May 2023 arXiv papers — page 80

Kaiwen Guo, Yanjun Liu

In this paper, we investigate sharp singular Trudinger-Moser inequalities involving the anisotropic Dirichlet norm $\left(\int_{\Omega}F^{N}(\nabla u)\;\mathrm{d}x\right)^{\frac{1}{N}}$ in the Sobolev-type space $D^{N,q}(\mathbb{R}^{N})$, $q\geq 1$, here $F:\mathbb{R}^{N}\rightarrow[0,+\infty)$ is a convex function of class $C^{2}(\mathbb{R}^{N}\setminus\{0\

Detai Xin, Shinnosuke Takamichi, Ai Morimatsu, Hiroshi Saruwatari

We present a large-scale in-the-wild Japanese laughter corpus and a laughter synthesis method. Previous work on laughter synthesis lacks not only data but also proper ways to represent laughter. To solve these problems, we first propose an in-the-wild corpus comprising $3.5$ hours of laughter, which is to our best knowledge the largest laughter corpus design

Gongyao Jiang, Shuang Liu, Meishan Zhang, Min Zhang

Dialogue-level dependency parsing has received insufficient attention, especially for Chinese. To this end, we draw on ideas from syntactic dependency and rhetorical structure theory (RST), developing a high-quality human-annotated corpus, which contains 850 dialogues and 199,803 dependencies. Considering that such tasks suffer from high annotation costs, we

Xiangxiang Gao, Wei Zhu, Jiasheng Gao, Congrui Yin

Computational complexity and overthinking problems have become the bottlenecks for pre-training language models (PLMs) with millions or even trillions of parameters. A Flexible-Patience-Based Early Exiting method (F-PABEE) has been proposed to alleviate the problems mentioned above for single-label classification (SLC) and multi-label classification (MLC) ta

Serban Matei Mihalache

We give a construction of a state sum invariant of a closed spin 3-manifold based on a super 3-cocycle $(\widetilde{\alpha}, \omega)$ and a combinatorial representation of a spin 3-manifold, where $\omega$ is a $\mathbb{Z}_2$-valued cocycle and $\widetilde{\alpha}$ is a 3-cochain satisfying a 3-cocycle condition with a sign coming from the 2-cocycle $\omega$

Yi-Shiuan Huang, Yang-Hao Chan, Guang-Yu Guo

We perform {\it ab initio} many-body calculations to investigate the exciton shift current in small diameter zigzag BN nanotubes and also single BN sheet, using the GW plus Bethe-Salpeter equation (GW-BSE) method with the newly developed efficient algorithms. Our GW-BSE calculations reveal a giant in-gap peak in the shift current spectrum in all the studied

Mihai Florincescu, Gaven Martin

This article identifies the conformal energy (or mean distortion) of extremal mappings of finite distortion with a given quasisymmetric mapping of the circle as boundary data. The conformal energy of $g_o:\IS\to\IS$ is \begin{equation}\label{energy} \mathcal E(g_o)=-\frac{1}{4\pi^2}\iint_{\mathbb{S}\times \mathbb{S}}\log |g_o(\zeta)-g_o(\eta)| \: d\zeta d\ba

Xijun Wang, Ruiqi Xian, Tianrui Guan, Fuxiao Liu

We present a new learning approach, Soft Conditional Prompt Learning (SCP), which leverages the strengths of prompt learning for aerial video action recognition. Our approach is designed to predict the action of each agent by helping the models focus on the descriptions or instructions associated with actions in the input videos for aerial/robot visual perce

S. De Gaetano, L. Di Venere, F. Gargano, F. Loparco

All known Small Solar System Bodies have diameters between a few meters and a few thousands of kilometers. Based on the collisional evolution of Solar System Bodies, a larger number of asteroids with diameters down to $\sim 2$ m is thought to exist. As all Solar System Bodies, Small Bodies can be passive sources of high-energy gamma rays, produced by the int

Dong Xie, Chunling Xu

Tripartite interactions play a fundamental role in the quantum information processing and quantum technology. However, it is generally difficult to realize strong tripartite coupling. We investigate the estimation of a tripartite coupling strength in a hybrid setup composed of a single nitrogen-vacancy (NV) center and a micromagnet. A time-independent parame

