id
stringlengths 9
10
| submitter
stringlengths 2
52
⌀ | authors
stringlengths 4
6.51k
| title
stringlengths 4
246
| comments
stringlengths 1
523
⌀ | journal-ref
stringlengths 4
345
⌀ | doi
stringlengths 11
120
⌀ | report-no
stringlengths 2
243
⌀ | categories
stringlengths 5
98
| license
stringclasses 9
values | abstract
stringlengths 33
3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2201.07188
|
Philip Lazos
|
Aggelos Kiayias, Philip Lazos
|
SoK: Blockchain Governance
| null | null | null | null |
cs.CR cs.CY cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Blockchain systems come with a promise of decentralization that often
stumbles on a roadblock when key decisions about modifying the software
codebase need to be made. This is attested by the fact that both of the two
major cryptocurrencies, Bitcoin and Ethereum, have undergone hard forks that
resulted in the creation of alternative systems, creating confusion and
opportunities for fraudulent activities. These events, and numerous others,
underscore the importance of Blockchain governance, namely the set of processes
that blockchain platforms utilize in order to perform decision-making and
converge to a widely accepted direction for the system to evolve. While a rich
topic of study in other areas, governance of blockchain platforms is lacking a
well established set of methods and practices that are adopted industry wide.
This makes the topic of blockchain governance a fertile domain for a thorough
systematization that we undertake in this work.
We start by distilling a comprehensive array of properties for sound
governance systems drawn from academic sources as well as grey literature of
election systems and blockchain white papers. These are divided into seven
categories, confidentiality, verifiability, accountability, sustainability,
Pareto efficiency, suffrage and liveness that capture the whole spectrum of
desiderata of governance systems. We proceed to classify ten well-documented
blockchain systems. While all properties are satisfied, even partially, by at
least one system, no system that satisfies most of them. Our work lays out a
foundation for assessing blockchain governance processes. While it highlights
shortcomings and deficiencies in currently deployed systems, it can also be a
catalyst for improving these processes to the highest possible standard with
appropriate trade-offs, something direly needed for blockchain platforms to
operate effectively in the long term.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 18:38:26 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jan 2022 18:51:20 GMT"
},
{
"version": "v3",
"created": "Tue, 17 May 2022 17:33:36 GMT"
},
{
"version": "v4",
"created": "Wed, 18 Jan 2023 18:46:18 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Kiayias",
"Aggelos",
""
],
[
"Lazos",
"Philip",
""
]
] |
new_dataset
| 0.953007 |
2211.09800
|
Aleksander Holynski
|
Tim Brooks, Aleksander Holynski, Alexei A. Efros
|
InstructPix2Pix: Learning to Follow Image Editing Instructions
|
Project page with code:
https://www.timothybrooks.com/instruct-pix2pix
| null | null | null |
cs.CV cs.AI cs.CL cs.GR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a method for editing images from human instructions: given an
input image and a written instruction that tells the model what to do, our
model follows these instructions to edit the image. To obtain training data for
this problem, we combine the knowledge of two large pretrained models -- a
language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to
generate a large dataset of image editing examples. Our conditional diffusion
model, InstructPix2Pix, is trained on our generated data, and generalizes to
real images and user-written instructions at inference time. Since it performs
edits in the forward pass and does not require per example fine-tuning or
inversion, our model edits images quickly, in a matter of seconds. We show
compelling editing results for a diverse collection of input images and written
instructions.
|
[
{
"version": "v1",
"created": "Thu, 17 Nov 2022 18:58:43 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Jan 2023 17:31:52 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Brooks",
"Tim",
""
],
[
"Holynski",
"Aleksander",
""
],
[
"Efros",
"Alexei A.",
""
]
] |
new_dataset
| 0.999647 |
2212.07601
|
Chase Mathews
|
Evangelos Chatziandreou, Chase W. Mathews, David J. Braun
|
Design of a Parallel Elastic Actuator with a Continuously-Adjustable
Equilibrium Position
|
6 pages, 5 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we present an adjustable-equilibrium parallel elastic actuator
(AE-PEA). The actuator consists of a motor, an equilibrium adjusting mechanism,
and a spring arranged into a cylindrical geometry, similar to a motor-gearbox
assembly. The novel component of the actuator is the equilibrium adjusting
mechanism which (i) does not require external energy to maintain the
equilibrium position of the actuator even if the spring is deformed and (ii)
enables equilibrium position control with low energy cost by rotating the
spring while keeping it undeformed. Adjustable equilibrium parallel elastic
actuators resolve the main limitation of parallel elastic actuators (PEAs) by
enabling energy-efficient operation at different equilibrium positions, instead
of being limited to energy-efficient operation at a single equilibrium
position. We foresee the use of AE-PEAs in industrial robots, mobile robots,
exoskeletons, and prostheses, where efficient oscillatory motion and gravity
compensation at different positions are required.
|
[
{
"version": "v1",
"created": "Thu, 15 Dec 2022 03:06:43 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 21:05:03 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Chatziandreou",
"Evangelos",
""
],
[
"Mathews",
"Chase W.",
""
],
[
"Braun",
"David J.",
""
]
] |
new_dataset
| 0.998255 |
2301.06249
|
Xiaowei Chen
|
Xiaowei Chen, Xiao Jiang, Jiawei Fang, Shihui Guo, Juncong Lin,
Minghong Liao, Guoliang Luo, Hongbo Fu
|
DisPad: Flexible On-Body Displacement of Fabric Sensors for Robust
Joint-Motion Tracking
|
25 pages, 14 figures
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The last few decades have witnessed an emerging trend of wearable soft
sensors; however, there are important signal-processing challenges for soft
sensors that still limit their practical deployment. They are error-prone when
displaced, resulting in significant deviations from their ideal sensor output.
In this work, we propose a novel prototype that integrates an elbow pad with a
sparse network of soft sensors. Our prototype is fully bio-compatible,
stretchable, and wearable. We develop a learning-based method to predict the
elbow orientation angle and achieve an average tracking error of 9.82 degrees
for single-user multi-motion experiments. With transfer learning, our method
achieves the average tracking errors of 10.98 degrees and 11.81 degrees across
different motion types and users, respectively. Our core contributions lie in a
solution that realizes robust and stable human joint motion tracking across
different device displacements.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 03:54:32 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Chen",
"Xiaowei",
""
],
[
"Jiang",
"Xiao",
""
],
[
"Fang",
"Jiawei",
""
],
[
"Guo",
"Shihui",
""
],
[
"Lin",
"Juncong",
""
],
[
"Liao",
"Minghong",
""
],
[
"Luo",
"Guoliang",
""
],
[
"Fu",
"Hongbo",
""
]
] |
new_dataset
| 0.999297 |
2301.07098
|
Andreas Ostermaier
|
Sebastian Kr\"ugel, Andreas Ostermaier, Matthias Uhl
|
The moral authority of ChatGPT
| null | null | null | null |
cs.CY cs.AI cs.HC cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
ChatGPT is not only fun to chat with, but it also searches information,
answers questions, and gives advice. With consistent moral advice, it might
improve the moral judgment and decisions of users, who often hold contradictory
moral beliefs. Unfortunately, ChatGPT turns out highly inconsistent as a moral
advisor. Nonetheless, it influences users' moral judgment, we find in an
experiment, even if they know they are advised by a chatting bot, and they
underestimate how much they are influenced. Thus, ChatGPT threatens to corrupt
rather than improves users' judgment. These findings raise the question of how
to ensure the responsible use of ChatGPT and similar AI. Transparency is often
touted but seems ineffective. We propose training to improve digital literacy.
|
[
{
"version": "v1",
"created": "Fri, 13 Jan 2023 20:24:38 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Krügel",
"Sebastian",
""
],
[
"Ostermaier",
"Andreas",
""
],
[
"Uhl",
"Matthias",
""
]
] |
new_dataset
| 0.996839 |
2301.07163
|
Shubham Atreja
|
Shubham Atreja, Jane Im, Paul Resnick, Libby Hemphill
|
AppealMod: Shifting Effort from Moderators to Users Making Appeals
|
under review
| null | null | null |
cs.CY cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As content moderation becomes a central aspect of all social media platforms
and online communities, interest has grown in how to make moderation decisions
contestable. On social media platforms where individual communities moderate
their own activities, the responsibility to address user appeals falls on
volunteers from within the community. While there is a growing body of work
devoted to understanding and supporting the volunteer moderators' workload,
little is known about their practice of handling user appeals. Through a
collaborative and iterative design process with Reddit moderators, we found
that moderators spend considerable effort in investigating user ban appeals and
desired to directly engage with users and retain their agency over each
decision. To fulfill their needs, we designed and built AppealMod, a system
that asks users to put more effort in their appeals, by providing additional
information, before their appeals are reviewed by human moderators. In addition
to giving moderators more information, we expected the friction in the appeal
process would lead to a selection effect among users, with many insincere and
toxic appeals being abandoned before getting any attention from human
moderators. To evaluate our system, we conducted a field experiment in a Reddit
community of over 29 million users that lasted for four months. As a result of
the selection effect, moderators viewed only 30\% of initial appeals and less
than 10\% of the toxically worded appeals; yet they granted roughly the same
number of appeals. Overall, our system is effective at reducing moderator
workload and minimizing their exposure to toxic content while honoring their
preference for direct engagement and agency in appeals.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 20:15:20 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Atreja",
"Shubham",
""
],
[
"Im",
"Jane",
""
],
[
"Resnick",
"Paul",
""
],
[
"Hemphill",
"Libby",
""
]
] |
new_dataset
| 0.982226 |
2301.07183
|
Runcong Zhao
|
Runcong Zhao and Lin Gui and Hanqi Yan and Yulan He
|
Tracking Brand-Associated Polarity-Bearing Topics in User Reviews
| null | null | null | null |
cs.IR cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Monitoring online customer reviews is important for business organisations to
measure customer satisfaction and better manage their reputations. In this
paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to
automatically detect and track brand-associated sentiment scores and
polarity-bearing topics from product reviews organised in temporally-ordered
time intervals. dBTM models the evolution of the latent brand polarity scores
and the topic-word distributions over time by Gaussian state space models. It
also incorporates a meta learning strategy to control the update of the
topic-word distribution in each time interval in order to ensure smooth topic
transitions and better brand score predictions. It has been evaluated on a
dataset constructed from MakeupAlley reviews and a hotel review dataset.
Experimental results show that dBTM outperforms a number of competitive
baselines in brand ranking, achieving a good balance of topic coherence and
uniqueness, and extracting well-separated polarity-bearing topics across time
intervals.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 18:30:34 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Zhao",
"Runcong",
""
],
[
"Gui",
"Lin",
""
],
[
"Yan",
"Hanqi",
""
],
[
"He",
"Yulan",
""
]
] |
new_dataset
| 0.985802 |
2301.07189
|
Gopika Ajaykumar
|
Gopika Ajaykumar and Chien-Ming Huang
|
Multimodal Robot Programming by Demonstration: A Preliminary Exploration
|
6 pages, 6 figures, 2021 RSS Workshop on Accessibility of Robot
Programming and the Work of the Future
| null | null | null |
cs.RO cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Recent years have seen a growth in the number of industrial robots working
closely with end-users such as factory workers. This growing use of
collaborative robots has been enabled in part due to the availability of
end-user robot programming methods that allow users who are not robot
programmers to teach robots task actions. Programming by Demonstration (PbD) is
one such end-user programming method that enables users to bypass the
complexities of specifying robot motions using programming languages by instead
demonstrating the desired robot behavior. Demonstrations are often provided by
physically guiding the robot through the motions required for a task action in
a process known as kinesthetic teaching. Kinesthetic teaching enables users to
directly demonstrate task behaviors in the robot's configuration space, making
it a popular end-user robot programming method for collaborative robots known
for its low cognitive burden. However, because kinesthetic teaching restricts
the programmer's teaching to motion demonstrations, it fails to leverage
information from other modalities that humans naturally use when providing
physical task demonstrations to one other, such as gaze and speech.
Incorporating multimodal information into the traditional kinesthetic
programming workflow has the potential to enhance robot learning by
highlighting critical aspects of a program, reducing ambiguity, and improving
situational awareness for the robot learner and can provide insight into the
human programmer's intent and difficulties. In this extended abstract, we
describe a preliminary study on multimodal kinesthetic demonstrations and
future directions for using multimodal demonstrations to enhance robot learning
and user programming experiences.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 21:05:13 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Ajaykumar",
"Gopika",
""
],
[
"Huang",
"Chien-Ming",
""
]
] |
new_dataset
| 0.995889 |
2301.07197
|
Mehmet Efe Tiryaki
|
Mehmet Efe Tiryaki, Fatih Dogangun, Cem Balda Dayan, Paul Wrede, Metin
Sitti
|
MRI-powered Magnetic Miniature Capsule Robot with HIFU-controlled
On-demand Drug Delivery
|
6 pages, 6 figures, accepted to ICRA2023
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Magnetic resonance imaging (MRI)-guided robotic systems offer great potential
for new minimally invasive medical tools, including MRI-powered miniature
robots. By re-purposing the imaging hardware of an MRI scanner, the magnetic
miniature robot could be navigated into the remote part of the patient's body
without needing tethered endoscopic tools. However, the state-of-art
MRI-powered magnetic miniature robots have limited functionality besides
navigation. Here, we propose an MRI-powered magnetic miniature capsule robot
benefiting from acoustic streaming forces generated by MRI-guided
high-intensity focus ultrasound (HIFU) for controlled drug release. Our design
comprises a polymer capsule shell with a submillimeter-diameter drug-release
hole that captures an air bubble functioning as a stopper. We use the HIFU
pulse to initiate drug release by removing the air bubble once the capsule
robot reaches the target location. By controlling acoustic pressure, we also
regulate the drug release rate for multiple location targeting during
navigation. We demonstrated that the proposed magnetic capsule robot could
travel at high speed up to 1.13 cm/s in ex vivo porcine small intestine and
release drug to multiple target sites in a single operation, using a
combination of MRI-powered actuation and HIFU-controlled release. The proposed
MRI-guided microrobotic drug release system will greatly impact minimally
invasive medical procedures by allowing on-demand targeted drug delivery.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 21:23:30 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Tiryaki",
"Mehmet Efe",
""
],
[
"Dogangun",
"Fatih",
""
],
[
"Dayan",
"Cem Balda",
""
],
[
"Wrede",
"Paul",
""
],
[
"Sitti",
"Metin",
""
]
] |
new_dataset
| 0.997814 |
2301.07202
|
Ali Abedi Abedi
|
Christopher Vattheuer, Charlie Liu, Ali Abedi, Omid Abari
|
Are Home Security Systems Reliable?
| null | null | null | null |
cs.CR cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Home security systems have become increasingly popular since they provide an
additional layer of protection and peace of mind. These systems typically
include battery-powered motion sensors, contact sensors, and smart locks.
Z-Wave is a very popular wireless communication technology for these low-power
systems. In this paper, we demonstrate two new attacks targeting Z-Wave
devices. First, we show how an attacker can remotely attack Z-Wave security
devices to increase their power consumption by three orders of magnitude,
reducing their battery life from a few years to just a few hours. Second, we
show multiple Denial of Service (DoS) attacks which enables an attacker to
interrupt the operation of security systems in just a few seconds. Our
experiments show that these attacks are effective even when the attacker device
is in a car 100 meters away from the targeted house.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 21:27:01 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Vattheuer",
"Christopher",
""
],
[
"Liu",
"Charlie",
""
],
[
"Abedi",
"Ali",
""
],
[
"Abari",
"Omid",
""
]
] |
new_dataset
| 0.998517 |
2301.07271
|
Tanj Bennett
|
Tanj Bennett
|
Chip Guard ECC: An Efficient, Low Latency Method
|
6 pages, 1 figure
| null | null | null |
cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
Chip Guard is a new approach to symbol-correcting error correction codes. It
can be scaled to various data burst sizes and reliability levels. A specific
version for DDR5 is described. It uses the usual DDR5 configuration of 8 data
chips, plus 2 chips for ECC and metadata, with 64-bit bursts per chip, to
support whole-chip correction reliably and with high probity (reporting of
uncorrectable faults). Various numbers of metadata bits may be supported with
defined tradeoffs for reliability and probity. The method should correct all
bounded faults of a single chip, with less than 1 in 10^12 chance of failing to
correct unbounded faults in one chip, or less than 1 in 10^12 chance of failure
to detect an uncorrected fault which affects multiple chips.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 02:27:25 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Bennett",
"Tanj",
""
]
] |
new_dataset
| 0.998536 |
2301.07301
|
Rui Wan
|
Rui Wan, Tianyun Zhao, Wei Zhao
|
PTA-Det: Point Transformer Associating Point cloud and Image for 3D
Object Detection
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In autonomous driving, 3D object detection based on multi-modal data has
become an indispensable approach when facing complex environments around the
vehicle. During multi-modal detection, LiDAR and camera are simultaneously
applied for capturing and modeling. However, due to the intrinsic discrepancies
between the LiDAR point and camera image, the fusion of the data for object
detection encounters a series of problems. Most multi-modal detection methods
perform even worse than LiDAR-only methods. In this investigation, we propose a
method named PTA-Det to improve the performance of multi-modal detection.
Accompanied by PTA-Det, a Pseudo Point Cloud Generation Network is proposed,
which can convert image information including texture and semantic features by
pseudo points. Thereafter, through a transformer-based Point Fusion Transition
(PFT) module, the features of LiDAR points and pseudo points from image can be
deeply fused under a unified point-based representation. The combination of
these modules can conquer the major obstacle in feature fusion across
modalities and realizes a complementary and discriminative representation for
proposal generation. Extensive experiments on the KITTI dataset show the
PTA-Det achieves a competitive result and support its effectiveness.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 04:35:49 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Wan",
"Rui",
""
],
[
"Zhao",
"Tianyun",
""
],
[
"Zhao",
"Wei",
""
]
] |
new_dataset
| 0.999031 |
2301.07315
|
Shrey Jain
|
Aaditya Bhat, Shrey Jain
|
Face Recognition in the age of CLIP & Billion image datasets
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
CLIP (Contrastive Language-Image Pre-training) models developed by OpenAI
have achieved outstanding results on various image recognition and retrieval
tasks, displaying strong zero-shot performance. This means that they are able
to perform effectively on tasks for which they have not been explicitly
trained. Inspired by the success of OpenAI CLIP, a new publicly available
dataset called LAION-5B was collected which resulted in the development of open
ViT-H/14, ViT-G/14 models that outperform the OpenAI L/14 model. The LAION-5B
dataset also released an approximate nearest neighbor index, with a web
interface for search & subset creation.
In this paper, we evaluate the performance of various CLIP models as
zero-shot face recognizers. Our findings show that CLIP models perform well on
face recognition tasks, but increasing the size of the CLIP model does not
necessarily lead to improved accuracy. Additionally, we investigate the
robustness of CLIP models against data poisoning attacks by testing their
performance on poisoned data. Through this analysis, we aim to understand the
potential consequences and misuse of search engines built using CLIP models,
which could potentially function as unintentional face recognition engines.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 05:34:57 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Bhat",
"Aaditya",
""
],
[
"Jain",
"Shrey",
""
]
] |
new_dataset
| 0.993824 |
2301.07322
|
Youbao Tang
|
Xiaoye Qian, Youbao Tang, Ning Zhang, Mei Han, Jing Xiao, Ming-Chun
Huang, Ruei-Sung Lin
|
HSTFormer: Hierarchical Spatial-Temporal Transformers for 3D Human Pose
Estimation
|
The first two authors have equal contribution
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transformer-based approaches have been successfully proposed for 3D human
pose estimation (HPE) from 2D pose sequence and achieved state-of-the-art
(SOTA) performance. However, current SOTAs have difficulties in modeling
spatial-temporal correlations of joints at different levels simultaneously.
This is due to the poses' spatial-temporal complexity. Poses move at various
speeds temporarily with various joints and body-parts movement spatially.
Hence, a cookie-cutter transformer is non-adaptable and can hardly meet the
"in-the-wild" requirement. To mitigate this issue, we propose Hierarchical
Spatial-Temporal transFormers (HSTFormer) to capture multi-level joints'
spatial-temporal correlations from local to global gradually for accurate 3D
HPE. HSTFormer consists of four transformer encoders (TEs) and a fusion module.
