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2307.02906
|
Orhan Konak
|
Orhan Konak, Alexander Wischmann, Robin van de Water, Bert Arnrich
|
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement
in Sensor-based Human Activity Recognition
| null | null | null | null |
cs.LG cs.CV eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Sensor-based Human Activity Recognition facilitates unobtrusive monitoring of
human movements. However, determining the most effective sensor placement for
optimal classification performance remains challenging. This paper introduces a
novel methodology to resolve this issue, using real-time 2D pose estimations
derived from video recordings of target activities. The derived skeleton data
provides a unique strategy for identifying the optimal sensor location. We
validate our approach through a feasibility study, applying inertial sensors to
monitor 13 different activities across ten subjects. Our findings indicate that
the vision-based method for sensor placement offers comparable results to the
conventional deep learning approach, demonstrating its efficacy. This research
significantly advances the field of Human Activity Recognition by providing a
lightweight, on-device solution for determining the optimal sensor placement,
thereby enhancing data anonymization and supporting a multimodal classification
approach.
|
[
{
"created": "Thu, 6 Jul 2023 10:38:14 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Konak",
"Orhan",
""
],
[
"Wischmann",
"Alexander",
""
],
[
"van de Water",
"Robin",
""
],
[
"Arnrich",
"Bert",
""
]
] |
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] |
2307.02909
|
Guinan Li
|
Guinan Li, Jiajun Deng, Mengzhe Geng, Zengrui Jin, Tianzi Wang, Shujie
Hu, Mingyu Cui, Helen Meng, Xunying Liu
|
Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation
and Recognition
|
IEEE/ACM Transactions on Audio, Speech, and Language Processing
| null | null | null |
eess.AS cs.AI cs.SD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate recognition of cocktail party speech containing overlapping
speakers, noise and reverberation remains a highly challenging task to date.
Motivated by the invariance of visual modality to acoustic signal corruption,
an audio-visual multi-channel speech separation, dereverberation and
recognition approach featuring a full incorporation of visual information into
all system components is proposed in this paper. The efficacy of the video
input is consistently demonstrated in mask-based MVDR speech separation,
DNN-WPE or spectral mapping (SpecM) based speech dereverberation front-end and
Conformer ASR back-end. Audio-visual integrated front-end architectures
performing speech separation and dereverberation in a pipelined or joint
fashion via mask-based WPD are investigated. The error cost mismatch between
the speech enhancement front-end and ASR back-end components is minimized by
end-to-end jointly fine-tuning using either the ASR cost function alone, or its
interpolation with the speech enhancement loss. Experiments were conducted on
the mixture overlapped and reverberant speech data constructed using simulation
or replay of the Oxford LRS2 dataset. The proposed audio-visual multi-channel
speech separation, dereverberation and recognition systems consistently
outperformed the comparable audio-only baseline by 9.1% and 6.2% absolute
(41.7% and 36.0% relative) word error rate (WER) reductions. Consistent speech
enhancement improvements were also obtained on PESQ, STOI and SRMR scores.
|
[
{
"created": "Thu, 6 Jul 2023 10:50:46 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Li",
"Guinan",
""
],
[
"Deng",
"Jiajun",
""
],
[
"Geng",
"Mengzhe",
""
],
[
"Jin",
"Zengrui",
""
],
[
"Wang",
"Tianzi",
""
],
[
"Hu",
"Shujie",
""
],
[
"Cui",
"Mingyu",
""
],
[
"Meng",
"Helen",
""
],
[
"Liu",
"Xunying",
""
]
] |
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] |
2307.02910
|
Agnese Lombardi
|
Agnese Lombardi, Alessandro Lenci
|
Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive
|
7 pages, in Italian language
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The goal of this study is to investigate whether a Transformer-based neural
language model infers lexical semantics and use this information for the
completion of morphosyntactic patterns. The semantic properties considered are
telicity (also combined with definiteness) and agentivity. Both act at the
interface between semantics and morphosyntax: they are semantically determined
and syntactically encoded. The tasks were submitted to both the computational
model and a group of Italian native speakers. The comparison between the two
groups of data allows us to investigate to what extent neural language models
capture significant aspects of human semantic competence.
|
[
{
"created": "Thu, 6 Jul 2023 10:52:22 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Lombardi",
"Agnese",
""
],
[
"Lenci",
"Alessandro",
""
]
] |
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] |
2307.02912
|
David Jimenez-Cabello
|
Mario Almagro, Emilio Almaz\'an, Diego Ortego, David Jim\'enez
|
LEA: Improving Sentence Similarity Robustness to Typos Using Lexical
Attention Bias
|
KDD'23 conference (main research track). (*) These authors
contributed equally
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Textual noise, such as typos or abbreviations, is a well-known issue that
penalizes vanilla Transformers for most downstream tasks. We show that this is
also the case for sentence similarity, a fundamental task in multiple domains,
e.g. matching, retrieval or paraphrasing. Sentence similarity can be approached
using cross-encoders, where the two sentences are concatenated in the input
allowing the model to exploit the inter-relations between them. Previous works
addressing the noise issue mainly rely on data augmentation strategies, showing
improved robustness when dealing with corrupted samples that are similar to the
ones used for training. However, all these methods still suffer from the token
distribution shift induced by typos. In this work, we propose to tackle textual
noise by equipping cross-encoders with a novel LExical-aware Attention module
(LEA) that incorporates lexical similarities between words in both sentences.
By using raw text similarities, our approach avoids the tokenization shift
problem obtaining improved robustness. We demonstrate that the attention bias
introduced by LEA helps cross-encoders to tackle complex scenarios with textual
noise, specially in domains with short-text descriptions and limited context.
Experiments using three popular Transformer encoders in five e-commerce
datasets for product matching show that LEA consistently boosts performance
under the presence of noise, while remaining competitive on the original
(clean) splits. We also evaluate our approach in two datasets for textual
entailment and paraphrasing showing that LEA is robust to typos in domains with
longer sentences and more natural context. Additionally, we thoroughly analyze
several design choices in our approach, providing insights about the impact of
the decisions made and fostering future research in cross-encoders dealing with
typos.
|
[
{
"created": "Thu, 6 Jul 2023 10:53:50 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Almagro",
"Mario",
""
],
[
"Almazán",
"Emilio",
""
],
[
"Ortego",
"Diego",
""
],
[
"Jiménez",
"David",
""
]
] |
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] |
2307.02913
|
Dominic Phillips
|
Dominic Phillips, Charles Matthews, Benedict Leimkuhler
|
Numerical Methods with Coordinate Transforms for Efficient Brownian
Dynamics Simulations
|
27 pages, including supplementary material
| null | null | null |
math.NA cs.NA
|
http://creativecommons.org/licenses/by/4.0/
|
Many stochastic processes in the physical and biological sciences can be
modelled using Brownian dynamics with multiplicative noise. However, numerical
integrators for these processes can lose accuracy or even fail to converge when
the diffusion term is configuration-dependent. One remedy is to construct a
transform to a constant-diffusion process and sample the transformed process
instead. In this work, we explain how coordinate-based and time-rescaling-based
transforms can be used either individually or in combination to map a general
class of variable-diffusion Brownian motion processes into constant-diffusion
ones. The transforms are invertible, thus allowing recovery of the original
dynamics. We motivate our methodology using examples in one dimension before
then considering multivariate diffusion processes. We illustrate the benefits
of the transforms through numerical simulations, demonstrating how the right
combination of integrator and transform can improve computational efficiency
and the order of convergence to the invariant distribution. Notably, the
transforms that we derive are applicable to a class of multibody, anisotropic
Stokes-Einstein diffusion that has applications in biophysical modelling.
|
[
{
"created": "Thu, 6 Jul 2023 10:56:20 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Phillips",
"Dominic",
""
],
[
"Matthews",
"Charles",
""
],
[
"Leimkuhler",
"Benedict",
""
]
] |
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] |
2307.02915
|
Mikhail Martynov
|
Mikhail Martynov, Zhanibek Darush, Miguel Altamirano Cabrera, Sausar
Karaf, Dzmitry Tsetserukou
|
MorphoArms: Morphogenetic Teleoperation of Multimanual Robot
|
IEEE International Conference on Automation Science and Engineering
(CASE 2023), Cordis, New Zeland, 26-30 August, 2023, in print
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nowadays, there are few unmanned aerial vehicles (UAVs) capable of flying,
walking and grasping. A drone with all these functionalities can significantly
improve its performance in complex tasks such as monitoring and exploring
different types of terrain, and rescue operations. This paper presents
MorphoArms, a novel system that consists of a morphogenetic chassis and a hand
gesture recognition teleoperation system. The mechanics, electronics, control
architecture, and walking behavior of the morphogenetic chassis are described.
This robot is capable of walking and grasping objects using four robotic limbs.
Robotic limbs with four degrees-of-freedom are used as pedipulators when
walking and as manipulators when performing actions in the environment. The
robot control system is implemented using teleoperation, where commands are
given by hand gestures. A motion capture system is used to track the user's
hands and to recognize their gestures. The method of controlling the robot was
experimentally tested in a study involving 10 users. The evaluation included
three questionnaires (NASA TLX, SUS, and UEQ). The results showed that the
proposed system was more user-friendly than 56% of the systems, and it was
rated above average in terms of attractiveness, stimulation, and novelty.
|
[
{
"created": "Thu, 6 Jul 2023 11:05:38 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Martynov",
"Mikhail",
""
],
[
"Darush",
"Zhanibek",
""
],
[
"Cabrera",
"Miguel Altamirano",
""
],
[
"Karaf",
"Sausar",
""
],
[
"Tsetserukou",
"Dzmitry",
""
]
] |
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] |
2307.02916
|
Harjinder Lallie Dr
|
Daeun Lee and Harjinder Singh Lallie and Nadine Michaelides
|
The impact of an employee's psychological contract breach on compliance
with information security policies: intrinsic and extrinsic motivation
|
27 pages, 3 figures
|
Cognition, Technology & Work, pp.1-17 (2023)
| null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the rapid rise in social engineering attacks, not all employees are
as compliant with information security policies (ISPs) to the extent that
organisations expect them to be. ISP non-compliance is caused by a variety of
psychological motivation. This study investigates the effect of psychological
contract breach (PCB) of employees on ISP compliance intention (ICI) by
dividing them into intrinsic and extrinsic motivation using the theory of
planned behaviour (TPB) and the general deterrence theory (GDT). Data analysis
from UK employees (\textit{n=206}) showed that the higher the PCB, the lower
the ICI. The study also found that PCBs significantly reduced intrinsic
motivation (attitude and perceived fairness) for ICI, whereas PCBs did not
moderate the relationship between extrinsic motivation (sanction severity and
sanctions certainty) and ICI. As a result, this study successfully addresses
the risks of PCBs in the field of IS security and proposes effective solutions
for employees with high PCBs.
|
[
{
"created": "Thu, 6 Jul 2023 11:07:39 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Lee",
"Daeun",
""
],
[
"Lallie",
"Harjinder Singh",
""
],
[
"Michaelides",
"Nadine",
""
]
] |
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] |
2307.02921
|
Claudia-Elisabeth Wulz
|
Claudia-Elisabeth Wulz
|
Memories from the W Boson Discovery
|
4 pages, 4 figures. Proceedings of 57th Rencontres de Moriond, QCD
and High Energy Interactions, La Thuile, Italy, 25 March - 1 April 2023
| null | null | null |
hep-ex physics.hist-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The fascinating story of a major discovery at CERN is outlined. The bold
decision to convert its most powerful, and only recently inaugurated, proton
accelerator to a proton-antiproton collider led to the discovery of the W and Z
bosons -- mediators of the weak interaction -- in a record time, at the
experiments UA1 and UA2. The decisive roles of Carlo Rubbia and Simon van der
Meer, who received the 1984 Nobel Prize for physics, are underlined.
|
[
{
"created": "Thu, 6 Jul 2023 11:18:59 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Wulz",
"Claudia-Elisabeth",
""
]
] |
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] |
2307.02922
|
William Barnes
|
William Leslie Barnes
|
Strong coupling and the C=O vibrational bond
|
technical note, 10 pages, 2 figures, 1 table
| null | null | null |
physics.chem-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In this technical note we calculate the strength of the expected Rabi
splitting for a molecular resonance. By way of an example we focus on the
molecular resonance associated with the C=O bond, specifically the stretch
resonance at $\sim$1730 cm$^{-1}$. This molecular resonance is common in a wide
range of polymeric materials that are convenient for many experiments, because
of the ease with which they may be spin cast to form optical micro-cavities,
polymers include PVA and PMMA. Two different approaches to modelling the
expected extent of the coupling are examined, and the results compared with
data from experiments. The approach adopted here indicates how material
parameters may be used to assess the potential of a material to exhibit strong
coupling, and also enable other useful parameters to be derived, including the
molecular dipole moment and the vacuum cavity field strength.
|
[
{
"created": "Thu, 6 Jul 2023 11:22:43 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Barnes",
"William Leslie",
""
]
] |
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] |
2307.02923
|
Fernando L. Guarnieri Dr.
|
Fernando L. Guarnieri, Bruce T. Tsurutani, Rajkumar Hajra, Ezequiel
Echer, and Gurbax S. Lakhina
|
NORAD Tracking of the February 2022 Starlink Satellites (and the
Possible Immediate Loss of 32 Satellites)
|
23 pages, 5 figures, 1 table. arXiv admin note: text overlap with
arXiv:2210.07902
| null | null | null |
physics.space-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The North American Aerospace Defense Command (NORAD) tracking of the SpaceX
Starlink satellite launch on February 03, 2022 is reviewed. Of the 49 Starlink
satellites released into orbit, 38 were eventually lost. Thirty-two of the
satellites were never tracked by NORAD. There have been three articles written
proposing physical mechanisms to explain the satellite losses. It is argued
that none of the proposed mechanisms can explain the immediate loss of 32 of
the 49 satellites. The non-availability of telemetry data from the lost
satellites has hindered the search for a physical mechanism to explain the
density increase observed in a short time interval.
|
[
{
"created": "Thu, 6 Jul 2023 11:23:24 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Guarnieri",
"Fernando L.",
""
],
[
"Tsurutani",
"Bruce T.",
""
],
[
"Hajra",
"Rajkumar",
""
],
[
"Echer",
"Ezequiel",
""
],
[
"Lakhina",
"Gurbax S.",
""
]
] |
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] |
2307.02924
|
Dennis Becker
|
Dennis Becker, Diana Rueda, Felix Beese, Brenda Scarleth Gutierrez
Torres, Myriem Lafdili, Kyra Ahrens, Di Fu, Erik Strahl, Tom Weber, Stefan
Wermter
|
The Emotional Dilemma: Influence of a Human-like Robot on Trust and
Cooperation
|
Accepted at 2023 32nd IEEE International Conference on Robot and
Human Interactive Communication (RO-MAN)
| null | null | null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Increasing anthropomorphic robot behavioral design could affect trust and
cooperation positively. However, studies have shown contradicting results and
suggest a task-dependent relationship between robots that display emotions and
trust. Therefore, this study analyzes the effect of robots that display
human-like emotions on trust, cooperation, and participants' emotions. In the
between-group study, participants play the coin entrustment game with an
emotional and a non-emotional robot. The results show that the robot that
displays emotions induces more anxiety than the neutral robot. Accordingly, the
participants trust the emotional robot less and are less likely to cooperate.
Furthermore, the perceived intelligence of a robot increases trust, while a
desire to outcompete the robot can reduce trust and cooperation. Thus, the
design of robots expressing emotions should be task dependent to avoid adverse
effects that reduce trust and cooperation.
|
[
{
"created": "Thu, 6 Jul 2023 11:23:40 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Becker",
"Dennis",
""
],
[
"Rueda",
"Diana",
""
],
[
"Beese",
"Felix",
""
],
[
"Torres",
"Brenda Scarleth Gutierrez",
""
],
[
"Lafdili",
"Myriem",
""
],
[
"Ahrens",
"Kyra",
""
],
[
"Fu",
"Di",
""
],
[
"Strahl",
"Erik",
""
],
[
"Weber",
"Tom",
""
],
[
"Wermter",
"Stefan",
""
]
] |
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] |
2307.02927
|
Ricardo Brito
|
Alonso Rodriguez-Navarro and Ricardo Brito
|
Rank analysis of most cited publications, a new approach for research
assessments
|
One PDF file, including figures and tables (31 pages)
| null | null | null |
cs.DL
|
http://creativecommons.org/licenses/by/4.0/
|
Citation metrics are the best tools for research assessments. However,
current metrics are misleading in research systems that pursue simultaneously
different goals, such as the advance of science and incremental innovations,
because their publications have different citation distributions. We estimate
the contribution to the progress of knowledge by studying only a limited number
of the most cited papers, which are dominated by publications pursuing this
progress. To field-normalize the metrics, we substitute the number of citations
by the rank position of papers from one country in the global list of papers.
Using synthetic series of lognormally distributed numbers, we developed the
Rk-index, which is calculated from the global ranks of the 10 highest numbers
in each series, and demonstrate its equivalence to the number of papers in top
percentiles, P top 0.1% and P top 0.01% . In real cases, the Rk-index is simple
and easy to calculate, and evaluates the contribution to the progress of
knowledge much better than commonly used metrics. Although further research is
needed, rank analysis of the most cited papers is a promising approach for
research evaluation. It is also demonstrated that, for this purpose, domestic
and collaborative papers should be studied independently.
|
[
{
"created": "Thu, 6 Jul 2023 11:28:51 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Rodriguez-Navarro",
"Alonso",
""
],
[
"Brito",
"Ricardo",
""
]
] |
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] |
2307.02928
|
Avishai Sintov
|
Osher Azulay, Nimrod Curtis, Rotem Sokolovsky, Guy Levitski, Daniel
Slomovik, Guy Lilling and Avishai Sintov
|
AllSight: A Low-Cost and High-Resolution Round Tactile Sensor with
Zero-Shot Learning Capability
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Tactile sensing is a necessary capability for a robotic hand to perform fine
manipulations and interact with the environment. Optical sensors are a
promising solution for high-resolution contact estimation. Nevertheless, they
are usually not easy to fabricate and require individual calibration in order
to acquire sufficient accuracy. In this letter, we propose AllSight, an optical
tactile sensor with a round 3D structure potentially designed for robotic
in-hand manipulation tasks. AllSight is mostly 3D printed making it low-cost,
modular, durable and in the size of a human thumb while with a large contact
surface. We show the ability of AllSight to learn and estimate a full contact
state, i.e., contact position, forces and torsion. With that, an experimental
benchmark between various configurations of illumination and contact elastomers
are provided. Furthermore, the robust design of AllSight provides it with a
unique zero-shot capability such that a practitioner can fabricate the
open-source design and have a ready-to-use state estimation model. A set of
experiments demonstrates the accurate state estimation performance of AllSight.
|
[
{
"created": "Thu, 6 Jul 2023 11:28:53 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Azulay",
"Osher",
""
],
[
"Curtis",
"Nimrod",
""
],
[
"Sokolovsky",
"Rotem",
""
],
[
"Levitski",
"Guy",
""
],
[
"Slomovik",
"Daniel",
""
],
[
"Lilling",
"Guy",
""
],
[
"Sintov",
"Avishai",
""
]
] |
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] |
AllSight: A Low-Cost and High-Resolution Round Tactile Sensor with Zero-Shot Learning Capability
|
https://arxiv.org/abs/2307.02928v1
|
https://arxiv.org/pdf/2307.02928v1.pdf
|
https://github.com/osheraz/allsight
| true | true |
https://paperswithcode.com/paper/allsight-a-low-cost-and-high-resolution-round
|
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] |
2307.02930
|
Niko Schmidt
|
Niko Schmidt
|
Global q-superlinear convergence of the infinite-dimensional Newton's
method for the regularized p-Stokes equations
|
26 pages, 4 figures
| null | null | null |
math.NA cs.NA
|
http://creativecommons.org/licenses/by/4.0/
|
The motion of glaciers can be simulated with the p-Stokes equations. We
present an algorithm that solves these equations faster than the Picard
iteration. We do that by proving q-superlinear global convergence of the
infinite-dimensional Newton's method with Armijo step sizes to the solution of
these equations. We only have to add an arbitrarily small diffusion term for
this convergence result. We also consider approximations of exact step sizes.