Yuxuan Wan, Wenxuan Wang, Pinjia He, Jiazhen Gu

Powered by advanced Artificial Intelligence (AI) techniques, conversational AI systems, such as ChatGPT and digital assistants like Siri, have been widely deployed in daily life. However, such systems may still produce content containing biases and stereotypes, causing potential social problems. Due to the data-driven, black-box nature of modern AI technique

Yaohua Zang, Gang Bao

Deep neural networks (DNNs) have been widely used to solve partial differential equations (PDEs) in recent years. In this work, a novel deep learning-based framework named Particle Weak-form based Neural Networks (ParticleWNN) is developed for solving PDEs in the weak form. In this framework, the trial space is defined as the space of DNNs, while the test sp

Idio Guarino, Chao Wang, Alessandro Finamore, Antonio Pescape

The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffic Classification (TC). However, to tame the dependency from task-specific large labeled datasets we need to find better ways to learn representations that are valid across tasks. I

P Aswathylakshmi, Radha Krishna Ganti

Massive MIMO OFDM waveforms help support a large number of users in the same time-frequency resource and also provide significant array gain for uplink reception in cellular systems. However, channel estimation in such large antenna systems can be tricky as pilot assignment for multiple users becomes more challenging with increasing number of users. Addition

Zhaolong Xue, Aoyu Gong, Yuan-Hsun Lo, Sirui Tian

This paper considers designing an optimal policy for deadline-constrained access in cognitive radio networks, where a secondary user needs to complete a packet transmission over the vacant spectrum within a delivery deadline. To minimize the total access cost, it is desirable to design an optimal opportunistic access policy by utilizing channel dynamics and

Jesse Gell-Redman, Sean Gomes, Andrew Hassell

Using the Fredholm theory of the linear time-dependent Schr\"odinger equation set up in our previous article arXiv:2201.03140, we solve the final-state problem for the nonlinear Schr\"odinger problem $$ (D_t + \Delta + V) u = N[u], \quad u(z,t) \sim (4\pi it)^{-n/2} e^{i|z|^2/4t} f\big( \frac{z}{2t} \big), \quad t \to -\infty, $$ where $u : \mathbb{R}^{n+1}

Zimu Wang, Jiashuo Liu, Hao Zou, Xingxuan Zhang

Massive amounts of data are the foundation of data-driven recommendation models. As an inherent nature of big data, data heterogeneity widely exists in real-world recommendation systems. It reflects the differences in the properties among sub-populations. Ignoring the heterogeneity in recommendation data could limit the performance of recommendation models,

Semiha Kosu, Mohammadreza Babaei, Serdar Özgür Ata, Lutfiye Durak-Ata

Vehicle-to-vehicle (V2V) communications is a part of next-generation wireless networks to create smart cities with the connectivity of intelligent transportation systems. Besides, green communications is considered in V2V communication systems for energy sustainability and carbon neutrality. In this scope, radio-frequency (RF) energy harvesting (EH) provides

Nikolaos Tsagkas, Oisin Mac Aodha, Chris Xiaoxuan Lu

We present Visual-Language Fields (VL-Fields), a neural implicit spatial representation that enables open-vocabulary semantic queries. Our model encodes and fuses the geometry of a scene with vision-language trained latent features by distilling information from a language-driven segmentation model. VL-Fields is trained without requiring any prior knowledge

Josselin Garnier, Laurent Mertz

This paper considers the motion of an object subjected to dry friction and an external random force. The objective is to characterize the role of the correlation time of the external random force. We develop efficient stochastic simulation methods for computing the diffusivity (the linear growth rate of the variance of the displacement) and other related qua

Ziqian Ning, Yuepeng Jiang, Pengcheng Zhu, Jixun Yao

Voice conversion is an increasingly popular technology, and the growing number of real-time applications requires models with streaming conversion capabilities. Unlike typical (non-streaming) voice conversion, which can leverage the entire utterance as full context, streaming voice conversion faces significant challenges due to the missing future information

Mengji Zhang, Yusuke Hiki, Akira Funahashi, Tetsuya J. Kobayashi

While visual and auditory information conveyed by wavelength of light and frequency of sound have been decoded, predicting olfactory information encoded by the combination of odorants remains challenging due to the unknown and potentially discontinuous perceptual space of smells and odorants. Herein, we develop a deep learning model called Mol-PECO (Molecula

