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---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2111.03534
|
Paul Krogmeier
|
Paul Krogmeier, P. Madhusudan
|
Learning Formulas in Finite Variable Logics
| null | null | null | null |
cs.LO cs.FL cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider grammar-restricted exact learning of formulas and terms in finite
variable logics. We propose a novel and versatile automata-theoretic technique
for solving such problems. We first show results for learning formulas that
classify a set of positively- and negatively-labeled structures. We give
algorithms for realizability and synthesis of such formulas along with upper
and lower bounds. We also establish positive results using our technique for
other logics and variants of the learning problem, including first-order logic
with least fixed point definitions, higher-order logics, and synthesis of
queries and terms with recursively-defined functions.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708244 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05283
|
Paul Kirkland
|
Paul Kirkland, Davide L. Manna, Alex Vicente-Sola and Gaetano Di
Caterina
|
Unsupervised Spiking Instance Segmentation on Event Data using STDP
|
20 Pages, 13 Figures
| null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Spiking Neural Networks (SNN) and the field of Neuromorphic Engineering has
brought about a paradigm shift in how to approach Machine Learning (ML) and
Computer Vision (CV) problem. This paradigm shift comes from the adaption of
event-based sensing and processing. An event-based vision sensor allows for
sparse and asynchronous events to be produced that are dynamically related to
the scene. Allowing not only the spatial information but a high-fidelity of
temporal information to be captured. Meanwhile avoiding the extra overhead and
redundancy of conventional high frame rate approaches. However, with this
change in paradigm, many techniques from traditional CV and ML are not
applicable to these event-based spatial-temporal visual streams. As such a
limited number of recognition, detection and segmentation approaches exist. In
this paper, we present a novel approach that can perform instance segmentation
using just the weights of a Spike Time Dependent Plasticity trained Spiking
Convolutional Neural Network that was trained for object recognition. This
exploits the spatial and temporal aspects of the network's internal feature
representations adding this new discriminative capability. We highlight the new
capability by successfully transforming a single class unsupervised network for
face detection into a multi-person face recognition and instance segmentation
network.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710446 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06397
|
Onofrio M. Marag\`o
|
P. Polimeno, A. Magazzu, M. A. Iati, R. Saija, L. Folco, D. Bronte
Ciriza, M. G. Donato, A. Foti, P. G. Gucciardi, A. Saidi, C.
Cecchi-Pestellini, A. Jimenez Escobar, E. Ammannito, G. Sindoni, I. Bertini,
V. Della Corte, L. Inno, A. Ciaravella, A. Rotundi, O. M. Marago
|
Optical tweezers in a dusty universe
|
18 pages, 4 figures, 1 table. Part of EPJ plus Focus Point Issues on
"Light Pressure across All Scales"
|
Eur. Phys. J. Plus (2021) 136:339
|
10.1140/epjp/s13360-021-01316-z
| null |
physics.space-ph astro-ph.EP astro-ph.IM physics.optics
|
http://creativecommons.org/licenses/by/4.0/
|
Optical tweezers are powerful tools based on focused laser beams. They are
able to trap, manipulate and investigate a wide range of microscopic and
nanoscopic particles in different media, such as liquids, air, and vacuum. Key
applications of this contactless technique have been developed in many fields.
Despite this progress, optical trapping applications to planetary exploration
is still to be developed. Here we describe how optical tweezers can be used to
trap and characterize extraterrestrial particulate matter. In particular, we
exploit light scattering theory in the T-matrix formalism to calculate
radiation pressure and optical trapping properties of a variety of complex
particles of astrophysical interest. Our results open perspectives in the
investigation of extraterrestrial particles on our planet, in controlled
laboratory experiments, aiming for space tweezers applications: optical
tweezers used to trap and characterize dust particles in space or on planetary
bodies surface.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710051 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06398
|
Yuan Xue
|
Jiarong Ye, Yuan Xue, Peter Liu, Richard Zaino, Keith Cheng, Xiaolei
Huang
|
A Multi-attribute Controllable Generative Model for Histopathology Image
Synthesis
|
MICCAI 2021
| null |
10.1007/978-3-030-87237-3_59
| null |
eess.IV cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generative models have been applied in the medical imaging domain for various
image recognition and synthesis tasks. However, a more controllable and
interpretable image synthesis model is still lacking yet necessary for
important applications such as assisting in medical training. In this work, we
leverage the efficient self-attention and contrastive learning modules and
build upon state-of-the-art generative adversarial networks (GANs) to achieve
an attribute-aware image synthesis model, termed AttributeGAN, which can
generate high-quality histopathology images based on multi-attribute inputs. In
comparison to existing single-attribute conditional generative models, our
proposed model better reflects input attributes and enables smoother
interpolation among attribute values. We conduct experiments on a
histopathology dataset containing stained H&E images of urothelial carcinoma
and demonstrate the effectiveness of our proposed model via comprehensive
quantitative and qualitative comparisons with state-of-the-art models as well
as different variants of our model. Code is available at
https://github.com/karenyyy/MICCAI2021AttributeGAN.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.695105 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06399
|
Yuan Xue
|
Yuan Xue, Jiarong Ye, Qianying Zhou, Rodney Long, Sameer Antani,
Zhiyun Xue, Carl Cornwell, Richard Zaino, Keith Cheng, Xiaolei Huang
|
Selective Synthetic Augmentation with HistoGAN for Improved
Histopathology Image Classification
|
Elsevier Medical Image Analysis Best Paper Award runner up. arXiv
admin note: substantial text overlap with arXiv:1912.03837
|
Medical Image Analysis 67 (2021): 101816
|
10.1016/j.media.2020.101816
| null |
eess.IV cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Histopathological analysis is the present gold standard for precancerous
lesion diagnosis. The goal of automated histopathological classification from
digital images requires supervised training, which requires a large number of
expert annotations that can be expensive and time-consuming to collect.
Meanwhile, accurate classification of image patches cropped from whole-slide
images is essential for standard sliding window based histopathology slide
classification methods. To mitigate these issues, we propose a carefully
designed conditional GAN model, namely HistoGAN, for synthesizing realistic
histopathology image patches conditioned on class labels. We also investigate a
novel synthetic augmentation framework that selectively adds new synthetic
image patches generated by our proposed HistoGAN, rather than expanding
directly the training set with synthetic images. By selecting synthetic images
based on the confidence of their assigned labels and their feature similarity
to real labeled images, our framework provides quality assurance to synthetic
augmentation. Our models are evaluated on two datasets: a cervical
histopathology image dataset with limited annotations, and another dataset of
lymph node histopathology images with metastatic cancer. Here, we show that
leveraging HistoGAN generated images with selective augmentation results in
significant and consistent improvements of classification performance (6.7% and
2.8% higher accuracy, respectively) for cervical histopathology and metastatic
cancer datasets.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.707424 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06400
|
Junwei Yang
|
Junwei Yang, Xiao-Xin Li, Feihong Liu, Dong Nie, Pietro Lio, Haikun
Qi, Dinggang Shen
|
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information
Complementary to Pre-acquired T1w MRI
| null | null | null | null |
eess.IV cs.CV physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent studies on T1-assisted MRI reconstruction for under-sampled images of
other modalities have demonstrated the potential of further accelerating MRI
acquisition of other modalities. Most of the state-of-the-art approaches have
achieved improvement through the development of network architectures for fixed
under-sampling patterns, without fully exploiting the complementary information
between modalities. Although existing under-sampling pattern learning
algorithms can be simply modified to allow the fully-sampled T1-weighted MR
image to assist the pattern learning, no significant improvement on the
reconstruction task can be achieved. To this end, we propose an iterative
framework to optimize the under-sampling pattern for MRI acquisition of another
modality that can complement the fully-sampled T1-weighted MR image at
different under-sampling factors, while jointly optimizing the T1-assisted MRI
reconstruction model. Specifically, our proposed method exploits the difference
of latent information between the two modalities for determining the sampling
patterns that can maximize the assistance power of T1-weighted MR image in
improving the MRI reconstruction. We have demonstrated superior performance of
our learned under-sampling patterns on a public dataset, compared to commonly
used under-sampling patterns and state-of-the-art methods that can jointly
optimize both the reconstruction network and the under-sampling pattern, up to
8-fold under-sampling factor.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710823 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06401
|
Mohammed A. Al-Masni Dr.
|
Mohammed A. Al-masni, Seul Lee, Jaeuk Yi, Sewook Kim, Sung-Min Gho,
Young Hun Choi, and Dong-Hyun Kim
|
Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of
Rigid Motion Artifact in Brain MRI
|
24 pages, 10 figures, 3 tables
| null | null | null |
eess.IV cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we develop an efficient retrospective deep learning method
called stacked U-Nets with self-assisted priors to address the problem of rigid
motion artifacts in MRI. The proposed work exploits the usage of additional
knowledge priors from the corrupted images themselves without the need for
additional contrast data. The proposed network learns missed structural details
through sharing auxiliary information from the contiguous slices of the same
distorted subject. We further design a refinement stacked U-Nets that
facilitates preserving of the image spatial details and hence improves the
pixel-to-pixel dependency. To perform network training, simulation of MRI
motion artifacts is inevitable. We present an intensive analysis using various
types of image priors: the proposed self-assisted priors and priors from other
image contrast of the same subject. The experimental analysis proves the
effectiveness and feasibility of our self-assisted priors since it does not
require any further data scans.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06402
|
Vittorio Del Tatto
|
Vittorio Del Tatto
|
A Fully Anisotropic Formulation of Stochastic Cell Rescaling
|
Thesis for the MSc in Theoretical and Computational Physics at
University of Trento (Academic Year 2020/2021)
| null | null | null |
physics.chem-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Anisotropic barostats are employed to carry out Molecular Dynamics
simulations where the volume is allowed to fluctuate with no constraints on the
shape of the simulation cell. Most of these algorithms are based on
second-order differential equations and share some common drawbacks, namely
they can lead to slowly damped oscillations in the equilibration phase, and
they do not allow to control efficiently the volume autocorrelation time. This
work develops the anisotropic version of stochastic cell rescaling, a
first-order stochastic barostat that overcomes these limits and can also be
employed in the production phase, resulting in the correct physical
fluctuations of the cell. The algorithm can be easily implemented in the
existing codes on top of the anisotropic Berendsen barostat. The validation
tests, performed on a number of crystal systems, show that the method is robust
against wide variations of the input parameter, which allows an efficient
control of the volume autocorrelation time.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710176 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06420
|
Waddah Saeed
|
Waddah Saeed, Christian Omlin
|
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and
Future Opportunities
|
29 pages, 2 figures, 4 tables
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The past decade has seen significant progress in artificial intelligence
(AI), which has resulted in algorithms being adopted for resolving a variety of
problems. However, this success has been met by increasing model complexity and
employing black-box AI models that lack transparency. In response to this need,
Explainable AI (XAI) has been proposed to make AI more transparent and thus
advance the adoption of AI in critical domains. Although there are several
reviews of XAI topics in the literature that identified challenges and
potential research directions in XAI, these challenges and research directions
are scattered. This study, hence, presents a systematic meta-survey for
challenges and future research directions in XAI organized in two themes: (1)
general challenges and research directions in XAI and (2) challenges and
research directions in XAI based on machine learning life cycle's phases:
design, development, and deployment. We believe that our meta-survey
contributes to XAI literature by providing a guide for future exploration in
the XAI area.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711387 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06435
|
Ruhui Jin
|
Ruhui Jin, Francesco Rizzi and Eric Parish
|
Space-time reduced-order modeling for uncertainty quantification
| null | null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work focuses on the space-time reduced-order modeling (ROM) method for
solving large-scale uncertainty quantification (UQ) problems with multiple
random coefficients. In contrast with the traditional space ROM approach, which
performs dimension reduction in the spatial dimension, the space-time ROM
approach performs dimension reduction on both the spatial and temporal domains,
and thus enables accurate approximate solutions at a low cost. We incorporate
the space-time ROM strategy with various classical stochastic UQ propagation
methods such as stochastic Galerkin and Monte Carlo. Numerical results
demonstrate that our methodology has significant computational advantages
compared to state-of-the-art ROM approaches. By testing the approximation
errors, we show that there is no obvious loss of simulation accuracy for
space-time ROM given its high computational efficiency.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711418 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06437
|
Abhinav Dahiya
|
Abhinav Dahiya, Nima Akbarzadeh, Aditya Mahajan and Stephen L. Smith
|
Scalable Operator Allocation for Multi-Robot Assistance: A Restless
Bandit Approach
|
11 pages + 4 page Appendix, 7 Figures
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we consider the problem of allocating human operators in a
system with multiple semi-autonomous robots. Each robot is required to perform
an independent sequence of tasks, subjected to a chance of failing and getting
stuck in a fault state at every task. If and when required, a human operator
can assist or teleoperate a robot. Conventional MDP techniques used to solve
such problems face scalability issues due to exponential growth of state and
action spaces with the number of robots and operators. In this paper we derive
conditions under which the operator allocation problem is indexable, enabling
the use of the Whittle index heuristic. The conditions can be easily checked to
verify indexability, and we show that they hold for a wide range of problems of
interest. Our key insight is to leverage the structure of the value function of
individual robots, resulting in conditions that can be verified separately for
each state of each robot. We apply these conditions to two types of transitions
commonly seen in remote robot supervision systems. Through numerical
simulations, we demonstrate the efficacy of Whittle index policy as a
near-optimal and scalable approach that outperforms existing scalable methods.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.706431 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06438
|
Chandralekha Singh
|
Chandralekha Singh, Abraham Asfaw and Jeremy Levy
|
Preparing students to be leaders of the quantum information revolution
|
Physics Today, 2021
| null |
10.1063/PT.6.5.20210927a
| null |
physics.ed-ph physics.soc-ph quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This Physics Today article discusses why the physics community needs to
embrace the challenge of educating students with diverse educational
backgrounds to meet future research and workforce demands and outlines some of
the efforts underway.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.70638 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06440
|
Gaurav Sahu
|
Alexandre Parmentier, Robin Cohen, Xueguang Ma, Gaurav Sahu and
Queenie Chen
|
Personalized multi-faceted trust modeling to determine trust links in
social media and its potential for misinformation management
|
28 pages
| null | null | null |
cs.SI cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present an approach for predicting trust links between
peers in social media, one that is grounded in the artificial intelligence area
of multiagent trust modeling. In particular, we propose a data-driven
multi-faceted trust modeling which incorporates many distinct features for a
comprehensive analysis. We focus on demonstrating how clustering of similar
users enables a critical new functionality: supporting more personalized, and
thus more accurate predictions for users. Illustrated in a trust-aware item
recommendation task, we evaluate the proposed framework in the context of a
large Yelp dataset. We then discuss how improving the detection of trusted
relationships in social media can assist in supporting online users in their
battle against the spread of misinformation and rumours, within a social
networking environment which has recently exploded in popularity. We conclude
with a reflection on a particularly vulnerable user base, older adults, in
order to illustrate the value of reasoning about groups of users, looking to
some future directions for integrating known preferences with insights gained
through data analysis.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710377 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06444
|
Mohsen Abedi
|
Pouria Nezhadmohammad, Mohsen Abedi, Mohammad Javad Emadi, Risto
Wichman
|
SWIPT-Enabled Multiple Access Channel: Effects of Decoding Cost and
Non-linear EH Model
|
IEEE Transactions on Communications
| null |
10.1109/TCOMM.2021.3121035
| null |
cs.IT math.IT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
We studied power splitting-based simultaneous wireless information and power
transfer (PS-SWIPT) in multiple access channels (MAC), considering the decoding
cost and non-linear energy harvesting (EH) constraints at the receiving nodes
to study practical limitations of an EH communication system. Under these
restrictions, we formulated and analyzed the achievable rate and maximum
departure regions in two well-studied scenarios, i.e., a classical PS-SWIPT MAC
and a PS-SWIPT MAC with user cooperation. In the classical PS-SWIPT MAC
setting, closed-form expressions for the optimal values of the PS factors are
derived for two fundamental decoding schemes: simultaneous decoding and
successive interference cancellation. In the PS-SWIPT MAC with user
cooperation, the joint optimal power allocation for users as well as the
optimal PS factor are derived. This reveals that one decoding scheme
outperforms the other in the classical PS-SWIPT MAC, depending on the function
type of the decoding cost. Finally, it is shown that the cooperation between
users can potentially boost the performance of a PS-SWIPT MAC under decoding
cost and non-linear EH constraints. Moreover, effects of the decoding cost
functions, non-linear EH model and channel quality between the users are
studied, and performance characteristics of the system are discussed.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06447
|
Mingming Qiu
|
Sibo Cheng, Mingming Qiu
|
Observation Error Covariance Specification in Dynamical Systems for Data
assimilation using Recurrent Neural Networks
|
The manuscript is accepted for publication in Neural computing and
applications
| null | null | null |
cs.LG cs.AI cs.NA math.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data assimilation techniques are widely used to predict complex dynamical
systems with uncertainties, based on time-series observation data. Error
covariance matrices modelling is an important element in data assimilation
algorithms which can considerably impact the forecasting accuracy. The
estimation of these covariances, which usually relies on empirical assumptions
and physical constraints, is often imprecise and computationally expensive
especially for systems of large dimension. In this work, we propose a
data-driven approach based on long short term memory (LSTM) recurrent neural
networks (RNN) to improve both the accuracy and the efficiency of observation
covariance specification in data assimilation for dynamical systems. Learning
the covariance matrix from observed/simulated time-series data, the proposed
approach does not require any knowledge or assumption about prior error
distribution, unlike classical posterior tuning methods. We have compared the
novel approach with two state-of-the-art covariance tuning algorithms, namely
DI01 and D05, first in a Lorenz dynamical system and then in a 2D shallow water
twin experiments framework with different covariance parameterization using
ensemble assimilation. This novel method shows significant advantages in
observation covariance specification, assimilation accuracy and computational
efficiency.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710829 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06449
|
Ryuji Imamura
|
Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger
|
Expert Human-Level Driving in Gran Turismo Sport Using Deep
Reinforcement Learning with Image-based Representation
|
Accepted at Deep Reinforcement Learning Workshop at Neural
Information Processing Systems 2021
| null | null | null |
cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When humans play virtual racing games, they use visual environmental
information on the game screen to understand the rules within the environments.