To the best of our knowledge, HSTFormer is the first to study hierarchical TEs
with multi-level fusion. Extensive experiments on three datasets (i.e.,
Human3.6M, MPI-INF-3DHP, and HumanEva) demonstrate that HSTFormer achieves
competitive and consistent performance on benchmarks with various scales and
difficulties. Specifically, it surpasses recent SOTAs on the challenging
MPI-INF-3DHP dataset and small-scale HumanEva dataset, with a highly
generalized systematic approach. The code is available at:
https://github.com/qianxiaoye825/HSTFormer.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 05:54:02 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Qian",
"Xiaoye",
""
],
[
"Tang",
"Youbao",
""
],
[
"Zhang",
"Ning",
""
],
[
"Han",
"Mei",
""
],
[
"Xiao",
"Jing",
""
],
[
"Huang",
"Ming-Chun",
""
],
[
"Lin",
"Ruei-Sung",
""
]
] |
new_dataset
| 0.999221 |
2301.07368
|
Marco Ruffini
|
M. Ruffini, C. Xie, L. shi and J. S. Wey
|
Connected OFCity Challenge: an updated perspective on technology for
connected cities
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
This paper gives an update on technologies discussed during three OFC events,
called 'The Connected OFCity Challenge', from 2016 to 2018. It focuses on
research development and field deployment of Passive Optical Networks and
Cloud-Based technologies.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 08:33:23 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Ruffini",
"M.",
""
],
[
"Xie",
"C.",
""
],
[
"shi",
"L.",
""
],
[
"Wey",
"J. S.",
""
]
] |
new_dataset
| 0.990774 |
2301.07378
|
Pardeep Singh
|
Pardeep Singh, Rabindra Lamsal, Monika, Satish Chand, Bhawna Shishodia
|
GeoCovaxTweets: COVID-19 Vaccines and Vaccination-specific Global
Geotagged Twitter Conversations
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Social media platforms provide actionable information during crises and
pandemic outbreaks. The COVID-19 pandemic has imposed a chronic public health
crisis worldwide, with experts considering vaccines as the ultimate prevention
to achieve herd immunity against the virus. A proportion of people may turn to
social media platforms to oppose vaccines and vaccination, hindering government
efforts to eradicate the virus. This paper presents the COVID-19 vaccines and
vaccination-specific global geotagged tweets dataset, GeoCovaxTweets, that
contains more than 1.8 million tweets, with location information and longer
temporal coverage, originating from 233 countries and territories between
January 2020 and November 2022. The paper discusses the dataset's curation
method and how it can be re-created locally, and later explores the dataset
through multiple tweets distributions and briefly discusses its potential use
cases. We anticipate that the dataset will assist the researchers in the crisis
computing domain to explore the conversational dynamics of COVID-19 vaccines
and vaccination Twitter discourse through numerous spatial and temporal
dimensions concerning trends, shifts in opinions, misinformation, and
anti-vaccination campaigns.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 09:12:21 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Singh",
"Pardeep",
""
],
[
"Lamsal",
"Rabindra",
""
],
[
"Monika",
"",
""
],
[
"Chand",
"Satish",
""
],
[
"Shishodia",
"Bhawna",
""
]
] |
new_dataset
| 0.999685 |
2301.07424
|
Sajad Tavakoil
|
Shafagh A. Pashaki, Ali Nahvi, Ahmad Ahmadi, Sajad Tavakoli, Shahin
Naeemi, Salar H. Shamchi
|
Autonomous Slalom Maneuver Based on Expert Drivers' Behavior Using
Convolutional Neural Network
| null | null | null | null |
cs.RO cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Lane changing and obstacle avoidance are one of the most important tasks in
automated cars. To date, many algorithms have been suggested that are generally
based on path trajectory or reinforcement learning approaches. Although these
methods have been efficient, they are not able to accurately imitate a smooth
path traveled by an expert driver. In this paper, a method is presented to
mimic drivers' behavior using a convolutional neural network (CNN). First,
seven features are extracted from a dataset gathered from four expert drivers
in a driving simulator. Then, these features are converted from 1D arrays to 2D
arrays and injected into a CNN. The CNN model computes the desired steering
wheel angle and sends it to an adaptive PD controller. Finally, the control
unit applies proper torque to the steering wheel. Results show that the CNN
model can mimic the drivers' behavior with an R2-squared of 0.83. Also, the
performance of the presented method was evaluated in the driving simulator for
17 trials, which avoided all traffic cones successfully. In some trials, the
presented method performed a smoother maneuver compared to the expert drivers.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 10:47:43 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Pashaki",
"Shafagh A.",
""
],
[
"Nahvi",
"Ali",
""
],
[
"Ahmadi",
"Ahmad",
""
],
[
"Tavakoli",
"Sajad",
""
],
[
"Naeemi",
"Shahin",
""
],
[
"Shamchi",
"Salar H.",
""
]
] |
new_dataset
| 0.977417 |
2301.07431
|
Ge Zhu
|
Ge Zhu, Jinbao Li and Yahong Guo
|
Sharp Eyes: A Salient Object Detector Working The Same Way as Human
Visual Characteristics
| null | null | null | null |
cs.CV cs.AI cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current methods aggregate multi-level features or introduce edge and skeleton
to get more refined saliency maps. However, little attention is paid to how to
obtain the complete salient object in cluttered background, where the targets
are usually similar in color and texture to the background. To handle this
complex scene, we propose a sharp eyes network (SENet) that first seperates the
object from scene, and then finely segments it, which is in line with human
visual characteristics, i.e., to look first and then focus. Different from
previous methods which directly integrate edge or skeleton to supplement the
defects of objects, the proposed method aims to utilize the expanded objects to
guide the network obtain complete prediction. Specifically, SENet mainly
consists of target separation (TS) brach and object segmentation (OS) branch
trained by minimizing a new hierarchical difference aware (HDA) loss. In the TS
branch, we construct a fractal structure to produce saliency features with
expanded boundary via the supervision of expanded ground truth, which can
enlarge the detail difference between foreground and background. In the OS
branch, we first aggregate multi-level features to adaptively select
complementary components, and then feed the saliency features with expanded
boundary into aggregated features to guide the network obtain complete
prediction. Moreover, we propose the HDA loss to further improve the structural
integrity and local details of the salient objects, which assigns weight to
each pixel according to its distance from the boundary hierarchically. Hard
pixels with similar appearance in border region will be given more attention
hierarchically to emphasize their importance in completeness prediction.
Comprehensive experimental results on five datasets demonstrate that the
proposed approach outperforms the state-of-the-art methods both quantitatively
and qualitatively.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 11:00:45 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Zhu",
"Ge",
""
],
[
"Li",
"Jinbao",
""
],
[
"Guo",
"Yahong",
""
]
] |
new_dataset
| 0.999524 |
2301.07566
|
Grigorii Trofimiuk
|
Grigorii Trofimiuk, Evgeny Belyaev, Peter Trifonov
|
Distributed Video Coding Based on Polar Codes
|
This is a slightly modified version of a paper accepted for
publication in IEEE Communications Letters
| null |
10.1109/LCOMM.2023.3237285
| null |
cs.IT eess.IV math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this letter we present an improved distributed video coding (DVC) scheme
based on polar coding techniques. Firstly, we adapt log-likelihood ratios
(LLRs) for DVC with integer implementation of a discrete cosine transform
(DCT). We propose a computationally efficient and numerically stable
modification of these LLRs based on the simplified methods of polar codes
decoding. We show that on average this approach provides 0.3 dB PSNR gain for
DVC with LDPC accumulated (LDPCA) codes. Secondly, we introduce the nested
shortened polar codes construction algorithm. We demonstrate that replacement
of LDPCA by polar codes improves PSNR by 0.1 dB on average, whereas, for videos
with relatively high motion level, the gain reaches up to 0.23, 0.39 and 0.55
dB for Group of Pictures (GOP) lengths 2, 4 and 8 frames, respectively.
Finally, experimental results demonstrate that DVC with polar codes and
Tal-Vardy list decoder operates up to two times faster than DVC with LDPCA code
and belief propagation (BP) decoder.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 14:36:50 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Trofimiuk",
"Grigorii",
""
],
[
"Belyaev",
"Evgeny",
""
],
[
"Trifonov",
"Peter",
""
]
] |
new_dataset
| 0.998408 |
2301.07613
|
Muhammad Ali Farooq
|
Muhammad Ali Farooq, Waseem Shariff, Faisal Khan, Peter Corcoran
|
Development, Optimization, and Deployment of Thermal Forward Vision
Systems for Advance Vehicular Applications on Edge Devices
|
The paper is accepted and in the publication phase at ICMV 2022
Conference. Link: http://icmv.org/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this research work, we have proposed a thermal tiny-YOLO multi-class
object detection (TTYMOD) system as a smart forward sensing system that should
remain effective in all weather and harsh environmental conditions using an
end-to-end YOLO deep learning framework. It provides enhanced safety and
improved awareness features for driver assistance. The system is trained on
large-scale thermal public datasets as well as newly gathered novel
open-sourced dataset comprising of more than 35,000 distinct thermal frames.
For optimal training and convergence of YOLO-v5 tiny network variant on thermal
data, we have employed different optimizers which include stochastic decent
gradient (SGD), Adam, and its variant AdamW which has an improved
implementation of weight decay. The performance of thermally tuned tiny
architecture is further evaluated on the public as well as locally gathered
test data in diversified and challenging weather and environmental conditions.
The efficacy of a thermally tuned nano network is quantified using various
qualitative metrics which include mean average precision, frames per second
rate, and average inference time. Experimental outcomes show that the network
achieved the best mAP of 56.4% with an average inference time/ frame of 4
milliseconds. The study further incorporates optimization of tiny network
variant using the TensorFlow Lite quantization tool this is beneficial for the
deployment of deep learning architectures on the edge and mobile devices. For
this study, we have used a raspberry pi 4 computing board for evaluating the
real-time feasibility performance of an optimized version of the thermal object
detection network for the automotive sensor suite. The source code, trained and
optimized models and complete validation/ testing results are publicly
available at
https://github.com/MAli-Farooq/Thermal-YOLO-And-Model-Optimization-Using-TensorFlowLite.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 15:45:33 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Farooq",
"Muhammad Ali",
""
],
[
"Shariff",
"Waseem",
""
],
[
"Khan",
"Faisal",
""
],
[
"Corcoran",
"Peter",
""
]
] |
new_dataset
| 0.99648 |
2301.07627
|
Huadeng Wang
|
Huadeng Wang, Hao Xu, Bingbing Li, Xipeng Pan, Lingqi Zeng, Rushi Lan,
Xiaonan Luo
|
A novel dataset and a two-stage mitosis nuclei detection method based on
hybrid anchor branch
|
22 pages,10 figures, 8 tables
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mitosis detection is one of the challenging problems in computational
pathology, and mitotic count is an important index of cancer grading for
pathologists. However, current counts of mitotic nuclei rely on pathologists
looking microscopically at the number of mitotic nuclei in hot spots, which is
subjective and time-consuming. In this paper, we propose a two-stage cascaded
network, named FoCasNet, for mitosis detection. In the first stage, a detection
network named M_det is proposed to detect as many mitoses as possible. In the
second stage, a classification network M_class is proposed to refine the
results of the first stage. In addition, the attention mechanism, normalization
method, and hybrid anchor branch classification subnet are introduced to
improve the overall detection performance. Our method achieves the current
highest F1-score of 0.888 on the public dataset ICPR 2012. We also evaluated
our method on the GZMH dataset released by our research team for the first time
and reached the highest F1-score of 0.563, which is also better than multiple
classic detection networks widely used at present. It confirmed the
effectiveness and generalization of our method. The code will be available at:
https://github.com/antifen/mitosis-nuclei-detection.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 16:11:09 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Wang",
"Huadeng",
""
],
[
"Xu",
"Hao",
""
],
[
"Li",
"Bingbing",
""
],
[
"Pan",
"Xipeng",
""
],
[
"Zeng",
"Lingqi",
""
],
[
"Lan",
"Rushi",
""
],
[
"Luo",
"Xiaonan",
""
]
] |
new_dataset
| 0.999746 |
2301.07652
|
Wei Xie
|
Wei Xie, Zhipeng Yu, Zimeng Zhao, Binghui Zuo, Yangang Wang
|
HMDO: Markerless Multi-view Hand Manipulation Capture with Deformable
Objects
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We construct the first markerless deformable interaction dataset recording
interactive motions of the hands and deformable objects, called HMDO (Hand
Manipulation with Deformable Objects). With our built multi-view capture
system, it captures the deformable interactions with multiple perspectives,
various object shapes, and diverse interactive forms. Our motivation is the
current lack of hand and deformable object interaction datasets, as 3D hand and
deformable object reconstruction is challenging. Mainly due to mutual
occlusion, the interaction area is difficult to observe, the visual features
between the hand and the object are entangled, and the reconstruction of the
interaction area deformation is difficult. To tackle this challenge, we propose
a method to annotate our captured data. Our key idea is to collaborate with
estimated hand features to guide the object global pose estimation, and then
optimize the deformation process of the object by analyzing the relationship
between the hand and the object. Through comprehensive evaluation, the proposed
method can reconstruct interactive motions of hands and deformable objects with
high quality. HMDO currently consists of 21600 frames over 12 sequences. In the
future, this dataset could boost the research of learning-based reconstruction
of deformable interaction scenes.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 16:55:15 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Xie",
"Wei",
""
],
[
"Yu",
"Zhipeng",
""
],
[
"Zhao",
"Zimeng",
""
],
[
"Zuo",
"Binghui",
""
],
[
"Wang",
"Yangang",
""
]
] |
new_dataset
| 0.99462 |
2301.07669
|
Regis Kopper
|
Zekun Cao and Regis Kopper
|
Real-Time Viewport-Aware Optical Flow Estimation in 360-degree Videos
for Visually-Induced Motion Sickness Mitigation
| null | null | null | null |
cs.HC cs.GR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Visually-induced motion sickness (VIMS), a side effect of illusionary motions
caused by visual stimulation, is one of the major obstacles to the widespread
use of Virtual Reality (VR). Along with scene object information, the visual
stimulation can be primarily indicated by the optical flow, which characterizes
the motion pattern, such as the intensity and direction of the moving image. We
estimated real-time optical flow in 360-degree videos targeted at immersive
user interactive visualization based on the user's current viewport. The
proposed method allows the estimation of the customized visual flow for each
experience of dynamic 360-degree videos and is an improvement over previous
methods which take into account a single optical flow value for the entire
equirectangular frame. We applied our method to modulate the opacity of
Granulated Rest Frames (GRF), a novel technique consisting of visual noise-like
randomly distributed visual references that are stable to the user's body
during the experience of immersive prerecorded 360-degree videos. We report the
results of a preliminary one-day between-subject study with 18 participants
where users watched a 2-minute high-intensity 360-degree video. Results show
that GRF combined with real-time optical flow estimation may help users be more
comfortable when they watch the 360-degree videos, although the improvement is
not significant.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 17:24:30 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"Cao",
"Zekun",
""
],
[
"Kopper",
"Regis",
""
]
] |
new_dataset
| 0.985984 |
2301.07673
|
Xingyi He
|
Xingyi He, Jiaming Sun, Yuang Wang, Di Huang, Hujun Bao, Xiaowei Zhou
|
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD
Models
|
Accepted to NeurIPS 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a new method for object pose estimation without CAD models. The
previous feature-matching-based method OnePose has shown promising results
under a one-shot setting which eliminates the need for CAD models or
object-specific training. However, OnePose relies on detecting repeatable image
keypoints and is thus prone to failure on low-textured objects. We propose a
keypoint-free pose estimation pipeline to remove the need for repeatable
keypoint detection. Built upon the detector-free feature matching method LoFTR,
we devise a new keypoint-free SfM method to reconstruct a semi-dense
point-cloud model for the object. Given a query image for object pose
estimation, a 2D-3D matching network directly establishes 2D-3D correspondences
between the query image and the reconstructed point-cloud model without first
detecting keypoints in the image. Experiments show that the proposed pipeline
outperforms existing one-shot CAD-model-free methods by a large margin and is
comparable to CAD-model-based methods on LINEMOD even for low-textured objects.
We also collect a new dataset composed of 80 sequences of 40 low-textured
objects to facilitate future research on one-shot object pose estimation. The
supplementary material, code and dataset are available on the project page:
https://zju3dv.github.io/onepose_plus_plus/.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 17:47:13 GMT"
}
] | 2023-01-19T00:00:00 |
[
[
"He",
"Xingyi",
""
],
[
"Sun",
"Jiaming",
""
],
[
"Wang",
"Yuang",
""
],
[
"Huang",
"Di",
""
],
[
"Bao",
"Hujun",
""
],
[
"Zhou",
"Xiaowei",
""
]
] |
new_dataset
| 0.970387 |
1804.09914
|
Hassan Habibi Gharakheili
|
Hassan Habibi Gharakheili, Minzhao Lyu, Yu Wang, Himal Kumar, Vijay
Sivaraman
|
iTeleScope: Intelligent Video Telemetry and Classification in Real-Time
using Software Defined Networking
|
12 pages, 16 figures
| null |
10.1109/TNSM.2019.2929511
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video continues to dominate network traffic, yet operators today have poor
visibility into the number, duration, and resolutions of the video streams
traversing their domain. Current approaches are inaccurate, expensive, or
unscalable, as they rely on statistical sampling, middle-box hardware, or
packet inspection software. We present {\em iTelescope}, the first intelligent,
inexpensive, and scalable SDN-based solution for identifying and classifying
video flows in real-time. Our solution is novel in combining dynamic flow rules
with telemetry and machine learning, and is built on commodity OpenFlow
switches and open-source software. We develop a fully functional system, train
it in the lab using multiple machine learning algorithms, and validate its
performance to show over 95\% accuracy in identifying and classifying video
streams from many providers including Youtube and Netflix. Lastly, we conduct
tests to demonstrate its scalability to tens of thousands of concurrent
streams, and deploy it live on a campus network serving several hundred real
users. Our system gives unprecedented fine-grained real-time visibility of
video streaming performance to operators of enterprise and carrier networks at
very low cost.
|
[
{
"version": "v1",
"created": "Thu, 26 Apr 2018 07:02:07 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Gharakheili",
"Hassan Habibi",
""
],
[
"Lyu",
"Minzhao",
""
],
[
"Wang",
"Yu",
""
],
[
"Kumar",
"Himal",
""
],
[
"Sivaraman",
"Vijay",
""
]
] |
new_dataset
| 0.995057 |
1911.03089
|
Om Prakash
|
Om Prakash, Habibul Islam and Ram Krishna Verma
|
Constacyclic codes of length $4p^s$ over the Galois ring $GR(p^a,m)$
|
There is mistakes in a few initial results that affecting the whole
paper
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
For prime $p$, $GR(p^a,m)$ represents the Galois ring of order $p^{am}$ and
characterise $p$, where $a$ is any positive integer. In this article, we study
the Type (1) $\lambda$-constacyclic codes of length $4p^s$ over the ring
$GR(p^a,m)$, where $\lambda=\xi_0+p\xi_1+p^2z$, $\xi_0,\xi_1\in T(p,m)$ are
nonzero elements and $z\in GR(p^a,m)$. In first case, when $\lambda$ is a
square, we show that any ideal of
$\mathcal{R}_p(a,m,\lambda)=\frac{GR(p^a,m)[x]}{\langle
x^{4p^s}-\lambda\rangle}$ is the direct sum of the ideals of
$\frac{GR(p^a,m)[x]}{\langle x^{2p^s}-\delta\rangle}$ and
$\frac{GR(p^a,m)[x]}{\langle x^{2p^s}+\delta\rangle}$. In second, when
$\lambda$ is not a square, we show that $\mathcal{R}_p(a,m,\lambda)$ is a chain
ring whose ideals are $\langle (x^4-\alpha)^i\rangle\subseteq
\mathcal{R}_p(a,m,\lambda)$, for $0\leq i\leq ap^s$ where $\alpha^{p^s}=\xi_0$.
Also, we prove the dual of the above code is $\langle
(x^4-\alpha^{-1})^{ap^s-i}\rangle\subseteq \mathcal{R}_p(a,m,\lambda^{-1})$ and
present the necessary and sufficient condition for these codes to be
self-orthogonal and self-dual, respectively. Moreover, the Rosenbloom-Tsfasman
(RT) distance, Hamming distance and weight distribution of Type (1)
$\lambda$-constacyclic codes of length $4p^s$ are obtained when $\lambda$ is
not a square.
|
[
{
"version": "v1",
"created": "Fri, 8 Nov 2019 07:04:34 GMT"
},
{
"version": "v2",
"created": "Sun, 15 Jan 2023 14:31:33 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Prakash",
"Om",
""
],
[
"Islam",
"Habibul",
""
],
[
"Verma",
"Ram Krishna",
""
]
] |
new_dataset
| 0.999687 |
2002.04979
|
Jingjin Yu
|
Mario Szegedy and Jingjin Yu
|
Rubik Tables and Object Rearrangement
|
Pre-print of extended manuscript accepted by IJRR in 2022
| null | null | null |
cs.RO cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A great number of robotics applications demand the rearrangement of many
mobile objects, e.g., organizing products on shelves, shuffling containers at
shipping ports, reconfiguring fleets of mobile robots, and so on. To boost the
throughput in systems designed for solving these rearrangement problems, it is
essential to minimize the number of atomic operations, e.g., the pick-n-places
of individual objects. However, this optimization task poses a rather difficult
challenge due to complex inter-dependency between objects, especially in
high-density settings.