Hongru Li, Wentao Yu, Hengtao He, Jiawei Shao

Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications. Most existing deep learning (DL)-based task-oriented communication systems adopt a closed-world scenario, assuming either the same data distribution for train

Vincent Guedj, Henri Guenancia, Ahmed Zeriahi

K\"ahler-Einstein currents, also known as singular K\"ahler-Einstein metrics, have been introduced and constructed a little over a decade ago. These currents live on mildly singular compact K\"ahler spaces $X$ and their two defining properties are the following: they are genuine K\"ahler-Einstein metrics on $X_{\rm reg}$ and they admit local bounded potentia

Cunxiang Wang, Sirui Cheng, Qipeng Guo, Yuanhao Yue

This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, Evaluating QA Evaluation (QA-Eval) and

Yue Xu, Hao Chen, Zefan Wang, Jianwen Yin

Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, users tend to browse a large number of items in succession, so the previously viewed items have a significant impact on users' behavior towards the following items. Therefore,

V. K. Ivanov, A. D. Chaikovskaia, D. V. Karlovets

We explore the opportunities of using electron scattering by screened Coulomb potential as a tool to retrieve properties of the relativistic vortex beams of electrons, such as their transverse momentum and orbital angular momentum (OAM). We focus on relativistic and ultra-relativistic regimes of the electron energies of at least several MeV and higher, in wh

Carlos Hinojosa, Karen Sanchez, Ariolfo Camacho, Henry Arguello

In recent years, fast technological advancements have led to the development of high-quality software and hardware, revolutionizing various industries such as the economy, health, industry, and agriculture. Specifically, applying information and communication technology (ICT) tools and the Internet of Things (IoT) in agriculture has improved productivity thr

Juan M. Gandarias, Alfonso J. García-Cerezo, Jesús M. Gómez-de-Gabriel

Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as Convolutional Neural Networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic end-effector to identify contacted objects. Two CNN-based a

Jinheon Baek, Alham Fikri Aji, Jens Lehmann, Sung Ju Hwang

There has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks. The conventional mechanism to retrieve facts in KGs usually involves three steps: entity span detection, entity disambiguation, and relation classification. However, this approach requires additional labels for training each of

Vedran Krčadinac, Mario Osvin Pavčević, Kristijan Tabak

We describe a construction of three-dimensional Hadamard matrices of even order $v$ such that $v-1$ is a prime power. The construction covers infinitely many orders for which the existence was previously open.

Tin Lai

We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade-off. This pipeline of networks can expl

Orphée Collin, Giambattista Giacomin, Yueyun Hu

We consider the continuum version of the random field Ising model in one dimension: this model arises naturally as weak disorder scaling limit of the original Ising model. Like for the Ising model, a spin configuration is conveniently described as a sequence of spin domains with alternating signs (domain-wall structure). We show that for fixed centered exter

Yiyang Li, Hai Zhao

Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to its previous utterance, the addressee of

Kaifeng Zhao, Yan Zhang, Shaofei Wang, Thabo Beeler

We present a novel method for populating 3D indoor scenes with virtual humans that can navigate in the environment and interact with objects in a realistic manner. Existing approaches rely on training sequences that contain captured human motions and the 3D scenes they interact with. However, such interaction data are costly, difficult to capture, and can ha

Xiangrong Zhang, Shunli Tian, Guanchun Wang, Huiyu Zhou

Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing due to its ability to detect subtle changes on the earth's surface. Recently, diffusional denoising probabilistic models (DDPM) have demonstrated remarkable performance in the generative domain. Apart from their image generation capability, the denoising pr

Zihang Wei, Rujiao Yan, Matthias Schreier

To implement autonomous driving, one essential step is to model the vehicle environment based on the sensor inputs. Radars, with their well-known advantages, became a popular option to infer the occupancy state of grid cells surrounding the vehicle. To tackle data sparsity and noise of radar detections, we propose a deep learning-based Inverse Sensor Model (

