May 2023 arXiv papers — page 8

Naoki Endo

For an Ulrich ideal in a Gorenstein local ring, the quotient ring is again Gorenstein. Aiming to further develop the theory of Ulrich ideals, this paper investigates a naive question of how many non-principal ideals whose quotient rings are Gorenstein exist in a given Gorenstein ring. The main result provides that the number of such graded ideals in a symmet

Fatih Erdem Kizilkaya, David Kempe

We revisit the recent breakthrough result of Gkatzelis et al. on (single-winner) metric voting, which showed that the optimal distortion of 3 can be achieved by a mechanism called Plurality Matching. The rule picks an arbitrary candidate for whom a certain candidate-specific bipartite graph contains a perfect matching, and thus, it is not neutral (i.e, symme

Katja Höflich, Gerhard Hobler, Frances I. Allen, Tom Wirtz

The focused ion beam (FIB) is a powerful tool for the fabrication, modification and characterization of materials down to the nanoscale. Starting with the gallium FIB, which was originally intended for photomask repair in the semiconductor industry, there are now many different types of FIB that are commercially available. These instruments use a range of io

Chigak Itoi, Yoshinori Sakamoto

Spontaneous symmetry breaking phenomena in the transverse field mixed $p$-spin glass model in finite dimensions are studied with Nishimori's gauge theory. Useful identities in the gauge theory enable us to study $\mathbb Z_2$-symmetry breaking. It is proven that ferromagnetic long-range order and spontaneous magnetization at an arbitrary temperature are boun

Sergi Nadal, Raquel Panadero, Javier Flores, Oscar Romero

We study the problem of discovering joinable datasets at scale. We approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compare

Sergio Contreras, Idit Zehavi

We use the TNG300 magneto-hydrodynamic simulation and mock catalogues built using subhalo abundance matching (SHAM) to study the origin of the redshift evolution of the halo occupation distribution (HOD). We analyse stellar-mass selected galaxy samples with fixed number densities, spanning the redshift range $0 \le z \le 3$. We measure their halo occupation

Jiakun Lei, Tao Meng, Yang Zhu, Kun Wang

This paper focuses on the problem of spacecraft attitude control in the presence of time-varying parameter uncertainties and multiple constraints, accounting for angular velocity limitation, performance requirements, and input saturation. To tackle this problem, we propose a modified framework called Compatible Performance Control (CPC), which integrates the

M. de Jong, E. van Campenhout

The probability density function of the arrival time of \v{C}erenkov light on a photo-multiplier tube has been studied. This study covers light production, transmission and detection. The light production includes the light from a muon, the light from a shower and the light due to the energy loss of a muon. For the transmission of light, the effects of dispe

Kazuhide Ichikawa, Satoru Ohuchi, Koki Ueno, Tomoyasu Yokoyama

This research demonstrates that Ising machines can effectively solve optimal elemental configuration searches in crystals, with Au-Cu alloys serving as an example. The energy function is derived using the cluster expansion method in the form of a QUBO function, enabling efficient problem-solving via Ising machines. We have successfully obtained reasonable so

Matthew Korban, Scott T. Acton, Peter Youngs

This paper proposes a novel multi-modal transformer network for detecting actions in untrimmed videos. To enrich the action features, our transformer network utilizes a new multi-modal attention mechanism that computes the correlations between different spatial and motion modalities combinations. Exploring such correlations for actions has not been attempted

Guofan Fan, Zekun Qi, Wenkai Shi, Kaisheng Ma

Geometry and color information provided by the point clouds are both crucial for 3D scene understanding. Two pieces of information characterize the different aspects of point clouds, but existing methods lack an elaborate design for the discrimination and relevance. Hence we explore a 3D self-supervised paradigm that can better utilize the relations of point

A. Yamasaki, T. Oguni, T. Hayashida, K. Miyazaki

In this study, we investigate the bulk electronic structure of Pb-substituted LaO$_{0.5}$F$_{0.5}$BiS$_2$ single crystals, using two types of hard-x-ray spectroscopy. High-energy-resolution fluorescence-detected x-ray absorption spectroscopy revealed a spectral change at low temperatures. Using density functional theory (DFT) simulations, we find that the te

