March 2024 arXiv papers — page 182

Lingmin Zhan, Yuanyuan Zhang, Yingdong Wang, Aoyi Wang

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show that TranscriptionNet, a deep learning model that integrates multiple biological networks to system

Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu

With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have been proposed, they mainly focus on fixed-budget attacks, aiming at finding the most adversarial perturbations within a fixed budget for target node. However, considering the varied

Purba Bhattacharya, Promita Roy, Tanay Dey, Jaydeep Datta

Aging and stability of gaseous ionization detectors are intricately related to charging up, accumulation of space charge and formation of discharges. All these phenomena, in their turn, depend on the dynamics of charged particles within the device. Because of the large number of particles involved and their complex interactions, the dynamic processes of gene

Yu-Tong Chen, Shigeki Matsumoto, Tian-Peng Tang, Yue-Lin Sming Tsai

This study explores a minimal renormalizable dark matter (DM) model, incorporating a sub-GeV Majorana DM and a singlet scalar particle $\phi$. Using scalar and pseudo-scalar interactions (couplings $c_s$ and $c_p$), we investigate implications for DM detection, considering $s$-wave, $p$-wave, and combined ($s$+$p$ wave) contributions in DM annihilation cross

Muriel Lepère, Olivier Browet, Jean Clément, Bastien Vispoel

To meet the challenges of high-resolution molecular spectroscopy, increasingly sophisticated spectroscopic techniques were developed. For a long time FTIR and laser-based spectroscopies were used for these studies. The recent development of dual-comb spectroscopy at high-resolution makes this technique a powerful tool for gas phase studies. We report on the

Yanbei Liu, Yu Zhao, Xiao Wang, Lei Geng

Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usually simply assume that a graph and its augmented graph as a positive pair, otherwise as a negative pair. However, it is well known that graph structure is always complex and multi

Mengyi Huang, Meng Xiao, Ludi Wang, Yi Du

Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data. Since the introduction of new relational tasks can overshadow previously learned information, catastrophic forgetting becomes a significant challenge in this domain. Current replay-based training paradigms prioritize all data uniformly an

Gaétan Guillot

This PhD thesis elaborates on a problem raised by Schmidt in 1967. We study Diophantine exponents for subspaces, which generalize the irrationality measure for real numbers. We construct subspaces with prescribed exponents and demonstrate properties of algebraic independence among certain exponents.

Xiuting Ge, Chunrong Fang, Quanjun Zhang, Daoyuan Wu

Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainly learn an AWI classifier from labeled warnings, are notably common. However, these approaches still face the problem of restricted performance due to the direct reliance on a limi

Sang T. Truong, Duc Q. Nguyen, Toan Nguyen, Dong D. Le

Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence. However, despite extensive pretraining on multilingual datasets, available open-sourced LLMs exhibit limited effectiveness in processing Vietnamese. The challenge is exacerbated by the absence of systematic benchmark datasets and

Feng Hou, Jin Yuan, Ying Yang, Yang Liu

Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-language models (VLMs), viewed as natural source models, the cross-domain task changes to directly adapt the pre-trained source model to arbitrary target domains equipped with prior dom

Jiwen Zhang, Jihao Wu, Yihua Teng, Minghui Liao

Large language model (LLM) leads to a surge of autonomous GUI agents for smartphone, which completes a task triggered by natural language through predicting a sequence of actions of API. Even though the task highly relies on past actions and visual observations, existing studies typically consider little semantic information carried out by intermediate scree

Liming Yang

Let $S$ be a subnormal operator on a separable complex Hilbert space $\mathcal H$ and let $\mu$ be the scalar-valued spectral measure for the minimal normal extension $N$ of $S.$ Let $R^\infty (\sigma(S),\mu)$ be the weak-star closure in $L^\infty (\mu)$ of rational functions with poles off $\sigma(S),$ the spectrum of $S.$ The multiplier algebra $M(S)$ cons

