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2111.06887
|
Ruth Bamford
|
Ruth A. Bamford, Barry J. Kellett, James L. Green, Chuanfei Dong,
Vladimir Airapetian and Bob Bingham
|
How to create an artificial magnetosphere for Mars
|
Accepted for publication Acta Astronautica Sept 2021
|
Acta Astronautica, Volume 190, January 2022, Pages 323-333
|
10.1016/j.actaastro.2021.09.023
| null |
physics.space-ph astro-ph.EP astro-ph.IM physics.pop-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
If humanity is ever to consider substantial, long-term colonization of Mars,
the resources needed are going to be extensive. For a long-term human presence
on Mars to be established, serious thought would need to be given to
terraforming the planet. One major requirement for such terraforming is having
the protection of a planetary magnetic field which Mars currently does not
have. In this article we explore comprehensively for the first time, the
practical and engineering challenges that affect the feasibility of creating an
artificial magnetic field capable of encompassing Mars. This includes the
concerns that define the design, where to locate the magnetic field generator
and possible construction strategies. The rationale here is not to justify the
need for a planetary magnetosphere but to put figures on the practicalities so
as to be able to weigh the pros and cons of the different engineering
approaches.
The optimum solution proposed is completely novel, although inspired by
natural situations and fusion plasma techniques. The solution with the lowest
power, assembly and mass is to create an artificial charged particle ring
(similar in form to a "radiation belt"), around the planet possibly formed by
ejecting matter from one of the moons of Mars (in fashion similar to that that
forms the Io-Jupiter plasma torus), but using electromagnetic and plasma waves
to drive a net current in the ring(s) that results in an overall magnetic
field.
With a new era of space exploration underway, this is the time to start
thinking about these new and bold future concepts and to begin filling
strategic knowledge gaps. Furthermore, the principles explored here are also
applicable to smaller scale objects like manned spacecraft, space stations or
moon bases, which would benefit from the creation of protective
mini-magnetospheres.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710465 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06888
|
Guy Lorberbom
|
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow,
Tamir Hazan
|
Learning Generalized Gumbel-max Causal Mechanisms
|
Accepted to NeurIPS 2021 (Spotlight)
| null | null | null |
cs.LG stat.CO stat.ML
|
http://creativecommons.org/publicdomain/zero/1.0/
|
To perform counterfactual reasoning in Structural Causal Models (SCMs), one
needs to know the causal mechanisms, which provide factorizations of
conditional distributions into noise sources and deterministic functions
mapping realizations of noise to samples. Unfortunately, the causal mechanism
is not uniquely identified by data that can be gathered by observing and
interacting with the world, so there remains the question of how to choose
causal mechanisms. In recent work, Oberst & Sontag (2019) propose Gumbel-max
SCMs, which use Gumbel-max reparameterizations as the causal mechanism due to
an intuitively appealing counterfactual stability property. In this work, we
instead argue for choosing a causal mechanism that is best under a quantitative
criteria such as minimizing variance when estimating counterfactual treatment
effects. We propose a parameterized family of causal mechanisms that generalize
Gumbel-max. We show that they can be trained to minimize counterfactual effect
variance and other losses on a distribution of queries of interest, yielding
lower variance estimates of counterfactual treatment effect than fixed
alternatives, also generalizing to queries not seen at training time.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709372 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06889
|
Parth Kothari
|
Parth Kothari, Christian Perone, Luca Bergamini, Alexandre Alahi,
Peter Ondruska
|
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
|
Accepted to NeurIPS 2021 ML4AD Workshop
| null | null | null |
cs.LG cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite promising progress in reinforcement learning (RL), developing
algorithms for autonomous driving (AD) remains challenging: one of the critical
issues being the absence of an open-source platform capable of training and
effectively validating the RL policies on real-world data. We propose
DriverGym, an open-source OpenAI Gym-compatible environment specifically
tailored for developing RL algorithms for autonomous driving. DriverGym
provides access to more than 1000 hours of expert logged data and also supports
reactive and data-driven agent behavior. The performance of an RL policy can be
easily validated on real-world data using our extensive and flexible
closed-loop evaluation protocol. In this work, we also provide behavior cloning
baselines using supervised learning and RL, trained in DriverGym. We make
DriverGym code, as well as all the baselines publicly available to further
stimulate development from the community.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708572 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06894
|
Adrian Galdran
|
Adrian Galdran, Gustavo Carneiro, Miguel A. Gonz\'alez Ballester
|
Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer
Classification
| null | null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper compares well-established Convolutional Neural Networks (CNNs) to
recently introduced Vision Transformers for the task of Diabetic Foot Ulcer
Classification, in the context of the DFUC 2021 Grand-Challenge, in which this
work attained the first position. Comprehensive experiments demonstrate that
modern CNNs are still capable of outperforming Transformers in a low-data
regime, likely owing to their ability for better exploiting spatial
correlations. In addition, we empirically demonstrate that the recent
Sharpness-Aware Minimization (SAM) optimization algorithm considerably improves
the generalization capability of both kinds of models. Our results demonstrate
that for this task, the combination of CNNs and the SAM optimization process
results in superior performance than any other of the considered approaches.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711638 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06902
|
Sujay Kumar Jauhar
|
Sujay Kumar Jauhar, Nirupama Chandrasekaran, Michael Gamon and Ryen W.
White
|
MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Tasks are a fundamental unit of work in the daily lives of people, who are
increasingly using digital means to keep track of, organize, triage and act on
them. These digital tools -- such as task management applications -- provide a
unique opportunity to study and understand tasks and their connection to the
real world, and through intelligent assistance, help people be more productive.
By logging signals such as text, timestamp information, and social connectivity
graphs, an increasingly rich and detailed picture of how tasks are created and
organized, what makes them important, and who acts on them, can be
progressively developed. Yet the context around actual task completion remains
fuzzy, due to the basic disconnect between actions taken in the real world and
telemetry recorded in the digital world. Thus, in this paper we compile and
release a novel, real-life, large-scale dataset called MS-LaTTE that captures
two core aspects of the context surrounding task completion: location and time.
We describe our annotation framework and conduct a number of analyses on the
data that were collected, demonstrating that it captures intuitive contextual
properties for common tasks. Finally, we test the dataset on the two problems
of predicting spatial and temporal task co-occurrence, concluding that
predictors for co-location and co-time are both learnable, with a BERT
fine-tuned model outperforming several other baselines. The MS-LaTTE dataset
provides an opportunity to tackle many new modeling challenges in contextual
task understanding and we hope that its release will spur future research in
task intelligence more broadly.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.713076 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06906
|
Pierre Moreau
|
Pierre Moreau (1), Michael Doggett (1), Erik Sintorn (2) ((1) Lund
University, Sweden, (2) Chalmers University of Technology, Sweden)
|
Path Verification for Dynamic Indirect Illumination
|
8 pages, 8 figures, 1 table
| null | null | null |
cs.GR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
In this paper we present a technique that improves rendering performance for
real-time scenes with ray traced lighting in the presence of dynamic lights and
objects. In particular we verify photon paths from the previous frame against
dynamic objects in the current frame, and show how most photon paths are still
valid. When using area lights, we use a data structure to store light
distribution that tracks light paths allowing photons to be reused when the
light source is moving in the scene. We also show that by reusing paths when
the error in the reflected energy is below a threshold value, even more paths
can be reused. We apply this technique to Indirect Illumination using a screen
space photon splatting rendering engine. By reusing photon paths and applying
our error threshold, our method can reduce the number of rays traced by up to
5x, and improve performance by up to 2x.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711625 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06907
|
Daniel Eug\^enio Neves
|
Daniel Eug\^enio Neves, Jo\~ao Pedro Oliveira Batisteli, Eduardo
Felipe Lopes, Lucila Ishitani and Zenilton Kleber Gon\c{c}alves do
Patroc\'inio J\'unior (Pontif\'icia Universidade Cat\'olica de Minas Gerais,
Belo Horizonte, Brazil)
|
Improving Experience Replay through Modeling of Similar Transitions'
Sets
|
41 pages
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we propose and evaluate a new reinforcement learning method,
COMPact Experience Replay (COMPER), which uses temporal difference learning
with predicted target values based on recurrence over sets of similar
transitions, and a new approach for experience replay based on two transitions
memories. Our objective is to reduce the required number of experiences to
agent training regarding the total accumulated rewarding in the long run. Its
relevance to reinforcement learning is related to the small number of
observations that it needs to achieve results similar to that obtained by
relevant methods in the literature, that generally demand millions of video
frames to train an agent on the Atari 2600 games. We report detailed results
from five training trials of COMPER for just 100,000 frames and about 25,000
iterations with a small experiences memory on eight challenging games of Arcade
Learning Environment (ALE). We also present results for a DQN agent with the
same experimental protocol on the same games set as the baseline. To verify the
performance of COMPER on approximating a good policy from a smaller number of
observations, we also compare its results with that obtained from millions of
frames presented on the benchmark of ALE.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710409 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06913
|
Ranjay Krishna
|
Ranjay Krishna, Mitchell Gordon, Li Fei-Fei, Michael Bernstein
|
Visual Intelligence through Human Interaction
|
This is a preprint of the following chapter: Ranjay Krishna, Mitchell
Gordon, Li Fei-Fei, Michael Bernstein, Visual Intelligence through Human
Interaction, published in Artificial Intelligence for Human Computer
Interaction: A Modern Approach, edited by Yang Li and Otmar Hilliges, 2021,
Springer reproduced with permission of Springer Nature. arXiv admin note:
substantial text overlap with arXiv:1602.04506, arXiv:1904.01121
| null |
10.1007/978-3-030-82681-9
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Over the last decade, Computer Vision, the branch of Artificial Intelligence
aimed at understanding the visual world, has evolved from simply recognizing
objects in images to describing pictures, answering questions about images,
aiding robots maneuver around physical spaces and even generating novel visual
content. As these tasks and applications have modernized, so too has the
reliance on more data, either for model training or for evaluation. In this
chapter, we demonstrate that novel interaction strategies can enable new forms
of data collection and evaluation for Computer Vision. First, we present a
crowdsourcing interface for speeding up paid data collection by an order of
magnitude, feeding the data-hungry nature of modern vision models. Second, we
explore a method to increase volunteer contributions using automated social
interventions. Third, we develop a system to ensure human evaluation of
generative vision models are reliable, affordable and grounded in psychophysics
theory. We conclude with future opportunities for Human-Computer Interaction to
aid Computer Vision.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711425 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06914
|
Gennady Gor
|
C. D. Dobrzanski and B. Gurevich and G. Y. Gor
|
Elastic Properties of Confined Fluids from Molecular Modeling to
Ultrasonic Experiments on Porous Solids
|
review paper, 20 pages
|
Applied Physics Reviews 8, 021317 (2021)
|
10.1063/5.0024114
| null |
cond-mat.soft physics.app-ph physics.chem-ph physics.geo-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Fluids confined in nanopores are ubiquitous in nature and technology. In
recent years, the interest in confined fluids has grown, driven by research on
unconventional hydrocarbon resources -- shale gas and shale oil, much of which
are confined in nanopores. When fluids are confined in nanopores, many of their
properties differ from those of the same fluid in the bulk. These properties
include density, freezing point, transport coefficients, thermal expansion
coefficient, and elastic properties. The elastic moduli of a fluid confined in
the pores contribute to the overall elasticity of the fluid-saturated porous
medium and determine the speed at which elastic waves traverse through the
medium. Wave propagation in fluid-saturated porous media is pivotal for
geophysics, as elastic waves are used for characterization of formations and
rock samples. In this paper, we present a comprehensive review of experimental
works on wave propagation in fluid-saturated nanoporous media, as well as
theoretical works focused on calculation of compressibility of fluids in
confinement. We discuss models that bridge the gap between experiments and
theory, revealing a number of open questions that are both fundamental and
applied in nature. While some results were demonstrated both experimentally and
theoretically (e.g. the pressure dependence of compressibility of fluids),
others were theoretically predicted, but not verified in experiments (e.g.
linear scaling of modulus with the pore size). Therefore, there is a demand for
the combined experimental-modeling studies on porous samples with various
characteristic pore sizes. The extension of molecular simulation studies from
simple model fluids to the more complex molecular fluids is another open area
of practical interest.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712589 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06915
|
Hind Alqurashi
|
Hind Alqurashi, Raad Haleoot, and Bothina Hamad
|
First-principles investigations of the electronic, magnetic and
thermoelectric properties of VTiRhZ (Z= Al, Ga, In) Quaternary Heusler Alloys
|
40 pages, 7 figures, 5 tables
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci
|
http://creativecommons.org/licenses/by/4.0/
|
Calculations using density functional theory (DFT) were performed to
investigate the structural, dynamical, mechanical, electronic, magnetic, and
thermoelectric properties of VTiRhZ (Z = Al, Ga, In) alloys. The most stable
structure of these alloys was found to be the type-I configuration. Using
GGA-PBE functional, VTiRhGa, and VTiRhIn alloys are predicted as half-metallic
ferromagnets with a 100% spin-polarization and a total magnetic moment of
3{\mu}B, which is promising for spintronic applications. The thermoelectric
properties and lattice thermal conductivity of VTiRhZ alloys were obtained
using the Boltzmann transport theory within the constant relaxation time and
Slack equation, respectively. The figure-of-merit (ZT) values of VTiRhAl,
VTiRhGa, and VTiRhIn alloys were found to be 0.96, 0.88 and 0.64, respectively,
which are promising for future thermoelectric applications.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71027 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06920
|
Johannes Friedrich
|
Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin,
Anirvan M. Sengupta, Dmitri B. Chklovskii
|
Neural optimal feedback control with local learning rules
|
Manuscript and supplementary material of NeurIPS 2021 paper
| null | null | null |
q-bio.NC cs.NE cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A major problem in motor control is understanding how the brain plans and
executes proper movements in the face of delayed and noisy stimuli. A prominent
framework for addressing such control problems is Optimal Feedback Control
(OFC). OFC generates control actions that optimize behaviorally relevant
criteria by integrating noisy sensory stimuli and the predictions of an
internal model using the Kalman filter or its extensions. However, a
satisfactory neural model of Kalman filtering and control is lacking because
existing proposals have the following limitations: not considering the delay of
sensory feedback, training in alternating phases, and requiring knowledge of
the noise covariance matrices, as well as that of systems dynamics. Moreover,
the majority of these studies considered Kalman filtering in isolation, and not
jointly with control. To address these shortcomings, we introduce a novel
online algorithm which combines adaptive Kalman filtering with a model free
control approach (i.e., policy gradient algorithm). We implement this algorithm
in a biologically plausible neural network with local synaptic plasticity
rules. This network performs system identification and Kalman filtering,
without the need for multiple phases with distinct update rules or the
knowledge of the noise covariances. It can perform state estimation with
delayed sensory feedback, with the help of an internal model. It learns the
control policy without requiring any knowledge of the dynamics, thus avoiding
the need for weight transport. In this way, our implementation of OFC solves
the credit assignment problem needed to produce the appropriate sensory-motor
control in the presence of stimulus delay.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708843 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06923
|
Srikrishna B.R
|
Vishwas N.S, Srikrishna B.R, Sudarshan T.S.B
|
ARC Nav -- A 3D Navigation Stack for Autonomous Robots
|
Submitted, ICRA 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Popular navigation stacks implemented on top of open-source frameworks such
as ROS(Robot Operating System) and ROS2 represent the robot workspace using a
discretized 2D occupancy grid. This method, while requiring less computation,
restricts the use of such navigation stacks to wheeled robots navigating on
flat surfaces. In this paper, we present a navigation stack that uses a
volumetric representation of the robot workspace, and hence can be extended to
aerial and legged robots navigating through uneven terrain. Additionally, we
present a new sampling-based motion planning algorithm which introduces a
bi-directional approach to the Batch Informed Trees (BIT*) motion planning
algorithm, whilst wrapping it with a strategy switching approach in order to
reduce the initial time taken to find a path, in addition to the time taken to
find the shortest path.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06924
|
Sanyam Kapoor
|
Sanyam Kapoor, Valerio Perrone
|
A Simple and Fast Baseline for Tuning Large XGBoost Models
|
Technical Report
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
XGBoost, a scalable tree boosting algorithm, has proven effective for many
prediction tasks of practical interest, especially using tabular datasets.