Paolo Aglianò

In this paper we investigate two logics from an algebraic point of view. The two logics are: MALL (multiplicative-additive Linear Logic) and LL (classical Linear Logic). Both logics turn out to be strongly algebraizable in the sense of Blok and Pigozzi and their equivalent algebraic semantics are, respectively, the variety of Girard algebras and the variety

Aldo Gael Carranza, Susan Athey

We consider the problem of learning personalized decision policies from observational bandit feedback data across multiple heterogeneous data sources. In our approach, we introduce a novel regret analysis that establishes finite-sample upper bounds on distinguishing notions of global regret for all data sources on aggregate and of local regret for any given

Yan V. Fyodorov, Elizaveta Safonova

Using the supersymmetric method of random matrix theory within the Heidelberg approach framework we provide statistical description of stationary intensity sampled in locations inside an open wave-chaotic cavity, assuming that the time-reversal invariance inside the cavity is fully broken. In particular, we show that when incoming waves are fed via a finite

Oisín Faust, Hamza Fawzi

Operator convex functions defined on the positive half-line play a prominent role in the theory of quantum information, where they are used to define quantum $f$-divergences. Such functions admit integral representations in terms of rational functions. Obtaining high-quality rational approximants of operator convex functions is particularly useful for solvin

Soham Chatterjee, Vivek Natarajan

We consider the problem of finding an input signal which transfers a linear boundary controlled 1D parabolic partial differential equation with spatially-varying coefficients from a given initial state to a desired final state. The initial and final states have certain smoothness and the transfer must occur over a given time interval. We address this motion

Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin

Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromise with conditional independence between time and space, which consider the temporal and spatial distributions separately. The failure to model the joint distribution leads to limit

Zongqi Wan, Jialin Zhang, Wei Chen, Xiaoming Sun

We investigate the online bandit learning of the monotone multi-linear DR-submodular functions, designing the algorithm $\mathtt{BanditMLSM}$ that attains $O(T^{2/3}\log T)$ of $(1-1/e)$-regret. Then we reduce submodular bandit with partition matroid constraint and bandit sequential monotone maximization to the online bandit learning of the monotone multi-li

Tianle Wang, Zihan Wang, Weitang Liu, Jingbo Shang

State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest. This is never easy to meet in practice when human explore new, large corpora without complete pictures. In this paper, we work on a novel yet important problem of weak

Koji Kudo, A. Sharma, G. J. Sreejith, J. K. Jain

While a parent Hamiltonian for Laughlin $1/3$ wave function has been long known in terms of the Haldane pseudopotentials, no parent Hamiltonians are known for the lowest-Landau-level projected wave functions of the composite fermion theory at $n/(2n+1)$ with $n\geq2$. If one takes the two lowest Landau levels to be degenerate, the Trugman-Kivelson interactio

Sven Ahrens, Chong Zhang, Ping Ge, Guweiyi Li

Theoretical spin properties of the Kapitza-Dirac effect beyond the plane-wave description are not known in detail. We develop a method for computing electron diffraction of the two-photon Kapitza-Dirac effect in a two-dimensional Gaussian beam standing light wave within a relativistic formulation. The solutions are computed on the basis of time-dependent per

Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, Timothy K. Shih

The utilization of Wi-Fi based human activity recognition has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on came

Haojun Xu, Yan Gao, Zheng Hui, Jie Li

How humans understand and recognize the actions of others is a complex neuroscientific problem that involves a combination of cognitive mechanisms and neural networks. Research has shown that humans have brain areas that recognize actions that process top-down attentional information, such as the temporoparietal association area. Also, humans have brain regi

Yuanyuan Jiang, Jianqin Yin

Audio-visual question answering (AVQA) is a challenging task that requires multistep spatio-temporal reasoning over multimodal contexts. Recent works rely on elaborate target-agnostic parsing of audio-visual scenes for spatial grounding while mistreating audio and video as separate entities for temporal grounding. This paper proposes a new target-aware joint

Lei Xu, Lei Chen, Rong Wang, Feiping Nie

Feature selection (FS) plays an important role in machine learning, which extracts important features and accelerates the learning process. In this paper, we propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning based on the Dirichlet Energy. The Dirichlet Energy identifies important features by me