In contrast, a state-of-the-art realistic racing game AI agent that outperforms
human players does not use image-based environmental information but the
compact and precise measurements provided by the environment. In this paper, a
vision-based control algorithm is proposed and compared with human player
performances under the same conditions in realistic racing scenarios using Gran
Turismo Sport (GTS), which is known as a high-fidelity realistic racing
simulator. In the proposed method, the environmental information that
constitutes part of the observations in conventional state-of-the-art methods
is replaced with feature representations extracted from game screen images. We
demonstrate that the proposed method performs expert human-level vehicle
control under high-speed driving scenarios even with game screen images as
high-dimensional inputs. Additionally, it outperforms the built-in AI in GTS in
a time trial task, and its score places it among the top 10% approximately
28,000 human players.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.7076 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06457
|
Zihao Deng
|
Zihao Deng and Michael Orshansky
|
Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for
Analog PIM
|
This is the preprint version of our paper accepted in DATE 2022
| null | null | null |
cs.LG cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
DNNs deployed on analog processing in memory (PIM) architectures are subject
to fabrication-time variability. We developed a new joint variability- and
quantization-aware DNN training algorithm for highly quantized analog PIM-based
models that is significantly more effective than prior work. It outperforms
variability-oblivious and post-training quantized models on multiple computer
vision datasets/models. For low-bitwidth models and high variation, the gain in
accuracy is up to 35.7% for ResNet-18 over the best alternative.
We demonstrate that, under a realistic pattern of within- and between-chip
components of variability, training alone is unable to prevent large DNN
accuracy loss (of up to 54% on CIFAR-100/ResNet-18). We introduce a self-tuning
DNN architecture that dynamically adjusts layer-wise activations during
inference and is effective in reducing accuracy loss to below 10%.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708187 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06458
|
Ladislav Mo\v{s}ner
|
Ladislav Mo\v{s}ner, Old\v{r}ich Plchot, Luk\'a\v{s} Burget, Jan
\v{C}ernock\'y
|
MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification
|
Submitted to ICASSP 2022
| null | null | null |
eess.AS cs.LG cs.SD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motivated by unconsolidated data situation and the lack of a standard
benchmark in the field, we complement our previous efforts and present a
comprehensive corpus designed for training and evaluating text-independent
multi-channel speaker verification systems. It can be readily used also for
experiments with dereverberation, denoising, and speech enhancement. We tackled
the ever-present problem of the lack of multi-channel training data by
utilizing data simulation on top of clean parts of the Voxceleb dataset. The
development and evaluation trials are based on a retransmitted Voices Obscured
in Complex Environmental Settings (VOiCES) corpus, which we modified to provide
multi-channel trials. We publish full recipes that create the dataset from
public sources as the MultiSV corpus, and we provide results with two of our
multi-channel speaker verification systems with neural network-based
beamforming based either on predicting ideal binary masks or the more recent
Conv-TasNet.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.708231 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06466
|
Vikram Jadhao
|
Prateek Sharma and Vikram Jadhao
|
Molecular Dynamics Simulations on Cloud Computing and Machine Learning
Platforms
|
4 pages, position paper appearing in the Proceedings of the 2021 IEEE
14th International Conference on Cloud Computing (CLOUD)
| null | null | null |
cs.DC cond-mat.soft cs.LG physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Scientific computing applications have benefited greatly from high
performance computing infrastructure such as supercomputers. However, we are
seeing a paradigm shift in the computational structure, design, and
requirements of these applications. Increasingly, data-driven and machine
learning approaches are being used to support, speed-up, and enhance scientific
computing applications, especially molecular dynamics simulations.
Concurrently, cloud computing platforms are increasingly appealing for
scientific computing, providing "infinite" computing powers, easier programming
and deployment models, and access to computing accelerators such as TPUs
(Tensor Processing Units). This confluence of machine learning (ML) and cloud
computing represents exciting opportunities for cloud and systems researchers.
ML-assisted molecular dynamics simulations are a new class of workload, and
exhibit unique computational patterns. These simulations present new challenges
for low-cost and high-performance execution. We argue that transient cloud
resources, such as low-cost preemptible cloud VMs, can be a viable platform for
this new workload. Finally, we present some low-hanging fruits and long-term
challenges in cloud resource management, and the integration of molecular
dynamics simulations into ML platforms (such as TensorFlow).
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710597 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06469
|
Andrew Litteken
|
Jonathan M. Baker, Andrew Litteken, Casey Duckering, Henry Hoffman,
Hannes Bernien, Frederic T. Chong
|
Exploiting Long-Distance Interactions and Tolerating Atom Loss in
Neutral Atom Quantum Architectures
|
14 pages, 14 figures, In ISCA '21: The 48th International Symposium
on Computer Architecture
| null | null | null |
quant-ph cs.AR cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum technologies currently struggle to scale beyond moderate scale
prototypes and are unable to execute even reasonably sized programs due to
prohibitive gate error rates or coherence times. Many software approaches rely
on heavy compiler optimization to squeeze extra value from noisy machines but
are fundamentally limited by hardware. Alone, these software approaches help to
maximize the use of available hardware but cannot overcome the inherent
limitations posed by the underlying technology. An alternative approach is to
explore the use of new, though potentially less developed, technology as a path
towards scalability. In this work we evaluate the advantages and disadvantages
of a Neutral Atom (NA) architecture. NA systems offer several promising
advantages such as long range interactions and native multiqubit gates which
reduce communication overhead, overall gate count, and depth for compiled
programs. Long range interactions, however, impede parallelism with restriction
zones surrounding interacting qubit pairs. We extend current compiler methods
to maximize the benefit of these advantages and minimize the cost. Furthermore,
atoms in an NA device have the possibility to randomly be lost over the course
of program execution which is extremely detrimental to total program execution
time as atom arrays are slow to load. When the compiled program is no longer
compatible with the underlying topology, we need a fast and efficient coping
mechanism. We propose hardware and compiler methods to increase system
resilience to atom loss dramatically reducing total computation time by
circumventing complete reloads or full recompilation every cycle.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710258 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06470
|
Gerardo Vega
|
Gerardo Vega and F\'elix Hern\'andez
|
The complete weight enumerator of a subclass of optimal three-weight
cyclic codes
|
arXiv admin note: text overlap with arXiv:1508.05077
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A class of optimal three-weight cyclic codes of dimension 3 over any finite
field was presented by Vega [Finite Fields Appl., 42 (2016) 23-38]. Shortly
thereafter, Heng and Yue [IEEE Trans. Inf. Theory, 62(8) (2016) 4501-4513]
generalized this result by presenting several classes of cyclic codes with
either optimal three weights or a few weights. On the other hand, a class of
optimal five-weight cyclic codes of dimension 4 over a prime field was recently
presented by Li, et al. [Adv. Math. Commun., 13(1) (2019) 137-156]. One of the
purposes of this work is to present a more general description for these
optimal five-weight cyclic codes, which gives place to an enlarged class of
optimal five-weight cyclic codes of dimension 4 over any finite field. As an
application of this enlarged class, we present the complete weight enumerator
of a subclass of the optimal three-weight cyclic codes over any finite field
that were studied by Vega [Finite Fields Appl., 42 (2016) 23-38]. In addition,
we study the dual codes in this enlarged class of optimal five-weight cyclic
codes, and show that they are cyclic codes of length $q^2-1$, dimension
$q^2-5$, and minimum Hamming distance 4. In fact, through several examples, we
see that those parameters are the best known parameters for linear codes.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711418 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06486
|
Negar Hassanpour
|
Negar Hassanpour, Russell Greiner
|
Variational Auto-Encoder Architectures that Excel at Causal Inference
| null | null | null | null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Estimating causal effects from observational data (at either an individual --
or a population -- level) is critical for making many types of decisions. One
approach to address this task is to learn decomposed representations of the
underlying factors of data; this becomes significantly more challenging when
there are confounding factors (which influence both the cause and the effect).
In this paper, we take a generative approach that builds on the recent advances
in Variational Auto-Encoders to simultaneously learn those underlying factors
as well as the causal effects. We propose a progressive sequence of models,
where each improves over the previous one, culminating in the Hybrid model. Our
empirical results demonstrate that the performance of all three proposed models
are superior to both state-of-the-art discriminative as well as other
generative approaches in the literature.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710666 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06489
|
Nguyen Phuong Dang
|
Nguyen Phuong Dang, Victor Gabriel Leandro Alves, Mahmoud Ahmed and
Jeffrey Siebers
|
Gross patient error detection via cine transmission dosimetry
| null | null | null | null |
physics.med-ph
|
http://creativecommons.org/licenses/by-sa/4.0/
|
$\textbf{Purpose:}$ To quantify the effectiveness of EPID-based cine
transmission dosimetry to detect gross patient anatomic errors. $\textbf{Method
and Materials:}$ EPID image frames resulting from fluence transmitted through
multiple patients anatomies are simulated for 100 msec delivery intervals for
hypothetical 6 MV VMAT deliveries. Frames simulated through 10 head-and-neck
CTs and 19 prostate CTs with and without 1-3 mm shift and 1-3 degree rotations
were used to quantify expected in-tolerance clinical setup variations.
Per-frame analysis methods to determine if simulated gross errors of (a) 10-20
mm patient miss alignment offsets and (b) 15-20 degree patient rotations could
be reliably distinguished from the above baseline variations. For the prostate
image sets, frames simulated through the reference CT are intercompared with
(c) frames through 8-13 different CT's for the same patient to quantify
expected inter-treatment frame variation. ROC analysis of per-frame error
discrimination based upon (i) frame image differences, (ii) frame histogram
comparisons, (iii) image feature matching, and (iv) image distance were used to
quantify error detectability. $\textbf{Results:}$ Each error detection method
was able to distinguish gross patient miss-alignment and gross rotations from
in-tolerance levels for both H&N and prostate datasets. The image distance
algorithm is the best method based on AUC. $\textbf{Conclusion:}$ In-field
gross error detection was possible for gross patient miss-alignments and
incorrect patients. For prostate cases, the methods used were able to
distinguish different patients from daily patient variations.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708036 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06494
|
Pavel Surynek
|
Martin \v{C}apek and Pavel Surynek
|
DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving
Technologies
| null | null | null | null |
cs.AI cs.MA
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In multi-agent path finding (MAPF), the task is to find non-conflicting paths
for multiple agents from their initial positions to given individual goal
positions. MAPF represents a classical artificial intelligence problem often
addressed by heuristic-search. An important alternative to search-based
techniques is compilation of MAPF to a different formalism such as Boolean
satisfiability (SAT). Contemporary SAT-based approaches to MAPF regard the SAT
solver as an external tool whose task is to return an assignment of all
decision variables of a Boolean model of input MAPF. We present in this short
paper a novel compilation scheme called DPLL(MAPF) in which the consistency
checking of partial assignments of decision variables with respect to the MAPF
rules is integrated directly into the SAT solver. This scheme allows for far
more automated compilation where the SAT solver and the consistency checking
procedure work together simultaneously to create the Boolean model and to
search for its satisfying assignment.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.707777 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06500
|
John Yang
|
John Yang, Yash Bhalgat, Simyung Chang, Fatih Porikli, Nojun Kwak
|
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While hand pose estimation is a critical component of most interactive
extended reality and gesture recognition systems, contemporary approaches are
not optimized for computational and memory efficiency. In this paper, we
propose a tiny deep neural network of which partial layers are recursively
exploited for refining its previous estimations. During its iterative
refinements, we employ learned gating criteria to decide whether to exit from
the weight-sharing loop, allowing per-sample adaptation in our model. Our
network is trained to be aware of the uncertainty in its current predictions to
efficiently gate at each iteration, estimating variances after each loop for
its keypoint estimates. Additionally, we investigate the effectiveness of
end-to-end and progressive training protocols for our recursive structure on
maximizing the model capacity. With the proposed setting, our method
consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches
in terms of both accuracy and efficiency for widely used benchmarks.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06502
|
Stefan Kollmannsberger
|
Frank Hartmann, Stefan Kollmannsberger
|
Enforcing essential boundary conditions on domains defined by point
clouds
| null | null | null | null |
math.NA cs.NA
|
http://creativecommons.org/licenses/by/4.0/
|
This paper develops and investigates a new method for the application of
Dirichlet boundary conditions for computational models defined by point clouds.
