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
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2210.13047
|
Gangtao Xin
|
Gangtao Xin and Pingyi Fan
|
EXK-SC: A Semantic Communication Model Based on Information Framework
Expansion and Knowledge Collision
| null | null |
10.3390/e24121842
| null |
cs.IT cs.GT cs.LG eess.SP math.IT math.ST stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Semantic communication is not focused on improving the accuracy of
transmitted symbols, but is concerned with expressing the expected meaning that
the symbol sequence exactly carries. However, the measurement of semantic
messages and their corresponding codebook generation are still open issues.
Expansion, which integrates simple things into a complex system and even
generates intelligence, is truly consistent with the evolution of the human
language system. We apply this idea to the semantic communication system,
quantifying semantic transmission by symbol sequences and investigating the
semantic information system in a similar way as Shannon's method for digital
communication systems. This work is the first to discuss semantic expansion and
knowledge collision in the semantic information framework. Some important
theoretical results are presented, including the relationship between semantic
expansion and the transmission information rate. We believe such a semantic
information framework may provide a new paradigm for semantic communications,
and semantic expansion and knowledge collision will be the cornerstone of
semantic information theory.
|
[
{
"version": "v1",
"created": "Mon, 24 Oct 2022 09:00:14 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Dec 2022 08:18:13 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Xin",
"Gangtao",
""
],
[
"Fan",
"Pingyi",
""
]
] |
new_dataset
| 0.967134 |
2212.00968
|
Danfeng Hong
|
Xin Wu and Danfeng Hong and Jocelyn Chanussot
|
UIU-Net: U-Net in U-Net for Infrared Small Object Detection
| null |
IEEE Transactions on Image Processing, 2022
|
10.1109/TIP.2022.3228497
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning-based infrared small object detection methods currently rely heavily
on the classification backbone network. This tends to result in tiny object
loss and feature distinguishability limitations as the network depth increases.
Furthermore, small objects in infrared images are frequently emerged bright and
dark, posing severe demands for obtaining precise object contrast information.
For this reason, we in this paper propose a simple and effective ``U-Net in
U-Net'' framework, UIU-Net for short, and detect small objects in infrared
images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net
backbone, enabling the multi-level and multi-scale representation learning of
objects. Moreover, UIU-Net can be trained from scratch, and the learned
features can enhance global and local contrast information effectively. More
specifically, the UIU-Net model is divided into two modules: the
resolution-maintenance deep supervision (RM-DS) module and the
interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks
into a deep supervision network to generate deep multi-scale
resolution-maintenance features while learning global context information.
Further, IC-A encodes the local context information between the low-level
details and high-level semantic features. Extensive experiments conducted on
two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets,
show the effectiveness and superiority of the proposed UIU-Net in comparison
with several state-of-the-art infrared small object detection methods. The
proposed UIU-Net also produces powerful generalization performance for video
sequence infrared small object datasets, e.g., ATR ground/air video sequence
dataset. The codes of this work are available openly at
\url{https://github.com/danfenghong/IEEE_TIP_UIU-Net}.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 04:52:26 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Wu",
"Xin",
""
],
[
"Hong",
"Danfeng",
""
],
[
"Chanussot",
"Jocelyn",
""
]
] |
new_dataset
| 0.966255 |
2212.14731
|
Asterios Bampakis
|
Asterios Bampakis, Sofia Yfantidou, Athena Vakali
|
UBIWEAR: An end-to-end, data-driven framework for intelligent physical
activity prediction to empower mHealth interventions
|
2022 IEEE International Conference on E-health Networking,
Application & Services (HealthCom), Pages 56-62
| null |
10.1109/HealthCom54947.2022.9982730
| null |
cs.AI cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
It is indisputable that physical activity is vital for an individual's health
and wellness. However, a global prevalence of physical inactivity has induced
significant personal and socioeconomic implications. In recent years, a
significant amount of work has showcased the capabilities of self-tracking
technology to create positive health behavior change. This work is motivated by
the potential of personalized and adaptive goal-setting techniques in
encouraging physical activity via self-tracking. To this end, we propose
UBIWEAR, an end-to-end framework for intelligent physical activity prediction,
with the ultimate goal to empower data-driven goal-setting interventions. To
achieve this, we experiment with numerous machine learning and deep learning
paradigms as a robust benchmark for physical activity prediction tasks. To
train our models, we utilize, "MyHeart Counts", an open, large-scale dataset
collected in-the-wild from thousands of users. We also propose a prescriptive
framework for self-tracking aggregated data preprocessing, to facilitate data
wrangling of real-world, noisy data. Our best model achieves a MAE of 1087
steps, 65% lower than the state of the art in terms of absolute error, proving
the feasibility of the physical activity prediction task, and paving the way
for future research.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 14:18:39 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Jan 2023 15:43:24 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Bampakis",
"Asterios",
""
],
[
"Yfantidou",
"Sofia",
""
],
[
"Vakali",
"Athena",
""
]
] |
new_dataset
| 0.998345 |
2301.00835
|
Manar Alalfi
|
Jian Chen and Manar H. Alalfi and Thomas R. Dean
|
Timed Model-Based Mutation Operators for Simulink Models
| null | null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Model-based mutation analysis is a recent research area, and real-time system
testing can benefit from using model mutants. Model-based mutation testing
(MBMT) is a particular branch of model-based testing. It generates faulty
versions of a model using mutation operators to evaluate and improve test
cases. Mutation testing is an effective way to ensure software correctness and
has been applied to various application areas. Simulink is a vital modeling
language for real-time systems. This paper introduces Simulink model mutation
analysis to improve Model-in-the-loop (MIL) testing. We propose a set of
Simulink mutation operators based on AUTOSAR, which reflects the temporal
correctness when a Simulink model is mapped to Operating System tasks. We
implement a mutation framework that generates mutants for implicit clock
Simulink models. Finally, we demonstrate how this framework generates mutants
to reveal task interference issues in the simulation. Our work integrates the
Simulink model with the timed systems to better support mutation testing
automation.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 19:05:17 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Chen",
"Jian",
""
],
[
"Alalfi",
"Manar H.",
""
],
[
"Dean",
"Thomas R.",
""
]
] |
new_dataset
| 0.970458 |
2301.00836
|
Vishweshwar Dixit
|
Vishweshwar V. Dixit
|
Kannudi -- A Reference Editor for Kannada
|
7 pages, 2 figures, 4 tables
| null | null | null |
cs.HC cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Kannudi is a reference editor for Kannada based on OPOK! and OHOK!
principles, and domain knowledge. It introduces a method of input for Kannada,
called OHOK!, that is, Ottu Haku Ottu Kodu! (apply pressure and give ottu).
This is especially suited for pressure sensitive input devices, though the
current online implementation uses the regular mechanical keyboard. OHOK! has
three possible modes, namely, sva-ottu (self-conjunct), kandante (as you see),
and andante (as you say). It may be noted that kandante mode does not follow
the phonetic order. However, this mode may work well for those who are inclined
to visualize as they type rather than vocalizing the sounds.
Kannudi also demonstrates how domain knowledge can be effectively used to
potentially increase speed, accuracy, and user friendliness. For example,
selection of a default vowel, automatic shunyification, and arkification. Also
implemented are four types Deletes that are necessary for phono-syllabic
languages like Kannada.
|
[
{
"version": "v1",
"created": "Sat, 24 Dec 2022 01:40:56 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Dixit",
"Vishweshwar V.",
""
]
] |
new_dataset
| 0.999638 |
2301.00880
|
Cristian Alecsa
|
Cristian Daniel Alecsa
|
OF-AE: Oblique Forest AutoEncoders
|
11 pages, 12 figures, 2 tables
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the present work we propose an unsupervised ensemble method consisting of
oblique trees that can address the task of auto-encoding, namely Oblique Forest
AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest
encoder introduced in [1]. More precisely, by employing oblique splits
consisting in multivariate linear combination of features instead of the
axis-parallel ones, we will devise an auto-encoder method through the
computation of a sparse solution of a set of linear inequalities consisting of
feature values constraints. The code for reproducing our results is available
at https://github.com/CDAlecsa/Oblique-Forest-AutoEncoders.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 21:23:37 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Alecsa",
"Cristian Daniel",
""
]
] |
new_dataset
| 0.99409 |
2301.00891
|
Samiran Gode
|
Samiran Gode, Supreeth Bare, Bhiksha Raj, Hyungon Yoo
|
Understanding Political Polarisation using Language Models: A dataset
and method
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Our paper aims to analyze political polarization in US political system using
Language Models, and thereby help candidates make an informed decision. The
availability of this information will help voters understand their candidates
views on the economy, healthcare, education and other social issues. Our main
contributions are a dataset extracted from Wikipedia that spans the past 120
years and a Language model based method that helps analyze how polarized a
candidate is. Our data is divided into 2 parts, background information and
political information about a candidate, since our hypothesis is that the
political views of a candidate should be based on reason and be independent of
factors such as birthplace, alma mater, etc. We further split this data into 4
phases chronologically, to help understand if and how the polarization amongst
candidates changes. This data has been cleaned to remove biases. To understand
the polarization we begin by showing results from some classical language
models in Word2Vec and Doc2Vec. And then use more powerful techniques like the
Longformer, a transformer based encoder, to assimilate more information and
find the nearest neighbors of each candidate based on their political view and
their background.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 22:15:04 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Gode",
"Samiran",
""
],
[
"Bare",
"Supreeth",
""
],
[
"Raj",
"Bhiksha",
""
],
[
"Yoo",
"Hyungon",
""
]
] |
new_dataset
| 0.997353 |
2301.00933
|
Wen Haifeng
|
Haifeng Wen, Weijie Yuan, Zilong Liu, Shuangyang Li
|
OTFS-SCMA: A Downlink NOMA Scheme for Massive Connectivity in High
Mobility Channels
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
This paper studies a downlink system that combines
orthogonal-time-frequency-space (OTFS) modulation and sparse code multiple
access (SCMA) to support massive connectivity in high-mobility environments. We
propose a cross-domain receiver for the considered OTFS-SCMA system which
efficiently carries out OTFS symbol estimation and SCMA decoding in a joint
manner. This is done by iteratively passing the extrinsic information between
the time domain and the delay-Doppler (DD) domain via the corresponding unitary
transformation to ensure the principal orthogonality of errors from each
domain. We show that the proposed OTFS-SCMA detection algorithm exists at a
fixed point in the state evolution when it converges. To further enhance the
error performance of the proposed OTFS-SCMA system, we investigate the
cooperation between downlink users to exploit the diversity gains and develop a
distributed cooperative detection (DCD) algorithm with the aid of belief
consensus. Our numerical results demonstrate the effectiveness and convergence
of the proposed algorithm and show an increased spectral efficiency compared to
the conventional OTFS transmission.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 02:42:08 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Wen",
"Haifeng",
""
],
[
"Yuan",
"Weijie",
""
],
[
"Liu",
"Zilong",
""
],
[
"Li",
"Shuangyang",
""
]
] |
new_dataset
| 0.989153 |
2301.00936
|
Ilya Semenov
|
Ilya Semenov, Robert Brown, Michael Otte
|
Control and Dynamic Motion Planning for a Hybrid Air-Underwater
Quadrotor: Minimizing Energy Use in a Flooded Cave Environment
|
8 pages, 9 figures, written in 2020
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a dynamic path planning algorithm to navigate an amphibious rotor
craft through a concave time-invariant obstacle field while attempting to
minimize energy usage. We create a nonlinear quaternion state model that
represents the rotor craft dynamics above and below the water. The 6 degree of
freedom dynamics used within a layered architecture to generate motion paths
for the vehicle to follow and the required control inputs. The rotor craft has
a 3 dimensional map of its surroundings that is updated via limited range
onboard sensor readings within the current medium (air or water). Path planning
is done via PRM and D* Lite.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 02:58:20 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Semenov",
"Ilya",
""
],
[
"Brown",
"Robert",
""
],
[
"Otte",
"Michael",
""
]
] |
new_dataset
| 0.996041 |
2301.00964
|
Aswani Kumar Cherukuri Dr
|
Abhiruph Chakravarty, Jatin Karthik Tripathy, Sibi Chakkaravarthy S,
Aswani Kumar Cherukuri, S. Anitha, Firuz Kamalov, Annapurna Jonnalagadda
|
e-Inu: Simulating A Quadruped Robot With Emotional Sentience
| null | null | null | null |
cs.RO cs.HC cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Quadruped robots are currently used in industrial robotics as mechanical aid
to automate several routine tasks. However, presently, the usage of such a
robot in a domestic setting is still very much a part of the research. This
paper discusses the understanding and virtual simulation of such a robot
capable of detecting and understanding human emotions, generating its gait, and
responding via sounds and expression on a screen. To this end, we use a
combination of reinforcement learning and software engineering concepts to
simulate a quadruped robot that can understand emotions, navigate through
various terrains and detect sound sources, and respond to emotions using
audio-visual feedback. This paper aims to establish the framework of simulating
a quadruped robot that is emotionally intelligent and can primarily respond to
audio-visual stimuli using motor or audio response. The emotion detection from
the speech was not as performant as ERANNs or Zeta Policy learning, still
managing an accuracy of 63.5%. The video emotion detection system produced
results that are almost at par with the state of the art, with an accuracy of
99.66%. Due to its "on-policy" learning process, the PPO algorithm was
extremely rapid to learn, allowing the simulated dog to demonstrate a
remarkably seamless gait across the different cadences and variations. This
enabled the quadruped robot to respond to generated stimuli, allowing us to
conclude that it functions as predicted and satisfies the aim of this work.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 06:28:45 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Chakravarty",
"Abhiruph",
""
],
[
"Tripathy",
"Jatin Karthik",
""
],
[
"S",
"Sibi Chakkaravarthy",
""
],
[
"Cherukuri",
"Aswani Kumar",
""
],
[
"Anitha",
"S.",
""
],
[
"Kamalov",
"Firuz",
""
],
[
"Jonnalagadda",
"Annapurna",
""
]
] |
new_dataset
| 0.990351 |
2301.00975
|
Jun Wan
|
Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Chenxu Zhao, Xu Zhang,
Stan Z. Li, Zhen Lei
|
Surveillance Face Anti-spoofing
|
15 pages, 9 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Face Anti-spoofing (FAS) is essential to secure face recognition systems from
various physical attacks. However, recent research generally focuses on
short-distance applications (i.e., phone unlocking) while lacking consideration
of long-distance scenes (i.e., surveillance security checks). In order to
promote relevant research and fill this gap in the community, we collect a
large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under
40 surveillance scenes, which has 101 subjects from different age groups with
232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and
screens), and 2 adversarial attacks. In this scene, low image resolution and
noise interference are new challenges faced in surveillance FAS. Together with
the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning
(CQIL) network to alleviate the performance degradation caused by image quality
from three aspects: (1) An Image Quality Variable module (IQV) is introduced to
recover image information associated with discrimination by combining the
super-resolution network. (2) Using generated sample pairs to simulate quality
variance distributions to help contrastive learning strategies obtain robust
feature representation under quality variation. (3) A Separate Quality Network
(SQN) is designed to learn discriminative features independent of image
quality. Finally, a large number of experiments verify the quality of the
SuHiFiMask dataset and the superiority of the proposed CQIL.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 07:09:57 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Fang",
"Hao",
""
],
[
"Liu",
"Ajian",
""
],
[
"Wan",
"Jun",
""
],
[
"Escalera",
"Sergio",
""
],
[
"Zhao",
"Chenxu",
""
],
[
"Zhang",
"Xu",
""
],
[
"Li",
"Stan Z.",
""
],
[
"Lei",
"Zhen",
""
]
] |
new_dataset
| 0.972233 |
2301.01057
|
Janne Mustaniemi
|
Janne Mustaniemi, Juho Kannala, Esa Rahtu, Li Liu and Janne Heikkil\"a
|
BS3D: Building-scale 3D Reconstruction from RGB-D Images
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Various datasets have been proposed for simultaneous localization and mapping
(SLAM) and related problems. Existing datasets often include small
environments, have incomplete ground truth, or lack important sensor data, such
as depth and infrared images. We propose an easy-to-use framework for acquiring
building-scale 3D reconstruction using a consumer depth camera. Unlike complex
and expensive acquisition setups, our system enables crowd-sourcing, which can
greatly benefit data-hungry algorithms. Compared to similar systems, we utilize
raw depth maps for odometry computation and loop closure refinement which
results in better reconstructions. We acquire a building-scale 3D dataset
(BS3D) and demonstrate its value by training an improved monocular depth
estimation model. As a unique experiment, we benchmark visual-inertial odometry
methods using both color and active infrared images.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 11:46:14 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Mustaniemi",
"Janne",
""
],
[
"Kannala",
"Juho",
""
],
[
"Rahtu",
"Esa",
""
],
[
"Liu",
"Li",
""
],
[
"Heikkilä",
"Janne",
""
]
] |
new_dataset
| 0.999561 |
2301.01116
|
Irene Marcovici
|
Chlo\'e Boisson, Damien Jamet and Ir\`ene Marcovici
|
On a probabilistic extension of the Oldenburger-Kolakoski sequence
| null | null | null | null |
cs.DM math.CO math.PR
|
http://creativecommons.org/licenses/by/4.0/
|
The Oldenburger-Kolakoski sequence is the only infinite sequence over the
alphabet $\{1,2\}$ that starts with $1$ and is its own run-length encoding. In
the present work, we take a step back from this largely known and studied
sequence by introducing some randomness in the choice of the letters written.
This enables us to provide some results on the convergence of the density of
$1$'s in the resulting sequence. When the choice of the letters is given by an
infinite sequence of i.i.d. random variables or by a Markov chain, the average
densities of letters converge. Moreover, in the case of i.i.d. random
variables, we are able to prove that the densities even almost surely converge.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 14:18:39 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Boisson",
"Chloé",
""
],
[
"Jamet",
"Damien",
""
],
[
"Marcovici",
"Irène",
""
]
] |
new_dataset
| 0.993859 |
2301.01134
|
Mika H\"am\"al\"ainen
|
Khalid Alnajjar, Mika H\"am\"al\"ainen, Shuo Zhang
|
Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in
Videos
|
Figlang 2022
| null | null | null |
cs.MM cs.CL cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present the first openly available multimodal metaphor annotated corpus.
