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Predicting Diseaserelated RNA Associations based on Graph Convolutional Attention Network Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are timeconsuming and laborintensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential diseaseRNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other diseaseRNAs association prediction methods, GCAN operates the computation process from global structure of diseaseRNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in diseaseRNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different diseaseRNAs networks: diseasemiRNA and diseaselncRNA. Comparisons of several stateoftheart methods using diseaseRNAs networks show that our novel frameworks outperform baselines by a wide margin in potential diseaseRNAs associations.	Predicting genedisease associations via graph embedding and graph convolutional networks Identifying diseaserelated genes provides essential information for physiology, pathology and pharmacology. With the rapid growth of highthroughput sequencing technology and genomewide association studies, a large number of genedisease associations have been accumulated. These associations, coupled with the existing generelated and diseaserelated databases, enable prediction of unknown associations by computational approaches. In this article, we proposed a new graphbased machine learning framework to predict the disease related genes. We first constructed a heterogeneous genedisease association graph by integrating multiple biomedical knowledge bases, and subsequently processed the graph by a method that utilizes graph embedding representation and graph convolutional networks to learn the genedisease associations. In our method, we defined a novel cluster loss function and a dropout mechanism to improve the generalization ability. We conducted experiments on DisGeNet dataset in 10fold cross validation. The experimental results suggested that our method significantly outperformed the existing methods according to the performance of gene prioritization. The performance comparison of the variations of our method showed the efficacy of each module in our method.	Boundary state feedback exponential stabilization for a onedimensional wave equation with velocity recirculation Abstract In this paper, we consider boundary state feedback stabilization of a onedimensional wave equation with indomain feedbackrecirculation of an intermediate point velocity. We firstly construct an auxiliary control system which has a nonlocal term of the displacement at the same intermediate point. Then by choosing a wellknown exponentially stable wave equation as its target system, we find one backstepping transformation from which a state feedback law for this auxiliary system is proposed. Finally, taking the resulting closedloop of the auxiliary system as a new target system, we obtain another backstepping transformation from which a boundary state feedback controller for the original system is designed. By the equivalence of three systems, the closedloop of original system is proved to be wellposed and exponentially stable. Some numerical simulations are presented to validate the theoretical results.
Predicting Diseaserelated RNA Associations based on Graph Convolutional Attention Network Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are timeconsuming and laborintensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential diseaseRNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other diseaseRNAs association prediction methods, GCAN operates the computation process from global structure of diseaseRNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in diseaseRNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different diseaseRNAs networks: diseasemiRNA and diseaselncRNA. Comparisons of several stateoftheart methods using diseaseRNAs networks show that our novel frameworks outperform baselines by a wide margin in potential diseaseRNAs associations.	Predicting genedisease associations via graph embedding and graph convolutional networks Identifying diseaserelated genes provides essential information for physiology, pathology and pharmacology. With the rapid growth of highthroughput sequencing technology and genomewide association studies, a large number of genedisease associations have been accumulated. These associations, coupled with the existing generelated and diseaserelated databases, enable prediction of unknown associations by computational approaches. In this article, we proposed a new graphbased machine learning framework to predict the disease related genes. We first constructed a heterogeneous genedisease association graph by integrating multiple biomedical knowledge bases, and subsequently processed the graph by a method that utilizes graph embedding representation and graph convolutional networks to learn the genedisease associations. In our method, we defined a novel cluster loss function and a dropout mechanism to improve the generalization ability. We conducted experiments on DisGeNet dataset in 10fold cross validation. The experimental results suggested that our method significantly outperformed the existing methods according to the performance of gene prioritization. The performance comparison of the variations of our method showed the efficacy of each module in our method.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
Predicting Diseaserelated RNA Associations based on Graph Convolutional Attention Network Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are timeconsuming and laborintensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential diseaseRNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other diseaseRNAs association prediction methods, GCAN operates the computation process from global structure of diseaseRNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in diseaseRNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different diseaseRNAs networks: diseasemiRNA and diseaselncRNA. Comparisons of several stateoftheart methods using diseaseRNAs networks show that our novel frameworks outperform baselines by a wide margin in potential diseaseRNAs associations.	Predicting genedisease associations via graph embedding and graph convolutional networks Identifying diseaserelated genes provides essential information for physiology, pathology and pharmacology. With the rapid growth of highthroughput sequencing technology and genomewide association studies, a large number of genedisease associations have been accumulated. These associations, coupled with the existing generelated and diseaserelated databases, enable prediction of unknown associations by computational approaches. In this article, we proposed a new graphbased machine learning framework to predict the disease related genes. We first constructed a heterogeneous genedisease association graph by integrating multiple biomedical knowledge bases, and subsequently processed the graph by a method that utilizes graph embedding representation and graph convolutional networks to learn the genedisease associations. In our method, we defined a novel cluster loss function and a dropout mechanism to improve the generalization ability. We conducted experiments on DisGeNet dataset in 10fold cross validation. The experimental results suggested that our method significantly outperformed the existing methods according to the performance of gene prioritization. The performance comparison of the variations of our method showed the efficacy of each module in our method.	Sensors for Ultrasonic Nondestructive Testing (NDT) in Harsh Environments In this special issue of Sensors, seven peerreviewed manuscripts appear on the topic of ultrasonic transducer design and operation in harsh environments: elevated temperature, high gamma and neutron fields, or the presence of chemically aggressive species. Motivations for these research and development projects are strongly focused on nuclear power plant inspections (particularly liquidsodium cooled reactors), and nondestructive testing of hightemperature piping installations. It is anticipated that we may eventually see extensive use of permanently mounted robust transducers for inservice monitoring of petrochemical plants and power generations stations; quality control in manufacturing plants; and primary and secondary process monitoring in the fabrication of engineering materials.
Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITONMB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLSGAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.	Within and betweenperson correlates of the temporal dynamics of resting EEG microstates Abstract Microstates reflect transient brain states resulting from the synchronous activity of brain networks that predominate in the broadband EEG. There has been increasing interest in how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent. However, little research has examined within and betweenperson correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyesclosed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five datadriven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with withinperson factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of wholebrain neuronal networks vary considerably withinindividuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on betweenperson differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to withinperson variation.	Unmanned agricultural product sales system The invention relates to the field of agricultural product sales, provides an unmanned agricultural product sales system, and aims to solve the problem of agricultural product waste caused by the factthat most farmers can only prepare goods according to guessing and experiences when selling agricultural products at present. The unmanned agricultural product sales system comprises an acquisition module for acquiring selection information of customers; a storage module which prestores a vegetable preparation scheme; a matching module which is used for matching a corresponding side dish schemefrom the storage module according to the selection information of the client; a pushing module which is used for pushing the matched side dish scheme back to the client; an acquisition module which isalso used for acquiring confirmation information of a client; an order module which is used for generating order information according to the confirmation information of the client, wherein the pushing module is used for pushing the order information to the client and the seller, and the acquisition module is also used for acquiring the delivery information of the seller; and a logistics trackingmodule which is used for tracking the delivery information to obtain logistics information, wherein the pushing module is used for pushing the logistics information to the client. The scheme is usedfor sales of unmanned agricultural product shops.
Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITONMB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLSGAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.	Within and betweenperson correlates of the temporal dynamics of resting EEG microstates Abstract Microstates reflect transient brain states resulting from the synchronous activity of brain networks that predominate in the broadband EEG. There has been increasing interest in how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent. However, little research has examined within and betweenperson correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyesclosed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five datadriven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with withinperson factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of wholebrain neuronal networks vary considerably withinindividuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on betweenperson differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to withinperson variation.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITONMB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLSGAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.	Within and betweenperson correlates of the temporal dynamics of resting EEG microstates Abstract Microstates reflect transient brain states resulting from the synchronous activity of brain networks that predominate in the broadband EEG. There has been increasing interest in how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent. However, little research has examined within and betweenperson correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyesclosed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five datadriven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with withinperson factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of wholebrain neuronal networks vary considerably withinindividuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on betweenperson differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to withinperson variation.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITONMB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLSGAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.	Within and betweenperson correlates of the temporal dynamics of resting EEG microstates Abstract Microstates reflect transient brain states resulting from the synchronous activity of brain networks that predominate in the broadband EEG. There has been increasing interest in how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent. However, little research has examined within and betweenperson correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyesclosed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five datadriven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with withinperson factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of wholebrain neuronal networks vary considerably withinindividuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on betweenperson differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to withinperson variation.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITONMB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLSGAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.	Within and betweenperson correlates of the temporal dynamics of resting EEG microstates Abstract Microstates reflect transient brain states resulting from the synchronous activity of brain networks that predominate in the broadband EEG. There has been increasing interest in how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent. However, little research has examined within and betweenperson correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyesclosed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five datadriven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with withinperson factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of wholebrain neuronal networks vary considerably withinindividuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on betweenperson differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to withinperson variation.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
A Fast Signal Integrity Design Model of Printed Circuit Board based on MonteCarlo Tree In this paper, we discuss the signal integrity of the printed circuit board(PCB) and put forward a new method of designing PCB parameters based on montecarlo tree search(MCTS). First, effects and measurements of the signal integrity are analysed and presented. Next, the paper introduces the algorithm MCTS on how to conduct parameters assignments and presents the improved upper confidence bound(UCB) algorithm that adapts to the PCB parameter design. We simulate and collect eye metrics data on the software and accelerate the process of designing a signal integrated PCB by employing MCTS. Compared to randomly searching for optimal parameters combination, the method generates an optimal global consequence rather than working out a suboptimum result. The total time cost is within ten minutes, while the random search takes hours to get the result.	A DataEfficient Training Model for Signal Integrity Analysis based on Transfer Learning The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a timeconsuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyperparameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
A Fast Signal Integrity Design Model of Printed Circuit Board based on MonteCarlo Tree In this paper, we discuss the signal integrity of the printed circuit board(PCB) and put forward a new method of designing PCB parameters based on montecarlo tree search(MCTS). First, effects and measurements of the signal integrity are analysed and presented. Next, the paper introduces the algorithm MCTS on how to conduct parameters assignments and presents the improved upper confidence bound(UCB) algorithm that adapts to the PCB parameter design. We simulate and collect eye metrics data on the software and accelerate the process of designing a signal integrated PCB by employing MCTS. Compared to randomly searching for optimal parameters combination, the method generates an optimal global consequence rather than working out a suboptimum result. The total time cost is within ten minutes, while the random search takes hours to get the result.	A DataEfficient Training Model for Signal Integrity Analysis based on Transfer Learning The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a timeconsuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyperparameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
A Fast Signal Integrity Design Model of Printed Circuit Board based on MonteCarlo Tree In this paper, we discuss the signal integrity of the printed circuit board(PCB) and put forward a new method of designing PCB parameters based on montecarlo tree search(MCTS). First, effects and measurements of the signal integrity are analysed and presented. Next, the paper introduces the algorithm MCTS on how to conduct parameters assignments and presents the improved upper confidence bound(UCB) algorithm that adapts to the PCB parameter design. We simulate and collect eye metrics data on the software and accelerate the process of designing a signal integrated PCB by employing MCTS. Compared to randomly searching for optimal parameters combination, the method generates an optimal global consequence rather than working out a suboptimum result. The total time cost is within ten minutes, while the random search takes hours to get the result.	A DataEfficient Training Model for Signal Integrity Analysis based on Transfer Learning The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a timeconsuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyperparameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.	General Data Protection Regulation in Health Clinics The focus on personal data has merited the EU concerns and attention, resulting in the legislative change regarding privacy and the protection of personal data. The General Data Protection Regulation (GDPR) aims to reform existing measures on the protection of personal data of European Union citizens, with a strong impact on the rights and freedoms of individuals in establishing rules for the processing of personal data. The GDPR considers a special category of personal data, the health data, being these considered as sensitive data and subject to special conditions regarding treatment and access by third parties. This work presents the evolution of the applicability of the Regulation (EU) 2016679 six months after its application in Portuguese health clinics. The results of the present study are discussed in the light of future literature and work are identified.
A Fast Signal Integrity Design Model of Printed Circuit Board based on MonteCarlo Tree In this paper, we discuss the signal integrity of the printed circuit board(PCB) and put forward a new method of designing PCB parameters based on montecarlo tree search(MCTS). First, effects and measurements of the signal integrity are analysed and presented. Next, the paper introduces the algorithm MCTS on how to conduct parameters assignments and presents the improved upper confidence bound(UCB) algorithm that adapts to the PCB parameter design. We simulate and collect eye metrics data on the software and accelerate the process of designing a signal integrated PCB by employing MCTS. Compared to randomly searching for optimal parameters combination, the method generates an optimal global consequence rather than working out a suboptimum result. The total time cost is within ten minutes, while the random search takes hours to get the result.	A DataEfficient Training Model for Signal Integrity Analysis based on Transfer Learning The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a timeconsuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyperparameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.	Using Electronic Patient Reported Outcomes to Foster Palliative Cancer Care: The MyPal Approach Palliative care is offered along with primary treatment to improve the quality of life of the patient by relieving the symptoms and stress of a serious illness such as cancer. As per modern definitions, palliative care is appropriate at any age and at any stage of the illness, regardless of the eventual outcome. Patientreported outcomes (PRO), i.e., health status measurements reported directly by the patients or their proxies, and especially their availability in electronic form (ePROs), are gradually gaining popularity as building blocks of innovative palliative care interventions. This paper presents MyPal, an ECfunded collaborative research project that aims to exploit advanced eHealth technologies to develop and evaluate two novel ePRObased general palliative care interventions for cancer patients. In particular, the paper presents: (1) a short overview of MyPal; (2) the target populations, i.e., adults suffering from chronic lymphocytic leukemia (CLL) or myelodysplastic syndromes (MDS), and children with solid or hematologic malignancies; (3) the ePRObased interventions being designed for the target populations, (4) the eHealth platform for delivering the interventions under development, and (5) the international, multicenter clinical studies to be conducted for assessing these interventions, i.e., a randomized controlled trial (RCT) and an observational study for adults and children, respectively.
A Fast Signal Integrity Design Model of Printed Circuit Board based on MonteCarlo Tree In this paper, we discuss the signal integrity of the printed circuit board(PCB) and put forward a new method of designing PCB parameters based on montecarlo tree search(MCTS). First, effects and measurements of the signal integrity are analysed and presented. Next, the paper introduces the algorithm MCTS on how to conduct parameters assignments and presents the improved upper confidence bound(UCB) algorithm that adapts to the PCB parameter design. We simulate and collect eye metrics data on the software and accelerate the process of designing a signal integrated PCB by employing MCTS. Compared to randomly searching for optimal parameters combination, the method generates an optimal global consequence rather than working out a suboptimum result. The total time cost is within ten minutes, while the random search takes hours to get the result.	A DataEfficient Training Model for Signal Integrity Analysis based on Transfer Learning The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a timeconsuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyperparameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.	What Makes a Social Robot Good at Interacting with Humans This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: xe2x80x9cDo social robots need to look like living creatures that already exist in the world for humans to interact well with them?xe2x80x9d; xe2x80x9cDo social robots need to have animated faces for humans to interact well with them?xe2x80x9d; xe2x80x9cDo social robots need to have the ability to speak a coherent human language for humans to interact well with them?xe2x80x9d and xe2x80x9cDo social robots need to have the capability to make physical gestures for humans to interact well with them?xe2x80x9d. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethicalmoral concerns have also been discussed.
