July 2022 arXiv papers — page 57

H. Olofsson, T. Khouri, B. A. Sargent, A. Winnberg

12CO and 13CO lines, as well as a mm-wave continuum, have been observed for a sample of 22 OH/IR stars in directions within 2 degrees of the Galactic Centre. Photometry data have been gathered from the literature to construct SEDs and to determine pulsational variability. Radiative transfer models have been used to interpret the data. All stars in the sample

Marta Lorenzo, Miriam Garcia, Francisco Najarro, Artemio Herrero

Local Group (LG) very metal-poor massive stars are the best proxy for the First Stars of the Universe and fundamental to modelling the evolution of early galaxies. These stars may follow new evolutionary pathways restricted to very low metallicities, such as chemically homogeneous evolution (CHE). However, given the great distance leap needed to reach very m

Joseph P. Vantassel, Brady R. Cox, Liam Wotherspoon, Andrew Stolte

Wellington's port (CentrePort) experienced significant damage from the $M_w$ 7.8 Kaik\=oura earthquake as a result of soil liquefaction, lateral spreading, and shaking-induced damage to structures. To investigate these ill effects, and propose mitigation measures to prevent similar damage in future earthquakes, there was a need to quantify the variations in

Shiro Sakai, Ryotaro Arita, Tomi Ohtsuki

We study an electron distribution under a quasiperiodic potential in light of hyperuniformity, aiming to establish a classification and analysis method for aperiodic but orderly density distributions realized in, e.g., quasicrystals. Using the Aubry-Andre-Harper model, we first reveal that the electron-charge distribution changes its character as the increas

Chengxin Liu, Kewei Wang, Hao Lu, Zhiguo Cao

Learning accurate object detectors often requires large-scale training data with precise object bounding boxes. However, labeling such data is expensive and time-consuming. As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data. In

Prasadini Mahapatra, Divya Singh

We construct the spectrum for a shift invariant space on Vilenkin group. We prove the results related to spectrum and Frame multiresolution analysis for Cantor dyadic group and Vilenkin group.

Pragna Mamidipaka, Shantanu Desai

We check if the first significant digit of the dispersion measure of pulsars and Fast Radio Bursts (using the CHIME catalog) is consistent with the Benford distribution. We find a large disagreement with Benford's law with $\chi^2$ close to 80 for 8 degrees of freedom for both these aforementioned datasets. This corresponds to a discrepancy of about 7$\sigma

Thierry Gallouët, Raphaèle Herbin, Jean-Claude Latché, David Maltese

The present paper addresses the convergence of a first order in time incremental projection scheme for the time-dependent incompressible Navier-Stokes equations to a weak solution, without any assumption of existence or regularity assumptions on the exact solution. We prove the convergence of the approximate solutions obtained by the semi-discrete scheme and

Yuichi Akaoka, Kazuki Okamura, Yoshiki Otobe

We consider power means of independent and identically distributed (i.i.d.) non-integrable random variables. The power mean is an example of a homogeneous quasi-arithmetic mean. Under certain conditions, several limit theorems hold for the power mean, similar to the case of the arithmetic mean of i.i.d. integrable random variables. Our feature is that the ge

Yuanhao Li, Badong Chen, Yuxi Shi, Natsue Yoshimura

Recent studies have utilized sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's intentions and mental states, selecting the relevant features automatically in the model training process. However, existing sparse classification models will likely be prone to the performance degradation which

Clément Jailin, Pablo Milioni, Zhijin Li, Răzvan Iordache

Background \& purpose: The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This approach was not yet developed in Contrast Enhanced Spectral Mammography (CESM) where access to large databases is complex. This work proposes a deep-learning-based Computer Aided Diagnostic develo

Xiaoqi Li, Jiaming Liu, Shizun Wang, Cheng Lyu

Recently, Deep Neural Networks (DNNs) are utilized to reduce the bandwidth and improve the quality of Internet video delivery. Existing methods train corresponding content-aware super-resolution (SR) model for each video chunk on the server, and stream low-resolution (LR) video chunks along with SR models to the client. Although they achieve promising result

