January 2022 arXiv papers — page 47

Keqi Wang, Ziteng Cui, Jieru Jia, Hao Xu

Convolution neural networks (CNNs) based methods have dominated the low-light image enhancement tasks due to their outstanding performance. However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps. Meanwhile, the self-attention based global relationship ag

Prateek Bansal, Rubal Dua

China and India, the world's two most populous developing economies, are also among the world's largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of new car bu

Shiwen He, Shaowen Xiong, Zhenyu An, Wei Zhang

In wireless network, the optimization problems generally have complex constraints, and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of network environments. In this paper, to overcome these shortcomings, an unsupervised deep unrolling framework

Stefania Sardellitti, Sergio Barbarossa

Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices of a graph) and to capture multiway relations of any order among the data. However, simplicial complexes

Kinshuk Sengupta, Praveen Ranjan Srivastava

Language data and models demonstrate various types of bias, be it ethnic, religious, gender, or socioeconomic. AI/NLP models, when trained on the racially biased dataset, AI/NLP models instigate poor model explainability, influence user experience during decision making and thus further magnifies societal biases, raising profound ethical implications for soc

Zhengrong Xue

This is a review on blind image deblurring. First, we formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and datasets, which is non-trivial to blind deblurr

Yi Hou, Chengyang Li, Yuheng Lu, Liping Zhu

In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity. The experimental results of using this dataset as data enhancement show that the performance of the proposed streamlined and efficient benchmark network ESA-Net can be improved by 8.4\%. The other tw

Atul Manikrao Bhagat, Partha Sarathi Goswami

The present study provides a detailed description of the forces on an ellipsoidal particle in the vicinity of the rough wall. Three-dimensional numerical simulations are performed using body-fitted mesh to estimate the drag, lift, and torque coefficients. A large number of simulations are conducted %(approximately 2400) over a range of parameters such as she

Farhad Rezazadeh, Hatim Chergui, Luis Blanco, Luis Alonso

Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital use-cases. In this paper, we propose a novel model-free deep reinforcement learning (DRL) framework, called collaborative statistical Ac

F. Alberto Grünbaum, Brian D. Vasquez, Jorge P. Zubelli

The bispectral problem is motivated by an effort to understand and extend a remarkable phenomenon in Fourier analysis on the real line: the operator of time-and-band limiting is an integral operator admitting a second-order differential operator with a simple spectrum in its commutator. In this article, we discuss a noncommutative version of the bispectral p

Dmitry Gribanov, Dmitry Malyshev, Nikolai Zolotykh

In our paper, we consider the following general problems: check feasibility, count the number of feasible solutions, find an optimal solution, and count the number of optimal solutions in $P \cap Z^n$, assuming that $P$ is a polyhedron, defined by systems $A x \leq b$ or $Ax = b,\, x \geq 0$ with a sparse matrix $A$. We develop algorithms for these problems

Ishrak Jahan Ratul, Ummay Habiba Wani, Mirza Muntasir Nishat, Abdullah Al-Monsur

Bone Marrow Transplant, a gradational rescue for a wide range of disorders emanating from the bone marrow, is an efficacious surgical treatment. Several risk factors, such as post-transplant illnesses, new malignancies, and even organ damage, can impair long-term survival. Therefore, technologies like Machine Learning are deployed for investigating the survi

Farhad Rezazadeh, Hatim Chergui, Loizos Christofi, Christos Verikoukis

To harness the full potential of beyond 5G (B5G) communication systems, zero-touch network slicing (NS) is viewed as a promising fully-automated management and orchestration (MANO) system. This paper proposes a novel knowledge plane (KP)-based MANO framework that accommodates and exploits recent NS technologies and is termed KB5G. Specifically, we deliberate

Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key resea

Yi Hou, Chengyang Li, Fan Yang, Cong Ma

In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. This makes the output of the network often found to contain incorrect responses, which may erroneously estimate the total number and not conducive to the interpretation of the a