Point cloud models often stem from laser or structured-light scanners which are
used to scan existing mechanical structures for which CAD models either do not
exist or from which the artifact under investigation deviates in shape or
topology. Instead of reconstructing a CAD model from point clouds via surface
reconstruction and a subsequent boundary conforming mesh generation, a direct
analysis without pre-processing is possible using embedded domain finite
element methods. These methods use non-boundary conforming meshes which calls
for a weak enforcement of Dirichlet boundary conditions. For point cloud based
models, Dirichlet boundary conditions are usually imposed using a diffuse
interface approach. This leads to a significant computational overhead due to
the necessary computation of domain integrals. Additionally, undesired side
effects on the gradients of the solution arise which can only be controlled to
some extent. This paper develops a new sharp interface approach for point cloud
based models which avoids both issues. The computation of domain integrals is
circumvented by an implicit approximation of corresponding Voronoi diagrams of
higher order and the resulting sharp approximation avoids the side-effects of
diffuse approaches. Benchmark examples from the graphics as well as the
computational mechanics community are used to verify the algorithm. All
algorithms are implemented in the FCMLab framework and provided at
https://gitlab.lrz.de/cie_sam_public/fcmlab/. Further, we discuss challenges
and limitations of point cloud based analysis w.r.t. application of Dirichlet
boundary conditions.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711481 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06503
|
Chuteng Zhou
|
Chuteng Zhou, Fernando Garcia Redondo, Julian B\"uchel, Irem Boybat,
Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian,
Manuel Le Gallo, Paul N. Whatmough
|
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On
Analog Compute-in-Memory Accelerator
| null | null | null | null |
cs.AR cs.ET cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Always-on TinyML perception tasks in IoT applications require very high
energy efficiency. Analog compute-in-memory (CiM) using non-volatile memory
(NVM) promises high efficiency and also provides self-contained on-chip model
storage. However, analog CiM introduces new practical considerations, including
conductance drift, read/write noise, fixed analog-to-digital (ADC) converter
gain, etc. These additional constraints must be addressed to achieve models
that can be deployed on analog CiM with acceptable accuracy loss. This work
describes $\textit{AnalogNets}$: TinyML models for the popular always-on
applications of keyword spotting (KWS) and visual wake words (VWW). The model
architectures are specifically designed for analog CiM, and we detail a
comprehensive training methodology, to retain accuracy in the face of analog
non-idealities, and low-precision data converters at inference time. We also
describe AON-CiM, a programmable, minimal-area phase-change memory (PCM) analog
CiM accelerator, with a novel layer-serial approach to remove the cost of
complex interconnects associated with a fully-pipelined design. We evaluate the
AnalogNets on a calibrated simulator, as well as real hardware, and find that
accuracy degradation is limited to 0.8$\%$/1.2$\%$ after 24 hours of PCM drift
(8-bit) for KWS/VWW. AnalogNets running on the 14nm AON-CiM accelerator
demonstrate 8.58/4.37 TOPS/W for KWS/VWW workloads using 8-bit activations,
respectively, and increasing to 57.39/25.69 TOPS/W with $4$-bit activations.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710007 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06505
|
Noah Kaufmann
|
Noah Kaufmann
|
Classifying All Degrees Below $N^3$
| null | null | null | null |
cs.FL math.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We answer an open question in the theory of transducer degrees initially
posed in [3], on the structure of polynomial transducer degrees, in particular
the question of what degrees, if any, lie below the degree of $n^3$. Transducer
degrees are the equivalence classes formed by word transformations which can be
realized by a finite-state transducer. While there are no general techniques to
tell if a word $w_1$ can be transformed into $w_2$ via an FST, the work of
Endrullis et al. in [2] provides a test for the class of spiralling functions,
which includes all polynomials. We classify fully the degrees of all cubic
polynomials which are below $n^3$, and many of the methods can also be used to
classify the degrees of polynomials of higher orders.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710377 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06517
|
Xiao Zeng
|
Xiao S. Zeng, Surya Dwarakanath, Wuyue Lu, Masaki Nakada, Demetri
Terzopoulos
|
Neuromuscular Control of the Face-Head-Neck Biomechanical Complex With
Learning-Based Expression Transfer From Images and Videos
|
12 pages, 7 figures, 2 tables
| null | null | null |
cs.GR cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The transfer of facial expressions from people to 3D face models is a classic
computer graphics problem. In this paper, we present a novel, learning-based
approach to transferring facial expressions and head movements from images and
videos to a biomechanical model of the face-head-neck complex. Leveraging the
Facial Action Coding System (FACS) as an intermediate representation of the
expression space, we train a deep neural network to take in FACS Action Units
(AUs) and output suitable facial muscle and jaw activation signals for the
musculoskeletal model. Through biomechanical simulation, the activations deform
the facial soft tissues, thereby transferring the expression to the model. Our
approach has advantages over previous approaches. First, the facial expressions
are anatomically consistent as our biomechanical model emulates the relevant
anatomy of the face, head, and neck. Second, by training the neural network
using data generated from the biomechanical model itself, we eliminate the
manual effort of data collection for expression transfer. The success of our
approach is demonstrated through experiments involving the transfer onto our
face-head-neck model of facial expressions and head poses from a range of
facial images and videos.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710057 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06524
|
Kaijie Xu
|
Kaijie Xu
|
An Enhanced Adaptive Bi-clustering Algorithm through Building a
Shielding Complex Sub-Matrix
| null | null | null | null |
cs.LG stat.ME
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bi-clustering refers to the task of finding sub-matrices (indexed by a group
of columns and a group of rows) within a matrix of data such that the elements
of each sub-matrix (data and features) are related in a particular way, for
instance, that they are similar with respect to some metric. In this paper,
after analyzing the well-known Cheng and Church (CC) bi-clustering algorithm
which has been proved to be an effective tool for mining co-expressed genes.
However, Cheng and Church bi-clustering algorithm and summarizing its
limitations (such as interference of random numbers in the greedy strategy;
ignoring overlapping bi-clusters), we propose a novel enhancement of the
adaptive bi-clustering algorithm, where a shielding complex sub-matrix is
constructed to shield the bi-clusters that have been obtained and to discover
the overlapping bi-clusters. In the shielding complex sub-matrix, the imaginary
and the real parts are used to shield and extend the new bi-clusters,
respectively, and to form a series of optimal bi-clusters. To assure that the
obtained bi-clusters have no effect on the bi-clusters already produced, a unit
impulse signal is introduced to adaptively detect and shield the constructed
bi-clusters. Meanwhile, to effectively shield the null data (zero-size data),
another unit impulse signal is set for adaptive detecting and shielding. In
addition, we add a shielding factor to adjust the mean squared residue score of
the rows (or columns), which contains the shielded data of the sub-matrix, to
decide whether to retain them or not. We offer a thorough analysis of the
developed scheme. The experimental results are in agreement with the
theoretical analysis. The results obtained on a publicly available real
microarray dataset show the enhancement of the bi-clusters performance thanks
to the proposed method.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711625 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06526
|
Akira Furui D.Eng.
|
Akira Furui, Tomoyuki Akiyama, and Toshio Tsuji
|
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure
for Epileptic Seizure Detection
|
Accepted at EMBC2021
| null | null | null |
eess.SP cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a time-series stochastic model based on a scale
mixture distribution with Markov transitions to detect epileptic seizures in
electroencephalography (EEG). In the proposed model, an EEG signal at each time
point is assumed to be a random variable following a Gaussian distribution. The
covariance matrix of the Gaussian distribution is weighted with a latent scale
parameter, which is also a random variable, resulting in the stochastic
fluctuations of covariances. By introducing a latent state variable with a
Markov chain in the background of this stochastic relationship, time-series
changes in the distribution of latent scale parameters can be represented
according to the state of epileptic seizures. In an experiment, we evaluated
the performance of the proposed model for seizure detection using EEGs with
multiple frequency bands decomposed from a clinical dataset. The results
demonstrated that the proposed model can detect seizures with high sensitivity
and outperformed several baselines.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710239 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06527
|
Qian Li
|
Kun He, Qian Li, and Xiaoming Sun
|
Moser-Tardos Algorithm: Beyond Shearer's Bound
|
32 pages
| null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a seminal paper (Moser and Tardos, JACM'10), Moser and Tardos developed a
simple and powerful algorithm to find solutions to combinatorial problems in
the variable Lov{\'a}sz Local Lemma (LLL) setting. Kolipaka and Szegedy
(STOC'11) proved that the Moser-Tardos algorithm is efficient up to the tight
condition of the abstract Lov{\'a}sz Local Lemma, known as Shearer's bound. A
fundamental problem around LLL is whether the efficient region of the
Moser-Tardos algorithm can be further extended. In this paper, we give a
positive answer to this problem. We show that the efficient region of the
Moser-Tardos algorithm goes beyond the Shearer's bound of the underlying
dependency graph, if the graph is not chordal. Otherwise, the dependency graph
is chordal, and it has been shown that Shearer's bound exactly characterizes
the efficient region for such graphs (Kolipaka and Szegedy, STOC'11; He, Li,
Liu, Wang and Xia, FOCS'17). Moreover, we demonstrate that the efficient region
can exceed Shearer's bound by a constant by explicitly calculating the gaps on
several infinite lattices. The core of our proof is a new criterion on the
efficiency of the Moser-Tardos algorithm which takes the intersection between
dependent events into consideration. Our criterion is strictly better than
Shearer's bound whenever the intersection exists between dependent events.
Meanwhile, if any two dependent events are mutually exclusive, our criterion
becomes the Shearer's bound, which is known to be tight in this situation for
the Moser-Tardos algorithm (Kolipaka and Szegedy, STOC'11; Guo, Jerrum and Liu,
JACM'19).
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712589 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06531
|
Byeonggeun Kim
|
Byeonggeun Kim, Seunghan Yang, Jangho Kim, Simyung Chang
|
Domain Generalization on Efficient Acoustic Scene Classification using
Residual Normalization
|
Proceedings of the Detection and Classification of Acoustic Scenes
and Events 2021 Workshop (DCASE2021)
| null | null | null |
cs.SD cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It is a practical research topic how to deal with multi-device audio inputs
by a single acoustic scene classification system with efficient design. In this
work, we propose Residual Normalization, a novel feature normalization method
that uses frequency-wise normalization % instance normalization with a shortcut
path to discard unnecessary device-specific information without losing useful
information for classification. Moreover, we introduce an efficient
architecture, BC-ResNet-ASC, a modified version of the baseline architecture
with a limited receptive field. BC-ResNet-ASC outperforms the baseline
architecture even though it contains the small number of parameters. Through
three model compression schemes: pruning, quantization, and knowledge
distillation, we can reduce model complexity further while mitigating the
performance degradation. The proposed system achieves an average test accuracy
of 76.3% in TAU Urban Acoustic Scenes 2020 Mobile, development dataset with
315k parameters, and average test accuracy of 75.3% after compression to 61.0KB
of non-zero parameters. The proposed method won the 1st place in DCASE 2021
challenge, TASK1A.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71027 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06532
|
Xiaopeng Li
|
Xiao Peng Li, Qi Liu and Hing Cheung So
|
Nonlinear Tensor Ring Network
| null | null | null | null |
cs.LG cs.AI eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The state-of-the-art deep neural networks (DNNs) have been widely applied for
various real-world applications, and achieved significant performance for
cognitive problems. However, the increment of DNNs' width and depth in
architecture results in a huge amount of parameters to challenge the storage
and memory cost, limiting to the usage of DNNs on resource-constrained
platforms, such as portable devices. By converting redundant models into
compact ones, compression technique appears to be a practical solution to
reducing the storage and memory consumption. In this paper, we develop a
nonlinear tensor ring network (NTRN) in which both fullyconnected and
convolutional layers are compressed via tensor ring decomposition. Furthermore,
to mitigate the accuracy loss caused by compression, a nonlinear activation
function is embedded into the tensor contraction and convolution operations
inside the compressed layer. Experimental results demonstrate the effectiveness
and superiority of the proposed NTRN for image classification using two basic
neural networks, LeNet-5 and VGG-11 on three datasets, viz. MNIST, Fashion
MNIST and Cifar-10.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710057 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06537
|
Raul Astudillo
|
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy,
Peter I. Frazier
|
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
|
In Advances in Neural Information Processing Systems, 2021
| null | null | null |
cs.LG math.OC stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Bayesian optimization (BO) is a sample-efficient approach to optimizing
costly-to-evaluate black-box functions. Most BO methods ignore how evaluation
costs may vary over the optimization domain. However, these costs can be highly
heterogeneous and are often unknown in advance. This occurs in many practical
settings, such as hyperparameter tuning of machine learning algorithms or
physics-based simulation optimization. Moreover, those few existing methods
that acknowledge cost heterogeneity do not naturally accommodate a budget
constraint on the total evaluation cost. This combination of unknown costs and
a budget constraint introduces a new dimension to the exploration-exploitation
trade-off, where learning about the cost incurs the cost itself. Existing
methods do not reason about the various trade-offs of this problem in a
principled way, leading often to poor performance. We formalize this claim by
proving that the expected improvement and the expected improvement per unit of
cost, arguably the two most widely used acquisition functions in practice, can
be arbitrarily inferior with respect to the optimal non-myopic policy. To
overcome the shortcomings of existing approaches, we propose the budgeted
multi-step expected improvement, a non-myopic acquisition function that
generalizes classical expected improvement to the setting of heterogeneous and
unknown evaluation costs. Finally, we show that our acquisition function
outperforms existing methods in a variety of synthetic and real problems.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709416 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06539
|
Kota Dohi
|
Kota Dohi, Takashi Endo, Yohei Kawaguchi
|
Disentangling Physical Parameters for Anomalous Sound Detection Under
Domain Shifts
|
4 pages, 4 figures
| null | null | null |
eess.AS cs.SD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To develop a sound-monitoring system for machines, a method for detecting
anomalous sound under domain shifts is proposed. A domain shift occurs when a
machine's physical parameters change. Because a domain shift changes the
distribution of normal sound data, conventional unsupervised anomaly detection
methods can output false positives. To solve this problem, the proposed method
constrains some latent variables of a normalizing flows (NF) model to represent
physical parameters, which enables disentanglement of the factors of domain
shifts and learning of a latent space that is invariant with respect to these
domain shifts. Anomaly scores calculated from this domain-shift-invariant
latent space are unaffected by such shifts, which reduces false positives and
improves the detection performance. Experiments were conducted with sound data
from a slide rail under different operation velocities. The results show that
the proposed method disentangled the velocity to obtain a latent space that was
invariant with respect to domain shifts, which improved the AUC by 13.2% for
Glow with a single block and 2.6% for Glow with multiple blocks.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711644 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06540
|
Ali Akhavan-Safaei
|
Ali Akhavan-Safaei, Mohsen Zayernouri
|
A Nonlocal Spectral Transfer Model and New Scaling Law for Scalar
Turbulence
|
18 pages, 9 figures
| null | null | null |
physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
In this study, we revisit the spectral transfer model for the turbulent
intensity in the passive scalar transport (under large-scale anisotropic
forcing), and a subsequent modification to the scaling of scalar variance
cascade is presented. From the modified spectral transfer model, we obtain a
revised scalar transport model using fractional-order Laplacian operator that
facilitates the robust inclusion of the nonlocal effects originated from
large-scale anisotropy transferred across the multitude of scales in the
turbulent cascade. We provide an $\textit{a priori}$ estimate for the nonlocal
model based on the scaling analysis of scalar spectrum, and later examine our
developed model through direct numerical simulation. We present a detailed
analysis on the evolution of the scalar variance, high-order statistics of
scalar gradient, and important two-point statistical metrics of the turbulent
transport to make a comprehensive comparison between the nonlocal model and its
standard version. Finally, we present an analysis that seamlessly reconciles
the similarities between the developed model with the fractional-order
subgrid-scale scalar flux model for the large-eddy simulation (Akhavan-Safaei
et al. 2021) when the filter scale approaches the dissipative scales of
turbulent transport. In order to perform this task, we employ a Gaussian
process regression model to predict the model coefficient for the
fractional-order subgrid model.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711619 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06546
|
Weijie Liu
|
Weijie Liu, Chao Zhang, Nenggan Zheng, Hui Qian
|
Approximating Optimal Transport via Low-rank and Sparse Factorization
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Optimal transport (OT) naturally arises in a wide range of machine learning
applications but may often become the computational bottleneck. Recently, one
line of works propose to solve OT approximately by searching the
\emph{transport plan} in a low-rank subspace. However, the optimal transport
plan is often not low-rank, which tends to yield large approximation errors.