Xianzhang Wu, Minquan Cheng, Li Chen, Congduan Li

Coded caching is an effective technique to decongest the amount of traffic in the backhaul link. In such a scheme, each file hosted in the server is divided into a number of packets to pursue a low transmission rate based on the delicate design of contents cached into users and broadcast messages. However, the implementation complexity of this scheme increas

Zhenqi Fu, Wu Wang, Yue Huang, Xinghao Ding

A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity of the reference map. In this paper, we

Iuri Macocco, Aldo Glielmo, Jacopo Grilli, Alessandro Laio

Real world-datasets characterized by discrete features are ubiquitous: from categorical surveys to clinical questionnaires, from unweighted networks to DNA sequences. Nevertheless, the most common unsupervised dimensional reduction methods are designed for continuous spaces, and their use for discrete spaces can lead to errors and biases. In this letter we i

Yukinobu Toda

We prove the existence of semiorthogonal decompositions of derived categories of Quot schemes of zero-dimensional quotients on curves in terms of derived categories of symmetric products of curves. The above result is a categorical analogue of a similar formula for the class of Quot schemes in the Grothendieck ring of varieties by Bagnarol-Fantechi-Perroni.

Qianyi Wu, Xian Liu, Yuedong Chen, Kejie Li

The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on holistic scene representation yet ignore individual objects inside it, thus limiting potential downstream applications. In order to learn object-compositional representation, a few

Geonung Kim, Kyoungkook Kang, Seongtae Kim, Hwayoon Lee

For realistic and vivid colorization, generative priors have recently been exploited. However, such generative priors often fail for in-the-wild complex images due to their limited representation space. In this paper, we propose BigColor, a novel colorization approach that provides vivid colorization for diverse in-the-wild images with complex structures. Wh

Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh

Comparing the functional behavior of neural network models, whether it is a single network over time or two (or more networks) during or post-training, is an essential step in understanding what they are learning (and what they are not), and for identifying strategies for regularization or efficiency improvements. Despite recent progress, e.g., comparing vis

Rita Giuliano, Milto Hadjikyriakou

In this work, we study convergence in probability and almost sure convergence for weighted partial sums of random variables that are related to the class of generalized Oppenheim expansions. It is worth noting that the random variables under study have infinite mean and the results are obtained without any dependence assumptions.

Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng

Recent years have witnessed significant success in Gradient Boosting Decision Trees (GBDT) for a wide range of machine learning applications. Generally, a consensus about GBDT's training algorithms is gradients and statistics are computed based on high-precision floating points. In this paper, we investigate an essentially important question which has been l

Zhai Mingqing, Liu Muhuo

In the 1960s, Erd\H{o}s and his cooperators initiated the research of the maximum numbers of edges in a graph or a planar graph on $n$ vertices without $k$ edge-disjoint cycles. This problem had been solved for $k\leq4$. As pointed out by Bollob\'{a}s, it is very difficult for general $k$. Recently, Tait and Tobin [J. Combin. Theory Ser. B, 2017] confirmed a

A. L. Ivanova, I. Astapov, P. Bezyazeekov, E. Bonvech

The Tunka-Grande experiment is a scintillation array with about 0.5 sq.km sensitive area at Tunka Valley, Siberia, for measuring charged particles and muons in extensive air showers (EASs). Tunka-Grande is optimized for cosmic ray studies in the energy range 10 PeV to about 1 EeV, where exploring the composition is of fundamental importance for understanding

Shichao Dong, Jin Wang, Jiajun Liang, Haoqiang Fan

This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels. To this end, three hypotheses from the perspective of image matching are proposed as follows. 1. Deepfake detection models indicate real/fake images based on visual concepts that are neither source-relevant nor target-relevant,

Lei Zhang, Pingwen Zhang, Xiangcheng Zheng

We present a mathematical and numerical investigation to the shrinkingdimer saddle dynamics for finding any-index saddle points in the solution landscape. Due to the dimer approximation of Hessian in saddle dynamics, the local Lipschitz assumptions and the strong nonlinearity for the saddle dynamics, it remains challenges for delicate analysis, such as the t