Exact step sizes are possible because we reformulate the problem as minimizing
a convex functional. Next, we prove that the additional diffusion term only
causes minor differences in the solution compared to the original p-Stokes
equations. Finally, we test our algorithms on a reformulation of the experiment
ISMIP-HOM B. The approximation of exact step sizes for the Picard iteration and
Newton's method is superior in the experiment compared to the Picard iteration.
Also, Newton's method with Armijo step sizes converges faster than the Picard
iteration. However, the reached accuracy of Newton's method with Armijo step
sizes depends more on the resolution of the domain.
|
[
{
"created": "Thu, 6 Jul 2023 11:41:21 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Schmidt",
"Niko",
""
]
] |
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] |
2307.02931
|
Henrik Gra{\ss}hoff
|
Henrik Gra{\ss}hoff, Florian Adamsky, Stefan Schiffner
|
Smartphones in a Microwave: Formal and Experimental Feasibility Study on
Fingerprinting the Corona-Warn-App
|
accepted for publication at TRUSTBUS
| null |
10.1145/3600160.3605011
| null |
cs.CR cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Contact Tracing Apps (CTAs) have been developed to contain the coronavirus
disease 19 (COVID-19) spread. By design, such apps invade their users' privacy
by recording data about their health, contacts, and partially location. Many
CTAs frequently broadcast pseudorandom numbers via Bluetooth to detect
encounters. These numbers are changed regularly to prevent individual
smartphones from being trivially trackable. However, the effectiveness of this
procedure has been little studied. We measured real smartphones and observed
that the German Corona-Warn-App (CWA) exhibits a device-specific latency
between two subsequent broadcasts. These timing differences provide a potential
attack vector for fingerprinting smartphones by passively recording Bluetooth
messages. This could conceivably lead to the tracking of users' trajectories
and, ultimately, the re-identification of users.
|
[
{
"created": "Thu, 6 Jul 2023 11:42:54 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Graßhoff",
"Henrik",
""
],
[
"Adamsky",
"Florian",
""
],
[
"Schiffner",
"Stefan",
""
]
] |
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] |
2307.02932
|
Hanzhao Wang
|
Xiaocheng Li, Shang Liu, Chunlin Sun, Hanzhao Wang
|
When No-Rejection Learning is Optimal for Regression with Rejection
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning with rejection is a prototypical model for studying the interaction
between humans and AI on prediction tasks. The model has two components, a
predictor and a rejector. Upon the arrival of a sample, the rejector first
decides whether to accept it; if accepted, the predictor fulfills the
prediction task, and if rejected, the prediction will be deferred to humans.
The learning problem requires learning a predictor and a rejector
simultaneously. This changes the structure of the conventional loss function
and often results in non-convexity and inconsistency issues. For the
classification with rejection problem, several works develop surrogate losses
for the jointly learning with provable consistency guarantees; in parallel,
there has been less work for the regression counterpart. We study the
regression with rejection (RwR) problem and investigate the no-rejection
learning strategy which treats the RwR problem as a standard regression task to
learn the predictor. We establish that the suboptimality of the no-rejection
learning strategy observed in the literature can be mitigated by enlarging the
function class of the predictor. Then we introduce the truncated loss to single
out the learning for the predictor and we show that a consistent surrogate
property can be established for the predictor individually in an easier way
than for the predictor and the rejector jointly. Our findings advocate for a
two-step learning procedure that first uses all the data to learn the predictor
and then calibrates the prediction loss for the rejector. It is better aligned
with the common intuition that more data samples will lead to a better
predictor and it calls for more efforts on a better design of calibration
algorithms for learning the rejector. While our discussions mainly focus on the
regression problem, the theoretical results and insights generalize to the
classification problem as well.
|
[
{
"created": "Thu, 6 Jul 2023 11:43:22 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Li",
"Xiaocheng",
""
],
[
"Liu",
"Shang",
""
],
[
"Sun",
"Chunlin",
""
],
[
"Wang",
"Hanzhao",
""
]
] |
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] |
2307.02933
|
Max Pascher
|
Max Pascher and Kirill Kronhardt and Felix Ferdinand Goldau and Udo
Frese and Jens Gerken
|
In Time and Space: Towards Usable Adaptive Control for Assistive Robotic
Arms
| null | null | null | null |
cs.HC cs.AI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Robotic solutions, in particular robotic arms, are becoming more frequently
deployed for close collaboration with humans, for example in manufacturing or
domestic care environments. These robotic arms require the user to control
several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving
grasping and manipulating objects. Standard input devices predominantly have
two DoFs, requiring time-consuming and cognitively demanding mode switches to
select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have
shown to decrease the necessary number of mode switches but were up to now not
able to significantly reduce the perceived workload. Users still bear the
mental workload of incorporating abstract mode switching into their workflow.
We address this by providing feed-forward multimodal feedback using updated
recommendations of ADMC, allowing users to visually compare the current and the
suggested mapping in real-time. We contrast the effectiveness of two new
approaches that a) continuously recommend updated DoF combinations or b) use
discrete thresholds between current robot movements and new recommendations.
Both are compared in a Virtual Reality (VR) in-person study against a classic
control method. Significant results for lowered task completion time, fewer
mode switches, and reduced perceived workload conclusively establish that in
combination with feedforward, ADMC methods can indeed outperform classic mode
switching. A lack of apparent quantitative differences between Continuous and
Threshold reveals the importance of user-centered customization options.
Including these implications in the development process will improve usability,
which is essential for successfully implementing robotic technologies with high
user acceptance.
|
[
{
"created": "Thu, 6 Jul 2023 11:51:43 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Pascher",
"Max",
""
],
[
"Kronhardt",
"Kirill",
""
],
[
"Goldau",
"Felix Ferdinand",
""
],
[
"Frese",
"Udo",
""
],
[
"Gerken",
"Jens",
""
]
] |
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] |
2307.02935
|
Xin Wang
|
Xin Wang, Tao Tan, Yuan Gao, Luyi Han, Tianyu Zhang, Chunyao Lu,
Regina Beets-Tan, Ruisheng Su, Ritse Mann
|
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral
Mammograms using Self-adversarial Learning
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when
abnormalities are developing. It is widely utilized by radiologists for
diagnosis. The question of 'what the symmetrical Bi-MG would look like when the
asymmetrical abnormalities have been removed ?' has not yet received strong
attention in the development of algorithms on mammograms. Addressing this
question could provide valuable insights into mammographic anatomy and aid in
diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet,
which utilizes asymmetrical abnormality transformer guided self-adversarial
learning for disentangling abnormalities and symmetric Bi-MG. At the same time,
our proposed method is partially guided by randomly synthesized abnormalities.
We conduct experiments on three public and one in-house dataset, and
demonstrate that our method outperforms existing methods in abnormality
classification, segmentation, and localization tasks. Additionally,
reconstructed normal mammograms can provide insights toward better
interpretable visual cues for clinical diagnosis. The code will be accessible
to the public.
|
[
{
"created": "Thu, 6 Jul 2023 11:52:36 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Wang",
"Xin",
""
],
[
"Tan",
"Tao",
""
],
[
"Gao",
"Yuan",
""
],
[
"Han",
"Luyi",
""
],
[
"Zhang",
"Tianyu",
""
],
[
"Lu",
"Chunyao",
""
],
[
"Beets-Tan",
"Regina",
""
],
[
"Su",
"Ruisheng",
""
],
[
"Mann",
"Ritse",
""
]
] |
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] |
2307.02936
|
Nuo Chen
|
Nuo Chen, Tetsuya Sakai
|
A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System
Ranking Consistency and Discriminative Power
| null | null | null | null |
cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, Moffat et al. proposed an analytic framework, namely C/W/L/A, for
offline evaluation metrics. This framework allows information retrieval (IR)
researchers to design evaluation metrics through the flexible combination of
user browsing models and user gain aggregations. However, the statistical
stability of C/W/L/A metrics with different aggregations is not yet
investigated. In this study, we investigate the statistical stability of
C/W/L/A metrics from the perspective of: (1) the system ranking similarity
among aggregations, (2) the system ranking consistency of aggregations and (3)
the discriminative power of aggregations. More specifically, we combined
various aggregation functions with the browsing model of Precision, Discounted
Cumulative Gain (DCG), Rank-Biased Precision (RBP), INST, Average Precision
(AP) and Expected Reciprocal Rank (ERR), examing their performances in terms of
system ranking similarity, system ranking consistency and discriminative power
on two offline test collections. Our experimental result suggests that, in
terms of system ranking consistency and discriminative power, the aggregation
function of expected rate of gain (ERG) has an outstanding performance while
the aggregation function of maximum relevance usually has an insufficient
performance. The result also suggests that Precision, DCG, RBP, INST and AP
with their canonical aggregation all have favourable performances in system
ranking consistency and discriminative power; but for ERR, replacing its
canonical aggregation with ERG can further strengthen the discriminative power
while obtaining a system ranking list similar to the canonical version at the
same time.
|
[
{
"created": "Thu, 6 Jul 2023 12:02:38 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Chen",
"Nuo",
""
],
[
"Sakai",
"Tetsuya",
""
]
] |
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] |
2307.02942
|
Gang Wang
|
Long Chen, Ying Zhou, He Zhang, Xuecong Ji, Ke Liao, Yu Ji, Ying Li,
Zhongnan Guo, Xi Shen, Richeng Yu, Xiaohui Yu, Hongming Weng, Gang Wang
|
Tunable magnetism and electron correlation in Titanium-based Kagome
metals RETi3Bi4 (RE = Yb, Pr, and Nd) by rare-earth engineering
|
Manuscript:17 pages, 5 figures; Supporting information:11 pages, 11
tables and 10 figures
| null | null | null |
cond-mat.str-el cond-mat.mtrl-sci physics.chem-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Rare-earth engineering is an effective way to introduce and tune the
magnetism in topological Kagome magnets, which has been acting as a fertile
platform to investigate the quantum interactions between geometry, topology,
spin, and correlation. Here we report the structure and properties of three
newly discovered Titanium-based Kagome metals RETi3Bi4 (RE = Yb, Pr, and Nd)
with various magnetic states. They crystalize in the orthogonal space group
Fmmm (No.69), where slightly distorted Ti Kagome lattice, RE triangular
lattice, Bi honeycomb and triangular lattices stack along the a axis. By
changing the rare earth atoms on RE zag-zig chains, the magnetism can be tuned
from nonmagnetic YbTi3Bi4 to short-range ordered PrTi3Bi4 (Tanomaly ~ 8.2 K),
and finally to ferromagnetic NdTi3Bi4 (Tc ~ 8.5 K). The measurements of
resistivity and specific heat capacity demonstrate an evolution of electron
correlation and density of states near the Fermi level with different rare
earth atoms. In-situ resistance measurements of NdTi3Bi4 under high pressure
further reveal a potential relationship between the electron correlation and
ferromagnetic ordering temperature. These results highlight RETi3Bi4 as another
family of topological Kagome magnets to explore nontrivial band topology and
exotic phases in Kagome materials.
|
[
{
"created": "Thu, 6 Jul 2023 12:17:34 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Chen",
"Long",
""
],
[
"Zhou",
"Ying",
""
],
[
"Zhang",
"He",
""
],
[
"Ji",
"Xuecong",
""
],
[
"Liao",
"Ke",
""
],
[
"Ji",
"Yu",
""
],
[
"Li",
"Ying",
""
],
[
"Guo",
"Zhongnan",
""
],
[
"Shen",
"Xi",
""
],
[
"Yu",
"Richeng",
""
],
[
"Yu",
"Xiaohui",
""
],
[
"Weng",
"Hongming",
""
],
[
"Wang",
"Gang",
""
]
] |
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] |
2307.02946
|
Guangyi Zhang
|
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
|
Finding Favourite Tuples on Data Streams with Provably Few Comparisons
|
To appear in KDD 2023
| null | null | null |
cs.DB cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the most fundamental tasks in data science is to assist a user with
unknown preferences in finding high-utility tuples within a large database. To
accurately elicit the unknown user preferences, a widely-adopted way is by
asking the user to compare pairs of tuples. In this paper, we study the problem
of identifying one or more high-utility tuples by adaptively receiving user
input on a minimum number of pairwise comparisons. We devise a single-pass
streaming algorithm, which processes each tuple in the stream at most once,
while ensuring that the memory size and the number of requested comparisons are
in the worst case logarithmic in $n$, where $n$ is the number of all tuples. An
important variant of the problem, which can help to reduce human error in
comparisons, is to allow users to declare ties when confronted with pairs of
tuples of nearly equal utility. We show that the theoretical guarantees of our
method can be maintained for this important problem variant. In addition, we
show how to enhance existing pruning techniques in the literature by leveraging
powerful tools from mathematical programming. Finally, we systematically
evaluate all proposed algorithms over both synthetic and real-life datasets,
examine their scalability, and demonstrate their superior performance over
existing methods.
|
[
{
"created": "Thu, 6 Jul 2023 12:31:32 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Zhang",
"Guangyi",
""
],
[
"Tatti",
"Nikolaj",
""
],
[
"Gionis",
"Aristides",
""
]
] |
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] |
Finding Favourite Tuples on Data Streams with Provably Few Comparisons
|
https://arxiv.org/abs/2307.02946v1
|
https://arxiv.org/pdf/2307.02946v1.pdf
|
https://github.com/guangyi-zhang/interactive-favourite-tuples
| true | true |
https://paperswithcode.com/paper/finding-favourite-tuples-on-data-streams-with
|
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] |
2307.02947
|
Sergio Chevtchenko
|
Sergio F. Chevtchenko, Yeshwanth Bethi, Teresa B. Ludermir, Saeed
Afshar
|
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued
Observations
| null | null | null | null |
cs.NE cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Reinforcement Learning (RL) provides a powerful framework for decision-making
in complex environments. However, implementing RL in hardware-efficient and
bio-inspired ways remains a challenge. This paper presents a novel Spiking
Neural Network (SNN) architecture for solving RL problems with real-valued
observations. The proposed model incorporates multi-layered event-based
clustering, with the addition of Temporal Difference (TD)-error modulation and
eligibility traces, building upon prior work. An ablation study confirms the
significant impact of these components on the proposed model's performance. A
tabular actor-critic algorithm with eligibility traces and a state-of-the-art
Proximal Policy Optimization (PPO) algorithm are used as benchmarks. Our
network consistently outperforms the tabular approach and successfully
discovers stable control policies on classic RL environments: mountain car,
cart-pole, and acrobot. The proposed model offers an appealing trade-off in
terms of computational and hardware implementation requirements. The model does
not require an external memory buffer nor a global error gradient computation,
and synaptic updates occur online, driven by local learning rules and a
broadcasted TD-error signal. Thus, this work contributes to the development of
more hardware-efficient RL solutions.
|
[
{
"created": "Thu, 6 Jul 2023 12:33:34 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Chevtchenko",
"Sergio F.",
""
],
[
"Bethi",
"Yeshwanth",
""
],
[
"Ludermir",
"Teresa B.",
""
],
[
"Afshar",
"Saeed",
""
]
] |
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] | null | null | null | null | null | null | null |
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] |
2307.02948
|
Kenji Koide
|
Kenji Koide, Shuji Oishi, Masashi Yokozuka, and Atsuhiko Banno
|
Exact Point Cloud Downsampling for Fast and Accurate Global Trajectory
Optimization
|
IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS2023)
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a point cloud downsampling algorithm for fast and
accurate trajectory optimization based on global registration error
minimization. The proposed algorithm selects a weighted subset of residuals of
the input point cloud such that the subset yields exactly the same quadratic
point cloud registration error function as that of the original point cloud at
the evaluation point. This method accurately approximates the original
registration error function with only a small subset of input points (29
residuals at a minimum). Experimental results using the KITTI dataset
demonstrate that the proposed algorithm significantly reduces processing time
(by 87\%) and memory consumption (by 99\%) for global registration error
minimization while retaining accuracy.
|
[
{
"created": "Thu, 6 Jul 2023 12:34:32 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Koide",
"Kenji",
""
],
[
"Oishi",
"Shuji",
""
],
[
"Yokozuka",
"Masashi",
""
],
[
"Banno",
"Atsuhiko",
""
]
] |
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] |
Exact Point Cloud Downsampling for Fast and Accurate Global Trajectory Optimization
|
https://arxiv.org/abs/2307.02948v1
|
https://arxiv.org/pdf/2307.02948v1.pdf
|
https://github.com/koide3/caratheodory2
| true | true |
https://paperswithcode.com/paper/exact-point-cloud-downsampling-for-fast-and
|
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] |
2307.02949
|
Avishai Sintov
|
Eran Bamani, Eden Nissinman, Lisa Koenigsberg, Inbar Meir, Yoav
Matalon and Avishai Sintov
|
Recognition and Estimation of Human Finger Pointing with an RGB Camera
for Robot Directive
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In communication between humans, gestures are often preferred or
complementary to verbal expression since the former offers better spatial
referral. Finger pointing gesture conveys vital information regarding some
point of interest in the environment. In human-robot interaction, a user can
easily direct a robot to a target location, for example, in search and rescue
or factory assistance. State-of-the-art approaches for visual pointing
estimation often rely on depth cameras, are limited to indoor environments and
provide discrete predictions between limited targets. In this paper, we explore
the learning of models for robots to understand pointing directives in various
indoor and outdoor environments solely based on a single RGB camera. A novel
framework is proposed which includes a designated model termed PointingNet.
PointingNet recognizes the occurrence of pointing followed by approximating the
position and direction of the index finger. The model relies on a novel
segmentation model for masking any lifted arm. While state-of-the-art human
pose estimation models provide poor pointing angle estimation accuracy of
28deg, PointingNet exhibits mean accuracy of less than 2deg. With the pointing
information, the target is computed followed by planning and motion of the
robot. The framework is evaluated on two robotic systems yielding accurate
target reaching.
|
[
{
"created": "Thu, 6 Jul 2023 12:36:20 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Bamani",
"Eran",
""
],
[
"Nissinman",
"Eden",
""
],
[
"Koenigsberg",
"Lisa",
""
],
[
"Meir",
"Inbar",
""
],
[
"Matalon",
"Yoav",
""
],
[
"Sintov",
"Avishai",
""
]
] |
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] |
2307.02953
|
Junlong Cheng
|
Junlong Cheng, Chengrui Gao, Fengjie Wang, Min Zhu
|
SegNetr: Rethinking the local-global interactions and skip connections
in U-shaped networks
| null | null | null | null |
eess.IV cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, U-shaped networks have dominated the field of medical image
segmentation due to their simple and easily tuned structure. However, existing
U-shaped segmentation networks: 1) mostly focus on designing complex
self-attention modules to compensate for the lack of long-term dependence based
on convolution operation, which increases the overall number of parameters and
computational complexity of the network; 2) simply fuse the features of encoder
and decoder, ignoring the connection between their spatial locations. In this
paper, we rethink the above problem and build a lightweight medical image
segmentation network, called SegNetr. Specifically, we introduce a novel
SegNetr block that can perform local-global interactions dynamically at any
stage and with only linear complexity. At the same time, we design a general
information retention skip connection (IRSC) to preserve the spatial location
information of encoder features and achieve accurate fusion with the decoder
features. We validate the effectiveness of SegNetr on four mainstream medical
image segmentation datasets, with 59\% and 76\% fewer parameters and GFLOPs
than vanilla U-Net, while achieving segmentation performance comparable to
state-of-the-art methods. Notably, the components proposed in this paper can be
applied to other U-shaped networks to improve their segmentation performance.
|
[
{
"created": "Thu, 6 Jul 2023 12:39:06 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Cheng",
"Junlong",
""
],
[
"Gao",
"Chengrui",
""
],
[
"Wang",
"Fengjie",
""
],
[
"Zhu",
"Min",
""
]
] |
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] |
2307.02954
|
Ignacio Amores-Sesar
|
Orestis Alpos, Ignacio Amores-Sesar, Christian Cachin, Michelle Yeo
|
Eating sandwiches: Modular and lightweight elimination of transaction
reordering attacks
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Traditional blockchains grant the miner of a block full control not only over
which transactions but also their order. This constitutes a major flaw
discovered with the introduction of decentralized finance and allows miners to
perform MEV attacks. In this paper, we address the issue of sandwich attacks by
providing a construction that takes as input a blockchain protocol and outputs
a new blockchain protocol with the same security but in which sandwich attacks
are not profitable. Furthermore, our protocol is fully decentralized with no
trusted third parties or heavy cryptography primitives and carries a linear
increase in latency and minimum computation overhead.
|
[
{
"created": "Thu, 6 Jul 2023 12:40:33 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Alpos",
"Orestis",
""
],
[
"Amores-Sesar",
"Ignacio",
""
],
[
"Cachin",
"Christian",
""
],
[
"Yeo",
"Michelle",
""
]
] |
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] |
2307.02960
|
Ali Mjalled
|
Ali Mjalled, Reza Namdar, Lucas Reineking, Mohammad Norouzi, Fathollah
Varnik, Martin M\"onnigmann
|
Parametric 3D Convolutional Autoencoder for the Prediction of Flow
Fields in a Bed Configuration of Hot Particles
| null | null | null | null |
physics.flu-dyn math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The use of deep learning methods for modeling fluid flow has drawn a lot of
attention in the past few years. In situations where conventional numerical
approaches can be computationally expensive, these techniques have shown
promise in offering accurate, rapid, and practical solutions for modeling
complex fluid flow problems. The success of deep learning is often due to its
ability to extract hidden patterns and features from the data, enabling the
creation of data-driven reduced models that can capture the underlying physics
of the domain. We present a data-driven reduced model for predicting flow
fields in a bed configuration of hot particles. The reduced model consists of a
parametric 3D convolutional autoencoder. The first part resolves the spatial
and temporal dependencies present in the input sequence, while the second part
of the architecture is responsible for predicting the solution at the
subsequent timestep based on the information gathered from the preceding part.