For example, when Monge's \emph{transport map} exists, the transport plan is
full rank. This paper concerns the computation of the OT distance with adequate
accuracy and efficiency. A novel approximation for OT is proposed, in which the
transport plan can be decomposed into the sum of a low-rank matrix and a sparse
one. We theoretically analyze the approximation error. An augmented Lagrangian
method is then designed to efficiently calculate the transport plan.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06548
|
Samuel Jackson
|
Daniel Keable, Alistair Jones, Samuel Krevor, Ann Muggeridge, Samuel
J. Jackson
|
The effect of viscosity ratio and Peclet number on miscible viscous
fingering in a Hele-Shaw cell: A combined numerical and experimental study
|
24 pages, 9 figures
| null | null | null |
physics.flu-dyn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The results from a series of well characterised, unstable, miscible
displacement experiments in a Hele Shaw cell with a quarter five-spot
source-sink geometry are presented, with comparisons to detailed numerical
simulation. We perform repeated experiments at adverse viscosity ratios from 1
- 20 and Peclet numbers from 10$^4$ - 10$^6$ capturing the transition from 2D
to 3D radial fingering and experimental uncertainty. The open-access dataset
provides time-lapse images of the fingering patterns, transient effluent
profiles, and meta-information for use in model validation. We find the
complexity of the fingering pattern increases with viscosity ratio and Peclet
number, and the onset of fingering is delayed compared to linear displacements,
likely due to Taylor dispersion stabilisation. The transition from 2D to 3D
fingering occurs at a critical Peclet number that is consistent with recent
experiments in the literature. 2D numerical simulations with hydrodynamic
dispersion and different mesh orientations provide good predictions of
breakthrough times and sweep efficiency obtained at intermediate Peclet numbers
across the range of viscosity ratios tested, generally within the experimental
uncertainty. Specific finger wavelengths, tip shapes, and growth are hard to
replicate; model predictions using velocity dependent longitudinal dispersion
or simple molecular diffusion bound the fingering evolution seen in the
experiments, but neither fully capture both fine-scale and macroscopic
measures. In both cases simulations predict sharper fingers than the
experiment. A weaker dispersion stabilisation seems necessary to capture the
experimental fingering at high viscosity ratio, which may also require
anisotropic components. 3D models with varying dispersion formulations should
be explored in future developments to capture the full range of effects at high
viscosity ratio and Peclet number.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.500671 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06552
|
Yu Li
|
Yu Li, Zijing Wang, Hehu Xie
|
GCGE: A Package for Solving Large Scale Eigenvalue Problems by Parallel
Block Damping Inverse Power Method
|
28 pages
| null | null |
GCGE-21-11
|
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose an eigensolver and the corresponding package, GCGE, for solving
large scale eigenvalue problems. This method is the combination of damping
idea, subspace projection method and inverse power method with dynamic shifts.
To reduce the dimensions of projection subspaces, a moving mechanism is
developed when the number of desired eigenpairs is large. The numerical
methods, implementing techniques and the structure of the package are
presented. Plenty of numerical results are provided to demonstrate the
efficiency, stability and scalability of the concerned eigensolver and the
package GCGE for computing many eigenpairs of large symmetric matrices arising
from applications.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.707632 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06553
|
Fei Tong
|
Ziyan Zhu and Fei Tong
|
The Distance Distribution between Mobile Node and Reference Node in
Regular Hexagon
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a new method to obtain the distance distribution between
the mobile node and any reference node in a regular hexagon. The existing
distance distribution research mainly focuses on static network deployment and
ignores node mobility. This paper studies the distribution of node distances
between mobile node and any reference node. A random waypoint (RWP) migration
model is adopted for mobile node. The Cumulative Distribution Function (CDF) of
the distance between any reference node (inside or outside the regular hexagon)
and the mobile node (inside the regular hexagon) is derived. The validity of
the results is verified by simulation.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06554
|
Yanhui Li
|
Y. H. Li, Y. X. Ren, Y. T. Su
|
A fourth-order finite difference scheme with accurate dispersion and
adaptive dissipation for computational aeroacoustics
| null | null | null | null |
physics.comp-ph
|
http://creativecommons.org/licenses/by/4.0/
|
For computational acoustics, schemes need to have low-dispersion and
low-dissipation properties in order to capture the amplitude and phase of the
wave correctly. To improve the spectral properties of the scheme, the authors
have previously proposed a scale sensor to automatically adjust the numerical
dissipation. In consequence, a fourth-order finite difference scheme with
minimized dispersion and adaptive dissipation (MDAD) has been proposed [1]. In
this study, we further investigate this method for the high-fidelity numerical
simulation of the acoustic problems and a new dispersion control method is
proposed which is different from the traditional dispersion relation preserving
(DRP) approach. Firstly, the scale sensor, which quantifies the local length
scale of the solution as the effective scaled wavenumber, is modified for
better performance on composite waves. Then the scale sensor is applied to
control both the dispersion and dissipation of the scheme. The relationships
between the dispersion/dissipation parameter and the effective scaled
wavenumber are analytically and artificially constructed respectively. Thus, a
fourth-order finite difference scheme with accurate dispersion and adaptive
dissipation (ADAD) is constructed. The approximate dispersion relation (ADR)
shows that the ADAD scheme achieves accurate dispersion property at k < 2.5.
The dissipation is negligible at low wave number and gradually increases after
k = 1 to suppress non-physical oscillations. Several benchmark cases of
computational acoustics are presented to verify the high resolution of the
proposed scheme compared with the conventional spectral optimized schemes.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71086 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06555
|
Wangyang Xu
|
Wangyang Xu, Lu Gan, and Chongwen Huang
|
A Robust Deep Learning-Based Beamforming Design for RIS-assisted
Multiuser MISO Communications with Practical Constraints
|
31 pages, 13 figures
| null | null | null |
cs.IT cs.LG eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reconfigurable intelligent surface (RIS) has become a promising technology to
improve wireless communication in recent years. It steers the incident signals
to create a favorable propagation environment by controlling the reconfigurable
passive elements with less hardware cost and lower power consumption. In this
paper, we consider a RIS-aided multiuser multiple-input single-output downlink
communication system. We aim to maximize the weighted sum-rate of all users by
joint optimizing the active beamforming at the access point and the passive
beamforming vector of the RIS elements. Unlike most existing works, we consider
the more practical situation with the discrete phase shifts and imperfect
channel state information (CSI). Specifically, for the situation that the
discrete phase shifts and perfect CSI are considered, we first develop a deep
quantization neural network (DQNN) to simultaneously design the active and
passive beamforming while most reported works design them alternatively. Then,
we propose an improved structure (I-DQNN) based on DQNN to simplify the
parameters decision process when the control bits of each RIS element are
greater than 1 bit. Finally, we extend the two proposed DQNN-based algorithms
to the case that the discrete phase shifts and imperfect CSI are considered
simultaneously. Our simulation results show that the two DQNN-based algorithms
have better performance than traditional algorithms in the perfect CSI case,
and are also more robust in the imperfect CSI case.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06562
|
Zhaozhuo Xu
|
Zhaozhuo Xu, Alan Baonan Ji, Andrew Woods, Beidi Chen and Anshumali
Shrivastava
|
Satellite Images and Deep Learning to Identify Discrepancy in Mailing
Addresses with Applications to Census 2020 in Houston
| null | null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The accuracy and completeness of population estimation would significantly
impact the allocation of public resources. However, the current census paradigm
experiences a non-negligible level of under-counting. Existing solutions to
this problem by the Census Bureau is to increase canvassing efforts, which
leads to expensive and inefficient usage of human resources. In this work, we
argue that the existence of hidden multi-family households is a significant
cause of under-counting. Accordingly, we introduce a low-cost but high-accuracy
method that combines satellite imagery and deep learning technologies to
identify hidden multi-family (HMF) households. With comprehensive knowledge of
the HMF households, the efficiency and effectiveness of the decennial census
could be vastly improved. An extensive experiment demonstrates that our
approach can discover over 1800 undetected HMF in a single zipcode of the
Houston area.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709629 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06563
|
Adel Nadjaran Toosi
|
Hamza Javed, Adel N. Toosi, Mohammad S. Aslanpour
|
Serverless Platforms on the Edge: A Performance Analysis
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
The exponential growth of Internet of Things (IoT) has given rise to a new
wave of edge computing due to the need to process data on the edge, closer to
where it is being produced and attempting to move away from a cloud-centric
architecture. This provides its own opportunity to decrease latency and address
data privacy concerns along with the ability to reduce public cloud costs. The
serverless computing model provides a potential solution with its event-driven
architecture to reduce the need for ever-running servers and convert the
backend services to an as-used model. This model is an attractive prospect in
edge computing environments with varying workloads and limited resources.
Furthermore, its setup on the edge of the network promises reduced latency to
the edge devices communicating with it and eliminates the need to manage the
underlying infrastructure. In this book chapter, first, we introduce the novel
concept of serverless edge computing, then, we analyze the performance of
multiple serverless platforms, namely, OpenFaaS, AWS Greengrass, Apache
OpenWhisk, when set up on the single-board computers (SBCs) on the edge and
compare it with public cloud serverless offerings, namely, AWS Lambda and Azure
Functions, to deduce the suitability of serverless architectures on the network
edge. These serverless platforms are set up on a cluster of Raspberry Pis and
we evaluate their performance by simulating different types of edge workloads.
