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2111.07598
|
Ke Chen
|
Xueli Wang, Kaili Chang, Weitao Liu, Hongqin Wang, Kaihui Liu, Ke Chen
|
Tunable mid-infrared hyperbolic van der Waals metasurfaces by strong
plasmon-phonon polaritons coupling
|
23 pages, 4 figures
| null | null | null |
physics.optics physics.app-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Hyperbolic metasurfaces based on van der Waals (vdW) materials support
propagation of extremely anisotropic polaritons towards nanoscale light
compression and manipulation, and thus has great potential in the applications
of planar hyperlens, nanolasing, quantum optics and ultrasensitive infrared
spectroscopy. Two-dimensional hexagonal boron nitride (h-BN) as a vdW
metasurface can manipulate the propagation of hyperbolic polaritons at the
level of single atomic layers, possessing higher degree of field confinement
and lower losses than the conventional media. However, active manipulation of
hyperbolic polaritonic waves in h-BN midinfrared metasurfaces remains elusive.
Herein, we provide an effective strategy for constructing tunable mid-infrared
hyperbolic vdW metasurfaces (HMSs). They are composed of meta-atoms that are
the in-plane heterostructures of thin-layer h-BN and monolayer graphene strips
(iHBNG). The strong coupling of h-BN phonons and graphene plasmons enables the
large tunability of light fields by tailoring chemical potentials of graphene
without frequency shift, which involves topological transitions of polaritonic
modes, unidirectional polariton propagation and local-density-of-state
enhancement. Simulated visual near-field distributions of iHBNG metasurfaces
reveal the unique transformations of hyperbolic polariton propagations,
distinguished from that of individual h-BN and graphene metasurfaces. Our
findings provide a platform of optical nanomanipulation towards emerging
on-chip polaritonic devices.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711281 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07599
|
Eduin Hernandez
|
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang, Stefano Rini
|
DNN gradient lossless compression: Can GenNorm be the answer?
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, the problem of optimal gradient lossless compression in Deep
Neural Network (DNN) training is considered. Gradient compression is relevant
in many distributed DNN training scenarios, including the recently popular
federated learning (FL) scenario in which each remote users are connected to
the parameter server (PS) through a noiseless but rate limited channel. In
distributed DNN training, if the underlying gradient distribution is available,
classical lossless compression approaches can be used to reduce the number of
bits required for communicating the gradient entries. Mean field analysis has
suggested that gradient updates can be considered as independent random
variables, while Laplace approximation can be used to argue that gradient has a
distribution approximating the normal (Norm) distribution in some regimes. In
this paper we argue that, for some networks of practical interest, the gradient
entries can be well modelled as having a generalized normal (GenNorm)
distribution. We provide numerical evaluations to validate that the hypothesis
GenNorm modelling provides a more accurate prediction of the DNN gradient tail
distribution. Additionally, this modeling choice provides concrete improvement
in terms of lossless compression of the gradients when applying classical
fix-to-variable lossless coding algorithms, such as Huffman coding, to the
quantized gradient updates. This latter results indeed provides an effective
compression strategy with low memory and computational complexity that has
great practical relevance in distributed DNN training scenarios.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710804 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07601
|
YuYang Sun
|
Yuyang Sun, Zhiyong Zhang, Changzhen Qiu, Liang Wang and Zekai Wang
|
FakeTransformer: Exposing Face Forgery From Spatial-Temporal
Representation Modeled By Facial Pixel Variations
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the rapid development of generation model, AI-based face manipulation
technology, which called DeepFakes, has become more and more realistic. This
means of face forgery can attack any target, which poses a new threat to
personal privacy and property security. Moreover, the misuse of synthetic video
shows potential dangers in many areas, such as identity harassment, pornography
and news rumors. Inspired by the fact that the spatial coherence and temporal
consistency of physiological signal are destroyed in the generated content, we
attempt to find inconsistent patterns that can distinguish between real videos
and synthetic videos from the variations of facial pixels, which are highly
related to physiological information. Our approach first applies Eulerian Video
Magnification (EVM) at multiple Gaussian scales to the original video to
enlarge the physiological variations caused by the change of facial blood
volume, and then transform the original video and magnified videos into a
Multi-Scale Eulerian Magnified Spatial-Temporal map (MEMSTmap), which can
represent time-varying physiological enhancement sequences on different
octaves. Then, these maps are reshaped into frame patches in column units and
sent to the vision Transformer to learn the spatio-time descriptors of frame
levels. Finally, we sort out the feature embedding and output the probability
of judging whether the video is real or fake. We validate our method on the
FaceForensics++ and DeepFake Detection datasets. The results show that our
model achieves excellent performance in forgery detection, and also show
outstanding generalization capability in cross-data domain.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710791 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07602
|
Bingxin Zhou
|
Bingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Li\`o,
YuGuang Wang
|
Spectral Transform Forms Scalable Transformer
| null | null | null | null |
cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Many real-world relational systems, such as social networks and biological
systems, contain dynamic interactions. When learning dynamic graph
representation, it is essential to employ sequential temporal information and
geometric structure. Mainstream work achieves topological embedding via message
passing networks (e.g., GCN, GAT). The temporal evolution, on the other hand,
is conventionally expressed via memory units (e.g., LSTM or GRU) that possess
convenient information filtration in a gate mechanism. Though, such a design
prevents large-scale input sequence due to the over-complicated encoding. This
work learns from the philosophy of self-attention and proposes an efficient
spectral-based neural unit that employs informative long-range temporal
interaction. The developed spectral window unit (SWINIT) model predicts
scalable dynamic graphs with assured efficiency. The architecture is assembled
with a few simple effective computational blocks that constitute randomized
SVD, MLP, and graph Framelet convolution. The SVD plus MLP module encodes the
long-short-term feature evolution of the dynamic graph events. A fast framelet
graph transform in the framelet convolution embeds the structural dynamics.
Both strategies enhance the model's ability on scalable analysis. In
particular, the iterative SVD approximation shrinks the computational
complexity of attention to O(Nd\log(d)) for the dynamic graph with N edges and
d edge features, and the multiscale transform of framelet convolution allows
sufficient scalability in the network training. Our SWINIT achieves
state-of-the-art performance on a variety of online continuous-time dynamic
graph learning tasks, while compared to baseline methods, the number of its
learnable parameters reduces by up to seven times.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708629 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07604
|
Binbin Yue
|
Binbin Yue, Wei Zhong, Ting Wen, Yonggang Wang, Hui Yu, Xiaohui Yu,
Fang Hong
|
Superconductivity in compressed SnPS3
| null | null | null | null |
cond-mat.supr-con physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Metal phosphorous trichalcogenides, MPX3, is a group of van der Waals
materials with rich electronic properties and even exotic magnetic behavior.
These properties can be well manipulated by pressure/strain via effective
control of interlayer interaction, lattice parameters and crystal structure.
Superconducting transition has been observed in compressed FePSe3. However, it
is the only one superconductor reported in the large MPX3 family. Is it
possible to achieve superconducting transition in other MPX3 compounds,
especially in a trisulfide compound? In this work, we tentatively compressed
the SnPS3 (an insulator with large band gap at ambient condition) up to 48.9
GPa, and managed to achieve the superconducting transition above 31.7 GPa with
Tc ranging from ~2.2 K to ~2.8 K. The upper critical field is estimated to be
~3.03 T at 40.5 GPa. Optical absorption measurements together with Raman
spectroscopy show a series of transitions under pressure, which is well
consistent with the electric transport results. This work provides direct
experimental evidence that SnPS3 undergoes an insulator-metal transition near
31.7 GPa. More importantly, it demonstrates that superconductivity can exist in
MPS3 compounds, which not only further enriches the electronic properties of
this kind of material but also paves a new avenue to explore the abundant
emergence phenomena in the whole MPX3 family, and it also benefits the study of
superconductor mechanism.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711825 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07606
|
Nunzio Alexandro Letizia Mr
|
Nunzio A. Letizia, Andrea M. Tonello
|
Discriminative Mutual Information Estimation for the Design of Channel
Capacity Driven Autoencoders
|
6 pages, 5 figures, submitted to ICC
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
The development of optimal and efficient machine learning-based communication
systems is likely to be a key enabler of beyond 5G communication technologies.
In this direction, physical layer design has been recently reformulated under a
deep learning framework where the autoencoder paradigm foresees the full
communication system as an end-to-end coding-decoding problem. Given the loss
function, the autoencoder jointly learns the coding and decoding optimal blocks
under a certain channel model. Because performance in communications typically
refers to achievable rates and channel capacity, the mutual information between
channel input and output can be included in the end-to-end training process,
thus, its estimation becomes essential. In this paper, we present a set of
novel discriminative mutual information estimators and we discuss how to
exploit them to design capacity-approaching codes and ultimately estimate the
channel capacity.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70557 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07608
|
Yufei Chen
|
Junhao Zhou, Yufei Chen, Chao Shen, Yang Zhang
|
Property Inference Attacks Against GANs
|
To Appear in NDSS 2022
| null | null | null |
cs.CR cs.AI cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
While machine learning (ML) has made tremendous progress during the past
decade, recent research has shown that ML models are vulnerable to various
security and privacy attacks. So far, most of the attacks in this field focus
on discriminative models, represented by classifiers. Meanwhile, little
attention has been paid to the security and privacy risks of generative models,
such as generative adversarial networks (GANs). In this paper, we propose the
first set of training dataset property inference attacks against GANs.
Concretely, the adversary aims to infer the macro-level training dataset
property, i.e., the proportion of samples used to train a target GAN with
respect to a certain attribute. A successful property inference attack can
allow the adversary to gain extra knowledge of the target GAN's training
dataset, thereby directly violating the intellectual property of the target
model owner. Also, it can be used as a fairness auditor to check whether the
target GAN is trained with a biased dataset. Besides, property inference can
serve as a building block for other advanced attacks, such as membership
inference. We propose a general attack pipeline that can be tailored to two
attack scenarios, including the full black-box setting and partial black-box
setting. For the latter, we introduce a novel optimization framework to
increase the attack efficacy. Extensive experiments over four representative
GAN models on five property inference tasks show that our attacks achieve
strong performance. In addition, we show that our attacks can be used to
enhance the performance of membership inference against GANs.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71022 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07611
|
Niall Taylor
|
Niall Taylor, Lei Sha, Dan W Joyce, Thomas Lukasiewicz, Alejo
Nevado-Holgado, Andrey Kormilitzin
|
Rationale production to support clinical decision-making
|
Machine Learning for Health (ML4H) - Extended Abstract
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The development of neural networks for clinical artificial intelligence (AI)
is reliant on interpretability, transparency, and performance. The need to
delve into the black-box neural network and derive interpretable explanations
of model output is paramount. A task of high clinical importance is predicting
the likelihood of a patient being readmitted to hospital in the near future to
enable efficient triage. With the increasing adoption of electronic health
records (EHRs), there is great interest in applications of natural language
processing (NLP) to clinical free-text contained within EHRs. In this work, we
apply InfoCal, the current state-of-the-art model that produces extractive
rationales for its predictions, to the task of predicting hospital readmission
using hospital discharge notes. We compare extractive rationales produced by
InfoCal to competitive transformer-based models pretrained on clinical text
data and for which the attention mechanism can be used for interpretation. We
find each presented model with selected interpretability or feature importance
methods yield varying results, with clinical language domain expertise and
pretraining critical to performance and subsequent interpretability.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708862 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07622
|
Yohan Payan
|
Alessio Trebbi (TIMC-BIOM\'ECA), Antoine Perrier (TIMC-BIOM\'ECA),
Mathieu Bailet, Yohan Payan (TIMC-BIOM\'ECA)
|
MR-compatible loading device for assessment of heel pad internal tissue
displacements under shearing load
| null |
Medical Engineering and Physics, Elsevier, 2021, 98, pp.125-132
|
10.1016/j.medengphy.2021.11.006
| null |
physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the last decade, the role of shearing loads has been increasingly
suspected to play a determinant impact in the formation of deep pressure
ulcers. In vivo observations of such deformations are complex to obtain.
Previous studies only provide global measurements of such deformations without
getting the quantitative values of the loads that generate these deformations.
To study the role that shearing loads have in the aetiology of heel pressure
ulcers, an MR-compatible device for the application of shearing and normal
loads was designed. Magnetic resonance imaging is a key feature that allows to
monitor deformations of soft tissues after loading in a non-invasive way.
Measuring applied forces in an MR-environment is challenging due to the
impossibility to use magnetic materials. In our device, forces are applied
through the compression of springs made in polylactide. Shearing and normal
loads were applied on the plantar skin of the human heel by means of an
indenting plate while acquiring MR images. The device materials did not
introduce any imaging artifact and allowed for high quality MR measurements
permitting to identify the deformation of the internal components of the heel.
The obtained subject-specific results are an original data set that can be used
in validations for Finite Element analysis and therefore contribute to a better
understanding of the factors involved in pressure ulcer development.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.704757 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07625
|
Gintautas Palubinskas
|
Gintautas Palubinskas
|
On the validation of pansharpening methods
|
18 pages, 5 figures, 6 tables. arXiv admin note: substantial text
overlap with arXiv:2103.03062
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Validation of the quality of pansharpening methods is a difficult task
because the reference is not directly available. In the meantime, two main
approaches have been established: validation in reduced resolution and original
resolution. In the former approach it is still not clear how the data are to be
processed to a lower resolution. Other open issues are related to the question
which resolution and measures should be used. In the latter approach the main
problem is how the appropriate measure should be selected. In the most
comparison studies the results of both approaches do not correspond, that means
in each case other methods are selected as the best ones. Thus, the developers
of the new pansharpening methods still stand in the front of dilemma: how to
perform a correct or appropriate comparison/evaluation/validation. It should be
noted, that the third approach is possible, that is to perform the comparison
of methods in a particular application with the usage of their ground truth.
But this is not always possible, because usually developers are not working
with applications. Moreover, it can be an additional computational load for a
researcher in a particular application. In this paper some of the
questions/problems raised above are approached/discussed. The following
component substitution (CS) and high pass filtering (HPF) pansharpening methods
with additive and multiplicative models and their enhancements such as haze
correction, histogram matching, usage of spectral response functions (SRF),
modulation transfer function (MTF) based lowpass filtering are investigated on
remote sensing data of WorldView-2 and WorldView-4 sensors.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711017 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07626
|
Mohammadjavad Salehi
|
MohammadJavad Salehi, Emanuele Parrinello, Hamidreza Bakhshzad
Mahmoodi, and Antti Tolli
|
Low-Subpacketization Multi-Antenna Coded Caching for Dynamic Networks
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Multi-antenna coded caching combines a global caching gain, proportional to
the total cache size in the network, with an additional spatial multiplexing
gain that stems from multiple transmitting antennas. However, classic
centralized coded caching schemes are not suitable for dynamic networks as they
require prior knowledge of the number of users to indicate what data should be
cached at each user during the placement phase. On the other hand, fully
decentralized schemes provide comparable gains to their centralized
counterparts only when the number of users is very large. In this paper, we
propose a novel multi-antenna coded caching scheme for dynamic networks, where
instead of defining individual cache contents, we associate users with a
limited set of predefined caching profiles. Then, during the delivery phase, we
aim at achieving a combined caching and spatial multiplexing gain, comparable
to a large extent with the ideal case of fully centralized schemes. The
resulting scheme imposes small subpacketization and beamforming overheads, is
robust under dynamic network conditions, and incurs small finite-SNR
performance loss compared with centralized schemes.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07627
|
Issam Lakkis
|
Ali Saab, Leila Issa, Salah Zeineddine, Daniel M. Tartakovsky, and
Issam Lakkis
|
Impact of Airways Geometry on Transport of Gases to Blood
| null | null | null | null |
physics.med-ph physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Topological structure of bronchial trees affects transport of gases and
aerosols in the respiratory system. We start by providing a quantitative
assessment of the ability of the alternative tree representations to predict
observable geometric and mechanistic characteristics, such as network
resistance, dead space volume, and path length. Then we present a model of
dynamic transport of oxygen and carbon dioxide along the airways, in the
alveoli, across the alveolar membrane, and along the pulmonary blood
capillaries. The model also accounts for the exchange of these two gases with
blood in the capillaries, as well as for age, gender and other in-species
characteristics. Our model's predictions are compared with corresponding
observations, providing an additional venue to assess the validity of the
existing representations of the lung's bronchial tree.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712251 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07634
|
Matthias Perkonigg
|
Matthias Perkonigg, Peter Mesenbrink, Alexander Goehler, Miljen
Martic, Ahmed Ba-Ssalamah, Georg Langs
|
Pseudo-domains in imaging data improve prediction of future disease
status in multi-center studies
|
Accepted at Medical Imaging Meets NeurIPS 2021
| null | null | null |
eess.IV cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In multi-center randomized clinical trials imaging data can be diverse due to
acquisition technology or scanning protocols. Models predicting future outcome
of patients are impaired by this data heterogeneity. Here, we propose a
prediction method that can cope with a high number of different scanning sites
and a low number of samples per site. We cluster sites into pseudo-domains
based on visual appearance of scans, and train pseudo-domain specific models.