We also propose the utilization of a post-processing non-trainable output layer
following the decoding path to incorporate the physical knowledge, e.g.,
no-slip condition, into the prediction. The evaluation of the reduced model for
a bed configuration with variable particle temperature showed accurate results
at a fraction of the computational cost required by traditional numerical
simulation methods.
|
[
{
"created": "Thu, 6 Jul 2023 12:49:48 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Mjalled",
"Ali",
""
],
[
"Namdar",
"Reza",
""
],
[
"Reineking",
"Lucas",
""
],
[
"Norouzi",
"Mohammad",
""
],
[
"Varnik",
"Fathollah",
""
],
[
"Mönnigmann",
"Martin",
""
]
] |
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] |
2307.02961
|
Vyacheslav Yukalov
|
V.I. Yukalov
|
Selected Topics of Social Physics: Equilibrium Systems
|
Review
|
Physics 5 (2023) 590--635
| null | null |
physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The present review is based on the lectures that the author had been giving
during several years at the Swiss Federal Institute of Technology in Zurich
(ETH Zurich). Being bounded by lecture frames, the selection of the material,
by necessity, is limited and is motivated by the author's research interests.
The paper gives an introduction to the physics of social systems, providing the
main definitions and notions used in the modeling of these systems. The
behavior of social systems is illustrated by several simple typical models. The
present part considers equilibrium systems. Nonequilibrium systems will be
presented in the second part of the lectures. The style of the paper combines
the features of a tutorial and a survey, which, from one side, makes it easy to
read for nonspecialists aiming at grasping the basics of social physics, and
from the other side, describes several rather recent original models containing
new ideas that could be of interest to experienced researchers in the field.
|
[
{
"created": "Thu, 6 Jul 2023 12:52:26 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Yukalov",
"V. I.",
""
]
] |
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] |
2307.02965
|
Lea Kopf
|
Lea Kopf, Rafael Barros, Robert Fickler
|
Spatio-Spectral Vector Beams
| null | null | null | null |
physics.optics quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Increasing the complexity of a light field through the advanced manipulation
of its degrees of freedom (DoF) provides new opportunities for fundamental
studies and technologies. Correlating polarization with the light's spatial or
spectral shape results in so-called spatial or spectral vector beams that are
fully polarized and have a spatially or spectrally varying polarization
structure. Here, we extend the general idea of vector beams by combining both
approaches and structuring a novel state of light in three non-separable DoF's,
i.e. space, wavelength, and polarization. We study in detail their complex
polarization structure, show that the degree of polarization of the field is
only unveiled when the field is narrowly defined in space and wavelength, and
demonstrate the analogy to the loss of coherence in non-separable quantum
systems. Such light fields allow fundamental studies on the non-separable
nature of a classical light field and new technological opportunities, e.g.
through applications in imaging or spectroscopy.
|
[
{
"created": "Thu, 6 Jul 2023 13:05:00 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Kopf",
"Lea",
""
],
[
"Barros",
"Rafael",
""
],
[
"Fickler",
"Robert",
""
]
] |
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] |
2307.02968
|
Sepehr Assadi
|
Sepehr Assadi
|
A Simple $(1-\epsilon)$-Approximation Semi-Streaming Algorithm for
Maximum (Weighted) Matching
| null | null | null | null |
cs.DS cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a simple semi-streaming algorithm for $(1-\epsilon)$-approximation
of bipartite matching in $O(\log{\!(n)}/\epsilon)$ passes. This matches the
performance of state-of-the-art "$\epsilon$-efficient" algorithms, while being
considerably simpler.
The algorithm relies on a "white-box" application of the multiplicative
weight update method with a self-contained primal-dual analysis that can be of
independent interest. To show case this, we use the same ideas, alongside
standard tools from matching theory, to present an equally simple
semi-streaming algorithm for $(1-\epsilon)$-approximation of weighted matchings
in general (not necessarily bipartite) graphs, again in
$O(\log{\!(n)}/\epsilon)$ passes.
|
[
{
"created": "Thu, 6 Jul 2023 13:08:27 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Assadi",
"Sepehr",
""
]
] |
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] |
2307.02969
|
Yara Sch\"utt
|
Yara Sch\"utt, Johannes Liebenow, Tanya Braun, Marcel Gehrke, Florian
Thaeter, Esfandiar Mohammadi
|
DPM: Clustering Sensitive Data through Separation
|
The first two authors equally contributed to this work. 21 pages, 9
figures
| null | null | null |
cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Privacy-preserving clustering groups data points in an unsupervised manner
whilst ensuring that sensitive information remains protected. Previous
privacy-preserving clustering focused on identifying concentration of point
clouds. In this paper, we take another path and focus on identifying
appropriate separators that split a data set. We introduce the novel
differentially private clustering algorithm DPM that searches for accurate data
point separators in a differentially private manner. DPM addresses two key
challenges for finding accurate separators: identifying separators that are
large gaps between clusters instead of small gaps within a cluster and, to
efficiently spend the privacy budget, prioritising separators that split the
data into large subparts. Using the differentially private Exponential
Mechanism, DPM randomly chooses cluster separators with provably high utility:
For a data set $D$, if there is a wide low-density separator in the central
$60\%$ quantile, DPM finds that separator with probability $1 -
\exp(-\sqrt{|D|})$. Our experimental evaluation demonstrates that DPM achieves
significant improvements in terms of the clustering metric inertia. With the
inertia results of the non-private KMeans++ as a baseline, for $\varepsilon =
1$ and $\delta=10^{-5}$ DPM improves upon the difference to the baseline by up
to $50\%$ for a synthetic data set and by up to $62\%$ for a real-world data
set compared to a state-of-the-art clustering algorithm by Chang and Kamath.
|
[
{
"created": "Thu, 6 Jul 2023 13:12:19 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Schütt",
"Yara",
""
],
[
"Liebenow",
"Johannes",
""
],
[
"Braun",
"Tanya",
""
],
[
"Gehrke",
"Marcel",
""
],
[
"Thaeter",
"Florian",
""
],
[
"Mohammadi",
"Esfandiar",
""
]
] |
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] |
2307.02971
|
Longyue Wang
|
Bingshuai Liu, Longyue Wang, Chenyang Lyu, Yong Zhang, Jinsong Su,
Shuming Shi, Zhaopeng Tu
|
On the Cultural Gap in Text-to-Image Generation
|
Equal contribution: Bingshuai Liu and Longyue Wang. Work done while
Bingshuai Liu and Chengyang Lyu were interning at Tencent AI Lab. Zhaopeng Tu
is the corresponding author
| null | null | null |
cs.CV cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
One challenge in text-to-image (T2I) generation is the inadvertent reflection
of culture gaps present in the training data, which signifies the disparity in
generated image quality when the cultural elements of the input text are rarely
collected in the training set. Although various T2I models have shown
impressive but arbitrary examples, there is no benchmark to systematically
evaluate a T2I model's ability to generate cross-cultural images. To bridge the
gap, we propose a Challenging Cross-Cultural (C3) benchmark with comprehensive
evaluation criteria, which can assess how well-suited a model is to a target
culture. By analyzing the flawed images generated by the Stable Diffusion model
on the C3 benchmark, we find that the model often fails to generate certain
cultural objects. Accordingly, we propose a novel multi-modal metric that
considers object-text alignment to filter the fine-tuning data in the target
culture, which is used to fine-tune a T2I model to improve cross-cultural
generation. Experimental results show that our multi-modal metric provides
stronger data selection performance on the C3 benchmark than existing metrics,
in which the object-text alignment is crucial. We release the benchmark, data,
code, and generated images to facilitate future research on culturally diverse
T2I generation (https://github.com/longyuewangdcu/C3-Bench).
|
[
{
"created": "Thu, 6 Jul 2023 13:17:55 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Liu",
"Bingshuai",
""
],
[
"Wang",
"Longyue",
""
],
[
"Lyu",
"Chenyang",
""
],
[
"Zhang",
"Yong",
""
],
[
"Su",
"Jinsong",
""
],
[
"Shi",
"Shuming",
""
],
[
"Tu",
"Zhaopeng",
""
]
] |
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] |
2307.02972
|
Axel Klawonn
|
Daniel Balzani, Alexander Heinlein, Axel Klawonn, Jascha Knepper,
Sharan Nurani Ramesh, Oliver Rheinbach, Lea Sassmannshausen, Klemens Uhlmann
|
A computational framework for pharmaco-mechanical interactions in
arterial walls using parallel monolithic domain decomposition methods
| null | null | null | null |
math.NA cs.CE cs.DC cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A computational framework is presented to numerically simulate the effects of
antihypertensive drugs, in particular calcium channel blockers, on the
mechanical response of arterial walls. A stretch-dependent smooth muscle model
by Uhlmann and Balzani is modified to describe the interaction of
pharmacological drugs and the inhibition of smooth muscle activation. The
coupled deformation-diffusion problem is then solved using the finite element
software FEDDLib and overlapping Schwarz preconditioners from the Trilinos
package FROSch. These preconditioners include highly scalable parallel GDSW
(generalized Dryja-Smith-Widlund) and RDSW (reduced GDSW) preconditioners.
Simulation results show the expected increase in the lumen diameter of an
idealized artery due to the drug-induced reduction of smooth muscle
contraction, as well as a decrease in the rate of arterial contraction in the
presence of calcium channel blockers. Strong and weak parallel scalability of
the resulting computational implementation are also analyzed.
|
[
{
"created": "Thu, 6 Jul 2023 13:18:23 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Balzani",
"Daniel",
""
],
[
"Heinlein",
"Alexander",
""
],
[
"Klawonn",
"Axel",
""
],
[
"Knepper",
"Jascha",
""
],
[
"Ramesh",
"Sharan Nurani",
""
],
[
"Rheinbach",
"Oliver",
""
],
[
"Sassmannshausen",
"Lea",
""
],
[
"Uhlmann",
"Klemens",
""
]
] |
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] |
2307.02973
|
Andrey Kuzmin
|
Andrey Kuzmin, Markus Nagel, Mart van Baalen, Arash Behboodi, Tijmen
Blankevoort
|
Pruning vs Quantization: Which is Better?
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Neural network pruning and quantization techniques are almost as old as
neural networks themselves. However, to date only ad-hoc comparisons between
the two have been published. In this paper, we set out to answer the question
on which is better: neural network quantization or pruning? By answering this
question, we hope to inform design decisions made on neural network hardware
going forward. We provide an extensive comparison between the two techniques
for compressing deep neural networks. First, we give an analytical comparison
of expected quantization and pruning error for general data distributions.
Then, we provide lower bounds for the per-layer pruning and quantization error
in trained networks, and compare these to empirical error after optimization.
Finally, we provide an extensive experimental comparison for training 8
large-scale models on 3 tasks. Our results show that in most cases quantization
outperforms pruning. Only in some scenarios with very high compression ratio,
pruning might be beneficial from an accuracy standpoint.
|
[
{
"created": "Thu, 6 Jul 2023 13:18:44 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Kuzmin",
"Andrey",
""
],
[
"Nagel",
"Markus",
""
],
[
"van Baalen",
"Mart",
""
],
[
"Behboodi",
"Arash",
""
],
[
"Blankevoort",
"Tijmen",
""
]
] |
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] |
2307.02974
|
Binglu Wang
|
Yuting Lu, Lingtong Min, Binglu Wang, Le Zheng, Xiaoxu Wang, Yongqiang
Zhao, Teng Long
|
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based
Transformer Network for Remote Sensing Image Super-Resolution
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Remote sensing image super-resolution (RSISR) plays a vital role in enhancing
spatial detials and improving the quality of satellite imagery. Recently,
Transformer-based models have shown competitive performance in RSISR. To
mitigate the quadratic computational complexity resulting from global
self-attention, various methods constrain attention to a local window,
enhancing its efficiency. Consequently, the receptive fields in a single
attention layer are inadequate, leading to insufficient context modeling.
Furthermore, while most transform-based approaches reuse shallow features
through skip connections, relying solely on these connections treats shallow
and deep features equally, impeding the model's ability to characterize them.
To address these issues, we propose a novel transformer architecture called
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based
Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively
enhances global cognition and understanding of the entire image, facilitating
efficient integration of features cross-stages. The model incorporates
cross-spatial pixel integration attention (CSPIA) to introduce contextual
information into a local window, while cross-stage feature fusion attention
(CSFFA) adaptively fuses features from the previous stage to improve feature
expression in line with the requirements of the current stage. We conducted
comprehensive experiments on multiple benchmark datasets, demonstrating the
superior performance of our proposed SPIFFNet in terms of both quantitative
metrics and visual quality when compared to state-of-the-art methods.
|
[
{
"created": "Thu, 6 Jul 2023 13:19:06 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Lu",
"Yuting",
""
],
[
"Min",
"Lingtong",
""
],
[
"Wang",
"Binglu",
""
],
[
"Zheng",
"Le",
""
],
[
"Wang",
"Xiaoxu",
""
],
[
"Zhao",
"Yongqiang",
""
],
[
"Long",
"Teng",
""
]
] |
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] |
2307.02975
|
Mattia Giovanni Campana
|
Mattia Giovanni Campana, Franca Delmastro, Elena Pagani
|
Transfer Learning for the Efficient Detection of COVID-19 from
Smartphone Audio Data
| null |
Pervasive and Mobile Computing, Volume 89, 2023
|
10.1016/j.pmcj.2023.101754
| null |
cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Disease detection from smartphone data represents an open research challenge
in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are
an important case study in this area and their early detection is a potential
real instrument to counteract the pandemic situation. The efficacy of this
solution mainly depends on the performances of AI algorithms applied to the
collected data and their possible implementation directly on the users' mobile
devices. Considering these issues, and the limited amount of available data, in
this paper we present the experimental evaluation of 3 different deep learning
models, compared also with hand-crafted features, and of two main approaches of
transfer learning in the considered scenario: both feature extraction and
fine-tuning. Specifically, we considered VGGish, YAMNET, and
L\textsuperscript{3}-Net (including 12 different configurations) evaluated
through user-independent experiments on 4 different datasets (13,447 samples in
total). Results clearly show the advantages of L\textsuperscript{3}-Net in all
the experimental settings as it overcomes the other solutions by 12.3\% in
terms of Precision-Recall AUC as features extractor, and by 10\% when the model
is fine-tuned. Moreover, we note that to fine-tune only the fully-connected
layers of the pre-trained models generally leads to worse performances, with an
average drop of 6.6\% with respect to feature extraction. %highlighting the
need for further investigations. Finally, we evaluate the memory footprints of
the different models for their possible applications on commercial mobile
devices.
|
[
{
"created": "Thu, 6 Jul 2023 13:19:27 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Campana",
"Mattia Giovanni",
""
],
[
"Delmastro",
"Franca",
""
],
[
"Pagani",
"Elena",
""
]
] |
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] |
Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data
|
https://arxiv.org/abs/2307.02975v1
|
https://arxiv.org/pdf/2307.02975v1.pdf
|
https://github.com/mattiacampana/transfer-learning-covid-19
| true | true |
https://paperswithcode.com/paper/transfer-learning-for-the-efficient-detection
|
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] |
2307.02977
|
David Abella
|
David Abella, Juan Carlos Gonz\'alez-Avella, Maxi San Miguel, Jos\'e
J. Ramasco
|
Ordering dynamics and aging in the Symmetrical Threshold model
|
17 pages, 15 figures
| null | null | null |
physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The so-called Granovetter-Watts model was introduced to capture a situation
in which the adoption of new ideas or technologies requires a certain
redundancy in the social environment of each agent to take effect. This model
has become a paradigm for complex contagion. Here we investigate a symmetric
version of the model: agents may be in two states that can spread equally
through the system via complex contagion. We find three possible phases: a
mixed one (dynamically active disordered state), an ordered one, and a
heterogeneous frozen phase. These phases exist for several configurations of
the contact network. Then we consider the effect of introducing aging as a
non-Markovian mechanism in the model, where agents become increasingly
resistant to change their state the longer they remain in it. We show that when
aging is present, the mixed phase is replaced, for sparse networks, by a new
phase with different dynamical properties. This new phase is characterized by
an initial disordering stage followed by a slow ordering process towards a
fully ordered absorbing state. In the ordered phase, aging modifies the
dynamical properties. For random contact networks, we develop a theoretical
description based on an Approximate Master Equation that describes with good
accuracy the results of numerical simulations for the model with and without
aging.
|
[
{
"created": "Thu, 6 Jul 2023 13:25:11 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Abella",
"David",
""
],
[
"González-Avella",
"Juan Carlos",
""
],
[
"Miguel",
"Maxi San",
""
],
[
"Ramasco",
"José J.",
""
]
] |
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] |
2307.02978
|
Sushanta Kumar Sahu
|
Sushanta Kumar Sahu, Ananda S. Chowdhury
|
Multi-modal multi-class Parkinson disease classification using CNN and
decision level fusion
|
10th International Conference on Pattern Recognition and Machine
Intelligence (Acepted)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Parkinson disease is the second most common neurodegenerative disorder, as
reported by the World Health Organization. In this paper, we propose a direct
three-Class PD classification using two different modalities, namely, MRI and
DTI. The three classes used for classification are PD, Scans Without Evidence
of Dopamine Deficit and Healthy Control. We use white matter and gray matter
from the MRI and fractional anisotropy and mean diffusivity from the DTI to
achieve our goal. We train four separate CNNs on the above four types of data.