The evaluation results show that OpenFaaS achieves the lowest response time on
the SBC edge computing infrastructure while serverless cloud offerings are the
most reliable with the highest success rate.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710829 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06574
|
Milton Kumar Kundu
|
Md. Ibrahim, A. S. M. Badrudduza, Md. Shakhawat Hossen, M. K. Kundu,
Imran Shafique Ansari
|
On Effective Secrecy Throughput of Underlay Spectrum Sharing
$\alpha$-$\mu$/ M\'alaga Hybrid Model under Interference-and-Transmit Power
Constraints
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The underlay cognitive radio-based hybrid radio frequency / free-space
optical (RF / FSO) systems have been emerged as a promising technology due to
its ability to eliminate spectrum scarcity and spectrum under-utilization
problems. Consequently, this work analyzes the physical layer security aspects
of a cognitive RF / FSO hybrid network that includes a primary user, a
secondary source, a secondary receiver, and an eavesdropper where the secret
communication takes place between two legitimate secondary peers over the RF
and FSO links simultaneously, and the eavesdropper can overhear the RF link
only. In particular, the maximum transmit power limitation at the secondary
user as well as the permissible interference power restriction at the primary
user are also taken into consideration. All the RF links are modeled with
$\alpha$-$\mu$ fading whereas the FSO link undergoes M\'alaga (M) turbulence
with link blockage and pointing error impairments. At the receiver, the
selection combining diversity technique is utilized to select the signal with
the best electrical signal-to-ratio (SNR). Moreover, the closed-form
expressions for the secrecy outage probability, probability of strictly
positive secrecy capacity, and effective secrecy throughput are derived to
analyze the secrecy performance. Besides, the impacts of fading,
primary-secondary interference, detection techniques, link blockage
probability, atmospheric turbulence, and pointing error are examined. Finally,
Monte-Carlo simulations are performed to corroborate the derived expressions.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710622 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06578
|
Xiyang Liu
|
Xiyang Liu, Weihao Kong, Sewoong Oh
|
Differential privacy and robust statistics in high dimensions
| null | null | null | null |
math.ST cs.CR cs.IT cs.LG math.IT stat.ML stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a universal framework for characterizing the statistical
efficiency of a statistical estimation problem with differential privacy
guarantees. Our framework, which we call High-dimensional Propose-Test-Release
(HPTR), builds upon three crucial components: the exponential mechanism, robust
statistics, and the Propose-Test-Release mechanism. Gluing all these together
is the concept of resilience, which is central to robust statistical
estimation. Resilience guides the design of the algorithm, the sensitivity
analysis, and the success probability analysis of the test step in
Propose-Test-Release. The key insight is that if we design an exponential
mechanism that accesses the data only via one-dimensional robust statistics,
then the resulting local sensitivity can be dramatically reduced. Using
resilience, we can provide tight local sensitivity bounds. These tight bounds
readily translate into near-optimal utility guarantees in several cases. We
give a general recipe for applying HPTR to a given instance of a statistical
estimation problem and demonstrate it on canonical problems of mean estimation,
linear regression, covariance estimation, and principal component analysis. We
introduce a general utility analysis technique that proves that HPTR nearly
achieves the optimal sample complexity under several scenarios studied in the
literature.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709617 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06580
|
Moontae Lee
|
Moontae Lee, Sungjun Cho, Kun Dong, David Mimno, David Bindel
|
On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Across many data domains, co-occurrence statistics about the joint appearance
of objects are powerfully informative. By transforming unsupervised learning
problems into decompositions of co-occurrence statistics, spectral algorithms
provide transparent and efficient algorithms for posterior inference such as
latent topic analysis and community detection. As object vocabularies grow,
however, it becomes rapidly more expensive to store and run inference
algorithms on co-occurrence statistics. Rectifying co-occurrence, the key
process to uphold model assumptions, becomes increasingly more vital in the
presence of rare terms, but current techniques cannot scale to large
vocabularies. We propose novel methods that simultaneously compress and rectify
co-occurrence statistics, scaling gracefully with the size of vocabulary and
the dimension of latent space. We also present new algorithms learning latent
variables from the compressed statistics, and verify that our methods perform
comparably to previous approaches on both textual and non-textual data.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06582
|
Hongping Liu
|
M. H. Cai, Z. S. Xu, S. H. You, H. P. Liu
|
Sensitivity improvement of Rydberg atom-based microwave sensing via
electromagnetically induced transparency
| null | null | null | null |
physics.atom-ph
|
http://creativecommons.org/licenses/by/4.0/
|
A highly excited Rydberg atom via electromagnetically induced transparency
with two color cascading lasers has extreme sensitivity to electric fields of
microwave ranging from 100 MHz to over 1 THz. It can be used as susceptible
atom-based microwave communication antennas where the carrier wave usually
works exactly resonant to the transition between a pair of adjacent Rydberg
states with large electric dipole moment. A technique of superheterodyne with a
strong on-resonant local microwave oscillator is employed to induce
considerable Autler-Townes splitting where the antennas has a highest dynamic
response to another weak target signal microwave carrier. To further improve
the sensitivity of atomic antenna in communication, we detune the carrier
microwave frequency off resonance forming an asymmetrically optical splitting
and fix the coupling laser frequency at the shoulder of the stronger one, and
optimize the local field strength simultaneously. It gives a sensitivity of
12.50(04) $\rm{nVcm^{-1}\cdot Hz^{-1/2}}$. Its enhancement mechanism of
sensitivity is also proved by a theoretical simulation.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06584
|
John Demme PhD
|
John Demme (Microsoft)
|
Elastic Silicon Interconnects: Abstracting Communication in Accelerator
Design
| null | null | null | null |
cs.AR cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Communication is an important part of accelerator design, though it is under
researched and under developed. Today, designers often face relatively
low-level communication tools requiring them to design straightforward but
error-prone plumbing. In this paper, we argue that raising the level of
abstraction could yield correctness, productivity, and performance benefits not
only for RTL-level designers but also for high level language developers.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71287 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06590
|
Sayak Mukherjee
|
Sayak Mukherjee, Ramij R. Hossain
|
Data-Driven Pole Placement in LMI Regions with Robustness Constraints
|
This version contains 12 pages
| null | null | null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes a robust learning methodology to place the closed-loop
poles in desired convex regions in the complex plane. We considered the system
state and input matrices to be unknown and can only use the measurements of the
system trajectories. The closed-loop pole placement problem in the linear
matrix inequality (LMI) regions is considered a classic robust control problem;
however, that requires knowledge about the state and input matrices of the
linear system. We bring in ideas from the behavioral system theory and
persistency of excitation condition-based fundamental lemma to develop a
data-driven counterpart that satisfies multiple closed-loop robustness
specifications, such as $\mathcal{D}$-stability and mixed $H_2/H_{\infty}$
performance specifications. Our formulations lead to data-driven semi-definite
programs (SDPs) that are coupled with sufficient theoretical guarantees. We
validate the theoretical results with numerical simulations on a third-order
dynamic system.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06593
|
Yanyi Ding
|
Yanyi Ding, Zhiyi Kuang, Yuxin Pei, Jeff Tan, Ziyu Zhang, Joseph Konan
|
Using Deep Learning Sequence Models to Identify SARS-CoV-2 Divergence
| null | null | null | null |
q-bio.QM cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
SARS-CoV-2 is an upper respiratory system RNA virus that has caused over 3
million deaths and infecting over 150 million worldwide as of May 2021. With
thousands of strains sequenced to date, SARS-CoV-2 mutations pose significant
challenges to scientists on keeping pace with vaccine development and public
health measures. Therefore, an efficient method of identifying the divergence
of lab samples from patients would greatly aid the documentation of SARS-CoV-2
genomics. In this study, we propose a neural network model that leverages
recurrent and convolutional units to directly take in amino acid sequences of
spike proteins and classify corresponding clades. We also compared our model's
performance with Bidirectional Encoder Representations from Transformers (BERT)
pre-trained on protein database. Our approach has the potential of providing a
more computationally efficient alternative to current homology based
intra-species differentiation.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06596
|
Nasir Saeed
|
Zakria Qadir, Hafiz Suliman Munawar, Nasir Saeed and Khoa Le
|
Towards 6G Internet of Things: Recent Advances, Use Cases, and Open
Challenges
|
Submitted to IEEE IoT Journal
| null | null | null |
eess.SY cs.NI cs.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Smart services based on the Internet of Everything (IoE) are gaining
considerable popularity due to the ever-increasing demands of wireless
networks. This demands the appraisal of the wireless networks with enhanced
properties as next-generation communication systems. Although 5G networks show
great potential to support numerous IoE based services, it is not adequate to
meet the complete requirements of the new smart applications. Therefore, there
is an increased demand for envisioning the 6G wireless communication systems to
overcome the major limitations in the existing 5G networks. Moreover,
incorporating artificial intelligence in 6G will provide solutions for very
complex problems relevant to network optimization. Furthermore, to add further
value to the future 6G networks, researchers are investigating new
technologies, such as THz and quantum communications. The requirements of
future 6G wireless communications demand to support massive data-driven
applications and the increasing number of users. This paper presents recent
advances in the 6G wireless networks, including the evolution from 1G to 5G
communications, the research trends for 6G, enabling technologies, and
state-of-the-art 6G projects.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709617 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06599
|
Parth Patwa
|
Mohsin Ali, Kandukuri Sai Teja, Sumanth Manduru, Parth Patwa, Amitava
Das
|
PESTO: Switching Point based Dynamic and Relative Positional Encoding
for Code-Mixed Languages
|
Accepted as Student Abstract at AAAI 2022
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
NLP applications for code-mixed (CM) or mix-lingual text have gained a
significant momentum recently, the main reason being the prevalence of language
mixing in social media communications in multi-lingual societies like India,
Mexico, Europe, parts of USA etc. Word embeddings are basic build-ing blocks of
any NLP system today, yet, word embedding for CM languages is an unexplored
territory. The major bottleneck for CM word embeddings is switching points,
where the language switches. These locations lack in contextually and
statistical systems fail to model this phenomena due to high variance in the
seen examples. In this paper we present our initial observations on applying
switching point based positional encoding techniques for CM language,
specifically Hinglish (Hindi - English). Results are only marginally better
than SOTA, but it is evident that positional encoding could bean effective way
to train position sensitive language models for CM text.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709233 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06601
|
Milos Cernak
|
Damien Ronssin, Milos Cernak
|
AC-VC: Non-parallel Low Latency Phonetic Posteriorgrams Based Voice
Conversion
|
ASRU 2021
| null | null | null |
eess.AS cs.SD
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents AC-VC (Almost Causal Voice Conversion), a phonetic
posteriorgrams based voice conversion system that can perform any-to-many voice
conversion while having only 57.5 ms future look-ahead. The complete system is
composed of three neural networks trained separately with non-parallel data.
While most of the current voice conversion systems focus primarily on quality
irrespective of algorithmic latency, this work elaborates on designing a method
using a minimal amount of future context thus allowing a future real-time
implementation. According to a subjective listening test organized in this
work, the proposed AC-VC system achieves parity with the non-causal ASR-TTS
baseline of the Voice Conversion Challenge 2020 in naturalness with a MOS of
3.5. In contrast, the results indicate that missing future context impacts
speaker similarity. Obtained similarity percentage of 65% is lower than the
similarity of current best voice conversion systems.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709416 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06609
|
Mohammad Abu Hamed
|
Mohammad Abu Hamed and Alexander A. Nepomnyashchy
|
Phase field model for phagocytosis dynamics
|
arXiv admin note: text overlap with arXiv:2106.12799
| null | null | null |
physics.bio-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The basic process of the innate immune system when phagocyte (white blood
cell) engulf or swallow a target particle (bacterium or dead cell), is called
phagocytosis. We apply the phase field approach in the spirit of [1], that
couples the order parameter $u$ with 3D polarization (orientation) vector field
$\textbf{P}$ of the actin network of the phagocyte cytoskeleton. We derive a
single closed scalar integro-differential equation governing the 3D phagocyte
membrane dynamics during bead engulfment, which includes the normal velocity of
the membrane, curvature, volume relaxation rate, a function determined by the
molecular effects of the subcell level, and the adhesion effect of the
motionless rigid spherical bead. This equation is easily solved numerically.
The simulation manifests the pedestal and the cup phases but not the final
complete bead internalization.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710998 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06623
|
Iurii Timrov
|
Iurii Timrov, Nathalie Vast, Ralph Gebauer, Stefano Baroni
|
turboEELS -- A code for the simulation of the electron energy loss and
inelastic X-ray scattering spectra using the Liouville-Lanczos approach to
time-dependent density-functional perturbation theory
| null |
Comput. Phys. Commun. 196, 460 (2015)
|
10.1016/j.cpc.2015.05.021
| null |
cond-mat.mtrl-sci physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce turboEELS, an implementation of the Liouville-Lanczos approach
to linearized time-dependent density-functional theory, designed to simulate
electron energy loss and inelastic X-ray scattering spectra in periodic solids.
turboEELS is open-source software distributed under the terms of the GPL as a
component of Quantum ESPRESSO. As with other components, turboEELS is optimized
to run on a variety of different platforms, from laptops to massively parallel
architectures, using native mathematical libraries (LAPACK and FFTW) and a
hierarchy of custom parallelization layers built on top of MPI.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06625
|
Ovishake Sen
|
Ovishake Sen, Al-Mahmud and Pias Roy
|
A Convolutional Neural Network Based Approach to Recognize Bangla Spoken
Digits from Speech Signal
|
4 pages, 5 figures, 2021 International Conference on Electronics,
Communications and Information Technology (ICECIT), 14 to 16 September 2021,
Khulna, Bangladesh
| null | null | null |
cs.SD cs.AI cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Speech recognition is a technique that converts human speech signals into
text or words or in any form that can be easily understood by computers or
other machines. There have been a few studies on Bangla digit recognition
systems, the majority of which used small datasets with few variations in
genders, ages, dialects, and other variables. Audio recordings of Bangladeshi
people of various genders, ages, and dialects were used to create a large
speech dataset of spoken '0-9' Bangla digits in this study. Here, 400 noisy and
noise-free samples per digit have been recorded for creating the dataset. Mel
Frequency Cepstrum Coefficients (MFCCs) have been utilized for extracting
meaningful features from the raw speech data. Then, to detect Bangla numeral
digits, Convolutional Neural Networks (CNNs) were utilized. The suggested
technique recognizes '0-9' Bangla spoken digits with 97.1% accuracy throughout
the whole dataset. The efficiency of the model was also assessed using 10-fold
crossvalidation, which yielded a 96.7% accuracy.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.706418 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06626
|
Serhat Tonkul
|
Serhat Tonkul, Alper Baba, Mustafa M. Demir, Simona Regenspurg
|
Characterization of Sb scaling and fluids in saline geothermal power
plants: A case study for Germencik Region (B\"uy\"uk Menderes Graben, Turkey)
| null |
Geothermics 96 (2021) 102227
|
10.1016/j.geothermics.2021.102227
| null |
physics.geo-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Turkey is located on the seismically active Alpine-Himalayan belt. Although
tectonic activity causes seismicity in the Anatolian plate, it also constitutes
an important geothermal energy resource. Today, geothermal energy production is
heavily concentrated in Turkey's Western Anatolia region. Graben systems in
this region are very suitable for geothermal resources. The B\"uy\"uk Menderes
Graben (BMG) is an area of complex geology with active tectonics and high
geothermal potential power. Germencik (Aydin) is located in the BMG, where the
geothermal waters include mainly Na-Cl-HCO3 water types. This study examined
the stibnite scaling formed in the preheater system of the Germencik Geothermal
Field (GGF). The formation of the stibnite scaling on the preheater system
dramatically reduces the energy harvesting of the GGF. Considering the stibnite
scaling in the surface equipment, the optimum reinjection temperature was
determined as 95 $^\circ$C to prevent stibnite scaling in the GGF.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06638
|
Yunxiao Qin
|
Yunxiao Qin, Zitong Yu, Longbin Yan, Zezheng Wang, Chenxu Zhao, Zhen
Lei
|
Meta-Teacher For Face Anti-Spoofing
|
Accepted by IEEE TPAMI-2021
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Face anti-spoofing (FAS) secures face recognition from presentation attacks
(PAs). Existing FAS methods usually supervise PA detectors with handcrafted
binary or pixel-wise labels. However, handcrafted labels may are not the most
adequate way to supervise PA detectors learning sufficient and intrinsic
spoofing cues. Instead of using the handcrafted labels, we propose a novel
Meta-Teacher FAS (MT-FAS) method to train a meta-teacher for supervising PA
detectors more effectively. The meta-teacher is trained in a bi-level
optimization manner to learn the ability to supervise the PA detectors learning
rich spoofing cues. The bi-level optimization contains two key components: 1) a
lower-level training in which the meta-teacher supervises the detector's
learning process on the training set; and 2) a higher-level training in which