Results show that they improve the prediction accuracy for steatosis after 48
weeks from imaging data acquired at an initial visit and 12-weeks follow-up in
liver disease
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07635
|
Issam Lakkis
|
Elissar Al Aawar, Abdelkader Baayoun, Alaa Imad, Jad El Helou, Lama
Halabi, Mohamad Ghadban, Ali Moukhadder, Marya El Malki, Sara Najem, Najat A.
Saliba, Alan Shihadeh, Issam Lakkis1
|
Identifying urban air pollution hot-spots by dispersion modeling when
data are scarce: application to diesel generators in Beirut, Lebanon
| null | null | null | null |
physics.ao-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Diesel generators are emerging as community-initiated solutions to compensate
for electricity shortage in cities marred by economical crisis and/or conflict.
The resulting pollution distribution in dense urban environments is a major
source of concern to the population. In the absence of periodic observations
from properly distributed sensors, as is the case in Beirut, physically based
computational modeling stand out as an effective tool for predicting the
pollutant distribution in complex environments, and a cost-effective framework
for investigating what-if scenarios and assessing mitigation strategies. Here,
we present a Lagrangian transport model-based study of PM2.5 dispersion
originating from a large number of diesel generators in Beirut. We explore
large and small scale dispersion patterns in selected smalls domains and over
the entire city. The scenarios considered investigate the impact of topography,
atmospheric stability, presence of buildings, diesel generators distribution,
and stacks elevations for representative meteorological conditions. Assessment
of these scenarios is carried out in terms of small and large scale dispersion
patterns and the mean concentration at street level and population exposure
proxy indicators. We also report on the efficacy of elevating the stack height
as a mitigation measure at different representative wind and atmospheric
stability conditions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.704122 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07645
|
Paolo Malgaretti Mr
|
P. Malgaretti, A.M. Puertas, I. Pagonabarraga
|
Active microrheology in corrugated channels: comparison of thermal and
colloidal baths
| null | null | null | null |
cond-mat.soft physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
The dynamics of colloidal suspension confined within porous materials
strongly differs from that in the bulk. In particular, within porous materials,
the presence of boundaries with complex shapes entangles the longitudinal and
transverse degrees of freedom inducing a coupling between the transport of the
suspension and the density inhomogeneities induced by the walls.
Colloidal suspension confined within model porous media are characterized by
means of active microrheology where a net force is applied on a single colloid
(tracer particle) whose transport properties are then studied. The trajectories
provided by active microrheology are exploited to determine the local transport
coefficients. In order to asses the role of the colloid-colloid interactions we
compare the case of a tracer embedded in a colloidal suspension to the case of
a tracer suspended in an ideal bath.
Our results show that the friction coefficient increases and the passage time
distribution widens upon increasing the corrugation of the channel. These
features are obtained for a tracer suspended in a (thermalized) colloidal bath
as well as for the case of an ideal thermal bath. These results highlight the
relevance of the confinement on the transport and show a mild dependence on the
colloidal/thermal bath. Finally, we rationalize our numerical results with
a semi-analytical model. Interestingly, the predictions of the model are
quantitatively reliable for mild external forces, hence providing a reliable
tool for predicting the transport across porous materials.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710804 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07646
|
Dwarikanath Mahapatra
|
Dwarikanath Mahapatra
|
Multimodal Generalized Zero Shot Learning for Gleason Grading using
Self-Supervised Learning
| null | null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Gleason grading from histopathology images is essential for accurate prostate
cancer (PCa) diagnosis. Since such images are obtained after invasive tissue
resection quick diagnosis is challenging under the existing paradigm. We
propose a method to predict Gleason grades from magnetic resonance (MR) images
which are non-interventional and easily acquired. We solve the problem in a
generalized zero-shot learning (GZSL) setting since we may not access training
images of every disease grade. Synthetic MRI feature vectors of unseen grades
(classes) are generated by exploiting Gleason grades' ordered nature through a
conditional variational autoencoder (CVAE) incorporating self-supervised
learning. Corresponding histopathology features are generated using cycle GANs,
and combined with MR features to predict Gleason grades of test images.
Experimental results show our method outperforms competing feature generating
approaches for GZSL, and comes close to performance of fully supervised
methods.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708591 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07648
|
Gonzalo Imaz
|
Gonzalo E. Imaz
|
The Possibilistic Horn Non-Clausal Knowledge Bases
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Posibilistic logic is the most extended approach to handle uncertain and
partially inconsistent information. Regarding normal forms, advances in
possibilistic reasoning are mostly focused on clausal form. Yet, the encoding
of real-world problems usually results in a non-clausal (NC) formula and
NC-to-clausal translators produce severe drawbacks that heavily limit the
practical performance of clausal reasoning. Thus, by computing formulas in its
original NC form, we propose several contributions showing that notable
advances are also possible in possibilistic non-clausal reasoning.
{\em Firstly,} we define the class of {\em Possibilistic Horn Non-Clausal
Knowledge Bases,} or $\mathcal{\overline{H}}_\Sigma$, which subsumes the
classes: possibilistic Horn and propositional Horn-NC.
$\mathcal{\overline{H}}_\Sigma $ is shown to be a kind of NC analogous of the
standard Horn class.
{\em Secondly}, we define {\em Possibilistic Non-Clausal Unit-Resolution,} or
$ \mathcal{UR}_\Sigma $, and prove that $ \mathcal{UR}_\Sigma $ correctly
computes the inconsistency degree of $\mathcal{\overline{H}}_\Sigma $members.
$\mathcal{UR}_\Sigma $ had not been proposed before and is formulated in a
clausal-like manner, which eases its understanding, formal proofs and future
extension towards non-clausal resolution.
{\em Thirdly}, we prove that computing the inconsistency degree of
$\mathcal{\overline{H}}_\Sigma $ members takes polynomial time. Although there
already exist tractable classes in possibilistic logic, all of them are
clausal, and thus, $\mathcal{\overline{H}}_\Sigma $ turns out to be the first
characterized polynomial non-clausal class within possibilistic reasoning.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708572 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07657
|
Sangjun Han
|
Sangjun Han, Hyeongrae Ihm, Woohyung Lim
|
Symbolic Music Loop Generation with VQ-VAE
| null | null | null | null |
cs.SD cs.MM eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Music is a repetition of patterns and rhythms. It can be composed by
repeating a certain number of bars in a structured way. In this paper, the
objective is to generate a loop of 8 bars that can be used as a building block
of music. Even considering musical diversity, we assume that music patterns
familiar to humans can be defined in a finite set. With explicit rules to
extract loops from music, we found that discrete representations are sufficient
to model symbolic music sequences. Among VAE family, musical properties from
VQ-VAE are better observed rather than other models. Further, to emphasize
musical structure, we have manipulated discrete latent features to be
repetitive so that the properties are more strengthened. Quantitative and
qualitative experiments are extensively conducted to verify our assumptions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708364 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07666
|
Rostyslav Vlokh O
|
Oksana Mys, Myroslav Kostyrko, Ivan Orykhivskyi, Dmitro Adamenko, Ihor
Skab and Rostyslav Vlokh
|
Enhancement of the efficiency of acousto-optic Bragg diffraction due to
optical activity. A case of Pb$_5$Ge$_3$O$_{11}$ crystals
|
23 pages, 11 figures
| null | null | null |
physics.optics
|
http://creativecommons.org/licenses/by/4.0/
|
We show that the existence of optical activity in an optical material can
lead to essential enhancement of acousto-optic (AO) figure of merit for this
material. The reason is that the ellipticity of interacting optical eigenwaves
approaches unity near the optic axis and so additional elasto-optic (EO) tensor
components with relatively large values become involved into the effective EO
coefficient. We demonstrate on the example of lead germanate crystals,
Pb$_5$Ge$_3$O$_{11}$, that the increase in the efficiency of AO diffraction
manifests itself for all the types of isotropic and anisotropic interactions,
whenever the incident optical wave propagates close to the optic axis. We find
that, in the particular case of diffraction in the interaction plane XZ of
Pb$_5$Ge$_3$O$_{11}$ crystals, the maximal enhancement of the AO figure of
merit takes place under conditions of the types V and VI of isotropic
diffraction, with the AO figure of merit increasing from zero up to
13.3x10$^{-15}$ s$^3$/kg, and the type IX of anisotropic diffraction when the
AO figure of merit increases more than twice (from 12.5x10$^{-15}$ up to
26.5x10$^{-15}$ s$^3$/kg). The maximal AO efficiency in the XZ interaction
plane is reached at the types I and II of isotropic AO interactions. In these
cases the AO figure of merit increases from 6.8x10$^{-15}$ up to
37.9x10$^{-15}$ s$^3$/kg and from 31.1x10$^{-15}$ to 37.9x10$^{-15}$ s$^3$/kg,
respectively.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712251 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07667
|
Niklas Jordi Freymuth
|
Niklas Freymuth and Philipp Becker and Gerhard Neumann
|
Versatile Inverse Reinforcement Learning via Cumulative Rewards
|
Accepted as a workshop paper in 4th Robot Learning Workshop:
Self-Supervised and Lifelong Learning @NeurIPS 2021
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Inverse Reinforcement Learning infers a reward function from expert
demonstrations, aiming to encode the behavior and intentions of the expert.
Current approaches usually do this with generative and uni-modal models,
meaning that they encode a single behavior. In the common setting, where there
are various solutions to a problem and the experts show versatile behavior this
severely limits the generalization capabilities of these methods. We propose a
novel method for Inverse Reinforcement Learning that overcomes these problems
by formulating the recovered reward as a sum of iteratively trained
discriminators. We show on simulated tasks that our approach is able to recover
general, high-quality reward functions and produces policies of the same
quality as behavioral cloning approaches designed for versatile behavior.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709849 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07668
|
Robin Hesse
|
Robin Hesse, Simone Schaub-Meyer, Stefan Roth
|
Fast Axiomatic Attribution for Neural Networks
|
To appear at NeurIPS*2021. Project page and code:
https://visinf.github.io/fast-axiomatic-attribution
| null | null | null |
cs.LG cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mitigating the dependence on spurious correlations present in the training
dataset is a quickly emerging and important topic of deep learning. Recent
approaches include priors on the feature attribution of a deep neural network
(DNN) into the training process to reduce the dependence on unwanted features.
However, until now one needed to trade off high-quality attributions,
satisfying desirable axioms, against the time required to compute them. This in
turn either led to long training times or ineffective attribution priors. In
this work, we break this trade-off by considering a special class of
efficiently axiomatically attributable DNNs for which an axiomatic feature
attribution can be computed with only a single forward/backward pass. We
formally prove that nonnegatively homogeneous DNNs, here termed
$\mathcal{X}$-DNNs, are efficiently axiomatically attributable and show that
they can be effortlessly constructed from a wide range of regular DNNs by
simply removing the bias term of each layer. Various experiments demonstrate
the advantages of $\mathcal{X}$-DNNs, beating state-of-the-art generic
attribution methods on regular DNNs for training with attribution priors.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710258 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07679
|
Masanori Koyama
|
Masanori Koyama and Kentaro Minami and Takeru Miyato and Yarin Gal
|
Contrastive Representation Learning with Trainable Augmentation Channel
| null | null | null | null |
stat.ML cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In contrastive representation learning, data representation is trained so
that it can classify the image instances even when the images are altered by
augmentations. However, depending on the datasets, some augmentations can
damage the information of the images beyond recognition, and such augmentations
can result in collapsed representations. We present a partial solution to this
problem by formalizing a stochastic encoding process in which there exist a
tug-of-war between the data corruption introduced by the augmentations and the
information preserved by the encoder. We show that, with the infoMax objective
based on this framework, we can learn a data-dependent distribution of
augmentations to avoid the collapse of the representation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710842 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07685
|
Gerg\H{o} Pint\'er
|
Gerg\H{o} Pint\'er and Imre Felde
|
Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile
Phone Network Perspective: Euro 2016, a Case Study
|
arXiv admin note: substantial text overlap with arXiv:2108.09291
|
Information 2021, 12(11), 468
|
10.3390/info12110468
| null |
cs.SI physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In this study, Call Detail Records (CDRs), covering Budapest, for the month
of June in 2016 has been analyzed. During this observation period, the 2016
UEFA European Football Championship took place, which affected significantly
the habit of the residents, despite the fact that not a single match was played
in the city. We evaluated the fans' behavior in Budapest, during and after the
Hungarian matches, and found that the mobile phone network activity reflects
the football fans' behavior, demonstrating the potential of mobile phone
network data within a social sensing system. The Call Detail Records are
enriched with mobile phone properties to analyze the subscribers' devices.
Applying the device information (Type Allocation Code) from the activity
records, the Subscriber Identity Modules, that do not operate in cell phones
are omitted from mobility analyses, allowing to focus on people. The mobile
phone price is proposed and evaluated as a socioeconomic indicator, and
correlation between the phone price and the mobility customs have been found.