Adam B. Cahaya, Ansell Alvarez Anderson, Anugrah Azhar, Muhammad Aziz Majidi

Exchange bias is a unidirectional magnetic anisotropy that often arise from interfacial interaction of a ferromagnetic and antiferromagnetic layers. In this article, we show that a metallic layer with spin-orbit coupling can induces an exchange bias via an interface magnetoelectric effect. In linear response regime, the interface magnetoelectric effect is in

Siyu Ren, Kenny Q. Zhu

Iterative pruning is one of the most effective compression methods for pre-trained language models. We discovered that finding the optimal pruning decision is an equality-constrained 0-1 Integer Linear Programming problem. The solution to this optimization problem leads to a principled importance criterion which we use to rank parameters during iterative mod

Guy Lorberbom, Itai Gat, Yossi Adi, Alex Schwing

Backpropagation, which uses the chain rule, is the de-facto standard algorithm for optimizing neural networks nowadays. Recently, Hinton (2022) proposed the forward-forward algorithm, a promising alternative that optimizes neural nets layer-by-layer, without propagating gradients throughout the network. Although such an approach has several advantages over b

Jiuzhou Han, Nigel Collier, Wray Buntine, Ehsan Shareghi

Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains. Due to the training objective of LLMs and their pre-training data, LLMs are not very well equipped for tasks involving structured data generation. We propose a framework, Prompting with Iterative Verification (PiVe), to improve graph-based

Li Zhang, Yong Liu, Xinpeng Zhang, Hanzhou Wu

Protecting deep neural networks (DNNs) against intellectual property (IP) infringement has attracted an increasing attention in recent years. Recent advances focus on IP protection of generative models, which embed the watermark information into the image generated by the model to be protected. Although the generated marked image has good visual quality, it

Adam B. Cahaya, Alejandro O. Leon, Mohammad H. Fauzi

Spin-transfer and spin-orbit torques allow controlling magnetic degrees of freedom in various materials and devices. However, while the transfer of angular momenta between electrons has been widely studied, the contribution of nuclear spins has yet to be explored further. This article demonstrates that the hyperfine coupling, which consists of Fermi contact

Jun-Yu Ma, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu

Zero-shot cross-lingual information extraction(IE) aims at constructing an IE model for some low-resource target languages, given annotations exclusively in some rich-resource languages. Recent studies based on language-universal features have shown their effectiveness and are attracting increasing attention. However, prior work has neither explored the pote

Aadhitya A, Rajapriya R, Vineetha R S, Anurag M Bagde

Stock market is often important as it represents the ownership claims on businesses. Without sufficient stocks, a company cannot perform well in finance. Predicting a stock market performance of a company is nearly hard because every time the prices of a company stock keeps changing and not constant. So, its complex to determine the stock data. But if the pr

S. Y. Lou

A new type of symmetry, ren-symmetry describing anyon physics and the corresponding topological physics, is proposed. Ren-symmetry is a generalization of super-symmetry which is widely applied in super-symmetric physics such as the super-symmetric quantum mechanics, super-symmetric gravity, super-symmetric string theory, super-symmetric integrable systems an

Alexander Tyurin, Peter Richtárik

Parallelization is a popular strategy for improving the performance of iterative algorithms. Optimization methods are no exception: design of efficient parallel optimization methods and tight analysis of their theoretical properties are important research endeavors. While the minimax complexities are well known for sequential optimization methods, the theory

David Morselli, Marcello Edoardo Delitala, Federico Frascoli

The use of oncolytic viruses as cancer treatment has received considerable attention in recent years, however the spatial dynamics of this viral infection is still poorly understood. We present here a stochastic agent-based model describing infected and uninfected cells for solid tumours, which interact with viruses in the absence of an immune response. Two

Sandra M Jose, Komal Sah, Rejish Nath

We analyze the formation of transient patterns and spin-spin correlations in quasi-two-dimensional spin-1 homogeneous Bose-Einstein condensates subjected to parametric driving of $s$-wave scattering lengths. The dynamics for an initial ferromagnetic phase is identical to that of a scalar condensate. In contrast, intriguing dynamics emerges for an initial pol

Toni Albert, Bjoern Eskofier, Dario Zanca

As the field of deep learning steadily transitions from the realm of academic research to practical application, the significance of self-supervised pretraining methods has become increasingly prominent. These methods, particularly in the image domain, offer a compelling strategy to effectively utilize the abundance of unlabeled image data, thereby enhancing