LongRange Distributed Solar Irradiance Sensing Using Optical Fibers Until recently, the amount of solar irradiance reaching the Earth surface was considered to be a steady value over the years. However, there is increasing observational evidence showing that this quantity undergoes substantial variations over time, which need to be addressed in different scenarios ranging from climate change to solar energy applications. With the growing interest in developing solar energy technology with enhanced efficiency and optimized management, the monitoring of solar irradiance at the ground level is now considered to be a fundamental input in the pursuit of that goal. Here, we propose the first fiberbased distributed sensor able of monitoring ground solar irradiance in real time, with meter scale spatial resolutions over distances of several tens of kilometers (up to 100 km). The technique is based on an optical fiber reflectometry technique (CPxcfx95OTDR), which enables real time and longrange highsensitivity bolometric measurements of solar radiance with a single optical fiber cable and a single interrogator unit. The method is explained and analyzed theoretically. A validation of the method is proposed using a solar simulator irradiating standard optical fibers, where we demonstrate the ability to detect and quantify solar irradiance with less than a 0.1 Wm2 resolution.	Noise Resilient Outdoor Traffic Light Visible Light Communications System Based on Logarithmic Transimpedance Circuit: Experimental Demonstration of a 50 m Reliable Link in Direct Sun Exposure The usage of Visible Light Communications (VLC) technology in automotive applications is very promising. Nevertheless, in outdoor conditions, the performances of existing VLC systems are strongly affected by the sun or other sources of light. In such situations, the strong parasitic light can saturate the photosensitive element and block data communication. To address the issue, this article analyzes the usage of an adaptive logarithmic transimpedance circuit as an alternative to the classical linear transimpedance circuit. The simulation and experimental evaluation demonstrate benefits of the proposed technique, as it significantly expands the communication distance and optical noise functionality range of the VLC systems and reduces the possibility of photoelement saturation. As a result, this approach might enable outdoor VLC sensors to work in strong sun conditions, the experimental results confirming its validity not only in the laboratory but also in outdoor conditions. A reliable 50 m communication distance is reported for outdoor sunny conditions using a standard power traffic light VLC emitter and a PIN photodiode VLC sensor.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
LongRange Distributed Solar Irradiance Sensing Using Optical Fibers Until recently, the amount of solar irradiance reaching the Earth surface was considered to be a steady value over the years. However, there is increasing observational evidence showing that this quantity undergoes substantial variations over time, which need to be addressed in different scenarios ranging from climate change to solar energy applications. With the growing interest in developing solar energy technology with enhanced efficiency and optimized management, the monitoring of solar irradiance at the ground level is now considered to be a fundamental input in the pursuit of that goal. Here, we propose the first fiberbased distributed sensor able of monitoring ground solar irradiance in real time, with meter scale spatial resolutions over distances of several tens of kilometers (up to 100 km). The technique is based on an optical fiber reflectometry technique (CPxcfx95OTDR), which enables real time and longrange highsensitivity bolometric measurements of solar radiance with a single optical fiber cable and a single interrogator unit. The method is explained and analyzed theoretically. A validation of the method is proposed using a solar simulator irradiating standard optical fibers, where we demonstrate the ability to detect and quantify solar irradiance with less than a 0.1 Wm2 resolution.	Noise Resilient Outdoor Traffic Light Visible Light Communications System Based on Logarithmic Transimpedance Circuit: Experimental Demonstration of a 50 m Reliable Link in Direct Sun Exposure The usage of Visible Light Communications (VLC) technology in automotive applications is very promising. Nevertheless, in outdoor conditions, the performances of existing VLC systems are strongly affected by the sun or other sources of light. In such situations, the strong parasitic light can saturate the photosensitive element and block data communication. To address the issue, this article analyzes the usage of an adaptive logarithmic transimpedance circuit as an alternative to the classical linear transimpedance circuit. The simulation and experimental evaluation demonstrate benefits of the proposed technique, as it significantly expands the communication distance and optical noise functionality range of the VLC systems and reduces the possibility of photoelement saturation. As a result, this approach might enable outdoor VLC sensors to work in strong sun conditions, the experimental results confirming its validity not only in the laboratory but also in outdoor conditions. A reliable 50 m communication distance is reported for outdoor sunny conditions using a standard power traffic light VLC emitter and a PIN photodiode VLC sensor.	A Critical Look at the 2019 College Admissions Scandal Discusses the 2019 College admissions scandal. Let me begin with a disclaimer: I am making no legal excuses for the participants in the current scandal. I am only offering contextual background that places it in the broader academic, cultural, and political perspective required for understanding. It is only the most recent installment of a wellworn narrative: the controlling elite make their own rules and live by them, if they can get away with it. Unfortunately, some of the participants, who are either serving or facing jail time, didnxe2x80x99t know to not go into a gunfight with a sharp stick. Money alone is not enough to avoid prosecution for fraud: you need political clout. The best protection a defendant can have is a prosecutor who fears political reprisal. Compare how the Koch brothers escaped prosecution for stealing millions of oil dollars from Native American tribes1,2 with the fate of actresses Lori Loughlin and Felicity Huffman, who, at the time of this writing, face jail time for paying bribes to get their children into good universities.3,4 In the former case, the federal prosecutor who dared to empanel a grand jury to get at the truth was fired for cause, which put a quick end to the prosecution. In the latter case, the prosecutors pushed for jail terms and public admonishment with the zeal of Oliver Cromwell. There you have it: stealing oil from Native Americans versus trying to bribe your kids into a great university. Where is the greater crime? Admittedly, these actresses and their
LongRange Distributed Solar Irradiance Sensing Using Optical Fibers Until recently, the amount of solar irradiance reaching the Earth surface was considered to be a steady value over the years. However, there is increasing observational evidence showing that this quantity undergoes substantial variations over time, which need to be addressed in different scenarios ranging from climate change to solar energy applications. With the growing interest in developing solar energy technology with enhanced efficiency and optimized management, the monitoring of solar irradiance at the ground level is now considered to be a fundamental input in the pursuit of that goal. Here, we propose the first fiberbased distributed sensor able of monitoring ground solar irradiance in real time, with meter scale spatial resolutions over distances of several tens of kilometers (up to 100 km). The technique is based on an optical fiber reflectometry technique (CPxcfx95OTDR), which enables real time and longrange highsensitivity bolometric measurements of solar radiance with a single optical fiber cable and a single interrogator unit. The method is explained and analyzed theoretically. A validation of the method is proposed using a solar simulator irradiating standard optical fibers, where we demonstrate the ability to detect and quantify solar irradiance with less than a 0.1 Wm2 resolution.	Noise Resilient Outdoor Traffic Light Visible Light Communications System Based on Logarithmic Transimpedance Circuit: Experimental Demonstration of a 50 m Reliable Link in Direct Sun Exposure The usage of Visible Light Communications (VLC) technology in automotive applications is very promising. Nevertheless, in outdoor conditions, the performances of existing VLC systems are strongly affected by the sun or other sources of light. In such situations, the strong parasitic light can saturate the photosensitive element and block data communication. To address the issue, this article analyzes the usage of an adaptive logarithmic transimpedance circuit as an alternative to the classical linear transimpedance circuit. The simulation and experimental evaluation demonstrate benefits of the proposed technique, as it significantly expands the communication distance and optical noise functionality range of the VLC systems and reduces the possibility of photoelement saturation. As a result, this approach might enable outdoor VLC sensors to work in strong sun conditions, the experimental results confirming its validity not only in the laboratory but also in outdoor conditions. A reliable 50 m communication distance is reported for outdoor sunny conditions using a standard power traffic light VLC emitter and a PIN photodiode VLC sensor.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
LongRange Distributed Solar Irradiance Sensing Using Optical Fibers Until recently, the amount of solar irradiance reaching the Earth surface was considered to be a steady value over the years. However, there is increasing observational evidence showing that this quantity undergoes substantial variations over time, which need to be addressed in different scenarios ranging from climate change to solar energy applications. With the growing interest in developing solar energy technology with enhanced efficiency and optimized management, the monitoring of solar irradiance at the ground level is now considered to be a fundamental input in the pursuit of that goal. Here, we propose the first fiberbased distributed sensor able of monitoring ground solar irradiance in real time, with meter scale spatial resolutions over distances of several tens of kilometers (up to 100 km). The technique is based on an optical fiber reflectometry technique (CPxcfx95OTDR), which enables real time and longrange highsensitivity bolometric measurements of solar radiance with a single optical fiber cable and a single interrogator unit. The method is explained and analyzed theoretically. A validation of the method is proposed using a solar simulator irradiating standard optical fibers, where we demonstrate the ability to detect and quantify solar irradiance with less than a 0.1 Wm2 resolution.	Noise Resilient Outdoor Traffic Light Visible Light Communications System Based on Logarithmic Transimpedance Circuit: Experimental Demonstration of a 50 m Reliable Link in Direct Sun Exposure The usage of Visible Light Communications (VLC) technology in automotive applications is very promising. Nevertheless, in outdoor conditions, the performances of existing VLC systems are strongly affected by the sun or other sources of light. In such situations, the strong parasitic light can saturate the photosensitive element and block data communication. To address the issue, this article analyzes the usage of an adaptive logarithmic transimpedance circuit as an alternative to the classical linear transimpedance circuit. The simulation and experimental evaluation demonstrate benefits of the proposed technique, as it significantly expands the communication distance and optical noise functionality range of the VLC systems and reduces the possibility of photoelement saturation. As a result, this approach might enable outdoor VLC sensors to work in strong sun conditions, the experimental results confirming its validity not only in the laboratory but also in outdoor conditions. A reliable 50 m communication distance is reported for outdoor sunny conditions using a standard power traffic light VLC emitter and a PIN photodiode VLC sensor.	Classifying unavoidable Tverberg partitions Let T(d,r) (r1)(d1)1 be the parameter in Tverbergu0027s theorem, and call a partition mathcal I of 1,2,ldots,T(d,r) into r parts a Tverberg type . We say that mathcal I o ccurs xc2xa0in an ordered point sequence P if P contains a subsequence Pu0027 of T(d,r) points such that the partition of Pu0027 that is orderisomorphic to mathcal I is a Tverberg partition. We say that mathcal I is unavoidable xc2xa0if it occurs in every sufficiently long point sequence. In this paper we study the problem of determining which Tverberg types are unavoidable. We conjecture a complete characterization of the unavoidable Tverberg types, and we prove some cases of our conjecture for dle 4. Along the way, we study the avoidability of many other geometric predicates. Our techniques also yield a large family of T(d,r)point sets for which the number of Tverberg partitions is exactly (r1)!d. This lends further support for Sierksmau0027s conjecture on the number of Tverberg partitions.
LongRange Distributed Solar Irradiance Sensing Using Optical Fibers Until recently, the amount of solar irradiance reaching the Earth surface was considered to be a steady value over the years. However, there is increasing observational evidence showing that this quantity undergoes substantial variations over time, which need to be addressed in different scenarios ranging from climate change to solar energy applications. With the growing interest in developing solar energy technology with enhanced efficiency and optimized management, the monitoring of solar irradiance at the ground level is now considered to be a fundamental input in the pursuit of that goal. Here, we propose the first fiberbased distributed sensor able of monitoring ground solar irradiance in real time, with meter scale spatial resolutions over distances of several tens of kilometers (up to 100 km). The technique is based on an optical fiber reflectometry technique (CPxcfx95OTDR), which enables real time and longrange highsensitivity bolometric measurements of solar radiance with a single optical fiber cable and a single interrogator unit. The method is explained and analyzed theoretically. A validation of the method is proposed using a solar simulator irradiating standard optical fibers, where we demonstrate the ability to detect and quantify solar irradiance with less than a 0.1 Wm2 resolution.	Noise Resilient Outdoor Traffic Light Visible Light Communications System Based on Logarithmic Transimpedance Circuit: Experimental Demonstration of a 50 m Reliable Link in Direct Sun Exposure The usage of Visible Light Communications (VLC) technology in automotive applications is very promising. Nevertheless, in outdoor conditions, the performances of existing VLC systems are strongly affected by the sun or other sources of light. In such situations, the strong parasitic light can saturate the photosensitive element and block data communication. To address the issue, this article analyzes the usage of an adaptive logarithmic transimpedance circuit as an alternative to the classical linear transimpedance circuit. The simulation and experimental evaluation demonstrate benefits of the proposed technique, as it significantly expands the communication distance and optical noise functionality range of the VLC systems and reduces the possibility of photoelement saturation. As a result, this approach might enable outdoor VLC sensors to work in strong sun conditions, the experimental results confirming its validity not only in the laboratory but also in outdoor conditions. A reliable 50 m communication distance is reported for outdoor sunny conditions using a standard power traffic light VLC emitter and a PIN photodiode VLC sensor.	Shifted Set Families, Degree Sequences, and Plethysm We study, in three parts, degree sequences of kfamilies (or kuniform hypergraphs) and shifted kfamilies. bullet The first part collects for the first time in one place, various implications such as scriptstyle hboxThreshold Rightarrow hboxUniquely Realizable Rightarrow hboxDegreeMaximal Rightarrow hboxShifted which are equivalent concepts for 2families ( simple graphs), but strict implications for kfamilies with k geq 3. The implication that uniquely realizable implies degreemaximal seems to be new. bullet The second part recalls Merris and Robyu0027s reformulation of the characterization due to Ruch and Gutman for graphical degree sequences and shifted 2families. It then introduces two generalizations which are characterizations of shifted kfamilies. bullet The third part recalls the connection between degree sequences of kfamilies of size m and the plethysm of elementary symmetric functions e_me_k. It then uses highest weight theory to explain how shifted kfamilies provide the top part of these plethysm expansions, along with offering a conjecture about a further relation.
3D Vertical RRAM Array and Device Codesign with Physicsbased Spice Model This paper demonstrates the codesign of threedimension (3D) Vertical Resistive Random Access Memory (RRAM) and the RRAM device. It presents a design consideration of 3D Vertical RRAM array in terms of array performance from the device point of view. A physicsbased RRAM Spice model is used to evaluate the performance of 3D RRAM array, including write access voltage, read margin, energy consumption and switching speed. The effects of device parameters, device parasitic capacitance, device variation and the 3D array size are discussed for design consideration. The simulation results show that with carefully choosing the RRAM device material and structure, a fastswitching, low energy consumption 3D RRAM array can be realized.	RRAM Endurance and Retention: Challenges, Opportunities and Implications on Reliable Design We provide an overview of metal oxide RRAM design challenges and opportunities in terms of endurance, retention and variability and the implications on the reliability and security of memory designs. We revise multiple level cell programming, endurance enhancing mechanisms, shortterm memory opportunities and variabilityaware design methods. We study the restore yield of nonvolatile SRAM designs for different endurance enhancing memory windows and device variability ranges citing power, reliability, endurance, and store energy tradeoffs taking into consideration program instability. A resistor window with a low high resistance state, which offers 100x enhanced endurance compared to a full range window, increases the restore power by around 2x while maintaining good yield in the absence of program instability. A resistor window with high low resistance state, which has 10x enhanced endurance, results in low restore power and store energy, and maintains good yield at low device process variations.	Estimation of the 2020 US Presidential Election Competition and Election Stratagies The 2020 US presidential election is still more than a year away, but the media is noisy due to the continuous registration of candidates that will face Trump in the election. Trump has already started to check is rivals through media. So far, Joe Biden and Bernie Sanders seem to have to most possibility to face Trump in the election. Sensitivity analysis was conducted to the data collected from Twitter from the year 2019. The positivity scores have been proved to effect approval ratings, they are estimated to effect the likeliness of becoming the most popular candidate. The data was compared to the past election from 2008, 2012, and 2016. The elections included the past rival background of Obama and McCain, Obama and Romney, Trump and Clinton to show how positive ratings effect the election. Tweets were collected through HTML and Python. The collected data was analyzed using SPSS and MS Excel. Data was defined into three major statuses; positive, negative, and neutral by a lexicon named Valence Aware Dictionary and Sediment Reasoner (VADER). The null hypothesis was rejected through Independent Sample TTest, MannWhitney U Test, Kruskal Wallis Test to show the difference between means. Research results show who will become Trumpu0027s estimated competitor for the 2020 election.
3D Vertical RRAM Array and Device Codesign with Physicsbased Spice Model This paper demonstrates the codesign of threedimension (3D) Vertical Resistive Random Access Memory (RRAM) and the RRAM device. It presents a design consideration of 3D Vertical RRAM array in terms of array performance from the device point of view. A physicsbased RRAM Spice model is used to evaluate the performance of 3D RRAM array, including write access voltage, read margin, energy consumption and switching speed. The effects of device parameters, device parasitic capacitance, device variation and the 3D array size are discussed for design consideration. The simulation results show that with carefully choosing the RRAM device material and structure, a fastswitching, low energy consumption 3D RRAM array can be realized.	RRAM Endurance and Retention: Challenges, Opportunities and Implications on Reliable Design We provide an overview of metal oxide RRAM design challenges and opportunities in terms of endurance, retention and variability and the implications on the reliability and security of memory designs. We revise multiple level cell programming, endurance enhancing mechanisms, shortterm memory opportunities and variabilityaware design methods. We study the restore yield of nonvolatile SRAM designs for different endurance enhancing memory windows and device variability ranges citing power, reliability, endurance, and store energy tradeoffs taking into consideration program instability. A resistor window with a low high resistance state, which offers 100x enhanced endurance compared to a full range window, increases the restore power by around 2x while maintaining good yield in the absence of program instability. A resistor window with high low resistance state, which has 10x enhanced endurance, results in low restore power and store energy, and maintains good yield at low device process variations.	Securing workers beyond the perimeter Although the delayed WeWork IPO has had a troubled journey, the growth of the startup highlights the wider shift in the commercial real estate market as more organisations embrace new working practices. Globally, teleworking is expanding, with a recent survey suggesting that at least 70% of knowledge workers work at least one day a week out of the office. 1 However, for some organisations in areas such as financial services and the public sector, one of the objections against teleworking is security. The fear that remote workers are more vulnerable to cyber attack means that these sectors are remaining locked into the old office model. Teleworking is expanding, but some organisations xe2x80x93 especially in financial services and the public sector xe2x80x93 remain concerned about security. Part of the issue concerns organisations that have failed to evolve IT security to match the growth of teleworking. Security tools are also lacking. Securing the remote workforce requires IT or security teams to conduct regular audit refreshes and IT security policy training sessions. This must be within the context of maintaining bestpractice IT security processes, says Scott Gordon of Pulse Secure.
3D Vertical RRAM Array and Device Codesign with Physicsbased Spice Model This paper demonstrates the codesign of threedimension (3D) Vertical Resistive Random Access Memory (RRAM) and the RRAM device. It presents a design consideration of 3D Vertical RRAM array in terms of array performance from the device point of view. A physicsbased RRAM Spice model is used to evaluate the performance of 3D RRAM array, including write access voltage, read margin, energy consumption and switching speed. The effects of device parameters, device parasitic capacitance, device variation and the 3D array size are discussed for design consideration. The simulation results show that with carefully choosing the RRAM device material and structure, a fastswitching, low energy consumption 3D RRAM array can be realized.	RRAM Endurance and Retention: Challenges, Opportunities and Implications on Reliable Design We provide an overview of metal oxide RRAM design challenges and opportunities in terms of endurance, retention and variability and the implications on the reliability and security of memory designs. We revise multiple level cell programming, endurance enhancing mechanisms, shortterm memory opportunities and variabilityaware design methods. We study the restore yield of nonvolatile SRAM designs for different endurance enhancing memory windows and device variability ranges citing power, reliability, endurance, and store energy tradeoffs taking into consideration program instability. A resistor window with a low high resistance state, which offers 100x enhanced endurance compared to a full range window, increases the restore power by around 2x while maintaining good yield in the absence of program instability. A resistor window with high low resistance state, which has 10x enhanced endurance, results in low restore power and store energy, and maintains good yield at low device process variations.	A Critical Look at the 2019 College Admissions Scandal Discusses the 2019 College admissions scandal. Let me begin with a disclaimer: I am making no legal excuses for the participants in the current scandal. I am only offering contextual background that places it in the broader academic, cultural, and political perspective required for understanding. It is only the most recent installment of a wellworn narrative: the controlling elite make their own rules and live by them, if they can get away with it. Unfortunately, some of the participants, who are either serving or facing jail time, didnxe2x80x99t know to not go into a gunfight with a sharp stick. Money alone is not enough to avoid prosecution for fraud: you need political clout. The best protection a defendant can have is a prosecutor who fears political reprisal. Compare how the Koch brothers escaped prosecution for stealing millions of oil dollars from Native American tribes1,2 with the fate of actresses Lori Loughlin and Felicity Huffman, who, at the time of this writing, face jail time for paying bribes to get their children into good universities.3,4 In the former case, the federal prosecutor who dared to empanel a grand jury to get at the truth was fired for cause, which put a quick end to the prosecution. In the latter case, the prosecutors pushed for jail terms and public admonishment with the zeal of Oliver Cromwell. There you have it: stealing oil from Native Americans versus trying to bribe your kids into a great university. Where is the greater crime? Admittedly, these actresses and their
3D Vertical RRAM Array and Device Codesign with Physicsbased Spice Model This paper demonstrates the codesign of threedimension (3D) Vertical Resistive Random Access Memory (RRAM) and the RRAM device. It presents a design consideration of 3D Vertical RRAM array in terms of array performance from the device point of view. A physicsbased RRAM Spice model is used to evaluate the performance of 3D RRAM array, including write access voltage, read margin, energy consumption and switching speed. The effects of device parameters, device parasitic capacitance, device variation and the 3D array size are discussed for design consideration. The simulation results show that with carefully choosing the RRAM device material and structure, a fastswitching, low energy consumption 3D RRAM array can be realized.	RRAM Endurance and Retention: Challenges, Opportunities and Implications on Reliable Design We provide an overview of metal oxide RRAM design challenges and opportunities in terms of endurance, retention and variability and the implications on the reliability and security of memory designs. We revise multiple level cell programming, endurance enhancing mechanisms, shortterm memory opportunities and variabilityaware design methods. We study the restore yield of nonvolatile SRAM designs for different endurance enhancing memory windows and device variability ranges citing power, reliability, endurance, and store energy tradeoffs taking into consideration program instability. A resistor window with a low high resistance state, which offers 100x enhanced endurance compared to a full range window, increases the restore power by around 2x while maintaining good yield in the absence of program instability. A resistor window with high low resistance state, which has 10x enhanced endurance, results in low restore power and store energy, and maintains good yield at low device process variations.	Managing Information From the :2,Information highlights the increasing value of information and IT within organizations and shows how organizations use it. It also deals with the crucial relationship between information and personal effectiveness. The use of computer software and communications in a management context are discussed in detail, including how to mould an information system to your needs. The book explains the basics using reallife examples and brings managers uptodate with the latest developments in electronic commerce and the Internet. The book is based on the Management Charter Initiativeu0027s Occupational Standards for Management NVQs and SVQs at level 4. It is particularly suitable for managers on the Certificate in Management, or Part I of the Diploma, especially those accredited by the IM and BTEC.