the meta-teacher's teaching performance is optimized by minimizing the
detector's validation loss. Our meta-teacher differs significantly from
existing teacher-student models because the meta-teacher is explicitly trained
for better teaching the detector (student), whereas existing teachers are
trained for outstanding accuracy neglecting teaching ability. Extensive
experiments on five FAS benchmarks show that with the proposed MT-FAS, the
trained meta-teacher 1) provides better-suited supervision than both
handcrafted labels and existing teacher-student models; and 2) significantly
improves the performances of PA detectors.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06639
|
Anay Majee
|
Ashutosh Agarwal and Anay Majee and Anbumani Subramanian and Chetan
Arora
|
Attention Guided Cosine Margin For Overcoming Class-Imbalance in
Few-Shot Road Object Detection
|
8 pages, 4 figures
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Few-shot object detection (FSOD) localizes and classifies objects in an image
given only a few data samples. Recent trends in FSOD research show the adoption
of metric and meta-learning techniques, which are prone to catastrophic
forgetting and class confusion. To overcome these pitfalls in metric learning
based FSOD techniques, we introduce Attention Guided Cosine Margin (AGCM) that
facilitates the creation of tighter and well separated class-specific feature
clusters in the classification head of the object detector. Our novel Attentive
Proposal Fusion (APF) module minimizes catastrophic forgetting by reducing the
intra-class variance among co-occurring classes. At the same time, the proposed
Cosine Margin Cross-Entropy loss increases the angular margin between confusing
classes to overcome the challenge of class confusion between already learned
(base) and newly added (novel) classes. We conduct our experiments on the
challenging India Driving Dataset (IDD), which presents a real-world
class-imbalanced setting alongside popular FSOD benchmark PASCAL-VOC. Our
method outperforms State-of-the-Art (SoTA) approaches by up to 6.4 mAP points
on the IDD-OS and up to 2.0 mAP points on the IDD-10 splits for the 10-shot
setting. On the PASCAL-VOC dataset, we outperform existing SoTA approaches by
up to 4.9 mAP points.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06643
|
Florian Henkel
|
Florian Henkel, Stephanie Schwaiger, Gerhard Widmer
|
Fully Automatic Page Turning on Real Scores
|
ISMIR 2021 Late Breaking/Demo
| null | null | null |
cs.SD cs.CV cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a prototype of an automatic page turning system that works
directly on real scores, i.e., sheet images, without any symbolic
representation. Our system is based on a multi-modal neural network
architecture that observes a complete sheet image page as input, listens to an
incoming musical performance, and predicts the corresponding position in the
image. Using the position estimation of our system, we use a simple heuristic
to trigger a page turning event once a certain location within the sheet image
is reached. As a proof of concept we further combine our system with an actual
machine that will physically turn the page on command.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.703957 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06644
|
Qinxuan Wu
|
Qinxuan Wu and Allyson Ettinger
|
Variation and generality in encoding of syntactic anomaly information in
sentence embeddings
|
BlackBoxNLP, EMNLP
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
While sentence anomalies have been applied periodically for testing in NLP,
we have yet to establish a picture of the precise status of anomaly information
in representations from NLP models. In this paper we aim to fill two primary
gaps, focusing on the domain of syntactic anomalies. First, we explore
fine-grained differences in anomaly encoding by designing probing tasks that
vary the hierarchical level at which anomalies occur in a sentence. Second, we
test not only models' ability to detect a given anomaly, but also the
generality of the detected anomaly signal, by examining transfer between
distinct anomaly types. Results suggest that all models encode some information
supporting anomaly detection, but detection performance varies between
anomalies, and only representations from more recent transformer models show
signs of generalized knowledge of anomalies. Follow-up analyses support the
notion that these models pick up on a legitimate, general notion of sentence
oddity, while coarser-grained word position information is likely also a
contributor to the observed anomaly detection.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708377 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06647
|
Aseem Srivastava
|
Ganeshan Malhotra, Abdul Waheed, Aseem Srivastava, Md Shad Akhtar,
Tanmoy Chakraborty
|
Speaker and Time-aware Joint Contextual Learning for Dialogue-act
Classification in Counselling Conversations
|
9 pages; Accepted to WSDM 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The onset of the COVID-19 pandemic has brought the mental health of people
under risk. Social counselling has gained remarkable significance in this
environment. Unlike general goal-oriented dialogues, a conversation between a
patient and a therapist is considerably implicit, though the objective of the
conversation is quite apparent. In such a case, understanding the intent of the
patient is imperative in providing effective counselling in therapy sessions,
and the same applies to a dialogue system as well. In this work, we take
forward a small but an important step in the development of an automated
dialogue system for mental-health counselling. We develop a novel dataset,
named HOPE, to provide a platform for the dialogue-act classification in
counselling conversations. We identify the requirement of such conversation and
propose twelve domain-specific dialogue-act (DAC) labels. We collect 12.9K
utterances from publicly-available counselling session videos on YouTube,
extract their transcripts, clean, and annotate them with DAC labels. Further,
we propose SPARTA, a transformer-based architecture with a novel speaker- and
time-aware contextual learning for the dialogue-act classification. Our
evaluation shows convincing performance over several baselines, achieving
state-of-the-art on HOPE. We also supplement our experiments with extensive
empirical and qualitative analyses of SPARTA.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.712038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06649
|
John Paul Miranda
|
John Paul P. Miranda
|
Dataset of Philippine Presidents Speeches from 1935 to 2016
|
11 pages, 4 figures, 4 tables, dataset
|
International Journal of Computing Sciences Research 5 (2021) 1-11
|
10.25147/ijcsr.2017.001.1.72
| null |
cs.CY cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
The dataset was collected to examine and identify possible key topics within
these texts. Data preparation such as data cleaning, transformation,
tokenization, removal of stop words from both English and Filipino, and word
stemming was employed in the dataset before feeding it to sentiment analysis
and the LDA model. The topmost occurring word within the dataset is
"development" and there are three (3) likely topics from the speeches of
Philippine presidents: economic development, enhancement of public services,
and addressing challenges. The dataset was able to provide valuable insights
contained among official documents. While the study showed that presidents have
used their annual address to express their visions for the country. It also
presented that the presidents from 1935 to 2016 faced the same problems during
their term. Future researchers may collect other speeches made by presidents
during their term; combine them to the dataset used in this study to further
investigate these important texts by subjecting them to the same methodology
used in this study. The dataset may be requested from the authors and it is
recommended for further analysis. For example, determine how the speeches of
the president reflect the preamble or foundations of the Philippine
constitution.
| 2021-11-15T00:00:00 |
new_dataset
| true | 0.686987 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06650
|
Yonggang Jiang
|
Yonggang Jiang, Chaodong Zheng
|
Robust and Optimal Contention Resolution without Collision Detection
| null | null | null | null |
cs.DC cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the classical contention resolution problem where nodes arrive
over time, each with a message to send. In each synchronous slot, each node can
send or remain idle. If in a slot one node sends alone, it succeeds; otherwise,
if multiple nodes send simultaneously, messages collide and none succeeds.
Nodes can differentiate collision and silence only if collision detection is
available. Ideally, a contention resolution algorithm should satisfy three
criteria: low time complexity (or high throughput); low energy complexity,
meaning each node does not make too many broadcast attempts; strong robustness,
meaning the algorithm can maintain good performance even if slots can be
jammed. Previous work has shown, with collision detection, there are "perfect"
contention resolution algorithms satisfying all three criteria. On the other
hand, without collision detection, it was not until 2020 that an algorithm was
discovered which can achieve optimal time complexity and low energy cost,
assuming there is no jamming. More recently, the trade-off between throughput
and robustness was studied. However, an intriguing and important question
remains unknown: without collision detection, are there robust algorithms
achieving both low total time complexity and low per-node energy cost? In this
paper, we answer the above question affirmatively. Specifically, we develop a
new randomized algorithm for robust contention resolution without collision
detection. Lower bounds show that it has both optimal time and energy
complexity. If all nodes start execution simultaneously, we design another
algorithm that is even faster, with similar energy complexity as the first
algorithm. The separation on time complexity suggests for robust contention
resolution without collision detection, ``batch'' instances (nodes start
simultaneously) are inherently easier than ``scattered'' ones (nodes arrive
over time).
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711469 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06660
|
Silvia-Laura Pintea
|
Nikhil Saldanha, Silvia L. Pintea, Jan C. van Gemert, Nergis Tomen
|
Frequency learning for structured CNN filters with Gaussian fractional
derivatives
|
Accepted at BMVC 2021
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Frequency information lies at the base of discriminating between textures,
and therefore between different objects. Classical CNN architectures limit the
frequency learning through fixed filter sizes, and lack a way of explicitly
controlling it. Here, we build on the structured receptive field filters with
Gaussian derivative basis. Yet, rather than using predetermined derivative
orders, which typically result in fixed frequency responses for the basis
functions, we learn these. We show that by learning the order of the basis we
can accurately learn the frequency of the filters, and hence adapt to the
optimal frequencies for the underlying learning task. We investigate the
well-founded mathematical formulation of fractional derivatives to adapt the
filter frequencies during training. Our formulation leads to parameter savings
and data efficiency when compared to the standard CNNs and the Gaussian
derivative CNN filter networks that we build upon.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709265 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06661
|
Viola Wenz
|
Viola Wenz, Arno Kesper, Gabriele Taentzer
|
Detecting Quality Problems in Data Models by Clustering Heterogeneous
Data Values
|
17 pages. This paper is an extended version of a paper to be
published in "MoDELS '21: ACM/IEEE 24th International Conference on Model
Driven Engineering Languages and Systems: Companion Proceedings". It was
presented at the 3rd Workshop on Artificial Intelligence and Model-driven
Engineering
| null | null | null |
cs.LG cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data is of high quality if it is fit for its intended use. The quality of
data is influenced by the underlying data model and its quality. One major
quality problem is the heterogeneity of data as quality aspects such as
understandability and interoperability are impaired. This heterogeneity may be
caused by quality problems in the data model. Data heterogeneity can occur in
particular when the information given is not structured enough and just
captured in data values, often due to missing or non-suitable structure in the
underlying data model. We propose a bottom-up approach to detecting quality
problems in data models that manifest in heterogeneous data values. It supports
an explorative analysis of the existing data and can be configured by domain
experts according to their domain knowledge. All values of a selected data
field are clustered by syntactic similarity. Thereby an overview of the data
values' diversity in syntax is provided. It shall help domain experts to
understand how the data model is used in practice and to derive potential
quality problems of the data model. We outline a proof-of-concept
implementation and evaluate our approach using cultural heritage data.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.7116 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06662
|
Monika Syga
|
Agnieszka Kaliszewska and Monika Syga
|
A comprehensive study of clustering a class of 2D shapes
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The paper concerns clustering with respect to the shape and size of 2D
contours that are boundaries of cross-sections of 3D objects of revolution. We
propose a number of similarity measures based on combined disparate Procrustes
analysis (PA) and Dynamic Time Warping (DTW) distances. Motivation and the main
application for this study comes from archaeology. The performed computational
experiments refer to the clustering of archaeological pottery.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.706222 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06664
|
Yi Huang
|
Igor Kulev, Berkay K\"opr\"u, Raul Rodriguez-Esteban, Diego Saldana,
Yi Huang, Alessandro La Torraca, Elif Ozkirimli
|
Extraction of Medication Names from Twitter Using Augmentation and an
Ensemble of Language Models
|
Proceedings of the BioCreative VII Challenge Evaluation Workshop
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The BioCreative VII Track 3 challenge focused on the identification of
medication names in Twitter user timelines. For our submission to this
challenge, we expanded the available training data by using several data
augmentation techniques. The augmented data was then used to fine-tune an
ensemble of language models that had been pre-trained on general-domain Twitter
content. The proposed approach outperformed the prior state-of-the-art
algorithm Kusuri and ranked high in the competition for our selected objective
function, overlapping F1 score.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712238 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06667
|
Anja Thieme Dr
|
Anja Thieme
|
Understanding the Information Needs and Practices of Human Supporters of
an Online Mental Health Intervention to Inform Machine Learning Applications
|
41 pages, 3 figures, 3 tables
| null | null | null |
cs.HC cs.CY cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In the context of digital therapy interventions, such as internet-delivered
Cognitive Behavioral Therapy (iCBT) for the treatment of depression and
anxiety, extensive research has shown how the involvement of a human supporter
or coach, who assists the person undergoing treatment, improves user engagement
in therapy and leads to more effective health outcomes than unsupported
interventions. Seeking to maximize the effects and outcomes of this human
support, the research investigates how new opportunities provided through
recent advances in the field of AI and machine learning (ML) can contribute
useful data insights to effectively support the work practices of iCBT
supporters. This paper reports detailed findings of an interview study with 15
iCBT supporters that deepens understanding of their existing work practices and
information needs with the aim to meaningfully inform the development of
useful, implementable ML applications particularly in the context of iCBT
treatment for depression and anxiety. The analysis contributes (1) a set of six
themes that summarize the strategies and challenges that iCBT supporters
encounter in providing effective, personalized feedback to their mental health
clients; and in response to these learnings, (2) presents for each theme
concrete opportunities for how methods of ML could help support and address
identified challenges and information needs. It closes with reflections on
potential social, emotional and pragmatic implications of introducing new
machine-generated data insights within supporter-led client review practices.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711844 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06670
|
Ebenezer Isaac
|
Ebenezer R.H.P. Isaac
|
Robust Analytics for Video-Based Gait Biometrics
|
Ph.D. Thesis, Anna University, Chennai, Feb. 2018
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Gait analysis is the study of the systematic methods that assess and quantify
animal locomotion. Gait finds a unique importance among the many
state-of-the-art biometric systems since it does not require the subject's
cooperation to the extent required by other modalities. Hence by nature, it is
an unobtrusive biometric.
This thesis discusses both hard and soft biometric characteristics of gait.
It shows how to identify gender based on gait alone through the Posed-Based
Voting scheme. It then describes improving gait recognition accuracy using
Genetic Template Segmentation. Members of a wide population can be
authenticated using Multiperson Signature Mapping. Finally, the mapping can be
improved in a smaller population using Bayesian Thresholding. All methods
proposed in this thesis have outperformed their existing state of the art with
adequate experimentation and results.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709233 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06676
|
Rick Fritschek
|
Gerhard Wunder, Benedikt Gro{\ss}, Rick Fritschek, Rafael F. Schaefer
|
A Reverse Jensen Inequality Result with Application to Mutual
Information Estimation
|
6 pages, ITW 2021
| null | null | null |
cs.IT cs.LG math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Jensen inequality is a widely used tool in a multitude of fields, such as
for example information theory and machine learning. It can be also used to
derive other standard inequalities such as the inequality of arithmetic and
geometric means or the H\"older inequality. In a probabilistic setting, the
Jensen inequality describes the relationship between a convex function and the
expected value. In this work, we want to look at the probabilistic setting from
the reverse direction of the inequality. We show that under minimal constraints
and with a proper scaling, the Jensen inequality can be reversed. We believe
that the resulting tool can be helpful for many applications and provide a
variational estimation of mutual information, where the reverse inequality
leads to a new estimator with superior training behavior compared to current
estimators.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709849 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06677
|
Xue Yang
|
Xue Yang, Yue Zhou, Junchi Yan
|
AlphaRotate: A Rotation Detection Benchmark using TensorFlow
|
7 pages, 1 figure, 1 table
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
AlphaRotate is an open-source Tensorflow benchmark for performing scalable
rotation detection on various datasets. It currently provides more than 18
popular rotation detection models under a single, well-documented API designed
for use by both practitioners and researchers. AlphaRotate regards high
performance, robustness, sustainability and scalability as the core concept of
design, and all models are covered by unit testing, continuous integration,
code coverage, maintainability checks, and visual monitoring and analysis.