Hyperparameter tuning can further improve the predictive performance, but
unlike neural networks, full-batch training of many models on large datasets
can be time consuming. Owing to the discovery that (i) there is a strong linear
relation between dataset size & training time, (ii) XGBoost models satisfy the
ranking hypothesis, and (iii) lower-fidelity models can discover promising
hyperparameter configurations, we show that uniform subsampling makes for a
simple yet fast baseline to speed up the tuning of large XGBoost models using
multi-fidelity hyperparameter optimization with data subsets as the fidelity
dimension. We demonstrate the effectiveness of this baseline on large-scale
tabular datasets ranging from $15-70\mathrm{GB}$ in size.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709982 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06934
|
Alex Andonian
|
Alex Andonian, Taesung Park, Bryan Russell, Phillip Isola, Jun-Yan
Zhu, Richard Zhang
|
Contrastive Feature Loss for Image Prediction
|
Appeared in Advances in Image Manipulation Workshop at ICCV 2021.
GitHub: https://github.com/alexandonian/contrastive-feature-loss
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Training supervised image synthesis models requires a critic to compare two
images: the ground truth to the result. Yet, this basic functionality remains
an open problem. A popular line of approaches uses the L1 (mean absolute error)
loss, either in the pixel or the feature space of pretrained deep networks.
However, we observe that these losses tend to produce overly blurry and grey
images, and other techniques such as GANs need to be employed to fight these
artifacts. In this work, we introduce an information theory based approach to
measuring similarity between two images. We argue that a good reconstruction
should have high mutual information with the ground truth. This view enables
learning a lightweight critic to "calibrate" a feature space in a contrastive
manner, such that reconstructions of corresponding spatial patches are brought
together, while other patches are repulsed. We show that our formulation
immediately boosts the perceptual realism of output images when used as a
drop-in replacement for the L1 loss, with or without an additional GAN loss.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710672 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06937
|
Christian Porter
|
Christian Porter, Cong Ling
|
Reduction Theory of Algebraic Modules and their Successive Minima
|
23 pages, including appendix. Submitted to Journal of Number Theory
| null | null | null |
math.NT cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Lattices defined as modules over algebraic rings or orders have garnered
interest recently, particularly in the fields of cryptography and coding
theory. Whilst there exist many attempts to generalise the conditions for LLL
reduction to such lattices, there do not seem to be any attempts so far to
generalise stronger notions of reduction such as Minkowski, HKZ and BKZ
reduction. Moreover, most lattice reduction methods for modules over algebraic
rings involve applying traditional techniques to the embedding of the module
into real space, which distorts the structure of the algebra. In this paper, we
generalise some classical notions of reduction theory to that of free modules
defined over an order. Moreover, we extend the definitions of Minkowski, HKZ
and BKZ reduction to that of such modules and show that bases reduced in this
manner have vector lengths that can be bounded above by the successive minima
of the lattice multiplied by a constant that depends on the algebra and the
dimension of the module. In particular, we show that HKZ reduced bases are
polynomially close to the successive minima of the lattice in terms of the
module dimension. None of our definitions require the module to be embedded and
thus preserve the structure of the module.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06939
|
Yosef Razin
|
Yosef S. Razin and Karen M. Feigh
|
Committing to Interdependence: Implications from Game Theory for
Human-Robot Trust
| null | null | null | null |
cs.HC cs.GT cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Human-robot interaction and game theory have developed distinct theories of
trust for over three decades in relative isolation from one another.
Human-robot interaction has focused on the underlying dimensions, layers,
correlates, and antecedents of trust models, while game theory has concentrated
on the psychology and strategies behind singular trust decisions. Both fields
have grappled to understand over-trust and trust calibration, as well as how to
measure trust expectations, risk, and vulnerability. This paper presents
initial steps in closing the gap between these fields. Using insights and
experimental findings from interdependence theory and social psychology, this
work starts by analyzing a large game theory competition data set to
demonstrate that the strongest predictors for a wide variety of human-human
trust interactions are the interdependence-derived variables for commitment and
trust that we have developed. It then presents a second study with human
subject results for more realistic trust scenarios, involving both human-human
and human-machine trust. In both the competition data and our experimental
data, we demonstrate that the interdependence metrics better capture social
`overtrust' than either rational or normative psychological reasoning, as
proposed by game theory. This work further explores how interdependence
theory--with its focus on commitment, coercion, and cooperation--addresses many
of the proposed underlying constructs and antecedents within human-robot trust,
shedding new light on key similarities and differences that arise when robots
replace humans in trust interactions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.704999 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06942
|
Andr\'e Ofner
|
Andre Ofner, Raihan Kabir Ratul, Suhita Ghosh, Sebastian Stober
|
Predictive coding, precision and natural gradients
| null | null | null | null |
cs.LG cs.AI cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
There is an increasing convergence between biologically plausible
computational models of inference and learning with local update rules and the
global gradient-based optimization of neural network models employed in machine
learning. One particularly exciting connection is the correspondence between
the locally informed optimization in predictive coding networks and the error
backpropagation algorithm that is used to train state-of-the-art deep
artificial neural networks. Here we focus on the related, but still largely
under-explored connection between precision weighting in predictive coding
networks and the Natural Gradient Descent algorithm for deep neural networks.
Precision-weighted predictive coding is an interesting candidate for scaling up
uncertainty-aware optimization -- particularly for models with large parameter
spaces -- due to its distributed nature of the optimization process and the
underlying local approximation of the Fisher information metric, the adaptive
learning rate that is central to Natural Gradient Descent. Here, we show that
hierarchical predictive coding networks with learnable precision indeed are
able to solve various supervised and unsupervised learning tasks with
performance comparable to global backpropagation with natural gradients and
outperform their classical gradient descent counterpart on tasks where high
amounts of noise are embedded in data or label inputs. When applied to
unsupervised auto-encoding of image inputs, the deterministic network produces
hierarchically organized and disentangled embeddings, hinting at the close
connections between predictive coding and hierarchical variational inference.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708843 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06945
|
Raed Alharbi
|
Raed Alharbi, Minh N. Vu, My T. Thai
|
Learning Interpretation with Explainable Knowledge Distillation
|
Accepted at IEEE BigData 2021
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Knowledge Distillation (KD) has been considered as a key solution in model
compression and acceleration in recent years. In KD, a small student model is
generally trained from a large teacher model by minimizing the divergence
between the probabilistic outputs of the two. However, as demonstrated in our
experiments, existing KD methods might not transfer critical explainable
knowledge of the teacher to the student, i.e. the explanations of predictions
made by the two models are not consistent. In this paper, we propose a novel
explainable knowledge distillation model, called XDistillation, through which
both the performance the explanations' information are transferred from the
teacher model to the student model. The XDistillation model leverages the idea
of convolutional autoencoders to approximate the teacher explanations. Our
experiments shows that models trained by XDistillation outperform those trained
by conventional KD methods not only in term of predictive accuracy but also
faithfulness to the teacher models.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712676 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06951
|
Max Cubillos
|
Max Cubillos and Edwin Jimenez
|
Diffraction integral computation using sinc approximation
|
17 pages, 2 figures. Submitted for publication
| null | null | null |
physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a method based on sinc series approximations for computing the
Rayleigh-Sommerfeld and Fresnel diffraction integrals of optics. The
diffraction integrals are given in terms of a convolution, and our proposed
numerical approach is not only super-algebraically convergent, but it also
satisfies an important property of the convolution -- namely, the preservation
of bandwidth. Furthermore, the accuracy of the proposed method depends only on
how well the source field is approximated; it is independent of wavelength,
propagation distance, and observation plane discretization. In contrast,
methods based on the fast Fourier transform (FFT), such as the angular spectrum
method (ASM) and its variants, approximate the optical fields in the source and
observation planes using Fourier series. We will show that the ASM introduces
artificial periodic boundary conditions and violates the preservation of
bandwidth property, resulting in limited accuracy which decreases for longer
propagation distances. The sinc-based approach avoids both of these problems.
Numerical results are presented for Gaussian beam propagation and circular
aperture diffraction to demonstrate the high-order accuracy of the sinc method
for both short-range and long-range propagation. For comparison, we also
present numerical results obtained with the angular spectrum method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06955
|
Kaiyuan Yao
|
Kaiyuan Yao, Shuai Zhang, Emanuil Yanev, Kathleen McCreary, Hsun-Jen
Chuang, Matthew R. Rosenberger, Thomas Darlington, Andrey Krayev, Berend T.
Jonker, James C. Hone, D.N. Basov, P. James Schuck
|
Nanoscale Optical Imaging of 2D Semiconductor Stacking Orders by
Exciton-Enhanced Second Harmonic Generation
| null | null | null | null |
physics.optics cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Second harmonic generation (SHG) is a nonlinear optical response arising
exclusively from broken inversion symmetry in the electric-dipole limit.
Recently, SHG has attracted widespread interest as a versatile and noninvasive
tool for characterization of crystal symmetry and emerging ferroic or
topological orders in quantum materials. However, conventional far-field optics
is unable to probe local symmetry at the deep subwavelength scale. Here, we
demonstrate near-field SHG imaging of 2D semiconductors and heterostructures
with the spatial resolution down to 20 nm using a scattering-type nano-optical
apparatus. We show that near-field SHG efficiency is greatly enhanced by
excitons in atomically thin transition metal dichalcogenides. Furthermore, by
correlating nonlinear and linear scattering-type nano-imaging, we resolve
nanoscale variations of interlayer stacking order in bilayer WSe2, and reveal
the stacking-tuned excitonic light-matter-interactions. Our work demonstrates
nonlinear optical interrogation of crystal symmetry and structure-property
relationships at the nanometer length scales relevant to emerging properties in
quantum materials.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06956
|
Lawrence Chan
|
Lawrence Chan, Andrew Critch, Anca Dragan
|
Human irrationality: both bad and good for reward inference
|
12 pages, 10 figures
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Assuming humans are (approximately) rational enables robots to infer reward
functions by observing human behavior. But people exhibit a wide array of
irrationalities, and our goal with this work is to better understand the effect
they can have on reward inference. The challenge with studying this effect is
that there are many types of irrationality, with varying degrees of
mathematical formalization. We thus operationalize irrationality in the
language of MDPs, by altering the Bellman optimality equation, and use this
framework to study how these alterations would affect inference.