The corpus consists of videos including audio and subtitles that have been
annotated by experts. Furthermore, we present a method for detecting metaphors
in the new dataset based on the textual content of the videos. The method
achieves a high F1-score (62\%) for metaphorical labels. We also experiment
with other modalities and multimodal methods; however, these methods did not
out-perform the text-based model. In our error analysis, we do identify that
there are cases where video could help in disambiguating metaphors, however,
the visual cues are too subtle for our model to capture. The data is available
on Zenodo.
|
[
{
"version": "v1",
"created": "Thu, 15 Dec 2022 17:11:35 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Alnajjar",
"Khalid",
""
],
[
"Hämäläinen",
"Mika",
""
],
[
"Zhang",
"Shuo",
""
]
] |
new_dataset
| 0.998783 |
2301.01145
|
Joseph Saverin Dr.-Ing.
|
Joseph Saverin
|
SailFFish: A Lightweight, Parallelised Fast Poisson Solver Library
| null | null | null | null |
cs.MS cs.NA math.NA
|
http://creativecommons.org/licenses/by/4.0/
|
A solver for the Poisson equation for 1D, 2D and 3D regular grids is
presented. The solver applies the convolution theorem in order to efficiently
solve the Poisson equation in spectral space over a rectangular computational
domain. Conversion to and from the spectral space is achieved through the use
of discrete Fourier transforms, allowing for the application of highly
optimised O(NlogN) algorithms. The data structure is configured to be modular
such that the underlying interface for operations to, from and within the
spectral space may be interchanged. For computationally demanding tasks, the
library is optimised by making use of parallel processing architectures. A
range of boundary conditions can be applied to the domain including periodic,
Dirichlet, Neumann and fully unbounded. In the case of Neumann and Dirichlet
boundary conditions, arbitrary inhomogeneous boundary conditions may be
specified. The desired solution may be found either on regular (cell-boundary)
or staggered (cell-centre) grid configurations. For problems with periodic,
Dirichlet or Neumann boundary conditions either a pseudo-spectral or a
second-order finite difference operator may be applied. For unbounded boundary
conditions a range of Green's functions are available. In addition to this, a
range of differential operators may be applied in the spectral space in order
to treat different forms of the Poisson equation or to extract highly accurate
gradients of the input fields. The underlying framework of the solver is first
detailed, followed by a range of validations for each of the available boundary
condition types. Finally, the performance of the library is investigated. The
code is free and publicly available under a GNU v3.0 license.
|
[
{
"version": "v1",
"created": "Sun, 1 Jan 2023 23:02:19 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Saverin",
"Joseph",
""
]
] |
new_dataset
| 0.963256 |
2301.01147
|
Patrick Wenzel
|
Patrick Wenzel, Nan Yang, Rui Wang, Niclas Zeller, Daniel Cremers
|
4Seasons: Benchmarking Visual SLAM and Long-Term Localization for
Autonomous Driving in Challenging Conditions
|
arXiv admin note: substantial text overlap with arXiv:2009.06364
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present a novel visual SLAM and long-term localization
benchmark for autonomous driving in challenging conditions based on the
large-scale 4Seasons dataset. The proposed benchmark provides drastic
appearance variations caused by seasonal changes and diverse weather and
illumination conditions. While significant progress has been made in advancing
visual SLAM on small-scale datasets with similar conditions, there is still a
lack of unified benchmarks representative of real-world scenarios for
autonomous driving. We introduce a new unified benchmark for jointly evaluating
visual odometry, global place recognition, and map-based visual localization
performance which is crucial to successfully enable autonomous driving in any
condition. The data has been collected for more than one year, resulting in
more than 300 km of recordings in nine different environments ranging from a
multi-level parking garage to urban (including tunnels) to countryside and
highway. We provide globally consistent reference poses with up to
centimeter-level accuracy obtained from the fusion of direct stereo-inertial
odometry with RTK GNSS. We evaluate the performance of several state-of-the-art
visual odometry and visual localization baseline approaches on the benchmark
and analyze their properties. The experimental results provide new insights
into current approaches and show promising potential for future research. Our
benchmark and evaluation protocols will be available at
https://www.4seasons-dataset.com/.
|
[
{
"version": "v1",
"created": "Sat, 31 Dec 2022 13:52:36 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Wenzel",
"Patrick",
""
],
[
"Yang",
"Nan",
""
],
[
"Wang",
"Rui",
""
],
[
"Zeller",
"Niclas",
""
],
[
"Cremers",
"Daniel",
""
]
] |
new_dataset
| 0.99966 |
2301.01191
|
Kevin Moran
|
Carlos Bernal-C\'ardenas, Nathan Cooper, Madeleine Havranek, Kevin
Moran, Oscar Chaparro, Denys Poshyvanyk, Andrian Marcus
|
Translating Video Recordings of Complex Mobile App UI Gestures into
Replayable Scenarios
|
Accepted to IEEE Transactions on Software Engineering. arXiv admin
note: substantial text overlap with arXiv:2005.09057
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Screen recordings of mobile applications are easy to obtain and capture a
wealth of information pertinent to software developers (e.g., bugs or feature
requests), making them a popular mechanism for crowdsourced app feedback. Thus,
these videos are becoming a common artifact that developers must manage. In
light of unique mobile development constraints, including swift release cycles
and rapidly evolving platforms, automated techniques for analyzing all types of
rich software artifacts provide benefit to mobile developers. Unfortunately,
automatically analyzing screen recordings presents serious challenges, due to
their graphical nature, compared to other types of (textual) artifacts. To
address these challenges, this paper introduces V2S+, an automated approach for
translating video recordings of Android app usages into replayable scenarios.
V2S+ is based primarily on computer vision techniques and adapts recent
solutions for object detection and image classification to detect and classify
user gestures captured in a video, and convert these into a replayable test
scenario. Given that V2S+ takes a computer vision-based approach, it is
applicable to both hybrid and native Android applications. We performed an
extensive evaluation of V2S+ involving 243 videos depicting 4,028 GUI-based
actions collected from users exercising features and reproducing bugs from a
collection of over 90 popular native and hybrid Android apps. Our results
illustrate that V2S+ can accurately replay scenarios from screen recordings,
and is capable of reproducing $\approx$ 90.2% of sequential actions recorded in
native application scenarios on physical devices, and $\approx$ 83% of
sequential actions recorded in hybrid application scenarios on emulators, both
with low overhead. A case study with three industrial partners illustrates the
potential usefulness of V2S+ from the viewpoint of developers.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 16:47:42 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Bernal-Cárdenas",
"Carlos",
""
],
[
"Cooper",
"Nathan",
""
],
[
"Havranek",
"Madeleine",
""
],
[
"Moran",
"Kevin",
""
],
[
"Chaparro",
"Oscar",
""
],
[
"Poshyvanyk",
"Denys",
""
],
[
"Marcus",
"Andrian",
""
]
] |
new_dataset
| 0.950884 |
2301.01234
|
Dmytro Humeniuk
|
Dmytro Humeniuk, Foutse Khomh and Giuliano Antoniol
|
AmbieGen: A Search-based Framework for Autonomous Systems Testing
|
17 pages, 10 figures
| null | null | null |
cs.RO cs.NE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Thorough testing of safety-critical autonomous systems, such as self-driving
cars, autonomous robots, and drones, is essential for detecting potential
failures before deployment. One crucial testing stage is model-in-the-loop
testing, where the system model is evaluated by executing various scenarios in
a simulator. However, the search space of possible parameters defining these
test scenarios is vast, and simulating all combinations is computationally
infeasible. To address this challenge, we introduce AmbieGen, a search-based
test case generation framework for autonomous systems. AmbieGen uses
evolutionary search to identify the most critical scenarios for a given system,
and has a modular architecture that allows for the addition of new systems
under test, algorithms, and search operators. Currently, AmbieGen supports test
case generation for autonomous robots and autonomous car lane keeping assist
systems. In this paper, we provide a high-level overview of the framework's
architecture and demonstrate its practical use cases.
|
[
{
"version": "v1",
"created": "Sun, 1 Jan 2023 23:42:32 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Humeniuk",
"Dmytro",
""
],
[
"Khomh",
"Foutse",
""
],
[
"Antoniol",
"Giuliano",
""
]
] |
new_dataset
| 0.997723 |
2301.01237
|
Brahim Tamadazte
|
Bassem Dahroug and Brahim Tamadazte and Nicolas Andreff
|
Safe Path following for Middle Ear Surgery
|
40 pages, 26 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This article formulates a generic representation of a path-following
controller operating under contained motion, which was developed in the context
of surgical robotics. It reports two types of constrained motion: i) Bilateral
Constrained Motion, also called Remote Center Motion (RCM), and ii)
Unilaterally Constrained Motion (UCM). In the first case, the incision hole has
almost the same diameter as the robotic tool. In contrast, in the second state,
the diameter of the incision orifice is larger than the tool diameter. The
second case offers more space where the surgical instrument moves freely
without constraints before touching the incision wall. The proposed method
combines two tasks that must operate hierarchically: i) respect the RCM or UCM
constraints formulated by equality or inequality, respectively, and ii) perform
a surgical assignment, e.g., scanning or ablation expressed as a 3D
path-following task. The proposed methods and materials were tested first on
our simulator that mimics realistic conditions of middle ear surgery, and then
on an experimental platform. Different validation scenarios were carried out
experimentally to assess quantitatively and qualitatively each developed
approach. Although ultimate precision was not the goal of this work, our
concept is validated with enough accuracy (inferior to 100 micrometres) for ear
surgery.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 17:31:19 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Dahroug",
"Bassem",
""
],
[
"Tamadazte",
"Brahim",
""
],
[
"Andreff",
"Nicolas",
""
]
] |
new_dataset
| 0.986776 |
2301.01282
|
Krishnan Shankar
|
Krishnan Shankar
|
RSA+: An algorithm at least as secure as RSA
|
8 pages, no figures
| null | null | null |
cs.CR math.NT
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The RSA algorithm has been around for nearly five decades and remains one of
the most studied public key cryptosystems. Many attempts have been made to
break it or improve it and questions remain about the equivalence of the
strength of its security to well known hard problems in computational number
theory. In this note we propose a modified version which we call RSA+ which is
at least as secure as RSA and show that breaking RSA+ is probably
computationally equivalent to factoring $n$, the public modulus. The motivation
came from wanting to obscure the encryption exponent in RSA.
|
[
{
"version": "v1",
"created": "Sat, 31 Dec 2022 02:48:17 GMT"
}
] | 2023-01-04T00:00:00 |
[
[
"Shankar",
"Krishnan",
""
]
] |
new_dataset
| 0.992265 |
2011.03669
|
Gang Liu
|
Gang Liu, Kenli Li, Zheng Xiao and Rujia Wang
|
EHAP-ORAM: Efficient Hardware-Assisted Persistent ORAM System for
Non-volatile Memory
|
In Proceedings of The 49th Annual International Symposium on Computer
Architecture (ISCA' 22)
| null |
10.1145/3470496.3527425
| null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Oblivious RAM (ORAM) is a provable secure primitive to prevent access pattern
leakage on the memory bus. It serves as the intermediate layer between the
trusted on-chip components and the untrusted external memory systems to
modulate the original memory access patterns into indistinguishable memory
sequences. By randomly remapping the data blocks and accessing redundant
blocks, ORAM prevents access pattern leakage through obfuscation. While there
is much prior work focusing on improving ORAM's performance on the conventional
DRAM-based memory system, when the memory technology shifts to use non-volatile
memory (NVM), new challenges come up as to how to efficiently support crash
consistency for ORAM.
In this work, we propose EHAP-ORAM, which studies how to persist ORAM
construction with an NVM-based memory system. We first analyze the design
requirements for a persistent ORAM system and discuss the need to preserve
crash consistency and atomicity for both data and ORAM metadata. Next, we
discuss some of the challenges in the design of a persistent ORAM system and
propose some solutions to those challenges. Then, we propose the modified
on-chip ORAM controller architecture. Based on the improved hardware
architecture of the ORAM controller on-chip, we propose different persistency
protocols to ensure the crash consistency of the ORAM system and satisfy that
the metadata in PosMap is safe when it is persisted to NVM in trusted/untrusted
off-chip. The proposed architecture and persistency protocol steps minimize the
overhead and leakage during the write-back process. Finally, we compared our
persistent ORAM with the system without crash consistency support, show that in
non-recursive and recursive cases, EHAP-ORAM only incurs 3.36% and 3.65%
performance overhead. The results show that the EHAP-ORAM can support efficient
crash consistency with minimal performance and hardware overhead.
|
[
{
"version": "v1",
"created": "Sat, 7 Nov 2020 03:15:50 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Nov 2020 22:02:32 GMT"
},
{
"version": "v3",
"created": "Sun, 15 May 2022 03:36:06 GMT"
},
{
"version": "v4",
"created": "Thu, 19 May 2022 12:59:25 GMT"
},
{
"version": "v5",
"created": "Sat, 31 Dec 2022 03:44:17 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Liu",
"Gang",
""
],
[
"Li",
"Kenli",
""
],
[
"Xiao",
"Zheng",
""
],
[
"Wang",
"Rujia",
""
]
] |
new_dataset
| 0.997721 |
2101.00756
|
Brittany Reid
|
Brittany Reid, Marcelo d`Amorim, Markus Wagner, Christoph Treude
|
NCQ: Code reuse support for node.js developers
|
Submitted to IEEE Transactions on Software Engineering
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Code reuse is an important part of software development. The adoption of code
reuse practices is especially common among Node.js developers. The Node.js
package manager, NPM, indexes over 1 Million packages and developers often seek
out packages to solve programming tasks. Due to the vast number of packages,
selecting the right package is difficult and time consuming. With the goal of
improving productivity of developers that heavily reuse code through
third-party packages, we present Node Code Query (NCQ), a Read-Eval-Print-Loop
environment that allows developers to 1) search for NPM packages using natural
language queries, 2) search for code snippets related to those packages, 3)
automatically correct errors in these code snippets, 4) quickly setup new
environments for testing those snippets, and 5) transition between search and
editing modes. In two user studies with a total of 20 participants, we find
that participants begin programming faster and conclude tasks faster with NCQ
than with baseline approaches, and that they like, among other features, the
search for code snippets and packages. Our results suggest that NCQ makes
Node.js developers more efficient in reusing code.
|
[
{
"version": "v1",
"created": "Mon, 4 Jan 2021 03:54:02 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Jun 2022 09:32:59 GMT"
},
{
"version": "v3",
"created": "Mon, 2 Jan 2023 06:02:37 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Reid",
"Brittany",
""
],
[
"d`Amorim",
"Marcelo",
""
],
[
"Wagner",
"Markus",
""
],
[
"Treude",
"Christoph",
""
]
] |
new_dataset
| 0.999351 |
2109.02325
|
Ahmet Yavuz Uluslu
|
Ibrahim Faruk Ceylan and Necmettin Bera Calik
|
MyProfessors: Mining Turkish Student Reviews
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce Hocalarim (MyProfessors), the largest student review dataset
available for the Turkish language. It consists of over 5000 professor reviews
left online by students, with different aspects of education rated on a scale
of 1 to 5 stars. We investigate the properties of the dataset and present its
statistics. We examine the impact of students' institution type on their
ratings and the correlation of students' bias to give positive or negative
feedback.
|
[
{
"version": "v1",
"created": "Mon, 6 Sep 2021 09:55:58 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 14:54:44 GMT"
},
{
"version": "v3",
"created": "Sat, 5 Nov 2022 05:54:45 GMT"
},
{
"version": "v4",
"created": "Sat, 31 Dec 2022 08:13:36 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Ceylan",
"Ibrahim Faruk",
""
],
[
"Calik",
"Necmettin Bera",
""
]
] |
new_dataset
| 0.998491 |
2110.10067
|
Sam Powers
|
Sam Powers, Eliot Xing, Eric Kolve, Roozbeh Mottaghi, Abhinav Gupta
|
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual
Reinforcement Learning Agents
|
Repository available at https://github.com/AGI-Labs/continual_rl
|
Proceedings of The 1st Conference on Lifelong Learning Agents,
PMLR 199:705-743, 2022
| null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Progress in continual reinforcement learning has been limited due to several
barriers to entry: missing code, high compute requirements, and a lack of
suitable benchmarks. In this work, we present CORA, a platform for Continual
Reinforcement Learning Agents that provides benchmarks, baselines, and metrics
in a single code package. The benchmarks we provide are designed to evaluate
different aspects of the continual RL challenge, such as catastrophic
forgetting, plasticity, ability to generalize, and sample-efficient learning.
Three of the benchmarks utilize video game environments (Atari, Procgen,
NetHack). The fourth benchmark, CHORES, consists of four different task
sequences in a visually realistic home simulator, drawn from a diverse set of
task and scene parameters. To compare continual RL methods on these benchmarks,
we prepare three metrics in CORA: Continual Evaluation, Isolated Forgetting,
and Zero-Shot Forward Transfer. Finally, CORA includes a set of performant,
open-source baselines of existing algorithms for researchers to use and expand
on. We release CORA and hope that the continual RL community can benefit from
our contributions, to accelerate the development of new continual RL
algorithms.
|
[
{
"version": "v1",
"created": "Tue, 19 Oct 2021 15:48:26 GMT"
},
{
"version": "v2",
"created": "Sat, 31 Dec 2022 07:10:45 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Powers",
"Sam",
""
],
[
"Xing",
"Eliot",
""
],
[
"Kolve",
"Eric",
""
],
[
"Mottaghi",
"Roozbeh",
""
],
[
"Gupta",
"Abhinav",
""
]
] |
new_dataset
| 0.99936 |
2112.01924
|
Wu Ran
|
Wu Ran, Bohong Yang, Peirong Ma, and Hong Lu
|
TRNR: Task-Driven Image Rain and Noise Removal with a Few Images Based
on Patch Analysis
|
16 pages
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The recent success of learning-based image rain and noise removal can be
attributed primarily to well-designed neural network architectures and large
labeled datasets. However, we discover that current image rain and noise
removal methods result in low utilization of images. To alleviate the reliance
of deep models on large labeled datasets, we propose the task-driven image rain
and noise removal (TRNR) based on a patch analysis strategy. The patch analysis
strategy samples image patches with various spatial and statistical properties
for training and can increase image utilization. Furthermore, the patch
analysis strategy encourages us to introduce the N-frequency-K-shot learning
task for the task-driven approach TRNR. TRNR allows neural networks to learn
from numerous N-frequency-K-shot learning tasks, rather than from a large
amount of data. To verify the effectiveness of TRNR, we build a Multi-Scale
Residual Network (MSResNet) for both image rain removal and Gaussian noise
removal. Specifically, we train MSResNet for image rain removal and noise
removal with a few images (for example, 20.0\% train-set of Rain100H).