Nirmal Kotal, Manoj Kummini

In this paper, we prove some sufficient conditions for Cohen-Macaulay normal Rees algebras to be $F$-rational. Let $(R,\mathfrak{m})$ be a Gorenstein normal local domain of dimension $d\geq 2$ and of characteristic $p > 0$. Let $I$ be a $\mathfrak{m}$-primary ideal. Our first set of results give conditions on the test ideals $\tau(I^n)$, $n \geq 1$ which wou

Yifeng Cao, Di Jin, Nir Kampf, Jacob Klein

The very low sliding friction of articular cartilage in the major synovial joints such as hips and knees is crucial for their well-being, and has been attributed to lubrication by phospholipid boundary layers. While single-component lipid layers have demonstrated efficient lubricity in model studies, in living joints there is a large number of different lipi

Hao Zeng, Yuki Wakata, Xing Chao, Mingbo Li

Hypothesis: Multi-component droplet evaporation has received significant attention in recent years due to the broad range of applications such as material science, environmental monitoring, and pharmaceuticals. The selective evaporation induced by the different physicochemical properties of components is expected to influence the concentration distributions

Dario Zanca, Andrea Zugarini, Simon Dietz, Thomas R. Altstidl

Understanding the mechanisms underlying human attention is a fundamental challenge for both vision science and artificial intelligence. While numerous computational models of free-viewing have been proposed, less is known about the mechanisms underlying task-driven image exploration. To address this gap, we present CapMIT1003, a database of captions and clic

Alexander Tyurin, Peter Richtárik

We consider distributed convex optimization problems in the regime when the communication between the server and the workers is expensive in both uplink and downlink directions. We develop a new and provably accelerated method, which we call 2Direction, based on fast bidirectional compressed communication and a new bespoke error-feedback mechanism which may

M. I. Krivoruchenko, F. Simkovic

Techniques are developed for constructing amplitudes of neutrino-related processes in terms of the neutrino mass matrix, with no reference to the neutrino mixing matrix. The amplitudes of neutrino oscillations in vacuum and medium, quasi-elastic neutrino scattering, $\beta$ decays and double-$\beta$ decays are considered. The proposed approach makes extensiv

Yinghui Wang, Huanyao Wen

We consider the initial-boundary value problem for an incompressible Oldroyd-B model with stress diffusion in two-dimensional upper half plane which describes the motion of viscoelastic polymeric fluids. From the physical point of view, the diffusive coefficient is several orders of magnitude smaller than other parameters in the model, and is usually assumed

Nadav Borenstein, Karolina Stańczak, Thea Rolskov, Natália da Silva Perez

Data-driven analyses of biases in historical texts can help illuminate the origin and development of biases prevailing in modern society. However, digitised historical documents pose a challenge for NLP practitioners as these corpora suffer from errors introduced by optical character recognition (OCR) and are written in an archaic language. In this paper, we

Zhou-Run Zhu, Defu Hou

In this paper, we consider the Einstein-Maxwell-dilaton holographic model for light quarks with nonzero magnetic field and chemical potential. First, we study the phase diagrams in $T-\mu$ and $T-B$ planes. We observe inverse magnetic catalysis which is consistent with the lattice QCD results. We discuss the influence of the magnetic field and chemical poten

Kiichi Tashiro

We investigate a mean curvature flow obtained via elliptic regularization, and prove that it is not only a Brakke flow, but additionally a generalized BV flow proposed by Stuvard and Tonegawa. In particular, we show that the change in volume of the evolving phase can be expressed in terms of the generalized mean curvature of the Brakke flow.