We find that wrongly modeling a systematically irrational human as
noisy-rational performs a lot worse than correctly capturing these biases -- so
much so that it can be better to skip inference altogether and stick to the
prior! More importantly, we show that an irrational human, when correctly
modelled, can communicate more information about the reward than a perfectly
rational human can. That is, if a robot has the correct model of a human's
irrationality, it can make an even stronger inference than it ever could if the
human were rational. Irrationality fundamentally helps rather than hinder
reward inference, but it needs to be correctly accounted for.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711819 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06958
|
Loizos Michael
|
Emmanuelle Dietz, Antonis Kakas, Loizos Michael
|
Computational Argumentation and Cognition
| null | null | null | null |
cs.AI cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper examines the interdisciplinary research question of how to
integrate Computational Argumentation, as studied in AI, with Cognition, as can
be found in Cognitive Science, Linguistics, and Philosophy. It stems from the
work of the 1st Workshop on Computational Argumentation and Cognition
(COGNITAR), which was organized as part of the 24th European Conference on
Artificial Intelligence (ECAI), and took place virtually on September 8th,
2020. The paper begins with a brief presentation of the scientific motivation
for the integration of Computational Argumentation and Cognition, arguing that
within the context of Human-Centric AI the use of theory and methods from
Computational Argumentation for the study of Cognition can be a promising
avenue to pursue. A short summary of each of the workshop presentations is
given showing the wide spectrum of problems where the synthesis of the theory
and methods of Computational Argumentation with other approaches that study
Cognition can be applied. The paper presents the main problems and challenges
in the area that would need to be addressed, both at the scientific level but
also at the epistemological level, particularly in relation to the synthesis of
ideas and approaches from the various disciplines involved.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711462 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06961
|
Priya Donti
|
Priya L. Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha, Larry
Pileggi, J. Zico Kolter
|
Adversarially Robust Learning for Security-Constrained Optimal Power
Flow
|
Accepted at Neural Information Processing Systems (NeurIPS) 2021
| null | null | null |
math.OC cs.LG cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, the ML community has seen surges of interest in both
adversarially robust learning and implicit layers, but connections between
these two areas have seldom been explored. In this work, we combine innovations
from these areas to tackle the problem of N-k security-constrained optimal
power flow (SCOPF). N-k SCOPF is a core problem for the operation of electrical
grids, and aims to schedule power generation in a manner that is robust to
potentially k simultaneous equipment outages. Inspired by methods in
adversarially robust training, we frame N-k SCOPF as a minimax optimization
problem - viewing power generation settings as adjustable parameters and
equipment outages as (adversarial) attacks - and solve this problem via
gradient-based techniques. The loss function of this minimax problem involves
resolving implicit equations representing grid physics and operational
decisions, which we differentiate through via the implicit function theorem. We
demonstrate the efficacy of our framework in solving N-3 SCOPF, which has
traditionally been considered as prohibitively expensive to solve given that
the problem size depends combinatorially on the number of potential outages.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711212 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06964
|
Marco Coraggio Dr
|
Marco Coraggio, Pietro DeLellis, S. John Hogan, Mario di Bernardo
|
Synchronization of networks of piecewise-smooth systems
| null | null |
10.1109/lcsys.2018.2845302
| null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We study convergence in networks of piecewise-smooth (PWS) systems that
commonly arise in applications to model dynamical systems whose evolution is
affected by macroscopic events such as switches and impacts. Existing
approaches were typically oriented toward guaranteeing global bounded
synchronizability, local stability of the synchronization manifold, or
achieving synchronization by exerting a control action on each node. Here we
start by generalizing existing results on QUAD systems to the case of PWS
systems, accounting for a large variety of nonlinear coupling laws. Then, we
propose that a discontinuous coupling can be used to guarantee global
synchronizability of a network of N PWS agents under mild assumptions on the
individual dynamics. We provide extensive numerical simulations to gain
insights on larger networks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707979 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06968
|
Zhen Liu
|
Wen-Bo Xie, Zhen Liu, Jaideep Srivastava
|
Hierarchical clustering by aggregating representatives in
sub-minimum-spanning-trees
| null | null | null | null |
stat.ML cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the main challenges for hierarchical clustering is how to
appropriately identify the representative points in the lower level of the
cluster tree, which are going to be utilized as the roots in the higher level
of the cluster tree for further aggregation. However, conventional hierarchical
clustering approaches have adopted some simple tricks to select the
"representative" points which might not be as representative as enough. Thus,
the constructed cluster tree is less attractive in terms of its poor robustness
and weak reliability. Aiming at this issue, we propose a novel hierarchical
clustering algorithm, in which, while building the clustering dendrogram, we
can effectively detect the representative point based on scoring the reciprocal
nearest data points in each sub-minimum-spanning-tree. Extensive experiments on
UCI datasets show that the proposed algorithm is more accurate than other
benchmarks. Meanwhile, under our analysis, the proposed algorithm has O(nlogn)
time-complexity and O(logn) space-complexity, indicating that it has the
scalability in handling massive data with less time and storage consumptions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710653 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06972
|
Marco Coraggio Dr
|
Daniel A. Burbano-Lombana, Marco Coraggio, Mario di Bernardo, Franco
Garofalo, Michele Pugliese
|
Adaptive and quasi-sliding control of shimmy in landing gears
| null | null |
10.23919/ecc.2018.8550431
| null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Shimmy is a dangerous phenomenon that occurs when aircraft's nose landing
gears oscillate in a rapid and uncontrollable fashion. In this paper, we
propose the use of two nonlinear control approaches (zero average control and
model reference adaptive control based on minimal control synthesis) as simple
yet effective strategies to suppress undesired oscillations, even in the
presence of uncertainties and partial state measurements. Numerical results are
presented to validate the proposed control approaches.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06974
|
Chuyuan Tao
|
Chuyuan Tao, Hunmin Kim, Hyungjin Yoon, Naira Hovakimyan, and Petros
Voulgaris
|
Control Barrier Function Augmentation in Sampling-based Control
Algorithm for Sample Efficiency
| null | null | null | null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For a nonlinear stochastic path planning problem, sampling-based algorithms
generate thousands of random sample trajectories to find the optimal path while
guaranteeing safety by Lagrangian penalty methods. However, the sampling-based
algorithm can perform poorly in obstacle-rich environments because most samples
might violate safety constraints, invalidating the corresponding samples. To
improve the sample efficiency of sampling-based algorithms in cluttered
environments, we propose an algorithm based on model predictive path integral
control and control barrier functions. The proposed algorithm needs fewer
samples and time-steps and has a better performance in cluttered environments
compared to the original model predictive path integral control algorithm.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712282 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06979
|
Jenelle Feather
|
Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David D. Cox,
Josh H. McDermott, James J. DiCarlo, SueYeon Chung
|
Neural Population Geometry Reveals the Role of Stochasticity in Robust
Perception
|
35th Conference on Neural Information Processing Systems (NeurIPS
2021)
| null | null | null |
q-bio.NC cs.LG cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Adversarial examples are often cited by neuroscientists and machine learning
researchers as an example of how computational models diverge from biological
sensory systems. Recent work has proposed adding biologically-inspired
components to visual neural networks as a way to improve their adversarial
robustness. One surprisingly effective component for reducing adversarial
vulnerability is response stochasticity, like that exhibited by biological
neurons. Here, using recently developed geometrical techniques from
computational neuroscience, we investigate how adversarial perturbations
influence the internal representations of standard, adversarially trained, and
biologically-inspired stochastic networks. We find distinct geometric
signatures for each type of network, revealing different mechanisms for
achieving robust representations. Next, we generalize these results to the
auditory domain, showing that neural stochasticity also makes auditory models
more robust to adversarial perturbations. Geometric analysis of the stochastic
networks reveals overlap between representations of clean and adversarially
perturbed stimuli, and quantitatively demonstrates that competing geometric
effects of stochasticity mediate a tradeoff between adversarial and clean
performance. Our results shed light on the strategies of robust perception
utilized by adversarially trained and stochastic networks, and help explain how
stochasticity may be beneficial to machine and biological computation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711281 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06980
|
Yu Huang
|
Yu Huang, Chao Zhang, Jaswanth Yella, Sergei Petrov, Xiaoye Qian,
Yufei Tang, Xingquan Zhu, Sthitie Bom
|
GraSSNet: Graph Soft Sensing Neural Networks
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the era of big data, data-driven based classification has become an
essential method in smart manufacturing to guide production and optimize
inspection. The industrial data obtained in practice is usually time-series
data collected by soft sensors, which are highly nonlinear, nonstationary,
imbalanced, and noisy. Most existing soft-sensing machine learning models focus
on capturing either intra-series temporal dependencies or pre-defined
inter-series correlations, while ignoring the correlation between labels as
each instance is associated with multiple labels simultaneously. In this paper,
we propose a novel graph based soft-sensing neural network (GraSSNet) for
multivariate time-series classification of noisy and highly-imbalanced
soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series
and intra-series dependencies jointly in the spectral domain; 2) exploit the
label correlations by superimposing label graph that built from statistical
co-occurrence information; 3) learn features with attention mechanism from both
textual and numerical domain; and 4) leverage unlabeled data and mitigate data
imbalance by semi-supervised learning. Comparative studies with other commonly
used classifiers are carried out on Seagate soft sensing data, and the
experimental results validate the competitive performance of our proposed
method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71145 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06981
|
Yu Huang
|
Jaswanth Yella, Chao Zhang, Sergei Petrov, Yu Huang, Xiaoye Qian, Ali
A. Minai, Sthitie Bom
|
Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional
Transformer
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Over the last few decades, modern industrial processes have investigated
several cost-effective methodologies to improve the productivity and yield of
semiconductor manufacturing. While playing an essential role in facilitating
real-time monitoring and control, the data-driven soft-sensors in industries
have provided a competitive edge when augmented with deep learning approaches
for wafer fault-diagnostics. Despite the success of deep learning methods
across various domains, they tend to suffer from bad performance on
multi-variate soft-sensing data domains. To mitigate this, we propose a
soft-sensing ConFormer (CONvolutional transFORMER) for wafer fault-diagnostic
classification task which primarily consists of multi-head convolution modules
that reap the benefits of fast and light-weight operations of convolutions, and
also the ability to learn the robust representations through multi-head design
alike transformers. Another key issue is that traditional learning paradigms
tend to suffer from low performance on noisy and highly-imbalanced soft-sensing
data. To address this, we augment our soft-sensing ConFormer model with a
curriculum learning-based loss function, which effectively learns easy samples
in the early phase of training and difficult ones later. To further demonstrate
the utility of our proposed architecture, we performed extensive experiments on
various toolsets of Seagate Technology's wafer manufacturing process which are
shared openly along with this work. To the best of our knowledge, this is the
first time that curriculum learning-based soft-sensing ConFormer architecture
has been proposed for soft-sensing data and our results show strong promise for
future use in soft-sensing research domain.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711005 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06982
|
Yu Huang
|
Xiaoye Qian, Chao Zhang, Jaswanth Yella, Yu Huang, Ming-Chun Huang,
Sthitie Bom
|
Soft Sensing Model Visualization: Fine-tuning Neural Network from What
Model Learned
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The growing availability of the data collected from smart manufacturing is
changing the paradigms of production monitoring and control. The increasing
complexity and content of the wafer manufacturing process in addition to the
time-varying unexpected disturbances and uncertainties, make it infeasible to
do the control process with model-based approaches. As a result, data-driven
soft-sensing modeling has become more prevalent in wafer process diagnostics.
Recently, deep learning has been utilized in soft sensing system with promising
performance on highly nonlinear and dynamic time-series data. Despite its
successes in soft-sensing systems, however, the underlying logic of the deep
learning framework is hard to understand. In this paper, we propose a deep
learning-based model for defective wafer detection using a highly imbalanced
dataset. To understand how the proposed model works, the deep visualization
approach is applied. Additionally, the model is then fine-tuned guided by the
deep visualization. Extensive experiments are performed to validate the
effectiveness of the proposed system. The results provide an interpretation of
how the model works and an instructive fine-tuning method based on the
interpretation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711857 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06994
|
Rongkai Ma
|
Tianyu Zhu, Rongkai Ma, Mehrtash Harandi and Tom Drummond
|
Learning Online for Unified Segmentation and Tracking Models
| null |
International Joint Conference on Neural Networks (IJCNN), 2021,
pp. 1-8
|
10.1109/IJCNN52387.2021.9533455.
| null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tracking requires building a discriminative model for the target in the
inference stage. An effective way to achieve this is online learning, which can
comfortably outperform models that are only trained offline. Recent research
shows that visual tracking benefits significantly from the unification of
visual tracking and segmentation due to its pixel-level discrimination.
However, it imposes a great challenge to perform online learning for such a
unified model. A segmentation model cannot easily learn from prior information
given in the visual tracking scenario. In this paper, we propose TrackMLP: a
novel meta-learning method optimized to learn from only partial information to
resolve the imposed challenge. Our model is capable of extensively exploiting
limited prior information hence possesses much stronger target-background
discriminability than other online learning methods. Empirically, we show that
our model achieves state-of-the-art performance and tangible improvement over
competing models. Our model achieves improved average overlaps of66.0%,67.1%,
and68.5% in VOT2019, VOT2018, and VOT2016 datasets, which are 6.4%,7.3%,
and6.4% higher than our baseline. Code will be made publicly available.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711017 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06995
|
Zhimin Gao
|
Shuangyan Miao, Yonghong Hou, Zhimin Gao, Mingliang Xu, and Wanqing Li
|
A Central Difference Graph Convolutional Operator for Skeleton-Based
Action Recognition
|
Accepted by IEEE Transactions on Circuits and Systems for Video
Technology (TCSVT)
| null |
10.1109/TCSVT.2021.3124562
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper proposes a new graph convolutional operator called central
difference graph convolution (CDGC) for skeleton based action recognition. It
is not only able to aggregate node information like a vanilla graph
convolutional operation but also gradient information. Without introducing any
additional parameters, CDGC can replace vanilla graph convolution in any
existing Graph Convolutional Networks (GCNs). In addition, an accelerated
version of the CDGC is developed which greatly improves the speed of training.
Experiments on two popular large-scale datasets NTU RGB+D 60 & 120 have
demonstrated the efficacy of the proposed CDGC. Code is available at
https://github.com/iesymiao/CD-GCN.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7114 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06997
|
James Melbourne
|
James Melbourne and Gerardo Palafox-Castillo
|
A discrete complement of Lyapunov's inequality and its information
theoretic consequences
|
16 pages
| null | null | null |
cs.IT math.FA math.IT math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We establish a reversal of Lyapunov's inequality for monotone log-concave
sequences, settling a conjecture of Havrilla-Tkocz and Melbourne-Tkocz. A
strengthened version of the same conjecture is disproved through counter
example. We also derive several information theoretic inequalities as
consequences. In particular sharp bounds are derived for the varentropy,
R\'enyi entropies, and the concentration of information of monotone log-concave
random variables. Moreover, the majorization approach utilized in the proof of
the main theorem, is applied to derive analogous information theoretic results
in the symmetric setting, where the Lyapunov reversal is known to fail.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710879 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07001
|
Dilini Rajapaksha
|
Dilini Rajapaksha, Christoph Bergmeir, Rob J Hyndman
|
LoMEF: A Framework to Produce Local Explanations for Global Model Time
Series Forecasts
|
46 pages, 11 figures, 21 tables
| null | null | null |
cs.LG cs.AI stat.ML
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Global Forecasting Models (GFM) that are trained across a set of multiple
time series have shown superior results in many forecasting competitions and
real-world applications compared with univariate forecasting approaches. One
aspect of the popularity of statistical forecasting models such as ETS and
ARIMA is their relative simplicity and interpretability (in terms of relevant
lags, trend, seasonality, and others), while GFMs typically lack
interpretability, especially towards particular time series. This reduces the
trust and confidence of the stakeholders when making decisions based on the
forecasts without being able to understand the predictions. To mitigate this
problem, in this work, we propose a novel local model-agnostic interpretability
approach to explain the forecasts from GFMs. We train simpler univariate
surrogate models that are considered interpretable (e.g., ETS) on the
predictions of the GFM on samples within a neighbourhood that we obtain through
bootstrapping or straightforwardly as the one-step-ahead global black-box model
forecasts of the time series which needs to be explained. After, we evaluate
the explanations for the forecasts of the global models in both qualitative and
quantitative aspects such as accuracy, fidelity, stability and
comprehensibility, and are able to show the benefits of our approach.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71145 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07002
|
Eman AlOmar
|
Eman Abdullah AlOmar and Tianjia Wang and Vaibhavi Raut and Mohamed
Wiem Mkaouer and Christian Newman and Ali Ouni
|
Refactoring for Reuse: An Empirical Study
| null | null | null | null |
cs.SE
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Refactoring is the de-facto practice to optimize software health. While
several studies propose refactoring strategies to optimize software design
through applying design patterns and removing design defects, little is known
about how developers actually refactor their code to improve its reuse.
Therefore, we extract, from 1,828 open-source projects, a set of refactorings
that were intended to improve the software reusability. We analyze the impact
of reusability refactorings on the state-of-the-art reusability metrics, and we
compare the distribution of reusability refactoring types, with the
distribution of the remaining mainstream refactorings. Overall, we found that
the distribution of refactoring types, applied in the context of reusability,
is different from the distribution of refactoring types in mainstream
development. In the refactorings performed to improve reusability, source files
are subject to more design-level types of refactorings. Reusability
refactorings significantly impact, high-level code elements, such as packages,
classes, and methods, while typical refactorings, impact all code elements,
including identifiers, and parameters. These findings provide practical
insights into the current practice of refactoring in the context of code reuse
involving the act of refactoring.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709189 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07004
|
Yanyan Shen
|
Yanyan Shen and Khashayar Khorasani
|
Fault Diagnosis of Nonlinear Systems Using a Hybrid-Degree Dual
Cubature-based Estimation Scheme
| null | null | null | null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, a novel hybrid-degree dual estimation approach based on
cubature rules and cubature-based nonlinear filters is proposed for fault
diagnosis of nonlinear systems through simultaneous state and time-varying
parameter estimation. Our proposed dual nonlinear filtering scheme is developed
based on case-dependent cubature rules that are motivated by the following
observations and facts, namely (i) dynamic characteristics of nonlinear system
states and parameters generally are distinct and posses different degrees of
complexities, and (ii) performance of cubature rules depend on the system
dynamics and vary due to handling of high-dimensional integrations
approximations. For improving the robustness capability of our proposed
methodologies modified cubature point propagation method is incorporated. The
performance of our proposed dual estimation strategy is demonstrated and
evaluated by application to a nonlinear gas turbine engine for addressing the
component fault diagnosis problem within an integrated fault detection,
isolation and identification framework. Robustness analysis is implemented to
verify the capability of our proposed approaches to deal with parametric
uncertainties and unmodeled dynamics. Extensive simulation case studies and
discussions with respect to component fouling, erosion or abrupt faults are
provided to substantiate and justify the superiority of our proposed fault
diagnosis methodology when compared to other well-known alternative diagnostic
techniques such as the Unscented Kalman Filters (UKF) and Particle Filters (PF)
that are commonly available in the literature.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709252 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07005
|
Jorge Maestre Vidal
|
\'Alvaro Luis Mart\'inez, Jorge Maestre Vidal, Victor A. Villagr\'a
Gonz\'alez
|
Understanding and Assessment of Mission-Centric Key Cyber Terrains for
joint Military Operations
|
Preprint of an extended version of the conference "A novel automatic
discovery system of critical assets in cyberspace-oriented military
missions", in Proc. of the First Workshop on Recent Advances in Cyber
Situational Awareness on Military Operations (CSA 2020) held by the 15th ARES
International Conference in August 2020.