Experimental results demonstrate that TRNR enables MSResNet to learn more
effectively when data is scarce. TRNR has also been shown in experiments to
improve the performance of existing methods. Furthermore, MSResNet trained with
a few images using TRNR outperforms most recent deep learning methods trained
data-driven on large labeled datasets. These experimental results have
confirmed the effectiveness and superiority of the proposed TRNR. The source
code is available on \url{https://github.com/Schizophreni/MSResNet-TRNR}.
|
[
{
"version": "v1",
"created": "Fri, 3 Dec 2021 14:12:15 GMT"
},
{
"version": "v2",
"created": "Sun, 1 Jan 2023 09:20:40 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Ran",
"Wu",
""
],
[
"Yang",
"Bohong",
""
],
[
"Ma",
"Peirong",
""
],
[
"Lu",
"Hong",
""
]
] |
new_dataset
| 0.999413 |
2112.12042
|
Suthee Ruangwises
|
Suthee Ruangwises, Toshiya Itoh
|
Physical ZKP for Makaro Using a Standard Deck of Cards
|
This paper has appeared at TAMC 2022
| null |
10.1007/978-3-031-20350-3_5
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Makaro is a logic puzzle with an objective to fill numbers into a rectangular
grid to satisfy certain conditions. In 2018, Bultel et al. developed a physical
zero-knowledge proof (ZKP) protocol for Makaro using a deck of cards, which
allows a prover to physically convince a verifier that he/she knows a solution
of the puzzle without revealing it. However, their protocol requires several
identical copies of some cards, making it impractical as a deck of playing
cards found in everyday life typically consists of all different cards. In this
paper, we propose a new ZKP protocol for Makaro that can be implemented using a
standard deck (a deck consisting of all different cards). Our protocol also
uses asymptotically less cards than the protocol of Bultel et al. Most
importantly, we develop a general method to encode a number with a sequence of
all different cards. This allows us to securely compute several numerical
functions using a standard deck, such as verifying that two given numbers are
different and verifying that a number is the largest one among the given
numbers.
|
[
{
"version": "v1",
"created": "Wed, 22 Dec 2021 17:11:32 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 15:46:10 GMT"
},
{
"version": "v3",
"created": "Tue, 25 Oct 2022 09:38:48 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Ruangwises",
"Suthee",
""
],
[
"Itoh",
"Toshiya",
""
]
] |
new_dataset
| 0.999803 |
2201.07425
|
Li Liu
|
Chunhui Zhang, Guanjie Huang, Li Liu, Shan Huang, Yinan Yang, Xiang
Wan, Shiming Ge, Dacheng Tao
|
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV
Tracking
|
25 pages
| null | null | null |
cs.CV
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Unmanned aerial vehicle (UAV) tracking is of great significance for a wide
range of applications, such as delivery and agriculture. Previous benchmarks in
this area mainly focused on small-scale tracking problems while ignoring the
amounts of data, types of data modalities, diversities of target categories and
scenarios, and evaluation protocols involved, greatly hiding the massive power
of deep UAV tracking. In this work, we propose WebUAV-3M, the largest public
UAV tracking benchmark to date, to facilitate both the development and
evaluation of deep UAV trackers. WebUAV-3M contains over 3.3 million frames
across 4,500 videos and offers 223 highly diverse target categories. Each video
is densely annotated with bounding boxes by an efficient and scalable
semiautomatic target annotation (SATA) pipeline. Importantly, to take advantage
of the complementary superiority of language and audio, we enrich WebUAV-3M by
innovatively providing both natural language specifications and audio
descriptions. We believe that such additions will greatly boost future research
in terms of exploring language features and audio cues for multimodal UAV
tracking. In addition, a fine-grained UAV tracking-under-scenario constraint
(UTUSC) evaluation protocol and seven challenging scenario subtest sets are
constructed to enable the community to develop, adapt and evaluate various
types of advanced trackers. We provide extensive evaluations and detailed
analyses of 43 representative trackers and envision future research directions
in the field of deep UAV tracking and beyond. The dataset, toolkits and
baseline results are available at \url{https://github.com/983632847/WebUAV-3M}.
|
[
{
"version": "v1",
"created": "Wed, 19 Jan 2022 05:39:42 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Jan 2022 12:07:09 GMT"
},
{
"version": "v3",
"created": "Sun, 7 Aug 2022 01:20:13 GMT"
},
{
"version": "v4",
"created": "Sat, 31 Dec 2022 02:00:27 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Zhang",
"Chunhui",
""
],
[
"Huang",
"Guanjie",
""
],
[
"Liu",
"Li",
""
],
[
"Huang",
"Shan",
""
],
[
"Yang",
"Yinan",
""
],
[
"Wan",
"Xiang",
""
],
[
"Ge",
"Shiming",
""
],
[
"Tao",
"Dacheng",
""
]
] |
new_dataset
| 0.995835 |
2203.05056
|
Ahmed Rida Sekkat
|
Ahmed Rida Sekkat, Yohan Dupuis, Varun Ravi Kumar, Hazem Rashed,
Senthil Yogamani, Pascal Vasseur, Paul Honeine
|
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for
Autonomous Driving
|
IEEE Robotics and Automation Letters (RA-L) and IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2022). An
initial sample of the dataset is released in
https://drive.google.com/drive/folders/1N5rrySiw1uh9kLeBuOblMbXJ09YsqO7I
|
IEEE Robotics and Automation Letters ( Volume: 7, Issue: 3, July
2022)
|
10.1109/LRA.2022.3188106
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Surround-view cameras are a primary sensor for automated driving, used for
near-field perception. It is one of the most commonly used sensors in
commercial vehicles primarily used for parking visualization and automated
parking. Four fisheye cameras with a 190{\deg} field of view cover the
360{\deg} around the vehicle. Due to its high radial distortion, the standard
algorithms do not extend easily. Previously, we released the first public
fisheye surround-view dataset named WoodScape. In this work, we release a
synthetic version of the surround-view dataset, covering many of its weaknesses
and extending it. Firstly, it is not possible to obtain ground truth for
pixel-wise optical flow and depth. Secondly, WoodScape did not have all four
cameras annotated simultaneously in order to sample diverse frames. However,
this means that multi-camera algorithms cannot be designed to obtain a unified
output in birds-eye space, which is enabled in the new dataset. We implemented
surround-view fisheye geometric projections in CARLA Simulator matching
WoodScape's configuration and created SynWoodScape. We release 80k images from
the synthetic dataset with annotations for 10+ tasks. We also release the
baseline code and supporting scripts.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 21:30:52 GMT"
},
{
"version": "v2",
"created": "Mon, 23 May 2022 08:44:28 GMT"
},
{
"version": "v3",
"created": "Sun, 26 Jun 2022 10:09:16 GMT"
},
{
"version": "v4",
"created": "Mon, 8 Aug 2022 05:14:19 GMT"
},
{
"version": "v5",
"created": "Mon, 2 Jan 2023 08:31:35 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Sekkat",
"Ahmed Rida",
""
],
[
"Dupuis",
"Yohan",
""
],
[
"Kumar",
"Varun Ravi",
""
],
[
"Rashed",
"Hazem",
""
],
[
"Yogamani",
"Senthil",
""
],
[
"Vasseur",
"Pascal",
""
],
[
"Honeine",
"Paul",
""
]
] |
new_dataset
| 0.999742 |
2205.05533
|
Azarakhsh Keipour
|
Mohammadreza Mousaei, Junyi Geng, Azarakhsh Keipour, Dongwei Bai, and
Sebastian Scherer
|
Design, Modeling and Control for a Tilt-rotor VTOL UAV in the Presence
of Actuator Failure
|
8 pages
|
Proceedings of 2022 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), pp. 4310-4317
|
10.1109/IROS47612.2022.9981806
| null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Enabling vertical take-off and landing while providing the ability to fly
long ranges opens the door to a wide range of new real-world aircraft
applications while improving many existing tasks. Tiltrotor vertical take-off
and landing (VTOL) unmanned aerial vehicles (UAVs) are a better choice than
fixed-wing and multirotor aircraft for such applications. Prior works on these
aircraft have addressed aerodynamic performance, design, modeling, and control.
However, a less explored area is the study of their potential fault tolerance
due to their inherent redundancy, which allows them to tolerate some degree of
actuation failure. This paper introduces tolerance to several types of actuator
failures in a tiltrotor VTOL aircraft. We discuss the design and modeling of a
custom tiltrotor VTOL UAV, which is a combination of a fixed-wing aircraft and
a quadrotor with tilting rotors, where the four propellers can be rotated
individually. Then, we analyze the feasible wrench space the vehicle can
generate and design the dynamic control allocation so that the system can adapt
to actuator failures, benefiting from the configuration redundancy. The
proposed approach is lightweight and is implemented as an extension to an
already-existing flight control stack. Extensive experiments validate that the
system can maintain the controlled flight under different actuator failures. To
the best of our knowledge, this work is the first study of the tiltrotor VTOL's
fault-tolerance that exploits the configuration redundancy. The source code and
simulation can be accessed at https://theairlab.org/vtol.
|
[
{
"version": "v1",
"created": "Wed, 11 May 2022 14:23:18 GMT"
},
{
"version": "v2",
"created": "Mon, 2 Jan 2023 15:23:24 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Mousaei",
"Mohammadreza",
""
],
[
"Geng",
"Junyi",
""
],
[
"Keipour",
"Azarakhsh",
""
],
[
"Bai",
"Dongwei",
""
],
[
"Scherer",
"Sebastian",
""
]
] |
new_dataset
| 0.985614 |
2205.13973
|
Carole Porrier
|
Thomas Fernique and Carole Porrier
|
Ammann Bars for Octagonal Tilings
|
sagemath code as an ancillary file
| null | null | null |
cs.DM math.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Ammann bars are formed by segments (decorations) on the tiles of a tiling
such that forming straight lines with them while tiling forces non-periodicity.
Only a few cases are known, starting with Robert Ammann's observations on
Penrose tiles, but there is no general explanation or construction. In this
article we propose a general method for cut and project tilings based on the
notion of subperiods and we illustrate it with an aperiodic set of 36 decorated
prototiles related to what we called Cyrenaic tilings.
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 13:42:56 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Nov 2022 05:20:11 GMT"
},
{
"version": "v3",
"created": "Sat, 31 Dec 2022 17:11:10 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Fernique",
"Thomas",
""
],
[
"Porrier",
"Carole",
""
]
] |
new_dataset
| 0.992445 |
2208.09787
|
Xue-Feng Zhu
|
Xue-Feng Zhu, Tianyang Xu, Zhangyong Tang, Zucheng Wu, Haodong Liu,
Xiao Yang, Xiao-Jun Wu, Josef Kittler
|
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
RGB-D object tracking has attracted considerable attention recently,
achieving promising performance thanks to the symbiosis between visual and
depth channels. However, given a limited amount of annotated RGB-D tracking
data, most state-of-the-art RGB-D trackers are simple extensions of
high-performance RGB-only trackers, without fully exploiting the underlying
potential of the depth channel in the offline training stage. To address the
dataset deficiency issue, a new RGB-D dataset named RGBD1K is released in this
paper. The RGBD1K contains 1,050 sequences with about 2.5M frames in total. To
demonstrate the benefits of training on a larger RGB-D data set in general, and
RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT,
as a baseline for future visual object tracking studies using the new dataset.
The results, of extensive experiments using the SPT tracker emonstrate the
potential of the RGBD1K dataset to improve the performance of RGB-D tracking,
inspiring future developments of effective tracker designs. The dataset and
codes will be available on the project homepage:
https://github.com/xuefeng-zhu5/RGBD1K.
|
[
{
"version": "v1",
"created": "Sun, 21 Aug 2022 03:07:36 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Dec 2022 10:30:06 GMT"
},
{
"version": "v3",
"created": "Fri, 30 Dec 2022 23:23:37 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Zhu",
"Xue-Feng",
""
],
[
"Xu",
"Tianyang",
""
],
[
"Tang",
"Zhangyong",
""
],
[
"Wu",
"Zucheng",
""
],
[
"Liu",
"Haodong",
""
],
[
"Yang",
"Xiao",
""
],
[
"Wu",
"Xiao-Jun",
""
],
[
"Kittler",
"Josef",
""
]
] |
new_dataset
| 0.999623 |
2209.00349
|
Jihoon Kim
|
Jihoon Kim, Jiseob Kim, Sungjoon Choi
|
FLAME: Free-form Language-based Motion Synthesis & Editing
|
AAAI 2023
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Text-based motion generation models are drawing a surge of interest for their
potential for automating the motion-making process in the game, animation, or
robot industries. In this paper, we propose a diffusion-based motion synthesis
and editing model named FLAME. Inspired by the recent successes in diffusion
models, we integrate diffusion-based generative models into the motion domain.
FLAME can generate high-fidelity motions well aligned with the given text.
Also, it can edit the parts of the motion, both frame-wise and joint-wise,
without any fine-tuning. FLAME involves a new transformer-based architecture we
devise to better handle motion data, which is found to be crucial to manage
variable-length motions and well attend to free-form text. In experiments, we
show that FLAME achieves state-of-the-art generation performances on three
text-motion datasets: HumanML3D, BABEL, and KIT. We also demonstrate that
editing capability of FLAME can be extended to other tasks such as motion
prediction or motion in-betweening, which have been previously covered by
dedicated models.
|
[
{
"version": "v1",
"created": "Thu, 1 Sep 2022 10:34:57 GMT"
},
{
"version": "v2",
"created": "Sun, 1 Jan 2023 11:46:43 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Kim",
"Jihoon",
""
],
[
"Kim",
"Jiseob",
""
],
[
"Choi",
"Sungjoon",
""
]
] |
new_dataset
| 0.999278 |
2209.10941
|
Ruslan Shevchenko
|
Ruslan Shevchenko
|
Embedding generic monadic transformer into Scala
|
Accepted to publication into "Trends of Functional Programming 2022"
| null |
10.1007/978-3-031-21314-4_1
| null |
cs.PL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Dotty-cps-async is an open-source package that consists of scala macro, which
implements generic async/await via monadic cps transform, and library, which
provides monadic substitutions for higher-order functions from the standard
library. It allows developers to use direct control flow constructions of the
base language instead of monadic DSL for various applications. Behind
well-known async/await operations, the package provides options for
transforming higher-order function applications, generating call-chain proxies,
and automatic coloring.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 11:46:03 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Shevchenko",
"Ruslan",
""
]
] |
new_dataset
| 0.995264 |
2210.12193
|
Jan Hohenheim
|
Jan Hohenheim, Zhaoyu Devon Liu, Tommaso Stecconi, Pietro Palopoli
|
A Trainable Sequence Learner that Learns and Recognizes Two-Input
Sequence Patterns
|
Submitted to IEEE TENCON 2022
| null |
10.1109/TENCON55691.2022.9977663
| null |
cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
We present two designs for an analog circuit that can learn to detect a
temporal sequence of two inputs. The training phase is done by feeding the
circuit with the desired sequence and, after the training is completed, each
time the trained sequence is encountered again the circuit will emit a signal
of correct recognition. Sequences are in the order of tens of nanoseconds. The
first design can reset the trained sequence on runtime but assumes very strict
timing of the inputs. The second design can only be trained once but is lenient
in the input's timing.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 18:43:18 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Hohenheim",
"Jan",
""
],
[
"Liu",
"Zhaoyu Devon",
""
],
[
"Stecconi",
"Tommaso",
""
],
[
"Palopoli",
"Pietro",
""
]
] |
new_dataset
| 0.998861 |
2212.02108
|
Ana Kotarcic
|
Ana Kotarcic, Dominik Hangartner, Fabrizio Gilardi, Selina Kurer,
Karsten Donnay
|
Human-in-the-Loop Hate Speech Classification in a Multilingual Context
|
Findings of EMNLP 2022
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The shift of public debate to the digital sphere has been accompanied by a
rise in online hate speech. While many promising approaches for hate speech
classification have been proposed, studies often focus only on a single
language, usually English, and do not address three key concerns:
post-deployment performance, classifier maintenance and infrastructural
limitations. In this paper, we introduce a new human-in-the-loop BERT-based
hate speech classification pipeline and trace its development from initial data
collection and annotation all the way to post-deployment. Our classifier,
trained using data from our original corpus of over 422k examples, is
specifically developed for the inherently multilingual setting of Switzerland
and outperforms with its F1 score of 80.5 the currently best-performing
BERT-based multilingual classifier by 5.8 F1 points in German and 3.6 F1 points
in French. Our systematic evaluations over a 12-month period further highlight
the vital importance of continuous, human-in-the-loop classifier maintenance to
ensure robust hate speech classification post-deployment.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 09:05:40 GMT"
},
{
"version": "v2",
"created": "Sun, 1 Jan 2023 14:39:09 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Kotarcic",
"Ana",
""
],
[
"Hangartner",
"Dominik",
""
],
[
"Gilardi",
"Fabrizio",
""
],
[
"Kurer",
"Selina",
""
],
[
"Donnay",
"Karsten",
""
]
] |
new_dataset
| 0.996903 |
2212.08996
|
Manuel Luis Delos Santos
|
Manuel Luis C. Delos Santos (1), Ronaldo S. Tinio (2), Darwin B. Diaz
(3) and Karlene Emily I. Tolosa (4), ((1)(3)(4) Asian Institute of Computer
Studies, Philippines, (2) Pamantasan ng Lungsod ng Valezuela, Philippines)
|
Smart Face Shield: A Sensor-Based Wearable Face Shield Utilizing
Computer Vision Algorithms
| null |
IJCSR Volume 6, October 2022, ISSN 2546-115X, pages 1-15
|
10.25147/ijcsr.2017.001.1.118
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The study aims the development of a wearable device to combat the onslaught
of covid-19. Likewise, to enhance the regular face shield available in the
market. Furthermore, to raise awareness of the health and safety protocols
initiated by the government and its affiliates in the enforcement of social
distancing with the integration of computer vision algorithms. The wearable
device was composed of various hardware and software components such as a
transparent polycarbonate face shield, microprocessor, sensors, camera,
thin-film transistor on-screen display, jumper wires, power bank, and python
programming language. The algorithm incorporated in the study was object
detection under computer vision machine learning. The front camera with OpenCV
technology determines the distance of a person in front of the user. Utilizing
TensorFlow, the target object identifies and detects the image or live feed to
get its bounding boxes. The focal length lens requires the determination of the
distance from the camera to the target object. To get the focal length,
multiply the pixel width by the known distance and divide it by the known width
(Rosebrock, 2020). The deployment of unit testing ensures that the parameters
are valid in terms of design and specifications.
|
[
{
"version": "v1",
"created": "Sun, 18 Dec 2022 03:23:38 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Santos",
"Manuel Luis C. Delos",
""
],
[
"Tinio",
"Ronaldo S.",
""
],
[
"Diaz",
"Darwin B.",
""
],
[
"Tolosa",
"Karlene Emily I.",
""
]
] |
new_dataset
| 0.993986 |
2212.09937
|
Emily Lines
|
Emily R. Lines, Matt Allen, Carlos Cabo, Kim Calders, Amandine Debus,
Stuart W. D. Grieve, Milto Miltiadou, Adam Noach, Harry J. F. Owen and
Stefano Puliti
|
AI applications in forest monitoring need remote sensing benchmark
datasets
| null | null | null | null |
cs.AI cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the rise in high resolution remote sensing technologies there has been
an explosion in the amount of data available for forest monitoring, and an
accompanying growth in artificial intelligence applications to automatically
derive forest properties of interest from these datasets. Many studies use
their own data at small spatio-temporal scales, and demonstrate an application
of an existing or adapted data science method for a particular task. This
approach often involves intensive and time-consuming data collection and
processing, but generates results restricted to specific ecosystems and sensor
types. There is a lack of widespread acknowledgement of how the types and
structures of data used affects performance and accuracy of analysis
algorithms. To accelerate progress in the field more efficiently, benchmarking
datasets upon which methods can be tested and compared are sorely needed.
Here, we discuss how lack of standardisation impacts confidence in estimation
of key forest properties, and how considerations of data collection need to be
accounted for in assessing method performance. We present pragmatic
requirements and considerations for the creation of rigorous, useful
benchmarking datasets for forest monitoring applications, and discuss how tools
from modern data science can improve use of existing data. We list a set of
example large-scale datasets that could contribute to benchmarking, and present
a vision for how community-driven, representative benchmarking initiatives
could benefit the field.
|
[
{
"version": "v1",
"created": "Tue, 20 Dec 2022 01:11:40 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Lines",
"Emily R.",
""
],
[
"Allen",
"Matt",
""
],
[
"Cabo",
"Carlos",
""
],
[
"Calders",
"Kim",
""
],
[
"Debus",
"Amandine",
""
],
[
"Grieve",
"Stuart W. D.",
""
],
[
"Miltiadou",
"Milto",
""
],
[
"Noach",
"Adam",
""
],
[
"Owen",
"Harry J. F.",
""
],
[
"Puliti",
"Stefano",
""
]
] |
new_dataset
| 0.981803 |
2212.13742
|
Haiyue Yuan
|
Jamie Knott, Haiyue Yuan, Matthew Boakes, Shujun Li
|
Cyber Security and Online Safety Education for Schools in the UK:
Looking through the Lens of Twitter Data
|
This is the full edition of a 4-page poster paper published in the
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23),
which can be accessed via the following DOI link:
https://doi.org/10.1145/3555776.3577805
| null | null | null |
cs.CY cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, digital technologies have grown in many ways. As a result,
many school-aged children have been exposed to the digital world a lot.