Chulong Zhang, Lin Liu, Jingjing Dai, Xuan Liu

Computed tomography (CT) scans offer a detailed, three-dimensional representation of patients' internal organs. However, conventional CT reconstruction techniques necessitate acquiring hundreds or thousands of x-ray projections through a complete rotational scan of the body, making navigation or positioning during surgery infeasible. In image-guided radiatio

Ali Ghalavand, Sandi Klavžar, Mostafa Tavakoli

The mixed metric dimension ${\rm mdim}(G)$ of a graph $G$ is the cardinality of a smallest set of vertices that (metrically) resolves each pair of elements from $V(G)\cup E(G)$. We say that $G$ is a max-mdim graph if ${\rm mdim}(G) = n(G)$. It is proved that a max-mdim graph $G$ with $n(G)\ge 7$ contains a vertex of degree at least $5$. Using the strong prod

Jakob D. Asmussen, Ltaief Ben Ltaief, Keshav Sishodia, Abdul R. Abid

Photoionization spectroscopy and mass spectrometry of doped helium (He) nanodroplets rely on the ability to efficiently detect ions and/or electrons. Using a commercial quadrupole mass spectrometer and a photoelectron-photoion coincidence (PEPICO) spectrometer, we systematically measure yields of ions and electrons created in pure and doped He nanodroplets i

Lital Dabush, Tirza Routtenberg

Graph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph. A crucial prerequisite for applying various GSP and graph neural network (GNN) approaches is that the examined signals are smooth graph signals with respect to the underlying graph

Shiqi Zhang, Yiqian Huang, Jiachen Sun, Wenqing Lin

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few influencers to promote the product. However, apart from the cost factor, the capacity of individuals to consum

Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin, Alexander Mitsos

We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standard machine learning (ML) frameworks enabling automatic differentiation. In contrast to recent h

Mao Ye, Haitao Wang, Zheqian Chen

To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup). This method feeds two different samples to the same deep neural network model, and then randomly select a specific layer and partially replace hidden fea

Makoto Kawashima

In this article, we prove a generalized Rodrigues formula for a wide class of holonomic Laurent series, which yields a new linear independence criterion concerning their values at algebraic points. This generalization yields a new construction of Pad\'e approximations including those for Gauss hypergeometric functions. In particular, we obtain a linear indep

Junyao Pan, Pengfei Guo

A set of permutations is called sign-balanced if the set contains the same number of even permutations as odd permutations. Let $S_n(\sigma_1, \sigma_2, \ldots, \sigma_r)$ be the set of permutations in the symmetric group $S_n$ which avoids patterns $\sigma_1, \sigma_2, \ldots, \sigma_r$. The aim of this paper is to investigate when, for certain patterns $\s

G. Mouloudakis, P. Lambropoulos

We report on the existence of exceptional points (EPs) in single-resonance autoionization and provide analytical expressions for their positions in parameter space, in terms of the Fano asymmetry parameter. We additionally propose a reliable method for the experimental determination of EPs, based solely on information about their ionization probability as a

Luciano S. de Souza, Jonathan H. A. de Carvalho, Henrique C. T. Santos, Tiago A. E. Ferreira

In this paper, we analyze the application of the Multi-self-loop Lackadaisical Quantum Walk on the hypercube that uses partial phase inversion to search for multiple adjacent marked vertices. We evaluate the influence of the relative position of non-adjacent marked vertices. The use of self-loops and the composition of their weights are an essential part of

Xiuliang Yuan, Bing Wang, Ying Sun, Huaiming Guo

The negative thermal expansion (NTE) materials, which can act as thermal-expansion compensators to counteract the positive thermal expansion, have great applications merit in precision engineering. However, the exploration of NTE behavior with a wide temperature range has reached its upper ceiling through traditional doping strategies due to composition limi