3D Vertical RRAM Array and Device Codesign with Physicsbased Spice Model This paper demonstrates the codesign of threedimension (3D) Vertical Resistive Random Access Memory (RRAM) and the RRAM device. It presents a design consideration of 3D Vertical RRAM array in terms of array performance from the device point of view. A physicsbased RRAM Spice model is used to evaluate the performance of 3D RRAM array, including write access voltage, read margin, energy consumption and switching speed. The effects of device parameters, device parasitic capacitance, device variation and the 3D array size are discussed for design consideration. The simulation results show that with carefully choosing the RRAM device material and structure, a fastswitching, low energy consumption 3D RRAM array can be realized.	RRAM Endurance and Retention: Challenges, Opportunities and Implications on Reliable Design We provide an overview of metal oxide RRAM design challenges and opportunities in terms of endurance, retention and variability and the implications on the reliability and security of memory designs. We revise multiple level cell programming, endurance enhancing mechanisms, shortterm memory opportunities and variabilityaware design methods. We study the restore yield of nonvolatile SRAM designs for different endurance enhancing memory windows and device variability ranges citing power, reliability, endurance, and store energy tradeoffs taking into consideration program instability. A resistor window with a low high resistance state, which offers 100x enhanced endurance compared to a full range window, increases the restore power by around 2x while maintaining good yield in the absence of program instability. A resistor window with high low resistance state, which has 10x enhanced endurance, results in low restore power and store energy, and maintains good yield at low device process variations.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
A Bidirectional Fuzzy Index and Approximate Search Algorithm for Next Generation Sequencing Sequence alignment is one of the most important problems in bioinformatics. However, the existing alignment tools may result in a large number of candidate locations which degradate alignment performance. Recent researches discard the high repetitive seeds for improving the alignment speed, which influences alignment accuracy. To this end, we propose a novel fuzzy index ( fBWT ), which is available at https:github.comweiquanappr_bwt. It allows approximate search and extending the length of seeds to reduce the candidate locations and accelerate the sequence alignment. The performance of our tool was compared with BWA using 150bp and 250bp length datasets. The result shows the number of misaligned reads (the correct position is not included in the highscore candidate position set) of the current mainstream tool is 510 times higher than it. The efficiency between the presented and the existing tools are also compared. Under the above conditions, the alignment time of the presented tool is very close. However, the alignment speed of fBWT is much faster than BWA under the requirement of similar alignment accuracy.	Efficient Construction of a Complete Index for PanGenomics Read Alignment While short read aligners, which predominantly use the FMindex, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FMindex in more detail, which is a rank data structure over the BurrowsWheeler Transform ((mathsfBWT)) of the string that will allow us to find the interval in the stringxe2x80x99s suffix array ((mathsfSA)) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the (mathsfSA) thatxe2x80x94when used with the rank data structurexe2x80x94allows us access to the (mathsfSA). The rank data structure can be kept small even for large genomic databases, by runlength compressing the (mathsfBWT), but until recently there was no means known to keep the (mathsfSA) sample small without greatly slowing down access to the (mathsfSA). Now that Gagie et al. (SODA 2018) have defined an (mathsfSA) sample that takes about the same space as the runlength compressed (mathsfBWT)xe2x80x94we have the design for efficient FMindexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the (mathsfBWT) of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.xe2x80x99s (mathsfSA) sample efficiently was left open. We compare our approach to stateoftheart methods for constructing the (mathsfSA) sample, and demonstrate that it is the fastest and most spaceefficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time.	Virtual Reality for training the public towards unexpected emergency situations Nowadays, unexpected situations in public spaces are quite frequent; for this reason, there is the need to provide valid decisionmaking tools to support peoplexe2x80x99s behavior in emergency situations. The aim of these support tools is to provide a training for the public on how to behave when something unexpected happens, in order to make them aware of how to manage and control their own emotions. Thanks to the introduction of new technologies, trainings are also feasible in Virtual Reality (VR), exploiting the chance to create virtual environments and situations that reflect real ones and test different scenarios on a sample of people in order to verify and validate training procedures. Virtual simulations in this context are paramount, because they offer the possibility to analyse reactions and behaviors in a safe, not real, so without health concern, environment. Three scenarios (fire, heart attack of a person in the environment and terrorist attack) have been reproduced in VR, analyzing how to define the context for emergency situations. Users approaching the training only know they are going to face a situation without having details on what is happening; this is fundamental to test the training efficiency on peoplexe2x80x99s reaction.
A Bidirectional Fuzzy Index and Approximate Search Algorithm for Next Generation Sequencing Sequence alignment is one of the most important problems in bioinformatics. However, the existing alignment tools may result in a large number of candidate locations which degradate alignment performance. Recent researches discard the high repetitive seeds for improving the alignment speed, which influences alignment accuracy. To this end, we propose a novel fuzzy index ( fBWT ), which is available at https:github.comweiquanappr_bwt. It allows approximate search and extending the length of seeds to reduce the candidate locations and accelerate the sequence alignment. The performance of our tool was compared with BWA using 150bp and 250bp length datasets. The result shows the number of misaligned reads (the correct position is not included in the highscore candidate position set) of the current mainstream tool is 510 times higher than it. The efficiency between the presented and the existing tools are also compared. Under the above conditions, the alignment time of the presented tool is very close. However, the alignment speed of fBWT is much faster than BWA under the requirement of similar alignment accuracy.	Efficient Construction of a Complete Index for PanGenomics Read Alignment While short read aligners, which predominantly use the FMindex, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FMindex in more detail, which is a rank data structure over the BurrowsWheeler Transform ((mathsfBWT)) of the string that will allow us to find the interval in the stringxe2x80x99s suffix array ((mathsfSA)) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the (mathsfSA) thatxe2x80x94when used with the rank data structurexe2x80x94allows us access to the (mathsfSA). The rank data structure can be kept small even for large genomic databases, by runlength compressing the (mathsfBWT), but until recently there was no means known to keep the (mathsfSA) sample small without greatly slowing down access to the (mathsfSA). Now that Gagie et al. (SODA 2018) have defined an (mathsfSA) sample that takes about the same space as the runlength compressed (mathsfBWT)xe2x80x94we have the design for efficient FMindexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the (mathsfBWT) of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.xe2x80x99s (mathsfSA) sample efficiently was left open. We compare our approach to stateoftheart methods for constructing the (mathsfSA) sample, and demonstrate that it is the fastest and most spaceefficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time.	Its time to rethink DDoS protection When you think of distributed denial of service (DDoS) attacks, chances are you conjure up an image of an overwhelming flood of traffic that incapacitates a network. This kind of cyber attack is all about overt, brute force used to take a target down. Some hackers are a little smarter, using DDoS as a distraction while they simultaneously attempt a more targeted strike, as was the case with a Carphone Warehouse hack in 2015. 1 But in general, DDoS isnu0027t subtle. Retailers are having to rethink how they approach distributed denial of service (DDoS) protection following the rise of a stealthier incarnation of the threat. There has been a significant increase in smallscale DDoS attacks and a corresponding reduction in conventional largescale events. The hackerxe2x80x99s aim is to remain below the conventional xe2x80x98detect and alertxe2x80x99 threshold that could trigger a DDoS mitigation strategy. Roy Reynolds of Vodat International explains the nature of the threat and the steps organisations can take to protect themselves.
A Bidirectional Fuzzy Index and Approximate Search Algorithm for Next Generation Sequencing Sequence alignment is one of the most important problems in bioinformatics. However, the existing alignment tools may result in a large number of candidate locations which degradate alignment performance. Recent researches discard the high repetitive seeds for improving the alignment speed, which influences alignment accuracy. To this end, we propose a novel fuzzy index ( fBWT ), which is available at https:github.comweiquanappr_bwt. It allows approximate search and extending the length of seeds to reduce the candidate locations and accelerate the sequence alignment. The performance of our tool was compared with BWA using 150bp and 250bp length datasets. The result shows the number of misaligned reads (the correct position is not included in the highscore candidate position set) of the current mainstream tool is 510 times higher than it. The efficiency between the presented and the existing tools are also compared. Under the above conditions, the alignment time of the presented tool is very close. However, the alignment speed of fBWT is much faster than BWA under the requirement of similar alignment accuracy.	Efficient Construction of a Complete Index for PanGenomics Read Alignment While short read aligners, which predominantly use the FMindex, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FMindex in more detail, which is a rank data structure over the BurrowsWheeler Transform ((mathsfBWT)) of the string that will allow us to find the interval in the stringxe2x80x99s suffix array ((mathsfSA)) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the (mathsfSA) thatxe2x80x94when used with the rank data structurexe2x80x94allows us access to the (mathsfSA). The rank data structure can be kept small even for large genomic databases, by runlength compressing the (mathsfBWT), but until recently there was no means known to keep the (mathsfSA) sample small without greatly slowing down access to the (mathsfSA). Now that Gagie et al. (SODA 2018) have defined an (mathsfSA) sample that takes about the same space as the runlength compressed (mathsfBWT)xe2x80x94we have the design for efficient FMindexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the (mathsfBWT) of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.xe2x80x99s (mathsfSA) sample efficiently was left open. We compare our approach to stateoftheart methods for constructing the (mathsfSA) sample, and demonstrate that it is the fastest and most spaceefficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
A Bidirectional Fuzzy Index and Approximate Search Algorithm for Next Generation Sequencing Sequence alignment is one of the most important problems in bioinformatics. However, the existing alignment tools may result in a large number of candidate locations which degradate alignment performance. Recent researches discard the high repetitive seeds for improving the alignment speed, which influences alignment accuracy. To this end, we propose a novel fuzzy index ( fBWT ), which is available at https:github.comweiquanappr_bwt. It allows approximate search and extending the length of seeds to reduce the candidate locations and accelerate the sequence alignment. The performance of our tool was compared with BWA using 150bp and 250bp length datasets. The result shows the number of misaligned reads (the correct position is not included in the highscore candidate position set) of the current mainstream tool is 510 times higher than it. The efficiency between the presented and the existing tools are also compared. Under the above conditions, the alignment time of the presented tool is very close. However, the alignment speed of fBWT is much faster than BWA under the requirement of similar alignment accuracy.	Efficient Construction of a Complete Index for PanGenomics Read Alignment While short read aligners, which predominantly use the FMindex, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FMindex in more detail, which is a rank data structure over the BurrowsWheeler Transform ((mathsfBWT)) of the string that will allow us to find the interval in the stringxe2x80x99s suffix array ((mathsfSA)) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the (mathsfSA) thatxe2x80x94when used with the rank data structurexe2x80x94allows us access to the (mathsfSA). The rank data structure can be kept small even for large genomic databases, by runlength compressing the (mathsfBWT), but until recently there was no means known to keep the (mathsfSA) sample small without greatly slowing down access to the (mathsfSA). Now that Gagie et al. (SODA 2018) have defined an (mathsfSA) sample that takes about the same space as the runlength compressed (mathsfBWT)xe2x80x94we have the design for efficient FMindexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the (mathsfBWT) of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.xe2x80x99s (mathsfSA) sample efficiently was left open. We compare our approach to stateoftheart methods for constructing the (mathsfSA) sample, and demonstrate that it is the fastest and most spaceefficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
A Bidirectional Fuzzy Index and Approximate Search Algorithm for Next Generation Sequencing Sequence alignment is one of the most important problems in bioinformatics. However, the existing alignment tools may result in a large number of candidate locations which degradate alignment performance. Recent researches discard the high repetitive seeds for improving the alignment speed, which influences alignment accuracy. To this end, we propose a novel fuzzy index ( fBWT ), which is available at https:github.comweiquanappr_bwt. It allows approximate search and extending the length of seeds to reduce the candidate locations and accelerate the sequence alignment. The performance of our tool was compared with BWA using 150bp and 250bp length datasets. The result shows the number of misaligned reads (the correct position is not included in the highscore candidate position set) of the current mainstream tool is 510 times higher than it. The efficiency between the presented and the existing tools are also compared. Under the above conditions, the alignment time of the presented tool is very close. However, the alignment speed of fBWT is much faster than BWA under the requirement of similar alignment accuracy.	Efficient Construction of a Complete Index for PanGenomics Read Alignment While short read aligners, which predominantly use the FMindex, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FMindex in more detail, which is a rank data structure over the BurrowsWheeler Transform ((mathsfBWT)) of the string that will allow us to find the interval in the stringxe2x80x99s suffix array ((mathsfSA)) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the (mathsfSA) thatxe2x80x94when used with the rank data structurexe2x80x94allows us access to the (mathsfSA). The rank data structure can be kept small even for large genomic databases, by runlength compressing the (mathsfBWT), but until recently there was no means known to keep the (mathsfSA) sample small without greatly slowing down access to the (mathsfSA). Now that Gagie et al. (SODA 2018) have defined an (mathsfSA) sample that takes about the same space as the runlength compressed (mathsfBWT)xe2x80x94we have the design for efficient FMindexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the (mathsfBWT) of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.xe2x80x99s (mathsfSA) sample efficiently was left open. We compare our approach to stateoftheart methods for constructing the (mathsfSA) sample, and demonstrate that it is the fastest and most spaceefficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time.	ARVR Based Smart Policing For Fast Response to Crimes in Safe City With advances in information and communication technologies, cities are getting smarter to enhance the quality of human life. In smart cities, safety (including security) is an essential issue. In this paper, by reviewing several safe city projects, smart city facilities for the safety are presented. With considering the facilities, a design for a crime intelligence system is introduced. Then, concentrating on how to support police activities (i.e., emergency call reporting reception, patrol activity, investigation activity, and arrest activity) with immersive technologies in order to reduce a crime rate and to quickly respond to emergencies in the safe city, smart policing with augmented reality (AR) and virtual reality (VR) is explained.
Efficient 3D Finite Element Modeling of a MuscleActivated Tongue We describe our investigation of a fast 3D finite element method (FEM) for biomedical simulation of a muscleactivated human tongue. Our method uses a linear stiffnesswarping scheme to achieve simulation speeds which are within a factor 10 of realtime rates at the expense of a small loss in accuracy. Muscle activations are produced by an arrangement of forces acting along selected edges of the FEM geometry. The modelu0027s dynamics are integrated using an implicit Euler formulation, which can be solved using either the conjugate gradient method or a direct sparse solver. To assess the utility of this model, we compare its accuracy against slower, but less approximate, simulations of a reference tongue model prepared using the FEM simulation package ANSYS.	A biomechanical model of the human tongue and its clinical implications Many surgical technics act on the upper airway in general, and on the tongue in particular. For example, tongue is one of the anatomical structures involved in the case of Pierre Robin syndrome, mandibular prognathism, or sleep apnoea syndrome. This paper presents the biomechanical and dynamical model of the human tongue we have developed, and the method we have used to fit this model to the anatomical and physical properties of a given patientu0027s tongue. Each step of the modeling process is precisely described: the soft tissues modeling through the Finite Element Method (geometrical design of the FE structure within the upper airway and representation of lingual musculature), and the motor control of the model with the corresponding dynamical simulations. Finally, the syndromes listed above are presented, with some focus on the clinical implications of the model.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Efficient 3D Finite Element Modeling of a MuscleActivated Tongue We describe our investigation of a fast 3D finite element method (FEM) for biomedical simulation of a muscleactivated human tongue. Our method uses a linear stiffnesswarping scheme to achieve simulation speeds which are within a factor 10 of realtime rates at the expense of a small loss in accuracy. Muscle activations are produced by an arrangement of forces acting along selected edges of the FEM geometry. The modelu0027s dynamics are integrated using an implicit Euler formulation, which can be solved using either the conjugate gradient method or a direct sparse solver. To assess the utility of this model, we compare its accuracy against slower, but less approximate, simulations of a reference tongue model prepared using the FEM simulation package ANSYS.	A biomechanical model of the human tongue and its clinical implications Many surgical technics act on the upper airway in general, and on the tongue in particular. For example, tongue is one of the anatomical structures involved in the case of Pierre Robin syndrome, mandibular prognathism, or sleep apnoea syndrome. This paper presents the biomechanical and dynamical model of the human tongue we have developed, and the method we have used to fit this model to the anatomical and physical properties of a given patientu0027s tongue. Each step of the modeling process is precisely described: the soft tissues modeling through the Finite Element Method (geometrical design of the FE structure within the upper airway and representation of lingual musculature), and the motor control of the model with the corresponding dynamical simulations. Finally, the syndromes listed above are presented, with some focus on the clinical implications of the model.	Virtually perfect democracy In the 2009 Security Protocols Workshop, the Pretty Good Democracy scheme was presented. This scheme has the appeal of allowing voters to cast votes remotely, e.g. via the Internet, and confirm correct receipt in a single session. The scheme provides a degree of endto end verifiability: receipt of the correct acknowledgement code provides assurance that the vote will be accurately included in the final tally. The scheme does not require any trust in a voter client device. It does however have a number of vulnerabilities: privacy and accuracy depend on vote codes being kept secret. It also suffers the usual coercion style threats common to most remote voting schemes.