Yadan Duan, Yuandeng Shen, Xinping Zhou, Zehao Tang

Using imaging and radio multi-wavelength observations, we studied the origin of two homologous accelerated electron beams and a quasi-periodic fast-propagating (QFP) wave train associated with a solar jet on 2012 July 14. The jet occurred in a small-scale fan-spine magnetic system embedding in a large-scale pseudostreamer, which associated with a GOES C1.4 f

Youri Davydov, Arkady Tempelman

We consider "randomized" statistics constructed by using a finite number of observations a random field at randomly chosen points. We generalize the invariance principle (the functional CLT), the Glivenko--Cantelli theorem, the theorem about convergence to the Brownian bridge and the Kolmogorov theorem about the limit distribution of the empirical distributi

Zhengrong Xue, Ziao Guo, Yiwei Guo

Popular node embedding methods such as DeepWalk follow the paradigm of performing random walks on the graph, and then requiring each node to be proximate to those appearing along with it. Though proved to be successful in various tasks, this paradigm reduces a graph with topology to a set of sequential sentences, thus omitting global information. To produce

Ricardo Arturo Lopez de la Cruz, Christian Diddens, Xuehua Zhang, Detlef Lohse

The dissolution or growth of a droplet in a host liquid is an important part for processes like chemical extraction, chromatography or emulsification. In this work we look at the dissolution of a pair of vertically aligned droplets immersed in water, both experimentally and with numerical simulations. The liquids used for the droplets are long chain alcohols

Moein Khazraee, Alex Forencich, George Papen, Alex C. Snoeren

We introduce an approach to designing FPGA-accelerated middleboxes that simplifies development, debugging, and performance tuning by decoupling the tasks of hardware-accelerator implementation and software-application programming. Rosebud is a framework that links hardware accelerators to a high-performance packet processing pipeline through a standardized h

Han Hu, Xinrong Liang, Yulin Ding, Qisen Shang

Deep learning methods are notoriously data-hungry, which requires a large number of labeled samples. Unfortunately, the large amount of interactive sample labeling efforts has dramatically hindered the application of deep learning methods, especially for 3D modeling tasks, which require heterogeneous samples. To alleviate the work of data annotation for lear

Teng Guo, Jingjin Yu

Graph-based multi-robot path planning (MRPP) is NP-hard to optimally solve. In this work, we propose the first low polynomial-time algorithm for MRPP achieving 1--1.5 asymptotic optimality guarantees on makespan for random instances under very high robot density, with high probability. The dual guarantee on computational efficiency and solution optimality su

Xuemei Tang, Jun Wang, Qi Su

In recent years, deep learning has achieved significant success in the Chinese word segmentation (CWS) task. Most of these methods improve the performance of CWS by leveraging external information, e.g., words, sub-words, syntax. However, existing approaches fail to effectively integrate the multi-level linguistic information and also ignore the structural f

Weixiao Wang, Miikka S. Väisälä, Hsien Shang, Ruben Krasnopolsky

Misalignment between rotation and magnetic field has been suggested to be one type of physical mechanisms which can easen the effects of magnetic braking during collapse of cloud cores leading to formation of protostellar disks. However, its essential factors are poorly understood. Therefore, we perform a more detailed analysis of the physics involved. We an

C. Britt, E. Church, T. Hossbach, B. Loer

Thick, Charge Coupled Devices (CCDs) have recently been explored for applied physics, such as nuclear explosion monitoring, and dark matter detection purposes. When run in fully-depleted mode, these devices are sensitive detectors for energy depositions by a variety of primary particles. In this study we are interested in applying the Deep Learning (DL) tech

Rupak Dey, Gadadhar Banerjee, Amar P. Misra

The propagation of arbitrary amplitude ion-acoustic (IA) solitary waves (SWs) is studied in unmagnetized, collisionless, homogeneous electron-positron-ion (e-p-i) plasmas with finite temperature degeneracy of both electrons and positrons. Starting from a set of fluid equations for classical ions and Fermi-Dirac distribution for degenerate electrons and posit