Yuan Xie, Jiawei Ren, Ji Xu

Underwater acoustic target recognition is an intractable task due to the complex acoustic source characteristics and sound propagation patterns. Limited by insufficient data and narrow information perspective, recognition models based on deep learning seem far from satisfactory in practical underwater scenarios. Although underwater acoustic signals are sever

Ziping Lei, Puchun Zhou

Combinatorial Calabi flows are introduced by Ge in his Ph.D. thesis (Combinatorial methods and geometric equations, Peking University, Beijing, 2012), and have been studied extensively in Euclidean and hyperbolic background geometry. In this paper, we introduce the combinatorial Calabi flow in spherical background geometry for finding ideal circle patterns w

Yabo Wang, Bing Yang, Xiaofei Li

Extracting direct-path spatial features is critical for sound source localization in adverse acoustic environments. This paper proposes a full-band and narrow-band fusion network for estimating direct-path inter-channel phase difference (DP-IPD) from microphone signals. The alternating full-band and narrow-band layers are responsible for learning the full-ba

Celina Treuillier, Sylvain Castagnos, Armelle Brun

Polarization is an increasingly worrying phenomenon within social media. Recent work has made it possible to detect and even quantify polarization. Nevertheless, the few existing metrics, although defined in a continuous space, often lead to a unimodal distribution of data once applied to users' interactions, making the distinction between polarized and non-

Yuanyuan Zhang, Huhu Zhang, Tingzeng Wu, Xing Gao

In the present paper, we propose the concepts of weighted differential ($q$-tri)dendriform algebras and give some basic properties of them. The corresponding free objects are constructed, in both the commutative and noncommutative contexts.

Alexander Pushnitski, František Štampach

We consider the class of bounded symmetric Jacobi matrices $J$ with positive off-diagonal elements and complex diagonal elements. With each matrix $J$ from this class, we associate the spectral data, which consists of a pair $(\nu,\psi)$. Here $\nu$ is the spectral measure of $|J|=\sqrt{J^*J}$ and $\psi$ is a $\textit{phase function}$ on the real line satisf

Ashim Gupta, Amrith Krishna

Clean-label (CL) attack is a form of data poisoning attack where an adversary modifies only the textual input of the training data, without requiring access to the labeling function. CL attacks are relatively unexplored in NLP, as compared to label flipping (LF) attacks, where the latter additionally requires access to the labeling function as well. While CL

Thomas K. Waring

Fill each box in a Young diagram with the number of paths from the bottom of its column to the end of its row, using steps north and east. Then, any square sub-matrix of this array starting on the south-east boundary has determinant one. We provide a - to our knowledge - new bijective argument for this result. Using the same ideas, we prove further identitie

Evgenii Chzhen, Christophe Giraud, Gilles Stoltz

We consider the problem of minimizing a convex function over a closed convex set, with Projected Gradient Descent (PGD). We propose a fully parameter-free version of AdaGrad, which is adaptive to the distance between the initialization and the optimum, and to the sum of the square norm of the subgradients. Our algorithm is able to handle projection steps, do

Sicen Liu, Xiaolong Wang, Xianbing Zhao, Hao Chen

Medication recommendation is a fundamental yet crucial branch of healthcare that presents opportunities to assist physicians in making more accurate medication prescriptions for patients with complex health conditions. Previous studies have primarily focused on learning patient representation from electronic health records (EHR). While considering the clinic

Jeongsoo Choi, Minsu Kim, Yong Man Ro

In this paper, we propose a novel Lip-to-Speech synthesis (L2S) framework, for synthesizing intelligible speech from a silent lip movement video. Specifically, to complement the insufficient supervisory signal of the previous L2S model, we propose to use quantized self-supervised speech representations, named speech units, as an additional prediction target

Tianyu Chen, Yuan Xie, Shuai Zhang, Shaohan Huang

Music representation learning is notoriously difficult for its complex human-related concepts contained in the sequence of numerical signals. To excavate better MUsic SEquence Representation from labeled audio, we propose a novel text-supervision pre-training method, namely MUSER. MUSER adopts an audio-spectrum-text tri-modal contrastive learning framework,