Children are using more digital technologies, so schools need to teach kids
more about cyber security and online safety. Because of this, there are now
more school programmes and projects that teach students about cyber security
and online safety and help them learn and improve their skills. Still, despite
many programmes and projects, there is not much proof of how many schools have
taken part and helped spread the word about them. This work shows how we can
learn about the size and scope of cyber security and online safety education in
schools in the UK, a country with a very active and advanced cyber security
education profile, using nearly 200k public tweets from over 15k schools. By
using simple techniques like descriptive statistics and visualisation as well
as advanced natural language processing (NLP) techniques like sentiment
analysis and topic modelling, we show some new findings and insights about how
UK schools as a sector have been doing on Twitter with their cyber security and
online safety education activities. Our work has led to a range of large-scale
and real-world evidence that can help inform people and organisations
interested in cyber security and teaching online safety in schools.
|
[
{
"version": "v1",
"created": "Wed, 28 Dec 2022 08:30:24 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Dec 2022 20:48:41 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Knott",
"Jamie",
""
],
[
"Yuan",
"Haiyue",
""
],
[
"Boakes",
"Matthew",
""
],
[
"Li",
"Shujun",
""
]
] |
new_dataset
| 0.969717 |
2301.00001
|
Tauheed Khan Mohd
|
Jordan Thompson, Ryan Benac, Kidus Olana, Talha Hassan, Andrew Sward,
Tauheed Khan Mohd
|
NFTrig
| null | null | null | null |
cs.HC
|
http://creativecommons.org/publicdomain/zero/1.0/
|
NFTrig is a web-based application created for use as an educational tool to
teach trigonometry and block chain technology. Creation of the application
includes front and back end development as well as integration with other
outside sources including MetaMask and OpenSea. The primary development
languages include HTML, CSS (Bootstrap 5), and JavaScript as well as Solidity
for smart contract creation. The application itself is hosted on Moralis
utilizing their Web3 API. This technical report describes how the application
was created, what the application requires, and smart contract design with
security considerations in mind. The NFTrig application has underwent
significant testing and validation prior to and after deployment. Future
suggestions and recommendations for further development, maintenance, and use
in other fields for education are also described.
|
[
{
"version": "v1",
"created": "Wed, 21 Dec 2022 18:07:06 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Thompson",
"Jordan",
""
],
[
"Benac",
"Ryan",
""
],
[
"Olana",
"Kidus",
""
],
[
"Hassan",
"Talha",
""
],
[
"Sward",
"Andrew",
""
],
[
"Mohd",
"Tauheed Khan",
""
]
] |
new_dataset
| 0.999741 |
2301.00023
|
Balamurugan Thambiraja
|
Balamurugan Thambiraja, Ikhsanul Habibie, Sadegh Aliakbarian, Darren
Cosker, Christian Theobalt, Justus Thies
|
Imitator: Personalized Speech-driven 3D Facial Animation
|
https://youtu.be/JhXTdjiUCUw
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Speech-driven 3D facial animation has been widely explored, with applications
in gaming, character animation, virtual reality, and telepresence systems.
State-of-the-art methods deform the face topology of the target actor to sync
the input audio without considering the identity-specific speaking style and
facial idiosyncrasies of the target actor, thus, resulting in unrealistic and
inaccurate lip movements. To address this, we present Imitator, a speech-driven
facial expression synthesis method, which learns identity-specific details from
a short input video and produces novel facial expressions matching the
identity-specific speaking style and facial idiosyncrasies of the target actor.
Specifically, we train a style-agnostic transformer on a large facial
expression dataset which we use as a prior for audio-driven facial expressions.
Based on this prior, we optimize for identity-specific speaking style based on
a short reference video. To train the prior, we introduce a novel loss function
based on detected bilabial consonants to ensure plausible lip closures and
consequently improve the realism of the generated expressions. Through detailed
experiments and a user study, we show that our approach produces temporally
coherent facial expressions from input audio while preserving the speaking
style of the target actors.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 19:00:02 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Thambiraja",
"Balamurugan",
""
],
[
"Habibie",
"Ikhsanul",
""
],
[
"Aliakbarian",
"Sadegh",
""
],
[
"Cosker",
"Darren",
""
],
[
"Theobalt",
"Christian",
""
],
[
"Thies",
"Justus",
""
]
] |
new_dataset
| 0.993421 |
2301.00044
|
Hisham A. Kholidy
|
Thomas Grippo, Hisham A. Kholidy
|
Detecting Forged Kerberos Tickets in an Active Directory Environment
| null | null | null | null |
cs.CR cs.CY cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Active Directory is the most popular service to manage users and devices on
the network. Its widespread deployment in the corporate world has made it a
popular target for threat actors. While there are many attacks that target
Active Directory and its authentication protocol Kerberos, ticket forgery
attacks are among the most dangerous. By exploiting weaknesses in Kerberos,
attackers can craft their own tickets that allow them to gain unauthorized
access to services on the network. These types of attacks are both dangerous
and hard to detect. They may require a powerful centralized log collecting
system to analyze Windows security logs across multiple services. This would
give additional visibility to be able to find these forged tickets in the
network.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 20:20:42 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Grippo",
"Thomas",
""
],
[
"Kholidy",
"Hisham A.",
""
]
] |
new_dataset
| 0.998414 |
2301.00047
|
Alexander Rubtsov
|
Alexander Rubtsov
|
The Simplest Proof of Parikh's Theorem via Derivation Trees
| null | null | null | null |
cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Parikh's theorem is a fundamental result of the formal language's theory.
There had been published many proofs and many papers claimed to provide a
simplified proof, but most of them are long and still complicated. We provide
the proof that is really short, simple and discloses the nature of this
fundamental result. We follow the technique closed to the original Parikh's
paper and our proof is similar to the proof by Ryoma Sin'ya 2019, but we
provide more detailed exposition and pretend to more simplicity as well. We
achieve the simplicity via nonconstructivenes that allows us avoiding many
difficulties met by other proofs.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 20:27:09 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Rubtsov",
"Alexander",
""
]
] |
new_dataset
| 0.994846 |
2301.00072
|
Shaobo Li
|
Jinghan Sun, Shaobo Li, Yunxin Sun, Chao Sun, Dejan Vucinic, and Jian
Huang
|
LeaFTL: A Learning-Based Flash Translation Layer for Solid-State Drives
|
This paper is accepted at the 28th Conference on Architectural
Support for Programming Languages and Operating Systems (ASPLOS 2023)
| null |
10.1145/3575693.3575744
| null |
cs.OS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In modern solid-state drives (SSDs), the indexing of flash pages is a
critical component in their storage controllers. It not only affects the data
access performance, but also determines the efficiency of the precious
in-device DRAM resource. A variety of address mapping schemes and optimization
techniques have been proposed. However, most of them were developed with
human-driven heuristics. They cannot automatically capture diverse data access
patterns at runtime in SSD controllers, which leaves a large room for
improvement. In this paper, we present a learning-based flash translation layer
(FTL), named LeaFTL, which learns the address mapping to tolerate dynamic data
access patterns via linear regression at runtime. By grouping a large set of
mapping entries into a learned segment, it significantly reduces the memory
footprint of the address mapping table, which further benefits the data caching
in SSD controllers. LeaFTL also employs various optimization techniques,
including out-of-band metadata verification to tolerate mispredictions,
optimized flash allocation, and dynamic compaction of learned index segments.
We implement LeaFTL with an SSD simulator and evaluate it with various storage
workloads. LeaFTL saves the memory consumption of the mapping table by 2.9x on
average and improves the storage performance by 1.4x on average, in comparison
with state-of-the-art FTL schemes.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 23:37:39 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Sun",
"Jinghan",
""
],
[
"Li",
"Shaobo",
""
],
[
"Sun",
"Yunxin",
""
],
[
"Sun",
"Chao",
""
],
[
"Vucinic",
"Dejan",
""
],
[
"Huang",
"Jian",
""
]
] |
new_dataset
| 0.996133 |
2301.00153
|
Peter \v{S}vec
|
Peter \v{S}vec, \v{S}tefan Balogh, Martin Homola, J\'an K\v{l}uka
|
Knowledge-Based Dataset for Training PE Malware Detection Models
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ontologies are a standard for semantic schemata in many knowledge-intensive
domains of human interest. They are now becoming increasingly important also in
areas until very recently dominated by subsymbolic representations and
machine-learning-based data processing. One such area is information security,
and more specifically malware detection. We propose PE Malware Ontology that
offers a reusable semantic schema for Portable Executable (PE, Windows binary
format) malware files. The ontology was inspired by the structure of the data
in the EMBER dataset and it currently covers the data intended for static
malware analysis. With this proposal, we hope to achieve: a) a unified semantic
representation for PE malware datasets that are available or will be published
in the future; (b) applicability of symbolic, neural-symbolic, or otherwise
explainable approaches in the PE Malware domain that may lead to improved
interpretability of results which may now be characterized by the terms defined
in the ontology; and (c)by joint publishing of semantically treated EMBER data,
including fractional datasets, also improved reproducibility of experiments.
|
[
{
"version": "v1",
"created": "Sat, 31 Dec 2022 08:46:02 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Švec",
"Peter",
""
],
[
"Balogh",
"Štefan",
""
],
[
"Homola",
"Martin",
""
],
[
"Kľuka",
"Ján",
""
]
] |
new_dataset
| 0.999789 |
2301.00200
|
Michael Rose PhD
|
Sebastian Erhardt, Mainak Ghosh, Erik Buunk, Michael E. Rose, Dietmar
Harhoff
|
Logic Mill -- A Knowledge Navigation System
|
9 pages, 2 figures, 1 table
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Logic Mill is a scalable and openly accessible software system that
identifies semantically similar documents within either one domain-specific
corpus or multi-domain corpora. It uses advanced Natural Language Processing
(NLP) techniques to generate numerical representations of documents. Currently
it leverages a large pre-trained language model to generate these document
representations. The system focuses on scientific publications and patent
documents and contains more than 200 million documents. It is easily accessible
via a simple Application Programming Interface (API) or via a web interface.
Moreover, it is continuously being updated and can be extended to text corpora
from other domains. We see this system as a general-purpose tool for future
research applications in the social sciences and other domains.
|
[
{
"version": "v1",
"created": "Sat, 31 Dec 2022 13:46:50 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Erhardt",
"Sebastian",
""
],
[
"Ghosh",
"Mainak",
""
],
[
"Buunk",
"Erik",
""
],
[
"Rose",
"Michael E.",
""
],
[
"Harhoff",
"Dietmar",
""
]
] |
new_dataset
| 0.999721 |
2301.00301
|
Yuqing Zhu
|
Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang
|
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with
Differential Privacy
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The ''Propose-Test-Release'' (PTR) framework is a classic recipe for
designing differentially private (DP) algorithms that are data-adaptive, i.e.
those that add less noise when the input dataset is nice. We extend PTR to a
more general setting by privately testing data-dependent privacy losses rather
than local sensitivity, hence making it applicable beyond the standard
noise-adding mechanisms, e.g. to queries with unbounded or undefined
sensitivity. We demonstrate the versatility of generalized PTR using private
linear regression as a case study. Additionally, we apply our algorithm to
solve an open problem from ''Private Aggregation of Teacher Ensembles (PATE)''
-- privately releasing the entire model with a delicate data-dependent
analysis.
|
[
{
"version": "v1",
"created": "Sat, 31 Dec 2022 22:22:53 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Redberg",
"Rachel",
""
],
[
"Zhu",
"Yuqing",
""
],
[
"Wang",
"Yu-Xiang",
""
]
] |
new_dataset
| 0.992758 |
2301.00395
|
Jiayi Geng
|
Ge Zhang, Yizhi Li, Yaoyao Wu, Linyuan Zhang, Chenghua Lin, Jiayi
Geng, Shi Wang, Jie Fu
|
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
| null | null | null | null |
cs.CL cs.AI cs.CY cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As natural language processing (NLP) for gender bias becomes a significant
interdisciplinary topic, the prevalent data-driven techniques such as
large-scale language models suffer from data inadequacy and biased corpus,
especially for languages with insufficient resources such as Chinese. To this
end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation
CORGI-PM, which contains 32.9k sentences with high-quality labels derived by
following an annotation scheme specifically developed for gender bias in the
Chinese context. Moreover, we address three challenges for automatic textual
gender bias mitigation, which requires the models to detect, classify, and
mitigate textual gender bias. We also conduct experiments with state-of-the-art
language models to provide baselines. To our best knowledge, CORGI-PM is the
first sentence-level Chinese corpus for gender bias probing and mitigation.
|
[
{
"version": "v1",
"created": "Sun, 1 Jan 2023 12:48:12 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Zhang",
"Ge",
""
],
[
"Li",
"Yizhi",
""
],
[
"Wu",
"Yaoyao",
""
],
[
"Zhang",
"Linyuan",
""
],
[
"Lin",
"Chenghua",
""
],
[
"Geng",
"Jiayi",
""
],
[
"Wang",
"Shi",
""
],
[
"Fu",
"Jie",
""
]
] |
new_dataset
| 0.987166 |
2301.00486
|
Joseph J. Boutros
|
Joseph J. Boutros and Emina Soljanin
|
Time-Entanglement QKD: Secret Key Rates and Information Reconciliation
Coding
|
We intend to publish this manuscript in an IEEE journal. 33 pages, 2
tables, and 10 figures
| null | null | null |
cs.IT math.IT quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In time entanglement-based quantum key distribution (QKD), Alice and Bob
extract the raw key bits from the (identical) arrival times of entangled photon
pairs by time-binning. Each of them individually discretizes time into bins and
groups them into frames. They retain only the frames with a single occupied
bin. Thus, Alice and Bob can use the position of the occupied bin within a
frame to generate random key bits, as in PPM modulation. Because of
entanglement, their occupied bins and their keys should be identical. However,
practical photon detectors suffer from time jitter errors. These errors cause
discrepancies between Alice's and Bob's keys. Alice sends information to Bob
through the public channel to reconcile the keys. The amount of information
determines the secret key rate. This paper computes the secret key rates
possible with detector jitter errors and constructs codes for information
reconciliation to approach these rates.
|
[
{
"version": "v1",
"created": "Sun, 1 Jan 2023 22:38:35 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Boutros",
"Joseph J.",
""
],
[
"Soljanin",
"Emina",
""
]
] |
new_dataset
| 0.963138 |
2301.00493
|
Benjamin Wilson
|
Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet
Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony
Kaesemodel Pontes, Deva Ramanan, Peter Carr, James Hays
|
Argoverse 2: Next Generation Datasets for Self-Driving Perception and
Forecasting
|
Proceedings of the Neural Information Processing Systems Track on
Datasets and Benchmarks
| null | null | null |
cs.CV cs.AI cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Argoverse 2 (AV2) - a collection of three datasets for
perception and forecasting research in the self-driving domain. The annotated
Sensor Dataset contains 1,000 sequences of multimodal data, encompassing
high-resolution imagery from seven ring cameras, and two stereo cameras in
addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain
3D cuboid annotations for 26 object categories, all of which are
sufficiently-sampled to support training and evaluation of 3D perception
models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point
clouds and map-aligned pose. This dataset is the largest ever collection of
lidar sensor data and supports self-supervised learning and the emerging task
of point cloud forecasting. Finally, the Motion Forecasting Dataset contains
250,000 scenarios mined for interesting and challenging interactions between
the autonomous vehicle and other actors in each local scene. Models are tasked
with the prediction of future motion for "scored actors" in each scenario and
are provided with track histories that capture object location, heading,
velocity, and category. In all three datasets, each scenario contains its own
HD Map with 3D lane and crosswalk geometry - sourced from data captured in six
distinct cities. We believe these datasets will support new and existing
machine learning research problems in ways that existing datasets do not. All
datasets are released under the CC BY-NC-SA 4.0 license.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 00:36:22 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Wilson",
"Benjamin",
""
],
[
"Qi",
"William",
""
],
[
"Agarwal",
"Tanmay",
""
],
[
"Lambert",
"John",
""
],
[
"Singh",
"Jagjeet",
""
],
[
"Khandelwal",
"Siddhesh",
""
],
[
"Pan",
"Bowen",
""
],
[
"Kumar",
"Ratnesh",
""
],
[
"Hartnett",
"Andrew",
""
],
[
"Pontes",
"Jhony Kaesemodel",
""
],
[
"Ramanan",
"Deva",
""
],
[
"Carr",
"Peter",
""
],
[
"Hays",
"James",
""
]
] |
new_dataset
| 0.999816 |
2301.00505
|
Adam Gamba
|
Adam Gamba and Andr\'es Monroy-Hern\'andez
|
PokAR: Facilitating Poker Play Through Augmented Reality
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce PokAR, an augmented reality (AR) application to facilitate poker
play. PokAR aims to alleviate three difficulties of traditional poker by
leveraging AR technology: (1) need to have physical poker chips, (2) complex
rules of poker, (3) slow game pace caused by laborious tasks. Despite the
potential benefits of AR in poker, not much research has been done in the
field. In fact, PokAR is the first application to enable AR poker on a mobile
device without requiring extra costly equipment. This has been done by creating
a Snapchat Lens which can be used on most mobile devices. We evaluated this
application by instructing 4 participant dyads to use PokAR to engage in poker
play and respond to survey questions about their experience. We found that most
PokAR features were positively received, AR did not significantly improve nor
hinder socialization, PokAR slightly increased the game pace, and participants
had an overall enjoyable experience with the Lens. These findings led to three
major conclusions: (1) AR has the potential to augment and simplify traditional
table games, (2) AR should not be used to replace traditional experiences, only
augment them, (3) Future work includes additional features like increased
tactility and statistical annotations.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 02:32:26 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Gamba",
"Adam",
""
],
[
"Monroy-Hernández",
"Andrés",
""
]
] |
new_dataset
| 0.999874 |
2301.00633
|
Ver\'onica Becher
|
Ver\'onica Becher and Olivier Carton
|
Nested perfect toroidal arrays
| null | null | null | null |
cs.IT cs.DM math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce two-dimensional toroidal arrays that are a variant of the de
Bruijn tori. We call them nested perfect toroidal arrays. Instead of asking
that every array of a given size has exactly one occurrence, we partition the
positions in congruence classes and we ask exactly one occurrence in each
congruence class. We also ask that this property applies recursively to each of
the subarrays. We give a method to construct nested perfect toroidal arrays
based on Pascal triangle matrix modulo 2. For the two-symbol alphabet, and for
$n$ being a power of $2$, our method yields $2^{n^2+n-1}$ different nested
perfect toroidal arrays allocating all the different $n\times n$ arrays in each
congruence class
that arises from taking the line number modulo $n$ and the column number
modulo $n$.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 12:51:30 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Becher",
"Verónica",
""
],
[
"Carton",
"Olivier",
""
]
] |
new_dataset
| 0.995817 |
2301.00704
|
Jarred Barber
|
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu
Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein,
Yuanzhen Li, Dilip Krishnan
|
Muse: Text-To-Image Generation via Masked Generative Transformers
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present Muse, a text-to-image Transformer model that achieves
state-of-the-art image generation performance while being significantly more
efficient than diffusion or autoregressive models. Muse is trained on a masked
modeling task in discrete token space: given the text embedding extracted from
a pre-trained large language model (LLM), Muse is trained to predict randomly
masked image tokens. Compared to pixel-space diffusion models, such as Imagen
and DALL-E 2, Muse is significantly more efficient due to the use of discrete
tokens and requiring fewer sampling iterations; compared to autoregressive
models, such as Parti, Muse is more efficient due to the use of parallel
decoding. The use of a pre-trained LLM enables fine-grained language
understanding, translating to high-fidelity image generation and the
understanding of visual concepts such as objects, their spatial relationships,
pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M,
with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88
on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also
directly enables a number of image editing applications without the need to
fine-tune or invert the model: inpainting, outpainting, and mask-free editing.