Chan-Jan Hsu, Chang-Le Liu, Feng-Ting Liao, Po-Chun Hsu

Breeze-7B is an open-source language model based on Mistral-7B, designed to address the need for improved language comprehension and chatbot-oriented capabilities in Traditional Chinese. This technical report provides an overview of the additional pretraining, finetuning, and evaluation stages for the Breeze-7B model. The Breeze-7B family of base and chat mo

Takao Miki

Integrating topology optimization and additive manufacturing (AM) technology can facilitate innovative product development. However, laser powder bed fusion, which is the predominant method in metal AM, can lead to issues such as residual stress and deformation. Recently, topology optimization methods considering these stresses and deformations have been pro

Jiawei Hou, Xiaoyan Li, Wenhao Guan, Gang Zhang

In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes compared with traditional perception tasks, such as 3D object detection and bird's-eye view (BEV) semantic segmentation. Recent researchers have extensively explored various aspects of this task, including view trans

Priya Sundaresan, Quan Vuong, Jiayuan Gu, Peng Xu

Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over-specified. In this work, we propose hand-drawn sketches as a modality for goal specification in visual imitation learning. Sketches are easy for users to provide on the fly like l

Songtao Peng, Tao Jin, Kailun Zhu, Qi Xuan

The rapid development of online media has significantly facilitated the public's information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has pr

Gang Liu, Hongyang Li, Zerui He, Shenjun Zhong

Leveraging pre-trained visual language models has become a widely adopted approach for improving performance in downstream visual question answering (VQA) applications. However, in the specialized field of medical VQA, the scarcity of available data poses a significant barrier to achieving reliable model generalization. Numerous methods have been proposed to

Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung, Shinwoo Kim

Optimizing molecular properties while preserving biological activity is a central challenge in drug design. Bioisosteric replacement, which substitutes a molecular fragment with a chemically or biologically analogous moiety, offers a powerful strategy for fine-tuning properties without disrupting target binding. However, existing in silico approaches often r

He-Xia Zhang, Ke-Ming Shen, Yu-Xin Xiao, Ben-Wei Zhang

We investigate the thermoelectric effect, which describes the generation of an electric field induced by temperature and conserved charge chemical potential gradients, in the hot and dense hadronic matter created in heavy-ion collisions. Utilizing the Boltzmann kinetic theory within the repulsive mean-field hadron resonance gas model, we evaluate both the di

Teng Zhang, Xing Fan

Most existing methodologies of estimating low-rank matrices rely on Burer-Monteiro factorization, but these approaches can suffer from slow convergence, especially when dealing with solutions characterized by a large condition number, defined by the ratio of the largest to the $r$-th singular values, where $r$ is the search rank. While methods such as Scaled

Firdous Ee Jannat, Rajat Kanti Nath

In this paper, we consider commuting conjugacy class graph (abbreviated as CCC-graph) of a finite group $G$ which is a graph with vertex set $\{x^G : x \in G \setminus Z(G)\}$ (where $x^G$ denotes the conjugacy class containing $x$) and two distinct vertices $x^G$ and $y^G$ are joined by an edge if there exist some elements $x'\in x^G$ and $y'\in y^G$ such t

Ivan Mogilnykh, Anna Taranenko, Konstantin Vorob'ev

We obtain a classification of the completely regular codes with covering radius 1 and the second eigenvalue in the Hamming graphs H(3,q) up to q and intersection array. Due to works of Meyerowitz, Mogilnykh and Valyuzenich, our result completes the classifications of completely regular codes with covering radius 1 and the second eigenvalue in the Hamming gra

Ibrahim Khan, Thai Van Nguyen, Chollakorn Nimpattanavong, Ruck Thawonmas

This paper presents our work to enhance the background music (BGM) in DareFightingICE by adding adaptive features. The adaptive BGM consists of three different categories of instruments playing the BGM of the winner sound design from the 2022 DareFightingICE Competition. The BGM adapts by changing the volume of each category of instruments. Each category is