M. Zubair, Mushayydha Farooq

This manuscript is based on an investigation of bouncing cosmology in a 4D Einstein Gauss-Bonnet gravity. Various bouncing models such as symmetric bounce, matter bounce, super bounce, and oscillatory bounce have been examined. Expressions for energy density, pressure, equation of state parameter have been derived in the most general manner and then reduced

Sophie Charlotte Stebner, Juri Martschin, Bahman Arian, Stefan Dietrich

As recent trends in manufacturing engineering disciplines show a clear development in the sustainable as well as economically efficient design of forming processes, monitoring techniques have been gaining in relevance. In terms of monitoring of product properties, most processes are currently open-loop controlled, entailing that the microstructure evolution,

M Yashwanth, Gaurav Kumar Nayak, Arya Singh, Yogesh Simmhan

Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can often be substantial heterogeneity (e.g., class imbalance) across the local data distributions observed by each of these clients. Under such non-i

Zutao Jiang, Guian Fang, Jianhua Han, Guansong Lu

Recent advances in text-to-image diffusion models have achieved remarkable success in generating high-quality, realistic images from textual descriptions. However, these approaches have faced challenges in precisely aligning the generated visual content with the textual concepts described in the prompts. In this paper, we propose a two-stage coarse-to-fine s

Xiao Luo, Yusheng Zhao, Yifang Qin, Wei Ju

Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied within the context of supervised end-to-end training, which necessities copious task-specific labels. However, in real-world circumstances, labeled data could be limited, and there could be a massive corpus of unlabeled data, even from

Yuan Xie, Jiawei Ren, Ji Xu

Analyzing the ocean acoustic environment is a tricky task. Background noise and variable channel transmission environment make it complicated to implement accurate ship-radiated noise recognition. Existing recognition systems are weak in addressing the variable underwater environment, thus leading to disappointing performance in practical application. In ord

Mir Sazzat Hossain, Sugandha Roy, K. M. B. Asad, Arshad Momen

Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the kn

Ruixiang Cui, Seolhwa Lee, Daniel Hershcovich, Anders Søgaard

Humans can effortlessly understand the coordinate structure of sentences such as "Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, respectively". In the context of natural language inference (NLI), we examine how language models (LMs) reason with respective readings (Gawron and Kehler, 2004) from two perspectives: syntactic-semantic and common

Nicolas Gauquelin, Filomena Forte, Daen Jannis, Rosalba Fittipaldi

The control of Mott phase is intertwined with the spatial reorganization of the electronic states. Out-of-equilibrium driving forces typically lead to electronic patterns that are absent at equilibrium, whose nature is however often elusive. Here, we unveil a nanoscale pattern formation in the Ca$_2$RuO$_4$ Mott insulator. We demonstrate how an applied elect

Sivan Doveh, Assaf Arbelle, Sivan Harary, Roei Herzig

Vision and Language (VL) models offer an effective method for aligning representation spaces of images and text, leading to numerous applications such as cross-modal retrieval, visual question answering, captioning, and more. However, the aligned image-text spaces learned by all the popular VL models are still suffering from the so-called `object bias' - the

P. An, C. Awe, P. S. Barbeau, B. Becker

Using an 185-kg NaI[Tl] array, COHERENT has measured the inclusive electron-neutrino charged-current cross section on ${}^{127}$I with pion decay-at-rest neutrinos produced by the Spallation Neutron Source at Oak Ridge National Laboratory. Iodine is one the heaviest targets for which low-energy ($\leq$ 50 MeV) inelastic neutrino-nucleus processes have been m

Mst Shapna Akter, Hossain Shahriar, Iysa Iqbal, MD Hossain

The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security sensitive applications. In this study, we present a comparative analysis of the vul

Hamidreza Simchi

Assuming that the first creatures of creation create a network, it is shown that how the network can be mapped to a topos discrete quantum manifold which is equipped with both the discrete calculus and the Alexandrov's algebra. We assign a locale to each nodes of the space-time network and show that in general, invariance under Lorentz transformations is no