More results are available at https://muse-model.github.io
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 14:43:38 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Chang",
"Huiwen",
""
],
[
"Zhang",
"Han",
""
],
[
"Barber",
"Jarred",
""
],
[
"Maschinot",
"AJ",
""
],
[
"Lezama",
"Jose",
""
],
[
"Jiang",
"Lu",
""
],
[
"Yang",
"Ming-Hsuan",
""
],
[
"Murphy",
"Kevin",
""
],
[
"Freeman",
"William T.",
""
],
[
"Rubinstein",
"Michael",
""
],
[
"Li",
"Yuanzhen",
""
],
[
"Krishnan",
"Dilip",
""
]
] |
new_dataset
| 0.977761 |
2301.00709
|
Ole-Christoffer Granmo
|
Bimal Bhattarai and Ole-Christoffer Granmo and Lei Jiao and Rohan
Yadav and Jivitesh Sharma
|
Tsetlin Machine Embedding: Representing Words Using Logical Expressions
|
9 pages, 7 figures
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Embedding words in vector space is a fundamental first step in
state-of-the-art natural language processing (NLP). Typical NLP solutions
employ pre-defined vector representations to improve generalization by
co-locating similar words in vector space. For instance, Word2Vec is a
self-supervised predictive model that captures the context of words using a
neural network. Similarly, GLoVe is a popular unsupervised model incorporating
corpus-wide word co-occurrence statistics. Such word embedding has
significantly boosted important NLP tasks, including sentiment analysis,
document classification, and machine translation. However, the embeddings are
dense floating-point vectors, making them expensive to compute and difficult to
interpret. In this paper, we instead propose to represent the semantics of
words with a few defining words that are related using propositional logic. To
produce such logical embeddings, we introduce a Tsetlin Machine-based
autoencoder that learns logical clauses self-supervised. The clauses consist of
contextual words like "black," "cup," and "hot" to define other words like
"coffee," thus being human-understandable. We evaluate our embedding approach
on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six
classification tasks. Furthermore, we investigate the interpretability of our
embedding using the logical representations acquired during training. We also
visualize word clusters in vector space, demonstrating how our logical
embedding co-locate similar words.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 15:02:45 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Bhattarai",
"Bimal",
""
],
[
"Granmo",
"Ole-Christoffer",
""
],
[
"Jiao",
"Lei",
""
],
[
"Yadav",
"Rohan",
""
],
[
"Sharma",
"Jivitesh",
""
]
] |
new_dataset
| 0.98237 |
2301.00716
|
Felix Hamann
|
Felix Hamann, Adrian Ulges, Maurice Falk
|
IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale
| null | null | null | null |
cs.LG cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We address the challenge of building domain-specific knowledge models for
industrial use cases, where labelled data and taxonomic information is
initially scarce. Our focus is on inductive link prediction models as a basis
for practical tools that support knowledge engineers with exploring text
collections and discovering and linking new (so-called open-world) entities to
the knowledge graph. We argue that - though neural approaches to text mining
have yielded impressive results in the past years - current benchmarks do not
reflect the typical challenges encountered in the industrial wild properly.
Therefore, our first contribution is an open benchmark coined IRT2 (inductive
reasoning with text) that (1) covers knowledge graphs of varying sizes
(including very small ones), (2) comes with incidental, low-quality text
mentions, and (3) includes not only triple completion but also ranking, which
is relevant for supporting experts with discovery tasks.
We investigate two neural models for inductive link prediction, one based on
end-to-end learning and one that learns from the knowledge graph and text data
in separate steps. These models compete with a strong bag-of-words baseline.
The results show a significant advance in performance for the neural approaches
as soon as the available graph data decreases for linking. For ranking, the
results are promising, and the neural approaches outperform the sparse
retriever by a wide margin.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 15:19:21 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Hamann",
"Felix",
""
],
[
"Ulges",
"Adrian",
""
],
[
"Falk",
"Maurice",
""
]
] |
new_dataset
| 0.976252 |
2301.00730
|
Quan Quan
|
Quan Quan, Wang Shuai, Gao Wenhan
|
Lifting-wing Quadcopter Modeling and Unified Control
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight
of fixed-wing and vertical takeoff and landing (VTOL) capabilities of
multicopter UAVs. This paper presents the modeling, control and simulation of a
new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The
airframe orientation of the lifting wing needs to tilt a specific angle often
within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees.
Compared with some convertiplane and tail-sitter UAVs, the lifting-wing
quadcopter has a highly reliable structure, robust wind resistance, low cruise
speed and reliable transition flight, making it potential to work
fully-autonomous outdoor or some confined airspace indoor. In the modeling
part, forces and moments generated by both lifting wing and rotors are
considered. Based on the established model, a unified controller for the full
flight phase is designed. The controller has the capability of uniformly
treating the hovering and forward flight, and enables a continuous transition
between two modes, depending on the velocity command. What is more, by taking
rotor thrust and aerodynamic force under consideration simultaneously, a
control allocation based on optimization is utilized to realize cooperative
control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL)
simulations are performed to verify the advantages of the designed aircraft and
the proposed controller.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 15:48:45 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Quan",
"Quan",
""
],
[
"Shuai",
"Wang",
""
],
[
"Wenhan",
"Gao",
""
]
] |
new_dataset
| 0.960081 |
2301.00764
|
Christian Lenz
|
Christian Lenz, Sven Behnke
|
Bimanual Telemanipulation with Force and Haptic Feedback through an
Anthropomorphic Avatar System
|
Published in Robotics and Autonomous Systems, 2022
(https://doi.org/10.1016/j.robot.2022.104338). arXiv admin note: substantial
text overlap with arXiv:2109.13382
| null |
10.1016/j.robot.2022.104338
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robotic teleoperation is a key technology for a wide variety of applications.
It allows sending robots instead of humans in remote, possibly dangerous
locations while still using the human brain with its enormous knowledge and
creativity, especially for solving unexpected problems. A main challenge in
teleoperation consists of providing enough feedback to the human operator for
situation awareness and thus create full immersion, as well as offering the
operator suitable control interfaces to achieve efficient and robust task
fulfillment. We present a bimanual telemanipulation system consisting of an
anthropomorphic avatar robot and an operator station providing force and haptic
feedback to the human operator. The avatar arms are controlled in Cartesian
space with a direct mapping of the operator movements. The measured forces and
torques on the avatar side are haptically displayed to the operator. We
developed a predictive avatar model for limit avoidance which runs on the
operator side, ensuring low latency. The system was successfully evaluated
during the ANA Avatar XPRIZE competition semifinals. In addition, we performed
in lab experiments and carried out a small user study with mostly untrained
operators.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 17:26:54 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Lenz",
"Christian",
""
],
[
"Behnke",
"Sven",
""
]
] |
new_dataset
| 0.998834 |
2301.00808
|
Saining Xie
|
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu,
In So Kweon and Saining Xie
|
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
|
Code and models available at
https://github.com/facebookresearch/ConvNeXt-V2
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Driven by improved architectures and better representation learning
frameworks, the field of visual recognition has enjoyed rapid modernization and
performance boost in the early 2020s. For example, modern ConvNets, represented
by ConvNeXt, have demonstrated strong performance in various scenarios. While
these models were originally designed for supervised learning with ImageNet
labels, they can also potentially benefit from self-supervised learning
techniques such as masked autoencoders (MAE). However, we found that simply
combining these two approaches leads to subpar performance. In this paper, we
propose a fully convolutional masked autoencoder framework and a new Global
Response Normalization (GRN) layer that can be added to the ConvNeXt
architecture to enhance inter-channel feature competition. This co-design of
self-supervised learning techniques and architectural improvement results in a
new model family called ConvNeXt V2, which significantly improves the
performance of pure ConvNets on various recognition benchmarks, including
ImageNet classification, COCO detection, and ADE20K segmentation. We also
provide pre-trained ConvNeXt V2 models of various sizes, ranging from an
efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a
650M Huge model that achieves a state-of-the-art 88.9% accuracy using only
public training data.
|
[
{
"version": "v1",
"created": "Mon, 2 Jan 2023 18:59:31 GMT"
}
] | 2023-01-03T00:00:00 |
[
[
"Woo",
"Sanghyun",
""
],
[
"Debnath",
"Shoubhik",
""
],
[
"Hu",
"Ronghang",
""
],
[
"Chen",
"Xinlei",
""
],
[
"Liu",
"Zhuang",
""
],
[
"Kweon",
"In So",
""
],
[
"Xie",
"Saining",
""
]
] |
new_dataset
| 0.998629 |
2107.05851
|
Jun Mao
|
Jun Mao, Lilian Zhang, Xiaofeng He, Hao Qu, Xiaoping Hu
|
A 2D Georeferenced Map Aided Visual-Inertial System for Precise UAV
Localization
| null |
2022 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS)
|
10.1109/IROS47612.2022.9982254.
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Precise geolocalization is crucial for unmanned aerial vehicles (UAVs).
However, most current deployed UAVs rely on the global navigation satellite
systems (GNSS) or high precision inertial navigation systems (INS) for
geolocalization. In this paper, we propose to use a lightweight visual-inertial
system with a 2D georeference map to obtain accurate and consecutive geodetic
positions for UAVs. The proposed system firstly integrates a micro inertial
measurement unit (MIMU) and a monocular camera as odometry to consecutively
estimate the navigation states and reconstruct the 3D position of the observed
visual features in the local world frame. To obtain the geolocation, the visual
features tracked by the odometry are further registered to the 2D georeferenced
map. While most conventional methods perform image-level aerial image
registration, we propose to align the reconstructed points to the map points in
the geodetic frame; this helps to filter out the large portion of outliers and
decouples the negative effects from the horizontal angles. The registered
points are then used to relocalize the vehicle in the geodetic frame. Finally,
a pose graph is deployed to fuse the geolocation from the aerial image
registration and the local navigation result from the visual-inertial odometry
(VIO) to achieve consecutive and drift-free geolocalization performance. We
have validated the proposed method by installing the sensors to a UAV body
rigidly and have conducted two flights in different environments with unknown
initials. The results show that the proposed method can achieve less than 4m
position error in flight at 100m high and less than 9m position error in flight
about 300m high.
|
[
{
"version": "v1",
"created": "Tue, 13 Jul 2021 05:10:02 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Dec 2022 03:17:16 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Mao",
"Jun",
""
],
[
"Zhang",
"Lilian",
""
],
[
"He",
"Xiaofeng",
""
],
[
"Qu",
"Hao",
""
],
[
"Hu",
"Xiaoping",
""
]
] |
new_dataset
| 0.993287 |
2110.07276
|
Soobee Lee
|
Soobee Lee, Minindu Weerakoon, Jonghyun Choi, Minjia Zhang, Di Wang,
Myeongjae Jeon
|
Carousel Memory: Rethinking the Design of Episodic Memory for Continual
Learning
|
This paper is the extended version of 'CarM: Hierarchical Episodic
Memory for Continual Learning' accepted at DAC 2022
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Continual Learning (CL) is an emerging machine learning paradigm that aims to
learn from a continuous stream of tasks without forgetting knowledge learned
from the previous tasks. To avoid performance decrease caused by forgetting,
prior studies exploit episodic memory (EM), which stores a subset of the past
observed samples while learning from new non-i.i.d. data. Despite the promising
results, since CL is often assumed to execute on mobile or IoT devices, the EM
size is bounded by the small hardware memory capacity and makes it infeasible
to meet the accuracy requirements for real-world applications. Specifically,
all prior CL methods discard samples overflowed from the EM and can never
retrieve them back for subsequent training steps, incurring loss of information
that would exacerbate catastrophic forgetting. We explore a novel hierarchical
EM management strategy to address the forgetting issue. In particular, in
mobile and IoT devices, real-time data can be stored not just in high-speed
RAMs but in internal storage devices as well, which offer significantly larger
capacity than the RAMs. Based on this insight, we propose to exploit the
abundant storage to preserve past experiences and alleviate the forgetting by
allowing CL to efficiently migrate samples between memory and storage without
being interfered by the slow access speed of the storage. We call it Carousel
Memory (CarM). As CarM is complementary to existing CL methods, we conduct
extensive evaluations of our method with seven popular CL methods and show that
CarM significantly improves the accuracy of the methods across different
settings by large margins in final average accuracy (up to 28.4%) while
retaining the same training efficiency.
|
[
{
"version": "v1",
"created": "Thu, 14 Oct 2021 11:27:45 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Oct 2021 03:49:25 GMT"
},
{
"version": "v3",
"created": "Thu, 29 Dec 2022 07:49:32 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Lee",
"Soobee",
""
],
[
"Weerakoon",
"Minindu",
""
],
[
"Choi",
"Jonghyun",
""
],
[
"Zhang",
"Minjia",
""
],
[
"Wang",
"Di",
""
],
[
"Jeon",
"Myeongjae",
""
]
] |
new_dataset
| 0.997482 |
2112.10374
|
Qi Tian
|
Qi Tian, Kun Kuang, Baoxiang Wang, Furui Liu, Fei Wu
|
CGIBNet: Bandwidth-constrained Communication with Graph Information
Bottleneck in Multi-Agent Reinforcement Learning
| null | null | null | null |
cs.AI cs.MA
|
http://creativecommons.org/licenses/by/4.0/
|
Communication is one of the core components for cooperative multi-agent
reinforcement learning (MARL). The communication bandwidth, in many real
applications, is always subject to certain constraints. To improve
communication efficiency, in this article, we propose to simultaneously
optimize whom to communicate with and what to communicate for each agent in
MARL. By initiating the communication between agents with a directed complete
graph, we propose a novel communication model, named Communicative Graph
Information Bottleneck Network (CGIBNet), to simultaneously compress the graph
structure and the node information with the graph information bottleneck
principle. The graph structure compression is designed to cut the redundant
edges for determining whom to communicate with. The node information
compression aims to address the problem of what to communicate via learning
compact node representations. Moreover, CGIBNet is the first universal module
for bandwidth-constrained communication, which can be applied to various
training frameworks (i.e., policy-based and value-based MARL frameworks) and
communication modes (i.e., single-round and multi-round communication).
Extensive experiments are conducted in Traffic Control and StarCraft II
environments. The results indicate that our method can achieve better
performance in bandwidth-constrained settings compared with state-of-the-art
algorithms.
|
[
{
"version": "v1",
"created": "Mon, 20 Dec 2021 07:53:44 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 17:25:01 GMT"
},
{
"version": "v3",
"created": "Fri, 10 Jun 2022 07:26:00 GMT"
},
{
"version": "v4",
"created": "Fri, 30 Dec 2022 10:24:54 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Tian",
"Qi",
""
],
[
"Kuang",
"Kun",
""
],
[
"Wang",
"Baoxiang",
""
],
[
"Liu",
"Furui",
""
],
[
"Wu",
"Fei",
""
]
] |
new_dataset
| 0.997955 |
2112.11763
|
Sascha Kurz
|
Sascha Kurz
|
Divisible Codes
|
105 pages; typos corrected
| null | null | null |
cs.IT math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A linear code over $\mathbb{F}_q$ with the Hamming metric is called
$\Delta$-divisible if the weights of all codewords are divisible by $\Delta$.
They have been introduced by Harold Ward a few decades ago. Applications
include subspace codes, partial spreads, vector space partitions, and distance
optimal codes. The determination of the possible lengths of projective
divisible codes is an interesting and comprehensive challenge.
|
[
{
"version": "v1",
"created": "Wed, 22 Dec 2021 10:03:31 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Dec 2022 09:05:04 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Kurz",
"Sascha",
""
]
] |
new_dataset
| 0.999423 |
2201.05729
|
Zhecan Wang
|
Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou, Jianwei Yang,
Xiyang Dai, Bin Xiao, Haoxuan You, Shih-Fu Chang, Lu Yuan
|
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks
|
This paper is greatly modified and updated to be re-submitted to
another conference. The new paper is under the name "Multimodal Adaptive
Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks",
https://doi.org/10.48550/arXiv.2204.10496
| null | null | null |
cs.CV cs.AI cs.CL cs.LG cs.MM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Contrastive language-image pretraining (CLIP) links vision and language
modalities into a unified embedding space, yielding the tremendous potential
for vision-language (VL) tasks. While early concurrent works have begun to
study this potential on a subset of tasks, important questions remain: 1) What
is the benefit of CLIP on unstudied VL tasks? 2) Does CLIP provide benefit in
low-shot or domain-shifted scenarios? 3) Can CLIP improve existing approaches
without impacting inference or pretraining complexity? In this work, we seek to
answer these questions through two key contributions. First, we introduce an
evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual
Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of
data availability constraints and conditions of domain shift. Second, we
propose an approach, named CLIP Targeted Distillation (CLIP-TD), to
intelligently distill knowledge from CLIP into existing architectures using a
dynamically weighted objective applied to adaptively selected tokens per
instance. Experiments demonstrate that our proposed CLIP-TD leads to
exceptional gains in the low-shot (up to 51.9%) and domain-shifted (up to
71.3%) conditions of VCR, while simultaneously improving performance under
standard fully-supervised conditions (up to 2%), achieving state-of-art
performance on VCR compared to other single models that are pretrained with
image-text data only. On SNLI-VE, CLIP-TD produces significant gains in
low-shot conditions (up to 6.6%) as well as fully supervised (up to 3%). On
VQA, CLIP-TD provides improvement in low-shot (up to 9%), and in
fully-supervised (up to 1.3%). Finally, CLIP-TD outperforms concurrent works
utilizing CLIP for finetuning, as well as baseline naive distillation
approaches. Code will be made available.
|
[
{
"version": "v1",
"created": "Sat, 15 Jan 2022 01:54:01 GMT"
},
{
"version": "v2",
"created": "Mon, 16 May 2022 15:47:52 GMT"
},
{
"version": "v3",
"created": "Wed, 28 Dec 2022 20:07:58 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Wang",
"Zhecan",
""
],
[
"Codella",
"Noel",
""
],
[
"Chen",
"Yen-Chun",
""
],
[
"Zhou",
"Luowei",
""
],
[
"Yang",
"Jianwei",
""
],
[
"Dai",
"Xiyang",
""
],
[
"Xiao",
"Bin",
""
],
[
"You",
"Haoxuan",
""
],
[
"Chang",
"Shih-Fu",
""
],
[
"Yuan",
"Lu",
""
]
] |
new_dataset
| 0.998568 |
2202.01811
|
Chong Xiang
|
Chong Xiang, Alexander Valtchanov, Saeed Mahloujifar, Prateek Mittal
|
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding
Attacks via Patch-agnostic Masking
|
IEEE Symposium on Security and Privacy 2023; extended version
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object detectors, which are widely deployed in security-critical systems such
as autonomous vehicles, have been found vulnerable to patch hiding attacks. An
attacker can use a single physically-realizable adversarial patch to make the
object detector miss the detection of victim objects and undermine the
functionality of object detection applications. In this paper, we propose
ObjectSeeker for certifiably robust object detection against patch hiding
attacks. The key insight in ObjectSeeker is patch-agnostic masking: we aim to
mask out the entire adversarial patch without knowing the shape, size, and
location of the patch. This masking operation neutralizes the adversarial
effect and allows any vanilla object detector to safely detect objects on the
masked images. Remarkably, we can evaluate ObjectSeeker's robustness in a
certifiable manner: we develop a certification procedure to formally determine
if ObjectSeeker can detect certain objects against any white-box adaptive
attack within the threat model, achieving certifiable robustness. Our
experiments demonstrate a significant (~10%-40% absolute and ~2-6x relative)
improvement in certifiable robustness over the prior work, as well as high
clean performance (~1% drop compared with undefended models).
|
[
{
"version": "v1",
"created": "Thu, 3 Feb 2022 19:34:25 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Dec 2022 19:03:52 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Xiang",
"Chong",
""
],
[
"Valtchanov",
"Alexander",
""
],
[
"Mahloujifar",
"Saeed",
""
],
[
"Mittal",
"Prateek",
""
]
] |
new_dataset
| 0.993836 |
2205.10019
|
Jiho Jin
|
Juhee Son, Jiho Jin, Haneul Yoo, JinYeong Bak, Kyunghyun Cho, Alice Oh
|
Translating Hanja Historical Documents to Contemporary Korean and
English
|
2022 EMNLP Findings
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of
Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals
were originally written in an archaic Korean writing system, `Hanja', and were
translated into Korean from 1968 to 1993. The resulting translation was however
too literal and contained many archaic Korean words; thus, a new expert
translation effort began in 2012. Since then, the records of only one king have
been completed in a decade. In parallel, expert translators are working on
English translation, also at a slow pace and produced only one king's records
in English so far. Thus, we propose H2KE, a neural machine translation model,
that translates historical documents in Hanja to more easily understandable
Korean and to English. Built on top of multilingual neural machine translation,
H2KE learns to translate a historical document written in Hanja, from both a
full dataset of outdated Korean translation and a small dataset of more
recently translated contemporary Korean and English. We compare our method
against two baselines: a recent model that simultaneously learns to restore and
translate Hanja historical document and a Transformer based model trained only
on newly translated corpora. The experiments reveal that our method
significantly outperforms the baselines in terms of BLEU scores for both
contemporary Korean and English translations. We further conduct extensive
human evaluation which shows that our translation is preferred over the
original expert translations by both experts and non-expert Korean speakers.