Naoki Kitazawa

Round fold maps are smooth maps on closed manifolds which are locally represented as the product maps of Morse functions and identity maps on open disks and whose singularity is realized as concentrically embedded spheres. The author previously introduced such maps. Our paper presents round fold maps on some $7$-dimensional simply-connected manifolds whose c

Lin Geng Foo, Tianjiao Li, Hossein Rahmani, Qiuhong Ke

Early action prediction aims to successfully predict the class label of an action before it is completely performed. This is a challenging task because the beginning stages of different actions can be very similar, with only minor subtle differences for discrimination. In this paper, we propose a novel Expert Retrieval and Assembly (ERA) module that retrieve

Laxmi Pandey, Debjyoti Paul, Pooja Chitkara, Yutong Pang

Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form. Traditional handcrafted ITN rules can be complex to transcribe and maintain. Meanwhile neural modeling approaches require quality large-scale spoken-written pair examples in the same or similar domain as the ASR system

Shihao Zhuang, Jia-Mian Hu

We investigate the role of polarization-photon coupling (specifically, polarization-oscillation-induced radiation electric field) in the excitation of ferroelectric thin films by an ultrafast terahertz (THz) electric-field pulse. Analytical theory is developed to predict how the frequencies and relaxation time of three-dimensional soft mode phonons (intrinsi

Juliette Opdenplatz, Umutcan Şimşek, Dieter Fensel

Completeness of a knowledge graph is an important quality dimension and factor on how well an application that makes use of it performs. Completeness can be improved by performing knowledge enrichment. Duplicate detection aims to find identity links between the instances of knowledge graphs and is a fundamental subtask of knowledge enrichment. Current soluti

Aminur Rahman, Angela Peace, Ramesh Kesawan, Souparno Ghosh

The surprisingly mercurial Covid-19 pandemic has highlighted the need to not only accelerate research on infectious disease, but to also study them using novel techniques and perspectives. A major contributor to the difficulty of containing the current pandemic is due to the highly asymptomatic nature of the disease. In this investigation, we develop a model

Venkata Karteek Yanumula, Panagiotis Typaldos, Dimitrios Troullinos, Milad Malekzadeh

The paper presents a movement strategy for Connected and Automated Vehicles (CAVs) in a lane-free traffic environment with vehicle nudging by use of an optimal control approach. State-dependent constraints on control inputs are considered to ensure that the vehicle moves within the road boundaries and to prevent collisions. An objective function, comprising

Larry González, Alex Ivliev, Markus Krötzsch, Stephan Mennicke

Several types of dependencies have been proposed for the static analysis of existential rule ontologies, promising insights about computational properties and possible practical uses of a given set of rules, e.g., in ontology-based query answering. Unfortunately, these dependencies are rarely implemented, so their potential is hardly realised in practice. We

Olaniyi S. Iyiola, Yekini Shehu

This paper proposes a two-point inertial proximal point algorithm to find zero of maximal monotone operators in Hilbert spaces. We obtain weak convergence results and non-asymptotic $O(1/n)$ convergence rate of our proposed algorithm in non-ergodic sense. Applications of our results to various well-known convex optimization methods, such as the proximal meth

Shany Biton, Mohsin Aldhafeeri, Erez Marcusohn, Kenta Tsutsui

To drive health innovation that meets the needs of all and democratize healthcare, there is a need to assess the generalization performance of deep learning (DL) algorithms across various distribution shifts to ensure that these algorithms are robust. This retrospective study is, to the best of our knowledge, the first to develop and assess the generalizatio

Van-Quang Nguyen, Masanori Suganuma, Takayuki Okatani

Current state-of-the-art methods for image captioning employ region-based features, as they provide object-level information that is essential to describe the content of images; they are usually extracted by an object detector such as Faster R-CNN. However, they have several issues, such as lack of contextual information, the risk of inaccurate detection, an

Cicy K Agnes, Akthar Naveed, Anitha Mary M O Chacko

Exoplanet detection opens the door to the discovery of new habitable worlds and helps us understand how planets were formed. With the objective of finding earth-like habitable planets, NASA launched Kepler space telescope and its follow up mission K2. The advancement of observation capabilities has increased the range of fresh data available for research, an