Yan Jiang, Hongyu Liu, Jiachuan Zhang, Kai Zhang

Consider the transmission eigenvalue problem for $u \in H^1(\Omega)$ and $v\in H^1(\Omega)$ associated with $(\Omega; \sigma, \mathbf{n}^2)$, where $\Omega$ is a ball in $\mathbb{R}^N$, $N=2,3$. If $\sigma$ and $\mathbf{n}$ are both radially symmetric, namely they are functions of the radial parameter $r$ only, we show that there exists a sequence of transmi

Siyuan Liang, Baoyuan Wu, Yanbo Fan, Xingxing Wei

Object detection has been widely used in many safety-critical tasks, such as autonomous driving. However, its vulnerability to adversarial examples has not been sufficiently studied, especially under the practical scenario of black-box attacks, where the attacker can only access the query feedback of predicted bounding-boxes and top-1 scores returned by the

Hongjia Wu, Ozgu Alay, Anna Brunstrom, Giuseppe Caso

Multipath transport protocols enable the concurrent use of different network paths, benefiting a fast and reliable data transmission. The scheduler of a multipath transport protocol determines how to distribute data packets over different paths. Existing multipath schedulers either conform to predefined policies or to online trained policies. The adoption of

Huang Huang, Michael Danielczuk, Chung Min Kim, Letian Fu

Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects will be fully occluded, resulting in a constrained lateral-access mechanical search problem. To address this prob

Oliver Couto, Seiji Tomita

Historically in math literature there are instances where solutions have been arrived at by different authors for equation 1 for six powers on both side of equation, for different degree 2,3,4,5,6,7,8,9. See reference number 1, by A. Bremner & J. Delorme and reference number 10, by Tito Piezas. The difference is that this paper has done systematic analysis o

Lei Tan, Ying Mei, Zhicun Liu, Yangping Luo

Hot subdwarf star is a particular type of star that is crucial for studying binary evolution and atmospheric diffusion processes. In recent years, identifying Hot subdwarfs by machine learning methods has become a hot topic, but there are still limitations in automation and accuracy. In this paper, we proposed a robust identification method based on the conv

Meng Chen, Louis Esser, Chengxi Wang

Let $n\geq 2$ be any integer. We study the optimal lower bound $v_{n, n-i}$ of the canonical volume and the optimal upper bound $r_{n,n-i}$ of the canonical stability index for minimal projective $n$-folds of general type, which are canonically fibered by $i$-folds ($i=0,1$). The results for $i = 0$, $v_{n,n}=2$ and $r_{n, n}=n+2$, are known to experts. In t

Tian-Ang Zheng, Ye Zheng, Lei Wang, Chang-Geng Liao

We propose an effective approach for generating significant amount of entanglement and asymmetric steering between photon and phonon in a cavity magnomechanical system which consists of a microwave cavity and a yttrium iron garnet sphere. By driving the magnon mode of the yttrium iron garnet sphere with blue-detuned microwave field, the magnon mode can be ac

Deniz Mengu, Aydogan Ozcan

Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of specimens. In modern implementations, the quantitative phase image of an object is reconstructed digitally through numerical methods running in a computer, often using iterative algorithms. Here, we demonstrate a diffractive QPI n

Luis Ángel Larios-Cárdenas, Frédéric Gibou

We present an error-neural-modeling-based strategy for approximating two-dimensional curvature in the level-set method. Our main contribution is a redesigned hybrid solver [Larios-C\'ardenas and Gibou, J. Comput. Phys. (May 2022), 10.1016/j.jcp.2022.111291] that relies on numerical schemes to enable machine-learning operations on demand. In particular, our r

Xuexing Lu

A causal-net is a finite acyclic directed graph. In this paper, we introduce a category, denoted by $\mathbf{Cau}$ and called causal-net category, whose objects are causal-nets and morphisms between two causal-nets are the functors between their path categories. The category $\mathbf{Cau}$ is in fact the Kleisli category of the "free category on a causal-net

Li Chu, Rui Wang, Xiao-Jun Wu

Collaborative representation-based classification (CRC) has demonstrated remarkable progress in the past few years because of its closed-form analytical solutions. However, the existing CRC methods are incapable of processing the nonlinear variational information directly. Recent advances illustrate that how to effectively model these nonlinear variational i