|
[
{
"version": "v1",
"created": "Fri, 20 May 2022 08:25:11 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Oct 2022 13:51:08 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Oct 2022 06:46:11 GMT"
},
{
"version": "v4",
"created": "Fri, 30 Dec 2022 08:11:29 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Son",
"Juhee",
""
],
[
"Jin",
"Jiho",
""
],
[
"Yoo",
"Haneul",
""
],
[
"Bak",
"JinYeong",
""
],
[
"Cho",
"Kyunghyun",
""
],
[
"Oh",
"Alice",
""
]
] |
new_dataset
| 0.999803 |
2206.07258
|
XiaoWen Wei
|
Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, Wenbin Hu
|
CLNode: Curriculum Learning for Node Classification
| null | null | null | null |
cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Node classification is a fundamental graph-based task that aims to predict
the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the
state-of-the-art methods. Current GNNs assume that nodes in the training set
contribute equally during training. However, the quality of training nodes
varies greatly, and the performance of GNNs could be harmed by two types of
low-quality training nodes: (1) inter-class nodes situated near class
boundaries that lack the typical characteristics of their corresponding
classes. Because GNNs are data-driven approaches, training on these nodes could
degrade the accuracy. (2) mislabeled nodes. In real-world graphs, nodes are
often mislabeled, which can significantly degrade the robustness of GNNs. To
mitigate the detrimental effect of the low-quality training nodes, we present
CLNode, which employs a selective training strategy to train GNN based on the
quality of nodes. Specifically, we first design a multi-perspective difficulty
measurer to accurately measure the quality of training nodes. Then, based on
the measured qualities, we employ a training scheduler that selects appropriate
training nodes to train GNN in each epoch. To evaluate the effectiveness of
CLNode, we conduct extensive experiments by incorporating it in six
representative backbone GNNs. Experimental results on real-world networks
demonstrate that CLNode is a general framework that can be combined with
various GNNs to improve their accuracy and robustness.
|
[
{
"version": "v1",
"created": "Wed, 15 Jun 2022 02:43:36 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Dec 2022 12:20:56 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Wei",
"Xiaowen",
""
],
[
"Gong",
"Xiuwen",
""
],
[
"Zhan",
"Yibing",
""
],
[
"Du",
"Bo",
""
],
[
"Luo",
"Yong",
""
],
[
"Hu",
"Wenbin",
""
]
] |
new_dataset
| 0.962747 |
2207.04690
|
Zhaohua Chen
|
Zhaohua Chen, Chang Wang, Qian Wang, Yuqi Pan, Zhuming Shi, Zheng Cai,
Yukun Ren, Zhihua Zhu, Xiaotie Deng
|
Dynamic Budget Throttling in Repeated Second-Price Auctions
|
45 pages, 1 figure, 1 table
| null | null | null |
cs.GT cs.LG econ.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In today's online advertising markets, an important demand for an advertiser
(buyer) is to control her total expenditure within a time span under some
budget. Among all budget control approaches, throttling stands out as a popular
one, where the buyer participates in only a part of auctions. This paper gives
a theoretical panorama of a single buyer's dynamic budget throttling process in
repeated second-price auctions, which is lacking in the literature. We first
establish a lower bound on the regret and an upper bound on the asymptotic
competitive ratio for any throttling algorithm, respectively, on whether the
buyer's values are stochastic or adversarial. Second, on the algorithmic side,
we consider two different information structures, with increasing difficulty in
learning the stochastic distribution of the highest competing bid. We further
propose the OGD-CB algorithm, which is oblivious to stochastic or adversarial
values and has asymptotically equal results under these two information
structures. Specifically, with stochastic values, we demonstrate that this
algorithm guarantees a near-optimal expected regret. When values are
adversarial, we prove that the proposed algorithm reaches the upper bound on
the asymptotic competitive ratio. At last, we compare throttling with pacing,
another widely adopted budget control method, in repeated second-price
auctions. In the stochastic case, we illustrate that pacing is generally better
than throttling for the buyer, which is an extension of known results that
pacing is asymptotically optimal in this scenario. However, in the adversarial
case, we give an exciting result indicating that throttling is the
asymptotically optimal dynamic bidding strategy. Our results fill the gaps in
the theoretical research of throttling in repeated auctions and comprehensively
reveal the ability of this popular budget-smoothing strategy.
|
[
{
"version": "v1",
"created": "Mon, 11 Jul 2022 08:12:02 GMT"
},
{
"version": "v2",
"created": "Tue, 12 Jul 2022 08:46:34 GMT"
},
{
"version": "v3",
"created": "Fri, 15 Jul 2022 02:04:52 GMT"
},
{
"version": "v4",
"created": "Wed, 21 Dec 2022 15:33:58 GMT"
},
{
"version": "v5",
"created": "Thu, 22 Dec 2022 05:01:10 GMT"
},
{
"version": "v6",
"created": "Tue, 27 Dec 2022 04:53:36 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Chen",
"Zhaohua",
""
],
[
"Wang",
"Chang",
""
],
[
"Wang",
"Qian",
""
],
[
"Pan",
"Yuqi",
""
],
[
"Shi",
"Zhuming",
""
],
[
"Cai",
"Zheng",
""
],
[
"Ren",
"Yukun",
""
],
[
"Zhu",
"Zhihua",
""
],
[
"Deng",
"Xiaotie",
""
]
] |
new_dataset
| 0.996335 |
2207.14556
|
Seyed Amir Tafrishi
|
Seyed Amir Tafrishi and Ankit A. Ravankar and Yasuhisa Hirata
|
PSM: A Predictive Safety Model for Body Motion Based On the
Spring-Damper Pendulum
|
Accepted to 2022 International Conference on Intelligent Robots and
Systems (IROS), 9 pages, 11 figures
| null |
10.1109/IROS47612.2022.9981274
| null |
cs.RO cs.SY eess.SY math.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Quantifying the safety of the human body orientation is an important issue in
human-robot interaction. Knowing the changing physical constraints on human
motion can improve inspection of safe human motions and bring essential
information about stability and normality of human body orientations with
real-time risk assessment. Also, this information can be used in cooperative
robots and monitoring systems to evaluate and interact in the environment more
freely. Furthermore, the workspace area can be more deterministic with the
known physical characteristics of safety. Based on this motivation, we propose
a novel predictive safety model (PSM) that relies on the information of an
inertial measurement unit on the human chest. The PSM encompasses a 3-Dofs
spring-damper pendulum model that predicts human motion based on a safe motion
dataset. The estimated safe orientation of humans is obtained by integrating a
safety dataset and an elastic spring-damper model in a way that the proposed
approach can realize complex motions at different safety levels. We did
experiments in a real-world scenario to verify our novel proposed model. This
novel approach can be used in different guidance/assistive robots and health
monitoring systems to support and evaluate the human condition, particularly
elders.
|
[
{
"version": "v1",
"created": "Fri, 29 Jul 2022 09:11:36 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Tafrishi",
"Seyed Amir",
""
],
[
"Ravankar",
"Ankit A.",
""
],
[
"Hirata",
"Yasuhisa",
""
]
] |
new_dataset
| 0.998094 |
2208.10657
|
Rayson Laroca
|
Rayson Laroca, Marcelo Santos, Valter Estevam, Eduardo Luz, David
Menotti
|
A First Look at Dataset Bias in License Plate Recognition
|
Accepted for presentation at the Conference on Graphics, Patterns and
Images (SIBGRAPI) 2022
| null |
10.1109/SIBGRAPI55357.2022.9991768
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Public datasets have played a key role in advancing the state of the art in
License Plate Recognition (LPR). Although dataset bias has been recognized as a
severe problem in the computer vision community, it has been largely overlooked
in the LPR literature. LPR models are usually trained and evaluated separately
on each dataset. In this scenario, they have often proven robust in the dataset
they were trained in but showed limited performance in unseen ones. Therefore,
this work investigates the dataset bias problem in the LPR context. We
performed experiments on eight datasets, four collected in Brazil and four in
mainland China, and observed that each dataset has a unique, identifiable
"signature" since a lightweight classification model predicts the source
dataset of a license plate (LP) image with more than 95% accuracy. In our
discussion, we draw attention to the fact that most LPR models are probably
exploiting such signatures to improve the results achieved in each dataset at
the cost of losing generalization capability. These results emphasize the
importance of evaluating LPR models in cross-dataset setups, as they provide a
better indication of generalization (hence real-world performance) than
within-dataset ones.
|
[
{
"version": "v1",
"created": "Tue, 23 Aug 2022 00:20:33 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Dec 2022 10:23:26 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Laroca",
"Rayson",
""
],
[
"Santos",
"Marcelo",
""
],
[
"Estevam",
"Valter",
""
],
[
"Luz",
"Eduardo",
""
],
[
"Menotti",
"David",
""
]
] |
new_dataset
| 0.952012 |
2209.14350
|
Linghao Song
|
Linghao Song, Licheng Guo, Suhail Basalama, Yuze Chi, Robert F. Lucas,
Jason Cong
|
Callipepla: Stream Centric Instruction Set and Mixed Precision for
Accelerating Conjugate Gradient Solver
|
To appear in FPGA 2023
| null |
10.1145/3543622.3573182
| null |
cs.AR cs.DC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The continued growth in the processing power of FPGAs coupled with high
bandwidth memories (HBM), makes systems like the Xilinx U280 credible platforms
for linear solvers which often dominate the run time of scientific and
engineering applications. In this paper, we present Callipepla, an accelerator
for a preconditioned conjugate gradient linear solver (CG). FPGA acceleration
of CG faces three challenges: (1) how to support an arbitrary problem and
terminate acceleration processing on the fly, (2) how to coordinate long-vector
data flow among processing modules, and (3) how to save off-chip memory
bandwidth and maintain double (FP64) precision accuracy. To tackle the three
challenges, we present (1) a stream-centric instruction set for efficient
streaming processing and control, (2) vector streaming reuse (VSR) and
decentralized vector flow scheduling to coordinate vector data flow among
modules and further reduce off-chip memory accesses with a double memory
channel design, and (3) a mixed precision scheme to save bandwidth yet still
achieve effective double precision quality solutions. To the best of our
knowledge, this is the first work to introduce the concept of VSR for data
reusing between on-chip modules to reduce unnecessary off-chip accesses for
FPGA accelerators. We prototype the accelerator on a Xilinx U280 HBM FPGA. Our
evaluation shows that compared to the Xilinx HPC product, the XcgSolver,
Callipepla achieves a speedup of 3.94x, 3.36x higher throughput, and 2.94x
better energy efficiency. Compared to an NVIDIA A100 GPU which has 4x the
memory bandwidth of Callipepla, we still achieve 77% of its throughput with
3.34x higher energy efficiency. The code is available at
https://github.com/UCLA-VAST/Callipepla.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 18:26:30 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Dec 2022 06:43:44 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Song",
"Linghao",
""
],
[
"Guo",
"Licheng",
""
],
[
"Basalama",
"Suhail",
""
],
[
"Chi",
"Yuze",
""
],
[
"Lucas",
"Robert F.",
""
],
[
"Cong",
"Jason",
""
]
] |
new_dataset
| 0.994391 |
2212.14125
|
Akash Mittal
|
Akash Mittal, Ragini Gupta
|
MuTable (Music Table): Turn any surface into musical instrument
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
With the rise in pervasive computing solutions, interactive surfaces have
gained a large popularity across multi-application domains including smart
boards for education, touch-enabled kiosks for smart retail and smart mirrors
for smart homes. Despite the increased popularity of such interactive surfaces,
existing platforms are mostly limited to custom built surfaces with attached
sensors and hardware, that are expensive and require complicated design
considerations. To address this, we design a low-cost, intuitive system called
MuTable that repurposes any flat surface (such as table tops) into a live
musical instrument. This provides a unique, close to real-time instrument
playing experience to the user to play any type of musical instrument. This is
achieved by projecting the instrument's shape on any tangible surface, sensor
calibration, user taps detection, tap position identification, and associated
sound generation. We demonstrate the performance of our working system by
reporting an accuracy of 83% for detecting softer taps, 100% accuracy for
detecting the regular taps, and a precision of 95.7% for estimating hand
location.
|
[
{
"version": "v1",
"created": "Wed, 28 Dec 2022 23:42:10 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Mittal",
"Akash",
""
],
[
"Gupta",
"Ragini",
""
]
] |
new_dataset
| 0.998954 |
2212.14143
|
Mai Nguyen
|
Siddhant Baldota, Shreyas Anantha Ramaprasad, Jaspreet Kaur Bhamra,
Shane Luna, Ravi Ramachandra, Eugene Zen, Harrison Kim, Daniel Crawl, Ismael
Perez, Ilkay Altintas, Garrison W. Cottrell, Mai H.Nguyen
|
Multimodal Wildland Fire Smoke Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Research has shown that climate change creates warmer temperatures and drier
conditions, leading to longer wildfire seasons and increased wildfire risks in
the United States. These factors have in turn led to increases in the
frequency, extent, and severity of wildfires in recent years. Given the danger
posed by wildland fires to people, property, wildlife, and the environment,
there is an urgency to provide tools for effective wildfire management. Early
detection of wildfires is essential to minimizing potentially catastrophic
destruction. In this paper, we present our work on integrating multiple data
sources in SmokeyNet, a deep learning model using spatio-temporal information
to detect smoke from wildland fires. Camera image data is integrated with
weather sensor measurements and processed by SmokeyNet to create a multimodal
wildland fire smoke detection system. We present our results comparing
performance in terms of both accuracy and time-to-detection for multimodal data
vs. a single data source. With a time-to-detection of only a few minutes,
SmokeyNet can serve as an automated early notification system, providing a
useful tool in the fight against destructive wildfires.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 01:16:06 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Baldota",
"Siddhant",
""
],
[
"Ramaprasad",
"Shreyas Anantha",
""
],
[
"Bhamra",
"Jaspreet Kaur",
""
],
[
"Luna",
"Shane",
""
],
[
"Ramachandra",
"Ravi",
""
],
[
"Zen",
"Eugene",
""
],
[
"Kim",
"Harrison",
""
],
[
"Crawl",
"Daniel",
""
],
[
"Perez",
"Ismael",
""
],
[
"Altintas",
"Ilkay",
""
],
[
"Cottrell",
"Garrison W.",
""
],
[
"Nguyen",
"Mai H.",
""
]
] |
new_dataset
| 0.999687 |
2212.14180
|
Juan Lagos
|
Juan Lagos, Esa Rahtu
|
PanDepth: Joint Panoptic Segmentation and Depth Completion
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Understanding 3D environments semantically is pivotal in autonomous driving
applications where multiple computer vision tasks are involved. Multi-task
models provide different types of outputs for a given scene, yielding a more
holistic representation while keeping the computational cost low. We propose a
multi-task model for panoptic segmentation and depth completion using RGB
images and sparse depth maps. Our model successfully predicts fully dense depth
maps and performs semantic segmentation, instance segmentation, and panoptic
segmentation for every input frame. Extensive experiments were done on the
Virtual KITTI 2 dataset and we demonstrate that our model solves multiple
tasks, without a significant increase in computational cost, while keeping high
accuracy performance. Code is available at
https://github.com/juanb09111/PanDepth.git
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 05:37:38 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Lagos",
"Juan",
""
],
[
"Rahtu",
"Esa",
""
]
] |
new_dataset
| 0.994227 |
2212.14201
|
Yuan Fang
|
Menghan Dou, Tianrui Zou, Yuan Fang, Jing Wang, Dongyi Zhao, Lei Yu,
Boying Chen, Wenbo Guo, Ye Li, Zhaoyun Chen, Guoping Guo
|
QPanda: high-performance quantum computing framework for multiple
application scenarios
| null | null | null | null |
cs.PL quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the birth of Noisy Intermediate Scale Quantum (NISQ) devices and the
verification of "quantum supremacy" in random number sampling and boson
sampling, more and more fields hope to use quantum computers to solve specific
problems, such as aerodynamic design, route allocation, financial option
prediction, quantum chemical simulation to find new materials, and the
challenge of quantum cryptography to automotive industry security. However,
these fields still need to constantly explore quantum algorithms that adapt to
the current NISQ machine, so a quantum programming framework that can face
multi-scenarios and application needs is required. Therefore, this paper
proposes QPanda, an application scenario-oriented quantum programming framework
with high-performance simulation. Such as designing quantum chemical simulation
algorithms based on it to explore new materials, building a quantum machine
learning framework to serve finance, etc. This framework implements
high-performance simulation of quantum circuits, a configuration of the fusion
processing backend of quantum computers and supercomputers, and compilation and
optimization methods of quantum programs for NISQ machines. Finally, the
experiment shows that quantum jobs can be executed with high fidelity on the
quantum processor using quantum circuit compile and optimized interface and
have better simulation performance.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 07:38:50 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Dou",
"Menghan",
""
],
[
"Zou",
"Tianrui",
""
],
[
"Fang",
"Yuan",
""
],
[
"Wang",
"Jing",
""
],
[
"Zhao",
"Dongyi",
""
],
[
"Yu",
"Lei",
""
],
[
"Chen",
"Boying",
""
],
[
"Guo",
"Wenbo",
""
],
[
"Li",
"Ye",
""
],
[
"Chen",
"Zhaoyun",
""
],
[
"Guo",
"Guoping",
""
]
] |
new_dataset
| 0.997678 |
2212.14209
|
Kangcheng Liu
|
Kangcheng Liu
|
An Enhanced LiDAR-Inertial SLAM System for Robotics Localization and
Mapping
|
ICCA 2022 (Oral)
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The LiDAR and inertial sensors based localization and mapping are of great
significance for Unmanned Ground Vehicle related applications. In this work, we
have developed an improved LiDAR-inertial localization and mapping system for
unmanned ground vehicles, which is appropriate for versatile search and rescue
applications. Compared with existing LiDAR-based localization and mapping
systems such as LOAM, we have two major contributions: the first is the
improvement of the robustness of particle swarm filter-based LiDAR SLAM, while
the second is the loop closure methods developed for global optimization to
improve the localization accuracy of the whole system. We demonstrate by
experiments that the accuracy and robustness of the LiDAR SLAM system are both
improved. Finally, we have done systematic experimental tests at the Hong Kong
science park as well as other indoor or outdoor real complicated testing
circumstances, which demonstrates the effectiveness and efficiency of our
approach. It is demonstrated that our system has high accuracy, robustness, as
well as efficiency. Our system is of great importance to the localization and
mapping of the unmanned ground vehicle in an unknown environment.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 08:01:19 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Liu",
"Kangcheng",
""
]
] |
new_dataset
| 0.999115 |
2212.14232
|
Xin Hu
|
Xin Hu, Lingling Zhang, Jun Liu, Jinfu Fan, Yang You, Yaqiang Wu
|
GPTR: Gestalt-Perception Transformer for Diagram Object Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Diagram object detection is the key basis of practical applications such as
textbook question answering. Because the diagram mainly consists of simple
lines and color blocks, its visual features are sparser than those of natural
images. In addition, diagrams usually express diverse knowledge, in which there
are many low-frequency object categories in diagrams. These lead to the fact
that traditional data-driven detection model is not suitable for diagrams. In
this work, we propose a gestalt-perception transformer model for diagram object
detection, which is based on an encoder-decoder architecture. Gestalt
perception contains a series of laws to explain human perception, that the
human visual system tends to perceive patches in an image that are similar,
close or connected without abrupt directional changes as a perceptual whole
object. Inspired by these thoughts, we build a gestalt-perception graph in
transformer encoder, which is composed of diagram patches as nodes and the
relationships between patches as edges. This graph aims to group these patches
into objects via laws of similarity, proximity, and smoothness implied in these
edges, so that the meaningful objects can be effectively detected. The
experimental results demonstrate that the proposed GPTR achieves the best
results in the diagram object detection task. Our model also obtains comparable
results over the competitors in natural image object detection.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 09:03:05 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Hu",
"Xin",
""
],
[
"Zhang",
"Lingling",
""
],
[
"Liu",
"Jun",
""
],
[
"Fan",
"Jinfu",
""
],
[
"You",
"Yang",
""
],
[
"Wu",
"Yaqiang",
""
]
] |
new_dataset
| 0.999525 |
2212.14377
|
Barak Hoffer
|
Barak Hoffer, Nicol\'as Wainstein, Christopher M. Neumann, Eric Pop,
Eilam Yalon, Shahar Kvatinsky
|
Stateful Logic using Phase Change Memory
| null |
IEEE Journal on Exploratory Solid-State Computational Devices and
Circuits (Volume: 8, Issue: 2, December 2022)
|
10.1109/JXCDC.2022.3219731
| null |
cs.ET
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Stateful logic is a digital processing-in-memory technique that could address
von Neumann memory bottleneck challenges while maintaining backward
compatibility with standard von Neumann architectures. In stateful logic,
memory cells are used to perform the logic operations without reading or moving
any data outside the memory array. Stateful logic has been previously
demonstrated using several resistive memory types, mostly by resistive RAM
(RRAM). Here we present a new method to design stateful logic using a different
resistive memory - phase change memory (PCM). We propose and experimentally
demonstrate four logic gate types (NOR, IMPLY, OR, NIMP) using commonly used
PCM materials. Our stateful logic circuits are different than previously
proposed circuits due to the different switching mechanism and functionality of
PCM compared to RRAM. Since the proposed stateful logic form a functionally
complete set, these gates enable sequential execution of any logic function
within the memory, paving the way to PCM-based digital processing-in-memory
systems.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 17:20:35 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Hoffer",
"Barak",
""
],
[
"Wainstein",
"Nicolás",
""
],
[
"Neumann",
"Christopher M.",
""
],
[
"Pop",
"Eric",
""
],
[
"Yalon",
"Eilam",
""
],
[
"Kvatinsky",
"Shahar",
""
]
] |
new_dataset
| 0.973457 |
2212.14397
|
Krzysztof Lis
|
Krzysztof Lis, Matthias Rottmann, Sina Honari, Pascal Fua, Mathieu
Salzmann
|
AttEntropy: Segmenting Unknown Objects in Complex Scenes using the
Spatial Attention Entropy of Semantic Segmentation Transformers
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision transformers have emerged as powerful tools for many computer vision
tasks. It has been shown that their features and class tokens can be used for
salient object segmentation. However, the properties of segmentation
transformers remain largely unstudied. In this work we conduct an in-depth
study of the spatial attentions of different backbone layers of semantic
segmentation transformers and uncover interesting properties.