Efficient 3D Finite Element Modeling of a MuscleActivated Tongue We describe our investigation of a fast 3D finite element method (FEM) for biomedical simulation of a muscleactivated human tongue. Our method uses a linear stiffnesswarping scheme to achieve simulation speeds which are within a factor 10 of realtime rates at the expense of a small loss in accuracy. Muscle activations are produced by an arrangement of forces acting along selected edges of the FEM geometry. The modelu0027s dynamics are integrated using an implicit Euler formulation, which can be solved using either the conjugate gradient method or a direct sparse solver. To assess the utility of this model, we compare its accuracy against slower, but less approximate, simulations of a reference tongue model prepared using the FEM simulation package ANSYS.	A biomechanical model of the human tongue and its clinical implications Many surgical technics act on the upper airway in general, and on the tongue in particular. For example, tongue is one of the anatomical structures involved in the case of Pierre Robin syndrome, mandibular prognathism, or sleep apnoea syndrome. This paper presents the biomechanical and dynamical model of the human tongue we have developed, and the method we have used to fit this model to the anatomical and physical properties of a given patientu0027s tongue. Each step of the modeling process is precisely described: the soft tissues modeling through the Finite Element Method (geometrical design of the FE structure within the upper airway and representation of lingual musculature), and the motor control of the model with the corresponding dynamical simulations. Finally, the syndromes listed above are presented, with some focus on the clinical implications of the model.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Efficient 3D Finite Element Modeling of a MuscleActivated Tongue We describe our investigation of a fast 3D finite element method (FEM) for biomedical simulation of a muscleactivated human tongue. Our method uses a linear stiffnesswarping scheme to achieve simulation speeds which are within a factor 10 of realtime rates at the expense of a small loss in accuracy. Muscle activations are produced by an arrangement of forces acting along selected edges of the FEM geometry. The modelu0027s dynamics are integrated using an implicit Euler formulation, which can be solved using either the conjugate gradient method or a direct sparse solver. To assess the utility of this model, we compare its accuracy against slower, but less approximate, simulations of a reference tongue model prepared using the FEM simulation package ANSYS.	A biomechanical model of the human tongue and its clinical implications Many surgical technics act on the upper airway in general, and on the tongue in particular. For example, tongue is one of the anatomical structures involved in the case of Pierre Robin syndrome, mandibular prognathism, or sleep apnoea syndrome. This paper presents the biomechanical and dynamical model of the human tongue we have developed, and the method we have used to fit this model to the anatomical and physical properties of a given patientu0027s tongue. Each step of the modeling process is precisely described: the soft tissues modeling through the Finite Element Method (geometrical design of the FE structure within the upper airway and representation of lingual musculature), and the motor control of the model with the corresponding dynamical simulations. Finally, the syndromes listed above are presented, with some focus on the clinical implications of the model.	Trust Degree Calculation Method Based on Trust Blockchain Node Due to the diversity and mobility of blockchain network nodes and the decentralized nature of blockchain networks, traditional trust value evaluation indicators cannot be directly used. In order to obtain trusted nodes, a trustworthiness calculation method based on trust blockchain nodes is proposed. Different from the traditional P2P network trust value calculation, the trust blockchain not only acquires the working state of the node, but also collects the special behavior information of the node, and calculates the joining time by synthesizing the trust value generated by the node transaction and the trust value generated by the node behavior. After the attenuation factor is comprehensively evaluated, the trusted nodes are selected to effectively ensure the security of the blockchain network environment, while reducing the average transaction delay and increasing the block rate.
Efficient 3D Finite Element Modeling of a MuscleActivated Tongue We describe our investigation of a fast 3D finite element method (FEM) for biomedical simulation of a muscleactivated human tongue. Our method uses a linear stiffnesswarping scheme to achieve simulation speeds which are within a factor 10 of realtime rates at the expense of a small loss in accuracy. Muscle activations are produced by an arrangement of forces acting along selected edges of the FEM geometry. The modelu0027s dynamics are integrated using an implicit Euler formulation, which can be solved using either the conjugate gradient method or a direct sparse solver. To assess the utility of this model, we compare its accuracy against slower, but less approximate, simulations of a reference tongue model prepared using the FEM simulation package ANSYS.	A biomechanical model of the human tongue and its clinical implications Many surgical technics act on the upper airway in general, and on the tongue in particular. For example, tongue is one of the anatomical structures involved in the case of Pierre Robin syndrome, mandibular prognathism, or sleep apnoea syndrome. This paper presents the biomechanical and dynamical model of the human tongue we have developed, and the method we have used to fit this model to the anatomical and physical properties of a given patientu0027s tongue. Each step of the modeling process is precisely described: the soft tissues modeling through the Finite Element Method (geometrical design of the FE structure within the upper airway and representation of lingual musculature), and the motor control of the model with the corresponding dynamical simulations. Finally, the syndromes listed above are presented, with some focus on the clinical implications of the model.	A Spatialxe2x80x93Temporal SubspaceBased Compressive Channel Estimation Technique in Unknown Interference MIMO Channels Spatialxe2x80x93temporal (ST) subspacebased channel estimation techniques formulated with ell 2 minimum mean square error (MMSE) criterion alleviate the multiaccess interference (MAI) problem when the interested signals exhibit lowrank property. However, the conventional ell 2 ST subspacebased methods suffer from mean squared error (MSE) deterioration in unknown interference channels, due to the difficulty to separate the interested signals from the channel covariance matrices (CCMs) contaminated with unknown interference. As a solution to the problem, we propose a new ell 1 regularized ST channel estimation algorithm by applying the expectationmaximization (EM) algorithm to iteratively examine the signal subspace and the corresponding sparsesupports. The new algorithm updates the CCM independently of the slotdependent ell 1 regularization, which enables it to correctly perform the sparseindependent component analysis (ICA) with a reasonable complexity order. Simulation results shown in this paper verify that the proposed technique significantly improves MSE performance in unknown interference MIMO channels, and hence, solves the BER floor problems from which the conventional receivers suffer.
PASCode: iOS App with Mobile Access to the International Classified Disease and Drug Databases for Health Informatics & Precision Medicine: PASCode with ICD and NDC Today, we stand on the threshold of the new medical revolution, and learning to read the code of life, and this revolution offers big hope to people suffering from all kinds of diseases. The complexity and variability in disease classification is a great challenge to overcome. Disease classification is routinely composed of healthcare units streaming data from multiple sources simultaneously e.g., pathology, genomics, imaging, and electrophysiology etc. To efficiently fathom the wealth of healthcare, there is a crucial need to generate appropriate diseasetreatment annotation repositories accessed through modern technologies. One platform that has proven to be an efficient tool in several areas including healthcare, is the multipurpose mobile computing devices e.g., smart phone and tablet computer. Our focus here was to develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. Innovative and smart systems are necessary to improve the quality and transition of healthcare by understanding heterogeneous healthcare and related data. In this manuscript, we present PASCode, an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. It enables users worldwide to install the application on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. PASCode is an exclusively academic application founded on the clinical, scientific, and modern technology premise to support healthcare and enable scientific data retrieval using efficient mobilebased tools. It has the potential to assist clinical researchers by providing enhanced exposure to ICD and NDC with greater visibility and easy browsing. These set of codes are highly significant at every level of healthcare.	PASSNP: iOS App with GWAS SNPDisease Database for Personalized Genomics Research: PASSNP for GWAS SNPDisease To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate genedisease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present nonprofit, academic and publicly available iOS application, PASSNP, which invites global users to freely download it on iPhone u0026 iPad devices, effortlessly adopt itu0027s easy to use interface and search for SNPs, genes and related diseases. PASSNP is developed with Swift multiparadigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNPdisease combinations. It includes SNPs released and organized by the Genomewide Association Study (GWAS). PASSNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
PASCode: iOS App with Mobile Access to the International Classified Disease and Drug Databases for Health Informatics & Precision Medicine: PASCode with ICD and NDC Today, we stand on the threshold of the new medical revolution, and learning to read the code of life, and this revolution offers big hope to people suffering from all kinds of diseases. The complexity and variability in disease classification is a great challenge to overcome. Disease classification is routinely composed of healthcare units streaming data from multiple sources simultaneously e.g., pathology, genomics, imaging, and electrophysiology etc. To efficiently fathom the wealth of healthcare, there is a crucial need to generate appropriate diseasetreatment annotation repositories accessed through modern technologies. One platform that has proven to be an efficient tool in several areas including healthcare, is the multipurpose mobile computing devices e.g., smart phone and tablet computer. Our focus here was to develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. Innovative and smart systems are necessary to improve the quality and transition of healthcare by understanding heterogeneous healthcare and related data. In this manuscript, we present PASCode, an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. It enables users worldwide to install the application on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. PASCode is an exclusively academic application founded on the clinical, scientific, and modern technology premise to support healthcare and enable scientific data retrieval using efficient mobilebased tools. It has the potential to assist clinical researchers by providing enhanced exposure to ICD and NDC with greater visibility and easy browsing. These set of codes are highly significant at every level of healthcare.	PASSNP: iOS App with GWAS SNPDisease Database for Personalized Genomics Research: PASSNP for GWAS SNPDisease To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate genedisease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present nonprofit, academic and publicly available iOS application, PASSNP, which invites global users to freely download it on iPhone u0026 iPad devices, effortlessly adopt itu0027s easy to use interface and search for SNPs, genes and related diseases. PASSNP is developed with Swift multiparadigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNPdisease combinations. It includes SNPs released and organized by the Genomewide Association Study (GWAS). PASSNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.	Boundary state feedback exponential stabilization for a onedimensional wave equation with velocity recirculation Abstract In this paper, we consider boundary state feedback stabilization of a onedimensional wave equation with indomain feedbackrecirculation of an intermediate point velocity. We firstly construct an auxiliary control system which has a nonlocal term of the displacement at the same intermediate point. Then by choosing a wellknown exponentially stable wave equation as its target system, we find one backstepping transformation from which a state feedback law for this auxiliary system is proposed. Finally, taking the resulting closedloop of the auxiliary system as a new target system, we obtain another backstepping transformation from which a boundary state feedback controller for the original system is designed. By the equivalence of three systems, the closedloop of original system is proved to be wellposed and exponentially stable. Some numerical simulations are presented to validate the theoretical results.
PASCode: iOS App with Mobile Access to the International Classified Disease and Drug Databases for Health Informatics & Precision Medicine: PASCode with ICD and NDC Today, we stand on the threshold of the new medical revolution, and learning to read the code of life, and this revolution offers big hope to people suffering from all kinds of diseases. The complexity and variability in disease classification is a great challenge to overcome. Disease classification is routinely composed of healthcare units streaming data from multiple sources simultaneously e.g., pathology, genomics, imaging, and electrophysiology etc. To efficiently fathom the wealth of healthcare, there is a crucial need to generate appropriate diseasetreatment annotation repositories accessed through modern technologies. One platform that has proven to be an efficient tool in several areas including healthcare, is the multipurpose mobile computing devices e.g., smart phone and tablet computer. Our focus here was to develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. Innovative and smart systems are necessary to improve the quality and transition of healthcare by understanding heterogeneous healthcare and related data. In this manuscript, we present PASCode, an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. It enables users worldwide to install the application on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. PASCode is an exclusively academic application founded on the clinical, scientific, and modern technology premise to support healthcare and enable scientific data retrieval using efficient mobilebased tools. It has the potential to assist clinical researchers by providing enhanced exposure to ICD and NDC with greater visibility and easy browsing. These set of codes are highly significant at every level of healthcare.	PASSNP: iOS App with GWAS SNPDisease Database for Personalized Genomics Research: PASSNP for GWAS SNPDisease To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate genedisease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present nonprofit, academic and publicly available iOS application, PASSNP, which invites global users to freely download it on iPhone u0026 iPad devices, effortlessly adopt itu0027s easy to use interface and search for SNPs, genes and related diseases. PASSNP is developed with Swift multiparadigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNPdisease combinations. It includes SNPs released and organized by the Genomewide Association Study (GWAS). PASSNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.	On Colorings Avoiding a Rainbow Cycle and a Fixed Monochromatic Subgraph Let H and G be two graphs on fixed number of vertices. An edge coloring of a complete graph is called (H,G)good if there is no monochromatic copy of G and no rainbow (totally multicolored) copy of H in this coloring. As shown by Jamison and West, an (H,G)good coloring of an arbitrarily large complete graph exists unless either G is a star or H is a forest. The largest number of colors in an (H,G)good coloring of K_n is denoted maxR(n, G,H). For graphs H which can not be vertexpartitioned into at most two induced forests, maxR(n, G,H) has been determined asymptotically. Determining maxR(n; G, H) is challenging for other graphs H, in particular for bipartite graphs or even for cycles. This manuscript treats the case when H is a cycle. The value of maxR(n, G, C_k) is determined for all graphs G whose edges do not induce a star.
PASCode: iOS App with Mobile Access to the International Classified Disease and Drug Databases for Health Informatics & Precision Medicine: PASCode with ICD and NDC Today, we stand on the threshold of the new medical revolution, and learning to read the code of life, and this revolution offers big hope to people suffering from all kinds of diseases. The complexity and variability in disease classification is a great challenge to overcome. Disease classification is routinely composed of healthcare units streaming data from multiple sources simultaneously e.g., pathology, genomics, imaging, and electrophysiology etc. To efficiently fathom the wealth of healthcare, there is a crucial need to generate appropriate diseasetreatment annotation repositories accessed through modern technologies. One platform that has proven to be an efficient tool in several areas including healthcare, is the multipurpose mobile computing devices e.g., smart phone and tablet computer. Our focus here was to develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. Innovative and smart systems are necessary to improve the quality and transition of healthcare by understanding heterogeneous healthcare and related data. In this manuscript, we present PASCode, an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. It enables users worldwide to install the application on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. PASCode is an exclusively academic application founded on the clinical, scientific, and modern technology premise to support healthcare and enable scientific data retrieval using efficient mobilebased tools. It has the potential to assist clinical researchers by providing enhanced exposure to ICD and NDC with greater visibility and easy browsing. These set of codes are highly significant at every level of healthcare.	PASSNP: iOS App with GWAS SNPDisease Database for Personalized Genomics Research: PASSNP for GWAS SNPDisease To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate genedisease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present nonprofit, academic and publicly available iOS application, PASSNP, which invites global users to freely download it on iPhone u0026 iPad devices, effortlessly adopt itu0027s easy to use interface and search for SNPs, genes and related diseases. PASSNP is developed with Swift multiparadigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNPdisease combinations. It includes SNPs released and organized by the Genomewide Association Study (GWAS). PASSNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.	Extremal Problems for tPartite and tColorable Hypergraphs Fix integers t ge r ge 2 and an runiform hypergraph F. We prove that the maximum number of edges in a tpartite runiform hypergraph on n vertices that contains no copy of F is c_t, Fn choose r o(nr), where c_t, F can be determined by a finite computation. We explicitly define a sequence F_1, F_2, ldots of runiform hypergraphs, and prove that the maximum number of edges in a tchromatic runiform hypergraph on n vertices containing no copy of F_i is alpha_t,r,in choose r o(nr), where alpha_t,r,i can be determined by a finite computation for each ige 1. In several cases, alpha_t,r,i is irrational. The main tool used in the proofs is the Lagrangian of a hypergraph.
PASCode: iOS App with Mobile Access to the International Classified Disease and Drug Databases for Health Informatics & Precision Medicine: PASCode with ICD and NDC Today, we stand on the threshold of the new medical revolution, and learning to read the code of life, and this revolution offers big hope to people suffering from all kinds of diseases. The complexity and variability in disease classification is a great challenge to overcome. Disease classification is routinely composed of healthcare units streaming data from multiple sources simultaneously e.g., pathology, genomics, imaging, and electrophysiology etc. To efficiently fathom the wealth of healthcare, there is a crucial need to generate appropriate diseasetreatment annotation repositories accessed through modern technologies. One platform that has proven to be an efficient tool in several areas including healthcare, is the multipurpose mobile computing devices e.g., smart phone and tablet computer. Our focus here was to develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. Innovative and smart systems are necessary to improve the quality and transition of healthcare by understanding heterogeneous healthcare and related data. In this manuscript, we present PASCode, an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. It enables users worldwide to install the application on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. PASCode is an exclusively academic application founded on the clinical, scientific, and modern technology premise to support healthcare and enable scientific data retrieval using efficient mobilebased tools. It has the potential to assist clinical researchers by providing enhanced exposure to ICD and NDC with greater visibility and easy browsing. These set of codes are highly significant at every level of healthcare.	PASSNP: iOS App with GWAS SNPDisease Database for Personalized Genomics Research: PASSNP for GWAS SNPDisease To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate genedisease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present nonprofit, academic and publicly available iOS application, PASSNP, which invites global users to freely download it on iPhone u0026 iPad devices, effortlessly adopt itu0027s easy to use interface and search for SNPs, genes and related diseases. PASSNP is developed with Swift multiparadigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNPdisease combinations. It includes SNPs released and organized by the Genomewide Association Study (GWAS). PASSNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.	Robust cluster consensus of general fractionalorder nonlinear multi agent systems via adaptive sliding mode controller Abstract In this paper robust cluster consensus is investigated for general fractionalorder multi agent systems with nonlinear dynamics with dynamic uncertainty and external disturbances via adaptive sliding mode controller. First, robust cluster consensus for general fractionalorder nonlinear multi agent systems is investigated with dynamic uncertainty and external disturbances in which multi agent systems are weakly heterogeneous because they have identical nominal dynamics with different normbounded parameter uncertainties. Then, robust cluster consensus for the fractionalorder nonlinear multi agent systems with general form dynamics is investigated by using adaptive sliding mode controller. Robust cluster consensus for general fractionalorder nonlinear multi agent systems is achieved asymptotically without disturbance. It is shown that the errors between agents can converge to a small region in the presence of disturbances based on the linear matrix inequality (LMI) and MittagLeffler stability theory. Finally, simulation examples are presented for general form multi agent systems, i.e. a singlelink flexible joint manipulator which demonstrates the efficiency of the proposed adaptive controller.