We also found that, beside the cell phone price, the subscriber age and the
subscription type also have an effect on the mobility. On the other hand, these
do not seem to affect the interest in football.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.703919 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07689
|
Manuel Array\'as
|
M. Array\'as, J. L. Trueba and C. Uriarte
|
Levitating frogs, machine learning and elliptic integrals
| null | null | null | null |
physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present the calculation of the stability region of a perfect diamagnet
levitated in a magnetic field created by a circular current loop. We make use
of the machine learning technique of automatic differentiation to illustrate
the calculations with the elliptic integrals involved.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710396 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07690
|
Gerrit van der Laan
|
Gerrit van der Laan
|
Determination of spin chirality using x-ray magnetic circular dichroism
|
7 pages, 5 figures
|
Physical Review B 104, 094414 (2021)
|
10.1103/PhysRevB.104.094414
| null |
cond-mat.mtrl-sci physics.app-ph
|
http://creativecommons.org/licenses/by/4.0/
|
A 3-fold symmetric kagome lattice that has negative spin chirality can give a
non-zero x-ray magnetic circular dichroism (XMCD) signal, despite that the
total spin moment amounts to zero. This is explained by a hitherto unnoticed
rule for the rotational symmetry invariance of the XMCD signal. A necessary
condition is the existence of an anisotropic XMCD signal for the local magnetic
atom, which can arise from a spin anisotropy either in the ground state or the
final state. The angular dependence of the XMCD as a function of the beam
direction has an unusual behavior. The maximum dichroism is not aligned along
the spin direction, but depends on the relative orientation of the spin with
respect to the atomic orientation. Therefore, different geometries can result
in the same angular dependence, and the spin direction can only be determined
if the atomic orientation is known. The consequences for the x-ray
magneto-optical sum rules are given. The integrated XMCD signals are
proportional to the anisotropy in the orbital moment and the magnetic dipole
term, where the isotropic spin moment drops out.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710427 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07693
|
Malte Krack
|
Carlo Monjaraz-Tec, Johann Gross, Malte Krack
|
A massless boundary component mode synthesis method for elastodynamic
contact problems
|
The final version of this article is available online at
https://doi.org/10.1016/j.compstruc.2021.106698
| null |
10.1016/j.compstruc.2021.106698
| null |
cs.CE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We propose to combine the ideas of mass redistribution and component mode
synthesis. More specifically, we employ the MacNeal method, which readily leads
to a singular mass matrix, and an accordingly modified version of the
Craig-Bampton method. Besides obtaining a massless boundary, we achieve a
drastic reduction of the mathematical model order in this way compared to the
parent finite element model. Contact is modeled using set-valued laws and time
stepping is carried out with a semi-explicit scheme. We assess the method's
computational performance by a series of benchmarks, including both
frictionless and frictional contact. The results indicate that the proposed
method achieves excellent energy conservation properties and superior
convergence behavior. It reduces the spurious oscillations and decreases the
computational effort by about 1-2 orders of magnitude compared to the current
state of the art (mass-carrying component mode synthesis method). We believe
that the computational performance and favorable energy conservation properties
will be valuable for the prediction of vibro-impact processes and physical
damping.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711619 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07699
|
Lifeng Han
|
Serge Gladkoff, Irina Sorokina, Lifeng Han, Alexandra Alekseeva
|
Measuring Uncertainty in Translation Quality Evaluation (TQE)
|
13 pages, 9 figures
| null | null | null |
cs.CL cs.NA math.NA stat.AP
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
From both human translators (HT) and machine translation (MT) researchers'
point of view, translation quality evaluation (TQE) is an essential task.
Translation service providers (TSPs) have to deliver large volumes of
translations which meet customer specifications with harsh constraints of
required quality level in tight time-frames and costs. MT researchers strive to
make their models better, which also requires reliable quality evaluation.
While automatic machine translation evaluation (MTE) metrics and quality
estimation (QE) tools are widely available and easy to access, existing
automated tools are not good enough, and human assessment from professional
translators (HAP) are often chosen as the golden standard
\cite{han-etal-2021-TQA}. Human evaluations, however, are often accused of
having low reliability and agreement. Is this caused by subjectivity or
statistics is at play? How to avoid the entire text to be checked and be more
efficient with TQE from cost and efficiency perspectives, and what is the
optimal sample size of the translated text, so as to reliably estimate the
translation quality of the entire material? This work carries out such
motivated research to correctly estimate the confidence intervals
\cite{Brown_etal2001Interval} depending on the sample size of the translated
text, e.g. the amount of words or sentences, that needs to be processed on TQE
workflow step for confident and reliable evaluation of overall translation
quality. The methodology we applied for this work is from Bernoulli Statistical
Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708855 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07706
|
Johannes Kraus
|
Johannes Kraus and Sergey Repin
|
A posteriori error estimates for domain decomposition methods
|
24 pages, 4 figures, 4 tables
| null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nowadays, a posteriori error control methods have formed a new important part
of the numerical analysis. Their purpose is to obtain computable error
estimates in various norms and error indicators that show distributions of
global and local errors of a particular numerical solution.
In this paper, we focus on a particular class of domain decomposition methods
(DDM), which are among the most efficient numerical methods for solving PDEs.
We adapt functional type a posteriori error estimates and construct a special
form of error majorant which allows efficient error control of approximations
computed via these DDM by performing only subdomain-wise computations. The
presented guaranteed error bounds use an extended set of admissible fluxes
which arise naturally in DDM.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707569 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07707
|
Qingsong Liu
|
Qingsong Liu, Wenfei Wu, Longbo Huang, Zhixuan Fang
|
Simultaneously Achieving Sublinear Regret and Constraint Violations for
Online Convex Optimization with Time-varying Constraints
|
31 pages, it has been accepted at Performance 2021
|
Proceedings of the 39th International Symposium on Computer
Performance, Modeling, Measurements and Evaluation (Performance), 2021
| null | null |
math.OC cs.LG
|
http://creativecommons.org/publicdomain/zero/1.0/
|
In this paper, we develop a novel virtual-queue-based online algorithm for
online convex optimization (OCO) problems with long-term and time-varying
constraints and conduct a performance analysis with respect to the dynamic
regret and constraint violations. We design a new update rule of dual variables
and a new way of incorporating time-varying constraint functions into the dual
variables. To the best of our knowledge, our algorithm is the first
parameter-free algorithm to simultaneously achieve sublinear dynamic regret and
constraint violations. Our proposed algorithm also outperforms the
state-of-the-art results in many aspects, e.g., our algorithm does not require
the Slater condition. Meanwhile, for a group of practical and widely-studied
constrained OCO problems in which the variation of consecutive constraints is
smooth enough across time, our algorithm achieves $O(1)$ constraint violations.
Furthermore, we extend our algorithm and analysis to the case when the time
horizon $T$ is unknown. Finally, numerical experiments are conducted to
validate the theoretical guarantees of our algorithm, and some applications of
our proposed framework will be outlined.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709403 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07716
|
Xiangfeng Wang
|
Wenhao Li and Qisen Xu and Chuyun Shen and Bin Hu and Fengping Zhu and
Yuxin Li and Bo Jin and Xiangfeng Wang
|
Interactive Medical Image Segmentation with Self-Adaptive Confidence
Calibration
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Medical image segmentation is one of the fundamental problems for artificial
intelligence-based clinical decision systems. Current automatic medical image
segmentation methods are often failed to meet clinical requirements. As such, a
series of interactive segmentation algorithms are proposed to utilize expert
correction information. However, existing methods suffer from some segmentation
refining failure problems after long-term interactions and some cost problems
from expert annotation, which hinder clinical applications. This paper proposes
an interactive segmentation framework, called interactive MEdical segmentation
with self-adaptive Confidence CAlibration (MECCA), by introducing the
corrective action evaluation, which combines the action-based confidence
learning and multi-agent reinforcement learning (MARL). The evaluation is
established through a novel action-based confidence network, and the corrective
actions are obtained from MARL. Based on the confidential information, a
self-adaptive reward function is designed to provide more detailed feedback,
and a simulated label generation mechanism is proposed on unsupervised data to
reduce over-reliance on labeled data. Experimental results on various medical
image datasets have shown the significant performance of the proposed
algorithm.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709818 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07724
|
Mounir Bensalem
|
Mounir Bensalem, Jasenka Dizdarevi\'c, Admela Jukan
|
Benchmarking Various ML Solutions in Complex Intent-Based Network
Management Systems
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Intent-based networking (IBN) solutions to managing complex ICT systems have
become one of the key enablers of intelligent and autonomous network
management. As the number of machine learning (ML) techniques deployed in IBN
increases, it becomes increasingly important to understand their expected
performance. Whereas IBN concepts are generally specific to the use case
envisioned, the underlying platforms are generally heterogenous, comprised of
complex processing units, including CPU/GPU, CPU/FPGA and CPU/TPU combinations,
which needs to be considered when running the ML techniques chosen. We focus on
a case study of IBNs in the so-called ICT supply chain systems, where multiple
ICT artifacts are integrated in one system based on heterogeneous hardware
platforms. Here, we are interested in the problem of benchmarking the
computational performance of ML technique defined by the intents. Our
benchmarking method is based on collaborative filtering techniques, relying on
ML-based methods like Singular Value Decomposition and Stochastic Gradient
Descent, assuming initial lack of explicit knowledge about the expected number
of operations, framework, or the device processing characteristics. We show
that it is possible to engineer a practical IBN system with various ML
techniques with an accurate estimated performance based on data from a few
benchmarks only.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709629 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07728
|
Yurui Fang PhD
|
Zhiguang Sun, Huan Chen, Zhenglong Zhang, Lujun Pan, Yiming Yang, Bin
Dong and Yurui Fang
|
2D wavelength-polarization dispersive microspectroscope based on a
hybrid plasmonic helical nanostructure
|
30 pages including supporting information
| null | null | null |
physics.optics cond-mat.mes-hall cond-mat.mtrl-sci
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Microspectrometer features remarkable portability and integration, and is
prospected to provide quick detection tools to daily life and revolutionary
techniques to researchers. For the extremely finite footprint,
microspectrometer can hardly work to analyze the polarization feature by
placing polarizer in the optical path like conventional spectrometers. Here, we
demonstrate a novel 2D wavelength-polarization dispersive microspectroscope
based on carbon nanocoil with plasmonic Au nanopariticles (Au/CNC) as a
dispersive component. Explored by the microspectrum and Fourier-space
microscopy, a unique 2D dispersive characteristic of the Au/CNC is revealed.
Along the axis of the coil, Au/CNC disperses light as wavelength to bands of
different diffraction orders like a grating. Wavelength of the incident light
can be obtained from the position of the signal lines in a quite large
visible-near-infrared wavelength range with an acceptable resolution. In the
direction perpendicular to the axis of the coil, incident light is dispersed as
polarization with bright and dark areas. We can distinguish left- and
right-circularly-polarized light, and also obtain the polarization orientation
of linearly-polarized light. Based on this effect, a wonderful 2D
wavelength-polarization microspectrometer can be built. It not only fulfills
the wavelength analysis with a tiny dispersive component, but also
simultaneously knows the polarization feature of the incident light in one
shot. Moreover, this powerful tool can further evolve new revolutionary
techniques via integrated with other systems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710245 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07734
|
Soeren Hougaard Mulvad
|
Nguyen Van Hoang and Soeren Hougaard Mulvad and Dexter Neo Yuan Rong
and Yang Yue
|
Zero-Shot Learning in Named-Entity Recognition with External Knowledge
|
4 main pages, 5 including broader impact and references. 4 figures. 2
equations. 2 tables. For code, see https://github.com/shmulvad/zero-for-ner
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
A significant shortcoming of current state-of-the-art (SOTA) named-entity
recognition (NER) systems is their lack of generalization to unseen domains,
which poses a major problem since obtaining labeled data for NER in a new
domain is expensive and time-consuming. We propose ZERO, a model that performs
zero-shot and few-shot learning in NER to generalize to unseen domains by
incorporating pre-existing knowledge in the form of semantic word embeddings.
ZERO first obtains contextualized word representations of input sentences using
the model LUKE, reduces their dimensionality, and compares them directly with
the embeddings of the external knowledge, allowing ZERO to be trained to
recognize unseen output entities. We find that ZERO performs well on unseen NER
domains with an average macro F1 score of 0.23, outperforms LUKE in few-shot
learning, and even achieves competitive scores on an in-domain comparison. The
performance across source-target domain pairs is shown to be inversely
correlated with the pairs' KL divergence.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710409 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07736
|
Oleksiy Ostapenko
|
Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, Laurent Charlin
|
Continual Learning via Local Module Composition
| null |
NeurIPS 2021
| null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modularity is a compelling solution to continual learning (CL), the problem
of modeling sequences of related tasks. Learning and then composing modules to
solve different tasks provides an abstraction to address the principal
challenges of CL including catastrophic forgetting, backward and forward
transfer across tasks, and sub-linear model growth. We introduce local module
composition (LMC), an approach to modular CL where each module is provided a
local structural component that estimates a module's relevance to the input.
Dynamic module composition is performed layer-wise based on local relevance
scores. We demonstrate that agnosticity to task identities (IDs) arises from
(local) structural learning that is module-specific as opposed to the task-
and/or model-specific as in previous works, making LMC applicable to more CL
settings compared to previous works. In addition, LMC also tracks statistics
about the input distribution and adds new modules when outlier samples are
detected. In the first set of experiments, LMC performs favorably compared to
existing methods on the recent Continual Transfer-learning Benchmark without
requiring task identities. In another study, we show that the locality of
structural learning allows LMC to interpolate to related but unseen tasks
(OOD), as well as to compose modular networks trained independently on
different task sequences into a third modular network without any fine-tuning.
Finally, in search for limitations of LMC we study it on more challenging
sequences of 30 and 100 tasks, demonstrating that local module selection
becomes much more challenging in presence of a large number of candidate
modules. In this setting best performing LMC spawns much fewer modules compared
to an oracle based baseline, however, it reaches a lower overall accuracy. The
codebase is available under https://github.com/oleksost/LMC.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709598 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07739
|
Kui Liu
|
Shangwen Wang, Kui Liu, Bo Lin, Li Li, Jacques Klein, Xiaoguang Mao,
Tegawend\'e F. Bissyand\'e
|
Beep: Fine-grained Fix Localization by Learning to Predict Buggy Code
Elements
| null | null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Software Fault Localization refers to the activity of finding code elements
(e.g., statements) that are related to a software failure. The state-of-the-art
fault localization techniques, however, produce coarse-grained results that can
deter manual debugging or mislead automated repair tools. In this work, we
focus specifically on the fine-grained identification of code elements (i.e.,
tokens) that must be changed to fix a buggy program: we refer to it as fix
localization. This paper introduces a neural network architecture (named Beep)
that builds on AST paths to predict the buggy code element as well as the
change action that must be applied to repair a program. Leveraging massive data
of bugs and patches within the CoCoNut dataset, we trained a model that was (1)
effective in localizing the buggy tokens with the Mean First Rank significantly
higher than a statistics based baseline and a machine learning-based baseline,
and (2) effective in predicting the repair operators (with the associated buggy
code elements) with a Recall@1= 30-45% and the Mean First Rank=7-12 (evaluated
by CoCoNut, ManySStuBs4J, and Defects4J datasets). To showcase how fine-grained
fix localization can help program repair, we employ it in two repair pipelines
where we use either a code completion engine to predict the correct token or a
set of heuristics to search for the suitable donor code. A key strength of
accurate fix localization for program repair is that it reduces the chance of
patch overfitting, a challenge in generate-and-validate automated program
repair: both two repair pipelines achieve a correctness ratio of 100%, i.e.,
all generated patches are found to be correct. Moreover, accurate fix
localization helps enhance the efficiency of program repair.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711619 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07746
|
Abhinav Lahariya
|
Abhinav Lahariya, Varsha Singh, Uma Shanker Tiwary
|
Real-time Emotion and Gender Classification using Ensemble CNN
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Analysing expressions on the person's face plays a very vital role in
identifying emotions and behavior of a person. Recognizing these expressions
automatically results in a crucial component of natural human-machine
interfaces. Therefore research in this field has a wide range of applications
in bio-metric authentication, surveillance systems , emotion to emoticons in
various social media platforms. Another application includes conducting
customer satisfaction surveys. As we know that the large corporations made huge
investments to get feedback and do surveys but fail to get equitable responses.