Yang Yu, Zixu Zhao, Yueming Jin, Guangyong Chen

Surgical scene segmentation is fundamentally crucial for prompting cognitive assistance in robotic surgery. However, pixel-wise annotating surgical video in a frame-by-frame manner is expensive and time consuming. To greatly reduce the labeling burden, in this work, we study semi-supervised scene segmentation from robotic surgical video, which is practically

Junwoo Cho, Seungtae Nam, Daniel Rho, Jong Hwan Ko

Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network parameters, the data transfer by sending and receiving the entire model parameters prevents this emerging technology from being used in many practical scenarios. We propose streamab

Tae-Kyung Kang, Gun-Hee Lee, Seong-Whan Lee

Temporal action localization (TAL) is a task of identifying a set of actions in a video, which involves localizing the start and end frames and classifying each action instance. Existing methods have addressed this task by using predefined anchor windows or heuristic bottom-up boundary-matching strategies, which are major bottlenecks in inference time. Addit

Xiangyu Miao, Jiahe Wang, Yanan Chang, Yi Wu

Learning from synthetic images plays an important role in facial expression recognition task due to the difficulties of labeling the real images, and it is challenging because of the gap between the synthetic images and real images. The fourth Affective Behavior Analysis in-the-wild Competition raises the challenge and provides the synthetic images generated

Kabir Aladin Chandrasekher, Mengqi Lou, Ashwin Pananjady

We consider the problem of estimating the factors of a rank-$1$ matrix with i.i.d. Gaussian, rank-$1$ measurements that are nonlinearly transformed and corrupted by noise. Considering two prototypical choices for the nonlinearity, we study the convergence properties of a natural alternating update rule for this nonconvex optimization problem starting from a

Rongxing Xu, Xuding Zhu

A decomposition of a graph $G$ is a family of subgraphs of $G$ whose edge sets form a partition of $E(G)$. In this paper, we prove that every triangle-free planar graph $G$ can be decomposed into a $2$-degenerate graph and a matching. Consequently, every triangle-free planar graph $G$ has a matching $M$ such that $G-M$ is online 3-DP-colorable. This strength

Changyeon Won, Hae-Gon Jeon

For better photography, most recent commercial cameras including smartphones have either adopted large-aperture lens to collect more light or used a burst mode to take multiple images within short times. These interesting features lead us to examine depth from focus/defocus. In this work, we present a convolutional neural network-based depth estimation from

Tuong Do, Binh X. Nguyen, Vuong Pham, Toan Tran

Federated learning is an active research topic since it enables several participants to jointly train a model without sharing local data. Currently, cross-silo federated learning is a popular training setting that utilizes a few hundred reliable data silos with high-speed access links to training a model. While this approach has been widely applied in real-w

Jayeon Yoo, Inseop Chung, Nojun Kwak

Most existing domain adaptive object detection methods exploit adversarial feature alignment to adapt the model to a new domain. Recent advances in adversarial feature alignment strives to reduce the negative effect of alignment, or negative transfer, that occurs because the distribution of features varies depending on the category of objects. However, by an

Fakhri Momeni, Philipp Mayr, Stefan Dietze

Evaluation of researchers' output is vital for hiring committees and funding bodies, and it is usually measured via their scientific productivity, citations, or a combined metric such as h-index. Assessing young researchers is more critical because it takes a while to get citations and increment of h-index. Hence, predicting the h-index can help to discover

Saumya Gupta, Xiaoling Hu, James Kaan, Michael Jin

Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions among different classes (e.g., containment and exclusion). These constraints naturally arise in biomedical images and can be crucial in improving segmentation quality. In this pap

Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu

Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communication constraints. Existing iterative model averaging based FL algorithms require a large number of communication rounds to obtain a well-performed model due to extremely unbalanc