Fenglong Sun, Yutai Wang, Hongjian Yin

In this paper, we consider the finite time blow-up results for a parabolic equation coupled with superlinear source term and local linear boundary dissipation. Using a concavity argument, we derive the sufficient conditions for the solutions to blow up in finite time. In particular, we obtain the existence of finite time blow-up solutions with arbitrary high

Minglun Tian, Zeren Simon Wang, Kechen Wang

In our previous work [Phys. Rev. D 101 (2020) 075046], we have proposed to install FAr Detectors at the Electron Positron Collider (FADEPC) to enhance the discovery potential of long-lived particles (LLPs). In this study, we consider eight designs of far detectors with different locations, volumes and geometries and investigate their potential for discoverin

Zhiyuan You, Kai Yang, Wenhan Luo, Xin Lu

This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image. The major challenge lies in that the target objects can be densely packed in the query image, making it hard to recognize every single one. To tackle the obstacle, we propose a

Siyan Li, Yue Xiao, Yuhang Zhang, Lei Chu

It is a challenging problem to detect and recognize targets on complex large-scene Synthetic Aperture Radar (SAR) images. Recently developed deep learning algorithms can automatically learn the intrinsic features of SAR images, but still have much room for improvement on large-scene SAR images with limited data. In this paper, based on learning representatio

Wai Kiu Chan, Byeong-Kweon Oh

Let $\mathfrak o$ be the ring of integers of a totally real number field. If $f$ is a quadratic form over $\mathfrak o$ and $g$ is another quadratic form over $\mathfrak o$ which represents all proper subforms of $f$, does $g$ represent $f$? We show that if $g$ is indefinite, then $g$ indeed represents $f$. However, when $f$ is positive definite and indecomp

Fangyuan Zhu

Image super-resolution technology is the process of obtaining high-resolution images from one or more low-resolution images. With the development of deep learning, image super-resolution technology based on deep learning method is emerging. This paper reviews the research progress of the application of depth learning method in the field of image super-resolu

Muni Sreenivas Pydi, Varun Jog

Adversarial risk quantifies the performance of classifiers on adversarially perturbed data. Numerous definitions of adversarial risk -- not all mathematically rigorous and differing subtly in the details -- have appeared in the literature. In this paper, we revisit these definitions, make them rigorous, and critically examine their similarities and differenc

Qi Chang, Hui Qu, Zhennan Yan, Yunhe Gao

Multi-modality images have been widely used and provide comprehensive information for medical image analysis. However, acquiring all modalities among all institutes is costly and often impossible in clinical settings. To leverage more comprehensive multi-modality information, we propose a privacy secured decentralized multi-modality adaptive learning archite

Junjie Wang, Feng Gao, Junyu Dong, Shan Zhang

Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudo-labeled samples to guide subsequent training and testing. However, deep networks commonly require many high-quality samples for parameter optimization. The no

Jinbao Wang, Guoyang Xie, Yawen Huang, Jiayi Lyu

Utilizing multi-modal neuroimaging data has been proved to be effective to investigate human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination cost, long acquisition time, and image corruption. In additi

Hossein Khodavirdi, Amir Ashkan Mokhtari, Ankit Srivastava

In this paper, we consider the problem of mechanical wave scattering from a spatially finite system into an infinite surrounding environment. The goal is to illuminate why the scattering spectrum undergoes peaks and dips (resonances) at specific locations and how these locations connect to the vibrational properties of the scatterer. The resonance locations

Prarthana Bhattacharyya, Chenge Li, Xiaonan Zhao, István Fehérvári

Self-supervision has shown outstanding results for natural language processing, and more recently, for image recognition. Simultaneously, vision transformers and its variants have emerged as a promising and scalable alternative to convolutions on various computer vision tasks. In this paper, we are the first to question if self-supervised vision transformers

Zhuoran Zeng, Ernest Davis

Most work on physical reasoning, both in artificial intelligence and in cognitive science, has focused on closed-world reasoning, in which it is assumed that the problem specification specifies all relevant objects and substance, all their relations in an initial situation, and all exogenous events. However, in many situations, it is important to do open-wor