Emotion & Gender recognition through facial gestures is a technology that aims
to improve product and services performance by monitoring customer behavior to
specific products or service staff by their evaluation. In the past few years
there have been a wide variety of advances performed in terms of feature
extraction mechanisms , detection of face and also expression classification
techniques. This paper is the implementation of an Ensemble CNN for building a
real-time system that can detect emotion and gender of the person. The
experimental results shows accuracy of 68% for Emotion classification into 7
classes (angry, fear , sad , happy , surprise , neutral , disgust) on FER-2013
dataset and 95% for Gender classification (Male or Female) on IMDB dataset. Our
work can predict emotion and gender on single face images as well as multiple
face images. Also when input is given through webcam our complete pipeline of
this real-time system can take less than 0.5 seconds to generate results.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709189 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07751
|
Matthieu Davy
|
Matthieu Davy, Cl\'ement Ferise, Elie Ch\'eron, Simon F\'elix, and
Vincent Pagneux
|
Experimental evidence of enhanced broadband transmission in disordered
systems with mirror symmetry
| null |
Appl. Phys. Lett. 119, 141104 (2021)
|
10.1063/5.0062678
| null |
cond-mat.dis-nn physics.optics
|
http://creativecommons.org/licenses/by/4.0/
|
We demonstrate in microwave measurements the broadband enhancement of
transmission through an opaque barrier due to mirror symmetry. This enhancement
relies on constructive interference between mirror scattering paths resulting
from strong internal reflections at the left and right interfaces of a
multichannel cavity. We observe a strong sensitivity of the conductance to a
shift of the barrier from the center of the cavity. Remarkably, the impact of
mirror symmetry can be further increased by tuning the degree of disorder
within the cavity. We report an additional enhancement of the conductance found
by symmetrically placing randomly located scatterers. Our results illuminate
the impact of symmetry and disorder correlation on transmission through complex
systems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709617 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07752
|
Xinghua Patrick Cheng
|
Xinghua Cheng, Di Hu, Handong He, Guonian Lv, A-Xing Zhu
|
Parsing Data Formats of the Inputs and Outputs of Geographic Models with
Code Analysis
|
22 pages, 7 figures
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Model web services provide an approach for implementing and facilitating the
sharing of geographic models. The description and acquisition of inputs and
outputs (IO) of geographic models is a key issue in constructing and using
model web services. These approaches for describing and acquiring the data
formats of the IO of geographic models can be classified into two categories,
i.e., intermediate-data-format-based and native-data-format-based. Nonetheless,
these two categories mainly consider the description of the IO of geographical
models but relatively pay little attention to the acquisition. To address this
issue, this paper proposes an approach for automatically parsing data formats
of the IO utilizing the relationship between the IO and source codes. This
proposed approach can utilize such a strict and coupling relationship and the
expression form of the data formats in the source codes to retrospectively
derive the IO data format and automatically generate data format documentation.
The feasibility of the proposed approach has been verified via a geographical
model coded in the FORTRAN language, which shows that it significantly improves
the efficiency of writing data format specifications and promotes sharing
geographic models as model web services.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07753
|
Saif Sidhik
|
Saif Sidhik, Mohan Sridharan, Dirk Ruiken
|
An Adaptive Framework for Reliable Trajectory Following in
Changing-Contact Robot Manipulation Tasks
|
21 pages including references
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We describe a framework for changing-contact robot manipulation tasks that
require the robot to make and break contacts with objects and surfaces. The
discontinuous interaction dynamics of such tasks make it difficult to construct
and use a single dynamics model or control strategy, and the highly non-linear
nature of the dynamics during contact changes can be damaging to the robot and
the objects. We present an adaptive control framework that enables the robot to
incrementally learn to predict contact changes in a changing contact task,
learn the interaction dynamics of the piece-wise continuous system, and provide
smooth and accurate trajectory tracking using a task-space variable impedance
controller. We experimentally compare the performance of our framework against
that of representative control methods to establish that the adaptive control
and incremental learning components of our framework are needed to achieve
smooth control in the presence of discontinuous dynamics in changing-contact
robot manipulation tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711437 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07756
|
Sebastian Ritter
|
Sebastian Ritter
|
Detection Limits of NaI Scintillator Detector Based Aerial Source
Detection Systems
|
This paper has not been published in any conferences or publications
| null | null | null |
physics.ins-det nucl-ex
|
http://creativecommons.org/licenses/by/4.0/
|
Aerial source detection systems have the capability to rapidly provide
radiological data over a large area of land. Sodium Iodine (NaI) scintillator
based aerial radiation detection systems of compact physical sizes have the
potential aid nuclear security applications in a cost-effective manner when
deployed on aerial vehicle systems. The Minimum Detectable Activity (MDA) of
NaI scintillator airborne detectors is qualitatively evaluated as a function of
detector-source distance and as a function of detector-source relative speed.
It is found that the MDA increases exponentially with vehicle height and that
MDA increases directly proportionally with the relative speed plus the square
root of the relative speed. Furthermore, detection limits of an aerial
detection system are evaluated in a case study. MDA is evaluated for the
nuclear materials U-238 and Pu-239 as defined by the IAEA Safeguards Glossary
Table II and MDA is evaluated for arbitrarily selected isotopes found in Table
I.2 of the IAEA document TECDOC-1344.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.706621 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07758
|
Yuhan Fang
|
Yuhan Fang, Yuqiao Liu and Yanan Sun
|
Evolving Deep Neural Networks for Collaborative Filtering
|
8 pages, 2 figures, accepted by ICONIP2021
| null | null | null |
cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Collaborative Filtering (CF) is widely used in recommender systems to model
user-item interactions. With the great success of Deep Neural Networks (DNNs)
in various fields, advanced works recently have proposed several DNN-based
models for CF, which have been proven effective. However, the neural networks
are all designed manually. As a consequence, it requires the designers to
develop expertise in both CF and DNNs, which limits the application of deep
learning methods in CF and the accuracy of recommended results. In this paper,
we introduce the genetic algorithm into the process of designing DNNs. By means
of genetic operations like crossover, mutation, and environmental selection
strategy, the architectures and the connection weights initialization of the
DNNs can be designed automatically. We conduct extensive experiments on two
benchmark datasets. The results demonstrate the proposed algorithm outperforms
several manually designed state-of-the-art neural networks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712076 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07765
|
Jobst Landgrebe
|
Jobst Landgrebe, Barry Smith
|
An argument for the impossibility of machine intelligence
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Since the noun phrase `artificial intelligence' (AI) was coined, it has been
debated whether humans are able to create intelligence using technology. We
shed new light on this question from the point of view of themodynamics and
mathematics. First, we define what it is to be an agent (device) that could be
the bearer of AI. Then we show that the mainstream definitions of
`intelligence' proposed by Hutter and others and still accepted by the AI
community are too weak even to capture what is involved when we ascribe
intelligence to an insect. We then summarise the highly useful definition of
basic (arthropod) intelligence proposed by Rodney Brooks, and we identify the
properties that an AI agent would need to possess in order to be the bearer of
intelligence by this definition. Finally, we show that, from the perspective of
the disciplines needed to create such an agent, namely mathematics and physics,
these properties are realisable by neither implicit nor explicit mathematical
design nor by setting up an environment in which an AI could evolve
spontaneously.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71202 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07774
|
Christian Schmidt
|
Christian Schmidt, Ali Athar, Sabarinath Mahadevan, Bastian Leibe
|
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in
Videos
|
Accepted to WACV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite receiving significant attention from the research community, the task
of segmenting and tracking objects in monocular videos still has much room for
improvement. Existing works have simultaneously justified the efficacy of
dilated and deformable convolutions for various image-level segmentation tasks.
This gives reason to believe that 3D extensions of such convolutions should
also yield performance improvements for video-level segmentation tasks.
However, this aspect has not yet been explored thoroughly in existing
literature. In this paper, we propose Dynamic Dilated Convolutions (D^2Conv3D):
a novel type of convolution which draws inspiration from dilated and deformable
convolutions and extends them to the 3D (spatio-temporal) domain. We
experimentally show that D^2Conv3D can be used to improve the performance of
multiple 3D CNN architectures across multiple video segmentation related
benchmarks by simply employing D^2Conv3D as a drop-in replacement for standard
convolutions. We further show that D^2Conv3D out-performs trivial extensions of
existing dilated and deformable convolutions to 3D. Lastly, we set a new
state-of-the-art on the DAVIS 2016 Unsupervised Video Object Segmentation
benchmark. Code is made publicly available at
https://github.com/Schmiddo/d2conv3d .
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709856 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07775
|
Manan Tomar Mr.
|
Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor
|
Learning Representations for Pixel-based Control: What Matters and Why?
| null | null | null | null |
cs.LG cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Learning representations for pixel-based control has garnered significant
attention recently in reinforcement learning. A wide range of methods have been
proposed to enable efficient learning, leading to sample complexities similar
to those in the full state setting. However, moving beyond carefully curated
pixel data sets (centered crop, appropriate lighting, clear background, etc.)
remains challenging. In this paper, we adopt a more difficult setting,
incorporating background distractors, as a first step towards addressing this
challenge. We present a simple baseline approach that can learn meaningful
representations with no metric-based learning, no data augmentations, no
world-model learning, and no contrastive learning. We then analyze when and why
previously proposed methods are likely to fail or reduce to the same
performance as the baseline in this harder setting and why we should think
carefully about extending such methods beyond the well curated environments.
Our results show that finer categorization of benchmarks on the basis of
characteristics like density of reward, planning horizon of the problem,
presence of task-irrelevant components, etc., is crucial in evaluating
algorithms. Based on these observations, we propose different metrics to
consider when evaluating an algorithm on benchmark tasks. We hope such a
data-centric view can motivate researchers to rethink representation learning
when investigating how to best apply RL to real-world tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710434 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07779
|
Sekou Remy
|
Sekou L Remy, Aisha Walcott-Bryant, Nelson K Bore, Charles M Wachira,
Julian Kuenhert
|
Overcoming Digital Gravity when using AI in Public Health Decisions
|
Presented at AAAI FSS-21: Artificial Intelligence in Government and
Public Sector, Washington, DC, USA
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In popular usage, Data Gravity refers to the ability of a body of data to
attract applications, services and other data. In this work we introduce a
broader concept, "Digital Gravity" which includes not just data, but other
elements of the AI/ML workflow. This concept is born out of our recent
experiences in developing and deploying an AI-based decision support platform
intended for use in a public health context. In addition to data, examples of
additional considerations are compute (infrastructure and software), DevSecOps
(personnel and practices), algorithms/programs, control planes, middleware
(considered separately from programs), and even companies/service providers. We
discuss the impact of Digital Gravity on the pathway to adoption and suggest
preliminary approaches to conceptualize and mitigate the friction caused by it.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71145 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07783
|
Lewei Yao
|
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu,
Xiaodan Liang, Zhenguo Li, Xin Jiang, Chunjing Xu
|
FILIP: Fine-grained Interactive Language-Image Pre-Training
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised large-scale vision-language pre-training has shown promising
advances on various downstream tasks. Existing methods often model the
cross-modal interaction either via the similarity of the global feature of each
modality which misses sufficient information, or finer-grained interactions
using cross/self-attention upon visual and textual tokens. However,
cross/self-attention suffers from inferior efficiency in both training and
inference. In this paper, we introduce a large-scale Fine-grained Interactive
Language-Image Pre-training (FILIP) to achieve finer-level alignment through a
cross-modal late interaction mechanism, which uses a token-wise maximum
similarity between visual and textual tokens to guide the contrastive
objective. FILIP successfully leverages the finer-grained expressiveness
between image patches and textual words by modifying only contrastive loss,
while simultaneously gaining the ability to pre-compute image and text
representations offline at inference, keeping both large-scale training and
inference efficient. Furthermore, we construct a new large-scale image-text
pair dataset called FILIP300M for pre-training. Experiments show that FILIP
achieves state-of-the-art performance on multiple downstream vision-language
tasks including zero-shot image classification and image-text retrieval. The
visualization on word-patch alignment further shows that FILIP can learn
meaningful fine-grained features with promising localization ability.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.708395 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07785
|
Dongcheng Zhao
|
Dongcheng Zhao, Yang Li, Yi Zeng, Jihang Wang, Qian Zhang
|
Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible
Routing Rule Between Capsules
| null | null | null | null |
cs.NE cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Spiking neural network (SNN) has attracted much attention due to their
powerful spatio-temporal information representation ability. Capsule Neural
Network (CapsNet) does well in assembling and coupling features at different
levels. Here, we propose Spiking CapsNet by introducing the capsules into the
modelling of spiking neural networks. In addition, we propose a more
biologically plausible Spike Timing Dependent Plasticity routing mechanism. By
fully considering the spatio-temporal relationship between the low-level
spiking capsules and the high-level spiking capsules, the coupling ability
between them is further improved. We have verified experiments on the MNIST and
FashionMNIST datasets. Compared with other excellent SNN models, our algorithm
still achieves high performance. Our Spiking CapsNet fully combines the
strengthens of SNN and CapsNet, and shows strong robustness to noise and affine
transformation. By adding different Salt-Pepper and Gaussian noise to the test
dataset, the experimental results demonstrate that our Spiking CapsNet shows a
more robust performance when there is more noise, while the artificial neural
network can not correctly clarify. As well, our Spiking CapsNet shows strong
generalization to affine transformation on the AffNIST dataset.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710415 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07790
|
Ron Kupfer
|
Moshe Babaioff, Shahar Dobzinski, Ron Kupfer
|
A Note on the Gains from Trade of the Random-Offerer Mechanism
| null | null | null | null |
cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
We study the classic bilateral trade setting. Myerson and Satterthwaite show
that there is no Bayesian incentive compatible and budget-balanced mechanism
that obtains the gains from trade of the first-best mechanism. Consider the
random-offerer mechanism: with probability $\frac{1}{2}$ run the
\emph{seller-offering} mechanism, in which the seller offers the buyer a
take-it-or-leave-it price that maximizes the expected profit of the seller, and
with probability $\frac{1}{2}$ run the \emph{buyer-offering} mechanism. Very
recently, Deng, Mao, Sivan, and Wang showed that the gains from trade of the
random-offerer mechanism is at least a constant factor of $\frac 1
{8.23}\approx 0.121$ of the gains from trade of the first best mechanism.
Perhaps a natural conjecture is that the gains-from-trade of the random-offerer
mechanism, which is known to be at least half of the gains-from-trade of the
second-best mechanism, is also at least half of the gains-from-trade of the
first-best mechanism. However, in this note we exhibit distributions such as
the gains-from trade of the random-offerer mechanism is smaller than a
$0.495$-fraction of the gains-from-trade of the first-best mechanism.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712782 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07800
|
Isabelle Lemonon
|
Isabelle L\'emonon-Waxin (CERMES3, EHESS, CAK-CRHST)
|
From the Dining Room to the Coll{\`e}ge royal: The Scholarly Spaces of
the Female Collaborators in Astronomy of J{\'e}r{\^o}me Lalande
|
in French
|
Cahiers Fran{\c c}ois Vi{\`e}te, Centre Fran{\c c}ois Vi{\`e}te,
Universit{\'e} de Nantes, 2021, Une histoire genr{\'e}e des savoirs est-elle
possible ?, III (11)
| null | null |
physics.hist-ph astro-ph.IM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
J{\'e}r{\^o}me Lalande, a famous French astronomer in the 18th century,
collaborated throughout his career with several female calculators in
astronomy: Nicole Reine Lepaute, Marie Louise Dupi{\'e}ry and Marie Jeanne
Lefran{\c c}ois. Taking on highly technical tasks of calculation and sometimes
observation, they also took on the scientific ''intendance'' for the
astronomer. This management of a part of the scholarly enterprise was mainly
carried out from home, as were the astronomical calculations. This space was
therefore both a family living space and a space for the production of
knowledge. This article will focus on its material organization as well as on
the dynamics that took place between the different places of knowledge involved
here.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712282 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07801
|
Hygor Piaget Melo Dr.
|
Gabriel L. Maia, Caio Ponte, Carlos Caminha, Lara Furtado, Hygor P. M.