Continuousdiscrete time observers for a class of MIMO nonlinear systems In this paper, we investigate the possibility of designing an observer for a class of continuoustime dynamical systems with nonuniformly sampled measurements. More specifically, we propose an observer with a time varying gain witch converges exponentially under some conditions on the sampling partition diameter. The proposed observer is an impulsive system since it is described by a set of differential equations with instantaneous state impulses corresponding to the measured samples and their estimates. As it is customarily done in the literature, we show that such an impulsive system can be split into two subsystems and be put under the form of a hybrid system which is designed using a continuoustime observer together with an intersample output predictor. Simulations results involving a typical bioreactor are given to show the effectiveness of the proposed observer.	On the need for switchedgain observers for nonmonotonic nonlinear systems Abstract This paper focuses on the challenges in observer design for nonlinear systems which are nonmonotonic. A class of nonlinear systems is considered in which the process dynamics and output equations are both composed of nonlinear vector functions of scalar combinations of the states. The nonlinear functions are assumed to be differentiable with bounded derivatives. An observer design algorithm that requires solving just a single linear matrix inequality for exponentially convergent state estimation is developed. The developed algorithm works effectively when the involved nonlinear functions are monotonic. However, it fails when all or even some of the system functions are nonmonotonic. Both numerical computation and analytical results show that the observer design LMI has no feasible solutions when either all output functions or all process dynamics functions are nonmonotonic. Further, other constant gain LMIbased observer design methods from literature also fail when the involved nonlinear functions are all nonmonotonic, no matter how small the Lipschitz constant or the Jacobian bounds of the nonlinearities. This limitation has not previously been recognized in observer design literature. To overcome this limitation, a hybrid observer that switches between multiple constant observer gains is developed that can provide global asymptotic stability for systems with nonmonotonic nonlinear functions. Hybrid observers with switched gains enable existing observer design methods to be utilized for nonmonotonic nonlinear functions with finite local extrema. The application of the developed hybrid observer to two motion estimation applications, one a vehicle position tracking problem on roads and another a piston position estimation problem for an industrial actuator, are demonstrated.	Supporting BlockchainBased Cryptocurrency Mobile Payment With Smart Devices The smart device owning rate such as smart phone and smart watch is higher than ever before and mobile payment has become one of the major payment methods in many different areas. At the same time, blockchainbased cryptocurrency is becoming a nonnegligible type of currency and the total value of all types of cryptocurrency has reached USD 200 billion. Therefore, it is a natural demand to support cryptocurrency payment on mobile devices. Considering the poor infrastructure and low penetration of financial service in developing countries, this combination is especially attractive. The high storage cost and payment processing latency are the two main obstacles for mobile payment using cryptocurrency. We propose two different schemes for cryptocurrency mobile payment, one involves a centralized bank and the other one does not require any centralized party. We also provide a solution for the bank to meet KYC (know your customer)AML (antimoney laundering) compliance requirements when it is involved in cryptocurrency mobile payment processing.
Continuousdiscrete time observers for a class of MIMO nonlinear systems In this paper, we investigate the possibility of designing an observer for a class of continuoustime dynamical systems with nonuniformly sampled measurements. More specifically, we propose an observer with a time varying gain witch converges exponentially under some conditions on the sampling partition diameter. The proposed observer is an impulsive system since it is described by a set of differential equations with instantaneous state impulses corresponding to the measured samples and their estimates. As it is customarily done in the literature, we show that such an impulsive system can be split into two subsystems and be put under the form of a hybrid system which is designed using a continuoustime observer together with an intersample output predictor. Simulations results involving a typical bioreactor are given to show the effectiveness of the proposed observer.	On the need for switchedgain observers for nonmonotonic nonlinear systems Abstract This paper focuses on the challenges in observer design for nonlinear systems which are nonmonotonic. A class of nonlinear systems is considered in which the process dynamics and output equations are both composed of nonlinear vector functions of scalar combinations of the states. The nonlinear functions are assumed to be differentiable with bounded derivatives. An observer design algorithm that requires solving just a single linear matrix inequality for exponentially convergent state estimation is developed. The developed algorithm works effectively when the involved nonlinear functions are monotonic. However, it fails when all or even some of the system functions are nonmonotonic. Both numerical computation and analytical results show that the observer design LMI has no feasible solutions when either all output functions or all process dynamics functions are nonmonotonic. Further, other constant gain LMIbased observer design methods from literature also fail when the involved nonlinear functions are all nonmonotonic, no matter how small the Lipschitz constant or the Jacobian bounds of the nonlinearities. This limitation has not previously been recognized in observer design literature. To overcome this limitation, a hybrid observer that switches between multiple constant observer gains is developed that can provide global asymptotic stability for systems with nonmonotonic nonlinear functions. Hybrid observers with switched gains enable existing observer design methods to be utilized for nonmonotonic nonlinear functions with finite local extrema. The application of the developed hybrid observer to two motion estimation applications, one a vehicle position tracking problem on roads and another a piston position estimation problem for an industrial actuator, are demonstrated.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Continuousdiscrete time observers for a class of MIMO nonlinear systems In this paper, we investigate the possibility of designing an observer for a class of continuoustime dynamical systems with nonuniformly sampled measurements. More specifically, we propose an observer with a time varying gain witch converges exponentially under some conditions on the sampling partition diameter. The proposed observer is an impulsive system since it is described by a set of differential equations with instantaneous state impulses corresponding to the measured samples and their estimates. As it is customarily done in the literature, we show that such an impulsive system can be split into two subsystems and be put under the form of a hybrid system which is designed using a continuoustime observer together with an intersample output predictor. Simulations results involving a typical bioreactor are given to show the effectiveness of the proposed observer.	On the need for switchedgain observers for nonmonotonic nonlinear systems Abstract This paper focuses on the challenges in observer design for nonlinear systems which are nonmonotonic. A class of nonlinear systems is considered in which the process dynamics and output equations are both composed of nonlinear vector functions of scalar combinations of the states. The nonlinear functions are assumed to be differentiable with bounded derivatives. An observer design algorithm that requires solving just a single linear matrix inequality for exponentially convergent state estimation is developed. The developed algorithm works effectively when the involved nonlinear functions are monotonic. However, it fails when all or even some of the system functions are nonmonotonic. Both numerical computation and analytical results show that the observer design LMI has no feasible solutions when either all output functions or all process dynamics functions are nonmonotonic. Further, other constant gain LMIbased observer design methods from literature also fail when the involved nonlinear functions are all nonmonotonic, no matter how small the Lipschitz constant or the Jacobian bounds of the nonlinearities. This limitation has not previously been recognized in observer design literature. To overcome this limitation, a hybrid observer that switches between multiple constant observer gains is developed that can provide global asymptotic stability for systems with nonmonotonic nonlinear functions. Hybrid observers with switched gains enable existing observer design methods to be utilized for nonmonotonic nonlinear functions with finite local extrema. The application of the developed hybrid observer to two motion estimation applications, one a vehicle position tracking problem on roads and another a piston position estimation problem for an industrial actuator, are demonstrated.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Continuousdiscrete time observers for a class of MIMO nonlinear systems In this paper, we investigate the possibility of designing an observer for a class of continuoustime dynamical systems with nonuniformly sampled measurements. More specifically, we propose an observer with a time varying gain witch converges exponentially under some conditions on the sampling partition diameter. The proposed observer is an impulsive system since it is described by a set of differential equations with instantaneous state impulses corresponding to the measured samples and their estimates. As it is customarily done in the literature, we show that such an impulsive system can be split into two subsystems and be put under the form of a hybrid system which is designed using a continuoustime observer together with an intersample output predictor. Simulations results involving a typical bioreactor are given to show the effectiveness of the proposed observer.	On the need for switchedgain observers for nonmonotonic nonlinear systems Abstract This paper focuses on the challenges in observer design for nonlinear systems which are nonmonotonic. A class of nonlinear systems is considered in which the process dynamics and output equations are both composed of nonlinear vector functions of scalar combinations of the states. The nonlinear functions are assumed to be differentiable with bounded derivatives. An observer design algorithm that requires solving just a single linear matrix inequality for exponentially convergent state estimation is developed. The developed algorithm works effectively when the involved nonlinear functions are monotonic. However, it fails when all or even some of the system functions are nonmonotonic. Both numerical computation and analytical results show that the observer design LMI has no feasible solutions when either all output functions or all process dynamics functions are nonmonotonic. Further, other constant gain LMIbased observer design methods from literature also fail when the involved nonlinear functions are all nonmonotonic, no matter how small the Lipschitz constant or the Jacobian bounds of the nonlinearities. This limitation has not previously been recognized in observer design literature. To overcome this limitation, a hybrid observer that switches between multiple constant observer gains is developed that can provide global asymptotic stability for systems with nonmonotonic nonlinear functions. Hybrid observers with switched gains enable existing observer design methods to be utilized for nonmonotonic nonlinear functions with finite local extrema. The application of the developed hybrid observer to two motion estimation applications, one a vehicle position tracking problem on roads and another a piston position estimation problem for an industrial actuator, are demonstrated.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Continuousdiscrete time observers for a class of MIMO nonlinear systems In this paper, we investigate the possibility of designing an observer for a class of continuoustime dynamical systems with nonuniformly sampled measurements. More specifically, we propose an observer with a time varying gain witch converges exponentially under some conditions on the sampling partition diameter. The proposed observer is an impulsive system since it is described by a set of differential equations with instantaneous state impulses corresponding to the measured samples and their estimates. As it is customarily done in the literature, we show that such an impulsive system can be split into two subsystems and be put under the form of a hybrid system which is designed using a continuoustime observer together with an intersample output predictor. Simulations results involving a typical bioreactor are given to show the effectiveness of the proposed observer.	On the need for switchedgain observers for nonmonotonic nonlinear systems Abstract This paper focuses on the challenges in observer design for nonlinear systems which are nonmonotonic. A class of nonlinear systems is considered in which the process dynamics and output equations are both composed of nonlinear vector functions of scalar combinations of the states. The nonlinear functions are assumed to be differentiable with bounded derivatives. An observer design algorithm that requires solving just a single linear matrix inequality for exponentially convergent state estimation is developed. The developed algorithm works effectively when the involved nonlinear functions are monotonic. However, it fails when all or even some of the system functions are nonmonotonic. Both numerical computation and analytical results show that the observer design LMI has no feasible solutions when either all output functions or all process dynamics functions are nonmonotonic. Further, other constant gain LMIbased observer design methods from literature also fail when the involved nonlinear functions are all nonmonotonic, no matter how small the Lipschitz constant or the Jacobian bounds of the nonlinearities. This limitation has not previously been recognized in observer design literature. To overcome this limitation, a hybrid observer that switches between multiple constant observer gains is developed that can provide global asymptotic stability for systems with nonmonotonic nonlinear functions. Hybrid observers with switched gains enable existing observer design methods to be utilized for nonmonotonic nonlinear functions with finite local extrema. The application of the developed hybrid observer to two motion estimation applications, one a vehicle position tracking problem on roads and another a piston position estimation problem for an industrial actuator, are demonstrated.	Effects of Brownfield Remediation on Total Gaseous Mercury Concentrations in an Urban Landscape In order to obtain a better perspective of the impacts of brownfields on the landxe2x80x93atmosphere exchange of mercury in urban areas, total gaseous mercury (TGM) was measured at two heights (1.8 m and 42.7 m) prior to 2011xe2x80x932012 and after 2015xe2x80x932016 for the remediation of a brownfield and installation of a parking lot adjacent to the Syracuse Center of Excellence in Syracuse, NY, USA. Prior to brownfield remediation, the annual average TGM concentrations were 1.6 xc2xb1 0.6 and 1.4 xc2xb1 0.4 ng xc2xb7 m xe2x88x92 3 at the ground and upper heights, respectively. After brownfield remediation, the annual average TGM concentrations decreased by 32% and 22% at the ground and the upper height, respectively. Mercury soil flux measurements during summer after remediation showed net TGM deposition of 1.7 ng xc2xb7 m xe2x88x92 2 xc2xb7 day xe2x88x92 1 suggesting that the site transitioned from a mercury source to a net mercury sink. Measurements from the Atmospheric Mercury Network (AMNet) indicate that there was no regional decrease in TGM concentrations during the study period. This study demonstrates that evasion from mercurycontaminated soil significantly increased local TGM concentrations, which was subsequently mitigated after soil restoration. Considering the large number of brownfields, they may be an important source of mercury emissions source to local urban ecosystems and warrant future study at additional locations.
Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Virtually perfect democracy In the 2009 Security Protocols Workshop, the Pretty Good Democracy scheme was presented. This scheme has the appeal of allowing voters to cast votes remotely, e.g. via the Internet, and confirm correct receipt in a single session. The scheme provides a degree of endto end verifiability: receipt of the correct acknowledgement code provides assurance that the vote will be accurately included in the final tally. The scheme does not require any trust in a voter client device. It does however have a number of vulnerabilities: privacy and accuracy depend on vote codes being kept secret. It also suffers the usual coercion style threats common to most remote voting schemes.
Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	NonuniformlyRotating Ship Refocusing in SAR Imagery Based on the Bilinear Extended Fractional Fourier Transform Nonuniformlyrotating ship refocusing is very significant in the marine surveillance of satellite synthetic aperture radar (SAR). The majority of ship imaging algorithms is based on the inverse SAR (ISAR) technique. On the basis of the ISAR technique, several parameter estimation algorithms were proposed for nonuniformly rotating ships. But these algorithms still have problems on crossterms and noise suppression. In this paper, a refocusing algorithm for nonuniformly rotating ships based on the bilinear extended fractional Fourier transform (BEFRFT) is proposed. The ship signal in a range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. BEFRFT is a bilinear extension of fractional Fourier transform (FRFT), which can estimate the chirp rates and quadratic chirp rates of CPSs. Furthermore, BEFRFT has excellent performances on crossterms and noise suppression. The results of simulated data and Gaofen3 data verify the effectiveness of BEFRFT.
Detecting protein complex based on hierarchical compressing network embedding Detecting protein complexes from proteinprotein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining highdensity regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPCHCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPCHCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPCHCNE outperforms those stateoftheart complex detection methods.	Integrating Sequence and Network Information to Enhance ProteinProtein Interaction Prediction Using Graph Convolutional Networks Identification of proteinprotein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of largescale highthroughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the proteinu0027s position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the stateoftheart sequencebased methods on several benchmark datasets and our work computationally is more efficient compared with previous works.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Detecting protein complex based on hierarchical compressing network embedding Detecting protein complexes from proteinprotein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining highdensity regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPCHCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPCHCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPCHCNE outperforms those stateoftheart complex detection methods.	Integrating Sequence and Network Information to Enhance ProteinProtein Interaction Prediction Using Graph Convolutional Networks Identification of proteinprotein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of largescale highthroughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the proteinu0027s position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the stateoftheart sequencebased methods on several benchmark datasets and our work computationally is more efficient compared with previous works.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Detecting protein complex based on hierarchical compressing network embedding Detecting protein complexes from proteinprotein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining highdensity regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPCHCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPCHCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPCHCNE outperforms those stateoftheart complex detection methods.	Integrating Sequence and Network Information to Enhance ProteinProtein Interaction Prediction Using Graph Convolutional Networks Identification of proteinprotein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of largescale highthroughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the proteinu0027s position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the stateoftheart sequencebased methods on several benchmark datasets and our work computationally is more efficient compared with previous works.	Development and Flight Experiments of a Bluffbodied X4Blimp The body of X4blimp using four propellers manufactured in conventional research was a structure which has arranged four envelopes in which the buoyancy was equally divided centering on the gondola to which the propeller was attached. However, with this structure, the variation in the buoyancy arose among four envelopes, and there was a problem to which the body posture becomes unstable. In this research, it returns to the starting point which arranges one envelope at the center of the body, and the body of a fundamental structure of the nonstreamline is developed, in which the number of envelopes is suppressed to the minimum, and the variation in the buoyancy is avoided by attaching the special frame which can carry four propellers in the circumference of the envelope. The validity of the manufactured body is demonstrated through some flight experiments.
Detecting protein complex based on hierarchical compressing network embedding Detecting protein complexes from proteinprotein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining highdensity regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPCHCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPCHCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPCHCNE outperforms those stateoftheart complex detection methods.	Integrating Sequence and Network Information to Enhance ProteinProtein Interaction Prediction Using Graph Convolutional Networks Identification of proteinprotein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of largescale highthroughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the proteinu0027s position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the stateoftheart sequencebased methods on several benchmark datasets and our work computationally is more efficient compared with previous works.	A Study on Electric Scooters for the Elderly by Applying Fuzzy Theory This research is based on fuzzy comprehensive evaluation, and lists the fuzzy rule table for designers to control a scooter, in order to affect smoothness in the product design process of electric scooters for the elderly. Step 1: Use questionnaire survey method to understand the factors considered by the designer in designing the electric scooter for the elderly. Step 2: Establish hierarchical analysis and consider the factor weight set in electric scooter design. Step 3: Establish fuzzy hierarchical analysis, and sum up the evaluation result set, as based on the designeru0027s experience. Step 4: Comprehensively consider the influence of all factors and obtain the judgment result. Step 5: List fuzzy rules as an application method to improve the traditional design of electric scooters for the elderly. This study found that the travel speed showed the greatest influence 24.98% on the set of factors affecting smoothness.
Detecting protein complex based on hierarchical compressing network embedding Detecting protein complexes from proteinprotein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining highdensity regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPCHCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPCHCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPCHCNE outperforms those stateoftheart complex detection methods.	Integrating Sequence and Network Information to Enhance ProteinProtein Interaction Prediction Using Graph Convolutional Networks Identification of proteinprotein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of largescale highthroughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the proteinu0027s position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the stateoftheart sequencebased methods on several benchmark datasets and our work computationally is more efficient compared with previous works.	Research on the Integrated Navigation Technology of SINS with Couple Odometers for Land Vehicles Autonomous and accurate acquisition of the position and azimuth of the vehicle is critical to the combat effectiveness of landfighting vehicles. The integrated navigation system, consisting of a strapdown inertial navigation system (SINS) and odometer (OD), is commonly applied in vehicles. In the SINSOD integrated system, the odometer is installed around the vehiclexe2x80x99s wheel, while SINS is usually installed on the base of the vehicle. The distance along SINS and OD would cause a velocity difference when the vehicle maneuvers, which may lead to a significant influence on the integration positioning accuracy. Furthermore, SINS navigation errors, especially azimuth error, would diverge over time due to gyro drifts and accelerometer biases. The azimuth error would cause the divergence of deadreckoning positioning errors with the distance that the vehicle drives. To solve these problems, an integrated positioning and orientation method based on the configuration of SINS and couple odometers was proposed in this paper. The proposed method designed a high precision integrated navigation algorithm, which compensated the lever arm effect to eliminate the velocity difference between SINS and odometers. At the same time, by using the measured information of couple odometers, azimuth reference was calculated and used as an external measurement to suppress SINS azimuth errorxe2x80x99s divergence over time, thus could further improve the navigation precision of the integrated system, especially the orientation accuracy. The performance of the proposed method was verified by simulations. The results demonstrated that SINS2ODs integrated system could achieve a positioning accuracy of 0.01% D (total mileage) and orientation accuracy of xc2xb130xe2x80xb3 by using SINS with 0.01xc2xb0h FiberOptic Gyroscope (FOGs) and 50 xc2xb5g accelerometers.
An attentionbased neural network basecaller for Oxford Nanopore sequencing data Highly portable Oxford Nanopore sequencer producing long reads in real time at low cost has made many breakthroughts in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. Here we develops SACall, an endtoend basecaller based on convolution layers, transformer selfattention layers and CTC decoder. From the perspective of read accuracy, SACall yields better performance in the benchmark than ONT official basecaller Guppy and Albacore. SACall is an opensource, freely available basecaller, which gives a chance for researchers to train new basecalling models on specific data and basecall Nanopore reads.	FilterLAP: Filtering Falsepositive Mutation Calls via a Label Propagation Framework Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the falsepositive calls. The existing filters rely on the whole genomeexome sequencing data, which can provide sufficient samples for training the filters. However, the genepanel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semilearning filter for genepanel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on genepanel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.900.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on genepanel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for genepanel sequencing data.	The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and proactive aggression levels of the HMVE group were significantly higher than those of the LMVE group. Additionally, rumination and anger played a mediating role in the relationship between media violence exposure and aggression. Overall, this study enriches the theoretical understanding of the longterm effect of media violence exposure on individual aggression. Second, this study deepens our understanding of the relatively new and relevant phenomenon of the mechanism between media violence exposure and individual aggression.