In tackling the aforementioned challenges, we develop a novel algorithmic
tool, Rubik Tables, that provides a clean abstraction of object rearrangement
problems as the proxy problem of shuffling items stored in a table or lattice.
In its basic form, a Rubik Table is an $n\times n$ table containing $n^2$
items. We show that the reconfiguration of items in such a Rubik Table can be
achieved using at most $2n$ column/row shuffles in the partially labeled
setting, where each column (resp., row) shuffle may arbitrarily permute the
items stored in a column (resp., row) of the table. When items are fully
distinguishable, additional $n$ shuffles are needed. Rubik Tables allow many
generalizations, e.g., to higher dimensions.
Using Rubik Table, we have designed a first constant-factor optimal algorithm
for stack rearrangement problems. We show that, for $nd$ items stored in $n$
stacks of depth $d$ each, using one empty stack as the swap space, $O(nd)$
stack pop-push operations are sufficient for an arbitrary reconfiguration of
the stacks where $d \le n^{\frac{m}{2}}$ for arbitrary fixed $m >0$. Rubik
Table results also allow the development of constant-factor optimal solutions
for solving multi-robot motion planning problems under extreme robot density.
These algorithms based on Rubik Table results run in low-polynomial time.
|
[
{
"version": "v1",
"created": "Wed, 12 Feb 2020 13:37:23 GMT"
},
{
"version": "v2",
"created": "Thu, 7 May 2020 02:42:20 GMT"
},
{
"version": "v3",
"created": "Tue, 17 Jan 2023 15:48:58 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Szegedy",
"Mario",
""
],
[
"Yu",
"Jingjin",
""
]
] |
new_dataset
| 0.968416 |
2003.00839
|
Bin Fang
|
Bin Fang, Xingming Long, Yifan Zhang, GuoYi Luo, Fuchun Sun, Huaping
Liu
|
Fabric Defect Detection Using Vision-Based Tactile Sensor
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper introduces a new type of system for fabric defect detection with
the tactile inspection system. Different from existed visual inspection
systems, the proposed system implements a vision-based tactile sensor. The
tactile sensor, which mainly consists of a camera, four LEDs, and an elastic
sensing layer, captures detailed information about fabric surface structure and
ignores the color and pattern. Thus, the ambiguity between a defect and image
background related to fabric color and pattern is avoided. To utilize the
tactile sensor for fabric inspection, we employ intensity adjustment for image
preprocessing, Residual Network with ensemble learning for detecting defects,
and uniformity measurement for selecting ideal dataset for model training. An
experiment is conducted to verify the performance of the proposed tactile
system. The experimental results have demonstrated the feasibility of the
proposed system, which performs well in detecting structural defects for
various types of fabrics. In addition, the system does not require external
light sources, which skips the process of setting up and tuning a lighting
environment.
|
[
{
"version": "v1",
"created": "Mon, 2 Mar 2020 12:57:45 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 11:09:14 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Fang",
"Bin",
""
],
[
"Long",
"Xingming",
""
],
[
"Zhang",
"Yifan",
""
],
[
"Luo",
"GuoYi",
""
],
[
"Sun",
"Fuchun",
""
],
[
"Liu",
"Huaping",
""
]
] |
new_dataset
| 0.999403 |
2011.04230
|
Afshin Alipour
|
Afshin Alipour, Mohammad J. Mahjoob, Zahra Fakhari, and Ara Nazarian
|
A New 4-DOF Robot for Rehabilitation of Knee and Ankle-Foot Complex:
Simulation and Experiment
|
23 pages, 14 figures
|
Journal of Robotics and Control (JRC) [Online], 3.4 (2022):
483-495
|
10.18196/jrc.v3i4.14759
| null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Stationary robotic trainers are lower limb rehab robots which often
incorporate an exoskeleton attached to a stationary base. The issue observed in
the stationery trainers for simultaneous knee and ankle-foot complex joints is
that they restrict the natural motion of ankle-foot in the rehab trainings due
to the insufficient Degrees of Freedom (DOFs) of these trainers. A new
stationary knee-ankle-foot rehab robot with all necessary DOFs is developed
here. A typical rehab training is first implemented in simulation, and then
tested on a healthy subject. Results show that the proposed system functions
naturally and meets the requirements of the desired rehab training.
|
[
{
"version": "v1",
"created": "Mon, 9 Nov 2020 07:39:40 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Alipour",
"Afshin",
""
],
[
"Mahjoob",
"Mohammad J.",
""
],
[
"Fakhari",
"Zahra",
""
],
[
"Nazarian",
"Ara",
""
]
] |
new_dataset
| 0.996573 |
2109.00908
|
Adam Michael Roberts
|
Joe Gildea, Adrian Korban, Adam Michael Roberts, Alexander Tylyshchak
|
Binary self-dual codes of various lengths with new weight enumerators
from a modified bordered construction and neighbours
|
arXiv admin note: substantial text overlap with arXiv:2108.09184,
arXiv:2106.12355, arXiv:2102.10354
| null |
10.3934/amc.2022021
| null |
cs.IT math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we define a modification of a bordered construction for
self-dual codes which utilises $\lambda$-circulant matrices. We provide the
necessary conditions for the construction to produce self-dual codes over
finite commutative Frobenius rings of characteristic 2. Using the modified
construction together with the neighbour construction, we construct many binary
self-dual codes of lengths 54, 68, 82 and 94 with weight enumerators that have
previously not been known to exist.
|
[
{
"version": "v1",
"created": "Thu, 2 Sep 2021 13:05:53 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Gildea",
"Joe",
""
],
[
"Korban",
"Adrian",
""
],
[
"Roberts",
"Adam Michael",
""
],
[
"Tylyshchak",
"Alexander",
""
]
] |
new_dataset
| 0.999489 |
2109.12881
|
Ra'fat AL-Msie'deen Dr. Rafat
|
Ra'Fat Al-Msie'deen
|
SoftCloud: A Tool for Visualizing Software Artifacts as Tag Clouds
|
14 pages, 10 figures, 10 tables
|
Mu'tah Lil-Buhuth wad-Dirasat, Natural and Applied Sciences Series
Vol. 37. No.2, pp. 93-115, 2022
| null | null |
cs.SE
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Software artifacts visualization helps software developers to manage the size
and complexity of the software system. The tag cloud technique visualizes tags
within the cloud according to their frequencies in software artifacts. A font
size of the tag within the cloud indicates its frequency within a software
artifact, while the color of a tag within the cloud uses just for aesthetic
purposes. This paper suggests a new approach (SoftCloud) to visualize software
artifacts as a tag cloud. The originality of SoftCloud is visualizing all the
artifacts available to the software program as a tag cloud. Experiments have
conducted on different software artifacts to validate SoftCloud and demonstrate
its strengths. The results showed the ability of SoftCloud to correctly
retrieve all tags and their frequencies from available software artifacts.
|
[
{
"version": "v1",
"created": "Mon, 27 Sep 2021 09:04:19 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Al-Msie'deen",
"Ra'Fat",
""
]
] |
new_dataset
| 0.972297 |
2110.07689
|
Xinyu Wang
|
Xinyu Wang
|
First-Order Modal $\xi$-Calculus: On the Aspects of Application and
Bisimulation
| null | null | null | null |
cs.LO math.LO
|
http://creativecommons.org/licenses/by/4.0/
|
This paper proposes first-order modal $\xi$-calculus as well as genealogical
Kripke models. Inspired by modal $\mu$-calculus, first-order modal
$\xi$-calculus takes a quite similar form and extends its inductive
expressivity onto a different dimension. We elaborate on several vivid examples
that demonstrate this logic's profound utility, especially for depicting
genealogy of concurrent computer processes. Bisimulation notion for the logic
has also been thoroughly examined.
|
[
{
"version": "v1",
"created": "Thu, 14 Oct 2021 19:54:57 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Feb 2022 03:16:29 GMT"
},
{
"version": "v3",
"created": "Mon, 21 Mar 2022 14:06:13 GMT"
},
{
"version": "v4",
"created": "Tue, 17 Jan 2023 02:52:02 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Wang",
"Xinyu",
""
]
] |
new_dataset
| 0.980183 |
2111.01946
|
Lijun Sun Mr
|
Jiawei Wang, Lijun Sun
|
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement
Learning Approach
| null |
IEEE Transactions on Intelligent Transportation Systems (2022)
|
10.1109/TITS.2022.3229527
| null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bus system is a critical component of sustainable urban transportation.
However, the operation of a bus fleet is unstable in nature, and bus bunching
has become a common phenomenon that undermines the efficiency and reliability
of bus systems. Recently research has demonstrated the promising application of
multi-agent reinforcement learning (MARL) to achieve efficient vehicle holding
control to avoid bus bunching. However, existing studies essentially overlook
the robustness issue resulting from various events, perturbations and anomalies
in a transit system, which is of utmost importance when transferring the models
for real-world deployment/application. In this study, we integrate implicit
quantile network and meta-learning to develop a distributional MARL framework
-- IQNC-M -- to learn continuous control. The proposed IQNC-M framework
achieves efficient and reliable control decisions through better handling
various uncertainties/events in real-time transit operations. Specifically, we
introduce an interpretable meta-learning module to incorporate global
information into the distributional MARL framework, which is an effective
solution to circumvent the credit assignment issue in the transit system. In
addition, we design a specific learning procedure to train each agent within
the framework to pursue a robust control policy. We develop simulation
environments based on real-world bus services and passenger demand data and
evaluate the proposed framework against both traditional holding control models
and state-of-the-art MARL models. Our results show that the proposed IQNC-M
framework can effectively handle the various extreme events, such as traffic
state perturbations, service interruptions, and demand surges, thus improving
both efficiency and reliability of the system.
|
[
{
"version": "v1",
"created": "Tue, 2 Nov 2021 23:41:09 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Wang",
"Jiawei",
""
],
[
"Sun",
"Lijun",
""
]
] |
new_dataset
| 0.992728 |
2111.05481
|
Noah Kaufmann
|
Noah Kaufmann
|
A Diamond Structure in the Transducer Hierarchy
| null | null | null | null |
cs.FL math.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We answer an open question in the theory of transducer degrees initially
posed in [1] on the existence of a diamond structure in the transducer
hierarchy. Transducer degrees are the equivalence classes formed by word
transformations which can be realized by a finite state transducer, which form
an order based on which words can be transformed into other words. We provide a
construction which proves the existence of a diamond structure, while also
introducing a new function on streams which may be useful for proving more
results about the transducer hierarchy.
|
[
{
"version": "v1",
"created": "Wed, 10 Nov 2021 01:48:10 GMT"
},
{
"version": "v2",
"created": "Tue, 2 Aug 2022 21:49:43 GMT"
},
{
"version": "v3",
"created": "Sun, 15 Jan 2023 20:19:11 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Kaufmann",
"Noah",
""
]
] |
new_dataset
| 0.987681 |
2111.12263
|
Bin-Bin Gao
|
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu, Jing-Hao Xue, Chengjie Wang
and Qingmin Liao
|
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic
Segmentation
|
12 pages, 7 figures, Accepted to IEEE Trans. on Multimedia. arXiv
admin note: substantial text overlap with arXiv:2104.09216
| null |
10.1109/TMM.2022.3174405
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Few-shot semantic segmentation aims to segment novel-class objects in a given
query image with only a few labeled support images. Most advanced solutions
exploit a metric learning framework that performs segmentation through matching
each query feature to a learned class-specific prototype. However, this
framework suffers from biased classification due to incomplete feature
comparisons. To address this issue, we present an adaptive prototype
representation by introducing class-specific and class-agnostic prototypes and
thus construct complete sample pairs for learning semantic alignment with query
features. The complementary features learning manner effectively enriches
feature comparison and helps yield an unbiased segmentation model in the
few-shot setting. It is implemented with a two-branch end-to-end network (i.e.,
a class-specific branch and a class-agnostic branch), which generates
prototypes and then combines query features to perform comparisons. In
addition, the proposed class-agnostic branch is simple yet effective. In
practice, it can adaptively generate multiple class-agnostic prototypes for
query images and learn feature alignment in a self-contrastive manner.
Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ demonstrate the
superiority of our method. At no expense of inference efficiency, our model
achieves state-of-the-art results in both 1-shot and 5-shot settings for
semantic segmentation.
|
[
{
"version": "v1",
"created": "Wed, 24 Nov 2021 04:38:37 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 09:24:37 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Chen",
"Jiacheng",
""
],
[
"Gao",
"Bin-Bin",
""
],
[
"Lu",
"Zongqing",
""
],
[
"Xue",
"Jing-Hao",
""
],
[
"Wang",
"Chengjie",
""
],
[
"Liao",
"Qingmin",
""
]
] |
new_dataset
| 0.993113 |
2202.05917
|
Delaram Kahrobaei
|
Delaram Kahrobaei, Ram\'on Flores, Marialaura Noce
|
Group-based Cryptography in the Quantum Era
|
To appear in the Notices of the American Mathematical Society
| null | null | null |
cs.CR math.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this expository article we present an overview of the current
state-of-the-art in post-quantum group-based cryptography. We describe several
families of groups that have been proposed as platforms, with special emphasis
in polycyclic groups and graph groups, dealing in particular with their
algorithmic properties and cryptographic applications. We then, describe some
applications of combinatorial algebra in fully homomorphic encryption. In the
end we discussing several open problems in this direction.
|
[
{
"version": "v1",
"created": "Fri, 11 Feb 2022 22:01:45 GMT"
},
{
"version": "v2",
"created": "Sat, 19 Feb 2022 17:22:40 GMT"
},
{
"version": "v3",
"created": "Thu, 24 Feb 2022 15:01:28 GMT"
},
{
"version": "v4",
"created": "Tue, 17 Jan 2023 11:52:12 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Kahrobaei",
"Delaram",
""
],
[
"Flores",
"Ramón",
""
],
[
"Noce",
"Marialaura",
""
]
] |
new_dataset
| 0.986647 |
2204.01968
|
Soumik Mohian
|
Soumik Mohian, Christoph Csallner
|
PSDoodle: Searching for App Screens via Interactive Sketching
|
arXiv admin note: text overlap with arXiv:2204.01956
| null |
10.1145/3524613.3527807
| null |
cs.CV cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Keyword-based mobile screen search does not account for screen content and
fails to operate as a universal tool for all levels of users. Visual searching
(e.g., image, sketch) is structured and easy to adopt. Current visual search
approaches count on a complete screen and are therefore slow and tedious.
PSDoodle employs a deep neural network to recognize partial screen element
drawings instantly on a digital drawing interface and shows results in
real-time. PSDoodle is the first tool that utilizes partial sketches and
searches for screens in an interactive iterative way. PSDoodle supports
different drawing styles and retrieves search results that are relevant to the
user's sketch query. A short video demonstration is available online at:
https://youtu.be/3cVLHFm5pY4
|
[
{
"version": "v1",
"created": "Tue, 5 Apr 2022 03:46:48 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Apr 2022 18:53:24 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Mohian",
"Soumik",
""
],
[
"Csallner",
"Christoph",
""
]
] |
new_dataset
| 0.956047 |
2204.12384
|
Finn Voichick
|
Finn Voichick, Liyi Li, Robert Rand, Michael Hicks
|
Qunity: A Unified Language for Quantum and Classical Computing (Extended
Version)
|
76 pages, 37 figures. To appear at POPL 2023, previous version
presented at QPL 2022. Expanded with additional background information and a
characterization of the classical sublanguage
| null |
10.1145/3571225
| null |
cs.PL cs.LO quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce Qunity, a new quantum programming language designed to treat
quantum computing as a natural generalization of classical computing. Qunity
presents a unified syntax where familiar programming constructs can have both
quantum and classical effects. For example, one can use sum types to implement
the direct sum of linear operators, exception-handling syntax to implement
projective measurements, and aliasing to induce entanglement. Further, Qunity
takes advantage of the overlooked BQP subroutine theorem, allowing one to
construct reversible subroutines from irreversible quantum algorithms through
the uncomputation of "garbage" outputs. Unlike existing languages that enable
quantum aspects with separate add-ons (like a classical language with quantum
gates bolted on), Qunity provides a unified syntax and a novel denotational
semantics that guarantees that programs are quantum mechanically valid. We
present Qunity's syntax, type system, and denotational semantics, showing how
it can cleanly express several quantum algorithms. We also detail how Qunity
can be compiled into a low-level qubit circuit language like OpenQASM, proving
the realizability of our design.
|
[
{
"version": "v1",
"created": "Tue, 26 Apr 2022 15:34:22 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Jul 2022 12:31:06 GMT"
},
{
"version": "v3",
"created": "Tue, 15 Nov 2022 02:44:37 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Voichick",
"Finn",
""
],
[
"Li",
"Liyi",
""
],
[
"Rand",
"Robert",
""
],
[
"Hicks",
"Michael",
""
]
] |
new_dataset
| 0.99939 |
2206.02153
|
Amashi Niwarthana
|
Arulmolivarman Thieshanthan, Amashi Niwarthana, Pamuditha Somarathne,
Tharindu Wickremasinghe, Ranga Rodrigo
|
HPGNN: Using Hierarchical Graph Neural Networks for Outdoor Point Cloud
Processing
|
Accepted for ICPR 2022
| null |
10.1109/ICPR56361.2022.9956238
| null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inspired by recent improvements in point cloud processing for autonomous
navigation, we focus on using hierarchical graph neural networks for processing
and feature learning over large-scale outdoor LiDAR point clouds. We observe
that existing GNN based methods fail to overcome challenges of scale and
irregularity of points in outdoor datasets. Addressing the need to preserve
structural details while learning over a larger volume efficiently, we propose
Hierarchical Point Graph Neural Network (HPGNN). It learns node features at
various levels of graph coarseness to extract information. This enables to
learn over a large point cloud while retaining fine details that existing
point-level graph networks struggle to achieve. Connections between multiple
levels enable a point to learn features in multiple scales, in a few
iterations. We design HPGNN as a purely GNN-based approach, so that it offers
modular expandability as seen with other point-based and Graph network
baselines. To illustrate the improved processing capability, we compare
previous point based and GNN models for semantic segmentation with our HPGNN,
achieving a significant improvement for GNNs (+36.7 mIoU) on the SemanticKITTI
dataset.
|
[
{
"version": "v1",
"created": "Sun, 5 Jun 2022 11:18:09 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Thieshanthan",
"Arulmolivarman",
""
],
[
"Niwarthana",
"Amashi",
""
],
[
"Somarathne",
"Pamuditha",
""
],
[
"Wickremasinghe",
"Tharindu",
""
],
[
"Rodrigo",
"Ranga",
""
]
] |
new_dataset
| 0.995965 |
2206.07038
|
Yanze Wu
|
Yanze Wu, Xintao Wang, Gen Li, Ying Shan
|
AnimeSR: Learning Real-World Super-Resolution Models for Animation
Videos
|
NeurIPS 2022. Codes and models are available at
https://github.com/TencentARC/AnimeSR
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper studies the problem of real-world video super-resolution (VSR) for
animation videos, and reveals three key improvements for practical animation
VSR. First, recent real-world super-resolution approaches typically rely on
degradation simulation using basic operators without any learning capability,
such as blur, noise, and compression. In this work, we propose to learn such
basic operators from real low-quality animation videos, and incorporate the
learned ones into the degradation generation pipeline. Such
neural-network-based basic operators could help to better capture the
distribution of real degradations. Second, a large-scale high-quality animation
video dataset, AVC, is built to facilitate comprehensive training and
evaluations for animation VSR. Third, we further investigate an efficient
multi-scale network structure. It takes advantage of the efficiency of
unidirectional recurrent networks and the effectiveness of sliding-window-based
methods. Thanks to the above delicate designs, our method, AnimeSR, is capable
of restoring real-world low-quality animation videos effectively and
efficiently, achieving superior performance to previous state-of-the-art
methods. Codes and models are available at
https://github.com/TencentARC/AnimeSR.
|
[
{
"version": "v1",
"created": "Tue, 14 Jun 2022 17:57:11 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Jun 2022 11:20:53 GMT"
},
{
"version": "v3",
"created": "Tue, 17 Jan 2023 11:08:41 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Wu",
"Yanze",
""
],
[
"Wang",
"Xintao",
""
],
[
"Li",
"Gen",
""
],
[
"Shan",
"Ying",
""
]
] |
new_dataset
| 0.991098 |
2206.10881
|
Yuan Li
|
Jinjie Gao, Haibin Kan, Yuan Li, Qichun Wang
|
The Covering Radius of the Third-Order Reed-Muller Code RM(3,7) is 20
| null | null | null | null |
cs.IT cs.DM math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We prove the covering radius of the third-order Reed-Muller code RM(3,7) is
20, which was previously known to be between 20 and 23 (inclusive). The
covering radius of RM(3, 7) is the maximum third-order nonlinearity among all
7-variable Boolean functions. It was known that there exist 7-variable Boolean
functions with third-order nonlinearity 20. We prove the third-order
nonlinearity cannot achieve 21. According to the classification of the quotient
space of RM(6,6)/RM(3,6), we classify all 7-variable Boolean functions into 66
types. Firstly, we prove 62 types (among 66) cannot have third-order
nonlinearity 21; Secondly, we prove function of the remaining 4 types can be
transformed into a type (6, 10) function, if its third-order nonlinearity is
21; Finally, we transform type (6, 10) functions into a specific form, and
prove the functions in that form cannot achieve third-order nonlinearity 21
(with the assistance of computers). By the way, we prove that the affine
transformation group over any finite field can be generated by two elements.
|
[
{
"version": "v1",
"created": "Wed, 22 Jun 2022 07:10:37 GMT"
},
{
"version": "v2",
"created": "Sat, 27 Aug 2022 01:28:16 GMT"
},
{
"version": "v3",
"created": "Sun, 15 Jan 2023 01:44:01 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Gao",
"Jinjie",
""
],
[
"Kan",
"Haibin",
""
],
[
"Li",
"Yuan",
""
],
[
"Wang",
"Qichun",
""
]
] |
new_dataset
| 0.968456 |
2206.15398
|
Yanqin Jiang
|
Yanqin Jiang, Li Zhang, Zhenwei Miao, Xiatian Zhu, Jin Gao, Weiming
Hu, Yu-Gang Jiang
|
PolarFormer: Multi-camera 3D Object Detection with Polar Transformer
|
Accepted to AAAI2023
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
3D object detection in autonomous driving aims to reason "what" and "where"
the objects of interest present in a 3D world. Following the conventional
wisdom of previous 2D object detection, existing methods often adopt the
canonical Cartesian coordinate system with perpendicular axis. However, we
conjugate that this does not fit the nature of the ego car's perspective, as
each onboard camera perceives the world in shape of wedge intrinsic to the
imaging geometry with radical (non-perpendicular) axis. Hence, in this paper we
advocate the exploitation of the Polar coordinate system and propose a new
Polar Transformer (PolarFormer) for more accurate 3D object detection in the
bird's-eye-view (BEV) taking as input only multi-camera 2D images.