Melo, Vasco Furtado
|
The ubiquitous efficiency of going further: how street networks affect
travel speed
|
10 pages, 7 figures
| null | null | null |
physics.soc-ph cs.CY cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
As cities struggle to adapt to more ``people-centered'' urbanism,
transportation planning and engineering must innovate to expand the street
network strategically in order to ensure efficiency but also to deter sprawl.
Here, we conducted a study of over 200 cities around the world to understand
the impact that the patterns of deceleration points in streets due to traffic
signs has in trajectories done from motorized vehicles. We demonstrate that
there is a ubiquitous nonlinear relationship between time and distance in the
optimal trajectories within each city. More precisely, given a specific period
of time $\tau$, without any traffic, one can move on average up to the distance
$\left \langle D \right \rangle \sim\tau^\beta$. We found a super-linear
relationship for almost all cities in which $\beta>1.0$. This points to an
efficiency of scale when traveling large distances, meaning the average speed
will be higher for longer trips when compared to shorter trips. We demonstrate
that this efficiency is a consequence of the spatial distribution of large
segments of streets without deceleration points, favoring access to routes in
which a vehicle can cross large distances without stops. These findings show
that cities must consider how their street morphology can affect travel speed.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.705214 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07812
|
Satyam Singh
|
Minati De, Sambhav Khurana and Satyam Singh
|
Online Dominating Set and Independent Set
|
26 pages, 17 figures
| null | null | null |
cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
Finding minimum dominating set and maximum independent set for graphs in the
classical online setup are notorious due to their disastrous $\Omega(n)$ lower
bound of the competitive ratio that even holds for interval graphs, where $n$
is the number of vertices. In this paper, inspired by Newton number, first, we
introduce the independent kissing number $\zeta$ of a graph. We prove that the
well known online greedy algorithm for dominating set achieves optimal
competitive ratio $\zeta$ for any graph. We show that the same greedy algorithm
achieves optimal competitive ratio $\zeta$ for online maximum independent set
of a class of graphs with independent kissing number $\zeta$. For minimum
connected dominating set problem, we prove that online greedy algorithm
achieves an asymptotic competitive ratio of $2(\zeta-1)$, whereas for a family
of translated convex objects the lower bound is $\frac{2\zeta-1}{3}$. Finally,
we study the value of $\zeta$ for some specific families of geometric objects:
fixed and arbitrary oriented unit hyper-cubes in $I\!\!R^d$, congruent balls in
$I\!\!R^3$, fixed oriented unit triangles, fixed and arbitrary oriented regular
polygons in $I\!\!R^2$. For each of these families, we also present lower
bounds of the minimum connected dominating set problem.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710823 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07814
|
Marouan Mizmizi Dr
|
Silvia Mura, Francesco Linsalata, Marouan Mizmizi, Maurizio Magarini,
Majid Nasiri Khormuji, Peng Wang, Alberto Perotti, Umberto Spagnolini
|
Spatial-Interference Aware Cooperative Resource Allocation for 5G NR
Sidelink Communications
| null | null | null | null |
eess.SP cs.NI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Distributed resource allocation (RA) schemes have been introduced in cellular
vehicle-to-everything (C-V2X) standard for vehicle-to-vehicle (V2V) sidelink
(SL) communications to share the limited spectrum (sub-6GHz) efficiently.
However, the recent progress in connected and automated vehicles and mobility
services requires a huge amount of available spectrum resources. Therefore,
millimeter-wave and sub-THz frequencies are being considered as they offer a
large free bandwidth. However, they require beamforming techniques to
compensate for the higher path loss attenuation. The current fifth-generation
(5G) RA standard for SL communication is inherited from the previous C-V2X
standard, which is not suited for beam-based communication since it does not
explore the spatial dimension. In this context, we propose a novel RA scheme
that addresses the directional component by adding this third spatial dimension
to the bandwidth part structure and promotes cooperation between vehicles in
resource selection, namely cooperative three-dimensional RA. Numerical results
show an average of 10% improvement in packet delivery ratio, an average 50%
decrease in collision probability, and a 30% better channel busy ratio compared
to the current standard, thus, confirming the validity of the proposed method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711462 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07815
|
Yifei Yuan
|
Yifei Yuan and Wai Lam
|
Sentiment Analysis of Fashion Related Posts in Social Media
|
WSDM 2022
| null |
10.1145/3488560.3498423
| null |
cs.CL cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The role of social media in fashion industry has been blooming as the years
have continued on. In this work, we investigate sentiment analysis for fashion
related posts in social media platforms. There are two main challenges of this
task. On the first place, information of different modalities must be jointly
considered to make the final predictions. On the second place, some unique
fashion related attributes should be taken into account. While most existing
works focus on traditional multimodal sentiment analysis, they always fail to
exploit the fashion related attributes in this task. We propose a novel
framework that jointly leverages the image vision, post text, as well as
fashion attribute modality to determine the sentiment category. One
characteristic of our model is that it extracts fashion attributes and
integrates them with the image vision information for effective representation.
Furthermore, it exploits the mutual relationship between the fashion attributes
and the post texts via a mutual attention mechanism. Since there is no existing
dataset suitable for this task, we prepare a large-scale sentiment analysis
dataset of over 12k fashion related social media posts. Extensive experiments
are conducted to demonstrate the effectiveness of our model.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.714211 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07824
|
Pierre-Olivier Chapuis
|
Christophe Lucchesi (CETHIL), Rodolphe Vaillon (IES, M@CSEE),
Pierre-Olivier Chapuis (CETHIL)
|
Temperature dependence of near-field radiative heat transfer above room
temperature
| null |
Materials Today Physics, Elsevier, In press, pp.100562
|
10.1016/j.mtphys.2021.100562
| null |
physics.class-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Stefan-Boltzmann's law indicates that far-field blackbody radiation scales at
the fourth power of temperature. The temperature dependence of radiative heat
transfer in the near field is expected to be very different due to the
contribution of evanescent waves. In this work, we experimentally observe such
deviation on the radiative thermal conductance by bringing a hot micrometric
sphere in the nearfield of a room-temperature planar substrate, down to a
separation distance of few tens of nanometers. The influence of materials is
assessed by using either SiO2 or graphite spheres, and SiO2, graphite and InSb
substrates. Temperature differences as large as 900 K are imposed. A maximum
near-field radiative thermal conductance of about 70 nW.K-1 is found for a
graphite-graphite configuration. The experimental results demonstrate that the
temperature exponent weakens in the near field, ranging from 2.2 to 4.1,
depending on the gap distance and the materials. These results have broad
consequences, in particular on the design of high-temperature nanoscale
radiative energy devices.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712639 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07825
|
Pierre-Olivier Chapuis
|
Elo\"ise Guen (CETHIL), Pierre-Olivier Chapuis (CETHIL), Nupinder Jeet
Kaur, Petr Klapetek, S\'everine Gom\`es (CETHIL)
|
Impact of roughness on heat conduction involving nanocontacts
| null |
Applied Physics Letters, American Institute of Physics, 2021, 119
(16), pp.161602
|
10.1063/5.0064244
| null |
physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The impact of surface roughness on conductive heat transfer across nanoscale
contacts is investigated by means of scanning thermal microscopy. Silicon
surfaces with out-of-plane rms roughness of ~0, 0.5, 4, 7 and 11 nm are scanned
both under air and vacuum conditions. Three types of resistive SThM probes
spanning curvature radii over orders of magnitude are used. A correlation
between thermal conductance and adhesion force is highlighted. In comparison
with a flat surface, the contact thermal conductance can decrease as much as
90% for a microprobe and by about 50% for probes with curvature radius lower
than 50 nm. The effects of multi-contact and ballistic heat conduction are
discussed. Limits of contact techniques for thermal conductivity
characterization are also discussed.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07831
|
Abhishek Agarwal
|
Abhishek Agarwal, Michael Hughes, Jordi Mur-Petit
|
Phase diagram and post-quench dynamics in a double spin-chain system in
transverse fields
|
10 pages, 7 figures
| null | null | null |
quant-ph cond-mat.quant-gas physics.atom-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose and explore the physics of a toy multiferroic model by coupling
two distinct dipolar XXZ models in transverse fields. We determine first the
rich ground-state phase diagram of the model using density matrix
renormalization group techniques. Then, we explore the dynamics of the system
after global and local quenches, using the time-evolving block decimation
algorithm. After a global quench, the system displays decaying coupled
oscillations of the electric and magnetic spins, in agreement with the
Eigenstate Thermalization Hypothesis (ETH) for many-body interacting quantum
systems. Notably, the spin-spin interactions lead to a sizeable quadratic shift
in the oscillation frequency as the inter-chain coupling is increased. Local
quenches lead to a light-cone-like propagation of excitations. In this case,
the inter-chain coupling drives a transfer of energy between the chains that
generates a novel fast spin-wave mode along the 'magnetic' chain at the speed
of the 'electric' spin-wave. This suggests a limited control mechanism for
faster information transfer in magnetic spin chains using electric fields that
harnesses the electric dipoles as intermediaries.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709233 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07834
|
Leda Liang
|
Brendan Juba, Leda Liang
|
Conditional Linear Regression for Heterogeneous Covariances
| null | null | null | null |
cs.LG cs.DS stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Often machine learning and statistical models will attempt to describe the
majority of the data. However, there may be situations where only a fraction of
the data can be fit well by a linear regression model. Here, we are interested
in a case where such inliers can be identified by a Disjunctive Normal Form
(DNF) formula. We give a polynomial time algorithm for the conditional linear
regression task, which identifies a DNF condition together with the linear
predictor on the corresponding portion of the data. In this work, we improve on
previous algorithms by removing a requirement that the covariances of the data
satisfying each of the terms of the condition have to all be very similar in
spectral norm to the covariance of the overall condition.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07837
|
Abdullah Abuolaim
|
Abdullah Abuolaim and Mahmoud Afifi and Michael S. Brown
|
Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Portrait mode is widely available on smartphone cameras to provide an
enhanced photographic experience. One of the primary effects applied to images
captured in portrait mode is a synthetic shallow depth of field (DoF). The
synthetic DoF (or bokeh effect) selectively blurs regions in the image to
emulate the effect of using a large lens with a wide aperture. In addition,
many applications now incorporate a new image motion attribute (NIMAT) to
emulate background motion, where the motion is correlated with estimated depth
at each pixel. In this work, we follow the trend of rendering the NIMAT effect
by introducing a modification on the blur synthesis procedure in portrait mode.
In particular, our modification enables a high-quality synthesis of multi-view
bokeh from a single image by applying rotated blurring kernels. Given the
synthesized multiple views, we can generate aesthetically realistic image
motion similar to the NIMAT effect. We validate our approach qualitatively
compared to the original NIMAT effect and other similar image motions, like
Facebook 3D image. Our image motion demonstrates a smooth image view transition
with fewer artifacts around the object boundary.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07843
|
Benjamin Shires
|
Benjamin W. B. Shires and Chris J. Pickard
|
Visualising energy landscapes through manifold learning
| null |
PRX, 041026 (2021)
|
10.1103/PhysRevX.11.041026
| null |
physics.comp-ph cond-mat.mtrl-sci
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Energy landscapes provide a conceptual framework for structure prediction,
and a detailed understanding of their topological features is necessary to
develop efficient methods for their exploration. The ability to visualise these
surfaces is essential, but the high dimensionality of the corresponding
configuration spaces makes this difficult. Here we present Stochastic
Hyperspace Embedding and Projection (SHEAP), a method for energy landscape
visualisation inspired by state-of-the-art algorithms for dimensionality
reduction through manifold learning, such as t-SNE and UMAP. The performance of
SHEAP is demonstrated through its application to the energy landscapes of
Lennard-Jones clusters, solid-state carbon, and the quaternary system C+H+N+O.
It produces meaningful and interpretable low-dimensional representations of
these landscapes, reproducing well known topological features such as funnels,
and providing fresh insight into their layouts. In particular, an intrinsic low
dimensionality in the distribution of local minima across configuration space
is revealed.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713076 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07846
|
Joakim Bruslund Haurum
|
Joakim Bruslund Haurum, Meysam Madadi, Sergio Escalera, Thomas B.
Moeslund
|
Multi-Task Classification of Sewer Pipe Defects and Properties using a
Cross-Task Graph Neural Network Decoder
|
WACV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The sewerage infrastructure is one of the most important and expensive
infrastructures in modern society. In order to efficiently manage the sewerage
infrastructure, automated sewer inspection has to be utilized. However, while
sewer defect classification has been investigated for decades, little attention
has been given to classifying sewer pipe properties such as water level, pipe
material, and pipe shape, which are needed to evaluate the level of sewer pipe
deterioration.
In this work we classify sewer pipe defects and properties concurrently and
present a novel decoder-focused multi-task classification architecture
Cross-Task Graph Neural Network (CT-GNN), which refines the disjointed per-task
predictions using cross-task information. The CT-GNN architecture extends the
traditional disjointed task-heads decoder, by utilizing a cross-task graph and
unique class node embeddings. The cross-task graph can either be determined a
priori based on the conditional probability between the task classes or
determined dynamically using self-attention. CT-GNN can be added to any
backbone and trained end-to-end at a small increase in the parameter count. We
achieve state-of-the-art performance on all four classification tasks in the
Sewer-ML dataset, improving defect classification and water level
classification by 5.3 and 8.0 percentage points, respectively. We also
outperform the single task methods as well as other multi-task classification
approaches while introducing 50 times fewer parameters than previous
model-focused approaches. The code and models are available at the project page
http://vap.aau.dk/ctgnn
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07847
|
Martin Henze
|
Rafael Uetz, Christian Hemminghaus, Louis Hackl\"ander, Philipp
Schlipper, Martin Henze
|
Reproducible and Adaptable Log Data Generation for Sound Cybersecurity
Experiments
|
To be published in Proceedings of the 2021 Annual Computer Security
Applications Conference (ACSAC '21)
| null |
10.1145/3485832.3488020
| null |
cs.CR cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Artifacts such as log data and network traffic are fundamental for
cybersecurity research, e.g., in the area of intrusion detection. Yet, most
research is based on artifacts that are not available to others or cannot be
adapted to own purposes, thus making it difficult to reproduce and build on
existing work. In this paper, we identify the challenges of artifact generation
with the goal of conducting sound experiments that are valid, controlled, and
reproducible. We argue that testbeds for artifact generation have to be
designed specifically with reproducibility and adaptability in mind. To achieve
this goal, we present SOCBED, our proof-of-concept implementation and the first
testbed with a focus on generating realistic log data for cybersecurity
experiments in a reproducible and adaptable manner. SOCBED enables researchers
to reproduce testbed instances on commodity computers, adapt them according to
own requirements, and verify their correct functionality. We evaluate SOCBED
with an exemplary, practical experiment on detecting a multi-step intrusion of
an enterprise network and show that the resulting experiment is indeed valid,
controlled, and reproducible. Both SOCBED and the log dataset underlying our
evaluation are freely available.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.698214 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07849
|
Bahador Bakhshi
|
Carlos Ruiz De Mendoza, Bahador Bakhshi, Engin Zeydan, Josep
Mangues-Bafalluy
|
Near Optimal VNF Placement in Edge-Enabled 6G Networks
|
This version of the paper is submitted to ICIN 2022
| null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Softwarization and virtualization are key concepts for emerging industries
that require ultra-low latency. This is only possible if computing resources,
traditionally centralized at the core of communication networks, are moved
closer to the user, to the network edge. However, the realization of Edge
Computing (EC) in the sixth generation (6G) of mobile networks requires
efficient resource allocation mechanisms for the placement of the Virtual
Network Functions (VNFs). Machine learning (ML) methods, and more specifically,
Reinforcement Learning (RL), are a promising approach to solve this problem.