AlphaRotate can be installed from PyPI and is released under the Apache-2.0
License. Source code is available at
https://github.com/yangxue0827/RotationDetection.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708629 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06685
|
Kunal Dahiya
|
Kunal Dahiya, Deepak Saini, Anshul Mittal, Ankush Shaw, Kushal Dave,
Akshay Soni, Himanshu Jain, Sumeet Agarwal, Manik Varma
|
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short
Text Documents
| null |
Web Search and Data Mining 2021
|
10.1145/3437963.3441810
| null |
cs.LG cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Scalability and accuracy are well recognized challenges in deep extreme
multi-label learning where the objective is to train architectures for
automatically annotating a data point with the most relevant subset of labels
from an extremely large label set. This paper develops the DeepXML framework
that addresses these challenges by decomposing the deep extreme multi-label
task into four simpler sub-tasks each of which can be trained accurately and
efficiently. Choosing different components for the four sub-tasks allows
DeepXML to generate a family of algorithms with varying trade-offs between
accuracy and scalability. In particular, DeepXML yields the Astec algorithm
that could be 2-12% more accurate and 5-30x faster to train than leading deep
extreme classifiers on publically available short text datasets. Astec could
also efficiently train on Bing short text datasets containing up to 62 million
labels while making predictions for billions of users and data points per day
on commodity hardware. This allowed Astec to be deployed on the Bing search
engine for a number of short text applications ranging from matching user
queries to advertiser bid phrases to showing personalized ads where it yielded
significant gains in click-through-rates, coverage, revenue and other online
metrics over state-of-the-art techniques currently in production. DeepXML's
code is available at https://github.com/Extreme-classification/deepxml
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710245 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06686
|
Yuhong Song
|
Yuhong Song, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Rui Xu, Yongzhuo
Zhang, Bingzhe Li, Lei Yang
|
BSC: Block-based Stochastic Computing to Enable Accurate and Efficient
TinyML
|
Accept by ASP-DAC 2022
| null | null | null |
cs.LG cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
Along with the progress of AI democratization, machine learning (ML) has been
successfully applied to edge applications, such as smart phones and automated
driving. Nowadays, more applications require ML on tiny devices with extremely
limited resources, like implantable cardioverter defibrillator (ICD), which is
known as TinyML. Unlike ML on the edge, TinyML with a limited energy supply has
higher demands on low-power execution. Stochastic computing (SC) using
bitstreams for data representation is promising for TinyML since it can perform
the fundamental ML operations using simple logical gates, instead of the
complicated binary adder and multiplier. However, SC commonly suffers from low
accuracy for ML tasks due to low data precision and inaccuracy of arithmetic
units. Increasing the length of the bitstream in the existing works can
mitigate the precision issue but incur higher latency. In this work, we propose
a novel SC architecture, namely Block-based Stochastic Computing (BSC). BSC
divides inputs into blocks, such that the latency can be reduced by exploiting
high data parallelism. Moreover, optimized arithmetic units and output revision
(OUR) scheme are proposed to improve accuracy. On top of it, a global
optimization approach is devised to determine the number of blocks, which can
make a better latency-power trade-off. Experimental results show that BSC can
outperform the existing designs in achieving over 10% higher accuracy on ML
tasks and over 6 times power reduction.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709573 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06689
|
Lin Chen
|
Lin Chen, Fengli Xu, Zhenyu Han, Kun Tang, Pan Hui, James Evans, Yong
Li
|
Strategic COVID-19 vaccine distribution can simultaneously elevate
social utility and equity
|
25 pages, 4 figures
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Balancing social utility and equity in distributing limited vaccines
represents a critical policy concern for protecting against the prolonged
COVID-19 pandemic. What is the nature of the trade-off between maximizing
collective welfare and minimizing disparities between more and less privileged
communities? To evaluate vaccination strategies, we propose a novel epidemic
model that explicitly accounts for both demographic and mobility differences
among communities and their association with heterogeneous COVID-19 risks, then
calibrate it with large-scale data. Using this model, we find that social
utility and equity can be simultaneously improved when vaccine access is
prioritized for the most disadvantaged communities, which holds even when such
communities manifest considerable vaccine reluctance. Nevertheless, equity
among distinct demographic features are in tension due to their complex
correlation in society. We design two behavior-and-demography-aware indices,
community risk and societal harm, which capture the risks communities face and
those they impose on society from not being vaccinated, to inform the design of
comprehensive vaccine distribution strategies. Our study provides a framework
for uniting utility and equity-based considerations in vaccine distribution,
and sheds light on how to balance multiple ethical values in complex settings
for epidemic control.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.705943 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06692
|
Asaf Levin
|
G. Jaykrishnan and Asaf Levin
|
EPTAS for parallel identical machine scheduling with time restrictions
| null | null | null | null |
cs.DS cs.DM math.CO math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the non-preemptive scheduling problem on identical machines where
there is a parameter B and each machine in every unit length time interval can
process up to B different jobs. The goal function we consider is the makespan
minimization and we develop an EPTAS for this problem. Prior to our work a PTAS
was known only for the case of one machine and constant values of B, and even
the case of non-constant values of B on one machine was not known to admit a
PTAS.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708036 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06693
|
Matthias Griebel
|
Matthias Griebel, Dennis Segebarth, Nikolai Stein, Nina Schukraft,
Philip Tovote, Robert Blum, Christoph M. Flath
|
Deep-learning in the bioimaging wild: Handling ambiguous data with
deepflash2
| null | null | null | null |
q-bio.QM cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present deepflash2, a deep learning solution that facilitates the
objective and reliable segmentation of ambiguous bioimages through multi-expert
annotations and integrated quality assurance. Thereby, deepflash2 addresses
typical challenges that arise during training, evaluation, and application of
deep learning models in bioimaging. The tool is embedded in an easy-to-use
graphical user interface and offers best-in-class predictive performance for
semantic and instance segmentation under economical usage of computational
resources.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710879 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06706
|
Alexandre Cauquoin
|
Alexandre Cauquoin, Philippe Jean-Baptiste, Camille Risi, \'Elise
Fourr\'e, Barbara Stenni and Amaelle Landais
|
The global distribution of natural tritium in precipitation simulated
with an Atmospheric General Circulation Model and comparison with
observations
|
Accepted paper version. See published version in EPSL Elsevier
website
|
Earth and Planetary Science Letters, 427, October 2015, 160-170
|
10.1016/j.epsl.2015.06.043
| null |
physics.ao-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The description of the hydrological cycle in Atmospheric General Circulation
Models (GCMs) can be validated using water isotopes as tracers. Many GCMs now
simulate the movement of the stable isotopes of water, but here we present the
first GCM simulations modelling the content of natural tritium in water. These
simulations were obtained using a version of the LMDZ General Circulation Model
enhanced by water isotopes diagnostics, LMDZ-iso. To avoid tritium generated by
nuclear bomb testing, the simulations have been evaluated against a compilation
of published tritium datasets dating from before 1950, or measured recently.
LMDZ-iso correctly captures the observed tritium enrichment in precipitation as
oceanic air moves inland (the so-called continental effect) and the observed
north-south variations due to the latitudinal dependency of the cosmogenic
tritium production rate. The seasonal variability, linked to the stratospheric
intrusions of air masses with higher tritium content into the troposphere, is
correctly reproduced for Antarctica with a maximum in winter. LMDZ-iso
reproduces the spring maximum of tritium over Europe, but underestimates it and
produces a peak in winter that is not apparent in the data. This implementation
of tritium in a GCM promises to provide a better constraint on: (1) the
intrusions and transport of air masses from the stratosphere and (2) the
dynamics of the modelled water cycle. The method complements the existing
approach of using stable water isotopes.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708572 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06707
|
Ming Lu
|
Ming Lu, Peiyao Guo, Huiqing Shi, Chuntong Cao, and Zhan Ma
|
Transformer-based Image Compression
| null | null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A Transformer-based Image Compression (TIC) approach is developed which
reuses the canonical variational autoencoder (VAE) architecture with paired
main and hyper encoder-decoders. Both main and hyper encoders are comprised of
a sequence of neural transformation units (NTUs) to analyse and aggregate
important information for more compact representation of input image, while the
decoders mirror the encoder-side operations to generate pixel-domain image
reconstruction from the compressed bitstream. Each NTU is consist of a Swin
Transformer Block (STB) and a convolutional layer (Conv) to best embed both
long-range and short-range information; In the meantime, a casual attention
module (CAM) is devised for adaptive context modeling of latent features to
utilize both hyper and autoregressive priors. The TIC rivals with
state-of-the-art approaches including deep convolutional neural networks (CNNs)
based learnt image coding (LIC) methods and handcrafted rules-based intra
profile of recently-approved Versatile Video Coding (VVC) standard, and
requires much less model parameters, e.g., up to 45% reduction to
leading-performance LIC.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.708994 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06714
|
Rune Jacobsen H
|
Liping Shi, N\'estor J. Hern\'andez Marcano, and Rune Hylsberg
Jacobsen
|
A Review on Communication Protocols for Autonomous Unmanned Aerial
Vehicles for Inspection Application
|
28 pages
| null |
10.1016/j.micpro.2021.104340
| null |
cs.NI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The communication system is a critical part of the system design for the
autonomous UAV. It has to address different considerations, including
efficiency, reliability and mobility of the UAV. In addition, a multi-UAV
system requires a communication system to assist information sharing, task
allocation and collaboration in a team of UAVs. In this paper, we review
communication solutions for supporting a team of UAVs while considering an
application in the power line inspection industry. We provide a review of
candidate wireless communication technologies {for supporting communication in
UAV applications. Performance measurements and UAV-related channel modeling of
those candidate technologies are reviewed. A discussion of current technologies
for building UAV mesh networks is presented. We then analyze the structure,
interface and performance of robotic communication middleware, ROS and ROS2.
Based on our review, the features and dependencies of candidate solutions in
each layer of the communication system are presented.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709057 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06718
|
Denys Rybalka
|
D. O. Rybalka
|
SimpleTensor -- a user-friendly Mathematica package for elementary
tensor and differential-geometric calculations
|
13 pages
| null | null | null |
nucl-th cs.MS cs.SC hep-th physics.comp-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we present a short overview of the new Wolfram Mathematica
package intended for elementary "in-basis" tensor and differential-geometric
calculations. In contrast to alternatives our package is designed to be
easy-to-use, short, all-purpose, and hackable. It supports tensor contractions
using Einstein notation, transformations between different bases, tensor
derivative operator, expansion in basis vectors and forms, exterior derivative,
and interior product.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711224 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06720
|
Alexandre Cauquoin
|
Alexandre Cauquoin, Camille Risi and \'Etienne Vignon
|
Importance of the advection scheme for the simulation of water isotopes
over Antarctica by atmospheric general circulation models: A case study for
present-day and Last Glacial Maximum with LMDZ-iso
|
Accepted paper version. See published version in EPSL Elsevier
website
|
Earth and Planetary Science Letters, 524, October 2019, 115731
|
10.1016/j.epsl.2019.115731
| null |
physics.ao-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Atmospheric general circulation models (AGCMs) are known to have a warm and
isotopically enriched bias over Antarctica. We test here the hypothesis that
these biases are partly consequences of a too diffusive advection. Exploiting
the LMDZ-iso model, we show that a less diffusive representation of the
advection, especially on the horizontal, is very important to reduce the bias
in the isotopic contents of precipitation above this area. The choice of an
appropriate representation of the advection is thus essential when using GCMs
for paleoclimate applications based on polar water isotopes. Too much diffusive
mixing along the poleward transport leads to overestimated isotopic contents in
water vapor because dehydration by mixing follows a more enriched path than
dehydration by Rayleigh distillation. The near-air surface temperature is also
influenced, to a lesser extent, by the diffusive properties of the advection
scheme directly via the advection of the air and indirectly via the radiative
effects of changes in high cloud fraction and water vapor. A too diffusive
horizontal advection increases the temperature and so also contributes to
enrich the isotopic contents of water vapor over Antarctica through a reduction
of the distillation. The temporal relationship, from Last Glacial Maximum (LGM)
to present-day conditions, between the mean annual near-air surface temperature
and the water isotopic contents of precipitation for a specific location can
also be impacted, with significant consequences on the paleo-temperature
reconstruction from observed changes in water isotopes.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.70844 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06723
|
Donglin Wang
|
Donglin Wang, Qiuheng Zhou, Sanket Partani, Anjie Qiu and Hans D.
Schotten
|
Mobility prediction Based on Machine Learning Algorithms
|
5 pages, 7 figures, MKT'21 osnabruck
| null | null |
ITG-Fachbericht 299
|
cs.LG cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nowadays mobile communication is growing fast in the 5G communication
industry. With the increasing capacity requirements and requirements for
quality of experience, mobility prediction has been widely applied to mobile
communication and has becoming one of the key enablers that utilizes historical
traffic information to predict future locations of traffic users, Since
accurate mobility prediction can help enable efficient radio resource
management, assist route planning, guide vehicle dispatching, or mitigate
traffic congestion. However, mobility prediction is a challenging problem due
to the complicated traffic network. In the past few years, plenty of researches
have been done in this area, including Non-Machine-Learning (Non-ML)- based and
Machine-Learning (ML)-based mobility prediction. In this paper, firstly we
introduce the state of the art technologies for mobility prediction. Then, we
selected Support Vector Machine (SVM) algorithm, the ML algorithm for practical
traffic date training. Lastly, we analyse the simulation results for mobility
prediction and introduce a future work plan where mobility prediction will be
applied for improving mobile communication.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710597 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06725
|
Ivan Tanasijevi\'c
|
Ivan Tanasijevi\'c and Eric Lauga
|
Hydrodynamic interactions between a point force and a slender filament
| null | null | null | null |
physics.flu-dyn cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Green's function of the incompressible Stokes equations, the stokeslet,
represents the singular flow due to a point force. Its classical value in an
unbounded fluid has been extended near surfaces of various shapes, including
flat walls and spheres, and in most cases the presence of a surface leads to an
advection flow induced at the location of the point force. In this paper,
motivated by the biological transport of cargo along polymeric filaments inside
eukaryotic cells, we investigate the reaction flow at the location of the point
force due to a rigid slender filament located at a separation distance
intermediate between the filament radius and its length (i.e. we compute the
advection of the point force induced by the presence of the filament). An
asymptotic analysis of the problem reveals that the leading-order approximation
for the force distribution along the axis of the filament takes a form
analogous to resistive-force theory but with drag coefficients that depend
logarithmically on the distance between the point force and the filament. A
comparison of our theoretical prediction with boundary element computations
show good agreement. We finally briefly extend the model to the case of curved
filaments.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712001 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06726
|
Dongda Li
|
Dongda Li, Zhaoquan Gu, Yuexuan Wang, Changwei Ren, Francis C.M. Lau
|
One model Packs Thousands of Items with Recurrent Conditional Query
Learning
|
16 pages, 5 figures, 3 tables. Accepted to Knowledge-Based Systems,
2022
|
Knowledge-Based Systems, Volume 235, 2022, 107683, ISSN 0950-7051
|
10.1016/j.knosys.2021.107683
| null |
cs.AI cs.LG cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
Recent studies have revealed that neural combinatorial optimization (NCO) has
advantages over conventional algorithms in many combinatorial optimization
problems such as routing, but it is less efficient for more complicated
optimization tasks such as packing which involves mutually conditioned action
spaces. In this paper, we propose a Recurrent Conditional Query Learning (RCQL)
method to solve both 2D and 3D packing problems. We first embed states by a
recurrent encoder, and then adopt attention with conditional queries from
previous actions. The conditional query mechanism fills the information gap
between learning steps, which shapes the problem as a Markov decision process.