Specifically, we design a cross attention based Polar detection head without
restriction to the shape of input structure to deal with irregular Polar grids.
For tackling the unconstrained object scale variations along Polar's distance
dimension, we further introduce a multi-scalePolar representation learning
strategy. As a result, our model can make best use of the Polar representation
rasterized via attending to the corresponding image observation in a
sequence-to-sequence fashion subject to the geometric constraints. Thorough
experiments on the nuScenes dataset demonstrate that our PolarFormer
outperforms significantly state-of-the-art 3D object detection alternatives.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 16:32:48 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 09:27:56 GMT"
},
{
"version": "v3",
"created": "Sun, 10 Jul 2022 11:49:53 GMT"
},
{
"version": "v4",
"created": "Tue, 12 Jul 2022 08:18:01 GMT"
},
{
"version": "v5",
"created": "Fri, 23 Dec 2022 08:45:37 GMT"
},
{
"version": "v6",
"created": "Mon, 16 Jan 2023 02:24:33 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Jiang",
"Yanqin",
""
],
[
"Zhang",
"Li",
""
],
[
"Miao",
"Zhenwei",
""
],
[
"Zhu",
"Xiatian",
""
],
[
"Gao",
"Jin",
""
],
[
"Hu",
"Weiming",
""
],
[
"Jiang",
"Yu-Gang",
""
]
] |
new_dataset
| 0.996193 |
2207.08051
|
Samar Khanna
|
Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi,
Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon
|
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral
Satellite Imagery
|
Published at NeurIPS 2022. The first two listed names contributed
equally to this project
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Unsupervised pre-training methods for large vision models have shown to
enhance performance on downstream supervised tasks. Developing similar
techniques for satellite imagery presents significant opportunities as
unlabelled data is plentiful and the inherent temporal and multi-spectral
structure provides avenues to further improve existing pre-training strategies.
In this paper, we present SatMAE, a pre-training framework for temporal or
multi-spectral satellite imagery based on Masked Autoencoder (MAE). To leverage
temporal information, we include a temporal embedding along with independently
masking image patches across time. In addition, we demonstrate that encoding
multi-spectral data as groups of bands with distinct spectral positional
encodings is beneficial. Our approach yields strong improvements over previous
state-of-the-art techniques, both in terms of supervised learning performance
on benchmark datasets (up to $\uparrow$ 7%), and transfer learning performance
on downstream remote sensing tasks, including land cover classification (up to
$\uparrow$ 14%) and semantic segmentation. Code and data are available on the
project website: https://sustainlab-group.github.io/SatMAE/
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 01:35:29 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Oct 2022 01:04:57 GMT"
},
{
"version": "v3",
"created": "Sun, 15 Jan 2023 19:27:57 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Cong",
"Yezhen",
""
],
[
"Khanna",
"Samar",
""
],
[
"Meng",
"Chenlin",
""
],
[
"Liu",
"Patrick",
""
],
[
"Rozi",
"Erik",
""
],
[
"He",
"Yutong",
""
],
[
"Burke",
"Marshall",
""
],
[
"Lobell",
"David B.",
""
],
[
"Ermon",
"Stefano",
""
]
] |
new_dataset
| 0.997857 |
2208.00554
|
Noah Kaufmann
|
Noah Kaufmann
|
A Diamond Structure in the Transducer Hierarchy
|
Incorrectly uploaded
| null | null | null |
cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We answer an open question in the theory of transducer degrees initially
posed in [1] on the existence of a diamond structure in the transducer
hierarchy. Transducer degrees are the equivalence classes formed by word
transformations which can be realized by a finite state transducer, which form
an order based on which words can be transformed into other words. We provide a
construction which proves the existence of a diamond structure, while also
introducing a new function on streams which may be useful for proving more
results about the transducer hierarchy.
|
[
{
"version": "v1",
"created": "Mon, 1 Aug 2022 01:05:01 GMT"
},
{
"version": "v2",
"created": "Wed, 3 Aug 2022 15:23:55 GMT"
},
{
"version": "v3",
"created": "Thu, 4 Aug 2022 14:38:05 GMT"
},
{
"version": "v4",
"created": "Sat, 10 Sep 2022 02:25:24 GMT"
},
{
"version": "v5",
"created": "Sun, 15 Jan 2023 20:14:53 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Kaufmann",
"Noah",
""
]
] |
new_dataset
| 0.987681 |
2208.00751
|
Zhe Zhu
|
Zhe Zhu, Liangliang Nan, Haoran Xie, Honghua Chen, Mingqiang Wei, Jun
Wang, Jing Qin
|
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud
Completion
| null | null |
10.1109/TVCG.2023.3236061
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
How will you repair a physical object with some missings? You may imagine its
original shape from previously captured images, recover its overall (global)
but coarse shape first, and then refine its local details. We are motivated to
imitate the physical repair procedure to address point cloud completion. To
this end, we propose a cross-modal shape-transfer dual-refinement network
(termed CSDN), a coarse-to-fine paradigm with images of full-cycle
participation, for quality point cloud completion. CSDN mainly consists of
"shape fusion" and "dual-refinement" modules to tackle the cross-modal
challenge. The first module transfers the intrinsic shape characteristics from
single images to guide the geometry generation of the missing regions of point
clouds, in which we propose IPAdaIN to embed the global features of both the
image and the partial point cloud into completion. The second module refines
the coarse output by adjusting the positions of the generated points, where the
local refinement unit exploits the geometric relation between the novel and the
input points by graph convolution, and the global constraint unit utilizes the
input image to fine-tune the generated offset. Different from most existing
approaches, CSDN not only explores the complementary information from images
but also effectively exploits cross-modal data in the whole coarse-to-fine
completion procedure. Experimental results indicate that CSDN performs
favorably against ten competitors on the cross-modal benchmark.
|
[
{
"version": "v1",
"created": "Mon, 1 Aug 2022 11:20:56 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Dec 2022 03:13:40 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Zhu",
"Zhe",
""
],
[
"Nan",
"Liangliang",
""
],
[
"Xie",
"Haoran",
""
],
[
"Chen",
"Honghua",
""
],
[
"Wei",
"Mingqiang",
""
],
[
"Wang",
"Jun",
""
],
[
"Qin",
"Jing",
""
]
] |
new_dataset
| 0.998674 |
2208.05119
|
Tongzhou Shen
|
Atia Hamidizadeh, Tony Shen, Martin Ester
|
Semi-Supervised Junction Tree Variational Autoencoder for Molecular
Property Prediction
| null | null | null | null |
cs.LG physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Molecular Representation Learning is essential to solving many drug discovery
and computational chemistry problems. It is a challenging problem due to the
complex structure of molecules and the vast chemical space. Graph
representations of molecules are more expressive than traditional
representations, such as molecular fingerprints. Therefore, they can improve
the performance of machine learning models. We propose SeMole, a method that
augments the Junction Tree Variational Autoencoders, a state-of-the-art
generative model for molecular graphs, with semi-supervised learning. SeMole
aims to improve the accuracy of molecular property prediction when having
limited labeled data by exploiting unlabeled data. We enforce that the model
generates molecular graphs conditioned on target properties by incorporating
the property into the latent representation. We propose an additional
pre-training phase to improve the training process for our semi-supervised
generative model. We perform an experimental evaluation on the ZINC dataset
using three different molecular properties and demonstrate the benefits of
semi-supervision.
|
[
{
"version": "v1",
"created": "Wed, 10 Aug 2022 03:06:58 GMT"
},
{
"version": "v2",
"created": "Mon, 15 Aug 2022 19:13:45 GMT"
},
{
"version": "v3",
"created": "Tue, 23 Aug 2022 21:20:54 GMT"
},
{
"version": "v4",
"created": "Thu, 1 Sep 2022 16:16:06 GMT"
},
{
"version": "v5",
"created": "Sun, 15 Jan 2023 02:07:56 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Hamidizadeh",
"Atia",
""
],
[
"Shen",
"Tony",
""
],
[
"Ester",
"Martin",
""
]
] |
new_dataset
| 0.984459 |
2209.06909
|
Sebastian Wild
|
William Cawley Gelling and Markus E. Nebel and Benjamin Smith and
Sebastian Wild
|
Multiway Powersort
|
17 pages; accompanying source code at
https://github.com/sebawild/powersort; v2 adds new figure and text changes.
v2 is identical to the ALENEX 2023 version
|
ALENEX 2023
|
10.1137/1.9781611977561.ch16
| null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a stable mergesort variant, Multiway Powersort, that exploits
existing runs and finds nearly-optimal merging orders for k-way merges with
negligible overhead. This builds on Powersort (Munro & Wild, ESA2018), which
has recently replaced Timsort's suboptimal merge policy in the CPython
reference implementation of Python, as well as in PyPy and further libraries.
Multiway Powersort reduces the number of memory transfers, which increasingly
determine the cost of internal sorting (as observed with Multiway Quicksort
(Kushagra et al., ALENEX 2014; Aum\"uller & Dietzfelbinger, TALG 2016; Wild,
PhD thesis 2016) and the inclusion of Dual-Pivot Quicksort in the Java runtime
library). We demonstrate that our 4-way Powersort implementation can achieve
substantial speedups over standard (2-way) Powersort and other stable sorting
methods without compromising the optimally run-adaptive performance of
Powersort.
|
[
{
"version": "v1",
"created": "Wed, 14 Sep 2022 20:06:30 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 01:26:12 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Gelling",
"William Cawley",
""
],
[
"Nebel",
"Markus E.",
""
],
[
"Smith",
"Benjamin",
""
],
[
"Wild",
"Sebastian",
""
]
] |
new_dataset
| 0.995292 |
2209.07989
|
Erkang Cheng
|
Yifeng Bai, Zhirong Chen, Zhangjie Fu, Lang Peng, Pengpeng Liang,
Erkang Cheng
|
CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries
and Attention
|
Accepted at the IEEE Conference on Robotics and Automation, ICRA 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D lane detection is an integral part of autonomous driving systems. Previous
CNN and Transformer-based methods usually first generate a bird's-eye-view
(BEV) feature map from the front view image, and then use a sub-network with
BEV feature map as input to predict 3D lanes. Such approaches require an
explicit view transformation between BEV and front view, which itself is still
a challenging problem. In this paper, we propose CurveFormer, a single-stage
Transformer-based method that directly calculates 3D lane parameters and can
circumvent the difficult view transformation step. Specifically, we formulate
3D lane detection as a curve propagation problem by using curve queries. A 3D
lane query is represented by a dynamic and ordered anchor point set. In this
way, queries with curve representation in Transformer decoder iteratively
refine the 3D lane detection results. Moreover, a curve cross-attention module
is introduced to compute the similarities between curve queries and image
features. Additionally, a context sampling module that can capture more
relative image features of a curve query is provided to further boost the 3D
lane detection performance. We evaluate our method for 3D lane detection on
both synthetic and real-world datasets, and the experimental results show that
our method achieves promising performance compared with the state-of-the-art
approaches. The effectiveness of each component is validated via ablation
studies as well.
|
[
{
"version": "v1",
"created": "Fri, 16 Sep 2022 14:54:57 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 15:10:09 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Bai",
"Yifeng",
""
],
[
"Chen",
"Zhirong",
""
],
[
"Fu",
"Zhangjie",
""
],
[
"Peng",
"Lang",
""
],
[
"Liang",
"Pengpeng",
""
],
[
"Cheng",
"Erkang",
""
]
] |
new_dataset
| 0.954269 |
2210.12375
|
Marten Lienen
|
Marten Lienen and Stephan G\"unnemann
|
torchode: A Parallel ODE Solver for PyTorch
|
Accepted at The Symbiosis of Deep Learning and Differential Equations
Workshop, NeurIPS, 2022
| null | null | null |
cs.LG cs.NA math.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce an ODE solver for the PyTorch ecosystem that can solve multiple
ODEs in parallel independently from each other while achieving significant
performance gains. Our implementation tracks each ODE's progress separately and
is carefully optimized for GPUs and compatibility with PyTorch's JIT compiler.
Its design lets researchers easily augment any aspect of the solver and collect
and analyze internal solver statistics. In our experiments, our implementation
is up to 4.3 times faster per step than other ODE solvers and it is robust
against within-batch interactions that lead other solvers to take up to 4 times
as many steps.
Code available at https://github.com/martenlienen/torchode
|
[
{
"version": "v1",
"created": "Sat, 22 Oct 2022 07:08:17 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 09:02:47 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Lienen",
"Marten",
""
],
[
"Günnemann",
"Stephan",
""
]
] |
new_dataset
| 0.976232 |
2210.14791
|
Simar Kareer
|
Simar Kareer, Naoki Yokoyama, Dhruv Batra, Sehoon Ha, Joanne Truong
|
ViNL: Visual Navigation and Locomotion Over Obstacles
| null | null | null | null |
cs.RO cs.AI cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present Visual Navigation and Locomotion over obstacles (ViNL), which
enables a quadrupedal robot to navigate unseen apartments while stepping over
small obstacles that lie in its path (e.g., shoes, toys, cables), similar to
how humans and pets lift their feet over objects as they walk. ViNL consists
of: (1) a visual navigation policy that outputs linear and angular velocity
commands that guides the robot to a goal coordinate in unfamiliar indoor
environments; and (2) a visual locomotion policy that controls the robot's
joints to avoid stepping on obstacles while following provided velocity
commands. Both the policies are entirely "model-free", i.e. sensors-to-actions
neural networks trained end-to-end. The two are trained independently in two
entirely different simulators and then seamlessly co-deployed by feeding the
velocity commands from the navigator to the locomotor, entirely "zero-shot"
(without any co-training). While prior works have developed learning methods
for visual navigation or visual locomotion, to the best of our knowledge, this
is the first fully learned approach that leverages vision to accomplish both
(1) intelligent navigation in new environments, and (2) intelligent visual
locomotion that aims to traverse cluttered environments without disrupting
obstacles. On the task of navigation to distant goals in unknown environments,
ViNL using just egocentric vision significantly outperforms prior work on
robust locomotion using privileged terrain maps (+32.8% success and -4.42
collisions per meter). Additionally, we ablate our locomotion policy to show
that each aspect of our approach helps reduce obstacle collisions. Videos and
code at http://www.joannetruong.com/projects/vinl.html
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 15:38:28 GMT"
},
{
"version": "v2",
"created": "Sat, 14 Jan 2023 20:19:59 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Kareer",
"Simar",
""
],
[
"Yokoyama",
"Naoki",
""
],
[
"Batra",
"Dhruv",
""
],
[
"Ha",
"Sehoon",
""
],
[
"Truong",
"Joanne",
""
]
] |
new_dataset
| 0.999707 |
2211.00818
|
Yihong Dong
|
Yihong Dong, Xue Jiang, Yuchen Liu, Ge Li, Zhi Jin
|
CodePAD: Sequence-based Code Generation with Pushdown Automaton
|
Accepted to ISSTA 2023 (Technical Papers)
| null | null | null |
cs.SE cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In the process of code generation, it is essential to guarantee the generated
code satisfies grammar constraints of programming language (PL). However,
neglecting grammar constraints is a fatal drawback of commonly used
sequence-based code generation. In this paper, we devise a pushdown automaton
(PDA)-based methodology to address this problem, exploiting the principle that
PL is a subset of PDA recognizable language and code accepted by PDA is
grammatical. Specifically, we construct a PDA module and design an algorithm to
constrain the generation of sequence-based models to ensure grammatical
correctness. Guided by this methodology, we further propose CodePAD, a
sequence-based code generation framework equipped with a PDA module, to
integrate the deduction of PDA into deep learning. Additionally, this framework
can leverage states of PDA deduction (including state representation, state
prediction task, and joint prediction with state) to assist models in learning
PDA deduction. To comprehensively evaluate CodePAD, we construct a PDA for
Python and conduct extensive experiments on four public benchmark datasets.
CodePAD can leverage existing sequence-based models, and we show that it can
achieve 100\% grammatical correctness percentage on these benchmark datasets.
Thus, it relatively improve 17\% CodeBLEU on CONALA, 8\% EM on DJANGO, and 15\%
CodeBLEU on JUICE-10K compared to base models. In addition, our method
significantly enhances pre-trained models, e.g., CodeBLEU of CodeGen-350M
improvement from 3.21 to 21.54 on MBPP in zero-shot setting.
|
[
{
"version": "v1",
"created": "Wed, 2 Nov 2022 01:40:18 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Nov 2022 16:53:15 GMT"
},
{
"version": "v3",
"created": "Mon, 9 Jan 2023 06:14:56 GMT"
},
{
"version": "v4",
"created": "Tue, 17 Jan 2023 03:14:35 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Dong",
"Yihong",
""
],
[
"Jiang",
"Xue",
""
],
[
"Liu",
"Yuchen",
""
],
[
"Li",
"Ge",
""
],
[
"Jin",
"Zhi",
""
]
] |
new_dataset
| 0.998561 |
2211.14238
|
Huaxiu Yao
|
Huaxiu Yao, Caroline Choi, Bochuan Cao, Yoonho Lee, Pang Wei Koh,
Chelsea Finn
|
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
|
Accepted by NeurIPS 2022 Track on Datasets and Benchmarks; v2: fixed
some issues in FMoW and change the name from "FMoW" to "FMoW-Time"
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Distribution shift occurs when the test distribution differs from the
training distribution, and it can considerably degrade performance of machine
learning models deployed in the real world. Temporal shifts -- distribution
shifts arising from the passage of time -- often occur gradually and have the
additional structure of timestamp metadata. By leveraging timestamp metadata,
models can potentially learn from trends in past distribution shifts and
extrapolate into the future. While recent works have studied distribution
shifts, temporal shifts remain underexplored. To address this gap, we curate
Wild-Time, a benchmark of 5 datasets that reflect temporal distribution shifts
arising in a variety of real-world applications, including patient prognosis
and news classification. On these datasets, we systematically benchmark 13
prior approaches, including methods in domain generalization, continual
learning, self-supervised learning, and ensemble learning. We use two
evaluation strategies: evaluation with a fixed time split (Eval-Fix) and
evaluation with a data stream (Eval-Stream). Eval-Fix, our primary evaluation
strategy, aims to provide a simple evaluation protocol, while Eval-Stream is
more realistic for certain real-world applications. Under both evaluation
strategies, we observe an average performance drop of 20% from in-distribution
to out-of-distribution data. Existing methods are unable to close this gap.