The main contributions of this work are twofold: first, we obtain the
theoretical performance bound for VNF placement in EC-enabled6G networks by
formulating the problem mathematically as a finite Markov Decision Process
(MDP) and solving it using a dynamic programming method called Policy Iteration
(PI). Second, we develop a practical solution to the problem using RL, where
the problem is treated with Q-Learning that considers both computational and
communication resources when placing VNFs in the network. The simulation
results under different settings of the system parameters show that the
performance of the Q-Learning approach is close to the optimal PI algorithm
(without having its restrictive assumptions on service statistics). This is
particularly interesting when the EC resources are scarce and efficient
management of these resources is required.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711268 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07850
|
Nikhil Ravi
|
Nikhil Ravi, Anna Scaglione, Sean Peisert
|
Colored Noise Mechanism for Differentially Private Clustering
|
5 pages, 3 figures, preprint
| null | null | null |
cs.CR eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The goal of this paper is to propose and analyze a differentially private
randomized mechanism for the $K$-means query. The goal is to ensure that the
information received about the cluster-centroids is differentially private. The
method consists in adding Gaussian noise with an optimum covariance. The main
result of the paper is the analytical solution for the optimum covariance as a
function of the database. Comparisons with the state of the art prove the
efficacy of our approach.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07854
|
Brenda Vilas Boas
|
Brenda Vilas Boas and Wolfgang Zirwas and Martin Haardt
|
Machine Learning for CSI Recreation Based on Prior Knowledge
|
submitted for publication
| null | null | null |
eess.SP cs.IT cs.LG math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Knowledge of channel state information (CSI) is fundamental to many
functionalities within the mobile wireless communications systems. With the
advance of machine learning (ML) and digital maps, i.e., digital twins, we have
a big opportunity to learn the propagation environment and design novel methods
to derive and report CSI. In this work, we propose to combine untrained neural
networks (UNNs) and conditional generative adversarial networks (cGANs) for
MIMO channel recreation based on prior knowledge. The UNNs learn the prior-CSI
for some locations which are used to build the input to a cGAN. Based on the
prior-CSIs, their locations and the location of the desired channel, the cGAN
is trained to output the channel expected at the desired location. This
combined approach can be used for low overhead CSI reporting as, after
training, we only need to report the desired location. Our results show that
our method is successful in modelling the wireless channel and robust to
location quantization errors in line of sight conditions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7118 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07857
|
Narad Rampersad
|
James Currie and Lucas Mol and Narad Rampersad and Jeffrey Shallit
|
Extending Dekking's construction of an infinite binary word avoiding
abelian $4$-powers
|
11 pages
| null | null | null |
math.CO cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We construct an infinite binary word with critical exponent 3 that avoids
abelian 4-powers. Our method gives an algorithm to determine if certain types
of morphic sequences avoid additive powers. We also show that there are
$\Omega(1.172^n)$ binary words of length $n$ that avoid abelian 4-powers, which
improves on previous estimates.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.703995 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07858
|
Brenda Vilas Boas
|
Brenda Vilas Boas and Wolfgang Zirwas and Martin Haardt
|
Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI
Recreation
|
to be published
| null | null | null |
eess.SP cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine learning (ML) applications for wireless communications have gained
momentum on the standardization discussions for 5G advanced and beyond. One of
the biggest challenges for real world ML deployment is the need for labeled
signals and big measurement campaigns. To overcome those problems, we propose
the use of untrained neural networks (UNNs) for MIMO channel
recreation/estimation and low overhead reporting. The UNNs learn the
propagation environment by fitting a few channel measurements and we exploit
their learned prior to provide higher channel estimation gains. Moreover, we
present a UNN for simultaneous channel recreation for multiple users, or
multiple user equipment (UE) positions, in which we have a trade-off between
the estimated channel gain and the number of parameters. Our results show that
transfer learning techniques are effective in accessing the learned prior on
the environment structure as they provide higher channel gain for neighbouring
users. Moreover, we indicate how the under-parameterization of UNNs can further
enable low-overhead channel state information (CSI) reporting.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710653 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07868
|
Jathushan Rajasegaran
|
Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra
Malik
|
Tracking People with 3D Representations
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel approach for tracking multiple people in video. Unlike
past approaches which employ 2D representations, we focus on using 3D
representations of people, located in three-dimensional space. To this end, we
develop a method, Human Mesh and Appearance Recovery (HMAR) which in addition
to extracting the 3D geometry of the person as a SMPL mesh, also extracts
appearance as a texture map on the triangles of the mesh. This serves as a 3D
representation for appearance that is robust to viewpoint and pose changes.
Given a video clip, we first detect bounding boxes corresponding to people, and
for each one, we extract 3D appearance, pose, and location information using
HMAR. These embedding vectors are then sent to a transformer, which performs
spatio-temporal aggregation of the representations over the duration of the
sequence. The similarity of the resulting representations is used to solve for
associations that assigns each person to a tracklet. We evaluate our approach
on the Posetrack, MuPoTs and AVA datasets. We find that 3D representations are
more effective than 2D representations for tracking in these settings, and we
obtain state-of-the-art performance. Code and results are available at:
https://brjathu.github.io/T3DP.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07869
|
Ben Van Dusen
|
Ben Van Dusen, Jayson Nissen
|
How statistical model development can obscure inequities in STEM student
outcomes
|
33 pages, 4 figures
| null | null | null |
physics.ed-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Researchers often frame quantitative research as objective, but every step in
data collection and analysis can bias findings in often unexamined ways. In
this investigation, we examined how the process of selecting variables to
include in regression models (model specification) can bias findings about
inequities in science and math student outcomes. We identified the four most
used methods for model specification in discipline-based education research
about equity: a priori, statistical significance, variance explained, and
information criterion. Using a quantitative critical perspective that blends
statistical theory with critical theory, we reanalyzed the data from a prior
publication using each of the four methods and compared the findings from each.
We concluded that using information criterion produced models that best aligned
with our quantitative critical perspective's emphasis on intersectionality and
models with more accurate coefficients and uncertainties. Based on these
findings, we recommend researchers use information criterion for specifying
models about inequities in STEM student outcomes.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712389 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07874
|
Dawn Tan
|
Peng Xing, George F. R. Chen, Hongwei Gao, Anuradha M. Agarwal, Lionel
C. Kimerling, and Dawn T. H. Tan
|
Microresonator Frequency Comb Based High-Speed Transmission of Intensity
Modulated Direct Detection Data
| null | null | null | null |
physics.app-ph physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Globally, the long-haul transmission of ultra-high bandwidth data is enabled
through coherent communications. Driven by the rapid pace of growth in
interconnectivity over the last decade, long-haul data transmission has reached
capacities on the order of tens to hundreds of terabits per second, over fiber
reaches which may span thousands of kilometers. Data center communications
however operate in a different regime, featuring shorter reaches and
characterized as being more cost and power sensitive. While integrated
microresonator frequency combs are poised to revolutionize light sources used
for high-speed data transmission over fiber, their use has been limited to
coherent detection schemes. In this paper, we demonstrate the use of
microresonator frequency combs pumped with a single laser for the transmission
of high-speed data, importantly using direct detection schemes. We achieve 120
Gb/s and 240 Gb/s aggregate data transmission for 30 Gb/s non-return-to-zero
(NRZ) and 60 Gb/s pulse modulation amplitude 4 (PAM4) modulation formats
respectively over 2 km of optical fiber, exceeding the reach, single lane data
rate, and aggregate data rates specified in Parallel Single Mode 4 (PSM4) and
Course Wavelength Division Multiplex 4 (CWDM4) multi-source agreements.
Remarkably, we achieve an extremely low power penalty of 0.1 dB compared to
back-to-back characterization. The results firmly cement CMOS-compatible
micro-resonator frequency combs based high-speed data transmission as a viable
technology for the cost and power sensitive data center transceiver industry.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07875
|
Dominik Sobania
|
Dominik Sobania, Martin Briesch, Franz Rothlauf
|
Choose Your Programming Copilot: A Comparison of the Program Synthesis
Performance of GitHub Copilot and Genetic Programming
| null | null | null | null |
cs.SE cs.LG cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
GitHub Copilot, an extension for the Visual Studio Code development
environment powered by the large-scale language model Codex, makes automatic
program synthesis available for software developers. This model has been
extensively studied in the field of deep learning, however, a comparison to
genetic programming, which is also known for its performance in automatic
program synthesis, has not yet been carried out. In this paper, we evaluate
GitHub Copilot on standard program synthesis benchmark problems and compare the
achieved results with those from the genetic programming literature. In
addition, we discuss the performance of both approaches. We find that the
performance of the two approaches on the benchmark problems is quite similar,
however, in comparison to GitHub Copilot, the program synthesis approaches
based on genetic programming are not yet mature enough to support programmers
in practical software development. Genetic programming usually needs a huge
amount of expensive hand-labeled training cases and takes too much time to
generate solutions. Furthermore, source code generated by genetic programming
approaches is often bloated and difficult to understand. For future work on
program synthesis with genetic programming, we suggest researchers to focus on
improving the execution time, readability, and usability.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07876
|
Mugurel-Ionut Andreica
|
Mugurel-Ionut Andreica
|
Winning Solution of the AIcrowd SBB Flatland Challenge 2019-2020
|
Presented at the Flatland Challenge workshop at AMLD 2020
(https://appliedmldays.org/events/amld-epfl-2020/challenges/flatland-challenge)
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
This report describes the main ideas of the solution which won the AIcrowd
SBB Flatland Challenge 2019-2020, with a score of 99% (meaning that, on
average, 99% of the agents were routed to their destinations within the
allotted time steps). The details of the task can be found on the competition's
website. The solution consists of 2 major components: 1) A component which
(re-)generates paths over a time-expanded graph for each agent 2) A component
which updates the agent paths after a malfunction occurs, in order to try to
preserve the same agent ordering of entering each cell as before the
malfunction. The goal of this component is twofold: a) to (try to) avoid
deadlocks b) to bring the system back to a consistent state (where each agent
has a feasible path over the time-expanded graph). I am discussing both of
these components, as well as a series of potentially promising, but unexplored
ideas, below.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712614 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07877
|
Mariusz Semczuk
|
Jakub Dobosz, Mateusz Boche\'nski and Mariusz Semczuk
|
Bidirectional, Analog Current Source Benchmarked with Gray
Molasses-Assisted Stray Magnetic Field Compensation
|
12 pages, 3 figures, supplementary materials
|
Appl. Sci. 2021, 11(21), 10474
|
10.3390/app112110474
| null |
physics.atom-ph cond-mat.quant-gas
|
http://creativecommons.org/licenses/by/4.0/
|
In ultracold-atom and ion experiments, flexible control of the direction and
amplitude of a uniform magnetic field is necessary. It is achieved almost
exclusively by controlling the current flowing through coils surrounding the
experimental chamber. Here, we present the design and characterization of a
modular, analog electronic circuit that enables three-dimensional control of a
magnetic field via the amplitude and direction of a current flowing through
three perpendicular pairs of coils. Each pair is controlled by one module, and
we are able to continuously change the current flowing thorough the coils in
the $\pm$4 A range using analog waveforms such that smooth crossing through
zero as the current's direction changes is possible. With the electrical
current stability at the 10$^{-5}$ level, the designed circuit enables
state-of-the-art ultracold experiments. As a benchmark, we use the circuit to
compensate stray magnetic fields that hinder efficient sub-Doppler cooling of
alkali atoms in gray molasses. We demonstrate how such compensation can be
achieved without actually measuring the stray fields present, thus speeding up
the process of optimization of various laser cooling stages.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709585 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07880
|
Nuno Paulino
|
H\'elder Campos and Nuno Paulino and Jo\~ao F. Loureiro
|
Design of a SOIMUMPs Inertial Sensor and readout Charge Amplifier
| null | null | null | null |
physics.ins-det eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents the design and post-layout characteristics of a
differential capacitance based inertial accelerometer This includes a MEMS
based mechanical sensing element and a CMOS charge amplifier, which is the
first stage of a readout circuit. The mechanical sensor is designed according
to the SOIMUMPs fabrication process technology, and the readout circuit
targeted AMS 0.35um technology. Post layout simulations indicated a +/-5G
dynamic range, a maximum bandwidth of 1.58 kHz, non-linearity of 0.077% and a
resolution of 10.5 uG/Hz^0.5. The readout circuit charge amplifier is fully
differential and incorporated in a switched capacitor (SC) topology with CDS.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07884
|
Nitish Mital
|
Nitish Mital, Katina Kralevska, Cong Ling, Deniz Gunduz
|
Functional Broadcast Repair of Multiple Partial Failures in Wireless
Distributed Storage Systems
|
20 pages, 2 figures, 3 tables, to appear in IEEE JSAIT. arXiv admin
note: text overlap with arXiv:1807.00220
| null | null | null |
cs.DC cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider a distributed storage system with $n$ nodes, where a user can
recover the stored file from any $k$ nodes, and study the problem of repairing
$r$ partially failed nodes. We consider \textit{broadcast repair}, that is, $d$
surviving nodes transmit broadcast messages on an error-free wireless channel
to the $r$ nodes being repaired, which are then used, together with the
surviving data in the local memories of the failed nodes, to recover the lost
content. First, we derive the trade-off between the storage capacity and the
repair bandwidth for partial repair of multiple failed nodes, based on the
cut-set bound for information flow graphs. It is shown that utilizing the
broadcast nature of the wireless medium and the surviving contents at the
partially failed nodes reduces the repair bandwidth per node significantly.
Then, we list a set of invariant conditions that are sufficient for a
functional repair code to be feasible. We further propose a scheme for
functional repair of multiple failed nodes that satisfies the invariant
conditions with high probability, and its extension to the repair of partial
failures. The performance of the proposed scheme meets the cut-set bound on all
the points on the trade-off curve for all admissible parameters when $k$ is
divisible by $r$, while employing linear subpacketization, which is an
important practical consideration in the design of distributed storage codes.