Zhangyong Tang, Tianyang Xu, Xiao-Jun Wu

Visual object tracking with RGB and thermal infrared (TIR) spectra available, shorted in RGBT tracking, is a novel and challenging research topic which draws increasing attention nowadays. In this paper, we propose an RGBT tracker which takes spatio-temporal clues into account for robust appearance model learning, and simultaneously, constructs an adaptive f

Quinn Taylor, Glenn Starkman

Black holes are expected to evaporate through the process of Hawking radiation. This process is expected to cause the uncertainty in a black hole's position to grow to $\sim M^2/M_{Pl}^3$ over the course of it's lifetime, even as its momentum spreads only by $\sim M_{Pl}$. For the black holes that have been observed, which have $M\geq M_\odot$, this greatly

Kihong Kim, Seulong Kim

Mode conversion of transverse electromagnetic waves into longitudinal oscillations and the associated resonant absorption of wave energy in inhomogeneous plasmas is a phenomenon that has been studied extensively in plasma physics. We show that precisely analogous phenomena occur generically in electronic and photonic systems where dispersionless flat bands a

Fei Heng, Yanqing Sun, Peter B. Gilbert

Statistical methods are developed for analysis of clinical and virus genetics data from phase 3 randomized, placebo-controlled trials of vaccines against novel coronavirus COVID-19. Vaccine efficacy (VE) of a vaccine to prevent COVID-19 caused by one of finitely many genetic strains of SARS-CoV-2 may vary by strain. The problem of assessing differential VE b

Sumith Yesudasan

Evaporation studies of water using classical molecular dynamics simulations are largely limited due to their high computational expense. This study addresses that issue by developing coarse-grained molecular dynamics models based on Morse potential. Models are optimized based on multi-temperature and at room temperature using machine learning techniques like

Saiping Zhang, Luis Herranz, Marta Mrak, Marc Gorriz Blanch

In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on mu

Weixiang Yu, Gordon T. Richards, Michael S. Vogeley, Jackeline Moreno

We present damped harmonic oscillator (DHO) light-curve modeling for a sample of 12,714 spectroscopically confirmed quasars in the Sloan Digital Sky Survey Stripe 82 region. DHO is a second-order continuous-time autoregressive moving-average (CARMA) process, which can be fully described using four independent parameters: a natural oscillation frequency ($\om

Mohamed A. Shabara, Ossama Abdelkhalik

In the recently introduced Variable-Shape heaving wave energy converters, the buoy changes its shape in response to changing incident waves actively. In this study, the dynamic model for a spherical Variable-Shape Wave Energy Converter is developed using the Lagrangian approach. The classical bending theory is used to write the stress-strain equations for th

Jingwen Zhang, Qing Liu, Haorui Zhang, Michelle Dai

Background: Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-\b{eta} (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. Objectives: To disentangle the massive heterogeneities in AD progressions and

Jincheng Gao, Minling Li, Zheng-an Yao

In this paper, we aim to investigate the optimal decay rate for the higher order spatial derivative of global solution to the full compressible Navier-Stokes (CNS) equations with potential force in $\mathbb{R}^3$. We establish the optimal decay rate of the solution itself and its spatial derivatives (including the highest order spatial derivative) for global

Junjie Wang, Feng Gao, Junyu Dong, Qian Du

In recent years, hyperspectral image (HSI) classification based on generative adversarial networks (GAN) has achieved great progress. GAN-based classification methods can mitigate the limited training sample dilemma to some extent. However, several studies have pointed out that existing GAN-based HSI classification methods are heavily affected by the imbalan

Tong Wu, Yong Wang

In this paper, we define a semi-symmetric non-metric connection on super Riemannian manifolds. And we compute the curvature tensor and the Ricci tensor of a semi-symmetric non-metric connection on super warped product spaces. Next, we introduce two kinds of super warped product spaces with a semi-symmetric non-metric connection and give the conditions that t

Monika Soraisam, Thomas Matheson, Chien-Hsiu Lee, Abhijit Saha

Ongoing large-scale optical time-domain surveys, such as the Zwicky Transient Facility (ZTF), are producing alerts at unprecedented rates. Analysis of transient sources has so far followed two distinct paths: archival analysis of data on transient sources at a time when they are no longer observable and real-time analysis at the time when the sources are fir