At the decision level, the outputs of the four CNN models are fused with an
optimal weighted average fusion technique. We achieve an accuracy of 95.53
percentage for the direct three class classification of PD, HC and SWEDD on the
publicly available PPMI database. Extensive comparisons including a series of
ablation studies clearly demonstrate the effectiveness of our proposed
solution.
|
[
{
"created": "Thu, 6 Jul 2023 13:25:31 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Sahu",
"Sushanta Kumar",
""
],
[
"Chowdhury",
"Ananda S.",
""
]
] |
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] |
2307.02982
|
Andreas Opedal
|
Andreas Opedal, Ran Zmigrod, Tim Vieira, Ryan Cotterell, Jason Eisner
|
Efficient Semiring-Weighted Earley Parsing
|
Main conference long paper at ACL 2023
| null | null | null |
cs.CL cs.DS cs.FL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This paper provides a reference description, in the form of a deduction
system, of Earley's (1970) context-free parsing algorithm with various
speed-ups. Our presentation includes a known worst-case runtime improvement
from Earley's $O (N^3|G||R|)$, which is unworkable for the large grammars that
arise in natural language processing, to $O (N^3|G|)$, which matches the
runtime of CKY on a binarized version of the grammar $G$. Here $N$ is the
length of the sentence, $|R|$ is the number of productions in $G$, and $|G|$ is
the total length of those productions. We also provide a version that achieves
runtime of $O (N^3|M|)$ with $|M| \leq |G|$ when the grammar is represented
compactly as a single finite-state automaton $M$ (this is partly novel). We
carefully treat the generalization to semiring-weighted deduction,
preprocessing the grammar like Stolcke (1995) to eliminate deduction cycles,
and further generalize Stolcke's method to compute the weights of sentence
prefixes. We also provide implementation details for efficient execution,
ensuring that on a preprocessed grammar, the semiring-weighted versions of our
methods have the same asymptotic runtime and space requirements as the
unweighted methods, including sub-cubic runtime on some grammars.
|
[
{
"created": "Thu, 6 Jul 2023 13:33:59 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Opedal",
"Andreas",
""
],
[
"Zmigrod",
"Ran",
""
],
[
"Vieira",
"Tim",
""
],
[
"Cotterell",
"Ryan",
""
],
[
"Eisner",
"Jason",
""
]
] |
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] |
Efficient Semiring-Weighted Earley Parsing
|
https://arxiv.org/abs/2307.02982v1
|
https://arxiv.org/pdf/2307.02982v1.pdf
|
https://github.com/rycolab/earleys-algo
| true | true |
https://paperswithcode.com/paper/efficient-semiring-weighted-earley-parsing
|
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] |
2307.02984
|
Matteo Pennisi
|
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Simone
Palazzo, Ulas Bagci, Concetto Spampinato
|
A Privacy-Preserving Walk in the Latent Space of Generative Models for
Medical Applications
|
Accepted at MICCAI 2023
| null | null | null |
cs.LG cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Generative Adversarial Networks (GANs) have demonstrated their ability to
generate synthetic samples that match a target distribution. However, from a
privacy perspective, using GANs as a proxy for data sharing is not a safe
solution, as they tend to embed near-duplicates of real samples in the latent
space. Recent works, inspired by k-anonymity principles, address this issue
through sample aggregation in the latent space, with the drawback of reducing
the dataset by a factor of k. Our work aims to mitigate this problem by
proposing a latent space navigation strategy able to generate diverse synthetic
samples that may support effective training of deep models, while addressing
privacy concerns in a principled way. Our approach leverages an auxiliary
identity classifier as a guide to non-linearly walk between points in the
latent space, minimizing the risk of collision with near-duplicates of real
samples. We empirically demonstrate that, given any random pair of points in
the latent space, our walking strategy is safer than linear interpolation. We
then test our path-finding strategy combined to k-same methods and demonstrate,
on two benchmarks for tuberculosis and diabetic retinopathy classification,
that training a model using samples generated by our approach mitigate drops in
performance, while keeping privacy preservation.
|
[
{
"created": "Thu, 6 Jul 2023 13:35:48 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Pennisi",
"Matteo",
""
],
[
"Salanitri",
"Federica Proietto",
""
],
[
"Bellitto",
"Giovanni",
""
],
[
"Palazzo",
"Simone",
""
],
[
"Bagci",
"Ulas",
""
],
[
"Spampinato",
"Concetto",
""
]
] |
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A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
|
https://arxiv.org/abs/2307.02984v1
|
https://arxiv.org/pdf/2307.02984v1.pdf
|
https://github.com/perceivelab/plan
| true | true |
https://paperswithcode.com/paper/a-privacy-preserving-walk-in-the-latent-space
|
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] |
2307.02988
|
Kai Cui
|
Kai Cui, Lars Baumg\"artner, Burak Yilmaz, Mengguang Li, Christian
Fabian, Benjamin Becker, Lin Xiang, Maximilian Bauer, Heinz Koeppl
|
UAV Swarms for Joint Data Ferrying and Dynamic Cell Coverage via Optimal
Transport Descent and Quadratic Assignment
|
Accepted to IEEE LCN 2023 as full paper, pre-final version
| null | null | null |
cs.NI eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Both data ferrying with disruption-tolerant networking (DTN) and mobile
cellular base stations constitute important techniques for UAV-aided
communication in situations of crises where standard communication
infrastructure is unavailable. For optimal use of a limited number of UAVs, we
propose providing both DTN and a cellular base station on each UAV. Here, DTN
is used for large amounts of low-priority data, while capacity-constrained cell
coverage remains reserved for emergency calls or command and control. We
optimize cell coverage via a novel optimal transport-based formulation using
alternating minimization, while for data ferrying we periodically deliver data
between dynamic clusters by solving quadratic assignment problems. In our
evaluation, we consider different scenarios with varying mobility models and a
wide range of flight patterns. Overall, we tractably achieve optimal cell
coverage under quality-of-service costs with DTN-based data ferrying, enabling
large-scale deployment of UAV swarms for crisis communication.
|
[
{
"created": "Thu, 6 Jul 2023 13:42:21 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Cui",
"Kai",
""
],
[
"Baumgärtner",
"Lars",
""
],
[
"Yilmaz",
"Burak",
""
],
[
"Li",
"Mengguang",
""
],
[
"Fabian",
"Christian",
""
],
[
"Becker",
"Benjamin",
""
],
[
"Xiang",
"Lin",
""
],
[
"Bauer",
"Maximilian",
""
],
[
"Koeppl",
"Heinz",
""
]
] |
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] |
2307.02991
|
Asma Atamna
|
Abhijeet Pendyala, Justin Dettmer, Tobias Glasmachers, Asma Atamna
|
ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource
Allocation
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present ContainerGym, a benchmark for reinforcement learning inspired by a
real-world industrial resource allocation task. The proposed benchmark encodes
a range of challenges commonly encountered in real-world sequential decision
making problems, such as uncertainty. It can be configured to instantiate
problems of varying degrees of difficulty, e.g., in terms of variable
dimensionality. Our benchmark differs from other reinforcement learning
benchmarks, including the ones aiming to encode real-world difficulties, in
that it is directly derived from a real-world industrial problem, which
underwent minimal simplification and streamlining. It is sufficiently versatile
to evaluate reinforcement learning algorithms on any real-world problem that
fits our resource allocation framework. We provide results of standard baseline
methods. Going beyond the usual training reward curves, our results and the
statistical tools used to interpret them allow to highlight interesting
limitations of well-known deep reinforcement learning algorithms, namely PPO,
TRPO and DQN.
|
[
{
"created": "Thu, 6 Jul 2023 13:44:29 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Pendyala",
"Abhijeet",
""
],
[
"Dettmer",
"Justin",
""
],
[
"Glasmachers",
"Tobias",
""
],
[
"Atamna",
"Asma",
""
]
] |
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ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation
|
https://arxiv.org/abs/2307.02991v1
|
https://arxiv.org/pdf/2307.02991v1.pdf
|
https://github.com/pendu/containergym
| true | true |
https://paperswithcode.com/paper/containergym-a-real-world-reinforcement
|
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] |
2307.02994
|
Meenu Upadhyay
|
Meenu Upadhyay, Kai T\"opfer and Markus Meuwly
|
Molecular Simulation for Atmospheric Reaction Exploration and Discovery:
Non-Equilibrium Dynamics, Roaming and Glycolaldehyde Formation Following
Photo-Induced Decomposition of syn-Acetaldehyde Oxide
|
28+11pages, 7+14figures
| null | null | null |
physics.chem-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The decomposition and chemical dynamics for vibrationally excited
syn-CH$_3$CHOO is followed based on statistically significant numbers of
molecular dynamics simulations. Using a neural network-based reactive potential
energy surface, transfer learned to the CASPT2 level of theory, the final total
kinetic energy release and rotational state distributions of the OH fragment
are in quantitative agreement with experiment. In particular the widths of
these distributions are sensitive to the experimentally unknown strength of the
O--O bond strength, for which values $D_e \in [22,25]$ kcal/mol are found. Due
to the non-equilibrium nature of the process considered, the energy-dependent
rates do not depend appreciably on the O--O scission energy. Roaming dynamics
of the OH-photoproduct leads to formation of glycolaldehyde on the picosecond
time scale with subsequent decomposition into CH$_2$OH+HCO. Atomistic
simulations with global reactive machine-learned energy functions provide a
viable route to quantitatively explore the chemistry and reaction dynamics for
atmospheric reactions.
|
[
{
"created": "Thu, 6 Jul 2023 13:55:52 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Upadhyay",
"Meenu",
""
],
[
"Töpfer",
"Kai",
""
],
[
"Meuwly",
"Markus",
""
]
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] |
2307.02997
|
Xi Jia
|
Xi Jia, Alexander Thorley, Alberto Gomez, Wenqi Lu, Dipak Kotecha and
Jinming Duan
|
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D
Medical Image Registration
|
Under review. arXiv admin note: text overlap with arXiv:2211.16342
| null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
U-Net style networks are commonly utilized in unsupervised image registration
to predict dense displacement fields, which for high-resolution volumetric
image data is a resource-intensive and time-consuming task. To tackle this
challenge, we first propose Fourier-Net, which replaces the costly U-Net style
expansive path with a parameter-free model-driven decoder. Instead of directly
predicting a full-resolution displacement field, our Fourier-Net learns a
low-dimensional representation of the displacement field in the band-limited
Fourier domain which our model-driven decoder converts to a full-resolution
displacement field in the spatial domain. Expanding upon Fourier-Net, we then
introduce Fourier-Net+, which additionally takes the band-limited spatial
representation of the images as input and further reduces the number of
convolutional layers in the U-Net style network's contracting path. Finally, to
enhance the registration performance, we propose a cascaded version of
Fourier-Net+. We evaluate our proposed methods on three datasets, on which our
proposed Fourier-Net and its variants achieve comparable results with current
state-of-the art methods, while exhibiting faster inference speeds, lower
memory footprint, and fewer multiply-add operations. With such small
computational cost, our Fourier-Net+ enables the efficient training of
large-scale 3D registration on low-VRAM GPUs. Our code is publicly available at
\url{https://github.com/xi-jia/Fourier-Net}.
|
[
{
"created": "Thu, 6 Jul 2023 13:57:12 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Jia",
"Xi",
""
],
[
"Thorley",
"Alexander",
""
],
[
"Gomez",
"Alberto",
""
],
[
"Lu",
"Wenqi",
""
],
[
"Kotecha",
"Dipak",
""
],
[
"Duan",
"Jinming",
""
]
] |
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] |
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
|
https://arxiv.org/abs/2307.02997v1
|
https://arxiv.org/pdf/2307.02997v1.pdf
|
https://github.com/xi-jia/fourier-net
| true | true |
https://paperswithcode.com/paper/fourier-net-leveraging-band-limited
|
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] |
2307.02998
|
Ming Huang
|
Shan Li, Ming Huang, Yongfeng Song, Bo Lan, Xiongbing Li
|
Ultrasonic backscattering model for Rayleigh waves in polycrystals with
Born and independent scattering approximations
|
18 pages, 10 figures
| null | null | null |
physics.app-ph
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents theoretical and numerical models for the backscattering
of 2D Rayleigh waves in single-phase, untextured polycrystalline materials with
statistically equiaxed grains. The theoretical model, based on our prior
inclusion-induced Rayleigh wave scattering model and the independent scattering
approximation, considers single scattering of Rayleigh-to-Rayleigh (R-R) waves.
The numerical finite element model is established to accurately simulate the
scattering problem and evaluate the theoretical model. Good quantitative
agreement is observed between the theoretical model and the finite element
results, especially for weakly scattering materials. The agreement decreases
with the increase of the anisotropy index, owing to the reduced applicability
of the Born approximation. However, the agreement remains generally good when
weak multiple scattering is involved. In addition, the R-R backscattering
behaviour of 2D Rayleigh waves is similar to the longitudinal-to-longitudinal
and transverse-to-transverse backscattering of bulk waves, with the former
exhibiting stronger scattering. These findings establish a foundation for using
Rayleigh waves in quantitative characterisation of polycrystalline materials.
|
[
{
"created": "Thu, 6 Jul 2023 14:00:05 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Li",
"Shan",
""
],
[
"Huang",
"Ming",
""
],
[
"Song",
"Yongfeng",
""
],
[
"Lan",
"Bo",
""
],
[
"Li",
"Xiongbing",
""
]
] |
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] |
2307.03002
|
Matthieu G\'en\'evriez
|
Eduardo Marin-Bujedo and Matthieu G\'en\'evriez
|
Autoionization of high-$\ell$ core-excited Rydberg states of
alkaline-earth-metal atoms
|
16 pages, 11 figures
| null | null | null |
physics.atom-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The autoionization of core-excited Rydberg states is theoretically studied
for a broad range of principal and angular-momentum quantum numbers $n$ and
$\ell$ in alkaline-earth-metal atoms. We combined two theoretical methods to
calculate accurate autoionization rates for $n=10-65$ and $\ell=0-45$ over the
100 orders of magnitude that they span. The strong interaction between the two
valence electrons for low $\ell$ states is treated from first principles with
configuration interaction with exterior complex scaling, while at large $\ell$
the weak correlation is described by a perturbative approach and
arbitrary-precision floating-point arithmetics. The results, which we benchmark
against available experimental data, provide autoionization rates for the
$Np_{1/2, 3/2}$ and, when applicable, $(N-1)d_{3/2, 5/2}$ ion-core states of
Mg, Ca and Sr ($N=3-5$). Using the extensive set of calculated data, we analyze
the dependence of the rates on $\ell$ and identify five general laws of the
autoionization of high-$\ell$ states. An empirical formula describing the
scaling of the rates with $\ell$ is suggested.
|
[
{
"created": "Thu, 6 Jul 2023 14:05:39 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Marin-Bujedo",
"Eduardo",
""
],
[
"Génévriez",
"Matthieu",
""
]
] |
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] |
2307.03003
|
Johannes Jakubik
|
Johannes Jakubik, Daniel Weber, Patrick Hemmer, Michael V\"ossing,
Gerhard Satzger
|
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial
to Human Experts
|
Accepted at International Conference on Wirtschaftsinformatik, 2023
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Information systems increasingly leverage artificial intelligence (AI) and
machine learning (ML) to generate value from vast amounts of data. However, ML
models are imperfect and can generate incorrect classifications. Hence,
human-in-the-loop (HITL) extensions to ML models add a human review for
instances that are difficult to classify. This study argues that continuously
relying on human experts to handle difficult model classifications leads to a
strong increase in human effort, which strains limited resources. To address
this issue, we propose a hybrid system that creates artificial experts that
learn to classify data instances from unknown classes previously reviewed by
human experts. Our hybrid system assesses which artificial expert is suitable
for classifying an instance from an unknown class and automatically assigns it.
Over time, this reduces human effort and increases the efficiency of the
system. Our experiments demonstrate that our approach outperforms traditional
HITL systems for several benchmarks on image classification.
|
[
{
"created": "Thu, 6 Jul 2023 14:06:23 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Jakubik",
"Johannes",
""
],
[
"Weber",
"Daniel",
""
],
[
"Hemmer",
"Patrick",
""
],
[
"Vössing",
"Michael",
""
],
[
"Satzger",
"Gerhard",
""
]
] |
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] |
2307.03004
|
Johannes K\"ohler
|
Johannes K\"ohler, Matthas A. M\"uller, Frank Allg\"ower
|
Analysis and design of model predictive control frameworks for dynamic
operation -- An overview
| null | null | null | null |
eess.SY cs.SY math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article provides an overview of model predictive control (MPC)
frameworks for dynamic operation of nonlinear constrained systems. Dynamic
operation is often an integral part of the control objective, ranging from
tracking of reference signals to the general economic operation of a plant
under online changing time-varying operating conditions. We focus on the
particular challenges that arise when dealing with such more general control
goals and present methods that have emerged in the literature to address these
issues. The goal of this article is to present an overview of the
state-of-the-art techniques, providing a diverse toolkit to apply and further
develop MPC formulations that can handle the challenges intrinsic to dynamic
operation. We also critically assess the applicability of the different
research directions, discussing limitations and opportunities for further
research
|
[
{
"created": "Thu, 6 Jul 2023 14:07:51 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Köhler",
"Johannes",
""
],
[
"Müller",
"Matthas A.",
""
],
[
"Allgöwer",
"Frank",
""
]
] |
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] |
2307.03007
|
Christian Jauch
|
Christian Jauch, Timo Leitritz, Marco F. Huber
|
Self-supervised Optimization of Hand Pose Estimation using Anatomical
Features and Iterative Learning
|
Manuscript accepted at IEEE SMC 2023
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Manual assembly workers face increasing complexity in their work.
Human-centered assistance systems could help, but object recognition as an
enabling technology hinders sophisticated human-centered design of these
systems. At the same time, activity recognition based on hand poses suffers
from poor pose estimation in complex usage scenarios, such as wearing gloves.
This paper presents a self-supervised pipeline for adapting hand pose
estimation to specific use cases with minimal human interaction. This enables
cheap and robust hand posebased activity recognition. The pipeline consists of
a general machine learning model for hand pose estimation trained on a
generalized dataset, spatial and temporal filtering to account for anatomical
constraints of the hand, and a retraining step to improve the model. Different
parameter combinations are evaluated on a publicly available and annotated
dataset. The best parameter and model combination is then applied to unlabelled
videos from a manual assembly scenario. The effectiveness of the pipeline is
demonstrated by training an activity recognition as a downstream task in the
manual assembly scenario.
|
[
{
"created": "Thu, 6 Jul 2023 14:13:11 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Jauch",
"Christian",
""
],
[
"Leitritz",
"Timo",
""
],
[
"Huber",
"Marco F.",
""
]
] |
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] |
2307.03008
|
Jos\'e Morano
|
Jos\'e Morano, Guilherme Aresta, Dmitrii Lachinov, Julia Mai, Ursula
Schmidt-Erfurth, Hrvoje Bogunovi\'c
|
Self-supervised learning via inter-modal reconstruction and feature
projection networks for label-efficient 3D-to-2D segmentation
|
To appear in MICCAI 2023. Code:
https://github.com/j-morano/multimodal-ssl-fpn
| null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Deep learning has become a valuable tool for the automation of certain
medical image segmentation tasks, significantly relieving the workload of
medical specialists. Some of these tasks require segmentation to be performed
on a subset of the input dimensions, the most common case being 3D-to-2D.
However, the performance of existing methods is strongly conditioned by the
amount of labeled data available, as there is currently no data efficient
method, e.g. transfer learning, that has been validated on these tasks. In this
work, we propose a novel convolutional neural network (CNN) and self-supervised
learning (SSL) method for label-efficient 3D-to-2D segmentation. The CNN is
composed of a 3D encoder and a 2D decoder connected by novel 3D-to-2D blocks.
The SSL method consists of reconstructing image pairs of modalities with
different dimensionality. The approach has been validated in two tasks with
clinical relevance: the en-face segmentation of geographic atrophy and
reticular pseudodrusen in optical coherence tomography. Results on different
datasets demonstrate that the proposed CNN significantly improves the state of
the art in scenarios with limited labeled data by up to 8% in Dice score.