Unlike random linear codes, which are conventionally used for functional repair
of failed nodes, the proposed repair scheme has lower overhead, lower
input-output cost, and lower computational complexity during repair.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708377 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07885
|
Padraig Corcoran
|
Padraig Corcoran and Andrei Gagarin
|
Heuristics for k-domination models of facility location problems in
street networks
| null |
Computers & Operations Research (2021): 105368
|
10.1016/j.cor.2021.105368
| null |
cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
We present new greedy and beam search heuristic methods to find small-size
$k$-dominating sets in graphs. The methods are inspired by a new problem
formulation which explicitly highlights a certain structure of the problem. An
empirical evaluation of the new methods is done with respect to two existing
methods, using instances of graphs corresponding to street networks. The
k-domination problem with respect to this class of graphs can be used to model
real-world facility location problem scenarios. For the classic minimum
dominating set ($1$-domination) problem, all except one methods perform
similarly, which is due to their equivalence in this particular case. However,
for the k-domination problem with k>1, the new methods outperform the benchmark
methods, and the performance gain is more significant for larger values of k.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07889
|
Jonathan Roth
|
Jonathan Roth, Guillaume Saint-Jacques, YinYin Yu
|
An Outcome Test of Discrimination for Ranked Lists
| null | null | null | null |
econ.EM cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
This paper extends Becker (1957)'s outcome test of discrimination to settings
where a (human or algorithmic) decision-maker produces a ranked list of
candidates. Ranked lists are particularly relevant in the context of online
platforms that produce search results or feeds, and also arise when human
decisionmakers express ordinal preferences over a list of candidates. We show
that non-discrimination implies a system of moment inequalities, which
intuitively impose that one cannot permute the position of a lower-ranked
candidate from one group with a higher-ranked candidate from a second group and
systematically improve the objective. Moreover, we show that that these moment
inequalities are the only testable implications of non-discrimination when the
auditor observes only outcomes and group membership by rank. We show how to
statistically test the implied inequalities, and validate our approach in an
application using data from LinkedIn.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711437 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07892
|
Boyuan Ma
|
Boyuan Ma and Xiang Yin and Xiaojuan Ban and Haiyou Huang and Neng
Zhang and Hao Wang and Weihua Xue
|
Data privacy protection in microscopic image analysis for material data
mining
|
14 pages
| null | null | null |
eess.IV cond-mat.mtrl-sci cs.CR cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Recent progress in material data mining has been driven by high-capacity
models trained on large datasets. However, collecting experimental data has
been extremely costly owing to the amount of human effort and expertise
required. Therefore, material researchers are often reluctant to easily
disclose their private data, which leads to the problem of data island, and it
is difficult to collect a large amount of data to train high-quality models. In
this study, a material microstructure image feature extraction algorithm
FedTransfer based on data privacy protection is proposed. The core
contributions are as follows: 1) the federated learning algorithm is introduced
into the polycrystalline microstructure image segmentation task to make full
use of different user data to carry out machine learning, break the data island
and improve the model generalization ability under the condition of ensuring
the privacy and security of user data; 2) A data sharing strategy based on
style transfer is proposed. By sharing style information of images that is not
urgent for user confidentiality, it can reduce the performance penalty caused
by the distribution difference of data among different users.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709982 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07900
|
S. Mazdak Abulnaga
|
S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, P. Ellen
Grant, Justin Solomon, Polina Golland
|
Volumetric Parameterization of the Placenta to a Flattened Template
|
Accepted to IEEE TMI ( (c) IEEE). This manuscript expands the MICCAI
2019 paper (arXiv:1903.05044) by developing additional template models and
extensions to improve robustness, expanded evaluation on a significantly
larger dataset, and experiments and discussion demonstrating utility for
clinical research. Code is available at
https://github.com/mabulnaga/placenta-flattening
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present a volumetric mesh-based algorithm for parameterizing the placenta
to a flattened template to enable effective visualization of local anatomy and
function. MRI shows potential as a research tool as it provides signals
directly related to placental function. However, due to the curved and highly
variable in vivo shape of the placenta, interpreting and visualizing these
images is difficult. We address interpretation challenges by mapping the
placenta so that it resembles the familiar ex vivo shape. We formulate the
parameterization as an optimization problem for mapping the placental shape
represented by a volumetric mesh to a flattened template. We employ the
symmetric Dirichlet energy to control local distortion throughout the volume.
Local injectivity in the mapping is enforced by a constrained line search
during the gradient descent optimization. We validate our method using a
research study of 111 placental shapes extracted from BOLD MRI images. Our
mapping achieves sub-voxel accuracy in matching the template while maintaining
low distortion throughout the volume. We demonstrate how the resulting
flattening of the placenta improves visualization of anatomy and function. Our
code is freely available at https://github.com/mabulnaga/placenta-flattening .
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07901
|
Paul Han
|
Paul Kyu Han, Thibault Marin, Yanis Djebra, Vanessa Landes, Yue Zhuo,
Georges El Fakhri, and Chao Ma
|
Free-breathing 3D Cardiac T1 Mapping with Transmit B1 Correction at 3T
|
33 pages, 10 figures, 3 supplementary figures, 1 supplementary table
| null |
10.1002/mrm.29097
| null |
physics.med-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Purpose: To develop a cardiac T1 mapping method for free-breathing 3D T1
mapping of the whole heart at 3T with transmit B1 (B1+) correction Methods: A
free-breathing, ECG-gated inversion recovery sequence with spoiled
gradient-echo readout was developed and optimized for cardiac T1 mapping at 3T.
High-frame rate dynamic images were reconstructed from sparse (k,t)-space data
acquired along a stack-of-stars trajectory using a subspace-based method for
accelerated imaging. Joint T1 and flip-angle (FA) estimation was performed in
T1 mapping to improve its robustness to B1+ inhomogeneity. Subject-specific
timing of data acquisition was utilized in the estimation to account for
natural heart-rate variations during the imaging experiment. Results:
Simulations showed that accuracy and precision of T1 mapping can be improved
with joint T1 and FA estimation and optimized ECG-gated SPGR-based IR
acquisition scheme. The phantom study showed good agreement between the T1 maps
from the proposed method and the reference method. 3D cardiac T1 maps (40
slices) were obtained at a 1.9 mm in-plane and 4.5 mm through-plane spatial
resolution from healthy subjects (n=6) with an average imaging time of 14.2 +-
1.6 min (heartbeat rate: 64.2 +- 7.1 bpm), showing myocardial T1 values
comparable to those obtained from MOLLI. The proposed method generated B1+ maps
with spatially smooth variation showing 21-32% and 11-15% variations across the
septal-lateral and inferior-anterior regions of the myocardium in the left
ventricle. Conclusion: The proposed method allows free-breathing 3D T1 mapping
of the whole-heart with transmit B1 correction in a practical imaging time.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711036 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07906
|
Karthik Puranik
|
Karthik Puranik, Bharathi B, Senthil Kumar B
|
IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment
analysis of code-mixed text in Dravidian languages
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Sentiment analysis of social media posts and comments for various marketing
and emotional purposes is gaining recognition. With the increasing presence of
code-mixed content in various native languages, there is a need for ardent
research to produce promising results. This research paper bestows a tiny
contribution to this research in the form of sentiment analysis of code-mixed
social media comments in the popular Dravidian languages Kannada, Tamil and
Malayalam. It describes the work for the shared task conducted by
Dravidian-CodeMix at FIRE 2021 by employing pre-trained models like ULMFiT and
multilingual BERT fine-tuned on the code-mixed dataset, transliteration (TRAI)
of the same, English translations (TRAA) of the TRAI data and the combination
of all the three. The results are recorded in this research paper where the
best models stood 4th, 5th and 10th ranks in the Tamil, Kannada and Malayalam
tasks respectively.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707588 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07908
|
Ingmar Schubert
|
Ingmar Schubert and Danny Driess and Ozgur S. Oguz and Marc Toussaint
|
Learning to Execute: Efficient Learning of Universal Plan-Conditioned
Policies in Robotics
| null |
35th Conference on Neural Information Processing Systems (NeurIPS
2021), Sydney, Australia
| null | null |
cs.AI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Applications of Reinforcement Learning (RL) in robotics are often limited by
high data demand. On the other hand, approximate models are readily available
in many robotics scenarios, making model-based approaches like planning a
data-efficient alternative. Still, the performance of these methods suffers if
the model is imprecise or wrong. In this sense, the respective strengths and
weaknesses of RL and model-based planners are. In the present work, we
investigate how both approaches can be integrated into one framework that
combines their strengths. We introduce Learning to Execute (L2E), which
leverages information contained in approximate plans to learn universal
policies that are conditioned on plans. In our robotic manipulation
experiments, L2E exhibits increased performance when compared to pure RL, pure
planning, or baseline methods combining learning and planning.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712607 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07918
|
Shijie Liu
|
Shijie Liu, Hongyu Zhou, Xiaozhou Shi, Junwen Pan
|
Transformer for Polyp Detection
| null | null | null | null |
eess.IV cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, as the Transformer has performed increasingly well on NLP
tasks, many researchers have ported the Transformer structure to vision tasks
,bridging the gap between NLP and CV tasks. In this work, we evaluate some deep
learning network for the detection track. Because the ground truth is mask, so
we can try both the current detection and segmentation method. We select the
DETR as our baseline through experiment. Besides, we modify the train strategy
to fit the dataset.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710829 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07923
|
Aditya Gopimohan Nair
|
Kunihiko Taira and Aditya G. Nair
|
Network-based analysis of fluid flows: Progress and outlook
|
35 pages, 17 figures
| null | null | null |
physics.flu-dyn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The network of interactions among fluid elements and coherent structures
gives rise to the incredibly rich dynamics of vortical flows. These
interactions can be described with the use of mathematical tools from the
emerging field of network science, which leverages graph theory, dynamical
systems theory, data science, and control theory. The blending of network
science and fluid mechanics facilitates the extraction of the key interactions
and communities in terms of vortical elements, modal structures, and particle
trajectories. Phase-space techniques and time-delay embedding enable
network-based analysis in terms of visibility, recurrence, and cluster
transitions leveraging available time-series measurements. Equipped with the
knowledge of interactions and communities, the network-theoretic approach
enables the analysis, modeling, and control of fluid flows, with a particular
emphasis on interactive dynamics. In this article, we provide a brief
introduction to network science and an overview of the progress on
network-based strategies to study the complex dynamics of fluid flows. Case
studies are surveyed to highlight the utility of network-based techniques to
tackle a range of problems from fluid mechanics. Towards the end of the paper,
we offer an outlook on network-inspired approaches.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7114 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07928
|
Marcin Mazur
|
Marcin Mazur, {\L}ukasz Pustelnik, Szymon Knop, Patryk Pagacz,
Przemys{\l}aw Spurek
|
Target Layer Regularization for Continual Learning Using Cramer-Wold
Generator
|
The paper is under consideration at Computer Vision and Image
Understanding
| null | null | null |
cs.LG cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose an effective regularization strategy (CW-TaLaR) for solving
continual learning problems. It uses a penalizing term expressed by the
Cramer-Wold distance between two probability distributions defined on a target
layer of an underlying neural network that is shared by all tasks, and the
simple architecture of the Cramer-Wold generator for modeling output data
representation. Our strategy preserves target layer distribution while learning
a new task but does not require remembering previous tasks' datasets. We
perform experiments involving several common supervised frameworks, which prove
the competitiveness of the CW-TaLaR method in comparison to a few existing
state-of-the-art continual learning models.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70844 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07930
|
Xuan Kien Phung
|
Xuan Kien Phung
|
A geometric generalization of Kaplansky's direct finiteness conjecture
| null | null | null | null |
math.AG cs.DM math.GR math.RA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Let $G$ be a group and let $k$ be a field. Kaplansky's direct finiteness
conjecture states that every one-sided unit of the group ring $k[G]$ must be a
two-sided unit. In this paper, we establish a geometric direct finiteness
theorem for endomorphisms of symbolic algebraic varieties. Whenever $G$ is a
sofic group or more generally a surjunctive group, our result implies a
generalization of Kaplansky's direct finiteness conjecture for the near ring
$R(k, G)$ which is $k[X_g\colon g \in G]$ as a group and which contains
naturally $k[G]$ as the subring of homogeneous polynomials of degree one. We
also prove that Kaplansky's stable finiteness conjecture is a consequence of
Gottschalk's Surjunctivity conjecture.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.706994 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07936
|
Andreas Abel
|
Andreas Abel
|
Birkhoff's Completeness Theorem for Multi-Sorted Algebras Formalized in
Agda
| null | null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
This document provides a formal proof of Birkhoff's completeness theorem for
multi-sorted algebras which states that any equational entailment valid in all
models is also provable in the equational theory.
More precisely, if a certain equation is valid in all models that validate a
fixed set of equations, then this equation is derivable from that set using the
proof rules for a congruence.
The proof has been formalized in Agda version 2.6.2 with the Agda Standard
Library version 1.7 and this document reproduces the commented Agda code.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707424 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07937
|
Jyoti Prakash Panda
|
J P Panda and H V Warrior
|
Data-driven prediction of complex flow field over an axisymmetric body
of revolution using Machine Learning
| null | null | null | null |
physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
Computationally efficient and accurate simulations of the flow over
axisymmetric bodies of revolution (ABR) has been an important desideratum for
engineering design. In this article the flow field over an ABR is predicted
using machine learning (ML) algorithms, using trained ML models as surrogates
for classical computational fluid dynamics (CFD) approaches. The flow field is
approximated as functions of x and y coordinates of locations in the flow field
and the velocity at the inlet of the computational domain. The data required
for the development of the ML models were obtained from high fidelity Reynolds
stress transport model (RSTM) based simulations. The optimal hyper-parameters
of the trained ML models are determined using validation. The trained ML models
can predict the flow field rapidly and exhibits orders of magnitude speed up
over conventional CFD approaches. The predicted results of pressure, velocity
and turbulence kinetic energy are compared with the baseline CFD data, it is
found that the ML based surrogate model predictions are as accurate as CFD
results. This investigation offers a framework for fast and accurate
predictions for a flow scenario that is critically important in engineering
design.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712401 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07942
|
Yaoming Cai
|
Yaoming Cai, Zijia Zhang, Zhihua Cai, Xiaobo Liu, Yao Ding, Pedram
Ghamisi
|
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning
and Clustering
|
Under review by IEEE Trans. xxx
| null | null | null |
cs.LG cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents FLGC, a simple yet effective fully linear graph
convolutional network for semi-supervised and unsupervised learning. Instead of
using gradient descent, we train FLGC based on computing a global optimal
closed-form solution with a decoupled procedure, resulting in a generalized
linear framework and making it easier to implement, train, and apply. We show
that (1) FLGC is powerful to deal with both graph-structured data and regular
data, (2) training graph convolutional models with closed-form solutions
improve computational efficiency without degrading performance, and (3) FLGC
acts as a natural generalization of classic linear models in the non-Euclidean
domain, e.g., ridge regression and subspace clustering. Furthermore, we
implement a semi-supervised FLGC and an unsupervised FLGC by introducing an
initial residual strategy, enabling FLGC to aggregate long-range neighborhoods
and alleviate over-smoothing. We compare our semi-supervised and unsupervised
FLGCs against many state-of-the-art methods on a variety of classification and
clustering benchmarks, demonstrating that the proposed FLGC models consistently
outperform previous methods in terms of accuracy, robustness, and learning
efficiency. The core code of our FLGC is released at
https://github.com/AngryCai/FLGC.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07945
|
Yaoming Cai
|
Yaoming Cai, Zijia Zhang, Yan Liu, Pedram Ghamisi, Kun Li, Xiaobo Liu,
Zhihua Cai
|
Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
|
Under review by IEEE Trans. xxx
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Clustering of hyperspectral images is a fundamental but challenging task. The
recent development of hyperspectral image clustering has evolved from shallow
models to deep and achieved promising results in many benchmark datasets.