https://doi.org/10.1145/3407023.3409225
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Since the cyberspace consolidated as fifth warfare dimension, the different
actors of the defense sector began an arms race toward achieving cyber
superiority, on which research, academic and industrial stakeholders contribute
from a dual vision, mostly linked to a large and heterogeneous heritage of
developments and adoption of civilian cybersecurity capabilities. In this
context, augmenting the conscious of the context and warfare environment, risks
and impacts of cyber threats on kinetic actuations became a critical
rule-changer that military decision-makers are considering. A major challenge
on acquiring mission-centric Cyber Situational Awareness (CSA) is the dynamic
inference and assessment of the vertical propagations from situations that
occurred at the mission supportive Information and Communications Technologies
(ICT), up to their relevance at military tactical, operational and strategical
views. In order to contribute on acquiring CSA, this paper addresses a major
gap in the cyber defence state-of-the-art: the dynamic identification of Key
Cyber Terrains (KCT) on a mission-centric context. Accordingly, the proposed
KCT identification approach explores the dependency degrees among tasks and
assets defined by commanders as part of the assessment criteria. These are
correlated with the discoveries on the operational network and the asset
vulnerabilities identified thorough the supported mission development. The
proposal is presented as a reference model that reveals key aspects for
mission-centric KCT analysis and supports its enforcement and further
enforcement by including an illustrative application case.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709265 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07007
|
Dominique Orban
|
Alexis Montoison and Dominique Orban
|
GPMR: An Iterative Method for Unsymmetric Partitioned Linear Systems
| null | null |
10.13140/RG.2.2.24069.68326
|
G-2021-62
|
math.NA cs.NA
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce an iterative method named GPMR for solving 2x2 block unsymmetric
linear systems. GPMR is based on a new process that reduces simultaneously two
rectangular matrices to upper Hessenberg form and that is closely related to
the block-Arnoldi process. GPMR is tantamount to Block-GMRES with two
right-hand sides in which the two approximate solutions are summed at each
iteration, but requires less storage and work per iteration. We compare the
performance of GPMR with GMRES and Block-GMRES on linear systems from the
SuiteSparse Matrix Collection. In our experiments, GPMR terminates
significantly earlier than GMRES on a residual-based stopping condition with an
improvement ranging from around 10% up to 50% in terms of number of iterations.
We also illustrate by experiment that GPMR appears more resilient to loss of
orthogonality than Block-GMRES.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710069 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07009
|
Riddhish Bhalodia
|
Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan, Ross Whitaker
|
Leveraging Unsupervised Image Registration for Discovery of Landmark
Shape Descriptor
|
Published in Medical Image Analysis
| null |
10.1016/j.media.2021.102157
| null |
cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In current biological and medical research, statistical shape modeling (SSM)
provides an essential framework for the characterization of anatomy/morphology.
Such analysis is often driven by the identification of a relatively small
number of geometrically consistent features found across the samples of a
population. These features can subsequently provide information about the
population shape variation. Dense correspondence models can provide ease of
computation and yield an interpretable low-dimensional shape descriptor when
followed by dimensionality reduction. However, automatic methods for obtaining
such correspondences usually require image segmentation followed by significant
preprocessing, which is taxing in terms of both computation as well as human
resources. In many cases, the segmentation and subsequent processing require
manual guidance and anatomy specific domain expertise. This paper proposes a
self-supervised deep learning approach for discovering landmarks from images
that can directly be used as a shape descriptor for subsequent analysis. We use
landmark-driven image registration as the primary task to force the neural
network to discover landmarks that register the images well. We also propose a
regularization term that allows for robust optimization of the neural network
and ensures that the landmarks uniformly span the image domain. The proposed
method circumvents segmentation and preprocessing and directly produces a
usable shape descriptor using just 2D or 3D images. In addition, we also
propose two variants on the training loss function that allows for prior shape
information to be integrated into the model. We apply this framework on several
2D and 3D datasets to obtain their shape descriptors, and analyze their utility
for various applications.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71103 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07015
|
Chance DeSmet
|
Chance N DeSmet, Diane J Cook
|
HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data
Generation
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Synthetic data generation overcomes limitations of real-world machine
learning. Traditional methods are valuable for augmenting costly datasets but
only optimize one criterion: realism. In this paper, we tackle the problem of
generating synthetic data that optimize multiple criteria. This goal is
necessary when real data are replaced by synthetic for privacy preservation. We
introduce HydraGAN, a new approach to synthetic data generation that introduces
multiple generator and discriminator agents into the system. The multi-agent
GAN optimizes the goal of privacy-preservation as well as data realism. To
facilitate multi-agent training, we adapt game-theoretic principles to offer
equilibrium guarantees. We observe that HydraGAN outperforms baseline methods
for three datasets for multiple criteria of maximizing data realism, maximizing
model accuracy, and minimizing re-identification risk.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711819 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07018
|
Yahya Sattar
|
Yahya Sattar and Zhe Du and Davoud Ataee Tarzanagh and Laura Balzano
and Necmiye Ozay and Samet Oymak
|
Identification and Adaptive Control of Markov Jump Systems: Sample
Complexity and Regret Bounds
| null | null | null | null |
cs.LG cs.SY eess.SY math.OC stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning how to effectively control unknown dynamical systems is crucial for
intelligent autonomous systems. This task becomes a significant challenge when
the underlying dynamics are changing with time. Motivated by this challenge,
this paper considers the problem of controlling an unknown Markov jump linear
system (MJS) to optimize a quadratic objective. By taking a model-based
perspective, we consider identification-based adaptive control for MJSs. We
first provide a system identification algorithm for MJS to learn the dynamics
in each mode as well as the Markov transition matrix, underlying the evolution
of the mode switches, from a single trajectory of the system states, inputs,
and modes. Through mixing-time arguments, sample complexity of this algorithm
is shown to be $\mathcal{O}(1/\sqrt{T})$. We then propose an adaptive control
scheme that performs system identification together with certainty equivalent
control to adapt the controllers in an episodic fashion. Combining our sample
complexity results with recent perturbation results for certainty equivalent
control, we prove that when the episode lengths are appropriately chosen, the
proposed adaptive control scheme achieves $\mathcal{O}(\sqrt{T})$ regret, which
can be improved to $\mathcal{O}(polylog(T))$ with partial knowledge of the
system. Our proof strategy introduces innovations to handle Markovian jumps and
a weaker notion of stability common in MJSs. Our analysis provides insights
into system theoretic quantities that affect learning accuracy and control
performance. Numerical simulations are presented to further reinforce these
insights.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708395 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07021
|
Yulin Pan
|
Yukun Sun, Christopher Ruf, Thomas Bakker, and Yulin Pan
|
Effects of microplastics and surfactants on surface roughness of water
waves
| null | null | null | null |
physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we study the flow physics underlying the recently developed
remote sensing capability of detecting oceanic microplastics, which is based on
the measurable surface roughness reduction induced by the presence of
microplastics on the ocean surface. In particular, we are interested in whether
this roughness reduction is caused by the microplastics as floating particles,
or by the surfactants which follow similar transport paths as microplastics.
For this purpose, we experimentally test the effects of floating particles and
surfactants on surface roughness, quantified by the mean square slope (MSS),
with waves generated by a mechanical wave maker or by wind. For microplastics,
we find that their effect on wave energy and MSS critically depends on the
surface area fraction of coverage, irrespective of the particle sizes in the
test range. The damping by particles is observed only for fractions above
$O(5-10\%)$, which is much higher than the realistic ocean condition. For
surfactants, their damping effect on mechanically generated irregular waves
generally increases with the concentration of surfactants, but no optimal
concentration corresponding to maximum damping is observed, in contrast to
previous studies based on monochromatic waves. In wind-wave experiments, the
presence of surfactants suppresses the wave generation, due to the combined
effects of reduced wind shear stress and increased wave damping. For the same
wind speed, the wind stress is identified to depend on the concentration of
surfactants with a power-law relation. The implications of these findings to
remote sensing are discussed.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.714429 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07027
|
Chuanting Zhang
|
Chuanting Zhang, Ke-ke Shang, Jingping Qiao
|
Adaptive Similarity Function with Structural Features of Network
Embedding for Missing Link Prediction
| null | null | null | null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Link prediction is a fundamental problem of data science, which usually calls
for unfolding the mechanisms that govern the micro-dynamics of networks. In
this regard, using features obtained from network embedding for predicting
links has drawn widespread attention. Though edge features-based or node
similarity-based methods have been proposed to solve the link prediction
problem, many technical challenges still exist due to the unique structural
properties of networks, especially when the networks are sparse. From the graph
mining perspective, we first give empirical evidence of the inconsistency
between heuristic and learned edge features. Then we propose a novel link
prediction framework, AdaSim, by introducing an Adaptive Similarity function
using features obtained from network embedding based on random walks. The node
feature representations are obtained by optimizing a graph-based objective
function. Instead of generating edge features using binary operators, we
perform link prediction solely leveraging the node features of the network. We
define a flexible similarity function with one tunable parameter, which serves
as a penalty of the original similarity measure. The optimal value is learned
through supervised learning thus is adaptive to data distribution. To evaluate
the performance of our proposed algorithm, we conduct extensive experiments on
eleven disparate networks of the real world. Experimental results show that
AdaSim achieves better performance than state-of-the-art algorithms and is
robust to different sparsities of the networks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709453 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07031
|
Alisa Rahim
|
Alisa Rahim and Esley Torres
|
Improving the Otsu Thresholding Method of Global Binarization Using Ring
Theory for Ultrasonographies of Congestive Heart Failure
| null | null | null | null |
eess.IV cs.CV math.RA physics.med-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Ring Theory states that a ring is an algebraic structure where two binary
operations can be performed among the elements addition and multiplication.
Binarization is a method of image processing where values within pixels are
reduced to a scale from zero to one, with zero representing the most absence of
light and one representing the most presence of light. Currently, sonograms are
implemented in scanning for congestive heart failure. However, the renowned
Playboy Bunny symbol representing the ailment becomes increasingly difficult to
isolate due to surrounding organs and lower quality image productions. This
paper examines the Otsu thresholding method and incorporates new elements to
account for different image features meant to better isolate congestive heart
failure indicators in ultrasound images.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707613 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07032
|
Tiancheng Huang
|
Xintao Xiang and Tiancheng Huang and Donglin Wang
|
Learning to Evolve on Dynamic Graphs
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Representation learning in dynamic graphs is a challenging problem because
the topology of graph and node features vary at different time. This requires
the model to be able to effectively capture both graph topology information and
temporal information. Most existing works are built on recurrent neural
networks (RNNs), which are used to exact temporal information of dynamic
graphs, and thus they inherit the same drawbacks of RNNs. In this paper, we
propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that
jointly learns graph information and time information. Specifically, our
approach utilizes gradient-based meta-learning to learn updating strategies
that have better generalization ability than RNN on snapshots. It is
model-agnostic and thus can train any message passing based graph neural
network (GNN) on dynamic graphs. To enhance the representation power, we
disentangle the embeddings into time embeddings and graph intrinsic embeddings.
We conduct experiments on various datasets and down-stream tasks, and the
experimental results validate the effectiveness of our method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07037
|
Jueming Hu
|
Jueming Hu, Xuxi Yang, Weichang Wang, Peng Wei, Lei Ying, Yongming Liu
|
Obstacle Avoidance for UAS in Continuous Action Space Using Deep
Reinforcement Learning
| null | null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Obstacle avoidance for small unmanned aircraft is vital for the safety of
future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic
Management (UTM). There are many techniques for real-time robust drone
guidance, but many of them solve in discretized airspace and control, which
would require an additional path smoothing step to provide flexible commands
for UAS. To provide a safe and efficient computational guidance of operations
for unmanned aircraft, we explore the use of a deep reinforcement learning
algorithm based on Proximal Policy Optimization (PPO) to guide autonomous UAS
to their destinations while avoiding obstacles through continuous control. The
proposed scenario state representation and reward function can map the
continuous state space to continuous control for both heading angle and speed.
To verify the performance of the proposed learning framework, we conducted
numerical experiments with static and moving obstacles. Uncertainties
associated with the environments and safety operation bounds are investigated
in detail. Results show that the proposed model can provide accurate and robust
guidance and resolve conflict with a success rate of over 99%.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708421 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07039
|
Tuan Nguyen Dinh
|
Linh Nguyen Viet, Tuan Nguyen Dinh, Hoang Nguyen Viet, Duc Tran Minh,
Long Tran Quoc
|
UET-Headpose: A sensor-based top-view head pose dataset
| null | null | null | null |
cs.CV cs.AI cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Head pose estimation is a challenging task that aims to solve problems
related to predicting three dimensions vector, that serves for many
applications in human-robot interaction or customer behavior. Previous
researches have proposed some precise methods for collecting head pose data.
But those methods require either expensive devices like depth cameras or
complex laboratory environment setup. In this research, we introduce a new
approach with efficient cost and easy setup to collecting head pose images,
namely UET-Headpose dataset, with top-view head pose data. This method uses an
absolute orientation sensor instead of Depth cameras to be set up quickly and
small cost but still ensure good results. Through experiments, our dataset has
been shown the difference between its distribution and available dataset like
CMU Panoptic Dataset \cite{CMU}. Besides using the UET-Headpose dataset and
other head pose datasets, we also introduce the full-range model called
FSANet-Wide, which significantly outperforms head pose estimation results by
the UET-Headpose dataset, especially on top-view images. Also, this model is
very lightweight and takes small size images.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.712057 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07040
|
Deepak Saini Dr
|
Deepak Saini, Richard D. Sandberg
|
Compressibility Effects on the Linear-stability of Centrifugal
Buoyancy-induced Flow
| null | null | null | null |
physics.flu-dyn
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The focus of this study is to understand the evolution of instability in
centrifugal buoyancy-induced flow in a rotating system. The problem is of
interest in atmospheric flows as well as in engineering applications. In this
study, we perform direct numerical simulations (DNS) by solving the
compressible Navier-Stokes equations and multi-dimensional stability analyses
by using a forced DNS approach. We systematically and independently vary the
Rayleigh and Mach numbers. The heat transfer by thermal conduction is used as
base flow and maintained as a reference state, upon which the growth of small
perturbations is investigated. It is found that the critical wavenumber
obtained from the linear stability analysis at the onset of convection has a
much shorter wavelength than the one that eventually appears in the non-linear
regime. Further, the investigations show that compressibility effects lead to a
reduction of the growth rate of the dominant mode, and it modifies the overall
formation of convection cells in the cavity.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07042
|
Sreeja Nag
|
Rich Levinson, Sreeja Nag, Vinay Ravindra
|
Agile Satellite Planning for Multi-Payload Observations for Earth
Science
| null |
International Workshop on Planning & Scheduling for Space (IWPSS)
2021
| null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present planning challenges, methods and preliminary results for a new
model-based paradigm for earth observing systems in adaptive remote sensing.