Moreover, the proposed SSL method allows further improvement of this
performance by up to 23%, and we show that the SSL is beneficial regardless of
the network architecture.
|
[
{
"created": "Thu, 6 Jul 2023 14:16:25 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Morano",
"José",
""
],
[
"Aresta",
"Guilherme",
""
],
[
"Lachinov",
"Dmitrii",
""
],
[
"Mai",
"Julia",
""
],
[
"Schmidt-Erfurth",
"Ursula",
""
],
[
"Bogunović",
"Hrvoje",
""
]
] |
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] |
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
|
https://arxiv.org/abs/2307.03008v1
|
https://arxiv.org/pdf/2307.03008v1.pdf
|
https://github.com/j-morano/multimodal-ssl-fpn
| true | true |
https://paperswithcode.com/paper/self-supervised-learning-via-inter-modal
|
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] |
2307.03015
|
Hongzhan Yu
|
Hongzhan Yu, Chiaki Hirayama, Chenning Yu, Sylvia Herbert, Sicun Gao
|
Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance
|
To be published in IROS 2023
| null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There are two major challenges for scaling up robot navigation around dynamic
obstacles: the complex interaction dynamics of the obstacles can be hard to
model analytically, and the complexity of planning and control grows
exponentially in the number of obstacles. Data-driven and learning-based
methods are thus particularly valuable in this context. However, data-driven
methods are sensitive to distribution drift, making it hard to train and
generalize learned models across different obstacle densities. We propose a
novel method for compositional learning of Sequential Neural Control Barrier
models (SNCBFs) to achieve scalability. Our approach exploits an important
observation: the spatial interaction patterns of multiple dynamic obstacles can
be decomposed and predicted through temporal sequences of states for each
obstacle. Through decomposition, we can generalize control policies trained
only with a small number of obstacles, to environments where the obstacle
density can be 100x higher. We demonstrate the benefits of the proposed methods
in improving dynamic collision avoidance in comparison with existing methods
including potential fields, end-to-end reinforcement learning, and
model-predictive control. We also perform hardware experiments and show the
practical effectiveness of the approach in the supplementary video.
|
[
{
"created": "Thu, 6 Jul 2023 14:24:17 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Yu",
"Hongzhan",
""
],
[
"Hirayama",
"Chiaki",
""
],
[
"Yu",
"Chenning",
""
],
[
"Herbert",
"Sylvia",
""
],
[
"Gao",
"Sicun",
""
]
] |
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] |
2307.03016
|
Vladimir Dorovskyy Dr.
|
Vladimir Dorovskyy, Valentin Melnik and Anatolii Brazhenko
|
Ground Based Support of the Space Mission Parker Performed with
Ukrainian Low Frequency Radio Telescopes
|
14 pages, 4 figures, 2 tables
|
ISSN 1027-9636. Radio Physics and Radio Astronomy. Vol. 28, No. 2,
2023
|
10.15407/rpra28.02.117
| null |
astro-ph.SR astro-ph.IM physics.space-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The purpose of this work is to demonstrate the effectiveness of ground-based
support for space missions, primarily PSP, using large Ukrainian decameter
radio telescopes. Another goal of the work is to carry out cross calibration of
the radiometers onboard spacecraft using the calibrated data of the
ground-based radio telescopes. One of the most common methods of remote
diagnostics of the solar corona is the study of radio emission, the sources of
which are located in the solar corona at different heliocentric altitudes. The
technique of joint space terrestrial observations consists in the simultaneous
observation of individual events and their analysis in the widest possible
frequency band during the maximum approach of the PSP vehicle to the Sun. At
the same time, observation in the common frequency band is proposed to be used
for calibration of the onboard radio receivers. The methods of planning joint
space terrestrial observations are substantiated. Using the data of the UTR 2,
URAN 2 radio telescopes and the PSP probe, the dynamic and polarization spectra
of the simultaneously observed bursts on June 9, 2020 were obtained. The
identification and comparison of individual bursts was carried out. A common
dynamic spectrum of the bursts in the frequency band 0.5 ... 32 MHz was
obtained. Cross calibration of the HFR receiver of the FIELDS PSP module in the
frequency band 10...18 MHz was made using the calibrated data of terrestrial
radio telescopes. The effectiveness of ground-based support of the PSP mission
by the large Ukrainian radio telescopes is shown. Examples of joint
observations are given, and the method of cross calibration of the FIELD PSP
module receivers is demonstrated. Prospects for further ground based support
for solar space missions are presented
|
[
{
"created": "Thu, 6 Jul 2023 14:25:42 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Dorovskyy",
"Vladimir",
""
],
[
"Melnik",
"Valentin",
""
],
[
"Brazhenko",
"Anatolii",
""
]
] |
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] |
2307.03017
|
Yijie Deng
|
Yijie Deng, Lei Han, Tianpeng Lin, Lin Li, Jinzhi Zhang, and Lu Fang
|
EffLiFe: Efficient Light Field Generation via Hierarchical Sparse
Gradient Descent
|
Submitted to IEEE TPAMI
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the rise of Extended Reality (XR) technology, there is a growing need
for real-time light field generation from sparse view inputs. Existing methods
can be classified into offline techniques, which can generate high-quality
novel views but at the cost of long inference/training time, and online
methods, which either lack generalizability or produce unsatisfactory results.
However, we have observed that the intrinsic sparse manifold of Multi-plane
Images (MPI) enables a significant acceleration of light field generation while
maintaining rendering quality. Based on this insight, we introduce EffLiFe, a
novel light field optimization method, which leverages the proposed
Hierarchical Sparse Gradient Descent (HSGD) to produce high-quality light
fields from sparse view images in real time. Technically, the coarse MPI of a
scene is first generated using a 3D CNN, and it is further sparsely optimized
by focusing only on important MPI gradients in a few iterations. Nevertheless,
relying solely on optimization can lead to artifacts at occlusion boundaries.
Therefore, we propose an occlusion-aware iterative refinement module that
removes visual artifacts in occluded regions by iteratively filtering the
input. Extensive experiments demonstrate that our method achieves comparable
visual quality while being 100x faster on average than state-of-the-art offline
methods and delivering better performance (about 2 dB higher in PSNR) compared
to other online approaches.
|
[
{
"created": "Thu, 6 Jul 2023 14:31:01 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Deng",
"Yijie",
""
],
[
"Han",
"Lei",
""
],
[
"Lin",
"Tianpeng",
""
],
[
"Li",
"Lin",
""
],
[
"Zhang",
"Jinzhi",
""
],
[
"Fang",
"Lu",
""
]
] |
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] |
2307.03021
|
Guanghui Yang
|
Guanghui Yang, Zhijiang Shao, Rui Wang, Zuhua Xu, Lidan Cui
|
Shadow operator: Effective dynamic load change operation training in air
separation processes based on industrial nonlinear MPC and Bloom's taxonomy
|
16 pages, 18 figures
| null | null | null |
eess.SY cs.HC cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A novel human-machine interactive training method for dynamic load change
operation in air separation processes (ASPs) is proposed. A shadow operator
(SO) is developed in this method to train ASP operators through industrial
model predictive control (IMPC) and Bloom's taxonomy. First, a nonlinear
two-layer IMPC machine algorithm is developed for dynamic load change
operation. The IMPC uses a linear parameter varying prediction model and an
iterative multi-step linearization algorithm to compute accurate control
decisions. Second, a hierarchical human-machine cooperation model is
established to improve the effectiveness of operation training. The model is
inspired by an educational psychology framework (Bloom's taxonomy) and assists
ASP operators in enhancing their dynamic operational skills. Finally, five
dynamic training modes of the SO are designed based on the IMPC algorithm and
the human-machine cooperation model. The practical application results
demonstrate that the SO improves the effectiveness of skill acquisition for
novice operators and the safety of dynamic operations.
|
[
{
"created": "Thu, 6 Jul 2023 14:36:13 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Yang",
"Guanghui",
""
],
[
"Shao",
"Zhijiang",
""
],
[
"Wang",
"Rui",
""
],
[
"Xu",
"Zuhua",
""
],
[
"Cui",
"Lidan",
""
]
] |
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] |
2307.03022
|
Jeff Thompson
|
Sebastian P. Horvath, Christopher M. Phenicie, Salim Ourari, Mehmet T.
Uysal, Songtao Chen, {\L}ukasz Dusanowski, Mouktik Raha, Paul Stevenson, Adam
T. Turflinger, Robert J. Cava, Nathalie P. de Leon, and Jeff D. Thompson
|
Strong Purcell enhancement of an optical magnetic dipole transition
| null | null | null | null |
physics.optics physics.atom-ph quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Engineering the local density of states with nanophotonic structures is a
powerful tool to control light-matter interactions via the Purcell effect. At
optical frequencies, control over the electric field density of states is
typically used to couple to and manipulate electric dipole transitions.
However, it is also possible to engineer the magnetic density of states to
control magnetic dipole transitions. In this work, we experimentally
demonstrate the optical magnetic Purcell effect using a single rare earth ion
coupled to a nanophotonic cavity. We engineer a new single photon emitter,
Er$^{3+}$ in MgO, where the electric dipole decay rate is strongly suppressed
by the cubic site symmetry, giving rise to a nearly pure magnetic dipole
optical transition. This allows the unambiguous determination of a magnetic
Purcell factor $P_m=1040 \pm 30$. We further extend this technique to realize a
magnetic dipole spin-photon interface, performing optical spin initialization
and readout of a single Er$^{3+}$ electron spin. This work demonstrates the
fundamental equivalence of electric and magnetic density of states engineering,
and provides a new tool for controlling light-matter interactions for a broader
class of emitters.
|
[
{
"created": "Thu, 6 Jul 2023 14:37:58 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Horvath",
"Sebastian P.",
""
],
[
"Phenicie",
"Christopher M.",
""
],
[
"Ourari",
"Salim",
""
],
[
"Uysal",
"Mehmet T.",
""
],
[
"Chen",
"Songtao",
""
],
[
"Dusanowski",
"Łukasz",
""
],
[
"Raha",
"Mouktik",
""
],
[
"Stevenson",
"Paul",
""
],
[
"Turflinger",
"Adam T.",
""
],
[
"Cava",
"Robert J.",
""
],
[
"de Leon",
"Nathalie P.",
""
],
[
"Thompson",
"Jeff D.",
""
]
] |
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] |
2307.03025
|
Minghao Wu
|
Minghao Wu, Alham Fikri Aji
|
Style Over Substance: Evaluation Biases for Large Language Models
|
Work in progress, 16 pages, 4 tables, 12 figures
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As large language models (LLMs) continue to advance, accurately and
comprehensively evaluating their performance becomes increasingly challenging.
Conventionally, human evaluations are considered the gold standard in natural
language generation. Recent advancements incorporate state-of-the-art LLMs as
proxies for human judges in evaluation processes. Nonetheless, the extent to
which humans and LLMs are capable evaluators remains uncertain. This study aims
to investigate the behavior of both crowd-sourced human and LLM-based judges
when comparing outputs from different models. To accomplish this, we curate a
dataset comprising intentionally flawed machine-generated answers. Our findings
indicate that despite the potentially greater danger posed by factual errors,
answers with factual errors were still rated more favorably compared to answers
that were too short or contained grammatical errors. This highlights a
concerning bias in the evaluation process. To address this issue, we propose to
independently evaluate machine-generated text across multiple dimensions,
rather than merging all the evaluation aspects into a single score. We
instantiate this idea with the Elo rating system, resulting in the Multi-Elo
Rating System. Empirical results from our study reveal that this proposed
approach significantly enhances the quality of LLM-based evaluations,
particularly in terms of factual accuracy. However, notable improvement is not
observed in crowd-sourced-based evaluations, suggesting the need for further
investigation and refinement.
|
[
{
"created": "Thu, 6 Jul 2023 14:42:01 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Wu",
"Minghao",
""
],
[
"Aji",
"Alham Fikri",
""
]
] |
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] |
2307.03027
|
Sebastian Schelter
|
Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei, Meng
Cao, Sebastian Schelter, Ce Zhang
|
Improving Retrieval-Augmented Large Language Models via Data Importance
Learning
| null | null | null | null |
cs.LG cs.CL cs.IR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Retrieval augmentation enables large language models to take advantage of
external knowledge, for example on tasks like question answering and data
imputation. However, the performance of such retrieval-augmented models is
limited by the data quality of their underlying retrieval corpus. In this
paper, we propose an algorithm based on multilinear extension for evaluating
the data importance of retrieved data points. There are exponentially many
terms in the multilinear extension, and one key contribution of this paper is a
polynomial time algorithm that computes exactly, given a retrieval-augmented
model with an additive utility function and a validation set, the data
importance of data points in the retrieval corpus using the multilinear
extension of the model's utility function. We further proposed an even more
efficient ({\epsilon}, {\delta})-approximation algorithm. Our experimental
results illustrate that we can enhance the performance of large language models
by only pruning or reweighting the retrieval corpus, without requiring further
training. For some tasks, this even allows a small model (e.g., GPT-JT),
augmented with a search engine API, to outperform GPT-3.5 (without retrieval
augmentation). Moreover, we show that weights based on multilinear extension
can be computed efficiently in practice (e.g., in less than ten minutes for a
corpus with 100 million elements).
|
[
{
"created": "Thu, 6 Jul 2023 14:44:07 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Lyu",
"Xiaozhong",
""
],
[
"Grafberger",
"Stefan",
""
],
[
"Biegel",
"Samantha",
""
],
[
"Wei",
"Shaopeng",
""
],
[
"Cao",
"Meng",
""
],
[
"Schelter",
"Sebastian",
""
],
[
"Zhang",
"Ce",
""
]
] |
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Improving Retrieval-Augmented Large Language Models via Data Importance Learning
|
https://arxiv.org/abs/2307.03027v1
|
https://arxiv.org/pdf/2307.03027v1.pdf
|
https://github.com/amsterdata/ragbooster
| true | true |
https://paperswithcode.com/paper/improving-retrieval-augmented-large-language
|
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] |
2307.03028
|
Zeping Sui
|
Zeping Sui, Shefeng Yan, Hongming Zhang, Sumei Sun, Yonghong Zeng,
Lie-Liang Yang, Lajos Hanzo
|
Performance Analysis and Approximate Message Passing Detection of
Orthogonal Time Sequency Multiplexing Modulation
|
Accepted in IEEE Transactions on Wireless Communications
| null |
10.1109/TWC.2023.3293315
| null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In orthogonal time sequency multiplexing (OTSM) modulation, the information
symbols are conveyed in the delay-sequency domain upon exploiting the inverse
Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of
attaining a bit error ratio (BER) similar to that of orthogonal time-frequency
space (OTFS) modulation at a lower complexity, since the saving of
multiplication operations in the IWHT. Hence we provide its BER performance
analysis and characterize its detection complexity. We commence by deriving its
generalized input-output relationship and its unconditional pairwise error
probability (UPEP). Then, its BER upper bound is derived in closed form under
both ideal and imperfect channel estimation conditions, which is shown to be
tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel
approximate message passing (AMP) aided OTSM detection framework is proposed.
Specifically, to circumvent the high residual BER of the conventional AMP
detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM)
detector for performing joint data detection and noise variance estimation. The
variance auto-tuning algorithm based on the EM algorithm is designed for the
VAMP-EM detector to further improve the convergence performance. The simulation
results illustrate that the VAMP-EM detector is capable of striking an
attractive BER vs. complexity trade-off than the state-of-the-art schemes as
well as providing a better convergence. Finally, we propose AMP and VAMP-EM
turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is
demonstrated that our proposed VAMP-EM turbo receiver is capable of providing
both BER and convergence performance improvements over the conventional AMP
solution.
|
[
{
"created": "Thu, 6 Jul 2023 14:45:41 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Sui",
"Zeping",
""
],
[
"Yan",
"Shefeng",
""
],
[
"Zhang",
"Hongming",
""
],
[
"Sun",
"Sumei",
""
],
[
"Zeng",
"Yonghong",
""
],
[
"Yang",
"Lie-Liang",
""
],
[
"Hanzo",
"Lajos",
""
]
] |
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] | null | null | null | null | null | null | null |
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] |
2307.03029
|
Justin Tabbett
|
Justin Tabbett and Karen L. Aplin
|
Modelling the response of a CsI(Tl)-PiN photodiode Microscintillator
Detector
| null | null | null | null |
physics.ins-det
|
http://creativecommons.org/licenses/by/4.0/
|
The full instrument response of a CsI(Tl)-PiN photodiode radioactivity
detector, intended for deployment on a meteorological radiosonde, has been
modelled by combining a physics-based model of the sensor with the detector
circuit response, obtained via a LTSpice simulation. The model uses the
incident energy of a gamma ray as an input, and produces the pulse expected
from the detector. The detector response was verified by comparing the
simulated energy calibration with laboratory radioactive sources. The Schmitt
trigger part of the measurement circuit is found to control the observed
minimum detectable energy of 223 keV. Additionally, the energy sensitivity of
the PiN detector was found to be 0.529 $\pm$ 0.010 mV/keV in the 200-800 keV
range. The simulation and laboratory calibrations were consistent to better
than 20% over the operating range of the instrument, decreasing to 0.34% at 800
keV.
|
[
{
"created": "Thu, 6 Jul 2023 14:45:44 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Tabbett",
"Justin",
""
],
[
"Aplin",
"Karen L.",
""
]
] |
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] |
2307.03030
|
Artemii Zenkin
|
A.M. Zenkin, A.A. Peregudin, A.A. Bobtsov
|
Lyapunov function search method for analysis of nonlinear systems
stability using genetic algorithm
|
in Russian language
| null | null | null |
eess.SY cs.RO cs.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper considers a wide class of smooth continuous dynamic nonlinear
systems (control objects) with a measurable vector of state. The problem is to
find a special function (Lyapunov function), which in the framework of the
second Lyapunov method guarantees asymptotic stability for the above described
class of nonlinear systems. It is well known that the search for a Lyapunov
function is the "cornerstone" of mathematical stability theory. Methods for
selecting or finding the Lyapunov function to analyze the stability of closed
linear stationary systems, as well as for nonlinear objects with explicit
linear dynamic and nonlinear static parts, have been well studied (see works by
Lurie, Yakubovich, Popov, and many others). However, universal approaches to
the search for the Lyapunov function for a more general class of nonlinear
systems have not yet been identified. There is a large variety of methods for
finding the Lyapunov function for nonlinear systems, but they all operate
within the constraints imposed on the structure of the control object. In this
paper we propose another approach, which allows to give specialists in the
field of automatic control theory a new tool/mechanism of Lyapunov function
search for stability analysis of smooth continuous dynamic nonlinear systems
with measurable state vector. The essence of proposed approach consists in
representation of some function through sum of nonlinear terms, which are
elements of object's state vector, multiplied by unknown coefficients, raised
to positive degrees. Then the unknown coefficients are selected using genetic
algorithm, which should provide the function with all necessary conditions for
Lyapunov function (in the framework of the second Lyapunov method).
|
[
{
"created": "Thu, 6 Jul 2023 14:46:22 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Zenkin",
"A. M.",
""
],
[
"Peregudin",
"A. A.",
""
],
[
"Bobtsov",
"A. A.",
""
]
] |
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] |
2307.03034
|
Keqin Liu
|
Keqin Liu and Chengzhong Zhang
|
PCL-Indexability and Whittle Index for Restless Bandits with General
Observation Models
| null | null | null | null |
stat.ML cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we consider a general observation model for restless
multi-armed bandit problems. The operation of the player needs to be based on
certain feedback mechanism that is error-prone due to resource constraints or
environmental or intrinsic noises. By establishing a general probabilistic
model for dynamics of feedback/observation, we formulate the problem as a
restless bandit with a countable belief state space starting from an arbitrary
initial belief (a priori information). We apply the achievable region method
with partial conservation law (PCL) to the infinite-state problem and analyze
its indexability and priority index (Whittle index). Finally, we propose an
approximation process to transform the problem into which the AG algorithm of
Ni\~no-Mora and Bertsimas for finite-state problems can be applied to.
Numerical experiments show that our algorithm has an excellent performance.
|
[
{
"created": "Thu, 6 Jul 2023 14:56:13 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Liu",
"Keqin",
""
],
[
"Zhang",
"Chengzhong",
""
]
] |
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] |
2307.03039
|
Eric Postma
|
Ludovica Schaerf, Carina Popovici, Eric Postma
|
Art Authentication with Vision Transformers
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, Transformers, initially developed for language, have been
successfully applied to visual tasks. Vision Transformers have been shown to
push the state-of-the-art in a wide range of tasks, including image
classification, object detection, and semantic segmentation. While ample
research has shown promising results in art attribution and art authentication
tasks using Convolutional Neural Networks, this paper examines if the
superiority of Vision Transformers extends to art authentication, improving,
thus, the reliability of computer-based authentication of artworks. Using a
carefully compiled dataset of authentic paintings by Vincent van Gogh and two
contrast datasets, we compare the art authentication performances of Swin
Transformers with those of EfficientNet. Using a standard contrast set
containing imitations and proxies (works by painters with styles closely
related to van Gogh), we find that EfficientNet achieves the best performance
overall. With a contrast set that only consists of imitations, we find the Swin
Transformer to be superior to EfficientNet by achieving an authentication
accuracy of over 85%. These results lead us to conclude that Vision
Transformers represent a strong and promising contender in art authentication,
particularly in enhancing the computer-based ability to detect artistic
imitations.
|
[
{
"created": "Thu, 6 Jul 2023 15:04:18 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Schaerf",
"Ludovica",
""
],
[
"Popovici",
"Carina",
""
],
[
"Postma",
"Eric",
""
]
] |
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] |
2307.03040
|
Alexander Engelmann
|
Alexander Engelmann, Michael Kaupmann, Timm Faulwasser
|
Distributed Interior Point Methods for Optimization in Energy Networks
| null | null |
10.15495/EPub_UBT_00006809
| null |
math.OC cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This note discusses an essentially decentralized interior point method, which
is well suited for optimization problems arising in energy networks. Advantages
of the proposed method are guaranteed and fast local convergence also for
problems with non-convex constraints. Moreover, our method exhibits a small
communication footprint and it achieves a comparably high solution accuracy
with a limited number of iterations, whereby the local subproblems are of low
computational complexity. We illustrate the performance of the proposed method
on a problem from energy systems, i.e., we consider an optimal power flow
problem with 708 buses.
|
[
{
"created": "Thu, 6 Jul 2023 15:05:42 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Engelmann",
"Alexander",
""
],
[
"Kaupmann",
"Michael",
""
],
[
"Faulwasser",
"Timm",
""
]
] |
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] |
2307.03042
|
Aryo Gema
|
Aryo Gema, Luke Daines, Pasquale Minervini, Beatrice Alex
|
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Adapting pretrained language models to novel domains, such as clinical
applications, traditionally involves retraining their entire set of parameters.