Code is available at https://wild-time.github.io/.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 17:07:53 GMT"
},
{
"version": "v2",
"created": "Mon, 16 Jan 2023 03:13:33 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Yao",
"Huaxiu",
""
],
[
"Choi",
"Caroline",
""
],
[
"Cao",
"Bochuan",
""
],
[
"Lee",
"Yoonho",
""
],
[
"Koh",
"Pang Wei",
""
],
[
"Finn",
"Chelsea",
""
]
] |
new_dataset
| 0.994693 |
2301.05746
|
Jack Urbanek
|
Alexander Gurung, Mojtaba Komeili, Arthur Szlam, Jason Weston, and
Jack Urbanek
|
Infusing Commonsense World Models with Graph Knowledge
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While language models have become more capable of producing compelling
language, we find there are still gaps in maintaining consistency, especially
when describing events in a dynamically changing world. We study the setting of
generating narratives in an open world text adventure game, where a graph
representation of the underlying game state can be used to train models that
consume and output both grounded graph representations and natural language
descriptions and actions. We build a large set of tasks by combining
crowdsourced and simulated gameplays with a novel dataset of complex actions in
order to to construct such models. We find it is possible to improve the
consistency of action narration models by training on graph contexts and
targets, even if graphs are not present at test time. This is shown both in
automatic metrics and human evaluations. We plan to release our code, the new
set of tasks, and best performing models.
|
[
{
"version": "v1",
"created": "Fri, 13 Jan 2023 19:58:27 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Gurung",
"Alexander",
""
],
[
"Komeili",
"Mojtaba",
""
],
[
"Szlam",
"Arthur",
""
],
[
"Weston",
"Jason",
""
],
[
"Urbanek",
"Jack",
""
]
] |
new_dataset
| 0.999085 |
2301.05768
|
Morteza Rezanejad
|
Maciej Sypetkowski, Morteza Rezanejad, Saber Saberian, Oren Kraus,
John Urbanik, James Taylor, Ben Mabey, Mason Victors, Jason Yosinski, Alborz
Rezazadeh Sereshkeh, Imran Haque, Berton Earnshaw
|
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
High-throughput screening techniques are commonly used to obtain large
quantities of data in many fields of biology. It is well known that artifacts
arising from variability in the technical execution of different experimental
batches within such screens confound these observations and can lead to invalid
biological conclusions. It is therefore necessary to account for these batch
effects when analyzing outcomes. In this paper we describe RxRx1, a biological
dataset designed specifically for the systematic study of batch effect
correction methods. The dataset consists of 125,510 high-resolution
fluorescence microscopy images of human cells under 1,138 genetic perturbations
in 51 experimental batches across 4 cell types. Visual inspection of the images
alone clearly demonstrates significant batch effects. We propose a
classification task designed to evaluate the effectiveness of experimental
batch correction methods on these images and examine the performance of a
number of correction methods on this task. Our goal in releasing RxRx1 is to
encourage the development of effective experimental batch correction methods
that generalize well to unseen experimental batches. The dataset can be
downloaded at https://rxrx.ai.
|
[
{
"version": "v1",
"created": "Fri, 13 Jan 2023 21:49:12 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Sypetkowski",
"Maciej",
""
],
[
"Rezanejad",
"Morteza",
""
],
[
"Saberian",
"Saber",
""
],
[
"Kraus",
"Oren",
""
],
[
"Urbanik",
"John",
""
],
[
"Taylor",
"James",
""
],
[
"Mabey",
"Ben",
""
],
[
"Victors",
"Mason",
""
],
[
"Yosinski",
"Jason",
""
],
[
"Sereshkeh",
"Alborz Rezazadeh",
""
],
[
"Haque",
"Imran",
""
],
[
"Earnshaw",
"Berton",
""
]
] |
new_dataset
| 0.999724 |
2301.05776
|
Iurii Medvedev
|
Iurii Medvedev and Farhad Shadmand and Nuno Gon\c{c}alves
|
Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces
Recognition
|
11 pages, 3 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Face recognition has achieved outstanding performance in the last decade with
the development of deep learning techniques.
Nowadays, the challenges in face recognition are related to specific
scenarios, for instance, the performance under diverse image quality, the
robustness for aging and edge cases of person age (children and elders),
distinguishing of related identities.
In this set of problems, recognizing children's faces is one of the most
sensitive and important. One of the reasons for this problem is the existing
bias towards adults in existing face datasets.
In this work, we present a benchmark dataset for children's face recognition,
which is compiled similarly to the famous face recognition benchmarks LFW,
CALFW, CPLFW, XQLFW and AgeDB.
We also present a development dataset (separated into train and test parts)
for adapting face recognition models for face images of children.
The proposed data is balanced for African, Asian, Caucasian, and Indian
races. To the best of our knowledge, this is the first standartized data tool
set for benchmarking and the largest collection for development for children's
face recognition. Several face recognition experiments are presented to
demonstrate the performance of the proposed data tool set.
|
[
{
"version": "v1",
"created": "Fri, 13 Jan 2023 22:19:44 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Medvedev",
"Iurii",
""
],
[
"Shadmand",
"Farhad",
""
],
[
"Gonçalves",
"Nuno",
""
]
] |
new_dataset
| 0.971624 |
2301.05856
|
Mingjie Xie
|
Jian Guan, Mingjie Xie, Youtian Lin, Guangjun He, Pengming Feng
|
EARL: An Elliptical Distribution aided Adaptive Rotation Label
Assignment for Oriented Object Detection in Remote Sensing Images
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Label assignment is often employed in recent convolutional neural network
(CNN) based detectors to determine positive or negative samples during training
process. However, we note that current label assignment strategies barely
consider the characteristics of targets in remote sensing images thoroughly,
such as large variations in orientations, aspect ratios and scales, which lead
to insufficient sampling. In this paper, an Elliptical Distribution aided
Adaptive Rotation Label Assignment (EARL) is proposed to select positive
samples with higher quality in orientation detectors, and yields better
performance. Concretely, to avoid inadequate sampling of targets with extreme
scales, an adaptive scale sampling (ADS) strategy is proposed to dynamically
select samples on different feature levels according to the scales of targets.
To enhance ADS, positive samples are selected following a dynamic elliptical
distribution (DED), which can further exploit the orientation and shape
properties of targets. Moreover, a spatial distance weighting (SDW) module is
introduced to mitigate the influence from low-quality samples on detection
performance. Extensive experiments on popular remote sensing datasets, such as
DOTA and HRSC2016, demonstrate the effectiveness and the superiority of our
proposed EARL, where without bells and whistles, it achieves 72.87 of mAP on
DOTA dataset by being integrated with simple structure, which outperforms
current state-of-the-art anchor-free detectors and provides comparable
performance as anchor-based methods. The source code will be available at
https://github.com/Justlovesmile/EARL
|
[
{
"version": "v1",
"created": "Sat, 14 Jan 2023 08:32:16 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Guan",
"Jian",
""
],
[
"Xie",
"Mingjie",
""
],
[
"Lin",
"Youtian",
""
],
[
"He",
"Guangjun",
""
],
[
"Feng",
"Pengming",
""
]
] |
new_dataset
| 0.993991 |
2301.05945
|
Sarad Venugopalan
|
Sarad Venugopalan and Heiko Aydt
|
Dance of the DAOs: Building Data Assets as a Use Case
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Decentralised Autonomous Organisations (DAOs) have recently piqued the
interest of participants from diverse backgrounds, including business owners,
engineers, individual and institutional investors. In part, the promised
autonomy (less rigid structure and more voice) in decision making along with
ease of market access, has resulted in its participants pouring in their time
and economic resources. In a DAO, governance is typically enacted via posting
proposals and collectively voting on it. The winning proposals are then
implemented. However, governance alone may be insufficient, when its
participants economic incentives are misaligned. Governance and tokenomics need
to work in tandem to ensure business stability. We present a case study on an
example building data asset from the construction industry and present its
tokenomics. We show its working, both as a caretaker and strategic DAO, to
illustrate its effects on governance and DAO stability. The case study serves
as an example for participants to decide whether their DAO tokenomics are
aligned with participation incentives. Finally, we propose the DAO tension
quadrilateral to study DAO stability and build a tool to measure agreement
among its participants.
|
[
{
"version": "v1",
"created": "Sat, 14 Jan 2023 16:24:40 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Venugopalan",
"Sarad",
""
],
[
"Aydt",
"Heiko",
""
]
] |
new_dataset
| 0.995735 |
2301.05965
|
George Chernishev
|
George Chernishev, Michael Polyntsov, Anton Chizhov, Kirill Stupakov,
Ilya Shchuckin, Alexander Smirnov, Maxim Strutovsky, Alexey Shlyonskikh,
Mikhail Firsov, Stepan Manannikov, Nikita Bobrov, Daniil Goncharov, Ilia
Barutkin, Vladislav Shalnev, Kirill Muraviev, Anna Rakhmukova, Dmitriy
Shcheka, Anton Chernikov, Dmitrii Mandelshtam, Mikhail Vyrodov, Arthur
Saliou, Eduard Gaisin, Kirill Smirnov
|
Desbordante: from benchmarking suite to high-performance
science-intensive data profiler (preprint)
| null | null | null | null |
cs.DB cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Pioneering data profiling systems such as Metanome and OpenClean brought
public attention to science-intensive data profiling. This type of profiling
aims to extract complex patterns (primitives) such as functional dependencies,
data constraints, association rules, and others. However, these tools are
research prototypes rather than production-ready systems.
The following work presents Desbordante - a high-performance
science-intensive data profiler with open source code. Unlike similar systems,
it is built with emphasis on industrial application in a multi-user
environment. It is efficient, resilient to crashes, and scalable. Its
efficiency is ensured by implementing discovery algorithms in C++, resilience
is achieved by extensive use of containerization, and scalability is based on
replication of containers.
Desbordante aims to open industrial-grade primitive discovery to a broader
public, focusing on domain experts who are not IT professionals. Aside from the
discovery of various primitives, Desbordante offers primitive validation, which
not only reports whether a given instance of primitive holds or not, but also
points out what prevents it from holding via the use of special screens. Next,
Desbordante supports pipelines - ready-to-use functionality implemented using
the discovered primitives, for example, typo detection. We provide built-in
pipelines, and the users can construct their own via provided Python bindings.
Unlike other profilers, Desbordante works not only with tabular data, but with
graph and transactional data as well.
In this paper, we present Desbordante, the vision behind it and its
use-cases. To provide a more in-depth perspective, we discuss its current
state, architecture, and design decisions it is built on. Additionally, we
outline our future plans.
|
[
{
"version": "v1",
"created": "Sat, 14 Jan 2023 19:14:51 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Chernishev",
"George",
""
],
[
"Polyntsov",
"Michael",
""
],
[
"Chizhov",
"Anton",
""
],
[
"Stupakov",
"Kirill",
""
],
[
"Shchuckin",
"Ilya",
""
],
[
"Smirnov",
"Alexander",
""
],
[
"Strutovsky",
"Maxim",
""
],
[
"Shlyonskikh",
"Alexey",
""
],
[
"Firsov",
"Mikhail",
""
],
[
"Manannikov",
"Stepan",
""
],
[
"Bobrov",
"Nikita",
""
],
[
"Goncharov",
"Daniil",
""
],
[
"Barutkin",
"Ilia",
""
],
[
"Shalnev",
"Vladislav",
""
],
[
"Muraviev",
"Kirill",
""
],
[
"Rakhmukova",
"Anna",
""
],
[
"Shcheka",
"Dmitriy",
""
],
[
"Chernikov",
"Anton",
""
],
[
"Mandelshtam",
"Dmitrii",
""
],
[
"Vyrodov",
"Mikhail",
""
],
[
"Saliou",
"Arthur",
""
],
[
"Gaisin",
"Eduard",
""
],
[
"Smirnov",
"Kirill",
""
]
] |
new_dataset
| 0.980131 |
2301.06018
|
Cheng-Ze Lu
|
Cheng-Ze Lu, Xiaojie Jin, Zhicheng Huang, Qibin Hou, Ming-Ming Cheng,
Jiashi Feng
|
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
|
Technical Report
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework,
has shown its potential of learning expressive feature representations in
visual image recognition. This work shows that CMAE also trivially generalizes
well on video action recognition without modifying the architecture and the
loss criterion. By directly replacing the original pixel shift with the
temporal shift, our CMAE for visual action recognition, CMAE-V for short, can
generate stronger feature representations than its counterpart based on pure
masked autoencoders. Notably, CMAE-V, with a hybrid architecture, can achieve
82.2% and 71.6% top-1 accuracy on the Kinetics-400 and Something-something V2
datasets, respectively. We hope this report could provide some informative
inspiration for future works.
|
[
{
"version": "v1",
"created": "Sun, 15 Jan 2023 05:07:41 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Lu",
"Cheng-Ze",
""
],
[
"Jin",
"Xiaojie",
""
],
[
"Huang",
"Zhicheng",
""
],
[
"Hou",
"Qibin",
""
],
[
"Cheng",
"Ming-Ming",
""
],
[
"Feng",
"Jiashi",
""
]
] |
new_dataset
| 0.997444 |
2301.06160
|
Shu Zhong
|
Shu Zhong, Miriam Ribul, Youngjun Cho, Marianna Obrist
|
TextileNet: A Material Taxonomy-based Fashion Textile Dataset
|
10 papes, 4 figures, 2 tables
| null | null | null |
cs.DL cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The rise of Machine Learning (ML) is gradually digitalizing and reshaping the
fashion industry. Recent years have witnessed a number of fashion AI
applications, for example, virtual try-ons. Textile material identification and
categorization play a crucial role in the fashion textile sector, including
fashion design, retails, and recycling. At the same time, Net Zero is a global
goal and the fashion industry is undergoing a significant change so that
textile materials can be reused, repaired and recycled in a sustainable manner.
There is still a challenge in identifying textile materials automatically for
garments, as we lack a low-cost and effective technique for identifying them.
In light of this, we build the first fashion textile dataset, TextileNet, based
on textile material taxonomies - a fibre taxonomy and a fabric taxonomy
generated in collaboration with material scientists. TextileNet can be used to
train and evaluate the state-of-the-art Deep Learning models for textile
materials. We hope to standardize textile related datasets through the use of
taxonomies. TextileNet contains 33 fibres labels and 27 fabrics labels, and has
in total 760,949 images. We use standard Convolutional Neural Networks (CNNs)
and Vision Transformers (ViTs) to establish baselines for this dataset. Future
applications for this dataset range from textile classification to optimization
of the textile supply chain and interactive design for consumers. We envision
that this can contribute to the development of a new AI-based fashion platform.
|
[
{
"version": "v1",
"created": "Sun, 15 Jan 2023 19:02:18 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Zhong",
"Shu",
""
],
[
"Ribul",
"Miriam",
""
],
[
"Cho",
"Youngjun",
""
],
[
"Obrist",
"Marianna",
""
]
] |
new_dataset
| 0.999842 |
2301.06176
|
Isa Inuwa-Dutse
|
Bello Shehu Bello, Muhammad Abubakar Alhassan, Isa Inuwa-Dutse
|
#EndSARS Protest: Discourse and Mobilisation on Twitter
|
17 pages, 11 figures, 2 tables
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Using the @NGRPresident Twitter handle, the Government of Nigeria issued a
special directive banning Special Anti-Robbery Squad (SARS) with immediate
effect. The SARS is a special police unit under the Nigeria Police Force tasked
with the responsibility of fighting violent crimes. However, the unit has been
accused of waves of human rights abuse across the nation. According to a report
by Amnesty International, between January 2017 and May 2020, 82 cases of police
brutality have been committed. This has led to one of the major protests
demanding more measures to be taken. The #EndSARS hashtag was widely used by
the protesters to amplify their messages and reach out to wider communities on
Twitter. In this study, we present a critical analysis of how the online
protest unfolded. Essentially, we examine how the protest evolves on Twitter,
the nature of engagement with the protest themes, the factors influencing the
protest and public perceptions about the online movement. We found that the
mobilisation strategies include direct and indirect engagements with
influential users, sharing direct stories and vicarious experiences. Also,
there is evidence that suggests the deployment of automated accounts to promote
the course of the protest. In terms of participation, over 70% of the protest
is confined within a few states in Nigeria, and the diaspora communities also
lent their voices to the movement. The most active users are not those with
high followership, and the majority of the protesters utilised mobile devices,
accounting for 88% to mobilise and report on the protest. We also examined how
social media users interact with the movement and the response from the wider
online communities. Needless to say, the themes in the online discourse are
mostly about #EndSARS and vicarious experiences with the police, however, there
are topics around police reform and demand for regime change.
|
[
{
"version": "v1",
"created": "Sun, 15 Jan 2023 20:11:25 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Bello",
"Bello Shehu",
""
],
[
"Alhassan",
"Muhammad Abubakar",
""
],
[
"Inuwa-Dutse",
"Isa",
""
]
] |
new_dataset
| 0.999834 |
2301.06178
|
Anthony Rios
|
Xingmeng Zhao, Xavier Walton, Suhana Shrestha and Anthony Rios
|
Bike Frames: Understanding the Implicit Portrayal of Cyclists in the
News
| null | null | null | null |
cs.CY cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Increasing the number of cyclists, whether for general transport or
recreation, can provide health improvements and reduce the environmental impact
of vehicular transportation. However, the public's perception of cycling may be
driven by the ideologies and reporting standards of news agencies. For
instance, people may identify cyclists on the road as "dangerous" if news
agencies overly report cycling accidents, limiting the number of people that
cycle for transportation. Moreover, if fewer people cycle, there may be less
funding from the government to invest in safe infrastructure. In this paper, we
explore the perceived perception of cyclists within news headlines. To
accomplish this, we introduce a new dataset, "Bike Frames", that can help
provide insight into how headlines portray cyclists and help detect
accident-related headlines. Next, we introduce a multi-task (MT) regularization
approach that increases the detection accuracy of accident-related posts,
demonstrating improvements over traditional MT frameworks. Finally, we compare
and contrast the perceptions of cyclists with motorcyclist-related headlines to
ground the findings with another related activity for both male- and
female-related posts. Our findings show that general news websites are more
likely to report accidents about cyclists than other events. Moreover,
cyclist-specific websites are more likely to report about accidents than
motorcycling-specific websites, even though there is more potential danger for
motorcyclists. Finally, we show substantial differences in the reporting about
male vs. female-related persons, e.g., more male-related cyclists headlines are
related to accidents, but more female-related motorcycling headlines about
accidents. WARNING: This paper contains descriptions of accidents and death.
|
[
{
"version": "v1",
"created": "Sun, 15 Jan 2023 20:22:03 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Zhao",
"Xingmeng",
""
],
[
"Walton",
"Xavier",
""
],
[
"Shrestha",
"Suhana",
""
],
[
"Rios",
"Anthony",
""
]
] |
new_dataset
| 0.998824 |
2301.06184
|
Yiqin Zhao
|
Yiqin Zhao, Chongyang Ma, Haibin Huang, Tian Guo
|
LitAR: Visually Coherent Lighting for Mobile Augmented Reality
| null | null |
10.1145/3550291
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An accurate understanding of omnidirectional environment lighting is crucial
for high-quality virtual object rendering in mobile augmented reality (AR). In
particular, to support reflective rendering, existing methods have leveraged
deep learning models to estimate or have used physical light probes to capture
physical lighting, typically represented in the form of an environment map.
However, these methods often fail to provide visually coherent details or
require additional setups. For example, the commercial framework ARKit uses a
convolutional neural network that can generate realistic environment maps;
however the corresponding reflective rendering might not match the physical
environments. In this work, we present the design and implementation of a
lighting reconstruction framework called LitAR that enables realistic and
visually-coherent rendering. LitAR addresses several challenges of supporting
lighting information for mobile AR. First, to address the spatial variance
problem, LitAR uses two-field lighting reconstruction to divide the lighting
reconstruction task into the spatial variance-aware near-field reconstruction
and the directional-aware far-field reconstruction. The corresponding
environment map allows reflective rendering with correct color tones. Second,
LitAR uses two noise-tolerant data capturing policies to ensure data quality,
namely guided bootstrapped movement and motion-based automatic capturing.