The spatial attentions of a patch intersecting with an object tend to
concentrate within the object, whereas the attentions of larger, more uniform
image areas rather follow a diffusive behavior. In other words, vision
transformers trained to segment a fixed set of object classes generalize to
objects well beyond this set. We exploit this by extracting heatmaps that can
be used to segment unknown objects within diverse backgrounds, such as
obstacles in traffic scenes.
Our method is training-free and its computational overhead negligible. We use
off-the-shelf transformers trained for street-scene segmentation to process
other scene types.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 18:07:56 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Lis",
"Krzysztof",
""
],
[
"Rottmann",
"Matthias",
""
],
[
"Honari",
"Sina",
""
],
[
"Fua",
"Pascal",
""
],
[
"Salzmann",
"Mathieu",
""
]
] |
new_dataset
| 0.998227 |
2212.14402
|
Michael Bommarito Ii
|
Michael Bommarito II, Daniel Martin Katz
|
GPT Takes the Bar Exam
|
Additional material available online at
https://github.com/mjbommar/gpt-takes-the-bar-exam
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Nearly all jurisdictions in the United States require a professional license
exam, commonly referred to as "the Bar Exam," as a precondition for law
practice. To even sit for the exam, most jurisdictions require that an
applicant completes at least seven years of post-secondary education, including
three years at an accredited law school. In addition, most test-takers also
undergo weeks to months of further, exam-specific preparation. Despite this
significant investment of time and capital, approximately one in five
test-takers still score under the rate required to pass the exam on their first
try. In the face of a complex task that requires such depth of knowledge, what,
then, should we expect of the state of the art in "AI?" In this research, we
document our experimental evaluation of the performance of OpenAI's
`text-davinci-003` model, often-referred to as GPT-3.5, on the multistate
multiple choice (MBE) section of the exam. While we find no benefit in
fine-tuning over GPT-3.5's zero-shot performance at the scale of our training
data, we do find that hyperparameter optimization and prompt engineering
positively impacted GPT-3.5's zero-shot performance. For best prompt and
parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete
NCBE MBE practice exam, significantly in excess of the 25% baseline guessing
rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's
ranking of responses is also highly-correlated with correctness; its top two
and top three choices are correct 71% and 88% of the time, respectively,
indicating very strong non-entailment performance. While our ability to
interpret these results is limited by nascent scientific understanding of LLMs
and the proprietary nature of GPT, we believe that these results strongly
suggest that an LLM will pass the MBE component of the Bar Exam in the near
future.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 18:19:43 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Bommarito",
"Michael",
"II"
],
[
"Katz",
"Daniel Martin",
""
]
] |
new_dataset
| 0.999577 |
2212.14410
|
Niladri Das
|
Niladri Das and B. Sundar Rajan
|
Shared Cache Coded Caching Schemes Using Designs and Circuits of
Matrices
|
36 pages, the paper has been submitted to IEEE Transactions on
Information Theory
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we study shared cache coded caching (SC-CC): a set of caches
serves a larger set of users; each user access one cache, and a cache may serve
many users. For this problem, under uncoded placement, Parrinello, \"Unsal, and
Elia showed an optimal SC-CC scheme, in which the subpacketization level
depends upon the number of caches. We show an SC-CC scheme where the
subpacketization level does not directly depend upon the number of users or
caches; any number of caches and users can be accommodated for a fixed
subpacketization level. Furthermore, new caches can be added without re-doing
the placement of the existing caches. We show that given an upper limit on the
allowable subpacketization level, our SC-CC scheme may achieve a lesser rate
than other relevant SC-CC schemes. Our scheme is constructed using matrices and
designs. A matroid can be obtained from a matrix over a finite field; the
placement of our scheme is decided by a design constructed from a matrix; the
circuits of a matroid obtained from the matrix and the design is used to decide
the delivery.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 18:35:54 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Das",
"Niladri",
""
],
[
"Rajan",
"B. Sundar",
""
]
] |
new_dataset
| 0.987234 |
2212.14438
|
Edgar Martinez-Moro
|
G\"uls\"um G\"ozde Y{\i}lmazg\"u\c{c} and Javier de la Cruz and Edgar
Mart\'inez-Moro
|
Abelian and consta-Abelian polyadic codes over affine algebras with a
finite commutative chain coefficient ring
| null | null | null | null |
cs.IT cs.DM math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we define Abelian and consta-Abelian polyadic codes over rings
defined as affine algebras over chain rings. For that aim, we use the classical
construction via splittings and multipliers of the underlying Abelian group. We
also derive some results on the structure of the associated polyadic codes and
the number of codes under these conditions.
|
[
{
"version": "v1",
"created": "Thu, 29 Dec 2022 19:25:13 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Yılmazgüç",
"Gülsüm Gözde",
""
],
[
"de la Cruz",
"Javier",
""
],
[
"Martínez-Moro",
"Edgar",
""
]
] |
new_dataset
| 0.998042 |
2212.14494
|
Mario Rom\'an
|
Elena Di Lavore, Giovanni de Felice, Mario Rom\'an
|
Coinductive Streams in Monoidal Categories
|
Expanded version of Monoidal Streams for Dataflow Programming,
published in LiCS'22, arXiv:2202.02061. 57 pages, 33 figures
| null | null | null |
cs.LO math.CT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce monoidal streams. Monoidal streams are a generalization of
causal stream functions, which can be defined in cartesian monoidal categories,
to arbitrary symmetric monoidal categories. In the same way that streams
provide semantics to dataflow programming with pure functions, monoidal streams
provide semantics to dataflow programming with theories of processes
represented by a symmetric monoidal category. Monoidal streams also form a
feedback monoidal category. In the same way that we can use a coinductive
stream calculus to reason about signal flow graphs, we can use coinductive
string diagrams to reason about feedback monoidal categories. As an example, we
study syntax for a stochastic dataflow language, with semantics in stochastic
monoidal streams.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 00:25:12 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Di Lavore",
"Elena",
""
],
[
"de Felice",
"Giovanni",
""
],
[
"Román",
"Mario",
""
]
] |
new_dataset
| 0.998401 |
2212.14521
|
Hiram H. L\'opez
|
Sarah E. Anderson, Eduardo Camps-Moreno, Hiram H. L\'opez, Gretchen L.
Matthews, Diego Ruano, Ivan Soprunov
|
Relative hulls and quantum codes
| null | null | null | null |
cs.IT math.IT math.RA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The relative hull of a code $C_1$ with respect to another code $C_2$ is the
intersection $C_1\cap C_2^\perp$. We prove that the dimension of the relative
hull can always be repeatedly reduced by one by replacing any of the two codes
with an equivalent one, down to a specified lower bound. We show how to
construct an equivalent code $C_1^\prime$ of $C_1$ (or $C_2^\prime$ of $C_2$)
such that the dimension of $C_1^\prime \cap C_2^{\perp}$ (or $C_1 \cap
C_2^{\prime\perp}$) is one less than the dimension of $C_1\cap C_2^\perp$.
Given codes $C_1$ and $C_2$, we provide a method to specify a code equivalent
to $C_2$ which gives a relative hull of any specified dimension, between the
difference in dimensions of $C_1$ and $C_2$ and the dimension of the relative
hull of $C_1$ with respect to $C_2$. These results apply to hulls taken with
respect to the $e$-Galois inner product, which has as special cases both the
Euclidean and Hermitian inner products. We also give conditions under which the
dimension of the relative hull can be increased by one via equivalent codes. We
study the consequences of the relative hull properties on quantum codes
constructed via CSS construction. Finally, we use families of decreasing
monomial-Cartesian codes to generate pure or impure quantum codes.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 02:49:32 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Anderson",
"Sarah E.",
""
],
[
"Camps-Moreno",
"Eduardo",
""
],
[
"López",
"Hiram H.",
""
],
[
"Matthews",
"Gretchen L.",
""
],
[
"Ruano",
"Diego",
""
],
[
"Soprunov",
"Ivan",
""
]
] |
new_dataset
| 0.998575 |
2212.14569
|
Prafful Kumar Khoba
|
Prafful Kumar Khoba, Chirag Parikh, Rohit Saluja, Ravi Kiran
Sarvadevabhatla, C. V. Jawahar
|
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
| null | null |
10.1145/3571600.3571626
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The previous fine-grained datasets mainly focus on classification and are
often captured in a controlled setup, with the camera focusing on the objects.
We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the
wild, captured from a moving camera mounted on a car. It contains 5502 scene
images with 210 unique fine-grained labels of multiple vehicle types organized
in a three-level hierarchy. While previous classification datasets also include
makes for different kinds of cars, the FGVD dataset introduces new class labels
for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is
challenging as it has vehicles in complex traffic scenarios with intra-class
and inter-class variations in types, scale, pose, occlusion, and lighting
conditions. The current object detectors like yolov5 and faster RCNN perform
poorly on our dataset due to a lack of hierarchical modeling. Along with
providing baseline results for existing object detectors on FGVD Dataset, we
also present the results of a combination of an existing detector and the
recent Hierarchical Residual Network (HRN) classifier for the FGVD task.
Finally, we show that FGVD vehicle images are the most challenging to classify
among the fine-grained datasets.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 06:50:15 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Khoba",
"Prafful Kumar",
""
],
[
"Parikh",
"Chirag",
""
],
[
"Saluja",
"Rohit",
""
],
[
"Sarvadevabhatla",
"Ravi Kiran",
""
],
[
"Jawahar",
"C. V.",
""
]
] |
new_dataset
| 0.999894 |
2212.14574
|
DongKi Noh
|
DongKi Noh, Changki Sung, Teayoung Uhm, WooJu Lee, Hyungtae Lim,
Jaeseok Choi, Kyuewang Lee, Dasol Hong, Daeho Um, Inseop Chung, Hochul Shin,
MinJung Kim, Hyoung-Rock Kim, SeungMin Baek, and Hyun Myung
|
X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor
Surveillance in Real Environments
|
8 pages, 13 figures, IEEE Robotics and Automation Letters
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In robotics and computer vision communities, extensive studies have been
widely conducted regarding surveillance tasks, including human detection,
tracking, and motion recognition with a camera. Additionally, deep learning
algorithms are widely utilized in the aforementioned tasks as in other computer
vision tasks. Existing public datasets are insufficient to develop
learning-based methods that handle various surveillance for outdoor and extreme
situations such as harsh weather and low illuminance conditions. Therefore, we
introduce a new large-scale outdoor surveillance dataset named eXtremely
large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000
image pairs and the first-person view data annotated by well-trained
annotators. Moreover, a single pair contains multi-modal data (e.g. an IR
image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is
the first large-scale first-person view outdoor multi-modal dataset focusing on
surveillance tasks to the best of our knowledge. We present an overview of the
proposed dataset with statistics and present methods of exploiting our dataset
with deep learning-based algorithms. The latest information on the dataset and
our study are available at https://github.com/lge-robot-navi, and the dataset
will be available for download through a server.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 07:26:26 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Noh",
"DongKi",
""
],
[
"Sung",
"Changki",
""
],
[
"Uhm",
"Teayoung",
""
],
[
"Lee",
"WooJu",
""
],
[
"Lim",
"Hyungtae",
""
],
[
"Choi",
"Jaeseok",
""
],
[
"Lee",
"Kyuewang",
""
],
[
"Hong",
"Dasol",
""
],
[
"Um",
"Daeho",
""
],
[
"Chung",
"Inseop",
""
],
[
"Shin",
"Hochul",
""
],
[
"Kim",
"MinJung",
""
],
[
"Kim",
"Hyoung-Rock",
""
],
[
"Baek",
"SeungMin",
""
],
[
"Myung",
"Hyun",
""
]
] |
new_dataset
| 0.999804 |
2212.14641
|
Juan-Pablo Ortega
|
Lukas Gonon, Lyudmila Grigoryeva, and Juan-Pablo Ortega
|
Reservoir kernels and Volterra series
|
10 pages, 2 figures, 1 table
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A universal kernel is constructed whose sections approximate any causal and
time-invariant filter in the fading memory category with inputs and outputs in
a finite-dimensional Euclidean space. This kernel is built using the reservoir
functional associated with a state-space representation of the Volterra series
expansion available for any analytic fading memory filter. It is hence called
the Volterra reservoir kernel. Even though the state-space representation and
the corresponding reservoir feature map are defined on an infinite-dimensional
tensor algebra space, the kernel map is characterized by explicit recursions
that are readily computable for specific data sets when employed in estimation
problems using the representer theorem. We showcase the performance of the
Volterra reservoir kernel in a popular data science application in relation to
bitcoin price prediction.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 11:33:20 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Gonon",
"Lukas",
""
],
[
"Grigoryeva",
"Lyudmila",
""
],
[
"Ortega",
"Juan-Pablo",
""
]
] |
new_dataset
| 0.972246 |
2212.14649
|
Dmitry Yudin
|
Dmitry Yudin, Yaroslav Solomentsev, Ruslan Musaev, Aleksei Staroverov,
Aleksandr I. Panov
|
HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D
Images
|
Accepted for publishing in proceedings of the 29th International
Conference on Neural Information Processing (ICONIP 2022)
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel dataset named as HPointLoc, specially designed for
exploring capabilities of visual place recognition in indoor environment and
loop detection in simultaneous localization and mapping. The loop detection
sub-task is especially relevant when a robot with an on-board RGB-D camera can
drive past the same place (``Point") at different angles. The dataset is based
on the popular Habitat simulator, in which it is possible to generate
photorealistic indoor scenes using both own sensor data and open datasets, such
as Matterport3D. To study the main stages of solving the place recognition
problem on the HPointLoc dataset, we proposed a new modular approach named as
PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then
extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with
SuperGlue, and finally performs a camera pose optimization step with TEASER++.
Such a solution to the place recognition problem has not been previously
studied in existing publications. The PNTR approach has shown the best quality
metrics on the HPointLoc dataset and has a high potential for real use in
localization systems for unmanned vehicles. The proposed dataset and framework
are publicly available: https://github.com/metra4ok/HPointLoc.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 12:20:56 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Yudin",
"Dmitry",
""
],
[
"Solomentsev",
"Yaroslav",
""
],
[
"Musaev",
"Ruslan",
""
],
[
"Staroverov",
"Aleksei",
""
],
[
"Panov",
"Aleksandr I.",
""
]
] |
new_dataset
| 0.99979 |
2212.14671
|
Collin Connors
|
Collin Connors and Dilip Sarkar
|
Novel Architecture to Create and Maintain Personal Blockchains
| null | null | null | null |
cs.CY cs.CR cs.DB cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Blockchain has been touted as a revolutionary technology. However, despite
the excitement, blockchain has not been adopted in many fields. Many are
hesitant to adopt blockchain technology due to privacy concerns, barriers to
use, or lack of practical use cases. In this work, we outline a potential
blockchain use case for tracking financial transactions across multiple
financial institutions. We show the downsides of traditional centralized
approaches and that blockchain approaches fail to give all the privacy and
accessibility required for this use case. Thus we propose a novel blockchain
architecture to support our use case. This novel architecture combines the ease
of use of public blockchains with the privacy of private blockchains by
allowing users to create personal blockchains. We believe this novel personal
blockchain architecture will lead to more blockchain adoption, particularly in
use cases handling private data.
|
[
{
"version": "v1",
"created": "Mon, 12 Dec 2022 02:05:59 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Connors",
"Collin",
""
],
[
"Sarkar",
"Dilip",
""
]
] |
new_dataset
| 0.964341 |
2212.14674
|
G\"urkan Soykan
|
G\"urkan Soykan, Deniz Yuret, Tevfik Metin Sezgin
|
A Comprehensive Gold Standard and Benchmark for Comics Text Detection
and Recognition
|
33 pages, 10 figures, 16 tables
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This study focuses on improving the optical character recognition (OCR) data
for panels in the COMICS dataset, the largest dataset containing text and
images from comic books. To do this, we developed a pipeline for OCR processing
and labeling of comic books and created the first text detection and
recognition datasets for western comics, called "COMICS Text+: Detection" and
"COMICS Text+: Recognition". We evaluated the performance of state-of-the-art
text detection and recognition models on these datasets and found significant
improvement in word accuracy and normalized edit distance compared to the text
in COMICS. We also created a new dataset called "COMICS Text+", which contains
the extracted text from the textboxes in the COMICS dataset. Using the improved
text data of COMICS Text+ in the comics processing model from resulted in
state-of-the-art performance on cloze-style tasks without changing the model
architecture. The COMICS Text+ dataset can be a valuable resource for
researchers working on tasks including text detection, recognition, and
high-level processing of comics, such as narrative understanding, character
relations, and story generation. All the data and inference instructions can be
accessed in https://github.com/gsoykan/comics_text_plus.
|
[
{
"version": "v1",
"created": "Tue, 27 Dec 2022 12:05:23 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Soykan",
"Gürkan",
""
],
[
"Yuret",
"Deniz",
""
],
[
"Sezgin",
"Tevfik Metin",
""
]
] |
new_dataset
| 0.999776 |
2212.14710
|
Pengwei Yin
|
Pengwei Yin, Jiawu Dai, Jingjing Wang, Di Xie and Shiliang Pu
|
NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation
|
10 pages, 8 figures, submitted to CVPR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Gaze estimation is the fundamental basis for many visual tasks. Yet, the high
cost of acquiring gaze datasets with 3D annotations hinders the optimization
and application of gaze estimation models. In this work, we propose a novel
Head-Eye redirection parametric model based on Neural Radiance Field, which
allows dense gaze data generation with view consistency and accurate gaze
direction. Moreover, our head-eye redirection parametric model can decouple the
face and eyes for separate neural rendering, so it can achieve the purpose of
separately controlling the attributes of the face, identity, illumination, and
eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by
manipulating the latent code belonging to different face attributions in an
unsupervised manner. Extensive experiments on several benchmarks demonstrate
the effectiveness of our method in domain generalization and domain adaptation
for gaze estimation tasks.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 13:52:28 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Yin",
"Pengwei",
""
],
[
"Dai",
"Jiawu",
""
],
[
"Wang",
"Jingjing",
""
],
[
"Xie",
"Di",
""
],
[
"Pu",
"Shiliang",
""
]
] |
new_dataset
| 0.982636 |
2212.14742
|
Xinyuan Chen
|
Xinyuan Chen, Yangchen Xie, Li Sun and Yue Lu
|
DGFont++: Robust Deformable Generative Networks for Unsupervised Font
Generation
|
arXiv admin note: substantial text overlap with arXiv:2104.03064
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Automatic font generation without human experts is a practical and
significant problem, especially for some languages that consist of a large
number of characters. Existing methods for font generation are often in
supervised learning. They require a large number of paired data, which are
labor-intensive and expensive to collect. In contrast, common unsupervised
image-to-image translation methods are not applicable to font generation, as
they often define style as the set of textures and colors. In this work, we
propose a robust deformable generative network for unsupervised font generation
(abbreviated as DGFont++). We introduce a feature deformation skip connection
(FDSC) to learn local patterns and geometric transformations between fonts. The
FDSC predicts pairs of displacement maps and employs the predicted maps to
apply deformable convolution to the low-level content feature maps. The outputs
of FDSC are fed into a mixer to generate final results. Moreover, we introduce
contrastive self-supervised learning to learn a robust style representation for
fonts by understanding the similarity and dissimilarities of fonts. To
distinguish different styles, we train our model with a multi-task
discriminator, which ensures that each style can be discriminated
independently. In addition to adversarial loss, another two reconstruction
losses are adopted to constrain the domain-invariant characteristics between
generated images and content images. Taking advantage of FDSC and the adopted
loss functions, our model is able to maintain spatial information and generates
high-quality character images in an unsupervised manner. Experiments
demonstrate that our model is able to generate character images of higher
quality than state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 14:35:10 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Chen",
"Xinyuan",
""
],
[
"Xie",
"Yangchen",
""
],
[
"Sun",
"Li",
""
],
[
"Lu",
"Yue",
""
]
] |
new_dataset
| 0.980703 |
2212.14814
|
R\'emi Pellerin
|
Christophe Crespelle, R\'emi Pellerin, St\'ephan Thomass\'e
|
A quasi-quadratic vertex Kernel for Cograph edge editing
| null | null | null | null |
cs.DS cs.CC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We provide a $O(k^2 \mathrm{log} k)$ vertex kernel for cograph edge editing.