An attentionbased neural network basecaller for Oxford Nanopore sequencing data Highly portable Oxford Nanopore sequencer producing long reads in real time at low cost has made many breakthroughts in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. Here we develops SACall, an endtoend basecaller based on convolution layers, transformer selfattention layers and CTC decoder. From the perspective of read accuracy, SACall yields better performance in the benchmark than ONT official basecaller Guppy and Albacore. SACall is an opensource, freely available basecaller, which gives a chance for researchers to train new basecalling models on specific data and basecall Nanopore reads.	FilterLAP: Filtering Falsepositive Mutation Calls via a Label Propagation Framework Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the falsepositive calls. The existing filters rely on the whole genomeexome sequencing data, which can provide sufficient samples for training the filters. However, the genepanel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semilearning filter for genepanel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on genepanel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.900.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on genepanel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for genepanel sequencing data.	A Study on Electric Scooters for the Elderly by Applying Fuzzy Theory This research is based on fuzzy comprehensive evaluation, and lists the fuzzy rule table for designers to control a scooter, in order to affect smoothness in the product design process of electric scooters for the elderly. Step 1: Use questionnaire survey method to understand the factors considered by the designer in designing the electric scooter for the elderly. Step 2: Establish hierarchical analysis and consider the factor weight set in electric scooter design. Step 3: Establish fuzzy hierarchical analysis, and sum up the evaluation result set, as based on the designeru0027s experience. Step 4: Comprehensively consider the influence of all factors and obtain the judgment result. Step 5: List fuzzy rules as an application method to improve the traditional design of electric scooters for the elderly. This study found that the travel speed showed the greatest influence 24.98% on the set of factors affecting smoothness.
An attentionbased neural network basecaller for Oxford Nanopore sequencing data Highly portable Oxford Nanopore sequencer producing long reads in real time at low cost has made many breakthroughts in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. Here we develops SACall, an endtoend basecaller based on convolution layers, transformer selfattention layers and CTC decoder. From the perspective of read accuracy, SACall yields better performance in the benchmark than ONT official basecaller Guppy and Albacore. SACall is an opensource, freely available basecaller, which gives a chance for researchers to train new basecalling models on specific data and basecall Nanopore reads.	FilterLAP: Filtering Falsepositive Mutation Calls via a Label Propagation Framework Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the falsepositive calls. The existing filters rely on the whole genomeexome sequencing data, which can provide sufficient samples for training the filters. However, the genepanel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semilearning filter for genepanel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on genepanel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.900.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on genepanel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for genepanel sequencing data.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
An attentionbased neural network basecaller for Oxford Nanopore sequencing data Highly portable Oxford Nanopore sequencer producing long reads in real time at low cost has made many breakthroughts in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. Here we develops SACall, an endtoend basecaller based on convolution layers, transformer selfattention layers and CTC decoder. From the perspective of read accuracy, SACall yields better performance in the benchmark than ONT official basecaller Guppy and Albacore. SACall is an opensource, freely available basecaller, which gives a chance for researchers to train new basecalling models on specific data and basecall Nanopore reads.	FilterLAP: Filtering Falsepositive Mutation Calls via a Label Propagation Framework Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the falsepositive calls. The existing filters rely on the whole genomeexome sequencing data, which can provide sufficient samples for training the filters. However, the genepanel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semilearning filter for genepanel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on genepanel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.900.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on genepanel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for genepanel sequencing data.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
An attentionbased neural network basecaller for Oxford Nanopore sequencing data Highly portable Oxford Nanopore sequencer producing long reads in real time at low cost has made many breakthroughts in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. Here we develops SACall, an endtoend basecaller based on convolution layers, transformer selfattention layers and CTC decoder. From the perspective of read accuracy, SACall yields better performance in the benchmark than ONT official basecaller Guppy and Albacore. SACall is an opensource, freely available basecaller, which gives a chance for researchers to train new basecalling models on specific data and basecall Nanopore reads.	FilterLAP: Filtering Falsepositive Mutation Calls via a Label Propagation Framework Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the falsepositive calls. The existing filters rely on the whole genomeexome sequencing data, which can provide sufficient samples for training the filters. However, the genepanel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semilearning filter for genepanel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on genepanel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.900.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on genepanel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for genepanel sequencing data.	Unmanned agricultural product sales system The invention relates to the field of agricultural product sales, provides an unmanned agricultural product sales system, and aims to solve the problem of agricultural product waste caused by the factthat most farmers can only prepare goods according to guessing and experiences when selling agricultural products at present. The unmanned agricultural product sales system comprises an acquisition module for acquiring selection information of customers; a storage module which prestores a vegetable preparation scheme; a matching module which is used for matching a corresponding side dish schemefrom the storage module according to the selection information of the client; a pushing module which is used for pushing the matched side dish scheme back to the client; an acquisition module which isalso used for acquiring confirmation information of a client; an order module which is used for generating order information according to the confirmation information of the client, wherein the pushing module is used for pushing the order information to the client and the seller, and the acquisition module is also used for acquiring the delivery information of the seller; and a logistics trackingmodule which is used for tracking the delivery information to obtain logistics information, wherein the pushing module is used for pushing the logistics information to the client. The scheme is usedfor sales of unmanned agricultural product shops.
Dangerousness of dysplastic nevi: a Multiple Instance Learning Solution for Early Diagnosis Malignant melanoma is responsible for the highest number of deaths related to skin lesions. However, early diagnosis may allow positive treatment of this terrible form of cancer. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. The proposed algorithms and methods have had as main focus the dichotomous distinction of melanoma from benign lesions and they rarely focused on the case of melanoma against dysplastic nevi. This challenge is much more difficult due to the similarity of the injuries. Currently, there is debate about dysplastic nevi syndrome, or rather about the number of moles present on the human body as potential melanoma risk factors. In this document, we consider the challenging task of applying a multiinstance learning (MIL) algorithm for discriminating melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common nevi. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to skin lesion detection.	Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.	Unmanned agricultural product sales system The invention relates to the field of agricultural product sales, provides an unmanned agricultural product sales system, and aims to solve the problem of agricultural product waste caused by the factthat most farmers can only prepare goods according to guessing and experiences when selling agricultural products at present. The unmanned agricultural product sales system comprises an acquisition module for acquiring selection information of customers; a storage module which prestores a vegetable preparation scheme; a matching module which is used for matching a corresponding side dish schemefrom the storage module according to the selection information of the client; a pushing module which is used for pushing the matched side dish scheme back to the client; an acquisition module which isalso used for acquiring confirmation information of a client; an order module which is used for generating order information according to the confirmation information of the client, wherein the pushing module is used for pushing the order information to the client and the seller, and the acquisition module is also used for acquiring the delivery information of the seller; and a logistics trackingmodule which is used for tracking the delivery information to obtain logistics information, wherein the pushing module is used for pushing the logistics information to the client. The scheme is usedfor sales of unmanned agricultural product shops.
Dangerousness of dysplastic nevi: a Multiple Instance Learning Solution for Early Diagnosis Malignant melanoma is responsible for the highest number of deaths related to skin lesions. However, early diagnosis may allow positive treatment of this terrible form of cancer. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. The proposed algorithms and methods have had as main focus the dichotomous distinction of melanoma from benign lesions and they rarely focused on the case of melanoma against dysplastic nevi. This challenge is much more difficult due to the similarity of the injuries. Currently, there is debate about dysplastic nevi syndrome, or rather about the number of moles present on the human body as potential melanoma risk factors. In this document, we consider the challenging task of applying a multiinstance learning (MIL) algorithm for discriminating melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common nevi. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to skin lesion detection.	Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Dangerousness of dysplastic nevi: a Multiple Instance Learning Solution for Early Diagnosis Malignant melanoma is responsible for the highest number of deaths related to skin lesions. However, early diagnosis may allow positive treatment of this terrible form of cancer. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. The proposed algorithms and methods have had as main focus the dichotomous distinction of melanoma from benign lesions and they rarely focused on the case of melanoma against dysplastic nevi. This challenge is much more difficult due to the similarity of the injuries. Currently, there is debate about dysplastic nevi syndrome, or rather about the number of moles present on the human body as potential melanoma risk factors. In this document, we consider the challenging task of applying a multiinstance learning (MIL) algorithm for discriminating melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common nevi. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to skin lesion detection.	Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Dangerousness of dysplastic nevi: a Multiple Instance Learning Solution for Early Diagnosis Malignant melanoma is responsible for the highest number of deaths related to skin lesions. However, early diagnosis may allow positive treatment of this terrible form of cancer. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. The proposed algorithms and methods have had as main focus the dichotomous distinction of melanoma from benign lesions and they rarely focused on the case of melanoma against dysplastic nevi. This challenge is much more difficult due to the similarity of the injuries. Currently, there is debate about dysplastic nevi syndrome, or rather about the number of moles present on the human body as potential melanoma risk factors. In this document, we consider the challenging task of applying a multiinstance learning (MIL) algorithm for discriminating melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common nevi. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to skin lesion detection.	Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
Dangerousness of dysplastic nevi: a Multiple Instance Learning Solution for Early Diagnosis Malignant melanoma is responsible for the highest number of deaths related to skin lesions. However, early diagnosis may allow positive treatment of this terrible form of cancer. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. The proposed algorithms and methods have had as main focus the dichotomous distinction of melanoma from benign lesions and they rarely focused on the case of melanoma against dysplastic nevi. This challenge is much more difficult due to the similarity of the injuries. Currently, there is debate about dysplastic nevi syndrome, or rather about the number of moles present on the human body as potential melanoma risk factors. In this document, we consider the challenging task of applying a multiinstance learning (MIL) algorithm for discriminating melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common nevi. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to skin lesion detection.	Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.	Symmetric Simplicial Pseudoline Arrangements A simplicial arrangement of pseudolines is a collection of topological lines in the projective plane where each region that is formed is triangular. This paper refines and develops David Eppsteinu0027s notion of a kaleidoscope construction for symmetric pseudoline arrangements to construct and analyze several infinite families of simplicial pseudoline arrangements with high degrees of geometric symmetry. In particular, all simplicial pseudoline arrangements with the symmetries of a regular kgon and three symmetry classes of pseudolines, consisting of the mirrors of the kgon and two other symmetry classes, plus sometimes the line at infinity, are classified, and other interesting families (with more symmetry classes of pseudolines) are discussed.
Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Unmanned agricultural product sales system The invention relates to the field of agricultural product sales, provides an unmanned agricultural product sales system, and aims to solve the problem of agricultural product waste caused by the factthat most farmers can only prepare goods according to guessing and experiences when selling agricultural products at present. The unmanned agricultural product sales system comprises an acquisition module for acquiring selection information of customers; a storage module which prestores a vegetable preparation scheme; a matching module which is used for matching a corresponding side dish schemefrom the storage module according to the selection information of the client; a pushing module which is used for pushing the matched side dish scheme back to the client; an acquisition module which isalso used for acquiring confirmation information of a client; an order module which is used for generating order information according to the confirmation information of the client, wherein the pushing module is used for pushing the order information to the client and the seller, and the acquisition module is also used for acquiring the delivery information of the seller; and a logistics trackingmodule which is used for tracking the delivery information to obtain logistics information, wherein the pushing module is used for pushing the logistics information to the client. The scheme is usedfor sales of unmanned agricultural product shops.
Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.	Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.	Research on Target Deviation Measurement of Projectile Based on Shadow Imaging Method in Laser Screen Velocity Measuring System In the laser screen velocity measuring (LSVM) system, there is a deviation in the consistency of the optoelectronic response between the start light screen and the stop light screen. When the projectile passes through the light screen, the projectilexe2x80x99s overtarget position, at which the timing pulse of the LSVM system is triggered, deviates from the actual position of the light screen (i.e., the target deviation). Therefore, it brings errors to the measurement of the projectilexe2x80x99s velocity, which has become a bottleneck, affecting the construction of a higher precision optoelectronic velocity measuring system. To solve this problem, this paper proposes a method based on highspeed shadow imaging to measure the projectilexe2x80x99s target deviation, xcex94S, when the LSVM system triggers the timing pulse. The infrared pulse laser is collimated by the combination of the aspherical lens to form a parallel laser source that is used as the light source of the system. When the projectile passes through the light screen, the projectilexe2x80x99s overtarget signal is processed by the specially designed trigger circuit. It uses the rising and falling edges of this signal to trigger the camera and pulsed laser source, respectively, to ensure that the projectilexe2x80x99s overtarget image is adequately exposed. By capturing the images of the light screen of the LSVM system and the overtarget projectile separately, this method of image edge detection was used to calculate the target deviation, and this value was used to correct the target distance of the LSVM to improve the accuracy of the measurement of the projectilexe2x80x99s velocity.
Are Inductive Current Transformers Performance Really Affected by Actual Distorted Network Conditions? An Experimental Case Study. The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in offnominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or lowpower instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two offtheshelf current transformers. Then, their accuracy performances have been evaluated by means of the wellknown composite error index and an approximated version of it. The obtained results show that under realistic nonrated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes.	A lowcost online estimator for switchmode power supplies with saturating ferritecore inductors Ferritecore inductors are largely used in switchmode power supplies (SMPSs) and can be fruitfully exploited also when working in partial saturation. Several behavioral models have been recently proposed to represent both inductance and power losses in saturation conditions. In this paper we exploit this kind of models for estimating online relevant features of the SMPS inductor, such as current ripple and power loss, which are difficult to monitor through acquisition of easilymeasurable quantities only. The method works at electrical steadystate, but is able to follow the thermal transients. Models are implemented on a lowcost microcontroller and tests are performed on a boost converter, for different operating conditions.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Are Inductive Current Transformers Performance Really Affected by Actual Distorted Network Conditions? An Experimental Case Study. The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in offnominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or lowpower instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two offtheshelf current transformers. Then, their accuracy performances have been evaluated by means of the wellknown composite error index and an approximated version of it. The obtained results show that under realistic nonrated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes.	A lowcost online estimator for switchmode power supplies with saturating ferritecore inductors Ferritecore inductors are largely used in switchmode power supplies (SMPSs) and can be fruitfully exploited also when working in partial saturation. Several behavioral models have been recently proposed to represent both inductance and power losses in saturation conditions. In this paper we exploit this kind of models for estimating online relevant features of the SMPS inductor, such as current ripple and power loss, which are difficult to monitor through acquisition of easilymeasurable quantities only. The method works at electrical steadystate, but is able to follow the thermal transients. Models are implemented on a lowcost microcontroller and tests are performed on a boost converter, for different operating conditions.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Are Inductive Current Transformers Performance Really Affected by Actual Distorted Network Conditions? An Experimental Case Study. The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in offnominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or lowpower instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two offtheshelf current transformers. Then, their accuracy performances have been evaluated by means of the wellknown composite error index and an approximated version of it. The obtained results show that under realistic nonrated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes.	A lowcost online estimator for switchmode power supplies with saturating ferritecore inductors Ferritecore inductors are largely used in switchmode power supplies (SMPSs) and can be fruitfully exploited also when working in partial saturation. Several behavioral models have been recently proposed to represent both inductance and power losses in saturation conditions. In this paper we exploit this kind of models for estimating online relevant features of the SMPS inductor, such as current ripple and power loss, which are difficult to monitor through acquisition of easilymeasurable quantities only. The method works at electrical steadystate, but is able to follow the thermal transients. Models are implemented on a lowcost microcontroller and tests are performed on a boost converter, for different operating conditions.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Are Inductive Current Transformers Performance Really Affected by Actual Distorted Network Conditions? An Experimental Case Study. The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in offnominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or lowpower instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two offtheshelf current transformers. Then, their accuracy performances have been evaluated by means of the wellknown composite error index and an approximated version of it. The obtained results show that under realistic nonrated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes.	A lowcost online estimator for switchmode power supplies with saturating ferritecore inductors Ferritecore inductors are largely used in switchmode power supplies (SMPSs) and can be fruitfully exploited also when working in partial saturation. Several behavioral models have been recently proposed to represent both inductance and power losses in saturation conditions. In this paper we exploit this kind of models for estimating online relevant features of the SMPS inductor, such as current ripple and power loss, which are difficult to monitor through acquisition of easilymeasurable quantities only. The method works at electrical steadystate, but is able to follow the thermal transients. Models are implemented on a lowcost microcontroller and tests are performed on a boost converter, for different operating conditions.	Classifying unavoidable Tverberg partitions Let T(d,r) (r1)(d1)1 be the parameter in Tverbergu0027s theorem, and call a partition mathcal I of 1,2,ldots,T(d,r) into r parts a Tverberg type . We say that mathcal I o ccurs xc2xa0in an ordered point sequence P if P contains a subsequence Pu0027 of T(d,r) points such that the partition of Pu0027 that is orderisomorphic to mathcal I is a Tverberg partition. We say that mathcal I is unavoidable xc2xa0if it occurs in every sufficiently long point sequence. In this paper we study the problem of determining which Tverberg types are unavoidable. We conjecture a complete characterization of the unavoidable Tverberg types, and we prove some cases of our conjecture for dle 4. Along the way, we study the avoidability of many other geometric predicates. Our techniques also yield a large family of T(d,r)point sets for which the number of Tverberg partitions is exactly (r1)!d. This lends further support for Sierksmau0027s conjecture on the number of Tverberg partitions.
Are Inductive Current Transformers Performance Really Affected by Actual Distorted Network Conditions? An Experimental Case Study. The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in offnominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or lowpower instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two offtheshelf current transformers. Then, their accuracy performances have been evaluated by means of the wellknown composite error index and an approximated version of it. The obtained results show that under realistic nonrated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes.	A lowcost online estimator for switchmode power supplies with saturating ferritecore inductors Ferritecore inductors are largely used in switchmode power supplies (SMPSs) and can be fruitfully exploited also when working in partial saturation. Several behavioral models have been recently proposed to represent both inductance and power losses in saturation conditions. In this paper we exploit this kind of models for estimating online relevant features of the SMPS inductor, such as current ripple and power loss, which are difficult to monitor through acquisition of easilymeasurable quantities only. The method works at electrical steadystate, but is able to follow the thermal transients. Models are implemented on a lowcost microcontroller and tests are performed on a boost converter, for different operating conditions.	What Makes a Social Robot Good at Interacting with Humans This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: xe2x80x9cDo social robots need to look like living creatures that already exist in the world for humans to interact well with them?xe2x80x9d; xe2x80x9cDo social robots need to have animated faces for humans to interact well with them?xe2x80x9d; xe2x80x9cDo social robots need to have the ability to speak a coherent human language for humans to interact well with them?xe2x80x9d and xe2x80x9cDo social robots need to have the capability to make physical gestures for humans to interact well with them?xe2x80x9d. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethicalmoral concerns have also been discussed.