Chenghao Feng, Jiaqi Gu, Hanqing Zhu, Rongxing Tang

The optical neural network (ONN) is a promising hardware platform for next-generation neuromorphic computing due to its high parallelism, low latency, and low energy consumption. However, previous integrated photonic tensor cores (PTCs) consume numerous single-operand optical modulators for signal and weight encoding, leading to large area costs and high pro

Maryam Shaygan, Collin Meese, Wanxin Li, Xiaolong Zhao

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significan

Jiahui Huang, Zan Gojcic, Matan Atzmon, Or Litany

We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural Kernel Fields (NKF) representation. It enjoys similar generalization capabilities to NKF, while simultaneously addressing its main limitations: (a) We can scale to large scenes through c

Aditya Yadavalli, Alekhya Yadavalli, Vera Tobin

Second language acquisition (SLA) research has extensively studied cross-linguistic transfer, the influence of linguistic structure of a speaker's native language [L1] on the successful acquisition of a foreign language [L2]. Effects of such transfer can be positive (facilitating acquisition) or negative (impeding acquisition). We find that NLP literature ha

Muhammad Shoaib Farooq, Tabir Arif, Shamyla Riaz

=One of the most frequently farmed crops is the tomato crop. Late blight is the most prevalent tomato disease in the world, and often causes a significant reduction in the production of tomato crops. The importance of tomatoes as an agricultural product necessitates early detection of late blight. It is produced by the fungus Phytophthora. The earliest signs

Davin Choo, Themis Gouleakis, Arnab Bhattacharyya

We introduce the problem of active causal structure learning with advice. In the typical well-studied setting, the learning algorithm is given the essential graph for the observational distribution and is asked to recover the underlying causal directed acyclic graph (DAG) $G^*$ while minimizing the number of interventions made. In our setting, we are additio

Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic se

Joel Kuepper, Andres Erbsen, Jason Gross, Owen Conoly

Manual engineering of high-performance implementations typically consumes many resources and requires in-depth knowledge of the hardware. Compilers try to address these problems; however, they are limited by design in what they can do. To address this, we present CryptOpt, an automatic optimizer for long stretches of straightline code. Experimental results a

Jeremiah Milbauer, Annie Louis, Mohammad Javad Hosseini, Alex Fabrikant

Transformer encoders contextualize token representations by attending to all other tokens at each layer, leading to quadratic increase in compute effort with the input length. In practice, however, the input text of many NLP tasks can be seen as a sequence of related segments (e.g., the sequence of sentences within a passage, or the hypothesis and premise in

Kaousheik Jayakumar, Vrunda N. Sukhadia, A Arunkumar, S. Umesh

Building a multilingual Automated Speech Recognition (ASR) system in a linguistically diverse country like India can be a challenging task due to the differences in scripts and the limited availability of speech data. This problem can be solved by exploiting the fact that many of these languages are phonetically similar. These languages can be converted into

Mst Shapna Akter, Md Jobair Hossain Faruk, Nafisa Anjum, Mohammad Masum

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Lear

Hirotatsu Nagoji

In this paper, we consider the Gibbs measures associated with Euclidean quantum field theory with polynomial-type of interactions on the torus. We observe the (non-)normalizability of the multivariate version of $P(\Phi)_2$ models by the variational method. Moreover, we extend this setting to the "fractional model" in the "Wick renormalizable regime": the ba

Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao

Causal discovery with latent confounders is an important but challenging task in many scientific areas. Despite the success of some overcomplete independent component analysis (OICA) based methods in certain domains, they are computationally expensive and can easily get stuck into local optima. We notice that interestingly, by making use of higher-order cumu

Zuheng Kang, Jianzong Wang, Junqing Peng, Jing Xiao

Voice activity detection (VAD) improves the performance of speaker verification (SV) by preserving speech segments and attenuating the effects of non-speech. However, this scheme is not ideal: (1) it fails in noisy environments or multi-speaker conversations; (2) it is trained based on inaccurate non-SV sensitive labels. To address this, we propose a speaker