Emmanouil S. N. Raptakis

In this thesis we study classical aspects of superconformal field theory via symmetry principles. Specifically, by employing the powerful setup of conformal superspace, we obtain a plethora of new results in the fields of geometric and higher symmetries, (super)conformal higher-spin theory and conformal supergravity. These findings open up numerous novel res

C. Itoi, K. Fujiwara, Y. Sakamoto

The interpolation method for mean field spin glass models developed by Guerra and Talagrand is extended to a quantum mean field spin glass model. This extension enables us to obtain both replica-symmetric (RS) and one step replica-symmetry breaking (1RSB) solutions of the free energy density in the transverse field Sherrington-Kirkpatrick model. It is shown

ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, Winston H. Hsu

Vehicle flow, a crucial indicator for transportation, is often limited by detector coverage. With the advent of extensive mobile network coverage, we can leverage mobile user activities, or cellular traffic, on roadways as a proxy for vehicle flow. However, as counts of cellular traffic may not directly align with vehicle flow due to data from various user t

Congzhi Zhang, Linhai Zhang, Deyu Zhou

Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfa

Manfred K. Warmuth, Wojciech Kotłowski, Matt Jones, Ehsan Amid

It is well known that the class of rotation invariant algorithms are suboptimal even for learning sparse linear problems when the number of examples is below the "dimension" of the problem. This class includes any gradient descent trained neural net with a fully-connected input layer (initialized with a rotationally symmetric distribution). The simplest spar

Xin Li, Dongya Wu

High-dimensional matrix regression has been studied in various aspects, such as statistical properties, computational efficiency and application to specific instances including multivariate regression, system identification and matrix compressed sensing. Current studies mainly consider the idealized case that the covariate matrix is obtained without noise, w

Hoang Phan, Andrew Gordon Wilson, Qi Lei

Models trained on data composed of different groups or domains can suffer from severe performance degradation under distribution shifts. While recent methods have largely focused on optimizing the worst-group objective, this often comes at the expense of good performance on other groups. To address this problem, we introduce an optimization scheme to achieve

Waris Gill, Mohamed Elidrisi, Pallavi Kalapatapu, Ammar Ahmed

Large Language Models (LLMs) like ChatGPT and Llama have revolutionized natural language processing and search engine dynamics. However, these models incur exceptionally high computational costs. For instance, GPT-3 consists of 175 billion parameters, where inference demands billions of floating-point operations. Caching is a natural solution to reduce LLM i

Lei Zhang, Tao Long, Weizhen Xu, Laizhong Cui

Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is diffi

Wenjie Wang, Changsheng Wang, Fuli Feng, Wentao Shi

Recommender systems are vulnerable to injective attacks, which inject limited fake users into the platforms to manipulate the exposure of target items to all users. In this work, we identify that conventional injective attackers overlook the fact that each item has its unique potential audience, and meanwhile, the attack difficulty across different users var

Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang

Recent work has embodied LLMs as agents, allowing them to access tools, perform actions, and interact with external content (e.g., emails or websites). However, external content introduces the risk of indirect prompt injection (IPI) attacks, where malicious instructions are embedded within the content processed by LLMs, aiming to manipulate these agents into

HeeSun Bae, Seungjae Shin, Byeonghu Na, Il-Chul Moon

For learning with noisy labels, the transition matrix, which explicitly models the relation between noisy label distribution and clean label distribution, has been utilized to achieve the statistical consistency of either the classifier or the risk. Previous researches have focused more on how to estimate this transition matrix well, rather than how to utili

Yaoyan Zheng, Hongyu Yang, Di Huang

Recent advancements in video semantic segmentation have made substantial progress by exploiting temporal correlations. Nevertheless, persistent challenges, including redundant computation and the reliability of the feature propagation process, underscore the need for further innovation. In response, we present Deep Common Feature Mining (DCFM), a novel appro