Our heuristically guided constraint optimization planner produces coordinated
plans for multiple satellites, each with multiple instruments (payloads). The
satellites are agile, meaning they can quickly maneuver to change viewing
angles in response to rapidly changing phenomena. The planner operates in a
closed-loop context, updating the plan as it receives regular sensor data and
updated predictions. We describe the planner's search space and search
procedure, and present preliminary experiment results. Contributions include
initial identification of the planner's search space, constraints, heuristics,
and performance metrics applied to a soil moisture monitoring scenario using
spaceborne radars.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707979 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07043
|
Jyoti Prakash Panda
|
J P Panda
|
Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms
|
arXiv admin note: substantial text overlap with arXiv:2105.13641
| null | null | null |
physics.flu-dyn physics.comp-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Fluid turbulence is an important problem for physics and engineering.
Turbulence modeling deals with the development of simplified models that can
act as surrogates for representing the effects of turbulence on flow evolution.
Such models correspond to a range of different fidelities, from simple
eddy-viscosity-based closures to Reynolds Stress Models. Till now the focus of
the data-driven turbulence modeling efforts has focused on Machine Learning
augmented eddy-viscosity models. In this communication, we illustrate the
manner in which the eddy-viscosity framework delimits the efficacy and
performance of Machine learning algorithms. Based on this foundation we carry
out the first application of Machine learning algorithms for developing
improved Reynolds Stress Modeling-based closures for turbulence. Different
machine learning approaches are assessed for modeling the pressure strain
correlation in turbulence, a longstanding problem of singular importance. We
evaluate the performance of these algorithms in the learning dataset, as well
as their ability to generalize to different flow cases where the inherent
physical processes may vary. This explores the assertion that ML-based
data-driven turbulence models can overcome the modeling limitations associated
with the traditional turbulence models and ML models trained with large amounts
of data with different classes of flows can predict flow field with reasonable
accuracy for unknown flows with similar flow physics.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711049 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07044
|
Hang Zhou
|
Hang Zhou, Yanchi Su, Zhanshan Li
|
Hyperspectral Mixed Noise Removal via Subspace Representation and
Weighted Low-rank Tensor Regularization
| null | null | null | null |
cs.CV cs.GR eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, the low-rank property of different components extracted from the
image has been considered in man hyperspectral image denoising methods.
However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector
to exploit the prior information, such as nonlocal spatial self-similarity
(NSS) and global spectral correlation (GSC), which break the intrinsic
structure correlation of hyperspectral image (HSI) and thus lead to poor
restoration quality. In addition, most of them suffer from heavy computational
burden issues due to the involvement of singular value decomposition operation
on matrix and tensor in the original high-dimensionality space of HSI. We
employ subspace representation and the weighted low-rank tensor regularization
(SWLRTR) into the model to remove the mixed noise in the hyperspectral image.
Specifically, to employ the GSC among spectral bands, the noisy HSI is
projected into a low-dimensional subspace which simplified calculation. After
that, a weighted low-rank tensor regularization term is introduced to
characterize the priors in the reduced image subspace. Moreover, we design an
algorithm based on alternating minimization to solve the nonconvex problem.
Experiments on simulated and real datasets demonstrate that the SWLRTR method
performs better than other hyperspectral denoising methods quantitatively and
visually.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709221 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07046
|
Cheng-Chou Lan
|
Cheng-Chou Lan
|
Iterative Training: Finding Binary Weight Deep Neural Networks with
Layer Binarization
|
10 pages, 7 figures
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In low-latency or mobile applications, lower computation complexity, lower
memory footprint and better energy efficiency are desired. Many prior works
address this need by removing redundant parameters. Parameter quantization
replaces floating-point arithmetic with lower precision fixed-point arithmetic,
further reducing complexity.
Typical training of quantized weight neural networks starts from fully
quantized weights. Quantization creates random noise. As a way to compensate
for this noise, during training, we propose to quantize some weights while
keeping others in floating-point precision. A deep neural network has many
layers. To arrive at a fully quantized weight network, we start from one
quantized layer and then quantize more and more layers. We show that the order
of layer quantization affects accuracies. Order count is large for deep neural
networks. A sensitivity pre-training is proposed to guide the layer
quantization order.
Recent work in weight binarization replaces weight-input matrix
multiplication with additions. We apply the proposed iterative training to
weight binarization. Our experiments cover fully connected and convolutional
networks on MNIST, CIFAR-10 and ImageNet datasets. We show empirically that,
starting from partial binary weights instead of from fully binary ones,
training reaches fully binary weight networks with better accuracies for larger
and deeper networks. Layer binarization in the forward order results in better
accuracies. Guided layer binarization can further improve that. The
improvements come at a cost of longer training time.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710791 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07047
|
Ali Pourramezan Fard
|
Ali Pourramezan Fard, Mohammad H. Mahoor
|
Facial Landmark Points Detection Using Knowledge Distillation-Based
Neural Networks
|
Accepted in Computer Vision and Image Understanding Journal
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Facial landmark detection is a vital step for numerous facial image analysis
applications. Although some deep learning-based methods have achieved good
performances in this task, they are often not suitable for running on mobile
devices. Such methods rely on networks with many parameters, which makes the
training and inference time-consuming. Training lightweight neural networks
such as MobileNets are often challenging, and the models might have low
accuracy. Inspired by knowledge distillation (KD), this paper presents a novel
loss function to train a lightweight Student network (e.g., MobileNetV2) for
facial landmark detection. We use two Teacher networks, a Tolerant-Teacher and
a Tough-Teacher in conjunction with the Student network. The Tolerant-Teacher
is trained using Soft-landmarks created by active shape models, while the
Tough-Teacher is trained using the ground truth (aka Hard-landmarks) landmark
points. To utilize the facial landmark points predicted by the Teacher
networks, we define an Assistive Loss (ALoss) for each Teacher network.
Moreover, we define a loss function called KD-Loss that utilizes the facial
landmark points predicted by the two pre-trained Teacher networks
(EfficientNet-b3) to guide the lightweight Student network towards predicting
the Hard-landmarks. Our experimental results on three challenging facial
datasets show that the proposed architecture will result in a better-trained
Student network that can extract facial landmark points with high accuracy.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71103 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07048
|
Peiqi Wang
|
Peiqi Wang, Ruizhi Liao, Daniel Moyer, Seth Berkowitz, Steven Horng,
Polina Golland
|
Image Classification with Consistent Supporting Evidence
|
13 pages, 6 figures, proceedings of the Machine Learning for Health
NeurIPS Workshop, 2021
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Adoption of machine learning models in healthcare requires end users' trust
in the system. Models that provide additional supportive evidence for their
predictions promise to facilitate adoption. We define consistent evidence to be
both compatible and sufficient with respect to model predictions. We propose
measures of model inconsistency and regularizers that promote more consistent
evidence. We demonstrate our ideas in the context of edema severity grading
from chest radiographs. We demonstrate empirically that consistent models
provide competitive performance while supporting interpretation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711243 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07049
|
Haotian Jiang
|
Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha
|
Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector
Balancing
|
22 pages. Appear in ITCS 2022
| null | null | null |
cs.DS cs.DM math.CO math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A well-known result of Banaszczyk in discrepancy theory concerns the prefix
discrepancy problem (also known as the signed series problem): given a sequence
of $T$ unit vectors in $\mathbb{R}^d$, find $\pm$ signs for each of them such
that the signed sum vector along any prefix has a small $\ell_\infty$-norm?
This problem is central to proving upper bounds for the Steinitz problem, and
the popular Koml\'os problem is a special case where one is only concerned with
the final signed sum vector instead of all prefixes. Banaszczyk gave an
$O(\sqrt{\log d+ \log T})$ bound for the prefix discrepancy problem. We
investigate the tightness of Banaszczyk's bound and consider natural
generalizations of prefix discrepancy:
We first consider a smoothed analysis setting, where a small amount of
additive noise perturbs the input vectors. We show an exponential improvement
in $T$ compared to Banaszczyk's bound. Using a primal-dual approach and a
careful chaining argument, we show that one can achieve a bound of
$O(\sqrt{\log d+ \log\!\log T})$ with high probability in the smoothed setting.
Moreover, this smoothed analysis bound is the best possible without further
improvement on Banaszczyk's bound in the worst case.
We also introduce a generalization of the prefix discrepancy problem where
the discrepancy constraints correspond to paths on a DAG on $T$ vertices. We
show that an analog of Banaszczyk's $O(\sqrt{\log d+ \log T})$ bound continues
to hold in this setting for adversarially given unit vectors and that the
$\sqrt{\log T}$ factor is unavoidable for DAGs. We also show that the
dependence on $T$ cannot be improved significantly in the smoothed case for
DAGs.
We conclude by exploring a more general notion of vector balancing, which we
call combinatorial vector balancing. We obtain near-optimal bounds in this
setting, up to poly-logarithmic factors.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709252 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07060
|
Soumyadeep Dey
|
Varun Gumma, Barsha Mitra, Soumyadeep Dey, Pratik Shashikantbhai
Patel, Sourabh Suman, Saptarshi Das
|
PAMMELA: Policy Administration Methodology using Machine Learning
|
This work is under progress
| null | null | null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, Attribute-Based Access Control (ABAC) has become quite
popular and effective for enforcing access control in dynamic and collaborative
environments. Implementation of ABAC requires the creation of a set of
attribute-based rules which cumulatively form a policy. Designing an ABAC
policy ab initio demands a substantial amount of effort from the system
administrator. Moreover, organizational changes may necessitate the inclusion
of new rules in an already deployed policy. In such a case, re-mining the
entire ABAC policy will require a considerable amount of time and
administrative effort. Instead, it is better to incrementally augment the
policy. Keeping these aspects of reducing administrative overhead in mind, in
this paper, we propose PAMMELA, a Policy Administration Methodology using
Machine Learning to help system administrators in creating new ABAC policies as
well as augmenting existing ones. PAMMELA can generate a new policy for an
organization by learning the rules of a policy currently enforced in a similar
organization. For policy augmentation, PAMMELA can infer new rules based on the
knowledge gathered from the existing rules. Experimental results show that our
proposed approach provides a reasonably good performance in terms of the
various machine learning evaluation metrics as well as execution time.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712657 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07061
|
Ravi Banavar
|
Rama Seshan, Ravi N Banavar, D. H. S. Maithripala and Arun D.
Mahindrakar
|
Geometric PID Controller for Stabilization of Nonholonomic Mechanical
Systems on Lie Groups
| null | null | null | null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by/4.0/
|
The PID controller is an elegant and versatile controller for set point
tracking in double integrator systems of which mechanical systems evolving on
Euclidean space constitute a large class. But since mechanical systems are
typically constrained interconnections of rigid bodies whose configuration
space is $SE(3)$, which is not even topologically Euclidean, a geometric PID
controller has been developed for mechanical systems evolving on Lie groups. In
this work, we extend the framework to such systems which have nonholonomic
constraints. It encompasses many practically applicable mechanical systems
encountered in robotics as robots are constrained interconnections of rigid
bodies where the constraints could either be holonomic or nonholonomic.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709466 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07062
|
Lilas Alrahis
|
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif, Muhammad
Shafique, and Ozgur Sinanoglu
|
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural
Networks-based Link Prediction
|
Published in 2021 International Conference On Computer-Aided Design
(ICCAD)
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Logic locking aims to prevent intellectual property (IP) piracy and
unauthorized overproduction of integrated circuits (ICs). However, initial
logic locking techniques were vulnerable to the Boolean satisfiability
(SAT)-based attacks. In response, researchers proposed various SAT-resistant
locking techniques such as point function-based locking and symmetric
interconnection (SAT-hard) obfuscation. We focus on the latter since point
function-based locking suffers from various structural vulnerabilities. The
SAT-hard logic locking technique, InterLock [1], achieves a unified logic and
routing obfuscation that thwarts state-of-the-art attacks on logic locking. In
this work, we propose a novel link prediction-based attack, UNTANGLE, that
successfully breaks InterLock in an oracle-less setting without having access
to an activated IC (oracle). Since InterLock hides selected timing paths in
key-controlled routing blocks, UNTANGLE reveals the gates and interconnections
hidden in the routing blocks upon formulating this task as a link prediction
problem. The intuition behind our approach is that ICs contain a large amount
of repetition and reuse cores. Hence, UNTANGLE can infer the hidden timing
paths by learning the composition of gates in the observed locked netlist or a
circuit library leveraging graph neural networks. We show that circuits
withstanding SAT-based and other attacks can be unlocked in seconds with 100%
precision using UNTANGLE in an oracle-less setting. UNTANGLE is a generic
attack platform (which we also open source [2]) that applies to multiplexer
(MUX)-based obfuscation, as demonstrated through our experiments on ISCAS-85
and ITC-99 benchmarks locked using InterLock and random MUX-based locking.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708994 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07070
|
Quan-Lin Li
|
Jing-Yu Ma, Quan-Lin Li
|
Sensitivity-Based Optimization for Blockchain Selfish Mining
|
15 pages, 2 figures
| null | null | null |
cs.CR math.CO math.OC math.PR
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we provide a novel dynamic decision method of blockchain
selfish mining by applying the sensitivity-based optimization theory. Our aim
is to find the optimal dynamic blockchain-pegged policy of the dishonest mining
pool. To study the selfish mining attacks, two mining pools is designed by
means of different competitive criterions, where the honest mining pool follows
a two-block leading competitive criterion, while the dishonest mining pool
follows a modification of two-block leading competitive criterion through using
a blockchain-pegged policy. To find the optimal blockchain-pegged policy, we
set up a policy-based continuous-time Markov process and analyze some key
factors. Based on this, we discuss monotonicity and optimality of the long-run
average profit with respect to the blockchain-pegged reward and prove the
structure of the optimal blockchain-pegged policy. We hope the methodology and
results derived in this paper can shed light on the dynamic decision research
on the selfish mining attacks of blockchain selfish mining.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710998 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07072
|
Eun-Sung Jung
|
Jaemo Sung, Eun-Sung Jung
|
Factorial Convolution Neural Networks
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, GoogleNet has garnered substantial attention as one of the
base convolutional neural networks (CNNs) to extract visual features for object
detection. However, it experiences challenges of contaminated deep features
when concatenating elements with different properties. Also, since GoogleNet is
not an entirely lightweight CNN, it still has many execution overheads to apply
to a resource-starved application domain. Therefore, a new CNNs, FactorNet, has
been proposed to overcome these functional challenges. The FactorNet CNN is
composed of multiple independent sub CNNs to encode different aspects of the
deep visual features and has far fewer execution overheads in terms of weight
parameters and floating-point operations. Incorporating FactorNet into the
Faster-RCNN framework proved that FactorNet gives \ignore{a 5\%} better
accuracy at a minimum and produces additional speedup over GoolgleNet
throughout the KITTI object detection benchmark data set in a real-time object
detection system.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708648 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07078
|
Peng Yang
|
Peng Yang, Xianbin Cao, Tony Q. S. Quek, and Dapeng Oliver Wu
|
Networking of Internet of UAVs: Challenges and Intelligent Approaches
| null | null | null | null |
cs.NI cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish
sensing and transmission tasks quickly, robustly, and cost-efficiently via
effective cooperation among UAVs. To achieve the promising benefits, the
crucial I-UAV networking issue should be tackled. This article argues that
I-UAV networking can be classified into three categories, quality-of-service
(QoS) driven networking, quality-of-experience (QoE) driven networking, and
situation aware networking. Each category of networking poses emerging
challenges which have severe effects on the safe and efficient accomplishment
of I-UAV missions. This article elaborately analyzes these challenges and
expounds on the corresponding intelligent approaches to tackle the I-UAV
networking issue. Besides, considering the uplifting effect of extending the
scalability of I-UAV networks through cooperating with high altitude platforms
(HAPs), this article gives an overview of the integrated HAP and I-UAV networks
and presents the corresponding networking challenges and intelligent
approaches.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712839 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07080
|
Jakob Zech
|
Christoph Schwab and Jakob Zech
|
Deep Learning in High Dimension: Neural Network Approximation of
Analytic Functions in $L^2(\mathbb{R}^d,\gamma_d)$
| null | null | null | null |
math.NA cs.NA math.PR stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For artificial deep neural networks, we prove expression rates for analytic
functions $f:\mathbb{R}^d\to\mathbb{R}$ in the norm of
$L^2(\mathbb{R}^d,\gamma_d)$ where $d\in {\mathbb{N}}\cup\{ \infty \}$. Here
$\gamma_d$ denotes the Gaussian product probability measure on $\mathbb{R}^d$.