Elizabeth Zhang, Yuelang Chen, Zhiao Yu, Yi Cui

High degree of fluorination for ether electrolytes has resulted in improved cycling stability of lithium metal batteries (LMBs) due to stable SEI formation and good oxidative stability. However, the sluggish ion transport and environmental concerns of high fluorination degree drives the need to develop less fluorinated structures. Here, we introduce bis(2-fl

Marina Barinova, Olga Pochinka, Evgeniy Yakovlev

This paper belongs to a series of papers devoted to the study of the structure of the non-wandering set of an A-diffeomorphism. We study such set $NW(f)$ for an $\Omega$-stable diffeomorphism $f$, given on a closed connected 3-manifold $M^3$. Namely, we prove that if all basic sets in $NW(f)$ are trivial except attractors, then every non-trivial attractor is

Hyojung Lee, Lam Vu, Minsung Jang

This paper presents the economic impacts of spot instance service on the cloud service providers (CSPs) and the customers when the CSPs offer it along with the on-demand instance service to the customers. We model the interaction between CSPs and customers as a non-cooperative two-stage dynamic game. Our equilibrium analysis reveals (i) the techno-economic i

Nishta Arora, Priyanka Singh, Randhir Kumar, Rudra Pratap

Nonlinearities play a critical role in the dynamics of mechanical resonators, enhancing sensitivity and enabling signal manipulation. Understanding the parameters affecting nonlinearities is crucial for developing strategies to counteract their effects or manipulate them for improved device performance. This study investigates the impact of fabrication-induc

Harsha Hutridurga, Krishan Kumar, Amiya K. Pani

This paper deals with the Vlasov-Stokes' system in three dimensions with periodic boundary conditions in the spatial variable. We prove the existence of a unique strong solution to this two-phase model under the assumption that initial velocity moments of certain order are bounded. We use a fixed point argument to arrive at a global-in-time solution.

Jiacai Liu, Jinchi Chen, Ke Wei

The convergence of deterministic policy gradient under the Hadamard parameterization is studied in the tabular setting and the linear convergence of the algorithm is established. To this end, we first show that the error decreases at an $O(\frac{1}{k})$ rate for all the iterations. Based on this result, we further show that the algorithm has a faster local l

Lin Hu, Jiabing Fan, Hong Wen, Jie Tang

This paper investigates physical layer security (PLS) in wireless interference networks. Specifically, we consider confidential transmission from a legitimate transmitter (Alice) to a legitimate receiver (Bob), in the presence of non-colluding passive eavesdroppers (Eves), as well as multiple legitimate transceivers. To mitigate interference at legitimate re

Pitambar Khanra, Johan Nakuci, Sarah Muldoon, Takamitsu Watanabe

Energy landscape analysis is a data-driven method to analyze multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the en

Aniket Banerjee, Vaibhava Srivastava, Rana D. Parshad

We consider the effect of finite time extinction mechanisms (FTEM) such as (1) semi-linear harvesting terms, and (2) quasi-linear fast diffusion terms on two species Lokta-Volterra competition models. We show that these mechanisms can alter classical dynamics of competitive exclusion, and weak and strong competition by acting only on a \emph{small} portion o

Dian Feng, Zhiwei Yang, Sen Zou

In this article, we propose the fractional weak adversarial networks (f-WANs) for the stationary fractional advection dispersion equations (FADE) based on their weak formulas. This enables us to handle less regular solutions for the fractional equations. To handle the non-local property of the fractional derivatives, convolutional layers and special loss fun

Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan

This paper focuses on supervised and unsupervised online label shift, where the class marginals $Q(y)$ varies but the class-conditionals $Q(x|y)$ remain invariant. In the unsupervised setting, our goal is to adapt a learner, trained on some offline labeled data, to changing label distributions given unlabeled online data. In the supervised setting, we must b