A CMOS HalfBridge GaN Driver with 630V Input Voltage Range and 5.4ns Propagation Delay In this paper, a halfbridge Gallium Nitride (GaN) driver is proposed. By integrating a wide input voltage range linear regulator (LDO), which is the power supply of the GaN driver, the input voltage of the driver can extend to a wide range of 6xe2x80x9330 V. In order to reduce the propagation delay, a highspeed level shifter with subnanosecond delay is proposed. A dead time generator is designed to introduce a dead time less than 1 ns, which can minimize the reverse conduction loss. In addition, a separate drive output is designed to adjust the gate resistance of the GaN transistors. The designed GaN driver has been designed in 0.18xc2xb5m CMOS technology, and the postlayout simulation results show it can achieve a low propagation delay of 5.4 ns.	Smart Gate Driver ICs for GaN Power Transistors GaN power transistors are well suited in high frequency, high temperature and high powerdensity applications. This paper investigates different gate driver designs for GaN power transistors, including fundamental driving concerns, current commercial products, popular gate driving features, and design trends for the suppression of gate node ringing. Many smart gate drivers offer features such as intelligent onchip CPU for flexible digital control, adjustable gate voltage levels, multistage driving speed for slewrate control, precise timing control, current sensing capability for closeloop regulations, and active gate driving mode with continuous optimized deadtime. This paper provides an overview to modern gate driver design trends and challenges for GaN power transistors.	Erkundung und Erforschung. Alexander von Humboldts Amerikareise Zusammenfassung Ahnlich wie Adalbert Stifters Erzahler im Roman xe2x80x9eNachsommerxe2x80x9c verband A. v. Humboldt auf seiner Amerikareise Erkundung und Erforschung, Reiselust und Erkenntnisstreben. Humboldt hat sein doppeltes Ziel klar benannt: Bekanntmachung der besuchten Lander, Sammeln von Tatsachen zur Erweiterung der physikalischen Geographie. Der Aufsatz ist in funf Abschnitte gegliedert: Anliegen, Route, Methoden, Ergebnisse, Auswertung. Abstract In a similar way as Adalbert Stifteru0027s narrator in the novel xe2x80x9cLate summerxe2x80x9d A. v. Humboldt combined exploration with research, fondness for travelling with striving for findings during his travel through South America. Humboldt clearly indicated his double aim: to report on the visited countries, to collect facts in order to improve physical geography. The treatise consists of five sections: object, route, methods, results, evaluation.
A CMOS HalfBridge GaN Driver with 630V Input Voltage Range and 5.4ns Propagation Delay In this paper, a halfbridge Gallium Nitride (GaN) driver is proposed. By integrating a wide input voltage range linear regulator (LDO), which is the power supply of the GaN driver, the input voltage of the driver can extend to a wide range of 6xe2x80x9330 V. In order to reduce the propagation delay, a highspeed level shifter with subnanosecond delay is proposed. A dead time generator is designed to introduce a dead time less than 1 ns, which can minimize the reverse conduction loss. In addition, a separate drive output is designed to adjust the gate resistance of the GaN transistors. The designed GaN driver has been designed in 0.18xc2xb5m CMOS technology, and the postlayout simulation results show it can achieve a low propagation delay of 5.4 ns.	Smart Gate Driver ICs for GaN Power Transistors GaN power transistors are well suited in high frequency, high temperature and high powerdensity applications. This paper investigates different gate driver designs for GaN power transistors, including fundamental driving concerns, current commercial products, popular gate driving features, and design trends for the suppression of gate node ringing. Many smart gate drivers offer features such as intelligent onchip CPU for flexible digital control, adjustable gate voltage levels, multistage driving speed for slewrate control, precise timing control, current sensing capability for closeloop regulations, and active gate driving mode with continuous optimized deadtime. This paper provides an overview to modern gate driver design trends and challenges for GaN power transistors.	The semantic theory of language Abstract Traditional linguistics was based on the idea that language is an activity that links sounds and meaning, an idea that has been referred to as xe2x80x98the code view of languagexe2x80x99 because codes are the most familiar processes that generate meaning. Ever since the work of Noam Chomsky, however, this view has been increasingly replaced by xe2x80x98the syntax view of languagexe2x80x99, the idea that children learn a language because they have an innate mechanism that allows them to grasp the syntax of whatever language they grow up with. This innate mechanism has been given various names xe2x80x93 first Universal Grammar, then Language Acquisition Device (LAD), and finally Faculty of Language xe2x80x93 but despite decades of research attempts there still is no evidence that such a device actually exists. At the same time, it has become increasingly clear that codes are not the sole processes that generate meaning. Another such process is the ability of higher animals to interpret what goes on in the world, and interpretation is different from coding because it is not based on fixed rules but on a process that Charles Peirce called abduction. This allows us to generalize the code view of language into the semantic view of language, a theory which maintains that language is primarily a semantic activity that gives meaning to sounds either by codes or by processes of interpretation. This view, furthermore, gives us a new theoretical framework for studying the origin of language without resorting to any deus ex machina device. In this framework the origin of language is compared with the origin of life and the origin of mind, because those mega transitions generated the three great families of codes that we find in Nature xe2x80x93 the organic codes, the neural codes and the cultural codes xe2x80x93 and it is possible that a comparative study allows us to catch a glimpse of the mechanisms that gave origin to language.
A CMOS HalfBridge GaN Driver with 630V Input Voltage Range and 5.4ns Propagation Delay In this paper, a halfbridge Gallium Nitride (GaN) driver is proposed. By integrating a wide input voltage range linear regulator (LDO), which is the power supply of the GaN driver, the input voltage of the driver can extend to a wide range of 6xe2x80x9330 V. In order to reduce the propagation delay, a highspeed level shifter with subnanosecond delay is proposed. A dead time generator is designed to introduce a dead time less than 1 ns, which can minimize the reverse conduction loss. In addition, a separate drive output is designed to adjust the gate resistance of the GaN transistors. The designed GaN driver has been designed in 0.18xc2xb5m CMOS technology, and the postlayout simulation results show it can achieve a low propagation delay of 5.4 ns.	Smart Gate Driver ICs for GaN Power Transistors GaN power transistors are well suited in high frequency, high temperature and high powerdensity applications. This paper investigates different gate driver designs for GaN power transistors, including fundamental driving concerns, current commercial products, popular gate driving features, and design trends for the suppression of gate node ringing. Many smart gate drivers offer features such as intelligent onchip CPU for flexible digital control, adjustable gate voltage levels, multistage driving speed for slewrate control, precise timing control, current sensing capability for closeloop regulations, and active gate driving mode with continuous optimized deadtime. This paper provides an overview to modern gate driver design trends and challenges for GaN power transistors.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
A CMOS HalfBridge GaN Driver with 630V Input Voltage Range and 5.4ns Propagation Delay In this paper, a halfbridge Gallium Nitride (GaN) driver is proposed. By integrating a wide input voltage range linear regulator (LDO), which is the power supply of the GaN driver, the input voltage of the driver can extend to a wide range of 6xe2x80x9330 V. In order to reduce the propagation delay, a highspeed level shifter with subnanosecond delay is proposed. A dead time generator is designed to introduce a dead time less than 1 ns, which can minimize the reverse conduction loss. In addition, a separate drive output is designed to adjust the gate resistance of the GaN transistors. The designed GaN driver has been designed in 0.18xc2xb5m CMOS technology, and the postlayout simulation results show it can achieve a low propagation delay of 5.4 ns.	Smart Gate Driver ICs for GaN Power Transistors GaN power transistors are well suited in high frequency, high temperature and high powerdensity applications. This paper investigates different gate driver designs for GaN power transistors, including fundamental driving concerns, current commercial products, popular gate driving features, and design trends for the suppression of gate node ringing. Many smart gate drivers offer features such as intelligent onchip CPU for flexible digital control, adjustable gate voltage levels, multistage driving speed for slewrate control, precise timing control, current sensing capability for closeloop regulations, and active gate driving mode with continuous optimized deadtime. This paper provides an overview to modern gate driver design trends and challenges for GaN power transistors.	Understanding the value of social media in organisations : a taxonomic approach While organizations strive to leverage the vast information generated daily from social media platforms and both decision makers and consultants are keen to identify and exploit this informationxe2x80x99s value, there has been little research into social media in the business context. Social media are diverse, varying in scope and functionality, this diversity entailing a complex of attributes and characteristics, resulting in confusion for both researchers and are :66,important precursors in emerging fields and are foundational for rigorous theory building. Though aspects of social media have been studied from various discipline perspectives, this work has been largely descriptive. Thus, while the need for a rigorous taxonomy of social media is strong, previous efforts to classify social media suffer limitations xe2x80x93 e.g. lack of a systematic taxonomic method, overreliance on intuition, disregard for the usersxe2x80x99 perspective, and inadequate consideration this :138,study was mainly initiated by the overarching question xe2x80x9cHow can social media in the business context be usefully classified?xe2x80x9d In order to address this gap, the current paper proposes a systematic method for developing a taxonomy appropriate to :138,study social media in organizations context, combining Nickerson et al,xe2x80x99s (2012) IS taxonomy building guidelines and a Repertory grid (RepGrid) approach.
A CMOS HalfBridge GaN Driver with 630V Input Voltage Range and 5.4ns Propagation Delay In this paper, a halfbridge Gallium Nitride (GaN) driver is proposed. By integrating a wide input voltage range linear regulator (LDO), which is the power supply of the GaN driver, the input voltage of the driver can extend to a wide range of 6xe2x80x9330 V. In order to reduce the propagation delay, a highspeed level shifter with subnanosecond delay is proposed. A dead time generator is designed to introduce a dead time less than 1 ns, which can minimize the reverse conduction loss. In addition, a separate drive output is designed to adjust the gate resistance of the GaN transistors. The designed GaN driver has been designed in 0.18xc2xb5m CMOS technology, and the postlayout simulation results show it can achieve a low propagation delay of 5.4 ns.	Smart Gate Driver ICs for GaN Power Transistors GaN power transistors are well suited in high frequency, high temperature and high powerdensity applications. This paper investigates different gate driver designs for GaN power transistors, including fundamental driving concerns, current commercial products, popular gate driving features, and design trends for the suppression of gate node ringing. Many smart gate drivers offer features such as intelligent onchip CPU for flexible digital control, adjustable gate voltage levels, multistage driving speed for slewrate control, precise timing control, current sensing capability for closeloop regulations, and active gate driving mode with continuous optimized deadtime. This paper provides an overview to modern gate driver design trends and challenges for GaN power transistors.	General Data Protection Regulation in Health Clinics The focus on personal data has merited the EU concerns and attention, resulting in the legislative change regarding privacy and the protection of personal data. The General Data Protection Regulation (GDPR) aims to reform existing measures on the protection of personal data of European Union citizens, with a strong impact on the rights and freedoms of individuals in establishing rules for the processing of personal data. The GDPR considers a special category of personal data, the health data, being these considered as sensitive data and subject to special conditions regarding treatment and access by third parties. This work presents the evolution of the applicability of the Regulation (EU) 2016679 six months after its application in Portuguese health clinics. The results of the present study are discussed in the light of future literature and work are identified.
ObjectAdaptive LSTM Network for Realtime Visual Tracking with Adversarial Data Augmentation. In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classificationbased tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matchingbased tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel realtime visual tracking method, which adopts an objectadaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matchingbased tracking method to preestimate dense proposals and selects highquality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classificationbased tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the stateoftheart performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.	Enhanced Semantic Features via Attention for RealTime Visual Tracking The key to balance the tracking accuracy and speed for object tracking algorithms is to learn powerful features via offline training in a lightweight tracking framework. With the development of attention mechanisms, itxe2x80x99s facile to apply attention to enhance the features without modifying the basic structure of the network. In this paper, a novel combination of different attention modules is implemented into a siamesebased tracker and boosts the tracking performance with little computational burden. In particular, by applying nonlocal selfattention and dual pooling channel attention, the extracted features tend to be more discriminative and adaptive due to the offline learning with tracking targets of different classes. Meanwhile, an IndexDifferenceweight boosts the performance and reduces overfitting when full occlusion occurs. Our experimental results on OTB2013 and OTB2015 show that the tracker using the proposal to implement the attention modules can achieve stateoftheart performance with a speed of 49 frames per second.	How to Make a Medical Error Disclosure to Patients This paper aims to investigate Chinese publicu0027s expectations of medical error disclosure, and to develop guidelines for hospitals. A national questionnaire survey was conducted in 2019, collecting 1,008 valid responses. Respondentsu0027 were asked their views of the severity of error they would like to be disclosed and what, when, where and who they preferred in an error disclosure. Results showed that Chinese public would like to be disclosed any error reached them even no harm. For both moderate and severe outcome errors, they preferred to be disclosed facetoface, all the information as detail as possible, immediately after the error was recognized and in a prepared meeting room. Regarding attendance of patient side, disclosure was expected to be made to the patient and family. For hospital side, the healthcare provider who committed the error, hisher leader, patient safety manager and highpositioned person of the hospital were anticipated to be present. About the person to make the disclosure, respondents preferred the healthcare provider who committed the error in a moderate outcome case while the leader or highpositioned person in a severe case.
ObjectAdaptive LSTM Network for Realtime Visual Tracking with Adversarial Data Augmentation. In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classificationbased tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matchingbased tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel realtime visual tracking method, which adopts an objectadaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matchingbased tracking method to preestimate dense proposals and selects highquality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classificationbased tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the stateoftheart performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.	Enhanced Semantic Features via Attention for RealTime Visual Tracking The key to balance the tracking accuracy and speed for object tracking algorithms is to learn powerful features via offline training in a lightweight tracking framework. With the development of attention mechanisms, itxe2x80x99s facile to apply attention to enhance the features without modifying the basic structure of the network. In this paper, a novel combination of different attention modules is implemented into a siamesebased tracker and boosts the tracking performance with little computational burden. In particular, by applying nonlocal selfattention and dual pooling channel attention, the extracted features tend to be more discriminative and adaptive due to the offline learning with tracking targets of different classes. Meanwhile, an IndexDifferenceweight boosts the performance and reduces overfitting when full occlusion occurs. Our experimental results on OTB2013 and OTB2015 show that the tracker using the proposal to implement the attention modules can achieve stateoftheart performance with a speed of 49 frames per second.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
ObjectAdaptive LSTM Network for Realtime Visual Tracking with Adversarial Data Augmentation. In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classificationbased tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matchingbased tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel realtime visual tracking method, which adopts an objectadaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matchingbased tracking method to preestimate dense proposals and selects highquality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classificationbased tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the stateoftheart performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.	Enhanced Semantic Features via Attention for RealTime Visual Tracking The key to balance the tracking accuracy and speed for object tracking algorithms is to learn powerful features via offline training in a lightweight tracking framework. With the development of attention mechanisms, itxe2x80x99s facile to apply attention to enhance the features without modifying the basic structure of the network. In this paper, a novel combination of different attention modules is implemented into a siamesebased tracker and boosts the tracking performance with little computational burden. In particular, by applying nonlocal selfattention and dual pooling channel attention, the extracted features tend to be more discriminative and adaptive due to the offline learning with tracking targets of different classes. Meanwhile, an IndexDifferenceweight boosts the performance and reduces overfitting when full occlusion occurs. Our experimental results on OTB2013 and OTB2015 show that the tracker using the proposal to implement the attention modules can achieve stateoftheart performance with a speed of 49 frames per second.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
ObjectAdaptive LSTM Network for Realtime Visual Tracking with Adversarial Data Augmentation. In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classificationbased tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matchingbased tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel realtime visual tracking method, which adopts an objectadaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matchingbased tracking method to preestimate dense proposals and selects highquality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classificationbased tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the stateoftheart performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.	Enhanced Semantic Features via Attention for RealTime Visual Tracking The key to balance the tracking accuracy and speed for object tracking algorithms is to learn powerful features via offline training in a lightweight tracking framework. With the development of attention mechanisms, itxe2x80x99s facile to apply attention to enhance the features without modifying the basic structure of the network. In this paper, a novel combination of different attention modules is implemented into a siamesebased tracker and boosts the tracking performance with little computational burden. In particular, by applying nonlocal selfattention and dual pooling channel attention, the extracted features tend to be more discriminative and adaptive due to the offline learning with tracking targets of different classes. Meanwhile, an IndexDifferenceweight boosts the performance and reduces overfitting when full occlusion occurs. Our experimental results on OTB2013 and OTB2015 show that the tracker using the proposal to implement the attention modules can achieve stateoftheart performance with a speed of 49 frames per second.	General Data Protection Regulation in Health Clinics The focus on personal data has merited the EU concerns and attention, resulting in the legislative change regarding privacy and the protection of personal data. The General Data Protection Regulation (GDPR) aims to reform existing measures on the protection of personal data of European Union citizens, with a strong impact on the rights and freedoms of individuals in establishing rules for the processing of personal data. The GDPR considers a special category of personal data, the health data, being these considered as sensitive data and subject to special conditions regarding treatment and access by third parties. This work presents the evolution of the applicability of the Regulation (EU) 2016679 six months after its application in Portuguese health clinics. The results of the present study are discussed in the light of future literature and work are identified.