We consider in particular ReLU and ReLU${}^k$ activations for integer $k\geq
2$. For $d\in\mathbb{N}$, we show exponential convergence rates in
$L^2(\mathbb{R}^d,\gamma_d)$. In case $d=\infty$, under suitable smoothness and
sparsity assumptions on $f:\mathbb{R}^{\mathbb{N}}\to\mathbb{R}$, with
$\gamma_\infty$ denoting an infinite (Gaussian) product measure on
$\mathbb{R}^{\mathbb{N}}$, we prove dimension-independent expression rate
bounds in the norm of $L^2(\mathbb{R}^{\mathbb{N}},\gamma_\infty)$. The rates
only depend on quantified holomorphy of (an analytic continuation of) the map
$f$ to a product of strips in $\mathbb{C}^d$. As an application, we prove
expression rate bounds of deep ReLU-NNs for response surfaces of elliptic PDEs
with log-Gaussian random field inputs.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707007 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07083
|
Ghodai Abdelrahman
|
Ghodai Abdelrahman, Qing Wang
|
Learning Data Teaching Strategies Via Knowledge Tracing
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Teaching plays a fundamental role in human learning. Typically, a human
teaching strategy would involve assessing a student's knowledge progress for
tailoring the teaching materials in a way that enhances the learning progress.
A human teacher would achieve this by tracing a student's knowledge over
important learning concepts in a task. Albeit, such teaching strategy is not
well exploited yet in machine learning as current machine teaching methods tend
to directly assess the progress on individual training samples without paying
attention to the underlying learning concepts in a learning task. In this
paper, we propose a novel method, called Knowledge Augmented Data Teaching
(KADT), which can optimize a data teaching strategy for a student model by
tracing its knowledge progress over multiple learning concepts in a learning
task. Specifically, the KADT method incorporates a knowledge tracing model to
dynamically capture the knowledge progress of a student model in terms of
latent learning concepts. Then we develop an attention pooling mechanism to
distill knowledge representations of a student model with respect to class
labels, which enables to develop a data teaching strategy on critical training
samples. We have evaluated the performance of the KADT method on four different
machine learning tasks including knowledge tracing, sentiment analysis, movie
recommendation, and image classification. The results comparing to the
state-of-the-art methods empirically validate that KADT consistently
outperforms others on all tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711782 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07085
|
Edoardo Giusto PhD
|
Daniel Oliveira, Edoardo Giusto, Betis Baheri, Qiang Guan, Bartolomeo
Montrucchio, Paolo Rech
|
A Systematic Methodology to Compute the Quantum Vulnerability Factors
for Quantum Circuits
| null | null | null | null |
cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum computing is one of the most promising technology advances of the
latest years. Once only a conceptual idea to solve physics simulations, quantum
computation is today a reality, with numerous machines able to execute quantum
algorithms. One of the hardest challenges in quantum computing is reliability.
Qubits are highly sensitive to noise, which can make the output useless.
Moreover, lately it has been shown that superconducting qubits are extremely
susceptible to external sources of faults, such as ionizing radiation. When
adopted in large scale, radiation-induced errors are expected to become a
serious challenge for qubits reliability. In this paper, we propose an
evaluation of the impact of transient faults in the execution of quantum
circuits. Inspired by the Architectural and Program Vulnerability Factors,
widely adopted to characterize the reliability of classical computing
architectures and algorithms, we propose the Quantum Vulnerability Factor (QVF)
as a metric to measure the impact that the corruption of a qubit has on the
circuit output probability distribution. First, we model faults based on the
latest studies on real machines and recently performed radiation experiments.
Then, we design a quantum fault injector, built over Qiskit, and characterize
the propagation of faults in quantum circuits. We report the finding of more
than 15,000,000 fault injections, evaluating the reliability of three quantum
circuits and identifying the faults and qubits that are more likely than others
to impact the output. With our results, we give guidelines on how to map the
qubits in the real quantum computer to reduce the output error and to reduce
the probability of having a radiation-induced corruption to modify the output.
Finally, we compare the simulation results with experiments on physical quantum
computers.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711813 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07089
|
Dimitris Spathis
|
Kevalee Shah, Dimitris Spathis, Chi Ian Tang, Cecilia Mascolo
|
Evaluating Contrastive Learning on Wearable Timeseries for Downstream
Clinical Outcomes
|
Machine Learning for Health (ML4H) - Extended Abstract
| null | null | null |
cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vast quantities of person-generated health data (wearables) are collected but
the process of annotating to feed to machine learning models is impractical.
This paper discusses ways in which self-supervised approaches that use
contrastive losses, such as SimCLR and BYOL, previously applied to the vision
domain, can be applied to high-dimensional health signals for downstream
classification tasks of various diseases spanning sleep, heart, and metabolic
conditions. To this end, we adapt the data augmentation step and the overall
architecture to suit the temporal nature of the data (wearable traces) and
evaluate on 5 downstream tasks by comparing other state-of-the-art methods
including supervised learning and an adversarial unsupervised representation
learning method. We show that SimCLR outperforms the adversarial method and a
fully-supervised method in the majority of the downstream evaluation tasks, and
that all self-supervised methods outperform the fully-supervised methods. This
work provides a comprehensive benchmark for contrastive methods applied to the
wearable time-series domain, showing the promise of task-agnostic
representations for downstream clinical outcomes.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710879 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07094
|
Fatemeh Daneshfar
|
Fatemeh Daneshfar, Seyed Jahanshah Kabudian
|
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme
Learning Machine with a New Weighting Scheme and Spectro-Temporal Features
Along with Classical Feature Selection and A New Quantum-Inspired Dimension
Reduction Method
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Affective computing is very important in the relationship between man and
machine. In this paper, a system for speech emotion recognition (SER) based on
speech signal is proposed, which uses new techniques in different stages of
processing. The system consists of three stages: feature extraction, feature
selection, and finally feature classification. In the first stage, a complex
set of long-term statistics features is extracted from both the speech signal
and the glottal-waveform signal using a combination of new and diverse features
such as prosodic, spectral, and spectro-temporal features. One of the
challenges of the SER systems is to distinguish correlated emotions. These
features are good discriminators for speech emotions and increase the SER's
ability to recognize similar and different emotions. This feature vector with a
large number of dimensions naturally has redundancy. In the second stage, using
classical feature selection techniques as well as a new quantum-inspired
technique to reduce the feature vector dimensionality, the number of feature
vector dimensions is reduced. In the third stage, the optimized feature vector
is classified by a weighted deep sparse extreme learning machine (ELM)
classifier. The classifier performs classification in three steps: sparse
random feature learning, orthogonal random projection using the singular value
decomposition (SVD) technique, and discriminative classification in the last
step using the generalized Tikhonov regularization technique. Also, many
existing emotional datasets suffer from the problem of data imbalanced
distribution, which in turn increases the classification error and decreases
system performance. In this paper, a new weighting method has also been
proposed to deal with class imbalance, which is more efficient than existing
weighting methods. The proposed method is evaluated on three standard emotional
databases.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710641 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07100
|
Biswarup Mukherjee
|
Biswarup Mukherjee, Fabrizio Sossan
|
Optimal Planning of Single-Port and Multi-Port Charging Stations for
Electric Vehicles in Medium Voltage Distribution Networks
|
This manuscript has been submitted to IEEE Transactions on Smart Grid
| null | null | null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper describes a method based on mixed-integer linear programming to
cost-optimally locate and size chargers for electric vehicles (EVs) in
distribution grids as a function of the driving demand. The problem accounts
for the notion of single-port chargers (SPCs), where a charger can interface
one EV maximum, and multi-port chargers (MPCs), where the same charger can
interface multiple EVs. The advantage of MPCs is twofold. First, multiple ports
allow arbitraging the charging among multiple vehicles without requiring the
drivers to plug and unplug EVs. Second, the charger's power electronics is not
sized for the total number of charging ports, enabling cost savings when the
grid constraints are bottleneck of the problem. The proposed method can account
for different charger typologies, such as slow and fast chargers, and model the
drivers' flexibility of plugging and unplugging their EVs. Simulation results
from a synthetic case study show that implementing MPCs is beneficial over both
SPCs and drivers' flexibility in terms of total investments required for the
charging infrastructure.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709239 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07102
|
Ajoy Mondal Dr.
|
Rajdeep Das, Ajoy Mondal, Tapan Chakraborty, and Kuntal Ghosh
|
Deep Neural Networks for Automatic Grain-matrix Segmentation in Plane
and Cross-polarized Sandstone Photomicrographs
| null | null |
10.1007/s10489-021-02530-z
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Grain segmentation of sandstone that is partitioning the grain from its
surrounding matrix/cement in the thin section is the primary step for
computer-aided mineral identification and sandstone classification. The
microscopic images of sandstone contain many mineral grains and their
surrounding matrix/cement. The distinction between adjacent grains and the
matrix is often ambiguous, making grain segmentation difficult. Various
solutions exist in literature to handle these problems; however, they are not
robust against sandstone petrography's varied pattern. In this paper, we
formulate grain segmentation as a pixel-wise two-class (i.e., grain and
background) semantic segmentation task. We develop a deep learning-based
end-to-end trainable framework named Deep Semantic Grain Segmentation network
(DSGSN), a data-driven method, and provide a generic solution. As per the
authors' knowledge, this is the first work where the deep neural network is
explored to solve the grain segmentation problem. Extensive experiments on
microscopic images highlight that our method obtains better segmentation
accuracy than various segmentation architectures with more parameters.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711782 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07104
|
Shaoguo Wen
|
Shaoguo Wen, Junle Wang
|
A strong baseline for image and video quality assessment
| null | null | null | null |
eess.IV cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we present a simple yet effective unified model for perceptual
quality assessment of image and video. In contrast to existing models which
usually consist of complex network architecture, or rely on the concatenation
of multiple branches of features, our model achieves a comparable performance
by applying only one global feature derived from a backbone network (i.e.
resnet18 in the presented work). Combined with some training tricks, the
proposed model surpasses the current baselines of SOTA models on public and
private datasets. Based on the architecture proposed, we release the models
well trained for three common real-world scenarios: UGC videos in the wild, PGC
videos with compression, Game videos with compression. These three pre-trained
models can be directly applied for quality assessment, or be further fine-tuned
for more customized usages. All the code, SDK, and the pre-trained weights of
the proposed models are publicly available at
https://github.com/Tencent/CenseoQoE.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712639 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07109
|
Shao-Bo Lin
|
Zirui Sun, Mingwei Dai, Yao Wang, Shao-Bo Lin
|
Nystr\"{o}m Regularization for Time Series Forecasting
|
35 pages
| null | null | null |
cs.LG stat.ML
|
http://creativecommons.org/publicdomain/zero/1.0/
|
This paper focuses on learning rate analysis of Nystr\"{o}m regularization
with sequential sub-sampling for $\tau$-mixing time series. Using a recently
developed Banach-valued Bernstein inequality for $\tau$-mixing sequences and an
integral operator approach based on second-order decomposition, we succeed in
deriving almost optimal learning rates of Nystr\"{o}m regularization with
sequential sub-sampling for $\tau$-mixing time series. A series of numerical
experiments are carried out to verify our theoretical results, showing the
excellent learning performance of Nystr\"{o}m regularization with sequential
sub-sampling in learning massive time series data. All these results extend the
applicable range of Nystr\"{o}m regularization from i.i.d. samples to
non-i.i.d. sequences.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711067 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07116
|
Chao Xie
|
Chao Xie, Yi-Chiao Wu, Patrick Lumban Tobing, Wen-Chin Huang and
Tomoki Toda
|
Direct Noisy Speech Modeling for Noisy-to-Noisy Voice Conversion
| null | null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Beyond the conventional voice conversion (VC) where the speaker information
is converted without altering the linguistic content, the background sounds are
informative and need to be retained in some real-world scenarios, such as VC in
movie/video and VC in music where the voice is entangled with background
sounds. As a new VC framework, we have developed a noisy-to-noisy (N2N) VC
framework to convert the speaker's identity while preserving the background
sounds. Although our framework consisting of a denoising module and a VC module
well handles the background sounds, the VC module is sensitive to the
distortion caused by the denoising module. To address this distortion issue, in
this paper we propose the improved VC module to directly model the noisy speech
waveform while controlling the background sounds. The experimental results have
demonstrated that our improved framework significantly outperforms the previous
one and achieves an acceptable score in terms of naturalness, while reaching
comparable similarity performance to the upper bound of our framework.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713469 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07117
|
Nanbo Li
|
Li Nanbo, Cian Eastwood, Robert B. Fisher
|
Learning Object-Centric Representations of Multi-Object Scenes from
Multiple Views
|
Accepted at NeurIPS 2020 (Spotlight)
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Learning object-centric representations of multi-object scenes is a promising
approach towards machine intelligence, facilitating high-level reasoning and
control from visual sensory data. However, current approaches for unsupervised
object-centric scene representation are incapable of aggregating information
from multiple observations of a scene. As a result, these "single-view" methods
form their representations of a 3D scene based only on a single 2D observation
(view). Naturally, this leads to several inaccuracies, with these methods
falling victim to single-view spatial ambiguities. To address this, we propose
The Multi-View and Multi-Object Network (MulMON) -- a method for learning
accurate, object-centric representations of multi-object scenes by leveraging
multiple views. In order to sidestep the main technical difficulty of the
multi-object-multi-view scenario -- maintaining object correspondences across
views -- MulMON iteratively updates the latent object representations for a
scene over multiple views. To ensure that these iterative updates do indeed
aggregate spatial information to form a complete 3D scene understanding, MulMON
is asked to predict the appearance of the scene from novel viewpoints during
training. Through experiments, we show that MulMON better-resolves spatial
ambiguities than single-view methods -- learning more accurate and disentangled
object representations -- and also achieves new functionality in predicting
object segmentations for novel viewpoints.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709466 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07119
|
Matej Klemen
|
Matej Klemen, Marko Robnik-\v{S}ikonja
|
Extracting and filtering paraphrases by bridging natural language
inference and paraphrasing
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Paraphrasing is a useful natural language processing task that can contribute
to more diverse generated or translated texts. Natural language inference (NLI)
and paraphrasing share some similarities and can benefit from a joint approach.