This improves a cubic kernel found by Guillemot, Havet, Paul and Perez [1]
which involved four reduction rules. We generalize one of their rules, based on
packing of induced paths of length four, by introducing t-modules, which are
modules up to t edge modifications. The key fact is that large t-modules cannot
be edited more than t times, and this allows to obtain a near quadratic kernel.
The extra $\mathrm{log} k$ factor seems tricky to remove as it is necessary in
the combinatorial lemma on trees which is central in our proof. Nevertheless,
we think that a quadratic bound should be reachable.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 16:23:27 GMT"
}
] | 2023-01-02T00:00:00 |
[
[
"Crespelle",
"Christophe",
""
],
[
"Pellerin",
"Rémi",
""
],
[
"Thomassé",
"Stéphan",
""
]
] |
new_dataset
| 0.993814 |
2003.00982
|
Vijay Prakash Dwivedi
|
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas
Laurent, Yoshua Bengio, Xavier Bresson
|
Benchmarking Graph Neural Networks
|
Benchmarking framework on GitHub at
https://github.com/graphdeeplearning/benchmarking-gnns
|
Journal of Machine Learning Research (JMLR), 2022
| null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the last few years, graph neural networks (GNNs) have become the standard
toolkit for analyzing and learning from data on graphs. This emerging field has
witnessed an extensive growth of promising techniques that have been applied
with success to computer science, mathematics, biology, physics and chemistry.
But for any successful field to become mainstream and reliable, benchmarks must
be developed to quantify progress. This led us in March 2020 to release a
benchmark framework that i) comprises of a diverse collection of mathematical
and real-world graphs, ii) enables fair model comparison with the same
parameter budget to identify key architectures, iii) has an open-source,
easy-to-use and reproducible code infrastructure, and iv) is flexible for
researchers to experiment with new theoretical ideas. As of December 2022, the
GitHub repository has reached 2,000 stars and 380 forks, which demonstrates the
utility of the proposed open-source framework through the wide usage by the GNN
community. In this paper, we present an updated version of our benchmark with a
concise presentation of the aforementioned framework characteristics, an
additional medium-sized molecular dataset AQSOL, similar to the popular ZINC,
but with a real-world measured chemical target, and discuss how this framework
can be leveraged to explore new GNN designs and insights. As a proof of value
of our benchmark, we study the case of graph positional encoding (PE) in GNNs,
which was introduced with this benchmark and has since spurred interest of
exploring more powerful PE for Transformers and GNNs in a robust experimental
setting.
|
[
{
"version": "v1",
"created": "Mon, 2 Mar 2020 15:58:46 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Jun 2020 16:45:15 GMT"
},
{
"version": "v3",
"created": "Fri, 3 Jul 2020 16:38:28 GMT"
},
{
"version": "v4",
"created": "Wed, 11 May 2022 17:07:03 GMT"
},
{
"version": "v5",
"created": "Wed, 28 Dec 2022 04:57:24 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Dwivedi",
"Vijay Prakash",
""
],
[
"Joshi",
"Chaitanya K.",
""
],
[
"Luu",
"Anh Tuan",
""
],
[
"Laurent",
"Thomas",
""
],
[
"Bengio",
"Yoshua",
""
],
[
"Bresson",
"Xavier",
""
]
] |
new_dataset
| 0.982519 |
2103.14972
|
Francielle Alves Vargas
|
Francielle Alves Vargas, Isabelle Carvalho, Fabiana Rodrigues de
G\'oes, Fabr\'icio Benevenuto, Thiago Alexandre Salgueiro Pardo
|
HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments
for Offensive Language and Hate Speech Detection
|
Published at LREC 2022 Proceedings
|
https://aclanthology.org/2022.lrec-1.777/
| null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Due to the severity of the social media offensive and hateful comments in
Brazil, and the lack of research in Portuguese, this paper provides the first
large-scale expert annotated corpus of Brazilian Instagram comments for hate
speech and offensive language detection. The HateBR corpus was collected from
the comment section of Brazilian politicians' accounts on Instagram and
manually annotated by specialists, reaching a high inter-annotator agreement.
The corpus consists of 7,000 documents annotated according to three different
layers: a binary classification (offensive versus non-offensive comments),
offensiveness-level classification (highly, moderately, and slightly
offensive), and nine hate speech groups (xenophobia, racism, homophobia,
sexism, religious intolerance, partyism, apology for the dictatorship,
antisemitism, and fatphobia). We also implemented baseline experiments for
offensive language and hate speech detection and compared them with a
literature baseline. Results show that the baseline experiments on our corpus
outperform the current state-of-the-art for the Portuguese language.
|
[
{
"version": "v1",
"created": "Sat, 27 Mar 2021 19:43:16 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Apr 2021 22:15:40 GMT"
},
{
"version": "v3",
"created": "Tue, 6 Apr 2021 10:02:52 GMT"
},
{
"version": "v4",
"created": "Sun, 2 May 2021 20:58:41 GMT"
},
{
"version": "v5",
"created": "Sun, 9 May 2021 16:41:18 GMT"
},
{
"version": "v6",
"created": "Tue, 27 Dec 2022 12:24:13 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Vargas",
"Francielle Alves",
""
],
[
"Carvalho",
"Isabelle",
""
],
[
"de Góes",
"Fabiana Rodrigues",
""
],
[
"Benevenuto",
"Fabrício",
""
],
[
"Pardo",
"Thiago Alexandre Salgueiro",
""
]
] |
new_dataset
| 0.993083 |
2203.10193
|
Tomoyuki Yamakami
|
Tomoyuki Yamakami
|
Between SC and LOGDCFL: Families of Languages Accepted by
Logarithmic-Space Deterministic Auxiliary Depth-k Storage Automata
|
(A4, 10pt, p28) This exposition corrects and expands its preliminary
report, which appeared in the Proceedings of the 27th International
Conference on Computing and Combinatorics (COCOON 2021), Tainan, Taiwan,
October 24--26, 2021, Lecture Notes in Computer Science, Springer, vol.
13025, pp. 164--175, 2021. An oral presentation was given online due to the
coronavirus pandemic
| null | null | null |
cs.FL cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
The closure of deterministic context-free languages under logarithmic-space
many-one reductions ($\mathrm{L}$-m-reductions), known as LOGDCFL, has been
studied in depth from an aspect of parallel computability because it is nicely
situated between $\mathrm{L}$ and $\mathrm{AC}^{1}\cap\mathrm{SC}^2$. By
replacing a memory device from pushdown stacks with access-controlled storage
tapes, we introduce a computational model of one-way deterministic depth-$k$
storage automata ($k$-sda's) whose tape cells are freely modified during the
first $k$ accesses and then become blank forever. These $k$-sda's naturally
induce the language family $k\mathrm{SDA}$. Similarly to $\mathrm{LOGDCFL}$, we
study the closure $\mathrm{LOG}k\mathrm{SDA}$ of all languages in
$k\mathrm{SDA}$ under $\mathrm{L}$-m-reductions. We demonstrate that
$\mathrm{DCFL}\subseteq k\mathrm{SDA}\subseteq \mathrm{SC}^k$ by significantly
extending Cook's early result (1979) of $\mathrm{DCFL}\subseteq \mathrm{SC}^2$.
The entire hierarch of $\mathrm{LOG}k\mathrm{SDA}$ for all $k\geq1$ therefore
lies between $\mathrm{LOGDCFL}$ and $\mathrm{SC}$. As an immediate consequence,
we obtain the same simulation bounds for Hibbard's limited automata. We further
characterize $\mathrm{LOG}k\mathrm{SDA}$ in terms of a new machine model,
called logarithmic-space deterministic auxiliary depth-$k$ storage automata
that run in polynomial time. These machines are as powerful as a
polynomial-time two-way multi-head deterministic depth-$k$ storage automata. We
also provide a ``generic'' $\mathrm{LOG}k\mathrm{SDA}$-complete language under
$\mathrm{L}$-m-reductions by constructing a two-way universal simulator working
for all $k$-sda's.
|
[
{
"version": "v1",
"created": "Fri, 18 Mar 2022 23:44:27 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Dec 2022 00:40:41 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Yamakami",
"Tomoyuki",
""
]
] |
new_dataset
| 0.995498 |
2208.02494
|
Stefano Kalonaris
|
Stefano Kalonaris
|
Tokyo Kion-On: Query-Based Generative Sonification of Atmospheric Data
|
To appear in: Proceedings of the 27th International Conference on
Auditory Display (ICAD 2022)
| null |
10.21785/icad2022.039
| null |
cs.SD cs.HC cs.LG eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Amid growing environmental concerns, interactive displays of data constitute
an important tool for exploring and understanding the impact of climate change
on the planet's ecosystemic integrity. This paper presents Tokyo kion-on, a
query-based sonification model of Tokyo's air temperature from 1876 to 2021.
The system uses a recurrent neural network architecture known as LSTM with
attention trained on a small dataset of Japanese melodies and conditioned upon
said atmospheric data. After describing the model's implementation, a brief
comparative illustration of the musical results is presented, along with a
discussion on how the exposed hyper-parameters can promote active and
non-linear exploration of the data.
|
[
{
"version": "v1",
"created": "Thu, 4 Aug 2022 06:56:06 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Kalonaris",
"Stefano",
""
]
] |
new_dataset
| 0.999445 |
2212.08448
|
Hadar Shavit
|
Hadar Shavit and Filip Jatelnicki and Pol Mor-Puigvent\'os and Wojtek
Kowalczyk
|
From Xception to NEXcepTion: New Design Decisions and Neural
Architecture Search
|
Accepted at ICPRAM 2023 for a 20 minutes oral presentation
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present a modified Xception architecture, the NEXcepTion
network. Our network has significantly better performance than the original
Xception, achieving top-1 accuracy of 81.5% on the ImageNet validation dataset
(an improvement of 2.5%) as well as a 28% higher throughput. Another variant of
our model, NEXcepTion-TP, reaches 81.8% top-1 accuracy, similar to ConvNeXt
(82.1%), while having a 27% higher throughput. Our model is the result of
applying improved training procedures and new design decisions combined with an
application of Neural Architecture Search (NAS) on a smaller dataset. These
findings call for revisiting older architectures and reassessing their
potential when combined with the latest enhancements.
|
[
{
"version": "v1",
"created": "Fri, 16 Dec 2022 12:46:21 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Dec 2022 13:43:14 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Shavit",
"Hadar",
""
],
[
"Jatelnicki",
"Filip",
""
],
[
"Mor-Puigventós",
"Pol",
""
],
[
"Kowalczyk",
"Wojtek",
""
]
] |
new_dataset
| 0.998619 |
2212.13163
|
Wei Ji
|
Wei Ji, Long Chen, Yinwei Wei, Yiming Wu, Tat-Seng Chua
|
MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding
|
work in progress
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Given an untrimmed video and natural language query, video sentence grounding
aims to localize the target temporal moment in the video. Existing methods
mainly tackle this task by matching and aligning semantics of the descriptive
sentence and video segments on a single temporal resolution, while neglecting
the temporal consistency of video content in different resolutions. In this
work, we propose a novel multi-resolution temporal video sentence grounding
network: MRTNet, which consists of a multi-modal feature encoder, a
Multi-Resolution Temporal (MRT) module, and a predictor module. MRT module is
an encoder-decoder network, and output features in the decoder part are in
conjunction with Transformers to predict the final start and end timestamps.
Particularly, our MRT module is hot-pluggable, which means it can be seamlessly
incorporated into any anchor-free models. Besides, we utilize a hybrid loss to
supervise cross-modal features in MRT module for more accurate grounding in
three scales: frame-level, clip-level and sequence-level. Extensive experiments
on three prevalent datasets have shown the effectiveness of MRTNet.
|
[
{
"version": "v1",
"created": "Mon, 26 Dec 2022 13:48:05 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Dec 2022 05:14:51 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Ji",
"Wei",
""
],
[
"Chen",
"Long",
""
],
[
"Wei",
"Yinwei",
""
],
[
"Wu",
"Yiming",
""
],
[
"Chua",
"Tat-Seng",
""
]
] |
new_dataset
| 0.999733 |
2212.13283
|
Yaniv Sadeh
|
Yaniv Sadeh
|
On Ranges and Partitions in Optimal TCAMs
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Traffic splitting is a required functionality in networks, for example for
load balancing over paths or servers, or by the source's access restrictions.
The capacities of the servers (or the number of users with particular access
restrictions) determine the sizes of the parts into which traffic should be
split. A recent approach implements traffic splitting within the ternary
content addressable memory (TCAM), which is often available in switches. It is
important to reduce the amount of memory allocated for this task since TCAMs
are power consuming and are often also required for other tasks such as
classification and routing. In the longest-prefix model (LPM), Draves et al.
(INFOCOM 1999) find a minimal representation of a function, and Sadeh et al.
(INFOCOM 2019) find a minimal representation of a partition. In certain
situations, range-functions are of special interest, that is, all the addresses
with the same target, or action, are consecutive. In this paper we show that
minimizing the amount of TCAM entries to represent a partition comes at the
cost of fragmentation, such that for some partitions some actions must have
multiple ranges. Then, we also study the case where each target must have a
single segment of addresses.
|
[
{
"version": "v1",
"created": "Mon, 26 Dec 2022 19:29:49 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Sadeh",
"Yaniv",
""
]
] |
new_dataset
| 0.985647 |
2212.13312
|
Muhammad Lutfor Rahman
|
Muhammad Lutfor Rahman, Daniel Timko, Hamid Wali, and Ajaya Neupane
|
Users really do respond to smishing
|
CODASPY'23
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Text phish messages, referred to as Smishing is a type of social engineering
attack where fake text messages are created, and used to lure users into
responding to those messages. These messages aim to obtain user credentials,
install malware on the phones, or launch smishing attacks. They ask users to
reply to their message, click on a URL that redirects them to a phishing
website, or call the provided number. Thousands of mobile users are affected by
smishing attacks daily. Drawing inspiration by the works of Tu et al. (USENIX
Security, 2019) on Robocalls and Tischer et al. (IEEE Symposium on Security and
Privacy, 2016) on USB drives, this paper investigates why smishing works.
Accordingly, we designed smishing experiments and sent phishing SMSes to 265
users to measure the efficacy of smishing attacks. We sent eight fake text
messages to participants and recorded their CLICK, REPLY, and CALL responses
along with their feedback in a post-test survey. Our results reveal that 16.92%
of our participants had potentially fallen for our smishing attack. To test
repeat phishing, we subjected a set of randomly selected participants to a
second round of smishing attacks with a different message than the one they
received in the first round. As a result, we observed that 12.82% potentially
fell for the attack again. Using logistic regression, we observed that a
combination of user REPLY and CLICK actions increased the odds that a user
would respond to our smishing message when compared to CLICK. Additionally, we
found a similar statistically significant increase when comparing Facebook and
Walmart entity scenario to our IRS baseline.
|
[
{
"version": "v1",
"created": "Mon, 26 Dec 2022 22:29:12 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Rahman",
"Muhammad Lutfor",
""
],
[
"Timko",
"Daniel",
""
],
[
"Wali",
"Hamid",
""
],
[
"Neupane",
"Ajaya",
""
]
] |
new_dataset
| 0.994591 |
2212.13367
|
Chonghe Zhao
|
Chonghe Zhao, Taotao Wang, Shengli Zhang and Soung Chang Liew
|
HCB: Enabling Compact Block in Ethereum Network with Secondary Pool and
Transaction Prediction
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Compact block, which replaces transactions in the block with their hashes, is
an effective means to speed up block propagation in the Bitcoin network. The
compact block mechanism in Bitcoin counts on the fact that many nodes may
already have the transactions (or most of the transactions) in the block,
therefore sending the complete block containing the full transactions is
unnecessary. This fact, however, does not hold in the Ethereum network.
Adopting compact block directly in Ethereum may degrade the block propagation
speed significantly because the probability of a node not having a transaction
in the sending block is relatively high in Ethereum and requesting the missing
transactions after receiving the compact block takes much additional time. This
paper proposes hybrid-compact block (HCB), an efficient compact block
propagation scheme for Ethereum and other similar blockchains. First, we
develop a Secondary Pool to store the low-fee transactions, which are removed
from the primary transaction pool, to conserve storage space. As simple
auxiliary storage, the Secondary Pool does not affect the normal block
processing of the primary pool in Ethereum. Second, we design a machine
learning-based transaction prediction module to precisely predict the missing
transactions caused by network latency and selfish behaviors. We implemented
our HCB scheme and other compact-block-like schemes (as benchmarks) and
deployed a number of worldwide nodes over the Ethereum MainNet to
experimentally investigate them. Experimental results show that HCB performs
best among the existing compact-block-like schemes and can reduce propagation
time by more than half with respect to the current block propagation scheme in
Ethereum.
|
[
{
"version": "v1",
"created": "Tue, 27 Dec 2022 05:50:21 GMT"
}
] | 2022-12-29T00:00:00 |
[
[
"Zhao",
"Chonghe",
""
],
[
"Wang",
"Taotao",
""
],
[
"Zhang",
"Shengli",
""
],
[
"Liew",
"Soung Chang",
""
]
] |
new_dataset
| 0.998649 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.