Nathan McNew

Let $S_{\rm div}(n)$ denote the set of permutations $\pi$ of $n$ such that for each $1\leq j \leq n$ either $j \mid \pi(j)$ or $\pi(j) \mid j$. These permutations can also be viewed as vertex-disjoint directed cycle covers of the divisor graph $\mathcal{D}_{[1,n]}$ on vertices $v_1, \ldots, v_n$ with an edge between $v_i$ and $v_j$ if $i\mid j$ or $j \mid i$

Xun Shen, Satoshi Ito

A risk-aware decision-making problem can be formulated as a chance-constrained linear program in probability measure space. Chance-constrained linear program in probability measure space is intractable, and no numerical method exists to solve this problem. This paper presents numerical methods to solve chance-constrained linear programs in probability measur

Haris Aziz, Arindam Pal, Ali Pourmiri, Fahimeh Ramezani

Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples

Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun

In this paper, we examine self-supervised learning methods, particularly VICReg, to provide an information-theoretical understanding of their construction. As a first step, we demonstrate how information-theoretic quantities can be obtained for a deterministic network, offering a possible alternative to prior work that relies on stochastic models. This enabl

Qirui Huang, Bin Fu, Aozhong Zhang, Yu Qiao

Automatic artistic text generation is an emerging topic which receives increasing attention due to its wide applications. The artistic text can be divided into three components, content, font, and texture, respectively. Existing artistic text generation models usually focus on manipulating one aspect of the above components, which is a sub-optimal solution f

Cheng-Chun Lee, Tina Comes, Megan Finn, Ali Mostafavi

Novel data sensing and AI technologies are finding practical use in the analysis of crisis resilience, revealing the need to consider how responsible artificial intelligence (AI) practices can mitigate harmful outcomes and protect vulnerable populations. In this paper, we present a responsible AI roadmap that is embedded in the Crisis Information Management

Fuxin Zhang, Chunbo Luo, Jialang Xu, Yang Luo

Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communicat

Yiqi Zhong, Zhenyang Ni, Siheng Chen, Ulrich Neumann

The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in trajectory forecasting with the guidance of high-definition maps, only a few works have explored such local historical information. In this work, we re-introduce this information as a new

Yao Su, Chi Chu, Meng Wang, Jiarui Li

Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing undesired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexity of modeli

Yuxiao Chen, Long Zhao, Jianbo Yuan, Yu Tian

Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active field because acquiring task-specific skeleton annotations at large scales is difficult. Recent studies focus on learning video-level temporal and discriminative information using co

Bai Li

Transformer-based language models have recently achieved remarkable results in many natural language tasks. However, performance on leaderboards is generally achieved by leveraging massive amounts of training data, and rarely by encoding explicit linguistic knowledge into neural models. This has led many to question the relevance of linguistics for modern na

Alexander Bors, Qiang Wang

We determine the permutation groups $P_{\mathrm{comp}}(\mathbb{F}_q),P_{\mathrm{orth}}(\mathbb{F}_q)\leq\operatorname{Sym}(\mathbb{F}_q)$ generated by the complete mappings, respectively the orthomorphisms, of the finite field $\mathbb{F}_q$ -- both are equal to $\operatorname{Sym}(\mathbb{F}_q)$ unless $q\in\{2,3,4,5,8\}$. More generally, denote by $P_{\mat

Chae-min Yun, Jubin Park, Myung-Ki Cheoun, Dukjae Jang

We report that the standard evolution of radiation-dominated era (RDE) universe $a \propto t^{1/2}$ is a sufficient condition for solving a sixth order gravitational field equation derived from the Lagrangian containing $B R^{ab}R_{ab} + C R {R^{;c}}_{c}$ as well as a polynomial $f(R)$ for a spatially flat radiation FLRW universe. By virtue of the similarity

Sachin Goyal, Mingjie Sun, Aditi Raghunathan, Zico Kolter

Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts, with access to only the unlabeled test samples from the new domain at test-time. Prior TTA methods optimize over unsupervised objectives such as the entropy of model predictions in TENT [Wang et al., 2021], but it is unclear what exactly makes a good TTA loss. In this paper

Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh

Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset. As the size of datasets contemporary machine learning models rely on becomes increasingly large, condensation methods become a prominent direction for accelerating network training and reducing data storage.