Akihiro Inoue, Ken Ohsuga, Hiroyuki R. Takahashi, Yuta Asahina

We perform general relativistic radiation magnetohydrodynamics (MHD) simulations of super-Eddington accretion flows around a neutron star with a dipole magnetic field for modeling the galactic ultra-luminous X-ray source (ULX) exhibiting X-ray pulsations, Swift J0243.6+6124. Our simulations show the accretion columns near the magnetic poles, the accretion di

Zheng Zhai, Hengchao Chen, Qiang Sun

Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded constraint. Algorithmically, we introduce a new differentiable convex penalty and derive an alternating direction method of multipliers (ADMM) algorithm. Theoretically, we establish

Xinyu Bian, Yuyi Mao, Jun Zhang

Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice. Therefore, this paper considers asynchronous grant-free massive RA systems and develops novel algorithms for joint user activity detection, sync

Amit Kumar, Shantipriya Parida, Ajay Pratap, Anil Kumar Singh

The use of subword embedding has proved to be a major innovation in Neural Machine Translation (NMT). It helps NMT to learn better context vectors for Low Resource Languages (LRLs) so as to predict the target words by better modelling the morphologies of the two languages and also the morphosyntax transfer. Even so, their performance for translation in India

A. V. Tsiganov

We discuss some families of integrable and superintegrable systems in $n$-dimensional Euclidean space which are invariant to $m\geq n-2$ rotations. The integrable invariant Hamiltonian $H=\sum p_i^2+V(q)$ commutes with $n-2$ integrals of motion $M_\alpha $ and an additional integral of motion $G$, which are polynomials of first and fourth-order in momenta, r

Yubin Kim, Dong Won Lee, Paul Pu Liang, Sharifa Algohwinem

Accurately modeling affect dynamics, which refers to the changes and fluctuations in emotions and affective displays during human conversations, is crucial for understanding human interactions. By analyzing affect dynamics, we can gain insights into how people communicate, respond to different situations, and form relationships. However, modeling affect dyna

Mikhail Khristoforov, Mikhail Skopenkov, Stanislav Smirnov

We study critical site percolation on the triangular lattice. We find the difference of the probabilities of having a percolation interface to the right and to the left of two given points in the scaling limit. This generalizes both Cardy's and Schramm's formulae. The generalization involves a new interesting discrete analytic observable and an unexpected co

Tekin Dereli, Yorgo Senikoglu

We investigate complex quaternion-valued exterior differential forms over 4-dimensional Lorentzian spacetimes and explore Weyl spinor fields as minimal left ideals within the complex quaternion algebra. The variational derivation of the coupled Einstein-Weyl equations from an action is presented, and the resulting field equations for both first and second or

Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka

Recently, methods for learning diverse skills to generate various behaviors without external rewards have been actively studied as a form of unsupervised reinforcement learning. However, most of the existing methods learn a finite number of discrete skills, and thus the variety of behaviors that can be exhibited with the learned skills is limited. In this pa

Rand R. Wilcox, Guillaume A. Rousselet

When comparing two independent groups, shift functions are basically techniques that compare multiple quantiles rather than a single measure of location, the goal being to get a more detailed understanding of how the distributions differ. Various versions have been proposed and studied. This paper deals with extensions of these methods to main effects and in

Xinyang Wu, Elisabeth Wedernikow, Christof Nitsche, Marco F. Huber

In recent years, the development of Artificial Intelligence (AI) has shown tremendous potential in diverse areas. Among them, reinforcement learning (RL) has proven to be an effective solution for learning intelligent control strategies. As an inevitable trend for mitigating climate change, hybrid electric vehicles (HEVs) rely on efficient energy management

Taeisha Nundlall, Terence L Van Zyl

Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. Although Mean-Variance (MV) models successfully generate the highest possible return based on an investor's risk tolerance, MV models do not make provisions for additional constraints relevant to socially responsib

Laksh Nanwani, Anmol Agarwal, Kanishk Jain, Raghav Prabhakar

Humans have a natural ability to perform semantic associations with the surrounding objects in the environment. This allows them to create a mental map of the environment, allowing them to navigate on-demand when given linguistic instructions. A natural goal in Vision Language Navigation (VLN) research is to impart autonomous agents with similar capabilities

Jie Zhou

We extend the notion of regularized integrals introduced by Li-Zhou that aims to assign finite values to divergent integrals on configuration spaces of Riemann surfaces. We then give cohomological formulations for the extended notion using the tools of current cohomology and mixed Hodge structures. We also provide practical ways of constructing representativ

Tianyi Wang, Feipeng Ma, Zhenhua Liu, Fengyun Rao

With the development of multimedia technology, Video Copy Detection has been a crucial problem for social media platforms. Meta AI hold Video Similarity Challenge on CVPR 2023 to push the technology forward. In this paper, we share our winner solutions on both tracks to help progress in this area. For Descriptor Track, we propose a dual-level detection metho