ObjectAdaptive LSTM Network for Realtime Visual Tracking with Adversarial Data Augmentation. In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classificationbased tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matchingbased tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel realtime visual tracking method, which adopts an objectadaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matchingbased tracking method to preestimate dense proposals and selects highquality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classificationbased tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the stateoftheart performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.	Enhanced Semantic Features via Attention for RealTime Visual Tracking The key to balance the tracking accuracy and speed for object tracking algorithms is to learn powerful features via offline training in a lightweight tracking framework. With the development of attention mechanisms, itxe2x80x99s facile to apply attention to enhance the features without modifying the basic structure of the network. In this paper, a novel combination of different attention modules is implemented into a siamesebased tracker and boosts the tracking performance with little computational burden. In particular, by applying nonlocal selfattention and dual pooling channel attention, the extracted features tend to be more discriminative and adaptive due to the offline learning with tracking targets of different classes. Meanwhile, an IndexDifferenceweight boosts the performance and reduces overfitting when full occlusion occurs. Our experimental results on OTB2013 and OTB2015 show that the tracker using the proposal to implement the attention modules can achieve stateoftheart performance with a speed of 49 frames per second.	Atomistic insight into sequencedirected DNA bending and minicircle formation propensity in the absence and presence of phased Atracts Bending of doublestranded (ds) DNA plays a crucial role in many important biological processes and is relevant for nanotechnological applications. Among all the elements that have been studied in relation to dsDNA bending, Atracts stand out as one of the most controversial. The xe2x80x9cApA wedgexe2x80x9d theory was disproved when a series of linear polynucleotides containing phased 5xe2x80xb2A4T43xe2x80xb2 or 5xe2x80xb2T4A43xe2x80xb2 runs were shown to be bent or straight, respectively, and crystallographic evidence revealed that Atracts are unbent. Furthermore, some of the smallest dsDNA minicircles described to date ( 100 bp in size) lack Atracts and are subjected to varying levels of torsional stress. Representative DNA sequences from this experimental background were modeled in atomic detail and their dynamic behavior was simulated over hundreds of nanoseconds using the AMBER force field ParmBSC1. Subsequent analysis of the resulting trajectories allowed us to (i) unambiguously establish the location of the bends in all cases; (ii) identify the structural elements that are directly responsible for the macroscopically detected curvature; and (iii) reveal the importance not only of coherently summing the effects of the bending elements when they are in synchrony with the natural repeat of the helix (i.e. separated by an integral number of helical turns) but also when alternated with a halfintegral separation of opposite effects. We conclude that the major determinant of the macroscopically observed bending is the proper grouping and phasing of the positive roll imposed by pyrimidinepurine (YR) steps and the negative or null roll characteristic of RY steps and Atracts, respectively. This conclusion is in very good agreement with the structural parameters experimentally derived for much smaller DNA molecules either alone or as found in DNAxe2x80x93protein complexes. We expect that this work will pave the way for future studies on druginduced DNA bending, DNA shape readout by transcription factors, structure of circular extrachromosomal DNA, and custom design of curved DNA origami scaffolds.
Featureaware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimers disease Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimeru0027s disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimeru0027s disease. Multitask based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multitask learning approach via a joint sparsityinducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.	Deep and Joint Learning of Longitudinal Data for Alzheimers Disease Prediction Abstract Alzheimeru0027s disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score predictionu0027s accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimeru0027s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the stateoftheart methods in scores prediction.	Virtually perfect democracy In the 2009 Security Protocols Workshop, the Pretty Good Democracy scheme was presented. This scheme has the appeal of allowing voters to cast votes remotely, e.g. via the Internet, and confirm correct receipt in a single session. The scheme provides a degree of endto end verifiability: receipt of the correct acknowledgement code provides assurance that the vote will be accurately included in the final tally. The scheme does not require any trust in a voter client device. It does however have a number of vulnerabilities: privacy and accuracy depend on vote codes being kept secret. It also suffers the usual coercion style threats common to most remote voting schemes.
Featureaware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimers disease Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimeru0027s disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimeru0027s disease. Multitask based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multitask learning approach via a joint sparsityinducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.	Deep and Joint Learning of Longitudinal Data for Alzheimers Disease Prediction Abstract Alzheimeru0027s disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score predictionu0027s accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimeru0027s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the stateoftheart methods in scores prediction.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Featureaware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimers disease Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimeru0027s disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimeru0027s disease. Multitask based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multitask learning approach via a joint sparsityinducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.	Deep and Joint Learning of Longitudinal Data for Alzheimers Disease Prediction Abstract Alzheimeru0027s disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score predictionu0027s accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimeru0027s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the stateoftheart methods in scores prediction.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
Featureaware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimers disease Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimeru0027s disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimeru0027s disease. Multitask based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multitask learning approach via a joint sparsityinducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.	Deep and Joint Learning of Longitudinal Data for Alzheimers Disease Prediction Abstract Alzheimeru0027s disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score predictionu0027s accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimeru0027s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the stateoftheart methods in scores prediction.	Symmetric Simplicial Pseudoline Arrangements A simplicial arrangement of pseudolines is a collection of topological lines in the projective plane where each region that is formed is triangular. This paper refines and develops David Eppsteinu0027s notion of a kaleidoscope construction for symmetric pseudoline arrangements to construct and analyze several infinite families of simplicial pseudoline arrangements with high degrees of geometric symmetry. In particular, all simplicial pseudoline arrangements with the symmetries of a regular kgon and three symmetry classes of pseudolines, consisting of the mirrors of the kgon and two other symmetry classes, plus sometimes the line at infinity, are classified, and other interesting families (with more symmetry classes of pseudolines) are discussed.
Featureaware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimers disease Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimeru0027s disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimeru0027s disease. Multitask based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multitask learning approach via a joint sparsityinducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.	Deep and Joint Learning of Longitudinal Data for Alzheimers Disease Prediction Abstract Alzheimeru0027s disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score predictionu0027s accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimeru0027s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the stateoftheart methods in scores prediction.	Edge Provisioning and Fairness in VPNDiffServ Networks Customers of Virtual Private Networks (VPNs) over Differentiated Services (DiffServ) infrastructure are most likely to demand not only security but also guaranteed QualityofService (QoS) in pursuance of their desire to have leasedlinelike services. However, expectedly they will be unable or unwilling to predict the load between VPN endpoints. This paper proposes that customers specify their requirements as a range of quantitative services in the Service Level Agreements (SLAs). To support such services Internet Service Providers (ISPs) would need an automated provisioning system that can logically partition the capacity at the edges to various classes (or groups) of VPN connections and manage them efficiently to allow resource sharing among the groups in a dynamic and fair manner. While with edge provisioning a certain amount of resources based on SLAs (traffic contract at edge) are allocated to VPN connections, we also need to provision the interior nodes of a transit network to meet the assurances offered at the boundaries of the network. We, therefore, propose a twolayered model to provision such VPNDiffServ networks where the top layer is responsible for edge provisioning, and drives the lower layer in charge of interior resource provisioning with the help of a Bandwidth Broker (BB). Various algorithms with examples and analyses are presented to provision and allocate resources dynamically at the edges for VPN connections. We have developed a prototype BB performing the required provisioning and connection admission.
Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a realworld setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that oneu0027s choice of which to use should depend on the sample size.	Investigating the effect of binning on causal discovery Binning (a.k.a. discretization) of numerically continuous measurements is a widespread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.	Unmanned agricultural product sales system The invention relates to the field of agricultural product sales, provides an unmanned agricultural product sales system, and aims to solve the problem of agricultural product waste caused by the factthat most farmers can only prepare goods according to guessing and experiences when selling agricultural products at present. The unmanned agricultural product sales system comprises an acquisition module for acquiring selection information of customers; a storage module which prestores a vegetable preparation scheme; a matching module which is used for matching a corresponding side dish schemefrom the storage module according to the selection information of the client; a pushing module which is used for pushing the matched side dish scheme back to the client; an acquisition module which isalso used for acquiring confirmation information of a client; an order module which is used for generating order information according to the confirmation information of the client, wherein the pushing module is used for pushing the order information to the client and the seller, and the acquisition module is also used for acquiring the delivery information of the seller; and a logistics trackingmodule which is used for tracking the delivery information to obtain logistics information, wherein the pushing module is used for pushing the logistics information to the client. The scheme is usedfor sales of unmanned agricultural product shops.
Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a realworld setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that oneu0027s choice of which to use should depend on the sample size.	Investigating the effect of binning on causal discovery Binning (a.k.a. discretization) of numerically continuous measurements is a widespread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a realworld setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that oneu0027s choice of which to use should depend on the sample size.	Investigating the effect of binning on causal discovery Binning (a.k.a. discretization) of numerically continuous measurements is a widespread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a realworld setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that oneu0027s choice of which to use should depend on the sample size.	Investigating the effect of binning on causal discovery Binning (a.k.a. discretization) of numerically continuous measurements is a widespread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.	Development and Flight Experiments of a Bluffbodied X4Blimp The body of X4blimp using four propellers manufactured in conventional research was a structure which has arranged four envelopes in which the buoyancy was equally divided centering on the gondola to which the propeller was attached. However, with this structure, the variation in the buoyancy arose among four envelopes, and there was a problem to which the body posture becomes unstable. In this research, it returns to the starting point which arranges one envelope at the center of the body, and the body of a fundamental structure of the nonstreamline is developed, in which the number of envelopes is suppressed to the minimum, and the variation in the buoyancy is avoided by attaching the special frame which can carry four propellers in the circumference of the envelope. The validity of the manufactured body is demonstrated through some flight experiments.
Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a realworld setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that oneu0027s choice of which to use should depend on the sample size.	Investigating the effect of binning on causal discovery Binning (a.k.a. discretization) of numerically continuous measurements is a widespread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.	Research on Target Deviation Measurement of Projectile Based on Shadow Imaging Method in Laser Screen Velocity Measuring System In the laser screen velocity measuring (LSVM) system, there is a deviation in the consistency of the optoelectronic response between the start light screen and the stop light screen. When the projectile passes through the light screen, the projectilexe2x80x99s overtarget position, at which the timing pulse of the LSVM system is triggered, deviates from the actual position of the light screen (i.e., the target deviation). Therefore, it brings errors to the measurement of the projectilexe2x80x99s velocity, which has become a bottleneck, affecting the construction of a higher precision optoelectronic velocity measuring system. To solve this problem, this paper proposes a method based on highspeed shadow imaging to measure the projectilexe2x80x99s target deviation, xcex94S, when the LSVM system triggers the timing pulse. The infrared pulse laser is collimated by the combination of the aspherical lens to form a parallel laser source that is used as the light source of the system. When the projectile passes through the light screen, the projectilexe2x80x99s overtarget signal is processed by the specially designed trigger circuit. It uses the rising and falling edges of this signal to trigger the camera and pulsed laser source, respectively, to ensure that the projectilexe2x80x99s overtarget image is adequately exposed. By capturing the images of the light screen of the LSVM system and the overtarget projectile separately, this method of image edge detection was used to calculate the target deviation, and this value was used to correct the target distance of the LSVM to improve the accuracy of the measurement of the projectilexe2x80x99s velocity.
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation.	Learning ViaPoint Movement Primitives with Inter and Extrapolation Capabilities Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Viapoints Movement Primitive (VMP), that can adapt to arbitrary viapoints using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.	The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and proactive aggression levels of the HMVE group were significantly higher than those of the LMVE group. Additionally, rumination and anger played a mediating role in the relationship between media violence exposure and aggression. Overall, this study enriches the theoretical understanding of the longterm effect of media violence exposure on individual aggression. Second, this study deepens our understanding of the relatively new and relevant phenomenon of the mechanism between media violence exposure and individual aggression.
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation.	Learning ViaPoint Movement Primitives with Inter and Extrapolation Capabilities Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Viapoints Movement Primitive (VMP), that can adapt to arbitrary viapoints using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.	Greening Trends of Southern China Confirmed by GRACE As reported by the National Aeronautics and Space Administration (NASA), the world has been greening over the last two decades, with the highest greening occurring in China and India. The increasing vegetation will increase plant tissue accumulation and water storage capacity, and all of these variations will cause mass change. In this study, we found that the mass change related to greening in Southern China could be confirmed by Gravity Recovery and Climate Experiment (GRACE) observations. The mean mass change rate detected by GRACE is 6.7 xc2xb1 0.8 mmyr in equivalent water height during 2003xe2x80x932016 in our study region. This is consistent with the sum of vegetation tissue, soil water and groundwater change calculated using multisource data. The vegetation accumulation is approximately 3.8 xc2xb1 1.3 mmyr, which is the major contribution to region mass change. We also found that the change of water storage capacity related to vegetation can be detected by GRACE.
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation.	Learning ViaPoint Movement Primitives with Inter and Extrapolation Capabilities Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Viapoints Movement Primitive (VMP), that can adapt to arbitrary viapoints using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.	Managing Information From the :2,Information highlights the increasing value of information and IT within organizations and shows how organizations use it. It also deals with the crucial relationship between information and personal effectiveness. The use of computer software and communications in a management context are discussed in detail, including how to mould an information system to your needs. The book explains the basics using reallife examples and brings managers uptodate with the latest developments in electronic commerce and the Internet. The book is based on the Management Charter Initiativeu0027s Occupational Standards for Management NVQs and SVQs at level 4. It is particularly suitable for managers on the Certificate in Management, or Part I of the Diploma, especially those accredited by the IM and BTEC.
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation.	Learning ViaPoint Movement Primitives with Inter and Extrapolation Capabilities Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Viapoints Movement Primitive (VMP), that can adapt to arbitrary viapoints using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.	A Critical Look at the 2019 College Admissions Scandal Discusses the 2019 College admissions scandal. Let me begin with a disclaimer: I am making no legal excuses for the participants in the current scandal. I am only offering contextual background that places it in the broader academic, cultural, and political perspective required for understanding. It is only the most recent installment of a wellworn narrative: the controlling elite make their own rules and live by them, if they can get away with it. Unfortunately, some of the participants, who are either serving or facing jail time, didnxe2x80x99t know to not go into a gunfight with a sharp stick. Money alone is not enough to avoid prosecution for fraud: you need political clout. The best protection a defendant can have is a prosecutor who fears political reprisal. Compare how the Koch brothers escaped prosecution for stealing millions of oil dollars from Native American tribes1,2 with the fate of actresses Lori Loughlin and Felicity Huffman, who, at the time of this writing, face jail time for paying bribes to get their children into good universities.3,4 In the former case, the federal prosecutor who dared to empanel a grand jury to get at the truth was fired for cause, which put a quick end to the prosecution. In the latter case, the prosecutors pushed for jail terms and public admonishment with the zeal of Oliver Cromwell. There you have it: stealing oil from Native Americans versus trying to bribe your kids into a great university. Where is the greater crime? Admittedly, these actresses and their
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation.	Learning ViaPoint Movement Primitives with Inter and Extrapolation Capabilities Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Viapoints Movement Primitive (VMP), that can adapt to arbitrary viapoints using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.	How to Make a Medical Error Disclosure to Patients This paper aims to investigate Chinese publicu0027s expectations of medical error disclosure, and to develop guidelines for hospitals. A national questionnaire survey was conducted in 2019, collecting 1,008 valid responses. Respondentsu0027 were asked their views of the severity of error they would like to be disclosed and what, when, where and who they preferred in an error disclosure. Results showed that Chinese public would like to be disclosed any error reached them even no harm. For both moderate and severe outcome errors, they preferred to be disclosed facetoface, all the information as detail as possible, immediately after the error was recognized and in a prepared meeting room. Regarding attendance of patient side, disclosure was expected to be made to the patient and family. For hospital side, the healthcare provider who committed the error, hisher leader, patient safety manager and highpositioned person of the hospital were anticipated to be present. About the person to make the disclosure, respondents preferred the healthcare provider who committed the error in a moderate outcome case while the leader or highpositioned person in a severe case.
Blind Single Image Superresolution with a Mixture of Deep Networks Abstract Existing deep neural network based image superresolution (SR) methods are mostly designed for nonblind cases, where the blur kernel used to generate the lowresolution (LR) images is assumed to be known and fixed. However, this assumption does not hold in many real scenarios. Motivated by the observation that SR of LR images generated by different blur kernels are essentially different but also correlated, we propose a mixture model of deep networks, which is capable of clustering SR tasks of different blur kernels into a set of groups. Each group is composed of correlated SR tasks with similar blur kernels and can be effectively handled by a combination of specific networks in the mixture model. To achieve automatic SR tasks clustering and network selection, we model the blur kernel with a latent variable, which is inferred from the input image by an encoder network. Since the groundtruth of the latent variable is unknown in the training stage, we initialize the encoder network by pretraining it on the blur kernel classification task to avoid trivial solutions. To jointly train the mixture model and the encoder network, we further derive a lower bound of the likelihood function, which circumvents the intractability in direct maximum likelihood estimation. Extensive evaluations are performed on benchmark data sets and validate the effectiveness of the proposed method.	SuperResolution via Wavelet Transform and Advanced Learning Techniques Image superresolution aims to generate a highresolution (HR) image from a lowresolution (LR) input image. In this paper, we propose a deep learningbased approach for image superresolution. We use the wavelet transform to separate the input image into four frequency bands, and train a model for each subband. By processing information from different frequency bands via different CNN models, we can extract features more efficiently and learn better LRtoHR mapping. In addition, we add dense connection to the model to make better use of the internal features in the CNN model. Furthermore, geometric selfensemble is applied in the testing stage to maximize the potential performance. Extensive experiments on four benchmark datasets show the efficiency of the proposed method.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Blind Single Image Superresolution with a Mixture of Deep Networks Abstract Existing deep neural network based image superresolution (SR) methods are mostly designed for nonblind cases, where the blur kernel used to generate the lowresolution (LR) images is assumed to be known and fixed. However, this assumption does not hold in many real scenarios. Motivated by the observation that SR of LR images generated by different blur kernels are essentially different but also correlated, we propose a mixture model of deep networks, which is capable of clustering SR tasks of different blur kernels into a set of groups. Each group is composed of correlated SR tasks with similar blur kernels and can be effectively handled by a combination of specific networks in the mixture model. To achieve automatic SR tasks clustering and network selection, we model the blur kernel with a latent variable, which is inferred from the input image by an encoder network. Since the groundtruth of the latent variable is unknown in the training stage, we initialize the encoder network by pretraining it on the blur kernel classification task to avoid trivial solutions. To jointly train the mixture model and the encoder network, we further derive a lower bound of the likelihood function, which circumvents the intractability in direct maximum likelihood estimation. Extensive evaluations are performed on benchmark data sets and validate the effectiveness of the proposed method.	SuperResolution via Wavelet Transform and Advanced Learning Techniques Image superresolution aims to generate a highresolution (HR) image from a lowresolution (LR) input image. In this paper, we propose a deep learningbased approach for image superresolution. We use the wavelet transform to separate the input image into four frequency bands, and train a model for each subband. By processing information from different frequency bands via different CNN models, we can extract features more efficiently and learn better LRtoHR mapping. In addition, we add dense connection to the model to make better use of the internal features in the CNN model. Furthermore, geometric selfensemble is applied in the testing stage to maximize the potential performance. Extensive experiments on four benchmark datasets show the efficiency of the proposed method.	Development and Flight Experiments of a Bluffbodied X4Blimp The body of X4blimp using four propellers manufactured in conventional research was a structure which has arranged four envelopes in which the buoyancy was equally divided centering on the gondola to which the propeller was attached. However, with this structure, the variation in the buoyancy arose among four envelopes, and there was a problem to which the body posture becomes unstable. In this research, it returns to the starting point which arranges one envelope at the center of the body, and the body of a fundamental structure of the nonstreamline is developed, in which the number of envelopes is suppressed to the minimum, and the variation in the buoyancy is avoided by attaching the special frame which can carry four propellers in the circumference of the envelope. The validity of the manufactured body is demonstrated through some flight experiments.

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