Yi Yang, Chen Zhang, Benyou Wang, Dawei Song

Over-parameterized models, typically pretrained language models (LMs), have shown an appealing expressive power due to their small learning bias. However, the huge learning capacity of LMs can also lead to large learning variance. In a pilot study, we find that, when faced with multiple domains, a critical portion of parameters behave unexpectedly in a domai

Huiping Chen, Yong Chen, Yong Liu

We clearly characterize the relation between real and complex Wiener-Ito integrals. Given a complex multiple Wiener-Ito integral, we get explicit expressions for two kernels of its real and imaginary parts. Conversely, consider a two-dimensional real Wiener-Ito integral, we obtain the representation formula by a finite sum of complex Wiener-Ito integrals. Th

Chongjun Ouyang, Hao Xu, Xujie Zang, Hongwen Yang

This paper considers a lens antenna array-assisted millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) system. The base station's beam selection matrix and user terminals' phase-only beamformers are jointly designed with the aim of maximizing the uplink sum rate. In order to deal with the formulated mixed-integer optimization problem,

Shaohua Guan, Qiang Chang, Wen Yao

Superstatistics generalizes Boltzmann statistics by assuming spatio-temporal fluctuations of the intensive variables. It has many applications in the analysis of experimental and simulated data. The fluctuation of the intensity variable is the key to the validity of superstatistical theory, but the law of its distribution is still unclear. In the framework o

Meiqi Hu, Chen Wu, Liangpei Zhang

The fast development of self-supervised learning lowers the bar learning feature representation from massive unlabeled data and has triggered a series of research on change detection of remote sensing images. Challenges in adapting self-supervised learning from natural images classification to remote sensing images change detection arise from difference betw

Yong He, Yalin Wang, Long Yu, Wang Zhou

In this article, we first propose generalized row/column matrix Kendall's tau for matrix-variate observations that are ubiquitous in areas such as finance and medical imaging. For a random matrix following a matrix-variate elliptically contoured distribution, we show that the eigenspaces of the proposed row/column matrix Kendall's tau coincide with those of

Anna M. Suliga

Neutrinos are the second most abundant particles in the universe according to the Standard Model, yet they are the least likely to interact. This feature implies that detecting a neutrino can reveal valuable insights into its source. Among the known sources of neutrinos, core-collapse supernovae are one of the most efficient factories. On average, a single c

Dejun Luo, Danli Wang

We consider stochastic inviscid dyadic models with energy-preserving noise. It is shown that the models admit weak solutions which are unique in law. Under a certain scaling limit of the noise, the stochastic models converge weakly to a deterministic viscous dyadic model, for which we provide explicit convergence rates in terms of the parameters of noise. A

W. Domitrz, L. I. Hernández-Martínez, F. Sánchez-Bringas

The Gauss map of a generic immersion of a smooth, oriented surface into $\mathbb R^4$ is an immersion. But this map takes values on the Grassmanian of oriented 2-planes in $\mathbb R^4$. Since this manifold has a structure of a product of two spheres, the Gauss map has two components that take values on the sphere. We study the singularities of the component

Saif Salam, Khaldoun Al-Zoubi

Let $R$ be a $G$ graded commutative ring and $M$ be a $G$-graded $R$-module. The set of all graded second submodules of $M$ is denoted by $Spec_G^s(M)$ and it is called the graded second spectrum of $M$. In this paper, we discuss graded rings with Noetherian graded prime spectrum and obtain some conclusions. In addition, we introduce the notion of the graded

Guangcheng Chen, Li He, Yisheng Guan, Hong Zhang

Current polarimetric 3D reconstruction methods, including those in the well-established shape from polarization literature, are all developed under the orthographic projection assumption. In the case of a large field of view, however, this assumption does not hold and may result in significant reconstruction errors in methods that make this assumption. To ad

Harukuni Ikeda

The fluctuations of the physical quantities play a central role to characterize the critical phenomena. Here, we report that the nature of the fluctuation highly depends on the control parameter near the jamming transition point $\varphi_J$. We show that the fluctuations do not diverge when the pressure is used as the control parameter. On the contrary, if t