Third, to handle the mismatch between the mobile computation capability and the
high computation requirement of lighting reconstruction, LitAR employs two
novel real-time environment map rendering techniques called multi-resolution
projection and anchor extrapolation. These two techniques effectively remove
the need of time-consuming mesh reconstruction while maintaining visual
quality.
|
[
{
"version": "v1",
"created": "Sun, 15 Jan 2023 20:47:38 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Zhao",
"Yiqin",
""
],
[
"Ma",
"Chongyang",
""
],
[
"Huang",
"Haibin",
""
],
[
"Guo",
"Tian",
""
]
] |
new_dataset
| 0.998432 |
2301.06246
|
Yiding Feng
|
Ozan Candogan, Yiding Feng
|
Mobility Data in Operations: The Facility Location Problem
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The recent large scale availability of mobility data, which captures
individual mobility patterns, poses novel operational problems that are
exciting and challenging. Motivated by this, we introduce and study a variant
of the (cost-minimization) facility location problem where each individual is
endowed with two locations (hereafter, her home and work locations), and the
connection cost is the minimum distance between any of her locations and its
closest facility. We design a polynomial-time algorithm whose approximation
ratio is at most 3.103. We complement this positive result by showing that the
proposed algorithm is at least a 3.073-approximation, and there exists no
polynomial-time algorithm with approximation ratio $2-\epsilon$ under
UG-hardness. We further extend our results and analysis to the model where each
individual is endowed with K locations. Finally, we conduct numerical
experiments over both synthetic data and US census data (for NYC, greater LA,
greater DC, Research Triangle) and evaluate the performance of our algorithms.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 03:35:35 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Candogan",
"Ozan",
""
],
[
"Feng",
"Yiding",
""
]
] |
new_dataset
| 0.976367 |
2301.06269
|
Bo Zhang
|
Bo Zhang, Yuchen Guo, Runzhao Yang, Zhihong Zhang, Jiayi Xie, Jinli
Suo and Qionghai Dai
|
DarkVision: A Benchmark for Low-light Image/Video Perception
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Imaging and perception in photon-limited scenarios is necessary for various
applications, e.g., night surveillance or photography, high-speed photography,
and autonomous driving. In these cases, cameras suffer from low signal-to-noise
ratio, which degrades the image quality severely and poses challenges for
downstream high-level vision tasks like object detection and recognition.
Data-driven methods have achieved enormous success in both image restoration
and high-level vision tasks. However, the lack of high-quality benchmark
dataset with task-specific accurate annotations for photon-limited
images/videos delays the research progress heavily. In this paper, we
contribute the first multi-illuminance, multi-camera, and low-light dataset,
named DarkVision, serving for both image enhancement and object detection. We
provide bright and dark pairs with pixel-wise registration, in which the bright
counterpart provides reliable reference for restoration and annotation. The
dataset consists of bright-dark pairs of 900 static scenes with objects from 15
categories, and 32 dynamic scenes with 4-category objects. For each scene,
images/videos were captured at 5 illuminance levels using three cameras of
different grades, and average photons can be reliably estimated from the
calibration data for quantitative studies. The static-scene images and dynamic
videos respectively contain around 7,344 and 320,667 instances in total. With
DarkVision, we established baselines for image/video enhancement and object
detection by representative algorithms. To demonstrate an exemplary application
of DarkVision, we propose two simple yet effective approaches for improving
performance in video enhancement and object detection respectively. We believe
DarkVision would advance the state-of-the-arts in both imaging and related
computer vision tasks in low-light environment.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 05:55:59 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Zhang",
"Bo",
""
],
[
"Guo",
"Yuchen",
""
],
[
"Yang",
"Runzhao",
""
],
[
"Zhang",
"Zhihong",
""
],
[
"Xie",
"Jiayi",
""
],
[
"Suo",
"Jinli",
""
],
[
"Dai",
"Qionghai",
""
]
] |
new_dataset
| 0.999886 |
2301.06316
|
Ehsan Ul Haq
|
Ehsan-Ul Haq, Haris Bin Zia, Reza Hadi Mogavi, Gareth Tyson, Yang K.
Lu, Tristan Braud, Pan Hui
|
A Twitter Dataset for Pakistani Political Discourse
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We share the largest dataset for the Pakistani Twittersphere consisting of
over 49 million tweets, collected during one of the most politically active
periods in the country. We collect the data after the deposition of the
government by a No Confidence Vote in April 2022. This large-scale dataset can
be used for several downstream tasks such as political bias, bots detection,
trolling behavior, (dis)misinformation, and censorship related to Pakistani
Twitter users. In addition, this dataset provides a large collection of tweets
in Urdu and Roman Urdu that can be used for optimizing language processing
tasks.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 09:11:11 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Haq",
"Ehsan-Ul",
""
],
[
"Zia",
"Haris Bin",
""
],
[
"Mogavi",
"Reza Hadi",
""
],
[
"Tyson",
"Gareth",
""
],
[
"Lu",
"Yang K.",
""
],
[
"Braud",
"Tristan",
""
],
[
"Hui",
"Pan",
""
]
] |
new_dataset
| 0.999883 |
2301.06375
|
Kwanghee Choi
|
Jeongkyun Park, Jung-Wook Hwang, Kwanghee Choi, Seung-Hyun Lee, Jun
Hwan Ahn, Rae-Hong Park, Hyung-Min Park
|
OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset
| null | null | null | null |
cs.MM cs.AI cs.CL cs.CV cs.LG cs.SD
|
http://creativecommons.org/licenses/by/4.0/
|
Inspired by humans comprehending speech in a multi-modal manner, various
audio-visual datasets have been constructed. However, most existing datasets
focus on English, induce dependencies with various prediction models during
dataset preparation, and have only a small number of multi-view videos. To
mitigate the limitations, we recently developed the Open Large-scale Korean
Audio-Visual Speech (OLKAVS) dataset, which is the largest among publicly
available audio-visual speech datasets. The dataset contains 1,150 hours of
transcribed audio from 1,107 Korean speakers in a studio setup with nine
different viewpoints and various noise situations. We also provide the
pre-trained baseline models for two tasks, audio-visual speech recognition and
lip reading. We conducted experiments based on the models to verify the
effectiveness of multi-modal and multi-view training over uni-modal and
frontal-view-only training. We expect the OLKAVS dataset to facilitate
multi-modal research in broader areas such as Korean speech recognition,
speaker recognition, pronunciation level classification, and mouth motion
analysis.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 11:40:50 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Park",
"Jeongkyun",
""
],
[
"Hwang",
"Jung-Wook",
""
],
[
"Choi",
"Kwanghee",
""
],
[
"Lee",
"Seung-Hyun",
""
],
[
"Ahn",
"Jun Hwan",
""
],
[
"Park",
"Rae-Hong",
""
],
[
"Park",
"Hyung-Min",
""
]
] |
new_dataset
| 0.999829 |
2301.06400
|
Youmna Farag
|
Youmna Farag, Charlotte O. Brand, Jacopo Amidei, Paul Piwek, Tom
Stafford, Svetlana Stoyanchev, Andreas Vlachos
|
Opening up Minds with Argumentative Dialogues
| null |
Findings of EMNLP 2022
| null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Recent research on argumentative dialogues has focused on persuading people
to take some action, changing their stance on the topic of discussion, or
winning debates. In this work, we focus on argumentative dialogues that aim to
open up (rather than change) people's minds to help them become more
understanding to views that are unfamiliar or in opposition to their own
convictions. To this end, we present a dataset of 183 argumentative dialogues
about 3 controversial topics: veganism, Brexit and COVID-19 vaccination. The
dialogues were collected using the Wizard of Oz approach, where wizards
leverage a knowledge-base of arguments to converse with participants.
Open-mindedness is measured before and after engaging in the dialogue using a
questionnaire from the psychology literature, and success of the dialogue is
measured as the change in the participant's stance towards those who hold
opinions different to theirs. We evaluate two dialogue models: a
Wikipedia-based and an argument-based model. We show that while both models
perform closely in terms of opening up minds, the argument-based model is
significantly better on other dialogue properties such as engagement and
clarity.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 12:47:16 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Farag",
"Youmna",
""
],
[
"Brand",
"Charlotte O.",
""
],
[
"Amidei",
"Jacopo",
""
],
[
"Piwek",
"Paul",
""
],
[
"Stafford",
"Tom",
""
],
[
"Stoyanchev",
"Svetlana",
""
],
[
"Vlachos",
"Andreas",
""
]
] |
new_dataset
| 0.999722 |
2301.06422
|
Novel Certad
|
Novel Certad, Walter Morales-Alvarez, Georg Novotny, Cristina
Olaverri-Monreal
|
JKU-ITS Automobile for Research on Autonomous Vehicles
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present our brand-new platform for Automated Driving
research. The chosen vehicle is a RAV4 hybrid SUV from TOYOTA provided with
exteroceptive sensors such as a multilayer LIDAR, a monocular camera, Radar and
GPS; and proprioceptive sensors such as encoders and a 9-DOF IMU. These sensors
are integrated in the vehicle via a main computer running ROS1 under Linux
20.04. Additionally, we installed an open-source ADAS called Comma Two, that
runs Openpilot to control the vehicle. The platform is currently being used to
research in the field of autonomous vehicles, human and autonomous vehicles
interaction, human factors, and energy consumption.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 13:21:15 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Certad",
"Novel",
""
],
[
"Morales-Alvarez",
"Walter",
""
],
[
"Novotny",
"Georg",
""
],
[
"Olaverri-Monreal",
"Cristina",
""
]
] |
new_dataset
| 0.999759 |
2301.06433
|
Animesh Singhal
|
Animesh Singhal, Sahil Modi, Abhishek Gupta, Leena Vachhani
|
Wobble control of a pendulum actuated spherical robot
|
The length of the research paper is 20 pages, and it contains 15
graphs or illustrations
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Spherical robots can conduct surveillance in hostile, cluttered environments
without being damaged, as their protective shell can safely house sensors such
as cameras. However, lateral oscillations, also known as wobble, occur when
these sphere-shaped robots operate at low speeds, leading to shaky camera
feedback. These oscillations in a pendulum-actuated spherical robot are caused
by the coupling between the forward and steering motions due to nonholonomic
constraints. Designing a controller to limit wobbling in these robots is
challenging due to their underactuated nature. We propose a model-based
controller to navigate a pendulum-actuated spherical robot using wobble-free
turning maneuvers consisting of circular arcs and straight lines. The model is
developed using Lagrange-D'Alembert equations and accounts for the coupled
forward and steering motions. The model is further analyzed to derive
expressions for radius of curvature, precession rate, wobble amplitude, and
wobble frequency during circular motions. Finally, we design an input-output
feedback linearization-based controller to control the robot's heading
direction and wobble. Overall, the proposed controller enables a teleoperator
to command a specific forward velocity and pendulum angle as per the desired
turning radius while limiting the robot's lateral oscillations to enhance the
quality of camera feedback.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 13:48:49 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Singhal",
"Animesh",
""
],
[
"Modi",
"Sahil",
""
],
[
"Gupta",
"Abhishek",
""
],
[
"Vachhani",
"Leena",
""
]
] |
new_dataset
| 0.999082 |
2301.06446
|
Chengju Li
|
Hai Liu, Chengju Li, Cunsheng Ding
|
Five infinite families of binary cyclic codes and their related codes
with good parameters
|
33 pages
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cyclic codes are an interesting type of linear codes and have wide
applications in communication and storage systems due to their efficient
encoding and decoding algorithms. Inspired by the recent work on binary cyclic
codes published in IEEE Trans. Inf. Theory, vol. 68, no. 12, pp. 7842-7849,
2022, the objectives of this paper are the construction and analyses of five
infinite families of binary cyclic codes with parameters $[n, k]$ and $(n-6)/3
\leq k \leq 2(n+6)/3$. Three of the five families of binary cyclic codes and
their duals have a very good lower bound on their minimum distances and contain
distance-optimal codes. The other two families of binary cyclic codes are
composed of binary duadic codes with a square-root-like lower bound on their
minimum distances. As a by-product, two infinite families of self-dual binary
codes with a square-root-like lower bound on their minimum distances are
obtained.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 14:37:52 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Liu",
"Hai",
""
],
[
"Li",
"Chengju",
""
],
[
"Ding",
"Cunsheng",
""
]
] |
new_dataset
| 0.998428 |
2301.06475
|
Julian Linke Dipl. Ing.
|
Julian Linke, Saskia Wepner, Gernot Kubin and Barbara Schuppler
|
Using Kaldi for Automatic Speech Recognition of Conversational Austrian
German
|
10 pages, 2 figures, 4 tables
| null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
As dialogue systems are becoming more and more interactional and social, also
the accurate automatic speech recognition (ASR) of conversational speech is of
increasing importance. This shifts the focus from short, spontaneous,
task-oriented dialogues to the much higher complexity of casual face-to-face
conversations. However, the collection and annotation of such conversations is
a time-consuming process and data is sparse for this specific speaking style.
This paper presents ASR experiments with read and conversational Austrian
German as target. In order to deal with having only limited resources available
for conversational German and, at the same time, with a large variation among
speakers with respect to pronunciation characteristics, we improve a
Kaldi-based ASR system by incorporating a (large) knowledge-based pronunciation
lexicon, while exploring different data-based methods to restrict the number of
pronunciation variants for each lexical entry. We achieve best WER of 0.4% on
Austrian German read speech and best average WER of 48.5% on conversational
speech. We find that by using our best pronunciation lexicon a similarly high
performance can be achieved than by increasing the size of the data used for
the language model by approx. 360% to 760%. Our findings indicate that for
low-resource scenarios -- despite the general trend in speech technology
towards using data-based methods only -- knowledge-based approaches are a
successful, efficient method.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 15:28:28 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Linke",
"Julian",
""
],
[
"Wepner",
"Saskia",
""
],
[
"Kubin",
"Gernot",
""
],
[
"Schuppler",
"Barbara",
""
]
] |
new_dataset
| 0.954971 |
2301.06528
|
Rodrigo Ramele
|
Franco Paviotti, Esteban Buniak, Rodrigo Ramele, Orestes Freixes and
Juan Miguel Santos
|
Equilivest: A Robotic Vest to aid in Post-Stroke Dynamic Balance
Rehabilitation
|
This extended abstract was presented at the "Workshop on Assistive
Robotic Systems for Human Balancing and Walking: Emerging Trends and
Perspectives" at IROS2022
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Stroke is a medical condition that can affect motor function, particularly
dynamic balance. Biofeedback can aid in rehabilitation procedures which help
patients to regain lost motor activity and recover functionality. In this work,
we are presenting a robotic smart-vest device that can analyze Inertial
Measurement Unit (IMU) data and assist in rehabilitation procedures by
providing timed feedback in the form of vibrotactile stimulation. Information
provided by principal caregivers and patients in the form of surveys and
interviews, is used to hypothesize potential clinical causes and to derive
alternative three alternative clinical modalities: Artificial Vestibular
Feedback, Gait Pacemaker and Risk-Predictor.
|
[
{
"version": "v1",
"created": "Mon, 16 Jan 2023 17:25:21 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Paviotti",
"Franco",
""
],
[
"Buniak",
"Esteban",
""
],
[
"Ramele",
"Rodrigo",
""
],
[
"Freixes",
"Orestes",
""
],
[
"Santos",
"Juan Miguel",
""
]
] |
new_dataset
| 0.999088 |
2301.06652
|
Mamtaj Akter
|
Leena Alghamdi, Mamtaj Akter, Jess Kropczynski, Pamela Wisniewski and
Heather Lipford
|
Co-designing Community-based Sharing of Smarthome Devices for the
Purpose of Co-monitoring In-home Emergencies
|
21 pages
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
We conducted 26 co-design interviews with 50 smarthome device owners to
understand the perceived benefits, drawbacks, and design considerations for
developing a smarthome system that facilitates co-monitoring with emergency
contacts who live outside of one's home. Participants felt that such a system
would help ensure their personal safety, safeguard from material loss, and give
them peace of mind by ensuring quick response and verifying potential threats.
However, they also expressed concerns regarding privacy, overburdening others,
and other potential threats, such as unauthorized access and security breaches.
To alleviate these concerns, participants designed for flexible and granular
access control and fail-safe back-up features. Our study reveals why peer-based
co-monitoring of smarthomes for emergencies may be beneficial but also
difficult to implement. Based on the insights gained from our study, we provide
recommendations for designing technologies that facilitate such co-monitoring
while mitigating its risks.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 01:22:30 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Alghamdi",
"Leena",
""
],
[
"Akter",
"Mamtaj",
""
],
[
"Kropczynski",
"Jess",
""
],
[
"Wisniewski",
"Pamela",
""
],
[
"Lipford",
"Heather",
""
]
] |
new_dataset
| 0.991466 |
2301.06680
|
Prateek Chhikara
|
Prateek Chhikara, Harshul Kuhar, Anil Goyal, Chirag Sharma
|
DIGITOUR: Automatic Digital Tours for Real-Estate Properties
|
Published at CODS-COMAD '23
| null |
10.1145/3570991.3571060
| null |
cs.CV cs.GR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A virtual or digital tour is a form of virtual reality technology which
allows a user to experience a specific location remotely. Currently, these
virtual tours are created by following a 2-step strategy. First, a photographer
clicks a 360 degree equirectangular image; then, a team of annotators manually
links these images for the "walkthrough" user experience. The major challenge
in the mass adoption of virtual tours is the time and cost involved in manual
annotation/linking of images. Therefore, this paper presents an end-to-end
pipeline to automate the generation of 3D virtual tours using equirectangular
images for real-estate properties. We propose a novel HSV-based coloring scheme
for paper tags that need to be placed at different locations before clicking
the equirectangular images using 360 degree cameras. These tags have two
characteristics: i) they are numbered to help the photographer for placement of
tags in sequence and; ii) bi-colored, which allows better learning of tag
detection (using YOLOv5 architecture) in an image and digit recognition (using
custom MobileNet architecture) tasks. Finally, we link/connect all the
equirectangular images based on detected tags. We show the efficiency of the
proposed pipeline on a real-world equirectangular image dataset collected from
the Housing.com database.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 03:43:34 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Chhikara",
"Prateek",
""
],
[
"Kuhar",
"Harshul",
""
],
[
"Goyal",
"Anil",
""
],
[
"Sharma",
"Chirag",
""
]
] |
new_dataset
| 0.984168 |
2301.06702
|
Casey Clifton
|
Ike Smith, Casey Clifton
|
Shackled: a 3D Rendering Engine Programmed Entirely in Ethereum Smart
Contracts
|
Published in proceedings of 5th International Conference on
Blockchain, Honolulu, HI, USA, December 10-14, 2022, Proceedings:
https://link.springer.com/chapter/10.1007/978-3-031-23495-8_9
|
Chen, S., Shyamasundar, R. K., & Zhang, L. J. (Eds.).