We propose a novel methodology for the extraction of paraphrasing datasets from
NLI datasets and cleaning existing paraphrasing datasets. Our approach is based
on bidirectional entailment; namely, if two sentences can be mutually entailed,
they are paraphrases. We evaluate our approach using several large pretrained
transformer language models in the monolingual and cross-lingual setting. The
results show high quality of extracted paraphrasing datasets and surprisingly
high noise levels in two existing paraphrasing datasets.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07125
|
Priyesh Shukla
|
Priyesh Shukla, Shamma Nasrin, Nastaran Darabi, Wilfred Gomes, and
Amit Ranjan Trivedi
|
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge
Intelligence
| null | null | null | null |
cs.LG cs.AR cs.RO eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low
power, Bayesian edge intelligence. Deep neural networks (DNN) with
deterministic weights cannot express their prediction uncertainties, thereby
pose critical risks for applications where the consequences of mispredictions
are fatal such as surgical robotics. To address this limitation, Bayesian
inference of a DNN has gained attention. Using Bayesian inference, not only the
prediction itself, but the prediction confidence can also be extracted for
planning risk-aware actions. However, Bayesian inference of a DNN is
computationally expensive, ill-suited for real-time and/or edge deployment. An
approximation to Bayesian DNN using Monte Carlo Dropout (MC-Dropout) has shown
high robustness along with low computational complexity. Enhancing the
computational efficiency of the method, we discuss a novel CIM module that can
perform in-memory probabilistic dropout in addition to in-memory weight-input
scalar product to support the method. We also propose a compute-reuse
reformulation of MC-Dropout where each successive instance can utilize the
product-sum computations from the previous iteration. Even more, we discuss how
the random instances can be optimally ordered to minimize the overall
MC-Dropout workload by exploiting combinatorial optimization methods.
Application of the proposed CIM-based MC-Dropout execution is discussed for
MNIST character recognition and visual odometry (VO) of autonomous drones. The
framework reliably gives prediction confidence amidst non-idealities imposed by
MC-CIM to a good extent. Proposed MC-CIM with 16x31 SRAM array, 0.85 V supply,
16nm low-standby power (LSTP) technology consumes 27.8 pJ for 30 MC-Dropout
instances of probabilistic inference in its most optimal computing and
peripheral configuration, saving 43% energy compared to typical execution.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709177 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07126
|
Ziping Xu
|
Ziping Xu and Ambuj Tewari
|
On the Statistical Benefits of Curriculum Learning
| null | null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Curriculum learning (CL) is a commonly used machine learning training
strategy. However, we still lack a clear theoretical understanding of CL's
benefits. In this paper, we study the benefits of CL in the multitask linear
regression problem under both structured and unstructured settings. For both
settings, we derive the minimax rates for CL with the oracle that provides the
optimal curriculum and without the oracle, where the agent has to adaptively
learn a good curriculum. Our results reveal that adaptive learning can be
fundamentally harder than the oracle learning in the unstructured setting, but
it merely introduces a small extra term in the structured setting. To connect
theory with practice, we provide justification for a popular empirical method
that selects tasks with highest local prediction gain by comparing its
guarantees with the minimax rates mentioned above.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712251 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07129
|
Ajoy Mondal Dr.
|
Sachin Raja, Ajoy Mondal, and C V Jawahar
|
Visual Understanding of Complex Table Structures from Document Images
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Table structure recognition is necessary for a comprehensive understanding of
documents. Tables in unstructured business documents are tough to parse due to
the high diversity of layouts, varying alignments of contents, and the presence
of empty cells. The problem is particularly difficult because of challenges in
identifying individual cells using visual or linguistic contexts or both.
Accurate detection of table cells (including empty cells) simplifies structure
extraction and hence, it becomes the prime focus of our work. We propose a
novel object-detection-based deep model that captures the inherent alignments
of cells within tables and is fine-tuned for fast optimization. Despite
accurate detection of cells, recognizing structures for dense tables may still
be challenging because of difficulties in capturing long-range row/column
dependencies in presence of multi-row/column spanning cells. Therefore, we also
aim to improve structure recognition by deducing a novel rectilinear
graph-based formulation. From a semantics perspective, we highlight the
significance of empty cells in a table. To take these cells into account, we
suggest an enhancement to a popular evaluation criterion. Finally, we introduce
a modestly sized evaluation dataset with an annotation style inspired by human
cognition to encourage new approaches to the problem. Our framework improves
the previous state-of-the-art performance by a 2.7% average F1-score on
benchmark datasets.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.713076 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07138
|
Nayan Saxena
|
Nayan Saxena, Robert Wu and Rohan Jain
|
Towards One Shot Search Space Poisoning in Neural Architecture Search
|
(Student Abstract) In Proceedings of the 36th AAAI Conference on
Artificial Intelligence, Vancouver, BC,Canada, 2022. arXiv admin note:
substantial text overlap with arXiv:2106.14406
| null | null | null |
cs.LG cs.AI cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
We evaluate the robustness of a Neural Architecture Search (NAS) algorithm
known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the
original search space with carefully designed ineffective operations. We
empirically demonstrate how our one shot search space poisoning approach
exploits design flaws in the ENAS controller to degrade predictive performance
on classification tasks. With just two poisoning operations injected into the
search space, we inflate prediction error rates for child networks upto 90% on
the CIFAR-10 dataset.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711074 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07139
|
Yuan Zhou
|
Yuan Zhou, Haiyang Wang, Shuwei Huo and Boyu Wang
|
Full-attention based Neural Architecture Search using Context
Auto-regression
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Self-attention architectures have emerged as a recent advancement for
improving the performance of vision tasks. Manual determination of the
architecture for self-attention networks relies on the experience of experts
and cannot automatically adapt to various scenarios. Meanwhile, neural
architecture search (NAS) has significantly advanced the automatic design of
neural architectures. Thus, it is appropriate to consider using NAS methods to
discover a better self-attention architecture automatically. However, it is
challenging to directly use existing NAS methods to search attention networks
because of the uniform cell-based search space and the lack of long-term
content dependencies. To address this issue, we propose a full-attention based
NAS method. More specifically, a stage-wise search space is constructed that
allows various attention operations to be adopted for different layers of a
network. To extract global features, a self-supervised search algorithm is
proposed that uses context auto-regression to discover the full-attention
architecture. To verify the efficacy of the proposed methods, we conducted
extensive experiments on various learning tasks, including image
classification, fine-grained image recognition, and zero-shot image retrieval.
The empirical results show strong evidence that our method is capable of
discovering high-performance, full-attention architectures while guaranteeing
the required search efficiency.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710666 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07143
|
Zhuoqun Wei
|
Zhuoqun Wei, Yina Han, Shuang Zhao, Qingyu Liu and Jun Song
|
Motion Acoustic Flow Field: Motion Estimation for Blob Targets in Active
Sonar Echograph of Harbor Environments
| null | null | null | null |
physics.ao-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Motion feature is of great significance for blob targets recognition,
behavior analysis and threat estimation in active sonar echographs. Hence, it
is desirable to access the space-time variation of echo intensity on each
spatial-temporal resolution cell from the sonar echographs sequence. Then the
subtle motion information of the potential blob targets can be accurately
characterized. This idea has been conduced in optical image sequences by
solving an motion optical flow field (MOFF) function. Nonetheless, due to the
sparkle of the sonar echograph sequences, and strong interferences caused by
wake and cavitation noise of fast-moving ship in harbor environments, the
constraints underlying the traditional motion optical flow function that is
couples the brightness constancy constant along time dimension of each echo
intensity points and the motion field spatial smoothness does not hold in our
case. Hence, this paper presents a new motion acoustic flow field (MAFF)
function and its solving strategy to accurately characterize the subtle motion
information of blob targets in active sonar echographs of harbor environments.
Experiments on a series of cooperative targets in real-world harbor
environments demonstrate the efficacy of our proposed MAFF.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713781 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07144
|
Giacomo Livan
|
Giacomo Livan, Giuseppe Pappalardo, Rosario N. Mantegna
|
Quantifying the relationship between specialisation and reputation in an
online platform
| null | null | null | null |
physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Online platforms experience a tension between decentralisation and incentives
to steer user behaviour, which are usually implemented through digital
reputation systems. We provide a statistical characterisation of the user
behaviour emerging from the interplay of such competing forces in Stack
Overflow, a long-standing knowledge sharing platform. Over the 11 years covered
by our analysis, we find that the platform's user base consistently
self-organise into specialists and generalists, i.e., users who focus their
activity on narrow and broad sets of topics, respectively. We relate the
emergence of these behaviours to the platform's reputation system with a series
of data-driven models, and find specialisation to be statistically associated
with a higher ability to post the best answers to a question. Our findings are
in stark contrast with observations made in top-down environments - such as
firms and corporations - where generalist skills are consistently found to be
more successful.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07145
|
Ajoy Mondal Dr.
|
Ajoy Mondal
|
New Performance Measures for Object Tracking under Complex Environments
| null | null |
10.1007/s00530-021-00775-9.pdf
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Various performance measures based on the ground truth and without ground
truth exist to evaluate the quality of a developed tracking algorithm. The
existing popular measures - average center location error (ACLE) and average
tracking accuracy (ATA) based on ground truth, may sometimes create confusion
to quantify the quality of a developed algorithm for tracking an object under
some complex environments (e.g., scaled or oriented or both scaled and oriented
object). In this article, we propose three new auxiliary performance measures
based on ground truth information to evaluate the quality of a developed
tracking algorithm under such complex environments. Moreover, one performance
measure is developed by combining both two existing measures ACLE and ATA and
three new proposed measures for better quantifying the developed tracking
algorithm under such complex conditions. Some examples and experimental results
conclude that the proposed measure is better than existing measures to quantify
one developed algorithm for tracking objects under such complex environments.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711675 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07148
|
Ilia Karpov
|
Ilia Karpov and Nick Kartashev
|
SocialBERT -- Transformers for Online SocialNetwork Language Modelling
| null | null | null | null |
cs.CL cs.AI cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The ubiquity of the contemporary language understanding tasks gives relevance
to the development of generalized, yet highly efficient models that utilize all
knowledge, provided by the data source. In this work, we present SocialBERT -
the first model that uses knowledge about the author's position in the network
during text analysis. We investigate possible models for learning social
network information and successfully inject it into the baseline BERT model.
The evaluation shows that embedding this information maintains a good
generalization, with an increase in the quality of the probabilistic model for
the given author up to 7.5%. The proposed model has been trained on the
majority of groups for the chosen social network, and still able to work with
previously unknown groups. The obtained model, as well as the code of our
experiments, is available for download and use in applied tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710459 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07154
|
Tzu-Heng Lin
|
Tzu-Heng Lin, Chen Gao
|
Session-aware Item-combination Recommendation with Transformer Network
|
2nd place solution in IEEE Bigdata Cup 2021 (Track 1: Item
Combination Prediction). Our code is available at
https://github.com/lzhbrian/bigdatacup2021
| null | null | null |
cs.IR cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we detailedly describe our solution for the IEEE BigData Cup
2021: RL-based RecSys (Track 1: Item Combination Prediction). We first conduct
an exploratory data analysis on the dataset and then utilize the findings to
design our framework. Specifically, we use a two-headed transformer-based
network to predict user feedback and unlocked sessions, along with the proposed
session-aware reweighted loss, multi-tasking with click behavior prediction,
and randomness-in-session augmentation. In the final private leaderboard on
Kaggle, our method ranked 2nd with a categorization accuracy of 0.39224.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07160
|
Jonas Kusch
|
Jonas Kusch and Pia Stammer
|
A robust collision source method for rank adaptive dynamical low-rank
approximation in radiation therapy
| null | null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deterministic models for radiation transport describe the density of
radiation particles moving through a background material. In radiation therapy
applications, the phase space of this density is composed of energy, spatial
position and direction of flight. The resulting six-dimensional phase space
prohibits fine numerical discretizations, which are essential for the
construction of accurate and reliable treatment plans. In this work, we tackle
the high dimensional phase space through a dynamical low-rank approximation of
the particle density. Dynamical low-rank approximation (DLRA) evolves the
solution on a low-rank manifold in time. Interpreting the energy variable as a
pseudo-time lets us employ the DLRA framework to represent the solution of the
radiation transport equation on a low-rank manifold for every energy. Stiff
scattering terms are treated through an efficient implicit energy
discretization and a rank adaptive integrator is chosen to dynamically adapt
the rank in energy. To facilitate the use of boundary conditions and reduce the
overall rank, the radiation transport equation is split into collided and
uncollided particles through a collision source method. Uncollided particles
are described by a directed quadrature set guaranteeing low computational
costs, whereas collided particles are represented by a low-rank solution. It
can be shown that the presented method is L$^2$-stable under a time step
restriction which does not depend on stiff scattering terms. Moreover, the
implicit treatment of scattering does not require numerical inversions of
matrices. Numerical results for radiation therapy configurations as well as the
line source benchmark underline the efficiency of the proposed method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07163
|
Debajyoti Bera
|
Bhisham Dev Verma and Rameshwar Pratap and Debajyoti Bera
|
Efficient Binary Embedding of Categorical Data using BinSketch
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this work, we present a dimensionality reduction algorithm, aka.
sketching, for categorical datasets. Our proposed sketching algorithm Cabin
constructs low-dimensional binary sketches from high-dimensional categorical
vectors, and our distance estimation algorithm Cham computes a close
approximation of the Hamming distance between any two original vectors only
from their sketches. The minimum dimension of the sketches required by Cham to
ensure a good estimation theoretically depends only on the sparsity of the data
points - making it useful for many real-life scenarios involving sparse
datasets. We present a rigorous theoretical analysis of our approach and
supplement it with extensive experiments on several high-dimensional real-world
data sets, including one with over a million dimensions. We show that the Cabin
and Cham duo is a significantly fast and accurate approach for tasks such as
RMSE, all-pairs similarity, and clustering when compared to working with the
full dataset and other dimensionality reduction techniques.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710478 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07166
|
Srikrishna B.R
|
Suhas Thalanki, T Vijay Prashant, Harshith Kumar M B, Shayak
Bhadraray, Aravind S, Srikrishna BR, Sameer Dhole
|
Autonomous UAV for Building Monitoring, Detection and Localisation of
Faults
|
Submitted, ICRA 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Collapsing of structural buildings has been sighted commonly and the presence
of potential faults has proved to be damaging to the buildings, resulting in
accidents. It is essential to continuously monitor any building for faults
where human access is restricted. With UAVs (Unmanned Aerial Vehicles) emerging
in the field of computer vision, monitoring any building and detecting such
faults is seen as a possibility. This paper puts forth a novel approach where
an automated UAV traverses around the target building, detects any potential
faults in the building, and localizes the faults. With the dimensions of the
building provided, a path around the building is generated. The images captured
by the onboard camera of the UAV are passed through a neural network system to
confirm the presence of faults. Once a fault is detected, the UAV maneuvers
itself to the corresponding position where the crack is detected. The
simulation is done with ROS(Robot Operating System) using the AirSim
environment which initializes ROS Wrappers and provides an integrated interface
of ROS and AirSim to work with, The UAV is simulated in the same.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709598 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07167
|
Nikhil Ghosh
|
Nikhil Ghosh, Song Mei, Bin Yu
|
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods
| null | null | null | null |
stat.ML cs.LG math.ST stat.TH
|
http://creativecommons.org/licenses/by/4.0/
|
To understand how deep learning works, it is crucial to understand the
training dynamics of neural networks. Several interesting hypotheses about
these dynamics have been made based on empirically observed phenomena, but
there exists a limited theoretical understanding of when and why such phenomena
occur.