Md Mahfuz Al Hasan, Mohammad Tahsin Mostafiz, Thomas An Le, Jake Julia

Due to the ever-growing demands for electronic chips in different sectors the semiconductor companies have been mandated to offshore their manufacturing processes. This unwanted matter has made security and trustworthiness of their fabricated chips concerning and caused creation of hardware attacks. In this condition, different entities in the semiconductor

Nate Harman, Andrew Snowden

A cubic space is a vector space equipped with a symmetric trilinear form. Using categorical Fra\"iss\'e theory, we show that there is a universal ultrahomogeneous cubic space $V$ of countable infinite dimension, which is unique up to isomorphism. The automorphism group $G$ of $V$ is quite large and, in some respects, similar to the infinite orthogonal group.

Zhen Wang, Long Chen, Wenbo Ma, Guangxing Han

Given an image and a reference caption, the image caption editing task aims to correct the misalignment errors and generate a refined caption. However, all existing caption editing works are implicit models, ie, they directly produce the refined captions without explicit connections to the reference captions. In this paper, we introduce a new task: Explicit

Aaron Berk, Gulcenur Ozturan, Parsa Delavari, David Maberley

Deep learning has seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for developing automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging amenable to such automated approaches. Recent work in analyzing fundus images using CNNs relies on access to massive

Syamantak Das, Tanmoy Paul

We continue to study (strong) property-$(R_1)$ in Banach spaces. As discussed by Pai \& Nowroji in [{\it On restricted centers of sets}, J. Approx. Theory, {\bf 66}(2), 170--189 (1991)], this study corresponds to a triplet $(X,V,\mathcal{F})$, where $X$ is a Banach space, $V$ is a closed convex set, and $\mathcal{F}$ is a subfamily of closed, bounded subsets

Yun-Bin Zhao, Zhi-Quan Luo

The algorithms based on the technique of optimal $k$-thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods. However, the computational cost for OT-based algorithms remains high at the current stage of their development. This stimulates the development of the so-calle

Long Wang, Ataru Tanikawa, Michiko Fujii

Previous theoretical studies suggest that the Population III (Pop3) stars tend to form in extremely metal poor gas clouds with approximately $10^5 M_\odot$ embedded in mini dark matter halos. Very massive stars can form via multiple collisions in Pop3 star clusters and eventually evolve to intermediate-mass black holes (IMBHs). In this work, we conduct star-

Jungwon Lee, Bharathwaj Palvannan

An important ingredient in the Ferrero--Washington proof of the vanishing of cyclotomic $\mu$-invariant for Kubota--Leopoldt $p$-adic $L$-functions is an equidistribution result which they established using the Weyl criterion. The purpose of our manuscript is to provide an alternative proof by adopting a dynamical approach. A key ingredient to our methods is

Jonathan A. DeCastro, Deepak Gopinath, Guy Rosman, Emily Sumner

To construct effective teaming strategies between humans and AI systems in complex, risky situations requires an understanding of individual preferences and behaviors of humans. Previously this problem has been treated in case-specific or data-agnostic ways. In this paper, we build a framework capable of capturing a compact latent representation of the human

Mohammad Khairul Bashar, Antik Mallick, Avik W. Ghosh, Nikhil Shukla

The intrinsic energy minimization in dynamical systems offers a valuable tool for minimizing the objective functions of computationally challenging problems in combinatorial optimization. However, most prior works have focused on mapping such dynamics to combinatorial optimization problems whose objective functions have quadratic degree (e.g., MaxCut); such

M. H. B. M. Shariff

In this paper, we address the open problem (stated in Pennisi and Trovato, 1987. Int. J. Engng Sci., 25(8), 1059-1065) associated with the irreducibility of representations for isotropic functions. In particular, we prove that for isotropic functions that depend on $P$ vectors, $N$ symmetric tensors and $M$ non-symmetric tensors (a) the number of irreducible