However, this approach is increasingly proven to be impractical owing to the
substantial computational requirements associated with training such large
language models. To address this issue, Parameter-Efficient Fine-Tuning (PEFT)
techniques offer a viable solution by selectively fine-tuning a small subset of
additional parameters, significantly reducing the computational requirements
for domain adaptation. In this study, we propose Clinical LLaMA-LoRA, a PEFT
adapter layer built upon the open-sourced LLaMA model. Clinical LLaMA-LoRA is
trained using clinical notes obtained from the MIMIC-IV database, thereby
creating a specialised adapter designed for the clinical domain. Additionally,
we propose a two-step PEFT framework which fuses Clinical LLaMA-LoRA with
Downstream LLaMA-LoRA, another PEFT adapter specialised for downstream tasks.
We evaluate this framework on multiple clinical outcome prediction datasets,
comparing it to clinically trained language models. Our proposed framework
achieves a state-of-the-art AUROC score averaged across all clinical downstream
tasks. We observe substantial improvements of 6-9% AUROC score in the
large-scale multilabel classification tasks, such as diagnoses and procedures
classification.
|
[
{
"created": "Thu, 6 Jul 2023 15:06:41 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Gema",
"Aryo",
""
],
[
"Daines",
"Luke",
""
],
[
"Minervini",
"Pasquale",
""
],
[
"Alex",
"Beatrice",
""
]
] |
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] |
2307.03043
|
Sandeep Silwal
|
Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik
Waingarten
|
A Near-Linear Time Algorithm for the Chamfer Distance
| null | null | null | null |
cs.DS cs.CG cs.GR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
For any two point sets $A,B \subset \mathbb{R}^d$ of size up to $n$, the
Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A,B)=\sum_{a \in A}
\min_{b \in B} d_X(a,b)$, where $d_X$ is the underlying distance measure (e.g.,
the Euclidean or Manhattan distance). The Chamfer distance is a popular measure
of dissimilarity between point clouds, used in many machine learning, computer
vision, and graphics applications, and admits a straightforward $O(d n^2)$-time
brute force algorithm. Further, the Chamfer distance is often used as a proxy
for the more computationally demanding Earth-Mover (Optimal Transport)
Distance. However, the \emph{quadratic} dependence on $n$ in the running time
makes the naive approach intractable for large datasets.
We overcome this bottleneck and present the first $(1+\epsilon)$-approximate
algorithm for estimating the Chamfer distance with a near-linear running time.
Specifically, our algorithm runs in time $O(nd \log (n)/\varepsilon^2)$ and is
implementable. Our experiments demonstrate that it is both accurate and fast on
large high-dimensional datasets. We believe that our algorithm will open new
avenues for analyzing large high-dimensional point clouds. We also give
evidence that if the goal is to \emph{report} a $(1+\varepsilon)$-approximate
mapping from $A$ to $B$ (as opposed to just its value), then any sub-quadratic
time algorithm is unlikely to exist.
|
[
{
"created": "Thu, 6 Jul 2023 15:07:48 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Bakshi",
"Ainesh",
""
],
[
"Indyk",
"Piotr",
""
],
[
"Jayaram",
"Rajesh",
""
],
[
"Silwal",
"Sandeep",
""
],
[
"Waingarten",
"Erik",
""
]
] |
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] | null | null | null | null | null | null | null |
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] |
2307.03045
|
Guillaume Salha-Galvan
|
Walid Bendada and Th\'eo Bontempelli and Mathieu Morlon and Benjamin
Chapus and Thibault Cador and Thomas Bouab\c{c}a and Guillaume Salha-Galvan
|
Track Mix Generation on Music Streaming Services using Transformers
|
RecSys 2023 - Industry track with oral presentation
| null | null | null |
cs.IR cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper introduces Track Mix, a personalized playlist generation system
released in 2022 on the music streaming service Deezer. Track Mix automatically
generates "mix" playlists inspired by initial music tracks, allowing users to
discover music similar to their favorite content. To generate these mixes, we
consider a Transformer model trained on millions of track sequences from user
playlists. In light of the growing popularity of Transformers in recent years,
we analyze the advantages, drawbacks, and technical challenges of using such a
model for mix generation on the service, compared to a more traditional
collaborative filtering approach. Since its release, Track Mix has been
generating playlists for millions of users daily, enhancing their music
discovery experience on Deezer.
|
[
{
"created": "Thu, 6 Jul 2023 15:10:29 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Bendada",
"Walid",
""
],
[
"Bontempelli",
"Théo",
""
],
[
"Morlon",
"Mathieu",
""
],
[
"Chapus",
"Benjamin",
""
],
[
"Cador",
"Thibault",
""
],
[
"Bouabça",
"Thomas",
""
],
[
"Salha-Galvan",
"Guillaume",
""
]
] |
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] |
2307.03046
|
Fabian Danecker
|
Ralf Bornd\"orfer, Fabian Danecker, and Martin Weiser
|
Convergence Properties of Newton's Method for Globally Optimal Free
Flight Trajectory Optimization
| null | null | null | null |
math.OC cs.NA math.NA
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The algorithmic efficiency of Newton-based methods for Free Flight Trajectory
Optimization is heavily influenced by the size of the domain of convergence. We
provide numerical evidence that the convergence radius is much larger in
practice than what the theoretical worst case bounds suggest. The algorithm can
be further improved by a convergence-enhancing domain decomposition.
|
[
{
"created": "Thu, 6 Jul 2023 15:12:31 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Borndörfer",
"Ralf",
""
],
[
"Danecker",
"Fabian",
""
],
[
"Weiser",
"Martin",
""
]
] |
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] |
2307.03047
|
Peter Foukal
|
Peter Foukal
|
A Remarkably Accurate Predictor of Sunspot Cycle Amplitude
| null | null | null | null |
astro-ph.SR physics.space-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The area ratios of sunspots to white light faculae in the first two years of
sunspot cycles 12-21 correlate remarkably well with the peak amplitudes of
those cycles between 1878-1980 (Brown and Evans, 1980). This finding could not
be used to predict subsequent cycle amplitudes because the Royal Greenwich
Observatory program of facular area measurements was discontinued in 1976. We
use continuum images from the Michelson Doppler Imager (MDI) and the
Heliospheric and Magnetic Imager (HMI) to show that the close relation holds
also for cycle 24, and we predict an amplitude of approximately 185 for the
current cycle 25.
|
[
{
"created": "Thu, 6 Jul 2023 15:14:05 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Foukal",
"Peter",
""
]
] |
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] |
2307.03048
|
Yan Lin
|
Yan Lin, Huaiyu Wan, Jilin Hu, Shengnan Guo, Bin Yang, Youfang Lin,
Christian S. Jensen
|
Origin-Destination Travel Time Oracle for Map-based Services
|
15 pages, 12 figures, accepted by SIGMOD International Conference on
Management of Data 2024
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Given an origin (O), a destination (D), and a departure time (T), an
Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of
the time it takes to travel from O to D when departing at T. ODT-Oracles serve
important purposes in map-based services. To enable the construction of such
oracles, we provide a travel-time estimation (TTE) solution that leverages
historical trajectories to estimate time-varying travel times for OD pairs.
The problem is complicated by the fact that multiple historical trajectories
with different travel times may connect an OD pair, while trajectories may vary
from one another. To solve the problem, it is crucial to remove outlier
trajectories when doing travel time estimation for future queries.
We propose a novel, two-stage framework called Diffusion-based
Origin-destination Travel Time Estimation (DOT), that solves the problem.
First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that
enables building a diffusion-based PiT inference process by learning
correlations between OD pairs and historical trajectories. Specifically, given
an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a
Masked Vision Transformer~(MViT) that effectively and efficiently estimates a
travel time based on the inferred PiT. We report on extensive experiments on
two real-world datasets that offer evidence that DOT is capable of
outperforming baseline methods in terms of accuracy, scalability, and
explainability.
|
[
{
"created": "Thu, 6 Jul 2023 15:14:23 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Lin",
"Yan",
""
],
[
"Wan",
"Huaiyu",
""
],
[
"Hu",
"Jilin",
""
],
[
"Guo",
"Shengnan",
""
],
[
"Yang",
"Bin",
""
],
[
"Lin",
"Youfang",
""
],
[
"Jensen",
"Christian S.",
""
]
] |
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] |
2307.03053
|
Joerg Schumacher
|
Philipp Pfeffer, Florian Heyder and J\"org Schumacher
|
Reduced-order modeling of two-dimensional turbulent Rayleigh-B\'enard
flow by hybrid quantum-classical reservoir computing
|
11 pages, 7 figures
| null | null | null |
physics.flu-dyn quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Two hybrid quantum-classical reservoir computing models are presented to
reproduce low-order statistical properties of a two-dimensional turbulent
Rayleigh-B\'enard convection flow at a Rayleigh number Ra=1e5 and a Prandtl
number Pr=10. Both quantum algorithms differ by the arrangement of the circuit
layers in the quantum reservoir, in particular the entanglement layers. The
second of the two architectures, denoted as H2, enables a complete execution of
the reservoir update inside the quantum circuit. Their performance is compared
with that of a classical reservoir computing model. All three models have to
learn the nonlinear and chaotic dynamics of the flow in a lower-dimensional
latent data space which is spanned by the time series of the 16 most energetic
Proper Orthogonal Decomposition (POD) modes. These training data are generated
by a POD snapshot analysis from the turbulent flow. All reservoir computing
models are operated in the reconstruction or open-loop mode, i.e., they receive
3 POD modes as an input at each step and reconstruct the missing 13 ones. We
analyse the reconstruction error in dependence on the hyperparameters which are
specific for the quantum cases or shared with the classical counterpart, such
as the reservoir size and the leaking rate. We show that both quantum
algorithms are able to reconstruct essential statistical properties of the
turbulent convection flow successfully with a small number of qubits of n<=9.
These properties comprise the velocity and temperature fluctuation profiles
and, in particular, the turbulent convective heat flux, which quantifies the
turbulent heat transfer across the layer and manifests in coherent hot rising
and cold falling thermal plumes.
|
[
{
"created": "Thu, 6 Jul 2023 15:17:54 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Pfeffer",
"Philipp",
""
],
[
"Heyder",
"Florian",
""
],
[
"Schumacher",
"Jörg",
""
]
] |
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] |
2307.03054
|
Michael \'Alvarez MSc
|
Carlos Theran, Michael Alvarez, Emmanuel Arzuaga and Heidy Sierra
|
Multi-source imagery fusion using deep learning in a cloud computing
platform
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Given the high availability of data collected by different remote sensing
instruments, the data fusion of multi-spectral and hyperspectral images (HSI)
is an important topic in remote sensing. In particular, super-resolution as a
data fusion application using spatial and spectral domains is highly
investigated because its fused images is used to improve the classification and
tracking objects accuracy. On the other hand, the huge amount of data obtained
by remote sensing instruments represent a key concern in terms of data storage,
management and pre-processing. This paper proposes a Big Data Cloud platform
using Hadoop and Spark to store, manages, and process remote sensing data.
Also, a study over the parameter \textit{chunk size} is presented to suggest
the appropriate value for this parameter to download imagery data from Hadoop
into a Spark application, based on the format of our data. We also developed an
alternative approach based on Long Short Term Memory trained with different
patch sizes for super-resolution image. This approach fuse hyperspectral and
multispectral images. As a result, we obtain images with high-spatial and
high-spectral resolution. The experimental results show that for a chunk size
of 64k, an average of 3.5s was required to download data from Hadoop into a
Spark application. The proposed model for super-resolution provides a
structural similarity index of 0.98 and 0.907 for the used dataset.
|
[
{
"created": "Thu, 6 Jul 2023 15:18:11 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Theran",
"Carlos",
""
],
[
"Alvarez",
"Michael",
""
],
[
"Arzuaga",
"Emmanuel",
""
],
[
"Sierra",
"Heidy",
""
]
] |
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] |
2307.03055
|
Ronald Aznavourian
|
Ronald Aznavourian, Guillaume Demesy, S\'ebastien Guenneau, Julien
Marot
|
Electromagnetic cloak design with mono-objective and bi-objective
optimizers: seeking the best tradeoff between protection and invisibility
| null | null | null | null |
physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We revisit the design of cloaks, without resorting to any geometric
transform. Cancellation techniques and anomalous resonances have been applied
for this purpose. Instead of a deductive reasoning, we propose a novel
mono-objective optimization algorithm, namely a ternary grey wolf algorithm,
and we adapt a bi-objective optimization algorithm. Firstly, the proposed
chaotic ternary grey wolf algorithm searches three-valued spaces for all
permittivity values in the cloak while minimizing the summation of a protection
criterion and an invisibility criterion. Secondly, a bi-objective genetic
algorithm is adapted to find pairs of optimal values of invisibility and
protection.
|
[
{
"created": "Thu, 6 Jul 2023 15:19:32 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Aznavourian",
"Ronald",
""
],
[
"Demesy",
"Guillaume",
""
],
[
"Guenneau",
"Sébastien",
""
],
[
"Marot",
"Julien",
""
]
] |
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] |
2307.03056
|
Kevin Du
|
Kevin Du, Lucas Torroba Hennigen, Niklas Stoehr, Alexander Warstadt,
Ryan Cotterell
|
Generalizing Backpropagation for Gradient-Based Interpretability
|
Long paper accepted at ACL 2023
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Many popular feature-attribution methods for interpreting deep neural
networks rely on computing the gradients of a model's output with respect to
its inputs. While these methods can indicate which input features may be
important for the model's prediction, they reveal little about the inner
workings of the model itself. In this paper, we observe that the gradient
computation of a model is a special case of a more general formulation using
semirings. This observation allows us to generalize the backpropagation
algorithm to efficiently compute other interpretable statistics about the
gradient graph of a neural network, such as the highest-weighted path and
entropy. We implement this generalized algorithm, evaluate it on synthetic
datasets to better understand the statistics it computes, and apply it to study
BERT's behavior on the subject-verb number agreement task (SVA). With this
method, we (a) validate that the amount of gradient flow through a component of
a model reflects its importance to a prediction and (b) for SVA, identify which
pathways of the self-attention mechanism are most important.
|
[
{
"created": "Thu, 6 Jul 2023 15:19:53 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Du",
"Kevin",
""
],
[
"Hennigen",
"Lucas Torroba",
""
],
[
"Stoehr",
"Niklas",
""
],
[
"Warstadt",
"Alexander",
""
],
[
"Cotterell",
"Ryan",
""
]
] |
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Generalizing Backpropagation for Gradient-Based Interpretability
|
https://arxiv.org/abs/2307.03056v1
|
https://arxiv.org/pdf/2307.03056v1.pdf
|
https://github.com/kdu4108/semiring-backprop-exps
| true | true |
https://paperswithcode.com/paper/generalizing-backpropagation-for-gradient
|
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] |
2307.03059
|
Elaine Pimentel
|
Eben Blaisdell, Max Kanovich, Stepan L. Kuznetsov, Elaine Pimentel,
Andre Scedrov
|
Explorations in Subexponential non-associative non-commutative Linear
Logic
| null | null | null | null |
cs.LO math.LO
|
http://creativecommons.org/licenses/by/4.0/
|
In a previous work we introduced a non-associative non-commutative logic
extended by multimodalities, called subexponentials, licensing local
application of structural rules. Here, we further explore this system,
considering a classical one-sided multi-succedent classical version of the
system, following the exponential-free calculi of Buszkowski's and de Groote
and Lamarche's works, where the intuitionistic calculus is shown to embed
faithfully into the classical fragment.
|
[
{
"created": "Thu, 6 Jul 2023 15:24:40 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Blaisdell",
"Eben",
""
],
[
"Kanovich",
"Max",
""
],
[
"Kuznetsov",
"Stepan L.",
""
],
[
"Pimentel",
"Elaine",
""
],
[
"Scedrov",
"Andre",
""
]
] |
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] |
2307.03061
|
Souma Chowdhury
|
Feng Liu, Achira Boonrath, Prajit KrisshnaKumar, Elenora M. Botta,
Souma Chowdhury
|
Learning Constrained Corner Node Trajectories of a Tether Net System for
Space Debris Capture
|
This paper was presented at AIAA Aviation 2023 Forum
| null | null | null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The earth's orbit is becoming increasingly crowded with debris that poses
significant safety risks to the operation of existing and new spacecraft and
satellites. The active tether-net system, which consists of a flexible net with
maneuverable corner nodes launched from a small autonomous spacecraft, is a
promising solution for capturing and disposing of such space debris. The
requirement of autonomous operation and the need to generalize over scenarios
with debris scenarios in different rotational rates makes the capture process
significantly challenging. The space debris could rotate about multiple axes,
which, along with sensing/estimation and actuation uncertainties, calls for a
robust, generalizable approach to guiding the net launch and flight - one that
can guarantee robust capture. This paper proposes a decentralized actuation
system combined with reinforcement learning for planning and controlling this
tether-net system. In this new system, four microsatellites with cold gas type
thrusters act as the corner nodes of the net and can thus help control or
correct the flight of the net after launch. The microsatellites pull the net to
complete the task of approaching and capturing the space debris. The proposed
method uses a RL framework that integrates a proximal policy optimization to
find the optimal solution based on the dynamics simulation of the net and the
microsatellites performed in Vortex Studio. The RL framework finds the optimal
trajectory that is both fuel-efficient and ensures a desired level of capture
quality.
|
[
{
"created": "Thu, 6 Jul 2023 15:28:34 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Liu",
"Feng",
""
],
[
"Boonrath",
"Achira",
""
],
[
"KrisshnaKumar",
"Prajit",
""
],
[
"Botta",
"Elenora M.",
""
],
[
"Chowdhury",
"Souma",
""
]
] |
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] |
2307.03063
|
Ram\'on Pozuelo Ruiz
|
Ram\'on Pozuelo, Andr\'e V.G. Cavalieri, Philipp Schlatter and Ricardo
Vinuesa
|
Widest scales in turbulent channels
| null | null | null | null |
physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
The widest spanwise scales in turbulent channel flows are studied through the
use of three periodic channel-flow simulations at friction Reynolds number
$\mathrm{Re}_{\tau}=550$. The length and height of the channels are the same in
all cases ($L_x/h=8\pi$ and $L_y/h=2$ respectively), while the width is
progressively doubled: $L_z/h = \{4\pi, 8\pi, 16\pi\}$. The effects of
increasing the domain can not be determined with statistical significance in
our simulations, since the difference in the statistics between the simulations
is of the same order as the errors of convergence. A channel flow similar to
the smaller one ($\textit{J. Fluid Mech.}$, vol. 500, 2004, pp. 135--144),
which was averaged over a very long time, was used as a reference. The
one-dimensional spanwise spectrum of the streamwise velocity is computed with
the aim of assessing the domain-size effect on the widest scales. Our results
indicate that $90\%$ of the total streamwise energetic fluctuations is
recovered without a significant influence of the size of the domain. The
remaining $10\%$ of the energy reflects that the widest scales in the outer
layer are the ones most significantly affected by the spanwise length of the
domain. The power-spectral density for $k_z = 0$ remains constant even if the
size of the domain in the spanwise direction is increased up to 4 times the
standard spanwise length, indicating that wide, spanwise coherent structures
are not an artifact of domain truncation.
|
[
{
"created": "Thu, 6 Jul 2023 15:29:24 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Pozuelo",
"Ramón",
""
],
[
"Cavalieri",
"André V. G.",
""
],
[
"Schlatter",
"Philipp",
""
],
[
"Vinuesa",
"Ricardo",
""
]
] |
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] |
2307.03064
|
Jean-Christophe Bourin
|
Jean-Christophe Bourin
|
A Journey into Matrix Analysis
|
144 pages. This is the Habilitation Thesis manuscript presented at
Besan\c{c}on on January 5. Special thanks are due to my colleagues and
friends kindly acting as rewievers, Frank Hansen, Hideki Kosaki, and Fedor
Sukochev
| null | null | null |
math.FA cs.NA math.NA math.OA
|
http://creativecommons.org/licenses/by/4.0/
|
This is the Habilitation Thesis manuscript presented at Besan\c{c}on on
January 5, focusing on Matrix Analysis, Matrix Inequalities and Matrix
Decompositions. There are also some topics in (Hilbert space) Operator Theory.