Blockchain-ICBC 2022: 5th International Conference, Held as part of the
Services Conference Federation, SCF 2022, Honolulu, HI, USA, December 10-14,
2022, Proceedings
|
10.1007/978-3-031-23495-8_9
| null |
cs.CR cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
The Ethereum blockchain permits the development and deployment of smart
contracts which can store and execute code 'on-chain' - that is, entirely on
nodes in the blockchain's network. Smart contracts have traditionally been used
for financial purposes, but since smart contracts are Turing-complete, their
algorithmic scope is broader than any single domain. To that end, we design,
develop, and deploy a comprehensive 3D rendering engine programmed entirely in
Ethereum smart contracts, called Shackled. Shackled computes a 2D image from a
3D scene, executing every single computation on-chain, on Ethereum. To our
knowledge, Shackled is the first and only fully on-chain 3D rendering engine
for Ethereum. In this work, we 1) provide three unique datasets for the purpose
of using and benchmarking Shackled, 2) execute said benchmarks and provide
results, 3) demonstrate a potential use case of Shackled in the domain of
tokenised generative art, 4) provide a no-code user interface to Shackled, 5)
enumerate the challenges associated with programming complex algorithms in
Solidity smart contracts, and 6) outline potential directions for improving the
Shackled platform. It is our hope that this work increases the Ethereum
blockchain's native graphics processing capabilities, and that it enables
increased use of smart contracts for more complex algorithms, thus increasing
the overall richness of the Ethereum ecosystem.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 05:00:13 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Smith",
"Ike",
""
],
[
"Clifton",
"Casey",
""
]
] |
new_dataset
| 0.999603 |
2301.06715
|
Dongseok Shim
|
Dongseok Shim, H. Jin Kim
|
SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via
Swin Transformer and Densely Cascaded Network
|
ICRA 2023
| null | null | null |
cs.CV cs.LG cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Monocular depth estimation plays a critical role in various computer vision
and robotics applications such as localization, mapping, and 3D object
detection. Recently, learning-based algorithms achieve huge success in depth
estimation by training models with a large amount of data in a supervised
manner. However, it is challenging to acquire dense ground truth depth labels
for supervised training, and the unsupervised depth estimation using monocular
sequences emerges as a promising alternative. Unfortunately, most studies on
unsupervised depth estimation explore loss functions or occlusion masks, and
there is little change in model architecture in that ConvNet-based
encoder-decoder structure becomes a de-facto standard for depth estimation. In
this paper, we employ a convolution-free Swin Transformer as an image feature
extractor so that the network can capture both local geometric features and
global semantic features for depth estimation. Also, we propose a Densely
Cascaded Multi-scale Network (DCMNet) that connects every feature map directly
with another from different scales via a top-down cascade pathway. This densely
cascaded connectivity reinforces the interconnection between decoding layers
and produces high-quality multi-scale depth outputs. The experiments on two
different datasets, KITTI and Make3D, demonstrate that our proposed method
outperforms existing state-of-the-art unsupervised algorithms.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 06:01:46 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Shim",
"Dongseok",
""
],
[
"Kim",
"H. Jin",
""
]
] |
new_dataset
| 0.996419 |
2301.06721
|
Hai Lin
|
Hai Lin and Jinhong Yuan
|
On Delay-Doppler Plane Orthogonal Pulse
|
This paper was presented at the IEEE GLOBECOM 2022
| null |
10.1109/GLOBECOM48099.2022.10001406
| null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we analyze the recently discovered delay-Doppler plane
orthogonal pulse (DDOP), which is essential for delay-Doppler plane
multi-carrier modulation waveform. In particular, we introduce a local
orthogonality property of pulses corresponding to Weyl-Heisenberg (WH) subset
and justify the DDOP's existence, in contrast to global orthogonality
corresponding to WH set governed by the WH frame theory. Then, sufficient
conditions for locally-orthogonal pulses are presented and discussed. Based on
the analysis, we propose a general DDOP design. We also derive the frequency
domain representation of the DDOP, and compare the DDOP-based orthogonal
delay-Doppler division multiplexing (ODDM) modulation with other modulation
schemes, in terms of TF signal localization. Interestingly, we show perfect
local orthogonality property of the DDOP with respect to delay-Doppler
resolutions using its ambiguity function.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 06:43:10 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Lin",
"Hai",
""
],
[
"Yuan",
"Jinhong",
""
]
] |
new_dataset
| 0.999563 |
2301.06727
|
Giovanni Finocchio
|
Giovanni Finocchio, Supriyo Bandyopadhyay, Peng Lin, Gang Pan, J.
Joshua Yang, Riccardo Tomasello, Christos Panagopoulos, Mario Carpentieri,
Vito Puliafito, Johan {\AA}kerman, Hiroki Takesue, Amit Ranjan Trivedi,
Saibal Mukhopadhyay, Kaushik Roy, Vinod K. Sangwan, Mark C. Hersam, Anna
Giordano, Huynsoo Yang, Julie Grollier, Kerem Camsari, Peter Mcmahon, Supriyo
Datta, Jean Anne Incorvia, Joseph Friedman, Sorin Cotofana, Florin Ciubotaru,
Andrii Chumak, Azad J. Naeemi, Brajesh Kumar Kaushik, Yao Zhu, Kang Wang,
Belita Koiller, Gabriel Aguilar, Guilherme Tempor\~ao, Kremena Makasheva,
Aida Tordi- Sanial, Jennifer Hasler, William Levy, Vwani Roychowdhury,
Samiran Ganguly, Avik Ghosh, Davi Rodriquez, Satoshi Sunada, Karin
Evershor-Sitte, Amit Lal, Shubham Jadhav, Massimiliano Di Ventra, Yuriy
Pershin, Kosuke Tatsumura, Hayato Goto
|
Roadmap for Unconventional Computing with Nanotechnology
|
88 pages currently under peer review with Nano
| null | null | null |
cs.ET physics.app-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In the Beyond Moore Law era, with increasing edge intelligence,
domain-specific computing embracing unconventional approaches will become
increasingly prevalent. At the same time, the adoption of a wide variety of
nanotechnologies will offer benefits in energy cost, computational speed,
reduced footprint, cyber-resilience and processing prowess. The time is ripe to
lay out a roadmap for unconventional computing with nanotechnologies to guide
future research and this collection aims to fulfill that need. The authors
provide a comprehensive roadmap for neuromorphic computing with electron spins,
memristive devices, two-dimensional nanomaterials, nanomagnets and assorted
dynamical systems. They also address other paradigms such as Ising machines,
Bayesian inference engines, probabilistic computing with p-bits, processing in
memory, quantum memories and algorithms, computing with skyrmions and spin
waves, and brain inspired computing for incremental learning and solving
problems in severely resource constrained environments. All of these approaches
have advantages over conventional Boolean computing predicated on the
von-Neumann architecture. With the computational need for artificial
intelligence growing at a rate 50x faster than Moore law for electronics, more
unconventional approaches to computing and signal processing will appear on the
horizon and this roadmap will aid in identifying future needs and challenges.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 07:00:28 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Finocchio",
"Giovanni",
""
],
[
"Bandyopadhyay",
"Supriyo",
""
],
[
"Lin",
"Peng",
""
],
[
"Pan",
"Gang",
""
],
[
"Yang",
"J. Joshua",
""
],
[
"Tomasello",
"Riccardo",
""
],
[
"Panagopoulos",
"Christos",
""
],
[
"Carpentieri",
"Mario",
""
],
[
"Puliafito",
"Vito",
""
],
[
"Åkerman",
"Johan",
""
],
[
"Takesue",
"Hiroki",
""
],
[
"Trivedi",
"Amit Ranjan",
""
],
[
"Mukhopadhyay",
"Saibal",
""
],
[
"Roy",
"Kaushik",
""
],
[
"Sangwan",
"Vinod K.",
""
],
[
"Hersam",
"Mark C.",
""
],
[
"Giordano",
"Anna",
""
],
[
"Yang",
"Huynsoo",
""
],
[
"Grollier",
"Julie",
""
],
[
"Camsari",
"Kerem",
""
],
[
"Mcmahon",
"Peter",
""
],
[
"Datta",
"Supriyo",
""
],
[
"Incorvia",
"Jean Anne",
""
],
[
"Friedman",
"Joseph",
""
],
[
"Cotofana",
"Sorin",
""
],
[
"Ciubotaru",
"Florin",
""
],
[
"Chumak",
"Andrii",
""
],
[
"Naeemi",
"Azad J.",
""
],
[
"Kaushik",
"Brajesh Kumar",
""
],
[
"Zhu",
"Yao",
""
],
[
"Wang",
"Kang",
""
],
[
"Koiller",
"Belita",
""
],
[
"Aguilar",
"Gabriel",
""
],
[
"Temporão",
"Guilherme",
""
],
[
"Makasheva",
"Kremena",
""
],
[
"Sanial",
"Aida Tordi-",
""
],
[
"Hasler",
"Jennifer",
""
],
[
"Levy",
"William",
""
],
[
"Roychowdhury",
"Vwani",
""
],
[
"Ganguly",
"Samiran",
""
],
[
"Ghosh",
"Avik",
""
],
[
"Rodriquez",
"Davi",
""
],
[
"Sunada",
"Satoshi",
""
],
[
"Evershor-Sitte",
"Karin",
""
],
[
"Lal",
"Amit",
""
],
[
"Jadhav",
"Shubham",
""
],
[
"Di Ventra",
"Massimiliano",
""
],
[
"Pershin",
"Yuriy",
""
],
[
"Tatsumura",
"Kosuke",
""
],
[
"Goto",
"Hayato",
""
]
] |
new_dataset
| 0.967926 |
2301.06736
|
Kavya Manohar
|
Kavya Manohar, A. R. Jayan, Rajeev Rajan
|
Syllable Subword Tokens for Open Vocabulary Speech Recognition in
Malayalam
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In a hybrid automatic speech recognition (ASR) system, a pronunciation
lexicon (PL) and a language model (LM) are essential to correctly retrieve
spoken word sequences. Being a morphologically complex language, the vocabulary
of Malayalam is so huge and it is impossible to build a PL and an LM that cover
all diverse word forms. Usage of subword tokens to build PL and LM, and
combining them to form words after decoding, enables the recovery of many out
of vocabulary words. In this work we investigate the impact of using syllables
as subword tokens instead of words in Malayalam ASR, and evaluate the relative
improvement in lexicon size, model memory requirement and word error rate.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 07:29:47 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Manohar",
"Kavya",
""
],
[
"Jayan",
"A. R.",
""
],
[
"Rajan",
"Rajeev",
""
]
] |
new_dataset
| 0.956764 |
2301.06741
|
Tianyi Zhou
|
Zhao Song, Tianyi Zhou
|
Faster Sinkhorn's Algorithm with Small Treewidth
| null | null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Computing optimal transport (OT) distances such as the earth mover's distance
is a fundamental problem in machine learning, statistics, and computer vision.
In this paper, we study the problem of approximating the general OT distance
between two discrete distributions of size $n$. Given the cost matrix
$C=AA^\top$ where $A \in \mathbb{R}^{n \times d}$, we proposed a faster
Sinkhorn's Algorithm to approximate the OT distance when matrix $A$ has
treewidth $\tau$. To approximate the OT distance, our algorithm improves the
state-of-the-art results [Dvurechensky, Gasnikov, and Kroshnin ICML 2018] from
$\widetilde{O}(\epsilon^{-2} n^2)$ time to $\widetilde{O}(\epsilon^{-2} n
\tau)$ time.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 07:55:15 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Song",
"Zhao",
""
],
[
"Zhou",
"Tianyi",
""
]
] |
new_dataset
| 0.995921 |
2301.06762
|
Pragma Kar
|
Pragma Kar, Shyamvanshikumar Singh, Avijit Mandal, Samiran
Chattopadhyay, Sandip Chakraborty
|
ExpresSense: Exploring a Standalone Smartphone to Sense Engagement of
Users from Facial Expressions Using Acoustic Sensing
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Facial expressions have been considered a metric reflecting a person's
engagement with a task. While the evolution of expression detection methods is
consequential, the foundation remains mostly on image processing techniques
that suffer from occlusion, ambient light, and privacy concerns. In this paper,
we propose ExpresSense, a lightweight application for standalone smartphones
that relies on near-ultrasound acoustic signals for detecting users' facial
expressions. ExpresSense has been tested on different users in lab-scaled and
large-scale studies for both posed as well as natural expressions. By achieving
a classification accuracy of ~75% over various basic expressions, we discuss
the potential of a standalone smartphone to sense expressions through acoustic
sensing.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 08:55:59 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Kar",
"Pragma",
""
],
[
"Singh",
"Shyamvanshikumar",
""
],
[
"Mandal",
"Avijit",
""
],
[
"Chattopadhyay",
"Samiran",
""
],
[
"Chakraborty",
"Sandip",
""
]
] |
new_dataset
| 0.995531 |
2301.06782
|
Chongshan Lu
|
Chongshan Lu, Fukun Yin, Xin Chen, Tao Chen, Gang YU, Jiayuan Fan
|
A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View
Synthesis and Implicit Scene Reconstruction
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural Radiance Fields (NeRF) has achieved impressive results in single
object scene reconstruction and novel view synthesis, which have been
demonstrated on many single modality and single object focused indoor scene
datasets like DTU, BMVS, and NeRF Synthetic.However, the study of NeRF on
large-scale outdoor scene reconstruction is still limited, as there is no
unified outdoor scene dataset for large-scale NeRF evaluation due to expensive
data acquisition and calibration costs. In this paper, we propose a large-scale
outdoor multi-modal dataset, OMMO dataset, containing complex land objects and
scenes with calibrated images, point clouds and prompt annotations. Meanwhile,
a new benchmark for several outdoor NeRF-based tasks is established, such as
novel view synthesis, surface reconstruction, and multi-modal NeRF. To create
the dataset, we capture and collect a large number of real fly-view videos and
select high-quality and high-resolution clips from them. Then we design a
quality review module to refine images, remove low-quality frames and
fail-to-calibrate scenes through a learning-based automatic evaluation plus
manual review. Finally, a number of volunteers are employed to add the text
descriptions for each scene and key-frame to meet the potential multi-modal
requirements in the future. Compared with existing NeRF datasets, our dataset
contains abundant real-world urban and natural scenes with various scales,
camera trajectories, and lighting conditions. Experiments show that our dataset
can benchmark most state-of-the-art NeRF methods on different tasks. We will
release the dataset and model weights very soon.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 10:15:32 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Lu",
"Chongshan",
""
],
[
"Yin",
"Fukun",
""
],
[
"Chen",
"Xin",
""
],
[
"Chen",
"Tao",
""
],
[
"YU",
"Gang",
""
],
[
"Fan",
"Jiayuan",
""
]
] |
new_dataset
| 0.999792 |
2301.06790
|
Michel Pl\"uss
|
Michel Pl\"uss, Yanick Schraner, Christian Scheller, Manfred Vogel
|
2nd Swiss German Speech to Standard German Text Shared Task at SwissText
2022
|
3 pages, 0 figures, to appear in proceedings of SwissText 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present the results and findings of the 2nd Swiss German speech to
Standard German text shared task at SwissText 2022. Participants were asked to
build a sentence-level Swiss German speech to Standard German text system
specialized on the Grisons dialect. The objective was to maximize the BLEU
score on a test set of Grisons speech. 3 teams participated, with the
best-performing system achieving a BLEU score of 70.1.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 10:31:11 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Plüss",
"Michel",
""
],
[
"Schraner",
"Yanick",
""
],
[
"Scheller",
"Christian",
""
],
[
"Vogel",
"Manfred",
""
]
] |
new_dataset
| 0.994 |
2301.06826
|
Guanqun Cao
|
Guanqun Cao, Jiaqi Jiang, Ningtao Mao, Danushka Bollegala, Min Li, and
Shan Luo
|
Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation
|
This paper is accepted at ICRA 2023
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To assist robots in teleoperation tasks, haptic rendering which allows human
operators access a virtual touch feeling has been developed in recent years.
Most previous haptic rendering methods strongly rely on data collected by
tactile sensors. However, tactile data is not widely available for robots due
to their limited reachable space and the restrictions of tactile sensors. To
eliminate the need for tactile data, in this paper we propose a novel method
named as Vis2Hap to generate haptic rendering from visual inputs that can be
obtained from a distance without physical interaction. We take the surface
texture of objects as key cues to be conveyed to the human operator. To this
end, a generative model is designed to simulate the roughness and slipperiness
of the object's surface. To embed haptic cues in Vis2Hap, we use height maps
from tactile sensors and spectrograms from friction coefficients as the
intermediate outputs of the generative model. Once Vis2Hap is trained, it can
be used to generate height maps and spectrograms of new surface textures, from
which a friction image can be obtained and displayed on a haptic display. The
user study demonstrates that our proposed Vis2Hap method enables users to
access a realistic haptic feeling similar to that of physical objects. The
proposed vision-based haptic rendering has the potential to enhance human
operators' perception of the remote environment and facilitate robotic
manipulation.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 12:07:40 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Cao",
"Guanqun",
""
],
[
"Jiang",
"Jiaqi",
""
],
[
"Mao",
"Ningtao",
""
],
[
"Bollegala",
"Danushka",
""
],
[
"Li",
"Min",
""
],
[
"Luo",
"Shan",
""
]
] |
new_dataset
| 0.998466 |
2301.06868
|
Jarkko Kari
|
Jarkko Kari
|
Expansivity and periodicity in algebraic subshifts
|
DLT 2022 special issue
| null | null | null |
cs.DM math.CO math.DS
|
http://creativecommons.org/licenses/by/4.0/
|
A d-dimensional configuration c : Z^d -> A is a coloring of the d-dimensional
infinite grid by elements of a finite alphabet A \subseteq Z. The configuration
c has an annihilator if a non-trivial linear combination of finitely many
translations of c is the zero configuration. Writing c as a d-variate formal
power series, the annihilator is conveniently expressed as a d-variate Laurent
polynomial f whose formal product with c is the zero power series. More
generally, if the formal product is a strongly periodic configuration, we call
the polynomial f a periodizer of c. A common annihilator (periodizer) of a set
of configurations is called an annihilator (periodizer, respectively) of the
set. In particular, we consider annihilators and periodizers of d-dimensional
subshifts, that is, sets of configurations defined by disallowing some local
patterns. We show that a (d-1)-dimensional linear subspace S \subseteq R^d is
expansive for a subshift if the subshift has a periodizer whose support
contains exactly one element of S. As a subshift is known to be finite if all
(d-1)-dimensional subspaces are expansive, we obtain a simple necessary
condition on the periodizers that guarantees finiteness of a subshift or,
equivalently, strong periodicity of a configuration. We provide examples in
terms of tilings of Z^d by translations of a single tile.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 13:22:44 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Kari",
"Jarkko",
""
]
] |
new_dataset
| 0.998546 |
2301.06876
|
Junjie Xu H.
|
Junjie H. Xu and Yu Nakano and Lingrong Kong and Kojiro Iizuka
|
CS-lol: a Dataset of Viewer Comment with Scene in E-sports
Live-streaming
|
5 pages, 3 figures, In ACM SIGIR Conference on Human Information
Interaction and Retrieval (CHIIR 23)
| null |
10.1145/3576840.3578334
| null |
cs.MM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Billions of live-streaming viewers share their opinions on scenes they are
watching in real-time and interact with the event, commentators as well as
other viewers via text comments. Thus, there is necessary to explore viewers'
comments with scenes in E-sport live-streaming events. In this paper, we
developed CS-lol, a new large-scale dataset containing comments from viewers
paired with descriptions of game scenes in E-sports live-streaming. Moreover,
we propose a task, namely viewer comment retrieval, to retrieve the viewer
comments for the scene of the live-streaming event. Results on a series of
baseline retrieval methods derived from typical IR evaluation methods show our
task as a challenging task. Finally, we release CS-lol and baseline
implementation to the research community as a resource.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 13:34:06 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Xu",
"Junjie H.",
""
],
[
"Nakano",
"Yu",
""
],
[
"Kong",
"Lingrong",
""
],
[
"Iizuka",
"Kojiro",
""
]
] |
new_dataset
| 0.999874 |
2301.06944
|
Xi Xu
|
Yu Gao, Xi Xu, Tianji Jiang, Siyuan Chen, Yi Yang, Yufeng Yue, Mengyin
Fu
|
DR-WLC: Dimensionality Reduction cognition for object detection and pose
estimation by Watching, Learning and Checking
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object detection and pose estimation are difficult tasks in robotics and
autonomous driving. Existing object detection and pose estimation methods
mostly adopt the same-dimensional data for training. For example, 2D object
detection usually requires a large amount of 2D annotation data with high cost.