Haotian Lu, Sanmitra Banerjee, Jiaqi Gu

Photonic computing has emerged as a promising solution for accelerating computation-intensive artificial intelligence (AI) workloads, offering unparalleled speed and energy efficiency, especially in resource-limited, latency-sensitive edge computing environments. However, the deployment of analog photonic tensor accelerators encounters reliability challenges

Ibrahim Khan, Chollakorn Nimpattanavong, Thai Van Nguyen, Kantinan Plupattanakit

This paper presents a new and improved DareFightingICE platform, a fighting game platform with a focus on visually impaired players (VIPs), in the Unity game engine. It also introduces the separation of the DareFightingICE Competition into two standalone competitions called DareFightingICE Sound Design Competition and DareFightingICE AI Competition--at the 2

Shumpei Kobayashi, Quoc Hoan Tran, Kohei Nakajima

The echo state property (ESP) represents a fundamental concept in the reservoir computing (RC) framework that ensures output-only training of reservoir networks by being agnostic to the initial states and far past inputs. However, the traditional definition of ESP does not describe possible non-stationary systems in which statistical properties evolve. To ad

V. V. Flambaum, I. B. Samsonov

There is limited information about the interaction strength of a scalar dark matter candidate with hadrons and leptons for a scalar particle mass exceeding $10^{-3}$ eV while its interaction with photon is well studied. The scalar-photon coupling constant receives quantum corrections from one-loop Feynman diagrams which involve the scalar-lepton, scalar-quar

Kelin Kurzer-Ogul, Brian M. Haines, David S. Montgomery, Silvia Pandolfi

Shock-bubble interactions (SBI) are important across a wide range of physical systems. In inertial confinement fusion, interactions between laser-driven shocks and micro-voids in both ablators and foam targets generate instabilities that are a major obstacle in achieving ignition. Experiments imaging the collapse of such voids at high energy densities (HED)

Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala

The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert. All existing work on L2D assumes that each expert is well-identified, and if any expert were to change, the system should be re-trained. In this work, we alleviate this constraint, formulating an L2D system that can cope

Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi

Imagine generating a city's electricity demand pattern based on weather, the presence of an electric vehicle, and location, which could be used for capacity planning during a winter freeze. Such real-world time series are often enriched with paired heterogeneous contextual metadata (e.g., weather and location). Current approaches to time series generation of

Ying Sun, Hongwei Yong, Lei Zhang

Due to the effectiveness of second-order algorithms in solving classical optimization problems, designing second-order optimizers to train deep neural networks (DNNs) has attracted much research interest in recent years. However, because of the very high dimension of intermediate features in DNNs, it is difficult to directly compute and store the Hessian mat

Ziyuan Yang, Ming Kang, Andrew Beng Jin Teoh, Chengrui Gao

In recent years, palmprints have been widely used for individual verification. The rich privacy information in palmprint data necessitates its protection to ensure security and privacy without sacrificing system performance. Existing systems often use cancelable technologies to protect templates, but these technologies ignore the potential risk of data leaka

Neeraja P. K., Bindiya T. S., Raghu C.

This paper is an attempt to mitigate the beam squint happening due to frequency-dependent phase shifts in the wideband beamforming scenario, specifically in radar applications. The estimation of the direction of arrival is significant for precise target detection in radars. The undesirable beam squint effect due to the phase shift-only mechanism in conventio

Haoxuan Sun

The project aims to use machine learning algorithms to fit the free parameters of an isotopic scaling model to elemental observations. The processes considered are massive star nucleosynthesis, Type Ia SNe, the s-process, the r-process, and p-isotope production. The analysis on the successful fits seeks to minimize the reduced chi squared between the model a

Weizhi Wang, Khalil Mrini, Linjie Yang, Sateesh Kumar

We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g., CLIPScore) via integrating the recent advances in MLMs. We design four distinct yet complementary metrics to holistically measure the quality of image-text data. A new pipeline is