The text should be of interest for a large audience of researchers and
students in pure and applied mathematics.
We may divide it into five parts:
1) Chapter 1 is an introductory chapter, some results from the period
1999-2010 are given, and a few conjectures are proposed.
2) Chapters 2-4 deal with matrix inequalities, Chapter 2 is concerned with
norm inequalities and logmajorization and Chapters 3-4 with functional calculus
and a unitary orbit technique that I started to develop in 2003.
3) Chapter 5 is a time-break in infinite dimensional Hilbert space operators,
the essential numerical range plays a key role.
4) Chapters 6-8 establish several decompositions for partitioned matrices,
especially for positive block matrices. Some norm inequalities involving the
numerical range are derived.
5) Chapter 9 may be of special interest for students : a proof of the
Spectral Theorem for bounded operators is derived from the matrix case.
|
[
{
"created": "Thu, 6 Jul 2023 15:32:24 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Bourin",
"Jean-Christophe",
""
]
] |
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] |
2307.03065
|
Minki Hhan
|
Minki Hhan, Takashi Yamakawa, Aaram Yun
|
Quantum Complexity for Discrete Logarithms and Related Problems
| null | null | null | null |
quant-ph cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
This paper studies the quantum computational complexity of the discrete
logarithm (DL) and related group-theoretic problems in the context of generic
algorithms -- that is, algorithms that do not exploit any properties of the
group encoding.
We establish a generic model of quantum computation for group-theoretic
problems, which we call the quantum generic group model. Shor's algorithm for
the DL problem and related algorithms can be described in this model. We show
the quantum complexity lower bounds and almost matching algorithms of the DL
and related problems in this model. More precisely, we prove the following
results for a cyclic group $G$ of prime order.
- Any generic quantum DL algorithm must make $\Omega(\log |G|)$ depth of
group operations. This shows that Shor's algorithm is asymptotically optimal
among the generic quantum algorithms, even considering parallel algorithms.
- We observe that variations of Shor's algorithm can take advantage of
classical computations to reduce the number of quantum group operations. We
introduce a model for generic hybrid quantum-classical algorithms and show that
these algorithms are almost optimal in this model. Any generic hybrid algorithm
for the DL problem with a total number of group operations $Q$ must make
$\Omega(\log |G|/\log Q)$ quantum group operations of depth $\Omega(\log\log
|G| - \log\log Q)$.
- When the quantum memory can only store $t$ group elements and use quantum
random access memory of $r$ group elements, any generic hybrid algorithm must
make either $\Omega(\sqrt{|G|})$ group operations in total or $\Omega(\log
|G|/\log (tr))$ quantum group operations.
As a side contribution, we show a multiple DL problem admits a better
algorithm than solving each instance one by one, refuting a strong form of the
quantum annoying property suggested in the context of password-authenticated
key exchange protocol.
|
[
{
"created": "Thu, 6 Jul 2023 15:32:50 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Hhan",
"Minki",
""
],
[
"Yamakawa",
"Takashi",
""
],
[
"Yun",
"Aaram",
""
]
] |
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] |
2307.03067
|
Yuan He
|
Yuan He, Jiaoyan Chen, Hang Dong, Ian Horrocks, Carlo Allocca, Taehun
Kim, Brahmananda Sapkota
|
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
|
under review at Semantic Web Journal
| null | null | null |
cs.AI cs.CL cs.LG cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Applying deep learning techniques, particularly language models (LMs), in
ontology engineering has raised widespread attention. However, deep learning
frameworks like PyTorch and Tensorflow are predominantly developed for Python
programming, while widely-used ontology APIs, such as the OWL API and Jena, are
primarily Java-based. To facilitate seamless integration of these frameworks
and APIs, we present Deeponto, a Python package designed for ontology
engineering. The package encompasses a core ontology processing module founded
on the widely-recognised and reliable OWL API, encapsulating its fundamental
features in a more "Pythonic" manner and extending its capabilities to include
other essential components including reasoning, verbalisation, normalisation,
projection, and more. Building on this module, Deeponto offers a suite of
tools, resources, and algorithms that support various ontology engineering
tasks, such as ontology alignment and completion, by harnessing deep learning
methodologies, primarily pre-trained LMs. In this paper, we also demonstrate
the practical utility of Deeponto through two use-cases: the Digital Health
Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment
Evaluation Initiative (OAEI).
|
[
{
"created": "Thu, 6 Jul 2023 15:35:02 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"He",
"Yuan",
""
],
[
"Chen",
"Jiaoyan",
""
],
[
"Dong",
"Hang",
""
],
[
"Horrocks",
"Ian",
""
],
[
"Allocca",
"Carlo",
""
],
[
"Kim",
"Taehun",
""
],
[
"Sapkota",
"Brahmananda",
""
]
] |
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] |
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
|
https://arxiv.org/abs/2307.03067v1
|
https://arxiv.org/pdf/2307.03067v1.pdf
|
https://github.com/KRR-Oxford/DeepOnto
| true | true |
https://paperswithcode.com/paper/deeponto-a-python-package-for-ontology
|
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] |
2307.03068
|
Shadi Sartipi
|
Shadi Sartipi and Mastaneh Torkamani-Azar and Mujdat Cetin
|
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with
Graph-Smooth Signals for EEG Emotion Recognition
| null | null | null | null |
cs.LG eess.SP
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Recently, physiological data such as electroencephalography (EEG) signals
have attracted significant attention in affective computing. In this context,
the main goal is to design an automated model that can assess emotional states.
Lately, deep neural networks have shown promising performance in emotion
recognition tasks. However, designing a deep architecture that can extract
practical information from raw data is still a challenge. Here, we introduce a
deep neural network that acquires interpretable physiological representations
by a hybrid structure of spatio-temporal encoding and recurrent attention
network blocks. Furthermore, a preprocessing step is applied to the raw data
using graph signal processing tools to perform graph smoothing in the spatial
domain. We demonstrate that our proposed architecture exceeds state-of-the-art
results for emotion classification on the publicly available DEAP dataset. To
explore the generality of the learned model, we also evaluate the performance
of our architecture towards transfer learning (TL) by transferring the model
parameters from a specific source to other target domains. Using DEAP as the
source dataset, we demonstrate the effectiveness of our model in performing
cross-modality TL and improving emotion classification accuracy on DREAMER and
the Emotional English Word (EEWD) datasets, which involve EEG-based emotion
classification tasks with different stimuli.
|
[
{
"created": "Thu, 6 Jul 2023 15:35:14 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Sartipi",
"Shadi",
""
],
[
"Torkamani-Azar",
"Mastaneh",
""
],
[
"Cetin",
"Mujdat",
""
]
] |
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] |
2307.03071
|
Marco Calautti
|
Marco Calautti, Sergio Greco, Cristian Molinaro and Irina Trubitsyna
|
Querying Data Exchange Settings Beyond Positive Queries
|
Under consideration in Theory and Practice of Logic Programming
(TPLP)
| null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
Data exchange, the problem of transferring data from a source schema to a
target schema, has been studied for several years.
The semantics of answering positive queries over the target schema has been
defined in early work, but little attention has been paid to more general
queries. A few proposals of semantics for more general queries exist but they
either do not properly extend the standard semantics under positive queries,
giving rise to counterintuitive answers, or they make query answering
undecidable even for the most important data exchange settings, e.g., with
weakly-acyclic dependencies.
The goal of this paper is to provide a new semantics for data exchange that
is able to deal with general queries. At the same time, we want our semantics
to coincide with the classical one when focusing on positive queries, and to
not trade-off too much in terms of complexity of query answering. We show that
query answering is undecidable in general under the new semantics, but it is
$\co\NP\complete$ when the dependencies are weakly-acyclic.
Moreover, in the latter case, we show that exact answers under our semantics
can be computed by means of logic programs with choice, thus exploiting
existing efficient systems. For more efficient computations, we also show that
our semantics allows for the construction of a representative target instance,
similar in spirit to a universal solution, that can be exploited for computing
approximate answers in polynomial time. Under consideration in Theory and
Practice of Logic Programming (TPLP).
|
[
{
"created": "Thu, 6 Jul 2023 15:36:55 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Calautti",
"Marco",
""
],
[
"Greco",
"Sergio",
""
],
[
"Molinaro",
"Cristian",
""
],
[
"Trubitsyna",
"Irina",
""
]
] |
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] |
2307.03073
|
Jishnu Jaykumar P
|
Jishnu Jaykumar P, Kamalesh Palanisamy, Yu-Wei Chao, Xinya Du, Yu
Xiang
|
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning
| null | null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a novel framework for few-shot learning by leveraging large-scale
vision-language models such as CLIP. Motivated by the unimodal prototypical
networks for few-shot learning, we introduce PROTO-CLIP that utilizes image
prototypes and text prototypes for few-shot learning. Specifically, PROTO-CLIP
adapts the image encoder and text encoder in CLIP in a joint fashion using
few-shot examples. The two encoders are used to compute prototypes of image
classes for classification. During adaptation, we propose aligning the image
and text prototypes of corresponding classes. Such a proposed alignment is
beneficial for few-shot classification due to the contributions from both types
of prototypes. We demonstrate the effectiveness of our method by conducting
experiments on benchmark datasets for few-shot learning as well as in the real
world for robot perception.
|
[
{
"created": "Thu, 6 Jul 2023 15:41:53 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"P",
"Jishnu Jaykumar",
""
],
[
"Palanisamy",
"Kamalesh",
""
],
[
"Chao",
"Yu-Wei",
""
],
[
"Du",
"Xinya",
""
],
[
"Xiang",
"Yu",
""
]
] |
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] |
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning
|
https://arxiv.org/abs/2307.03073v2
|
https://arxiv.org/pdf/2307.03073v2.pdf
|
https://github.com/IRVLUTD/Proto-CLIP
| true | false |
https://paperswithcode.com/paper/proto-clip-vision-language-prototypical
|
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] |
2307.03077
|
Jinhong Jung
|
Geonwoo Ko and Jinhong Jung
|
Learning Disentangled Representations in Signed Directed Graphs without
Social Assumptions
|
26 pages, 11 figures
| null | null | null |
cs.LG cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Signed graphs are complex systems that represent trust relationships or
preferences in various domains. Learning node representations in such graphs is
crucial for many mining tasks. Although real-world signed relationships can be
influenced by multiple latent factors, most existing methods often oversimplify
the modeling of signed relationships by relying on social theories and treating
them as simplistic factors. This limits their expressiveness and their ability
to capture the diverse factors that shape these relationships. In this paper,
we propose DINES, a novel method for learning disentangled node representations
in signed directed graphs without social assumptions. We adopt a disentangled
framework that separates each embedding into distinct factors, allowing for
capturing multiple latent factors. We also explore lightweight graph
convolutions that focus solely on sign and direction, without depending on
social theories. Additionally, we propose a decoder that effectively classifies
an edge's sign by considering correlations between the factors. To further
enhance disentanglement, we jointly train a self-supervised factor
discriminator with our encoder and decoder. Throughout extensive experiments on
real-world signed directed graphs, we show that DINES effectively learns
disentangled node representations, and significantly outperforms its
competitors in the sign prediction task.
|
[
{
"created": "Thu, 6 Jul 2023 15:45:35 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Ko",
"Geonwoo",
""
],
[
"Jung",
"Jinhong",
""
]
] |
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] |
Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions
|
https://arxiv.org/abs/2307.03077v1
|
https://arxiv.org/pdf/2307.03077v1.pdf
|
https://github.com/geonwooko/dines
| true | true |
https://paperswithcode.com/paper/learning-disentangled-representations-in-1
|
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] |
2307.03079
|
Florian Brandl
|
Florian Brandl and Felix Brandt
|
A Robust Characterization of Nash Equilibrium
| null | null | null | null |
econ.TH cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We give a robust characterization of Nash equilibrium by postulating coherent
behavior across varying games: Nash equilibrium is the only solution concept
that satisfies consequentialism, consistency, and rationality. As a
consequence, every equilibrium refinement violates at least one of these
properties. We moreover show that every solution concept that approximately
satisfies consequentialism, consistency, and rationality returns approximate
Nash equilibria. The latter approximation can be made arbitrarily good by
increasing the approximation of the axioms. This result extends to various
natural subclasses of games such as two-player zero-sum games, potential games,
and graphical games.
|
[
{
"created": "Thu, 6 Jul 2023 15:47:40 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Brandl",
"Florian",
""
],
[
"Brandt",
"Felix",
""
]
] |
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] |
2307.03080
|
Riccardo Bertoglio
|
Riccardo Bertoglio, Veronica Carini, Stefano Arrigoni, Matteo
Matteucci
|
A Map-Free LiDAR-Based System for Autonomous Navigation in Vineyards
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Agricultural robots have the potential to increase production yields and
reduce costs by performing repetitive and time-consuming tasks. However, for
robots to be effective, they must be able to navigate autonomously in fields or
orchards without human intervention. In this paper, we introduce a navigation
system that utilizes LiDAR and wheel encoder sensors for in-row, turn, and
end-row navigation in row structured agricultural environments, such as
vineyards. Our approach exploits the simple and precise geometrical structure
of plants organized in parallel rows. We tested our system in both simulated
and real environments, and the results demonstrate the effectiveness of our
approach in achieving accurate and robust navigation. Our navigation system
achieves mean displacement errors from the center line of 0.049 m and 0.372 m
for in-row navigation in the simulated and real environments, respectively. In
addition, we developed an end-row points detection that allows end-row
navigation in vineyards, a task often ignored by most works.
|
[
{
"created": "Thu, 6 Jul 2023 15:48:29 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Bertoglio",
"Riccardo",
""
],
[
"Carini",
"Veronica",
""
],
[
"Arrigoni",
"Stefano",
""
],
[
"Matteucci",
"Matteo",
""
]
] |
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] |
A Map-Free LiDAR-Based System for Autonomous Navigation in Vineyards
|
https://arxiv.org/abs/2307.03080v1
|
https://arxiv.org/pdf/2307.03080v1.pdf
|
https://github.com/airlab-polimi/mflb-vineyard-navigation
| true | true |
https://paperswithcode.com/paper/a-map-free-lidar-based-system-for-autonomous
|
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] |
2307.03083
|
Abhay Goyal
|
Abhay Goyal, Nimay Parekh, Lam Yin Cheung, Koustuv Saha, Frederick L
Altice, Robin O'hanlon, Roger Ho Chun Man, Christian Poellabauer, Honoria
Guarino, Pedro Mateu Gelabert, Navin Kumar
|
Predicting Opioid Use Outcomes in Minoritized Communities
| null | null | null | null |
cs.CE
|
http://creativecommons.org/licenses/by/4.0/
|
Machine learning algorithms can sometimes exacerbate health disparities based
on ethnicity, gender, and other factors. There has been limited work at
exploring potential biases within algorithms deployed on a small scale, and/or
within minoritized communities. Understanding the nature of potential biases
may improve the prediction of various health outcomes. As a case study, we used
data from a sample of 539 young adults from minoritized communities who engaged
in nonmedical use of prescription opioids and/or heroin. We addressed the
indicated issues through the following contributions: 1) Using machine learning
techniques, we predicted a range of opioid use outcomes for participants in our
dataset; 2) We assessed if algorithms trained only on a majority sub-sample
(e.g., Non-Hispanic/Latino, male), could accurately predict opioid use outcomes
for a minoritized sub-sample (e.g., Latino, female). Results indicated that
models trained on a random sample of our data could predict a range of opioid
use outcomes with high precision. However, we noted a decrease in precision
when we trained our models on data from a majority sub-sample, and tested these
models on a minoritized sub-sample. We posit that a range of cultural factors
and systemic forms of discrimination are not captured by data from majority
sub-samples. Broadly, for predictions to be valid, models should be trained on
data that includes adequate representation of the groups of people about whom
predictions will be made. Stakeholders may utilize our findings to mitigate
biases in models for predicting opioid use outcomes within minoritized
communities.
|
[
{
"created": "Thu, 6 Jul 2023 15:50:58 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Goyal",
"Abhay",
""
],
[
"Parekh",
"Nimay",
""
],
[
"Cheung",
"Lam Yin",
""
],
[
"Saha",
"Koustuv",
""
],
[
"Altice",
"Frederick L",
""
],
[
"O'hanlon",
"Robin",
""
],
[
"Man",
"Roger Ho Chun",
""
],
[
"Poellabauer",
"Christian",
""
],
[
"Guarino",
"Honoria",
""
],
[
"Gelabert",
"Pedro Mateu",
""
],
[
"Kumar",
"Navin",
""
]
] |
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] |
2307.03084
|
Shengding Hu
|
Shengding Hu, Ning Ding, Weilin Zhao, Xingtai Lv, Zhen Zhang, Zhiyuan
Liu, Maosong Sun
|
OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of
Pre-trained Models
|
Accepted to ACL 2023 Demo track
| null | null | null |
cs.LG cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The scale of large pre-trained models (PTMs) poses significant challenges in
adapting to downstream tasks due to the high optimization overhead and storage
costs associated with full-parameter fine-tuning. To address this, many studies
explore parameter-efficient tuning methods, also framed as "delta tuning",
which updates only a small subset of parameters, known as "delta modules",
while keeping the backbone model's parameters fixed. However, the practicality
and flexibility of delta tuning have been limited due to existing
implementations that directly modify the code of the backbone PTMs and
hard-code specific delta tuning methods for each PTM. In this paper, we present
OpenDelta, an open-source library that overcomes these limitations by providing
a plug-and-play implementation of various delta tuning methods. Our novel
techniques eliminate the need to modify the backbone PTMs' code, making
OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to
be simple, modular, and extensible, providing a comprehensive platform for
researchers and practitioners to adapt large PTMs efficiently.
|
[
{
"created": "Wed, 5 Jul 2023 16:30:14 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Hu",
"Shengding",
""
],
[
"Ding",
"Ning",
""
],
[
"Zhao",
"Weilin",
""
],
[
"Lv",
"Xingtai",
""
],
[
"Zhang",
"Zhen",
""
],
[
"Liu",
"Zhiyuan",
""
],
[
"Sun",
"Maosong",
""
]
] |
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] |
2307.03089
|
Manuel Gomes
|
Manuel Gomes, Miguel Oliveira, and V\'itor Santos
|
Volumetric Occupancy Detection: A Comparative Analysis of Mapping
Algorithms
|
11 pages, 11 figures, 9 tables
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the growing interest in innovative functionalities for collaborative
robotics, volumetric detection remains indispensable for ensuring basic
security. However, there is a lack of widely used volumetric detection
frameworks specifically tailored to this domain, and existing evaluation
metrics primarily focus on time and memory efficiency. To bridge this gap, the
authors present a detailed comparison using a simulation environment, ground
truth extraction, and automated evaluation metrics calculation. This enables
the evaluation of state-of-the-art volumetric mapping algorithms, including
OctoMap, SkiMap, and Voxblox, providing valuable insights and comparisons
through the impact of qualitative and quantitative analyses. The study not only
compares different frameworks but also explores various parameters within each
framework, offering additional insights into their performance.
|
[
{
"created": "Thu, 6 Jul 2023 16:01:43 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Gomes",
"Manuel",
""
],
[
"Oliveira",
"Miguel",
""
],
[
"Santos",
"Vítor",
""
]
] |
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] |
2307.03093
|
Kenza Tazi
|
Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, Ti John,
Hong Ge, Richard E. Turner
|
Beyond Intuition, a Framework for Applying GPs to Real-World Data
|
Accepted at the 1st ICML Workshop on Structured Probabilistic
Inference and Generative Modelling (2023)
| null | null | null |
cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Gaussian Processes (GPs) offer an attractive method for regression over
small, structured and correlated datasets. However, their deployment is
hindered by computational costs and limited guidelines on how to apply GPs
beyond simple low-dimensional datasets. We propose a framework to identify the
suitability of GPs to a given problem and how to set up a robust and
well-specified GP model. The guidelines formalise the decisions of experienced
GP practitioners, with an emphasis on kernel design and options for
computational scalability. The framework is then applied to a case study of
glacier elevation change yielding more accurate results at test time.
|
[
{
"created": "Thu, 6 Jul 2023 16:08:47 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Tazi",
"Kenza",
""
],
[
"Lin",
"Jihao Andreas",
""
],
[
"Viljoen",
"Ross",
""
],
[
"Gardner",
"Alex",
""
],
[
"John",
"Ti",
""
],
[
"Ge",
"Hong",
""
],
[
"Turner",
"Richard E.",
""
]
] |
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] | null | null | null | null | null | null | null |
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] |
2307.03099
|
Michael Vignal
|
Michael Vignal, Gayle Geschwind, Marcos D. Caballero, H. J.