In this paper, we consider the training dynamics of gradient flow on kernel
least-squares objectives, which is a limiting dynamics of SGD trained neural
networks. Using precise high-dimensional asymptotics, we characterize the
dynamics of the fitted model in two "worlds": in the Oracle World the model is
trained on the population distribution and in the Empirical World the model is
trained on a sampled dataset. We show that under mild conditions on the kernel
and $L^2$ target regression function the training dynamics undergo three stages
characterized by the behaviors of the models in the two worlds. Our theoretical
results also mathematically formalize some interesting deep learning phenomena.
Specifically, in our setting we show that SGD progressively learns more complex
functions and that there is a "deep bootstrap" phenomenon: during the second
stage, the test error of both worlds remain close despite the empirical
training error being much smaller. Finally, we give a concrete example
comparing the dynamics of two different kernels which shows that faster
training is not necessary for better generalization.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710427 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07168
|
Narayan Kundu
|
Narayan Kundu, Dipayan Biswas, Vikrant Kumar, Anirban Paul and
Dhananjay Nandi
|
Effect of slicing in velocity map imaging for the study of dissociation
dynamics
| null | null | null | null |
physics.atom-ph physics.atm-clus
|
http://creativecommons.org/licenses/by/4.0/
|
Inelastic collision dynamics between isolated gas-phase carbon monoxide
molecules and low energetic electrons (< 50 eV) has been studied using
state-of-the-art velocity map imaging apparatus and reported previously. These
were based on data analysis using the time-gated parallel slicing technique,
which has recently revealed the drawback of lower momentum ion exaggeration
mainly due to the inclusion of whole Newton sphere's of diameter $\le$ parallel
slicing time window. To overcome this drawback, we report implementing a wedge
slicing technique so that every momentum sphere contributes equally to the
statistics. We also present a comparative study between these two techniques by
reanalyzing the data using the time-gated parallel slicing technique. Unlike
parallel slicing, the wedge slicing technique better represents the
dissociation dynamics, particularly for the ions with low kinetic energy.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713806 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07169
|
Gang Chen
|
Gang Chen
|
Where to Look: A Unified Attention Model for Visual Recognition with
Reinforcement Learning
|
11 pages
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The idea of using the recurrent neural network for visual attention has
gained popularity in computer vision community. Although the recurrent
attention model (RAM) leverages the glimpses with more large patch size to
increasing its scope, it may result in high variance and instability. For
example, we need the Gaussian policy with high variance to explore object of
interests in a large image, which may cause randomized search and unstable
learning. In this paper, we propose to unify the top-down and bottom-up
attention together for recurrent visual attention. Our model exploits the image
pyramids and Q-learning to select regions of interests in the top-down
attention mechanism, which in turn to guide the policy search in the bottom-up
approach. In addition, we add another two constraints over the bottom-up
recurrent neural networks for better exploration. We train our model in an
end-to-end reinforcement learning framework, and evaluate our method on visual
classification tasks. The experimental results outperform convolutional neural
networks (CNNs) baseline and the bottom-up recurrent attention models on visual
classification tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711481 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07177
|
Vladimir Gurvich
|
Vladimir Gurvich
|
On Nash-solvability of finite $n$-person shortest path games;
bi-shortest path conjecture
|
5 pages
| null | null | null |
cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
We formulate a conjecture from graph theory that is equivalent to
Nash-solvability of the finite two-person shortest path games with positive
local costs. For the three-person games such conjecture fails.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708767 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07180
|
Yizhen Zhang
|
Yizhen Zhang, Minkyu Choi, Kuan Han, Zhongming Liu
|
Explainable Semantic Space by Grounding Language to Vision with
Cross-Modal Contrastive Learning
|
10 pages, 7 figures, 1 appendix, to be published in Neurips 2021
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In natural language processing, most models try to learn semantic
representations merely from texts. The learned representations encode the
distributional semantics but fail to connect to any knowledge about the
physical world. In contrast, humans learn language by grounding concepts in
perception and action and the brain encodes grounded semantics for cognition.
Inspired by this notion and recent work in vision-language learning, we design
a two-stream model for grounding language learning in vision. The model
includes a VGG-based visual stream and a Bert-based language stream. The two
streams merge into a joint representational space. Through cross-modal
contrastive learning, the model first learns to align visual and language
representations with the MS COCO dataset. The model further learns to retrieve
visual objects with language queries through a cross-modal attention module and
to infer the visual relations between the retrieved objects through a bilinear
operator with the Visual Genome dataset. After training, the language stream of
this model is a stand-alone language model capable of embedding concepts in a
visually grounded semantic space. This semantic space manifests principal
dimensions explainable with human intuition and neurobiological knowledge. Word
embeddings in this semantic space are predictive of human-defined norms of
semantic features and are segregated into perceptually distinctive clusters.
Furthermore, the visually grounded language model also enables compositional
language understanding based on visual knowledge and multimodal image search
with queries based on images, texts, or their combinations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708824 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07185
|
Mohammed Abdul Basith
|
Angkita Mistry Tama, Subrata Das, Sagar Dutta, M. D. I. Bhuyan, M. N.
Islam, M. A. Basith
|
MoS$_{2}$ nanosheets incorporated {\alpha}-Fe$_{2}$O$_{3}$/ZnO
nanocomposite with enhanced photocatalytic dye degradation and hydrogen
production ability
| null |
RSC Adv., 9, 40357, 2019
|
10.1039/c9ra07526g
| null |
physics.app-ph cond-mat.mtrl-sci
|
http://creativecommons.org/licenses/by/4.0/
|
We have synthesized MoS$_{2}$ incorporated $\alpha$-Fe$_{2}$O$_{3}$/ZnO
nanocomposites by the hydrothermal process. The effect of incorporating
ultrasonically exfoliated MoS$_{2}$ on the photocatalytic performance of
$\alpha$-Fe$_{2}$O$_{3}$/ZnO nanocomposites has been demonstrated. Structural,
morphological and optical characteristics of the nanomaterials are investigated
by performing Rietveld refinement of powder X-ray diffraction patterns, field
emission scanning electron microscopy and UV-visible spectroscopy. The
photoluminescence spectra of the nanocomposites show that the recombination of
photogenerated electron-hole pairs is suppressed due to incorporating MoS$_{2}$
nanosheets. The ultrasonicated MoS$_{2}$ incorporated
$\alpha$-Fe$_{2}$O$_{3}$/ZnO nanocomposite shows 91% and 83% efficiency to
degrade RhB dye and antibiotic ciprofloxacin under solar illumination. Active
species trapping experiments reveal that the hydroxyl radicals play a
significant role in RhB degradation. Likewise, the dye degradation efficiency,
the amount of hydrogen produced by this nanocomposite via photocatalytic water
splitting is also higher as compared to non-ultrasonicated MoS$_{2}$
incorporated $\alpha$-Fe$_{2}$O$_{3}$/ZnO and $\alpha$-Fe$_{2}$O$_{3}$/ZnO
nanocomposites as well as Degussa P25 titania nanoparticles. This indicates the
promising potential of the incorporation of ultrasonicated MoS$_{2}$ with
$\alpha$-Fe$_{2}$O$_{3}$/ZnO nanocomposite for generation of carbon-free
hydrogen by water splitting. The substantial increase in the photocatalytic
efficiency of $\alpha$-Fe$_{2}$O$_{3}$/ZnO after incorporation of
ultrasonicated MoS$_{2}$ can be attributed to its favorable band structure,
large surface to volume ratio, effective segregation and migration of
photogenerated electron-hole pairs at the interface of heterojunction and the
active edge sites provided by few-layer MoS$_{2}$ nanosheets.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07187
|
Lei Zou
|
Lei Zou, Danqing Liao, Nina S.N. Lam, Michelle Meyer, Nasir G.
Gharaibeh, Heng Cai, Bing Zhou, Dongying Li
|
Social Media for Emergency Rescue: An Analysis of Rescue Requests on
Twitter during Hurricane Harvey
|
24 pages, 9 figures, 6 tables
| null | null | null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Social media plays increasingly significant roles in disaster response, but
effectively leveraging social media for rescue is challenging. This study
analyzed rescue requests on Twitter during the 2017 Hurricane Harvey, in which
many residents resorted to social media to call for help. The objectives
include (1) understanding the characteristics of rescue-request messages; (2)
revealing the spatial-temporal patterns of rescue requests; (3) determining the
social-geographical conditions of communities needing rescue; and (4)
identifying the challenges of using social media for rescue and propose
improvement strategies. About half of rescue requests either did not provide
sufficient information or neglected to include rescue-related hashtags or
accounts. Of the 824 geocoded unique rescue requests, 41% were from
FEMA-defined minimal flood risk zones. Communities sending more rescue requests
on Twitter were environmentally and socioeconomically more vulnerable. Finally,
we derived a framework summarizing the steps and strategies needed to improve
social media use for rescue operations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711049 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07188
|
Tom De Smedt
|
Tom De Smedt, Pierre Vou\'e, Sylvia Jaki, Emily Duffy, Lydia El-Khouri
|
A feast for trolls -- Engagement analysis of counternarratives against
online toxicity
|
15 pages
|
Detect Then Act Technical Report 4 (2021)
| null |
DTCT-TR-04
|
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This report provides an engagement analysis of counternarratives against
online toxicity. Between February 2020 and July 2021, we observed over 15
million toxic messages on social media identified by our fine-grained,
multilingual detection AI. Over 1,000 dashboard users responded to toxic
messages with combinations of visual memes, text, or AI-generated text, or they
reported content. This leads to new, real-life insights on self-regulatory
approaches for the mitigation of online hate.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.703944 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07189
|
Vinayak Gupta
|
Vinayak Gupta
|
Learning Neural Models for Continuous-Time Sequences
|
Outstanding Doctoral Symposium Paper Award at AI-ML Systems 2021
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The large volumes of data generated by human activities such as online
purchases, health records, spatial mobility etc. are stored as a sequence of
events over a continuous time. Learning deep learning methods over such
sequences is a non-trivial task as it involves modeling the ever-increasing
event timestamps, inter-event time gaps, event types, and the influences
between events -- within and across different sequences. This situation is
further exacerbated by the constraints associated with data collection e.g.
limited data, incomplete sequences, privacy restrictions etc. With the research
direction described in this work, we aim to study the properties of
continuous-time event sequences (CTES) and design robust yet scalable neural
network-based models to overcome the aforementioned problems. In this work, we
model the underlying generative distribution of events using marked temporal
point processes (MTPP) to address a wide range of real-world problems.
Moreover, we highlight the efficacy of the proposed approaches over the
state-of-the-art baselines and later report the ongoing research problems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710051 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07193
|
Evander Ramos
|
Evander Ramos, Takahiro Masuda, Zenji Horita, Suveen Mathaudhu
|
Electrical conductivity characterized at varying strains in spiral cut
high-pressure torsion discs
| null | null | null | null |
physics.app-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
High-pressure torsion (HPT) imparts inhomogeneous strain to process discs
with low strain in the center and higher strain at the outer edge. Microscopy
and microhardness indentation have been used to characterize and correlate this
inhomogeneity with strain, but similar exploration with other properties has
been uncommon. In this work, the electrical conductivity of pure copper discs
processed by HPT was characterized with respect to equivalent strain by cutting
them into spirals with an incremental, monotonic increase in strain. Electrical
conductivity varied with straining in agreement with the literature and
expectations based on grain boundary evolution. The spiral conductivity testing
method outlined in this work can improve characterization of HPT materials in
future studies.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710823 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07195
|
Jordi Sanchez-Riera
|
Jordi Sanchez-Riera, Albert Pumarola and Francesc Moreno-Noguer
|
PhysXNet: A Customizable Approach for LearningCloth Dynamics on Dressed
People
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce PhysXNet, a learning-based approach to predict the dynamics of
deformable clothes given 3D skeleton motion sequences of humans wearing these
clothes. The proposed model is adaptable to a large variety of garments and
changing topologies, without need of being retrained. Such simulations are
typically carried out by physics engines that require manual human expertise
and are subjectto computationally intensive computations. PhysXNet, by
contrast, is a fully differentiable deep network that at inference is able to
estimate the geometry of dense cloth meshes in a matter of milliseconds, and
thus, can be readily deployed as a layer of a larger deep learning
architecture. This efficiency is achieved thanks to the specific
parameterization of the clothes we consider, based on 3D UV maps encoding
spatial garment displacements. The problem is then formulated as a mapping
between the human kinematics space (represented also by 3D UV maps of the
undressed body mesh) into the clothes displacement UV maps, which we learn
using a conditional GAN with a discriminator that enforces feasible
deformations. We train simultaneously our model for three garment templates,
tops, bottoms and dresses for which we simulate deformations under 50 different
human actions. Nevertheless, the UV map representation we consider allows
encapsulating many different cloth topologies, and at test we can simulate
garments even if we did not specifically train for them. A thorough evaluation
demonstrates that PhysXNet delivers cloth deformations very close to those
computed with the physical engine, opening the door to be effectively
integrated within deeplearning pipelines.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709202 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07198
|
Yuchen Liang
|
Yuchen Liang and Mohammed J. Zaki
|
Keyphrase Extraction Using Neighborhood Knowledge Based on Word
Embeddings
| null | null | null | null |
cs.CL cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Keyphrase extraction is the task of finding several interesting phrases in a
text document, which provide a list of the main topics within the document.
Most existing graph-based models use co-occurrence links as cohesion indicators
to model the relationship of syntactic elements. However, a word may have
different forms of expression within the document, and may have several
synonyms as well. Simply using co-occurrence information cannot capture this
information. In this paper, we enhance the graph-based ranking model by
leveraging word embeddings as background knowledge to add semantic information
to the inter-word graph. Our approach is evaluated on established benchmark
datasets and empirical results show that the word embedding neighborhood
information improves the model performance.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07199
|
Andrea Colombi
|
R. Zaccherini, A. Palermo, A. Marzani, A. Colombi, V. K. Dertimanis,
E. N. Chatzi
|
Attenuation of surface modes in granular media
| null | null | null | null |
physics.geo-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, an unconsolidated granular medium, made of silica microbeads,
is experimentally tested in a laboratory setting. The objective is to
investigate the attenuation mechanisms of vertically polarized seismic waves
traveling at the surface of unconsolidated substrates that are characterized by
power-law rigidity profiles. Both geometric spreading and material damping due
to skeletal dissipation are considered. An electromagnetic shaker is employed
to excite the granular medium between 300 and 550 Hz, generating linear modes
that are localized near the surface. A densely sampled section is recorded at
the surface using a laser vibrometer. The explicit solution of the geometric
attenuation law of Rayleigh-like waves in layered media is employed to
calculate the geometric spreading function of the vertically polarized surface
modes within the granular material. In accordance with recent studies, the
dynamics of these small-amplitude multi-modal linear waves can be analysed by
considering the granular medium as perfectly continuous and elastic. By
performing a non-linear regression analysis on particle displacements,
extracted from experimental velocity data, we determine the frequency-dependent
attenuation coefficients, which account for the material damping.
The findings of this work show that laboratory-scale physical models can be
used to study the geometric spreading of vertically polarized seismic waves
induced by the soil inhomogeneity and characterize the material damping of the
medium.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707367 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
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