Minki Kim, Alan Lew

We present extensions of the Colorful Helly Theorem for $d$-collapsible and $d$-Leray complexes, providing a common generalization to the matroidal versions of the theorem due to Kalai and Meshulam, the ``very colorful" Helly theorem introduced by Arocha, B\'ar\'any, Bracho, Fabila and Montejano, and the ``semi-intersecting" colorful Helly theorem proved by

A. Khodam-Mohammadi, M. Monshizadeh

The investigation of modifications to the FLRW cosmology resulting from the consideration of a general entropy for the cosmological apparent horizon is the subject of this study. Building upon the work of Nojiri and collaborators in 2022, who introduced a class of generalized entropies with four parameters capable of converging to familiar entropies and addr

Navya Saxena, Anjana A. Mahesh, B. Sundar Rajan

This paper studies noisy index coding problems over single-input single-output broadcast channels. The codewords from a chosen index code of length $N$ are transmitted after $2^N$-PSK modulation over an AWGN channel. In "Index Coded PSK Modulation for prioritized Receivers," the authors showed that when a length-$N$ index code is transmitted as a $2^N$-PSK s

Mehdi Astaraki, Francesca De Benetti, Yousef Yeganeh, Iuliana Toma-Dasu

Robust and accurate detection and segmentation of heterogenous tumors appearing in different anatomical organs with supervised methods require large-scale labeled datasets covering all possible types of diseases. Due to the unavailability of such rich datasets and the high cost of annotations, unsupervised anomaly detection (UAD) methods have been developed

Sukrit Venkatagiri, Anirban Mukhopadhyay, David Hicks, Aaron Brantly

Crowdsourced investigations shore up democratic institutions by debunking misinformation and uncovering human rights abuses. However, current crowdsourcing approaches rely on simplistic collaborative or competitive models and lack technological support, limiting their collective impact. Prior research has shown that blending elements of competition and colla

Yijia Zhang, Lingran Zhao, Shijie Cao, Wenqiang Wang

Efficient deployment of large language models (LLMs) necessitates low-bit quantization to minimize model size and inference cost. While low-bit integer formats (e.g., INT8/INT4) have been the conventional choice, emerging low-bit floating-point formats (e.g., FP8/FP4) offer a compelling alternative and are gaining support from cutting-edge hardware, such as

Sayuka Kita, Koji Nakamura, Tomoka Imamura, Ikumi Goya

Low-Gain Avalanche Diode (LGAD) sensor is one of candidate sensors for the tracker at future hadron collider experiments. To use this sensor as a tracking detector, AC-LGAD sensor is being developed which has both timing and spatial resolutions. In high luminosity environments, good timing resolution (typically 30 ps) together with $\mathcal{O}$(10) ${\mathr

Yanjing Li, Sheng Xu, Mingbao Lin, Xianbin Cao

Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) that pushes the quantization of ViTs to its limit remain largely unexplored and a very challenging task yet, due to their unacceptable performance. Through extensive empirical analys

Sanskriti Das, Yi-Kuan Chiang, Smita Mathur

We report on the measurement of the thermal Sunyaev-Zel'dovich (tSZ) Effect in the circumgalactic medium (CGM) of 641,923 galaxies with $\rm M_\star$=$\rm 10^{9.8-11.3}M_\odot$ at $z<$0.5, pushing the exploration of tSZ Effect to lower-mass galaxies compared to previous studies. We cross-correlate the galaxy catalog of $WISE$ and $SuperCosmos$ with the Compt

Yanguang Chen, Wenzhi Gao, Wanyu Zhang, Dongdong Ge

In this paper, we propose a Pre-trained Mixed Integer Optimization framework (PreMIO) that accelerates online mixed integer program (MIP) solving with offline datasets and machine learning models. Our method is based on a data-driven multi-variable cardinality branching procedure that splits the MIP feasible region using hyperplanes chosen by the concentrati

Christopher Burger, Lingwei Chen, Thai Le

LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI) frameworks that is integrated into critical machine learning applications--e.g., healthcare and finance. However, its stability remains little explored, especially in the context of text data, due to the unique text-space constraints. To address these challenges, in this pa