However, their poor scalability, robustness, and generalization ability, mainly
resulting from their offline clustering scenarios, greatly limit their
application to large-scale hyperspectral data. To circumvent these problems, we
present a scalable deep online clustering model, named Spectral-Spatial
Contrastive Clustering (SSCC), based on self-supervised learning. Specifically,
we exploit a symmetric twin neural network comprised of a projection head with
a dimensionality of the cluster number to conduct dual contrastive learning
from a spectral-spatial augmentation pool. We define the objective function by
implicitly encouraging within-cluster similarity and reducing between-cluster
redundancy. The resulting approach is trained in an end-to-end fashion by
batch-wise optimization, making it robust in large-scale data and resulting in
good generalization ability for unseen data. Extensive experiments on three
hyperspectral image benchmarks demonstrate the effectiveness of our approach
and show that we advance the state-of-the-art approaches by large margins.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711813 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07950
|
Jiyang Qi
|
Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai,
Serge Belongie, Alan Yuille, Philip H.S. Torr, Song Bai
|
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge
|
Accepted by NeurIPS 2021 Datasets and Benchmarks Track. arXiv admin
note: text overlap with arXiv:2102.01558
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although deep learning methods have achieved advanced video object
recognition performance in recent years, perceiving heavily occluded objects in
a video is still a very challenging task. To promote the development of
occlusion understanding, we collect a large-scale dataset called OVIS for video
instance segmentation in the occluded scenario. OVIS consists of 296k
high-quality instance masks and 901 occluded scenes. While our human vision
systems can perceive those occluded objects by contextual reasoning and
association, our experiments suggest that current video understanding systems
cannot. On the OVIS dataset, all baseline methods encounter a significant
performance degradation of about 80% in the heavily occluded object group,
which demonstrates that there is still a long way to go in understanding
obscured objects and videos in a complex real-world scenario. To facilitate the
research on new paradigms for video understanding systems, we launched a
challenge based on the OVIS dataset. The submitted top-performing algorithms
have achieved much higher performance than our baselines. In this paper, we
will introduce the OVIS dataset and further dissect it by analyzing the results
of baselines and submitted methods. The OVIS dataset and challenge information
can be found at http://songbai.site/ovis .
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.712401 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07954
|
David Wu
|
David Wu and Yunnan Wu
|
QK Iteration: A Self-Supervised Representation Learning Algorithm for
Image Similarity
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Self-supervised representation learning is a fundamental problem in computer
vision with many useful applications (e.g., image search, instance level
recognition, copy detection). In this paper we present a new contrastive
self-supervised representation learning algorithm in the context of Copy
Detection in the 2021 Image Similarity Challenge hosted by Facebook AI
Research. Previous work in contrastive self-supervised learning has identified
the importance of being able to optimize representations while ``pushing''
against a large number of negative examples. Representative previous solutions
either use large batches enabled by modern distributed training systems or
maintain queues or memory banks holding recently evaluated representations
while relaxing some consistency properties. We approach this problem from a new
angle: We directly learn a query model and a key model jointly and push
representations against a very large number (e.g., 1 million) of negative
representations in each SGD step. We achieve this by freezing the backbone on
one side and by alternating between a Q-optimization step and a K-optimization
step. During the competition timeframe, our algorithms achieved a micro-AP
score of 0.3401 on the Phase 1 leaderboard, significantly improving over the
baseline $\mu$AP of 0.1556. On the final Phase 2 leaderboard, our model scored
0.1919, while the baseline scored 0.0526. Continued training yielded further
improvement. We conducted an empirical study to compare the proposed approach
with a SimCLR style strategy where the negative examples are taken from the
batch only. We found that our method ($\mu$AP of 0.3403) significantly
outperforms this SimCLR-style baseline ($\mu$AP of 0.2001).
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07959
|
Jonathan Nguyen
|
Jonathan Nguyen, Linfang Wang, Chester Hulse, Sahil Dani, Amaael
Antonini, Todd Chauvin, Divsalar Dariush and Richard Wesel
|
Neural Normalized Min-Sum Message-Passing vs. Viterbi Decoding for the
CCSDS Line Product Code
|
This paper has been submitted to ICC 2022
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The Consultative Committee for Space Data Systems (CCSDS) 141.11-O-1 Line
Product Code (LPC) provides a rare opportunity to compare maximum-likelihood
decoding and message passing. The LPC considered in this paper is intended to
serve as the inner code in conjunction with a (255,239) Reed Solomon (RS) code
whose symbols are bytes of data. This paper represents the 141.11-O-1 LPC as a
bipartite graph and uses that graph to formulate both maximum likelihood (ML)
and message passing algorithms. ML decoding must, of course, have the best
frame error rate (FER) performance. However, a fixed point implementation of a
Neural-Normalized MinSum (N-NMS) message passing decoder closely approaches ML
performance with a significantly lower complexity.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07960
|
Romana Boiger
|
Romana Boiger, Rob L. Modini, Alireza Moallemi, David Degen, Martin
Gysel-Beer, Andreas Adelmann
|
Retrieval of aerosol properties from in situ, multi-angle light
scattering measurements using invertible neural networks
| null | null | null | null |
physics.ao-ph physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Atmospheric aerosols have a major influence on the earths climate and public
health. Hence, studying their properties and recovering them from light
scattering measurements is of great importance. State of the art retrieval
methods such as pre-computed look-up tables and iterative, physics-based
algorithms can suffer from either accuracy or speed limitations. These
limitations are becoming increasingly restrictive as instrumentation technology
advances and measurement complexity increases. Machine learning algorithms
offer new opportunities to overcome these problems, by being quick and precise.
In this work we present a method, using invertible neural networks to retrieve
aerosol properties from in situ light scattering measurements. In addition, the
algorithm is capable of simulating the forward direction, from aerosol
properties to measurement data. The applicability and performance of the
algorithm are demonstrated with simulated measurement data, mimicking in situ
laboratory and field measurements. With a retrieval time in the millisecond
range and a weighted mean absolute percentage error of less than 1.5%, the
algorithm turned out to be fast and accurate. By introducing Gaussian noise to
the data, we further demonstrate that the method is robust with respect to
measurement errors. In addition, realistic case studies are performed to
demonstrate that the algorithm performs well even with missing measurement
data.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7116 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07971
|
David Acuna
|
David Acuna, Jonah Philion, Sanja Fidler
|
Towards Optimal Strategies for Training Self-Driving Perception Models
in Simulation
|
NeurIPS 2021; Project website:
https://nv-tlabs.github.io/simulation-strategies/
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autonomous driving relies on a huge volume of real-world data to be labeled
to high precision. Alternative solutions seek to exploit driving simulators
that can generate large amounts of labeled data with a plethora of content
variations. However, the domain gap between the synthetic and real data
remains, raising the following important question: What are the best ways to
utilize a self-driving simulator for perception tasks? In this work, we build
on top of recent advances in domain-adaptation theory, and from this
perspective, propose ways to minimize the reality gap. We primarily focus on
the use of labels in the synthetic domain alone. Our approach introduces both a
principled way to learn neural-invariant representations and a theoretically
inspired view on how to sample the data from the simulator. Our method is easy
to implement in practice as it is agnostic of the network architecture and the
choice of the simulator. We showcase our approach on the bird's-eye-view
vehicle segmentation task with multi-sensor data (cameras, lidar) using an
open-source simulator (CARLA), and evaluate the entire framework on a
real-world dataset (nuScenes). Last but not least, we show what types of
variations (e.g. weather conditions, number of assets, map design, and color
diversity) matter to perception networks when trained with driving simulators,
and which ones can be compensated for with our domain adaptation technique.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712676 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07974
|
Anastasios Sidiropoulos
|
Ken-ichi Kawarabayashi, Anastasios Sidiropoulos
|
Embeddings of Planar Quasimetrics into Directed \ell_1$ and
Polylogarithmic Approximation for Directed Sparsest-Cut
| null | null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
The multi-commodity flow-cut gap is a fundamental parameter that affects the
performance of several divide \& conquer algorithms, and has been extensively
studied for various classes of undirected graphs. It has been shown by Linial,
London and Rabinovich and by Aumann and Rabani that for general $n$-vertex
graphs it is bounded by $O(\log n)$ and the Gupta-Newman-Rabinovich-Sinclair
conjecture asserts that it is $O(1)$ for any family of graphs that excludes
some fixed minor.
We show that the multicommodity flow-cut gap on \emph{directed} planar graphs
is $O(\log^3 n)$. This is the first \emph{sub-polynomial} bound for any family
of directed graphs of super-constant treewidth. We remark that for general
directed graphs, it has been shown by Chuzhoy and Khanna that the gap is
$\widetilde{\Omega}(n^{1/7})$, even for directed acyclic graphs.
As a direct consequence of our result, we also obtain the first
polynomial-time polylogarithmic-approximation algorithms for the Directed
Non-Bipartite Sparsest-Cut, and the Directed Multicut problems for directed
planar graphs, which extends the long-standing result for undirectd planar
graphs by Rao (with a slightly weaker bound).
At the heart of our result we investigate low-distortion quasimetric
embeddings into \emph{directed} $\ell_1$. More precisely, we construct
$O(\log^2 n)$-Lipschitz quasipartitions for the shortest-path quasimetric
spaces of planar digraphs, which generalize the notion of Lipschitz partitions
from the theory of metric embeddings. This construction combines ideas from the
theory of bi-Lipschitz embeddings, with tools form data structures on directed
planar graphs.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07975
|
Walter Goodwin
|
Walter Goodwin, Sagar Vaze, Ioannis Havoutis, Ingmar Posner
|
Semantically Grounded Object Matching for Robust Robotic Scene
Rearrangement
|
8 pages, 5 figures
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Object rearrangement has recently emerged as a key competency in robot
manipulation, with practical solutions generally involving object detection,
recognition, grasping and high-level planning. Goal-images describing a desired
scene configuration are a promising and increasingly used mode of instruction.
A key outstanding challenge is the accurate inference of matches between
objects in front of a robot, and those seen in a provided goal image, where
recent works have struggled in the absence of object-specific training data. In
this work, we explore the deterioration of existing methods' ability to infer
matches between objects as the visual shift between observed and goal scenes
increases. We find that a fundamental limitation of the current setting is that
source and target images must contain the same $\textit{instance}$ of every
object, which restricts practical deployment. We present a novel approach to
object matching that uses a large pre-trained vision-language model to match
objects in a cross-instance setting by leveraging semantics together with
visual features as a more robust, and much more general, measure of similarity.
We demonstrate that this provides considerably improved matching performance in
cross-instance settings, and can be used to guide multi-object rearrangement
with a robot manipulator from an image that shares no object
$\textit{instances}$ with the robot's scene.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07978
|
Evgenia (Eugenia) Ternovska
|
Eugenia Ternovska
|
Towards Capturing PTIME with no Counting Construct (but with a Choice
Operator)
|
20 pages
| null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
The central open question in Descriptive Complexity is whether there is a
logic that characterizes deterministic polynomial time (PTIME) on relational
structures. Towards this goal, we define a logic that is obtained from
first-order logic with fixed points, FO(FP), by a series of transformations
that include restricting logical connectives and adding a dynamic version of
Hilbert's Choice operator Epsilon. The formalism can be viewed, simultaneously,
as an algebra of binary relations and as a linear-time modal dynamic logic,
where algebraic expressions describing ``proofs'' or ``programs'' appear inside
the modalities. We show how counting, reachability and ``mixed'' examples (that
include linear equations modulo two) are axiomatized in the logic, and how an
arbitrary PTIME Turing machine can be encoded. For each fixed Choice function,
the data complexity of model checking is in PTIME. However, there can be
exponentially many such functions. A crucial question is under what syntactic
conditions on algebraic terms checking just one Choice function is sufficient.
Answering this question requires a study of symmetries among computations. This
paper sets mathematical foundations towards such a study via algebraic and
automata-theoretic techniques.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708591 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07979
|
Shakeel Muhammad
|
Muhammad Shakeel, Katsutoshi Itoyama, Kenji Nishida, Kazuhiro Nakadai
|
Metric-based multimodal meta-learning for human movement identification
via footstep recognition
| null | null | null | null |
cs.SD cs.AI cs.LG cs.SY eess.AS eess.SY q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We describe a novel metric-based learning approach that introduces a
multimodal framework and uses deep audio and geophone encoders in siamese
configuration to design an adaptable and lightweight supervised model. This
framework eliminates the need for expensive data labeling procedures and learns
general-purpose representations from low multisensory data obtained from
omnipresent sensing systems. These sensing systems provide numerous
applications and various use cases in activity recognition tasks. Here, we
intend to explore the human footstep movements from indoor environments and
analyze representations from a small self-collected dataset of acoustic and
vibration-based sensors. The core idea is to learn plausible similarities
between two sensory traits and combining representations from audio and
geophone signals. We present a generalized framework to learn embeddings from
temporal and spatial features extracted from audio and geophone signals. We
then extract the representations in a shared space to maximize the learning of
a compatibility function between acoustic and geophone features. This, in turn,
can be used effectively to carry out a classification task from the learned
model, as demonstrated by assigning high similarity to the pairs with a human
footstep movement and lower similarity to pairs containing no footstep
movement. Performance analyses show that our proposed multimodal framework
achieves a 19.99\% accuracy increase (in absolute terms) and avoided
overfitting on the evaluation set when the training samples were increased from
200 pairs to just 500 pairs while satisfactorily learning the audio and
geophone representations. Our results employ a metric-based contrastive
learning approach for multi-sensor data to mitigate the impact of data scarcity
and perform human movement identification with limited data size.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.709221 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07984
|
Severine Atis
|
Sasan J. Ghaemsaidi, Michel Fruchart, Severine Atis
|
Internal wave crystals
| null | null | null | null |
physics.ao-ph cond-mat.other cond-mat.soft
|
http://creativecommons.org/licenses/by/4.0/
|
Geophysical fluids such as the ocean and atmosphere can be stratified: their
density depends on the depth. As a consequence, they can host internal gravity
waves that propagate in the bulk of the fluid, far from the surface. These
waves can transport energy and momentum over large distances, thereby affecting
large-scale circulation patterns, as well as the transport of heat, sediments,
nutrients and pollutants in the ocean. When the density stratification is not
uniform, internal waves can exhibit wave phenomena such as resonances,
tunneling, and frequency-dependent transmissions. Spatially periodic density
profiles formed by thermohaline staircases are commonly found in stratified
fluids ranging from the Arctic Ocean to giant planet interiors, and can produce
extended regions with periodically stratified fluid. Here, we report on the
experimental observation of band gaps for internal gravity waves, ranges of
frequencies over which the wave propagation is prohibited in the presence of a
periodic stratification. We show the existence of surface states controlled by
boundary conditions and discuss their topological origin. Our results suggest
that energy transport can be profoundly affected by the presence of periodic
stratifications in geophysical fluids ranging from Earth's oceans to gas
giants.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712845 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07986
|
Hamid Izadinia
|
Hamid Izadinia, Byron Boots, Steven M. Seitz
|
Nonprehensile Riemannian Motion Predictive Control
|
To appear at International Symposium on Experimental Robotics (ISER)
| null | null | null |
cs.RO cs.LG cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nonprehensile manipulation involves long horizon underactuated object
interactions and physical contact with different objects that can inherently
introduce a high degree of uncertainty. In this work, we introduce a novel
Real-to-Sim reward analysis technique, called Riemannian Motion Predictive
Control (RMPC), to reliably imagine and predict the outcome of taking possible
actions for a real robotic platform. Our proposed RMPC benefits from Riemannian
motion policy and second order dynamic model to compute the acceleration
command and control the robot at every location on the surface. Our approach
creates a 3D object-level recomposed model of the real scene where we can
simulate the effect of different trajectories. We produce a closed-loop
controller to reactively push objects in a continuous action space. We evaluate
the performance of our RMPC approach by conducting experiments on a real robot
platform as well as simulation and compare against several baselines. We
observe that RMPC is robust in cluttered as well as occluded environments and
outperforms the baselines.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709636 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
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