Jong Moon Ha, Olga Fink

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain

Shigetora Miyashita, Takahiko Satoh, Michihiko Sugawara, Naphan Benchasattabuse

We introduce a quantum algorithm for simulating the time-dependent Dirac equation in 3+1 dimensions using discrete-time quantum walks. Thus far, promising quantum algorithms have been proposed to simulate quantum dynamics in non-relativistic regimes efficiently. However, only some studies have attempted to simulate relativistic dynamics due to its theoretica

Yerin Choi, Myoung-Wan Koo

Despite the huge successes made in neutral TTS, content-leakage remains a challenge. In this paper, we propose a new input representation and simple architecture to achieve improved prosody modeling. Inspired by the recent success in the use of discrete code in TTS, we introduce discrete code to the input of the reference encoder. Specifically, we leverage t

Liwen Gao, Xuejun Guo

In this paper, we establish the explicit lower bound estimates for the rank of universal quadratic forms in some certain families of real cubic fields under the condition of density one. The more general results that represent all multiples of a given rational integer are obtained for totally positive definite quadratic lattices. Our main tools are some prop

Nghi D. Q. Bui, Hung Le, Yue Wang, Junnan Li

Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. However, the development and deployment o

Chunlei Liu

On a Goppa code whose structure polynomial has coefficients in the symbol field, the Frobenius acts. Its fixed codewords form a subcode. Deleting the naturally occurred redundance, we obtain a new code. It is proved that these new codes approach the Gilbert-Varshamov bound. It is also proved that these codes can be decoded within $O(n^2(\logn)^a)$ operations

Alain Finkel, Serge Haddad, Lina Ye

Decisiveness of infinite Markov chains with respect to some (finite or infinite) target set of states is a key property that allows to compute the reachability probability of this set up to an arbitrary precision. Most of the existing works assume constant weights for defining the probability of a transition in the considered models. However numerous probabi

Hongfu Liu, Mingqian Shi, Ye Wang

Automatic Pronunciation Assessment (APA) is vital for computer-assisted language learning. Prior methods rely on annotated speech-text data to train Automatic Speech Recognition (ASR) models or speech-score data to train regression models. In this work, we propose a novel zero-shot APA method based on the pre-trained acoustic model, HuBERT. Our method involv

Amin Karbasi, Grigoris Velegkas, Lin F. Yang, Felix Zhou

We initiate the mathematical study of replicability as an algorithmic property in the context of reinforcement learning (RL). We focus on the fundamental setting of discounted tabular MDPs with access to a generative model. Inspired by Impagliazzo et al. [2022], we say that an RL algorithm is replicable if, with high probability, it outputs the exact same po

Chong-Xing Yue, Han Wang, Xue-Jia Cheng, Yue-Qi Wang

Axion-like particles (ALPs) arise naturally in many extensions of the Standard Model (SM). We explore the discovery potential for ALPs of the Large Hadron electron Collider (LHeC) via the $W^{+}W^{-}$ fusion process. For concreteness, both cases of the ALP decaying to muon pairs and $b\overline{b}$ pairs are investigated. Our numerical results show that the

Nguyen Q. Vinh, Luan C. Doan, Ngoc L. H. Hoang, Jiarong R. Cui

Water is vital for life, and without it biomolecules and cells cannot maintain their structures and functions. The remarkable properties of water originate from its ability to form hydrogen-bonding networks and dynamics, which the connectivity constantly alters because of the orientation rotation of individual water molecules. Experimental investigation of t

Hesham Beshary, Ali Niknejad

This paper presents a novel technique to build squint-free massive phased arrays. This is accomplished by explicitly implementing a spatial IDFT to cancel out the DFT imposed by the array nature which causes beam squint. In addition, the paper analyzes the beam-squint issue, which arises from two mechanisms: the coherent bandwidth limitations and the systema

Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun

Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided

Ruxiao Chen, Shuaishuai Guo

Mobile augmented reality (MAR) blends a real scenario with overlaid virtual content, which has been envisioned as one of the ubiquitous interfaces to the Metaverse. Due to the limited computing power and battery life of MAR devices, it is common to offload the computation tasks to edge or cloud servers in close proximity. However, existing offloading solutio