Lewandowski
|
Couplet scoring for research based assessment instruments
|
13 pages, 2 figures
| null | null | null |
physics.ed-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Contemporary content-focused research-based assessment instruments typically
use instrument items (i.e., questions) as the unit of assessment for instrument
scoring, reporting, and validation. However, traditional item-based scoring has
a number of limitations, including several arising from the use of the common
assessment development conventions of single-construct items,
unidimensionality, and single-correct-answer items. Couplet scoring, introduced
in this paper, employs the couplet as an alternative unit of assessment, where
a couplet is essentially an item viewed and scored through the lens of a
specific assessment objective (AO). With couplet scoring, a single item may
have more than one AO and therefore more than one couplet. In this paper, we
outline the limitations of traditional item scoring, introduce couplet scoring
and discuss its affordances (especially as they relate to limitations of item
scoring), and use a recently developed content RBAI to ground our discussion.
|
[
{
"created": "Thu, 6 Jul 2023 16:15:23 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Vignal",
"Michael",
""
],
[
"Geschwind",
"Gayle",
""
],
[
"Caballero",
"Marcos D.",
""
],
[
"Lewandowski",
"H. J.",
""
]
] |
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] |
2307.03101
|
Jie Zhang
|
Jie Zhang, Masanori Suganuma, Takayuki Okatani
|
Contextual Affinity Distillation for Image Anomaly Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Previous works on unsupervised industrial anomaly detection mainly focus on
local structural anomalies such as cracks and color contamination. While
achieving significantly high detection performance on this kind of anomaly,
they are faced with logical anomalies that violate the long-range dependencies
such as a normal object placed in the wrong position. In this paper, based on
previous knowledge distillation works, we propose to use two students (local
and global) to better mimic the teacher's behavior. The local student, which is
used in previous studies mainly focuses on structural anomaly detection while
the global student pays attention to logical anomalies. To further encourage
the global student's learning to capture long-range dependencies, we design the
global context condensing block (GCCB) and propose a contextual affinity loss
for the student training and anomaly scoring. Experimental results show the
proposed method doesn't need cumbersome training techniques and achieves a new
state-of-the-art performance on the MVTec LOCO AD dataset.
|
[
{
"created": "Thu, 6 Jul 2023 16:18:14 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Zhang",
"Jie",
""
],
[
"Suganuma",
"Masanori",
""
],
[
"Okatani",
"Takayuki",
""
]
] |
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] | null | null | null | null | null | null | null |
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] |
2307.03102
|
William Foreman
|
MicroBooNE collaboration: P. Abratenko, O. Alterkait, D. Andrade
Aldana, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller,
G. Barr, D. Barrow, J. Barrow, V. Basque, O. Benevides Rodrigues, S. Berkman,
A. Bhanderi, A. Bhat, M. Bhattacharya, M. Bishai, A. Blake, B. Bogart, T.
Bolton, J.Y. Book, L. Camilleri, Y. Cao, D. Caratelli, I. Caro Terrazas, F.
Cavanna, G. Cerati, Y. Chen, J.M. Conrad, M. Convery, L. Cooper-Troendle,
J.I. Crespo-Anadon, R. Cross, M. Del Tutto, S.R. Dennis, P. Detje, A. Devitt,
R. Diurba, Z. Djurcic, R. Dorrill, K. Duffy, S. Dytman, B. Eberly, P.
Englezos, A. Ereditato, J.J. Evans, R. Fine, O.G. Finnerud, B.T. Fleming, N.
Foppiani, W. Foreman, D. Franco, A.P. Furmanski, D. Garcia-Gamez, S.
Gardiner, G. Ge, S. Gollapinni, O. Goodwin, E. Gramellini, P. Green, H.
Greenlee, W. Gu, R. Guenette, P. Guzowski, L. Hagaman, O. Hen, R. Hicks, C.
Hilgenberg, G.A. Horton-Smith, Z. Imani, B. Irwin, R. Itay, C. James, X. Ji,
L. Jiang, J.H. Jo, R.A. Johnson, Y.J. Jwa, D. Kalra, N. Kamp, G. Karagiorgi,
W. Ketchum, M. Kirby, T. Kobilarcik, I. Kreslo, M. B. Leibovitch, I. Lepetic,
J.-Y. Li, K. Li, Y. Li, K. Lin, B.R. Littlejohn, H. Liu, W.C. Louis, X. Luo,
C. Mariani, D. Marsden, J. Marshall, N. Martinez, D.A. Martinez Caicedo, S.
Martynenko, A. Mastbaum, N. McConkey, V. Meddage, J. Micallef, K. Miller, K.
Mistry, T. Mohayai, A. Mogan, M. Mooney, A.F. Moor, C.D. Moore, L. Mora
Lepin, M. Moudgalya, S. Mulleria Babu, D. Naples, A. Navrer-Agasson, N.
Nayak, M. Nebot-Guinot, J. Nowak, N. Oza, O. Palamara, N. Pallat, V. Paolone,
A. Papadopoulou, V. Papavassiliou, H. Parkinson, S.F. Pate, N. Patel, Z.
Pavlovic, E. Piasetzky, I. Ponce-Pinto, I. Pophale, X. Qian, J.L. Raaf, V.
Radeka, A. Rafique, M. Reggiani-Guzzo, L. Ren, L. Rochester, J. Rodriguez
Rondon, M. Rosenberg, M. Ross-Lonergan, C. Rudolph von Rohr, I. Safa, G.
Scanavini, D.W. Schmitz, A. Schukraft, W. Seligman, M.H. Shaevitz, R.
Sharankova, J. Shi, E.L. Snider, M. Soderberg, S. Soldner-Rembold, J. Spitz,
M. Stancari, J. St. John, T. Strauss, A.M. Szelc, W. Tang, N. Taniuchi, K.
Terao, C.Thorpe, D. Torbunov, D. Totani, M. Toups, Y.-T. Tsai, J. Tyler, M.A.
Uchida, T. Usher, B. Viren, M. Weber, H. Wei, A.J. White, Z. Williams, S.
Wolbers, T. Wongjirad, M. Wospakrik, K. Wresilo, N. Wright, W. Wu, E. Yandel,
T. Yang, L.E. Yates, H.W. Yu, G.P. Zeller, J. Zennamo, C. Zhang
|
Measurement of ambient radon daughter decay rates and energy spectra in
liquid argon using the MicroBooNE detector
| null | null | null |
FERMILAB-PUB-23-352-ND
|
hep-ex physics.ins-det
|
http://creativecommons.org/licenses/by/4.0/
|
We report measurements of radon daughters in liquid argon within the
MicroBooNE time projection chamber (LArTPC). The presence of radon in
MicroBooNE's 85 metric tons of active liquid argon bulk is probed with newly
developed charge-based low-energy reconstruction tools and analysis techniques
to detect correlated $^{214}$Bi-$^{214}$Po radioactive decays. Special datasets
taken during periods of active radon doping enable new demonstrations of the
calorimetric capabilities of single-phase neutrino LArTPCs for $\beta$ and
$\alpha$ particles with electron-equivalent energies ranging from 0.1 to 3.0
MeV. By applying $^{214}$Bi-$^{214}$Po detection algorithms to beam-external
physics data recorded over a 46-day period, no statistically significant
presence of radon is detected, corresponding to a limit of $<0.38$ mBq/kg at
the 95\% confidence level. The obtained radon radiopurity limit -- the first
ever reported for a noble element detector incorporating liquid-phase
purification -- is well below the target value of the future DUNE neutrino
detector.
|
[
{
"created": "Thu, 6 Jul 2023 16:22:09 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"MicroBooNE collaboration",
"",
""
],
[
"Abratenko",
"P.",
""
],
[
"Alterkait",
"O.",
""
],
[
"Aldana",
"D. Andrade",
""
],
[
"Arellano",
"L.",
""
],
[
"Asaadi",
"J.",
""
],
[
"Ashkenazi",
"A.",
""
],
[
"Balasubramanian",
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] |
2307.03103
|
Behzad Akbari
|
Behzad Akbari, Zikai Wang, Haibin Zhu, Lucas Wan, Ryan Adderson, and
Ya-Jun Pan
|
Role Engine Implementation for a Continuous and Collaborative
Multi-Robot System
|
10 pages, 18 figures, summited in IEEE Transactions on Systems, Man
and Cybernetics(T-SMC)
| null | null | null |
cs.RO cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In situations involving teams of diverse robots, assigning appropriate roles
to each robot and evaluating their performance is crucial. These roles define
the specific characteristics of a robot within a given context. The stream
actions exhibited by a robot based on its assigned role are referred to as the
process role. Our research addresses the depiction of process roles using a
multivariate probabilistic function. The main aim of this study is to develop a
role engine for collaborative multi-robot systems and optimize the behavior of
the robots. The role engine is designed to assign suitable roles to each robot,
generate approximately optimal process roles, update them on time, and identify
instances of robot malfunction or trigger replanning when necessary. The
environment considered is dynamic, involving obstacles and other agents. The
role engine operates hybrid, with central initiation and decentralized action,
and assigns unlabeled roles to agents. We employ the Gaussian Process (GP)
inference method to optimize process roles based on local constraints and
constraints related to other agents. Furthermore, we propose an innovative
approach that utilizes the environment's skeleton to address initialization and
feasibility evaluation challenges. We successfully demonstrated the proposed
approach's feasibility, and efficiency through simulation studies and
real-world experiments involving diverse mobile robots.
|
[
{
"created": "Thu, 6 Jul 2023 16:23:49 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Akbari",
"Behzad",
""
],
[
"Wang",
"Zikai",
""
],
[
"Zhu",
"Haibin",
""
],
[
"Wan",
"Lucas",
""
],
[
"Adderson",
"Ryan",
""
],
[
"Pan",
"Ya-Jun",
""
]
] |
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] |
2307.03104
|
Tim Schopf
|
Tim Schopf, Dennis Schneider, Florian Matthes
|
Efficient Domain Adaptation of Sentence Embeddings using Adapters
|
Accepted to the International Conference on Recent Advances in
Natural Language Processing (RANLP 2023)
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Sentence embeddings enable us to capture the semantic similarity of short
texts. Most sentence embedding models are trained for general semantic textual
similarity (STS) tasks. Therefore, to use sentence embeddings in a particular
domain, the model must be adapted to it in order to achieve good results.
Usually, this is done by fine-tuning the entire sentence embedding model for
the domain of interest. While this approach yields state-of-the-art results,
all of the model's weights are updated during fine-tuning, making this method
resource-intensive. Therefore, instead of fine-tuning entire sentence embedding
models for each target domain individually, we propose to train lightweight
adapters. These domain-specific adapters do not require fine-tuning all
underlying sentence embedding model parameters. Instead, we only train a small
number of additional parameters while keeping the weights of the underlying
sentence embedding model fixed. Training domain-specific adapters allows always
using the same base model and only exchanging the domain-specific adapters to
adapt sentence embeddings to a specific domain. We show that using adapters for
parameter-efficient domain adaptation of sentence embeddings yields competitive
performance within 1% of a domain-adapted, entirely fine-tuned sentence
embedding model while only training approximately 3.6% of the parameters.
|
[
{
"created": "Thu, 6 Jul 2023 16:26:34 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Schopf",
"Tim",
""
],
[
"Schneider",
"Dennis",
""
],
[
"Matthes",
"Florian",
""
]
] |
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] |
Efficient Domain Adaptation of Sentence Embeddings using Adapters
|
https://arxiv.org/abs/2307.03104v1
|
https://arxiv.org/pdf/2307.03104v1.pdf
|
https://github.com/sebischair/efficient-domain-adaptation-of-sentence-embeddings-using-adapters
| true | true |
https://paperswithcode.com/paper/efficient-domain-adaptation-of-sentence
|
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] |
2307.03105
|
Jagadeesh Harshan
|
Soumita Hazra and J. Harshan
|
On Distribution-Preserving Mitigation Strategies for Communication under
Cognitive Adversaries
|
Presented at IEEE ISIT 2023
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In wireless security, cognitive adversaries are known to inject jamming
energy on the victim's frequency band and monitor the same band for
countermeasures thereby trapping the victim. Under the class of cognitive
adversaries, we propose a new threat model wherein the adversary, upon
executing the jamming attack, measures the long-term statistic of
Kullback-Leibler Divergence (KLD) between its observations over each of the
network frequencies before and after the jamming attack. To mitigate this
adversary, we propose a new cooperative strategy wherein the victim takes the
assistance for a helper node in the network to reliably communicate its message
to the destination. The underlying idea is to appropriately split their energy
and time resources such that their messages are reliably communicated without
disturbing the statistical distribution of the samples in the network. We
present rigorous analyses on the reliability and the covertness metrics at the
destination and the adversary, respectively, and then synthesize tractable
algorithms to obtain near-optimal division of resources between the victim and
the helper. Finally, we show that the obtained near-optimal division of energy
facilitates in deceiving the adversary with a KLD estimator.
|
[
{
"created": "Thu, 6 Jul 2023 16:26:41 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Hazra",
"Soumita",
""
],
[
"Harshan",
"J.",
""
]
] |
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] |
2307.03108
|
Zhenting Wang
|
Zhenting Wang, Chen Chen, Yuchen Liu, Lingjuan Lyu, Dimitris Metaxas,
Shiqing Ma
|
How to Detect Unauthorized Data Usages in Text-to-image Diffusion Models
| null | null | null | null |
cs.CV cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent text-to-image diffusion models have shown surprising performance in
generating high-quality images. However, concerns have arisen regarding the
unauthorized usage of data during the training process. One example is when a
model trainer collects a set of images created by a particular artist and
attempts to train a model capable of generating similar images without
obtaining permission from the artist. To address this issue, it becomes crucial
to detect unauthorized data usage. In this paper, we propose a method for
detecting such unauthorized data usage by planting injected memorization into
the text-to-image diffusion models trained on the protected dataset.
Specifically, we modify the protected image dataset by adding unique contents
on the images such as stealthy image wrapping functions that are imperceptible
to human vision but can be captured and memorized by diffusion models. By
analyzing whether the model has memorization for the injected content (i.e.,
whether the generated images are processed by the chosen post-processing
function), we can detect models that had illegally utilized the unauthorized
data. Our experiments conducted on Stable Diffusion and LoRA model demonstrate
the effectiveness of the proposed method in detecting unauthorized data usages.
|
[
{
"created": "Thu, 6 Jul 2023 16:27:39 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Wang",
"Zhenting",
""
],
[
"Chen",
"Chen",
""
],
[
"Liu",
"Yuchen",
""
],
[
"Lyu",
"Lingjuan",
""
],
[
"Metaxas",
"Dimitris",
""
],
[
"Ma",
"Shiqing",
""
]
] |
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] |
2307.03109
|
Yuan Wu
|
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Kaijie Zhu, Hao Chen,
Linyi Yang, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi
Chang, Philip S. Yu, Qiang Yang, and Xing Xie
|
A Survey on Evaluation of Large Language Models
|
23 pages
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey.
|
[
{
"created": "Thu, 6 Jul 2023 16:28:35 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Chang",
"Yupeng",
""
],
[
"Wang",
"Xu",
""
],
[
"Wang",
"Jindong",
""
],
[
"Wu",
"Yuan",
""
],
[
"Zhu",
"Kaijie",
""
],
[
"Chen",
"Hao",
""
],
[
"Yang",
"Linyi",
""
],
[
"Yi",
"Xiaoyuan",
""
],
[
"Wang",
"Cunxiang",
""
],
[
"Wang",
"Yidong",
""
],
[
"Ye",
"Wei",
""
],
[
"Zhang",
"Yue",
""
],
[
"Chang",
"Yi",
""
],
[
"Yu",
"Philip S.",
""
],
[
"Yang",
"Qiang",
""
],
[
"Xie",
"Xing",
""
]
] |
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] |
A Survey on Evaluation of Large Language Models
|
https://arxiv.org/abs/2307.03109v2
|
https://arxiv.org/pdf/2307.03109v2.pdf
|
https://github.com/mlgroupjlu/llm-eval-survey
| true | true |
https://paperswithcode.com/paper/a-survey-on-evaluation-of-large-language
|
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] |
2307.03110
|
Arjun Sridhar
|
Bhavna Gopal, Arjun Sridhar, Tunhou Zhang and Yiran Chen
|
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural
Architecture Search
| null |
IJCAI 2023
| null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Search spaces hallmark the advancement of Neural Architecture Search (NAS).
Large and complex search spaces with versatile building operators and
structures provide more opportunities to brew promising architectures, yet pose
severe challenges on efficient exploration and exploitation. Subsequently,
several search space shrinkage methods optimize by selecting a single
sub-region that contains some well-performing networks. Small performance and
efficiency gains are observed with these methods but such techniques leave room
for significantly improved search performance and are ineffective at retaining
architectural diversity. We propose LISSNAS, an automated algorithm that
shrinks a large space into a diverse, small search space with SOTA search
performance. Our approach leverages locality, the relationship between
structural and performance similarity, to efficiently extract many pockets of
well-performing networks. We showcase our method on an array of search spaces
spanning various sizes and datasets. We accentuate the effectiveness of our
shrunk spaces when used in one-shot search by achieving the best Top-1 accuracy
in two different search spaces. Our method achieves a SOTA Top-1 accuracy of
77.6\% in ImageNet under mobile constraints, best-in-class Kendal-Tau,
architectural diversity, and search space size.
|
[
{
"created": "Thu, 6 Jul 2023 16:28:51 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Gopal",
"Bhavna",
""
],
[
"Sridhar",
"Arjun",
""
],
[
"Zhang",
"Tunhou",
""
],
[
"Chen",
"Yiran",
""
]
] |
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] |
2307.03113
|
Michael Mior
|
Michael J. Mior
|
JSONoid: Monoid-based Enrichment for Configurable and Scalable
Data-Driven Schema Discovery
| null | null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Schema discovery is an important aspect to working with data in formats such
as JSON. Unlike relational databases, JSON data sets often do not have
associated structural information. Consumers of such datasets are often left to
browse through data in an attempt to observe commonalities in structure across
documents to construct suitable code for data processing. However, this process
is time-consuming and error-prone. Existing distributed approaches to mining
schemas present a significant usability advantage as they provide useful
metadata for large data sources. However, depending on the data source, ad hoc
queries for estimating other properties to help with crafting an efficient data
pipeline can be expensive. We propose JSONoid, a distributed schema discovery
process augmented with additional metadata in the form of monoid data
structures that are easily maintainable in a distributed setting. JSONoid
subsumes several existing approaches to distributed schema discovery with
similar performance. Our approach also adds significant useful additional
information about data values to discovered schemas with linear scalability.
|
[
{
"created": "Thu, 6 Jul 2023 16:30:38 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Mior",
"Michael J.",
""
]
] |
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JSONoid: Monoid-based Enrichment for Configurable and Scalable Data-Driven Schema Discovery
|
https://arxiv.org/abs/2307.03113v1
|
https://arxiv.org/pdf/2307.03113v1.pdf
|
https://github.com/dataunitylab/jsonoid-discovery
| true | true |
https://paperswithcode.com/paper/jsonoid-monoid-based-enrichment-for
|
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] |
2307.03114
|
Pedro Henrique de Almeida Konzen
|
P. H. A. Konzen
|
ANN-MoC Method for Solving Unidimensional Neutral Particle Transport
Problems
|
9 pages, 2 figures
| null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neutral particle transport problems are fundamental in the modeling of energy
transfer by radiation (photons) and by neutrons with many important
applications. In this work, the novel ANN-MoC method for solving unidimensional
neutral particle transport problems is presented. Following the Method of
Discrete Ordinates (DOM) and decoupling with a Source Iteration (SI) scheme,
the proposed method applies Artificial Neural Networks (ANNs) together with the
Method of Characteristics (MoC) to solve the transport problem. Once the SI
scheme converges, the method gives an ANN that estimates the average flux of
particles at any points in the computational domain. Details of the proposed
method are given and results for two test cases are discussed. The achieve
results show the potential of this novel approach for solving neutral particle
transport problems.
|
[
{
"created": "Thu, 6 Jul 2023 16:33:19 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Konzen",
"P. H. A.",
""
]
] |
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] |
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