Yunhao Gao, Feng Gao, Junyu Dong, Heng-Chao Li

Traditional synthetic aperture radar image change detection methods based on convolutional neural networks (CNNs) face the challenges of speckle noise and deformation sensitivity. To mitigate these issues, we proposed a Multiscale Capsule Network (Ms-CapsNet) to extract the discriminative information between the changed and unchanged pixels. On the one hand,

Xing-Yu Chen, Qiu-Shi Zhu, Jie Zhang, Li-Rong Dai

In this work, we propose a bi-directional long short-term memory (BiLSTM) network based COVID-19 detection method using breath/speech/cough signals. By using the acoustic signals to train the network, respectively, we can build individual models for three tasks, whose parameters are averaged to obtain an average model, which is then used as the initializatio

S. M. Mazza, C. Gee, Y. Zhao, R. Padilla

Next generation Low Gain Avalanche Diodes (LGAD) produced by Hamamatsu photonics (HPK) and Fondazione Bruno Kessler (FBK) were tested before and after irradiation with ~1MeV neutrons at the JSI facility in Ljubljana. Sensors were irradiated to a maximum 1-MeV equivalent fluence of 2.5E15 Neq/cm2. The sensors analysed in this paper are an improvement after th

Kaiqi Zhao, Animesh Jain, Ming Zhao

Deploying complex deep learning models on edge devices is challenging because they have substantial compute and memory resource requirements, whereas edge devices' resource budget is limited. To solve this problem, extensive pruning techniques have been proposed for compressing networks. Recent advances based on the Lottery Ticket Hypothesis (LTH) show that

Frederik Wenkel, Yimeng Min, Matthew Hirn, Michael Perlmutter

Geometric deep learning has made great strides towards generalizing the design of structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise to graph neural networks (GNN) that can be applied to graph-structured data arising in, e.g., social networks, biochemistry, and material science. Graph convolutional networks (GCNs) in

Mohammed Rashid, Jeffrey A. Nanzer

A decentralized approach for joint frequency and phase synchronization in distributed antenna arrays is presented. The nodes in the array share their frequencies and phases with their neighboring nodes to align these parameters across the array. Our signal model includes the frequency drifts and phase jitters of the local oscillators as well as the frequency

Kaiqi Zhao, Yitao Chen, Ming Zhao

Deep learning models have introduced various intelligent applications to edge devices, such as image classification, speech recognition, and augmented reality. There is an increasing need of training such models on the devices in order to deliver personalized, responsive, and private learning. To address this need, this paper presents a new solution for depl

Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang, Ming-Hui Wu

Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.0 has a good robustness against the domain shift, while the noise robustness is still unclear. In this work, we therefore first analyze the noise robustness of wav2vec2.0 via exper

Sabari Subramaniyan, Kishor Kumar Ramavarmaraja, Radha Ramaswamy, Boris A Malomed

We investigate the nonlocal Gross-Pitaevskii (GP) equation with long-range dipole-dipole and contact interactions (including binary and three-body collisions). We address the impact of the three-body interaction on stabilizing trapless dipolar Bose-Einstein condensates (BECs). It is found that the dipolar BECs exhibit stability not only for the usual combina

Anna Muranova, Wolfgang Woess

We introduce a Green function and analogues of other related kernels for finite and infinite networks whose edge weights are complex-valued admittances with positive real part. We provide comparison results with the same kernels associated with corresponding reversible Markov chains, i.e., where the edge weights are positive. Under suitable conditions, these

Christoph Lüders, Thomas Sturm, Ovidiu Radulescu

Recently, symbolic computation and computer algebra systems have been successfully applied in systems biology, especially in chemical reaction network theory. One advantage of symbolic computation is its potential for qualitative answers to biological questions. Qualitative methods analyze dynamical input systems as formal objects, in contrast to investigati