Subhroshekhar Ghosh, Aaron Y. R. Low, Yong Sheng Soh, Zhuohang Feng

The dictionary learning problem can be viewed as a data-driven process to learn a suitable transformation so that data is sparsely represented directly from example data. In this paper, we examine the problem of learning a dictionary that is invariant under a pre-specified group of transformations. Natural settings include Cryo-EM, multi-object tracking, syn

Se Jin Park, Minsu Kim, Jeongsoo Choi, Yong Man Ro

Talking face generation is the challenging task of synthesizing a natural and realistic face that requires accurate synchronization with a given audio. Due to co-articulation, where an isolated phone is influenced by the preceding or following phones, the articulation of a phone varies upon the phonetic context. Therefore, modeling lip motion with the phonet

Gaël Gendron, Qiming Bao, Michael Witbrock, Gillian Dobbie

Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning. However, the mechanisms responsible for this success remain opaque, and it is unclear whether LLMs can achieve human-like cognitive capabilities or whether these models are still fundament

Claudio Corianò, Mario Cretì, Stefano Lionetti, Matteo Maria Maglio

A nonlocal version of Einstein-Gauss Bonnet (EGB) gravity can be generated by a procedure that follows closely the steps of the derivation of the conformal anomaly effective action. The action is obtained using a Weyl decomposition of the metric, with the conformal factor removed by a finite renormalization of the topological term. We outline the mains steps

C. T. Parzyck, C. A. Pennington, W. J. I. DeBenedetti, J. Balajka

Alkali antimonide semiconductor photocathodes provide a promising platform for the generation of high brightness electron beams, which are necessary for the development of cutting-edge probes including x-ray free electron lasers and ultrafast electron diffraction. However, to harness the intrinsic brightness limits in these compounds, extrinsic degrading fac

Mohammad Babar, Ziyan Zhu, Rachel Kurchin, Efthimios Kaxiras

In this work, we develop a twist-dependent electrochemical activity map, combining a tight-binding electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer graphene. We identify a counterintuitive rate enhancement region spanning the magic angle curve and incommensurate twists of the system geometry. We find a broad activity peak wit

SJ Dillon, Yueqing Liang, H. Russell Bernard, Kai Shu

With the emergence of short video-sharing platforms, engagement with social media sites devoted to opinion and knowledge dissemination has rapidly increased. Among these platforms, TikTok is one of the most popular globally and has become the platform of choice for transgender and nonbinary individuals, who have formed a large community to mobilize personal

Swetarekha Ram, Gwan Hyun Choi, Albert S. Lee, Seung-Cheol Lee

In this study, we address the significant challenge of overcoming limitations in catalytic efficiency for the oxygen evolution reaction (OER). The current linear scaling relationships hinder the optimization of electrocatalytic performance. To tackle this issue, we investigate the potential of designing single-atom catalysts (SACs) on Mo$_2$CO$_2$ MXenes for

Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal, Yoshua Bengio

The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a set of interchangeable modules called \emph{slots} or \emph{object files} that compete for local patches of an image. The competition has a weak inductive bias to preserve spatial continuity; consequently, one slot may

Huiqiang Jiang, Li Lyna Zhang, Yuang Li, Yu Wu

Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model sizes and high inference costs, posing a challenge to deploy on resource-limited devices. In this paper, we propose a novel compression strategy that leverages structured pruning an

Shin-Liang Chen, Jens Eisert

We develop a framework for characterizing quantum temporal correlations in a general temporal scenario, in which an initial quantum state is measured, sent through a quantum channel, and finally measured again. This framework does not make any assumptions on the system nor on the measurements, namely, it is device-independent. It is versatile enough, however

Yuxiang Wei, Zhilong Ji, Xiaohe Wu, Jinfeng Bai

Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can undoubtedly benefit image synthesis, but is bothersome and inconvenient to be provided by users. To improve part synthesis, this paper presents to infer Parts from Object ShapE (iP