Using high-dimensional information to supervise lower-dimensional tasks is a
feasible way to reduce datasets size. In this work, the DR-WLC, a
dimensionality reduction cognitive model, which can perform both object
detection and pose estimation tasks at the same time is proposed. The model
only requires 3D model of objects and unlabeled environment images (with or
without objects) to finish the training. In addition, a bounding boxes
generation strategy is also proposed to build the relationship between 3D model
and 2D object detection task. Experiments show that our method can qualify the
work without any manual annotations and it is easy to deploy for practical
applications. Source code is at https://github.com/IN2-ViAUn/DR-WLC.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 15:08:32 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Gao",
"Yu",
""
],
[
"Xu",
"Xi",
""
],
[
"Jiang",
"Tianji",
""
],
[
"Chen",
"Siyuan",
""
],
[
"Yang",
"Yi",
""
],
[
"Yue",
"Yufeng",
""
],
[
"Fu",
"Mengyin",
""
]
] |
new_dataset
| 0.996684 |
2301.06959
|
Sofia Reis M.D.
|
Sofia Reis, Corina Pasareanu, Rui Abreu, Hakan Erdogmus
|
SECOMlint: A linter for Security Commit Messages
| null | null | null | null |
cs.CR cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Transparent and efficient vulnerability and patch disclosure are still a
challenge in the security community, essentially because of the poor-quality
documentation stemming from the lack of standards. SECOM is a recently-proposed
standard convention for security commit messages that enables the writing of
well-structured and complete commit messages for security patches. The
convention prescribes different bits of security-related information essential
for a better understanding of vulnerabilities by humans and tools. SECOMlint is
an automated and configurable solution to help security and maintenance teams
infer compliance against the SECOM standard when submitting patches to security
vulnerabilities in their source version control systems. The tool leverages the
natural language processing technique Named-Entity Recognition (NER) to extract
security-related information from commit messages and uses it to match the
compliance standards designed. We demonstrate SECOMlint at
https://youtu.be/-1hzpMN_uFI; and documentation and its source code at
https://tqrg.github.io/secomlint/.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 15:33:38 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Reis",
"Sofia",
""
],
[
"Pasareanu",
"Corina",
""
],
[
"Abreu",
"Rui",
""
],
[
"Erdogmus",
"Hakan",
""
]
] |
new_dataset
| 0.999694 |
2301.06985
|
Carlos Pineda
|
Josu\'e Ely Molina, Jorge Flores, Carlos Gershenson and Carlos Pineda
|
Statistical analysis of word flow among five Indo-European languages
|
13 pages
| null | null | null |
cs.CL physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A recent increase in data availability has allowed the possibility to perform
different statistical linguistic studies. Here we use the Google Books Ngram
dataset to analyze word flow among English, French, German, Italian, and
Spanish. We study what we define as ``migrant words'', a type of loanwords that
do not change their spelling. We quantify migrant words from one language to
another for different decades, and notice that most migrant words can be
aggregated in semantic fields and associated to historic events. We also study
the statistical properties of accumulated migrant words and their rank
dynamics. We propose a measure of use of migrant words that could be used as a
proxy of cultural influence. Our methodology is not exempt of caveats, but our
results are encouraging to promote further studies in this direction.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 16:12:42 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Molina",
"Josué Ely",
""
],
[
"Flores",
"Jorge",
""
],
[
"Gershenson",
"Carlos",
""
],
[
"Pineda",
"Carlos",
""
]
] |
new_dataset
| 0.950634 |
2301.06987
|
Bassel El Mabsout
|
Bassel El Mabsout, Shahin Roozkhosh, Siddharth Mysore, Kate Saenko,
Renato Mancuso
|
The SwaNNFlight System: On-the-Fly Sim-to-Real Adaptation via Anchored
Learning
| null | null | null | null |
cs.RO cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Reinforcement Learning (RL) agents trained in simulated environments and then
deployed in the real world are often sensitive to the differences in dynamics
presented, commonly termed the sim-to-real gap. With the goal of minimizing
this gap on resource-constrained embedded systems, we train and live-adapt
agents on quadrotors built from off-the-shelf hardware. In achieving this we
developed three novel contributions. (i) SwaNNFlight, an open-source firmware
enabling wireless data capture and transfer of agents' observations.
Fine-tuning agents with new data, and receiving and swapping onboard NN
controllers -- all while in flight. We also design SwaNNFlight System (SwaNNFS)
allowing new research in training and live-adapting learning agents on similar
systems. (ii) Multiplicative value composition, a technique for preserving the
importance of each policy optimization criterion, improving training
performance and variability in learnt behavior. And (iii) anchor critics to
help stabilize the fine-tuning of agents during sim-to-real transfer, online
learning from real data while retaining behavior optimized in simulation. We
train consistently flight-worthy control policies in simulation and deploy them
on real quadrotors. We then achieve live controller adaptation via over-the-air
updates of the onboard control policy from a ground station. Our results
indicate that live adaptation unlocks a near-50\% reduction in power
consumption, attributed to the sim-to-real gap. Finally, we tackle the issues
of catastrophic forgetting and controller instability, showing the
effectiveness of our novel methods.
Project Website: https://github.com/BU-Cyber-Physical-Systems-Lab/SwaNNFS
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 16:16:53 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Mabsout",
"Bassel El",
""
],
[
"Roozkhosh",
"Shahin",
""
],
[
"Mysore",
"Siddharth",
""
],
[
"Saenko",
"Kate",
""
],
[
"Mancuso",
"Renato",
""
]
] |
new_dataset
| 0.992466 |
2301.06993
|
Lakmal Meegahapola
|
Emma Bouton--Bessac, Lakmal Meegahapola, Daniel Gatica-Perez
|
Your Day in Your Pocket: Complex Activity Recognition from Smartphone
Accelerometers
|
16th EAI International Conference on Pervasive Computing Technologies
for Healthcare (PervasiveHealth) 2022
| null | null | null |
cs.HC cs.AI cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Human Activity Recognition (HAR) enables context-aware user experiences where
mobile apps can alter content and interactions depending on user activities.
Hence, smartphones have become valuable for HAR as they allow large, and
diversified data collection. Although previous work in HAR managed to detect
simple activities (i.e., sitting, walking, running) with good accuracy using
inertial sensors (i.e., accelerometer), the recognition of complex daily
activities remains an open problem, specially in remote work/study settings
when people are more sedentary. Moreover, understanding the everyday activities
of a person can support the creation of applications that aim to support their
well-being. This paper investigates the recognition of complex activities
exclusively using smartphone accelerometer data. We used a large smartphone
sensing dataset collected from over 600 users in five countries during the
pandemic and showed that deep learning-based, binary classification of eight
complex activities (sleeping, eating, watching videos, online communication,
attending a lecture, sports, shopping, studying) can be achieved with AUROC
scores up to 0.76 with partially personalized models. This shows encouraging
signs toward assessing complex activities only using phone accelerometer data
in the post-pandemic world.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 16:22:30 GMT"
}
] | 2023-01-18T00:00:00 |
[
[
"Bouton--Bessac",
"Emma",
""
],
[
"Meegahapola",
"Lakmal",
""
],
[
"Gatica-Perez",
"Daniel",
""
]
] |
new_dataset
| 0.986618 |
1806.00749
|
Yang Yang
|
Yang Yang, Lei Zheng, Jiawei Zhang, Qingcai Cui, Zhoujun Li, Philip S.
Yu
|
TI-CNN: Convolutional Neural Networks for Fake News Detection
| null | null | null | null |
cs.CL cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the development of social networks, fake news for various commercial and
political purposes has been appearing in large numbers and gotten widespread in
the online world. With deceptive words, people can get infected by the fake
news very easily and will share them without any fact-checking. For instance,
during the 2016 US president election, various kinds of fake news about the
candidates widely spread through both official news media and the online social
networks. These fake news is usually released to either smear the opponents or
support the candidate on their side. The erroneous information in the fake news
is usually written to motivate the voters' irrational emotion and enthusiasm.
Such kinds of fake news sometimes can bring about devastating effects, and an
important goal in improving the credibility of online social networks is to
identify the fake news timely. In this paper, we propose to study the fake news
detection problem. Automatic fake news identification is extremely hard, since
pure model based fact-checking for news is still an open problem, and few
existing models can be applied to solve the problem. With a thorough
investigation of a fake news data, lots of useful explicit features are
identified from both the text words and images used in the fake news. Besides
the explicit features, there also exist some hidden patterns in the words and
images used in fake news, which can be captured with a set of latent features
extracted via the multiple convolutional layers in our model. A model named as
TI-CNN (Text and Image information based Convolutinal Neural Network) is
proposed in this paper. By projecting the explicit and latent features into a
unified feature space, TI-CNN is trained with both the text and image
information simultaneously. Extensive experiments carried on the real-world
fake news datasets have demonstrate the effectiveness of TI-CNN.
|
[
{
"version": "v1",
"created": "Sun, 3 Jun 2018 08:09:58 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 06:57:16 GMT"
},
{
"version": "v3",
"created": "Fri, 13 Jan 2023 02:42:37 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Yang",
"Yang",
""
],
[
"Zheng",
"Lei",
""
],
[
"Zhang",
"Jiawei",
""
],
[
"Cui",
"Qingcai",
""
],
[
"Li",
"Zhoujun",
""
],
[
"Yu",
"Philip S.",
""
]
] |
new_dataset
| 0.998359 |
2008.07292
|
Kees Middelburg
|
C. A. Middelburg
|
A classical-logic view of a paraconsistent logic
|
17 pages, error in the distinguishing laws of logical equivalence
corrected
| null | null | null |
cs.LO math.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper is concerned with the first-order paraconsistent logic
LPQ$^{\supset,\mathsf{F}}$. A sequent-style natural deduction proof system for
this logic is presented and, for this proof system, both a model-theoretic
justification and a logical justification by means of an embedding into
first-order classical logic is given. For no logic that is essentially the same
as LPQ$^{\supset,\mathsf{F}}$, a natural deduction proof system is currently
available in the literature. The given embedding provides both a
classical-logic explanation of this logic and a logical justification of its
proof system. The major properties of LPQ$^{\supset,\mathsf{F}}$ are also
treated.
|
[
{
"version": "v1",
"created": "Mon, 17 Aug 2020 13:17:25 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Jan 2021 13:31:18 GMT"
},
{
"version": "v3",
"created": "Wed, 6 Jul 2022 15:10:20 GMT"
},
{
"version": "v4",
"created": "Sat, 27 Aug 2022 11:46:24 GMT"
},
{
"version": "v5",
"created": "Sun, 23 Oct 2022 09:05:13 GMT"
},
{
"version": "v6",
"created": "Thu, 12 Jan 2023 21:14:59 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Middelburg",
"C. A.",
""
]
] |
new_dataset
| 0.994306 |
2205.15979
|
Johannes Betz Dr.
|
Johannes Betz, Tobias Betz, Felix Fent, Maximilian Geisslinger,
Alexander Heilmeier, Leonhard Hermansdorfer, Thomas Herrmann, Sebastian Huch,
Phillip Karle, Markus Lienkamp, Boris Lohmann, Felix Nobis, Levent
\"Ogretmen, Matthias Rowold, Florian Sauerbeck, Tim Stahl, Rainer Trauth,
Frederik Werner, Alexander Wischnewski
|
TUM autonomous motorsport: An autonomous racing software for the Indy
Autonomous Challenge
|
37 pages, 18 figures, 2 tables
|
Journal of Field Robotics, 2023, 1-27
|
10.1002/rob.22153
| null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
For decades, motorsport has been an incubator for innovations in the
automotive sector and brought forth systems like disk brakes or rearview
mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy
Autonomous Challenge (IAC) are envisioned as playing a similar role within the
autonomous vehicle sector, serving as a proving ground for new technology at
the limits of the autonomous systems capabilities. This paper outlines the
software stack and approach of the TUM Autonomous Motorsport team for their
participation in the Indy Autonomous Challenge, which holds two competitions: A
single-vehicle competition on the Indianapolis Motor Speedway and a passing
competition at the Las Vegas Motor Speedway. Nine university teams used an
identical vehicle platform: A modified Indy Lights chassis equipped with
sensors, a computing platform, and actuators. All the teams developed different
algorithms for object detection, localization, planning, prediction, and
control of the race cars. The team from TUM placed first in Indianapolis and
secured second place in Las Vegas. During the final of the passing competition,
the TUM team reached speeds and accelerations close to the limit of the
vehicle, peaking at around 270 km/h and 28 ms2. This paper will present details
of the vehicle hardware platform, the developed algorithms, and the workflow to
test and enhance the software applied during the two-year project. We derive
deep insights into the autonomous vehicle's behavior at high speed and high
acceleration by providing a detailed competition analysis. Based on this, we
deduce a list of lessons learned and provide insights on promising areas of
future work based on the real-world evaluation of the displayed concepts.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 17:35:52 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Jan 2023 08:33:31 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Betz",
"Johannes",
""
],
[
"Betz",
"Tobias",
""
],
[
"Fent",
"Felix",
""
],
[
"Geisslinger",
"Maximilian",
""
],
[
"Heilmeier",
"Alexander",
""
],
[
"Hermansdorfer",
"Leonhard",
""
],
[
"Herrmann",
"Thomas",
""
],
[
"Huch",
"Sebastian",
""
],
[
"Karle",
"Phillip",
""
],
[
"Lienkamp",
"Markus",
""
],
[
"Lohmann",
"Boris",
""
],
[
"Nobis",
"Felix",
""
],
[
"Ögretmen",
"Levent",
""
],
[
"Rowold",
"Matthias",
""
],
[
"Sauerbeck",
"Florian",
""
],
[
"Stahl",
"Tim",
""
],
[
"Trauth",
"Rainer",
""
],
[
"Werner",
"Frederik",
""
],
[
"Wischnewski",
"Alexander",
""
]
] |
new_dataset
| 0.999594 |
2207.08794
|
Weicai Ye
|
Weicai Ye, Xingyuan Yu, Xinyue Lan, Yuhang Ming, Jinyu Li, Hujun Bao,
Zhaopeng Cui and Guofeng Zhang
|
DeFlowSLAM: Self-Supervised Scene Motion Decomposition for Dynamic Dense
SLAM
|
Homepage: https://zju3dv.github.io/deflowslam
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present a novel dual-flow representation of scene motion that decomposes
the optical flow into a static flow field caused by the camera motion and
another dynamic flow field caused by the objects' movements in the scene. Based
on this representation, we present a dynamic SLAM, dubbed DeFlowSLAM, that
exploits both static and dynamic pixels in the images to solve the camera
poses, rather than simply using static background pixels as other dynamic SLAM
systems do. We propose a dynamic update module to train our DeFlowSLAM in a
self-supervised manner, where a dense bundle adjustment layer takes in
estimated static flow fields and the weights controlled by the dynamic mask and
outputs the residual of the optimized static flow fields, camera poses, and
inverse depths. The static and dynamic flow fields are estimated by warping the
current image to the neighboring images, and the optical flow can be obtained
by summing the two fields. Extensive experiments demonstrate that DeFlowSLAM
generalizes well to both static and dynamic scenes as it exhibits comparable
performance to the state-of-the-art DROID-SLAM in static and less dynamic
scenes while significantly outperforming DROID-SLAM in highly dynamic
environments. The code and pre-trained model will be available on the project
webpage: \urlstyle{tt}
\textcolor{url_color}{\url{https://zju3dv.github.io/deflowslam/}}.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 17:47:39 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Jan 2023 15:08:01 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Ye",
"Weicai",
""
],
[
"Yu",
"Xingyuan",
""
],
[
"Lan",
"Xinyue",
""
],
[
"Ming",
"Yuhang",
""
],
[
"Li",
"Jinyu",
""
],
[
"Bao",
"Hujun",
""
],
[
"Cui",
"Zhaopeng",
""
],
[
"Zhang",
"Guofeng",
""
]
] |
new_dataset
| 0.995545 |
2208.07363
|
Nolan Wagener
|
Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd,
Ching-An Cheng, Matthew Hausknecht
|
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
|
Appearing in NeurIPS 2022 Datasets and Benchmarks Track
| null | null | null |
cs.RO cs.GR cs.LG cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Simulated humanoids are an appealing research domain due to their physical
capabilities. Nonetheless, they are also challenging to control, as a policy
must drive an unstable, discontinuous, and high-dimensional physical system.
One widely studied approach is to utilize motion capture (MoCap) data to teach
the humanoid agent low-level skills (e.g., standing, walking, and running) that
can then be re-used to synthesize high-level behaviors. However, even with
MoCap data, controlling simulated humanoids remains very hard, as MoCap data
offers only kinematic information. Finding physical control inputs to realize
the demonstrated motions requires computationally intensive methods like
reinforcement learning. Thus, despite the publicly available MoCap data, its
utility has been limited to institutions with large-scale compute. In this
work, we dramatically lower the barrier for productive research on this topic
by training and releasing high-quality agents that can track over three hours
of MoCap data for a simulated humanoid in the dm_control physics-based
environment. We release MoCapAct (Motion Capture with Actions), a dataset of
these expert agents and their rollouts, which contain proprioceptive
observations and actions. We demonstrate the utility of MoCapAct by using it to
train a single hierarchical policy capable of tracking the entire MoCap dataset
within dm_control and show the learned low-level component can be re-used to
efficiently learn downstream high-level tasks. Finally, we use MoCapAct to
train an autoregressive GPT model and show that it can control a simulated
humanoid to perform natural motion completion given a motion prompt.
Videos of the results and links to the code and dataset are available at
https://microsoft.github.io/MoCapAct.
|
[
{
"version": "v1",
"created": "Mon, 15 Aug 2022 17:57:33 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Oct 2022 15:14:56 GMT"
},
{
"version": "v3",
"created": "Fri, 13 Jan 2023 14:42:44 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Wagener",
"Nolan",
""
],
[
"Kolobov",
"Andrey",
""
],
[
"Frujeri",
"Felipe Vieira",
""
],
[
"Loynd",
"Ricky",
""
],
[
"Cheng",
"Ching-An",
""
],
[
"Hausknecht",
"Matthew",
""
]
] |
new_dataset
| 0.999771 |
2210.04975
|
Vijja Wichitwechkarn
|
Vijja Wichitwechkarn and Charles Fox
|
MACARONS: A Modular and Open-Sourced Automation System for Vertical
Farming
| null | null |
10.5334/joh.53
| null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The Modular Automated Crop Array Online System (MACARONS) is an extensible,
scalable, open hardware system for plant transport in automated horticulture
systems such as vertical farms. It is specified to move trays of plants up to
1060mm x 630mm and 12.5kg at a rate of 100mm/s along the guide rails and
41.7mm/s up the lifts, such as between stations for monitoring and actuating
plants. The cost for the construction of one grow unit of MACARONS is 144.96USD
which equates to 128.85USD/m2 of grow area. The designs are released and meets
the requirements of CERN-OSH-W, which includes step-by-step graphical build
instructions and can be built by a typical technical person in one day at a
cost of 1535.50USD. Integrated tests are included in the build instructions are
used to validate against the specifications, and we report on a successful
build. Through a simple analysis, we demonstrate that MACARONS can operate at a
rate sufficient to automate tray loading/unloading, to reduce labour costs in a
vertical farm.
|
[
{
"version": "v1",
"created": "Mon, 10 Oct 2022 19:18:36 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Nov 2022 15:28:13 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Wichitwechkarn",
"Vijja",
""
],
[
"Fox",
"Charles",
""
]
] |
new_dataset
| 0.997961 |
2211.04993
|
Mauro Martini
|
Andrea Eirale, Mauro Martini, Marcello Chiaberge
|
RL-DWA Omnidirectional Motion Planning for Person Following in Domestic
Assistance and Monitoring
| null | null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Robot assistants are emerging as high-tech solutions to support people in
everyday life. Following and assisting the user in the domestic environment
requires flexible mobility to safely move in cluttered spaces. We introduce a
new approach to person following for assistance and monitoring. Our methodology
exploits an omnidirectional robotic platform to detach the computation of
linear and angular velocities and navigate within the domestic environment
without losing track of the assisted person. While linear velocities are
managed by a conventional Dynamic Window Approach (DWA) local planner, we
trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular
velocities commands and maintain the orientation of the robot towards the user.
We evaluate our navigation system on a real omnidirectional platform in various
indoor scenarios, demonstrating the competitive advantage of our solution
compared to a standard differential steering following.
|
[
{
"version": "v1",
"created": "Wed, 9 Nov 2022 16:11:41 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Jan 2023 14:12:08 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Eirale",
"Andrea",
""
],
[
"Martini",
"Mauro",
""
],
[
"Chiaberge",
"Marcello",
""
]
] |
new_dataset
| 0.997652 |
2212.12117
|
Alexander Barg
|
Alexander Barg, Moshe Schwartz, and Lev Yohananov
|
Storage codes on coset graphs with asymptotically unit rate
|
14 pages. In v2 we expanded the introduction to account for some new
references of which we were previously not aware
| null | null | null |
cs.IT math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A storage code on a graph $G$ is a set of assignments of symbols to the
vertices such that every vertex can recover its value by looking at its
neighbors. We consider the question of constructing large-size storage codes on
triangle-free graphs constructed as coset graphs of binary linear codes.
Previously it was shown that there are infinite families of binary storage
codes on coset graphs with rate converging to 3/4. Here we show that codes on
such graphs can attain rate asymptotically approaching 1.
Equivalently, this question can be phrased as a version of hat-guessing games
on graphs (e.g., P.J. Cameron e.a., \emph{Electronic J. Comb.} 2016). In this
language, we construct triangle-free graphs with success probability of the
players approaching one as the number of vertices tends to infinity.
Equivalently again, there exist linear index codes on such graphs of rate
approaching zero.
Another family of storage codes on triangle-free graphs of rate approaching 1
was constructed earlier by A. Golovnev and I. Haviv (36th Computational
Complexity Conf., 2021) relying on a different family of graphs.
|
[
{
"version": "v1",
"created": "Fri, 23 Dec 2022 03:01:10 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Jan 2023 03:25:05 GMT"
}
] | 2023-01-16T00:00:00 |
[
[
"Barg",
"Alexander",
""
],
[
"Schwartz",
"Moshe",
""
],
[
"Yohananov",
"Lev",
""
]
] |
new_dataset
| 0.99987 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.