Chuntao Ding, Zhichao Lu, Felix Juefei-Xu, Vishnu Naresh Boddeti

Deploying deep convolutional neural network (CNN) models on ubiquitous Internet of Things (IoT) devices has attracted much attention from industry and academia since it greatly facilitates our lives by providing various rapid-response services. Due to the limited resources of IoT devices, cloud-assisted training of CNN models has become the mainstream. Howev

Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky

Concept-based interpretability methods aim to explain deep neural network model predictions using a predefined set of semantic concepts. These methods evaluate a trained model on a new, "probe" dataset and correlate model predictions with the visual concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-und

Alfa Heryudono, Mehdi Raessi

The adaptive partition of unity interpolation method, introduced by Aiton and Driscoll, using Chebyshev local interpolants, is explored for interpolating functions with sharp gradients representing two-medium problems. For functions that evolve under vector fields, the partition of unity patches (covers) can be shifted and resized to follow the changing dyna

Mirae Do, Seogkyu Jeon, Pilhyeon Lee, Kibeom Hong

Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations. To tackle the problem, previous works focus on aligning features extracted from partial levels (e.g., image-level, instance-level, RPN-level) in a two-stage detector via adversarial training. However, in

Li-Jia Liu, Bo Peng, Lei Yu, Bin Liu

Observation of Interplanetary Scintillation (IPS) provides an important and effective way to study the solar wind and the space weather. A series of IPS observations were conducted by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The extraordinary sensitivity and the wide frequency coverage make FAST an ideal platform for IPS studies. In

Qiang Duan, Shijing Hu, Ruijun Deng, Zhihui Lu

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML mode

Dongliang Cao, Florian Bernard

3D shape matching is a long-standing problem in computer vision and computer graphics. While deep neural networks were shown to lead to state-of-the-art results in shape matching, existing learning-based approaches are limited in the context of multi-shape matching: (i) either they focus on matching pairs of shapes only and thus suffer from cycle-inconsisten

Hongjiang Li, Huanyi Shui, Alemayehu Admasu, Praveen Narayanan

Image classification with deep neural networks has seen a surge of technological breakthroughs with promising applications in areas such as face recognition, medical imaging, and autonomous driving. In engineering problems, however, such as high-speed imaging of engine fuel injector sprays or body paint sprays, deep neural networks face a fundamental challen

Noëlle Rakotondravony, Yiren Ding, Lane Harrison

Visualizations today are used across a wide range of languages and cultures. Yet the extent to which language impacts how we reason about data and visualizations remains unclear. In this paper, we explore the intersection of visualization and language through a cross-language study on estimative probability tasks with icon-array visualizations. Across Arabic

J. Salinas San Martin, J. Noronha-Hostler, H. Elfner, J. Hammelmann

Recent lattice QCD results, comparing to a hadron resonance gas model, have shown the need for hundreds of particles in hadronic models. These extra particles influence both the equation of state and hadronic interactions within hadron transport models. Here, we introduce the PDG21+ particle list, which contains the most up-to-date database of particles and

Maxim Olshanii

In Book 1, Proposition 7, Problem 2 of his 1687 Philosophiae Naturalis Principia Mathematica, Isaac Newton poses and answers the following question: Let the orbit of a particle moving in a central force field be an off-center circle. How does the magnitude of the force depend on the position of the particle onthat circle? In this article, we identify a poten

Tanmoy Chattopadhyay, Soumya Gupta, Shabnam Iyyani, Divita Saraogi

Cadmium Zinc Telluride Imager (CZTI) aboard AstroSat has been regularly detecting Gamma-Ray Bursts (GRBs) since its launch in 2015. Its sensitivity to polarization measurements at energies above 100 keV allows CZTI to attempt spectro-polarimetric studies of GRBs. Here, we present the first catalog of GRB polarization measurements made by CZTI during its firs

Patrick Carzon, Mauricio Martinez, Matthew D. Sievert, Douglas E. Wertepny

While it is well known that there is a significant amount of conserved charges in the initial state of nuclear collisions, the production of these due to gluon splitting has yet to be thoroughly investigated. The ICCING (Initial Conserved Charges in Nuclear Geometry) algorithm reconstructs these quark distributions, providing conserved strange, baryon, and e