Benefiting from the recurrence, a single RCQL model is capable of handling
different sizes of packing problems. Experiment results show that RCQL can
effectively learn strong heuristics for offline and online strip packing
problems (SPPs), outperforming a wide range of baselines in space utilization
ratio. RCQL reduces the average bin gap ratio by 1.83% in offline 2D 40-box
cases and 7.84% in 3D cases compared with state-of-the-art methods. Meanwhile,
our method also achieves 5.64% higher space utilization ratio for SPPs with
1000 items than the state of the art.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709598 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06735
|
Surajit Chakrabarti
|
Sanjoy Kumar Pal, Soumen sarkar, and Surajit Chakrabarti
|
Determination of the refractive index of water and glass using
smartphone cameras by estimating the apparent depth of an object
|
12 pages, 1 figure
| null | null | null |
physics.ed-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A smartphone camera can be used for measuring the width and distance of an
object by taking its photograph. The focal length of the camera lens can be
determined very accurately by finding the image width of an object on the
camera sensor to micron level accuracy. The level of accuracy achieved with the
help of camera sensors, allows us to determine the refractive index of water
upto four significant digits by finding the apparent depth of an object
immersed in it. We have also measured the refractive index of glass by the same
method, using three glass slides of different thicknesses, the smallest being
1.2 mm.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.698265 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06736
|
Burcu Sayin G\"unel
|
Burcu Sayin, Jie Yang, Andrea Passerini, Fabio Casati
|
The Science of Rejection: A Research Area for Human Computation
|
To appear in the Proceedings of The 9th AAAI Conference on Human
Computation and Crowdsourcing (HCOMP 2021)
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We motivate why the science of learning to reject model predictions is
central to ML, and why human computation has a lead role in this effort.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.706007 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06737
|
Marcello Calvanese Strinati
|
Marcello Calvanese Strinati, Davide Pierangeli, Claudio Conti
|
All-optical scalable spatial coherent Ising machine
|
7 pages, 3 figures
|
Phys. Rev. Applied 16, 054022 (2021)
|
10.1103/PhysRevApplied.16.054022
| null |
cs.ET physics.comp-ph physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Networks of optical oscillators simulating coupled Ising spins have been
recently proposed as a heuristic platform to solve hard optimization problems.
These networks, called coherent Ising machines (CIMs), exploit the fact that
the collective nonlinear dynamics of coupled oscillators can drive the system
close to the global minimum of the classical Ising Hamiltonian, encoded in the
coupling matrix of the network. To date, realizations of large-scale CIMs have
been demonstrated using hybrid optical-electronic setups, where optical
oscillators simulating different spins are subject to electronic feedback
mechanisms emulating their mutual interaction. While the optical evolution
ensures an ultrafast computation, the electronic coupling represents a
bottleneck that causes the computational time to severely depend on the system
size. Here, we propose an all-optical scalable CIM with fully-programmable
coupling. Our setup consists of an optical parametric amplifier with a spatial
light modulator (SLM) within the parametric cavity. The spin variables are
encoded in the binary phases of the optical wavefront of the signal beam at
different spatial points, defined by the pixels of the SLM. We first discuss
how different coupling topologies can be achieved by different configurations
of the SLM, and then benchmark our setup with a numerical simulation that
mimics the dynamics of the proposed machine. In our proposal, both the spin
dynamics and the coupling are fully performed in parallel, paving the way
towards the realization of size-independent ultrafast optical hardware for
large-scale computation purposes.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711994 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06738
|
Baoguang Shi
|
Baoguang Shi, Wenfeng Cheng, Yijuan Lu, Cha Zhang, Dinei Florencio
|
Improving Structured Text Recognition with Regular Expression Biasing
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We study the problem of recognizing structured text, i.e. text that follows
certain formats, and propose to improve the recognition accuracy of structured
text by specifying regular expressions (regexes) for biasing. A biased
recognizer recognizes text that matches the specified regexes with
significantly improved accuracy, at the cost of a generally small degradation
on other text. The biasing is realized by modeling regexes as a Weighted
Finite-State Transducer (WFST) and injecting it into the decoder via dynamic
replacement. A single hyperparameter controls the biasing strength. The method
is useful for recognizing text lines with known formats or containing words
from a domain vocabulary. Examples include driver license numbers, drug names
in prescriptions, etc. We demonstrate the efficacy of regex biasing on datasets
of printed and handwritten structured text and measures its side effects.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712076 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06742
|
Sriram Siva
|
Sriram Siva, Maggie Wigness, John G. Rogers, Long Quang, and Hao Zhang
|
Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road
Ground Navigation
|
13 pages, 7 figures, IJRR21
| null | null | null |
cs.RO cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Ground robots require the crucial capability of traversing unstructured and
unprepared terrains and avoiding obstacles to complete tasks in real-world
robotics applications such as disaster response. When a robot operates in
off-road field environments such as forests, the robot's actual behaviors often
do not match its expected or planned behaviors, due to changes in the
characteristics of terrains and the robot itself. Therefore, the capability of
robot adaptation for consistent behavior generation is essential for
maneuverability on unstructured off-road terrains. In order to address the
challenge, we propose a novel method of self-reflective terrain-aware
adaptation for ground robots to generate consistent controls to navigate over
unstructured off-road terrains, which enables robots to more accurately execute
the expected behaviors through robot self-reflection while adapting to varying
unstructured terrains. To evaluate our method's performance, we conduct
extensive experiments using real ground robots with various functionality
changes over diverse unstructured off-road terrains. The comprehensive
experimental results have shown that our self-reflective terrain-aware
adaptation method enables ground robots to generate consistent navigational
behaviors and outperforms the compared previous and baseline techniques.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712426 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06743
|
Dian Echevarr\'ia P\'erez
|
Dian Echevarr\'ia P\'erez, Onel L. Alcaraz L\'opez, Hirley Alves,
Matti Latva-aho
|
Self-energy recycling for low-power reliable networks: Half-duplex or
Full-duplex?
|
The paper is not published yet but it was accepted to be published in
IEEE Systems Journal
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Self-energy recycling (sER), which allows transmit energy re-utilization, has
emerged as a viable option for improving the energy efficiency (EE) in
low-power Internet of Things networks. In this work, we investigate its
benefits also in terms of reliability improvements and compare the performance
of full-duplex (FD) and half-duplex (HD) schemes when using multi-antenna
techniques in a communication system. We analyze the trade-offs when
considering not only the energy spent on transmission but also the circuitry
power consumption, thus making the analysis of much more practical interest. In
addition to the well known spectral efficiency improvements, results show that
FD also outperforms HD in terms of reliability. We show that sER introduces not
only benefits in EE matters but also some modifications on how to achieve
maximum reliability fairness between uplink and downlink transmissions, which
is the main goal in this work. In order to achieve this objective, we propose
the use of a dynamic FD scheme where the small base station (SBS) determines
the optimal allocation of antennas for transmission and reception. We show the
significant improvement gains of this strategy for the system outage
probability when compared to the simple HD and FD schemes.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711224 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06745
|
Scott Hertel
|
Ricochet Collaboration: G. Beaulieu, V. Belov, L. Berge, J. Billard,
G. Bres, J-.L. Bret, A. Broniatowski, M. Calvo, A. Cazes, D. Chaize, M.
Chapellier, L. Chaplinsky, G. Chemin, R. Chen, J. Colas, M. De Jesus, P. de
Marcillac, L. Dumoulin, O. Exshaw, S. Ferriol, E. Figueroa-Feliciano, J. B.
Filippini, J. A. Formaggio, S. Fuard, J. Gascon, A. Giuliani, J. Goupy, C.
Goy, C. Guerin, C. F. Hirjibehedin, P. Harrington, S. T. Heine, S. A. Hertel,
M. Heusch, C. Hoarau, Z. Hong, J.-C. Ianigro, Y. Jin, J. P. Johnston, A.
Juillard, S. Kazarcev, J. Lamblin, H. Lattaud, A. Lubashevskiy, D. W. Mayer,
S. Marnieros, J. Minet, D. Misiak, A. Monfardini, F. Mounier, E. Olivieri, C.
Oriol, P. K. Patel, E. Perbet, H. D. Pinckney, D. Ponomarev, D. Poda, F.
Rarbi, J.-S. Real, J.-S. Ricol, T. Redon, A. Robert, S. Rozov, I. Rozova, T.
Salagnac, V. Sanglard, B. Schmidt, Ye. Shevchik, V. Sibille, T. Soldner, J.
Stachurska, A. Stutz, L. Vagneron, W. Van De Ponteseele, F. Vezzu, S. Weber,
L. Winslow, E. Yakushev, D. Zinatulina
|
Ricochet Progress and Status
|
Proceedings for the 19th International Workshop on Low Temperature
Detectors (LTD19)
| null | null | null |
physics.ins-det nucl-ex
|
http://creativecommons.org/licenses/by/4.0/
|
We present an overview of recent progress towards the Ricochet coherent
elastic neutrino nucleus scattering CE$\nu$NS experiment. The ILL research
reactor in Grenoble, France has been selected as the experiment site, after in
situ studies of vibration and particle backgrounds. We present background rate
estimates specific to that site, along with descriptions of the planned
CryoCube and Q-Array detector payloads.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711875 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06746
|
Pengfei Nan
|
Pengfei Nan, Zhiyao Liang, Yue Zhang, Yangrui Liu, Dongsheng Song,
Binghui Ge
|
Fast determination of thickness through scanning moir\'e fringe in
scanning transmission electron microscopy
|
11 pages, 4 figures
| null | null | null |
cond-mat.mtrl-sci physics.ins-det
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sample thickness is an important parameter in transmission electron
microscopy (TEM) imaging, for interpreting image contrast and understanding the
relationship between properties and microstructure. In this study, we introduce
a method for determining thickness in scanning transmission electron microscopy
(STEM) mode based on scanning moir\'e fringe (SMF). Sample thickness can be
determined in situ in the medium magnification using focal-series SMF imaging,
with beam damage and contamination avoided to a large extent. This method
provides a fast and convenient way for determining thickness in TEM imaging,
which is particularly useful for beam-sensitive materials such as Metal-Organic
Frameworks.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.712876 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06748
|
Sunil Kumar Maurya
|
Sunil Kumar Maurya, Xin Liu and Tsuyoshi Murata
|
Simplifying approach to Node Classification in Graph Neural Networks
|
arXiv admin note: substantial text overlap with arXiv:2105.07634
| null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graph Neural Networks have become one of the indispensable tools to learn
from graph-structured data, and their usefulness has been shown in wide variety
of tasks. In recent years, there have been tremendous improvements in
architecture design, resulting in better performance on various prediction
tasks. In general, these neural architectures combine node feature aggregation
and feature transformation using learnable weight matrix in the same layer.
This makes it challenging to analyze the importance of node features aggregated
from various hops and the expressiveness of the neural network layers. As
different graph datasets show varying levels of homophily and heterophily in
features and class label distribution, it becomes essential to understand which
features are important for the prediction tasks without any prior information.
In this work, we decouple the node feature aggregation step and depth of graph
neural network, and empirically analyze how different aggregated features play
a role in prediction performance. We show that not all features generated via
aggregation steps are useful, and often using these less informative features
can be detrimental to the performance of the GNN model. Through our
experiments, we show that learning certain subsets of these features can lead
to better performance on wide variety of datasets. We propose to use softmax as
a regularizer and "soft-selector" of features aggregated from neighbors at
different hop distances; and L2-Normalization over GNN layers. Combining these
techniques, we present a simple and shallow model, Feature Selection Graph
Neural Network (FSGNN), and show empirically that the proposed model achieves
comparable or even higher accuracy than state-of-the-art GNN models in nine
benchmark datasets for the node classification task, with remarkable
improvements up to 51.1%.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.710176 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06754
|
Andreanne Lemay
|
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem
Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree
Kalpathy-Cramer
|
Monte Carlo dropout increases model repeatability
|
Machine Learning for Health (ML4H) at NeurIPS 2021 - Extended
Abstract
| null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
The integration of artificial intelligence into clinical workflows requires
reliable and robust models. Among the main features of robustness is
repeatability. Much attention is given to classification performance without
assessing the model repeatability, leading to the development of models that
turn out to be unusable in practice. In this work, we evaluate the
repeatability of four model types on images from the same patient that were
acquired during the same visit. We study the performance of binary,
multi-class, ordinal, and regression models on three medical image analysis
tasks: cervical cancer screening, breast density estimation, and retinopathy of
prematurity classification. Moreover, we assess the impact of sampling Monte
Carlo dropout predictions at test time on classification performance and
repeatability. Leveraging Monte Carlo predictions significantly increased
repeatability for all tasks on the binary, multi-class, and ordinal models
leading to an average reduction of the 95% limits of agreement by 17% points.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.71262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06757
|
J.-M. Chauvet
|
J.-M. Chauvet
|
Multiway Storage Modification Machines
|
15 pages, 6 figures
| null | null | null |
cs.AI cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
We present a parallel version of Sch\"onhage's Storage Modification Machine,
the Multiway Storage Modification Machine (MWSMM). Like the alternative
Association Storage Modification Machine of Tromp and van Emde Boas, MWSMMs
recognize in polynomial time what Turing Machines recognize in polynomial
space. Falling thus into the Second Machine Class, the MWSMM is a parallel
machine model conforming to the Parallel Computation Thesis. We illustrate
MWSMMs by a simple implementation of Wolfram's String Substitution System.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06760
|
Jan Kierfeld
|
Tobias A. Kampmann, Thevashangar Sathiyanesan, Jan Kierfeld
|
Kinetic Event-Chain Algorithm for Active Matter
|
5 pages + supplemental material
| null | null | null |
cond-mat.soft physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a cluster kinetic Monte-Carlo algorithm for active matter systems
of self-propelled hard particles. The kinetic event-chain algorithm is based on
the event-chain Monte-Carlo method and is applied to active hard disks in two
dimensions. The algorithm assigns Monte-Carlo moves of active disks a mean time
based on their mean move length in force direction. This time is used to
perform diffusional rotation of their propulsion force. We show that the
algorithm reproduces the motility induced phase separated region in the phase
diagram of hard disks correctly and efficiently. We extend the algorithm to
mixtures of active and passive particles and uncover the microscopic mechanism
behind the enhanced diffusion of a completely symmetric passive tracer disk in
a bath of active hard disks.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.711055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06762
|
Chao Zhang
|
Chunzhi Gu, Shuofeng Zhao, Chao Zhang
|
Diversity-Promoting Human Motion Interpolation via Conditional
Variational Auto-Encoder
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present a deep generative model based method to generate
diverse human motion interpolation results. We resort to the Conditional
Variational Auto-Encoder (CVAE) to learn human motion conditioned on a pair of
given start and end motions, by leveraging the Recurrent Neural Network (RNN)
structure for both the encoder and the decoder. Additionally, we introduce a
regularization loss to further promote sample diversity. Once trained, our
method is able to generate multiple plausible coherent motions by repetitively
sampling from the learned latent space. Experiments on the publicly available
dataset demonstrate the effectiveness of our method, in terms of sample
plausibility and diversity.
| 2021-11-15T00:00:00 |
no_new_dataset
| false | 0.709982 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
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