Sumin Jeong, Arman Farhang, Nemanja Stefan Perović, Mark F. Flanagan

Accurate channel estimation is essential to achieve the performance gains promised by the use of reconfigurable intelligent surfaces (RISs) in wireless communications. In the uplink of multi-user orthogonal frequency division multiple access (OFDMA) systems, synchronization errors such as carrier frequency offsets (CFOs) can significantly degrade the channel

Pierre J. Walker, Hon-Wa Yew, Andrés Riedemann

Thermodynamic models are often vital when characterising complex systems, particularly natural gas, electrolyte, polymer, pharmaceutical and biological systems. However, their implementations have historically been abstruse and cumbersome, and as such, the only options available were black-box commercial tools. In this article, we present Clapeyron.jl: a pio

Martin Bridgeman, Kenneth Bromberg

One can associate to a complex projective structure on a surface holomorphic quadratic differential $\Phi$ via the Schwarzian derivative and a bending lamination $\lambda$ via the Thurston parameterization. In this note we obtain upper bounds on the $L^2$-norm of $\Phi$ in terms of the length of $\lambda$. The proof uses the theory of $W$-volume introduced b

Miranda D. Louwerse, David A. Sivak

A system's configurational state can be manipulated using dynamic variation of control parameters, such as temperature, pressure, or magnetic field; for finite-duration driving, excess work is required above the equilibrium free-energy change. Minimum-work protocols in multidimensional control-parameter space have potential to significantly reduce work relat

Ido Ben-Shaul, Shai Dekel

Recent advances in theoretical Deep Learning have introduced geometric properties that occur during training, past the Interpolation Threshold -- where the training error reaches zero. We inquire into the phenomena coined Neural Collapse in the intermediate layers of the networks, and emphasize the innerworkings of Nearest Class-Center Mismatch inside the de

Kevin Sartori, Anamaria Musat, Fadi Choueikani, Jean Marc Grenèche

Nanoparticles (NPs) which combine several magnetic phases offer wide perspectives for cutting edge applications because of the high modularity of their magnetic properties. Besides the addition of the magnetic characteristics intrinsic to each phase, the interface that results from core-shell and, further, from onion structures leads to synergistic propertie

Tine Curk, Erik Luijten

The process of phase separation in elastic solids and viscous fluids is of fundamental importance to the stability and function of soft materials. We explore the dynamics of phase separation and domain growth in a viscoelastic material such as a polymer gel. Using analytical theory and Monte Carlo simulations we report a new domain growth regime, in which th

Alastair N. Fletcher, Daniel A. Nicks

Beardon and Minda gave a characterization of normal families of holomorphic and meromorphic functions in terms of a locally uniform Lipschitz condition. Here, we generalize this viewpoint to families of mappings in higher dimensions that are locally uniformly continuous with respect to a given modulus of continuity. Our main application is to the normality o

Chak Kui Wong, Jonathan P. K. Doye

Molecular simulations using coarse-grained models allow the structure, dynamics and mechanics of DNA origamis to be comprehensively characterized. Here, we focus on the free-energy landscape of a jointed DNA origami that has been designed to exhibit two mechanically stable states and for which a bistable landscape has been inferred from ensembles of structur

Zhaoxin Luo, Michael Zhu

How to obtain hierarchical representations with an increasing level of abstraction becomes one of the key issues of learning with deep neural networks. A variety of RNN models have recently been proposed to incorporate both explicit and implicit hierarchical information in modeling languages in the literature. In this paper, we propose a novel approach calle

Olivier Gingras, Nikita Allaglo, Reza Nourafkan, Michel Côté

The superconducting order parameter of strontium ruthenate is the center of a lasting puzzle calling for theoretical studies that include the seldom-considered effects of spin-orbit coupling and the frequency-dependence of the order parameters. Here we generalize the frequency-dependent theory of superconductivity mediated by spin and charge fluctuations to

Olivier Gingras, Nikita Allaglo, Reza Nourafkan, Michel Côté

The lasting puzzle of the superconducting order parameter of Sr$_2$RuO$_4$ calls for theoretical studies that include seldom-considered effects. Here we include spin-orbit coupling effects on the electronic structure and then solve the linearized Eliashberg equation in a pseudospin basis, including the possibility that spin and charge fluctuations induce fre

Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya, Michael Pellauer

Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common characteristic among all of these accelerators is that they target tensor algebra (typically matrix multiplications); yet dozens of new accelerators are proposed for every new applic

Ivan Shestakov, Sergey Sverchkov

A new series of central elements is found in the free alternative algebra. More exactly, let $Alt[X]$ and $SMalc[X]\subset Alt[X]$ be the free alternative algebra and the free special Malcev algebra over a field of characteristic 0 on a set of free generators $X$, and let $f(x,y,x_1,\ldots,x_n)\in SMalc[X]$ be a multilinear element which is trivial in the fr

Farjana Siddiqua, Xihui Xie

The classical Smagorinsky model's solution is an approximation to a (resolved) mean velocity. Since it is an eddy viscosity model, it cannot represent a flow of energy from unresolved fluctuations to the (resolved) mean velocity. This model has recently been modified to incorporate this flow and still be well-posed. Herein we first develop some basic propert

André Davis

We compute recursive approximations of the action of the height $h \geq 2$ Morava stabilizer group on the associated Lubin-Tate deformation ring. We then specialize to the case $h=3$ and $p>2$ to calculate the action explicitly. These results are new for $h>2$ and agree with computations by Lader at height $h=2$.

Zachary M. Miksis, Yong-Tao Zhang

Fixed-point fast sweeping methods are a class of explicit iterative methods developed in the literature to efficiently solve steady state solutions of hyperbolic partial differential equations (PDEs). As other types of fast sweeping schemes, fixed-point fast sweeping methods use the Gauss-Seidel iterations and alternating sweeping strategy to cover character

Jacco Vink, Daniel J. Patnaude, Daniel Castro

We report on proper motion measurements of the forward- and reverse-shock regions of the supernova remnant Cassiopeia A (Cas A), including deceleration/acceleration measurements of the forward shock. The measurements combine 19 years of observations with the Chandra X-ray Observatory, using the 4.2-6 keV continuum band, preferentially targeting X-ray synchro

Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen

A reservoir computer (RC) is a type of simplified recurrent neural network architecture that has demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A further advantage of RC is that it reproduces intrinsic dynamical quantities essential for its incorporation into numerical forecasting routines such as the ensemble Kalman fi

Hossein Kalantar-Neyestanaki, Madjid Soltani

The building sector accounts for almost 40 percent of the global energy consumption. This reveals a great opportunity to exploit renewable energy resources in buildings to achieve the climate target. In this context, this paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected photovoltaic therma

Eric S. Egge, Michaela A. Polley

Long, Reid, and Thistlewaite have shown that some groups generated by representations of the $\Delta 334$ triangle group in $SL_3({\mathbb Z})$ are thin, while the status of others is unknown. In this paper we take a new approach: for each group we introduce a new graph that captures information about representations of $\Delta 334$ in the group. We provide

Nour ElHouda Tellache, Frédéric Meunier, Axel Parmentier

Some airlines use the preferential bidding system to construct the schedules of their pilots. In this system, the pilots bid on the different activities and the schedules that lexicographically maximize the scores of the pilots according to their seniority are selected. A sequential approach to solve this maximization problem is natural: the problem is first

Kaiqi Zhao, Animesh Jain, Ming Zhao

Pruning is a promising approach to compress complex deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are based on unstructured pruning, which yields models that cannot efficiently run on commodity hardware and require users to manually explore and tune the pruning process, which is ti

Anup Pandey, Jonathan Gigax, Reeju Pokharel

We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys (HEAs). MLIPs enabled accurate high throughput calculations of elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very time consuming calculations when performed u

Dheeraj Baby, Yu-Xiang Wang

We study the framework of universal dynamic regret minimization with strongly convex losses. We answer an open problem in Baby and Wang 2021 by showing that in a proper learning setup, Strongly Adaptive algorithms can achieve the near optimal dynamic regret of $\tilde O(d^{1/3} n^{1/3}\text{TV}[u_{1:n}]^{2/3} \vee d)$ against any comparator sequence $u_1,\ld