Jesse Liebman, Kazuya Miyagawa, Kazushi Kanoda, Natalia Drichko

A family of molecular Mott insulators on triangular lattice provided a few S=1/2 triangular quantum spin liquid candidates, with $\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$ being the most studied material of this group. The large number experimental works present a conflicting set of evidence, with some suggesting spin liquid behavior, while others point towards a

V. V. Ryazanov

A simple and rapid method is proposed for assessing the reduction in the lifetime of steel walls of the reactor vessel under neutron irradiation. The method is based on modeling the number of radiation defects by the behavior of a general time-dependent random process of death and birth and queuing theory. Necessary data for assessments: the estimated operat

Masamune Kobayashi, Masato Mita, Mamoru Komachi

Metrics are the foundation for automatic evaluation in grammatical error correction (GEC), with their evaluation of the metrics (meta-evaluation) relying on their correlation with human judgments. However, conventional meta-evaluations in English GEC encounter several challenges including biases caused by inconsistencies in evaluation granularity, and an out

Pradeep Kumar Sahu, Nitin Gupta

The extropy measure, introduced by Lad, Sanfilippo, and Agro in their (2015) paper in Statistical Science, has garnered significant interest over the past years. In this study, we present a novel representation for the weighted extropy within the context of extreme ranked set sampling. Additionally, we offer related findings such as stochastic orders, charac

Ruggero Pagnan

We describe the construction of the slice fibration of a given one.

Wang Chen, Yong Zhao, Liping Tang, Xinmin Yang

This paper focuses on developing a conditional gradient algorithm for multiobjective optimization problems with an unbounded feasible region. We employ the concept of recession cone to establish the well-defined nature of the algorithm. The asymptotic convergence property and the iteration-complexity bound are established under mild assumptions. Numerical ex

Svante Janson, Warren D. Smith

Consider $n$ independent random numbers with a uniform distribution on $[0,1]$. The number of them that exceed their mean is shown to have an Eulerian distribution, i.e., it is described by the Eulerian numbers. This is related to, but distinct from, the well known fact that the integer part of the sum of independent random numbers uniform on $[0,1]$ has an

Haoqi Chu, Xuanting Ji, Ya-Wen Sun

We introduce a holographic model that exhibits a coexistence state of the Weyl semimetal and the topological nodal line state, providing us with a valuable tool to investigate the system's behavior in the strong coupling regime. Nine types of bulk solutions exhibiting different IR behaviors have been identified, corresponding to nine different types of bound

Lysander Miller, Airlie Chapman, Katie Auchettl, Jeremy M. C. Brown

Radiation detection is vital for space, medical imaging, homeland security, and environmental monitoring applications. In the past, the Monte Carlo radiation transport toolkit, Geant4, has been employed to enable the effective development of emerging technologies in these fields. Radiation detectors utilising scintillator crystals have benefited from Geant4;

Juan Zou, Weiwei Jiang, Yizhang Xia, Yuan Liu

The evolutionary paradigm has been successfully applied to neural network search(NAS) in recent years. Due to the vast search complexity of the global space, current research mainly seeks to repeatedly stack partial architectures to build the entire model or to seek the entire model based on manually designed benchmark modules. The above two methods are atte

Abdullah Al Raqibul Islam, Dong Dai

Dynamic graphs, featuring continuously updated vertices and edges, have grown in importance for numerous real-world applications. To accommodate this, graph frameworks, particularly their internal data structures, must support both persistent graph updates and rapid graph analysis simultaneously, leading to complex designs to orchestrate `fast but volatile'

I. Psaradaki, L. Corrales, J. Werk, A. G. Jensen

In this study, we investigate interstellar absorption lines along the line of sight toward the galactic low-mass X-ray binary Cygnus X-2. We combine absorption line data obtained from high-resolution X-ray spectra collected with Chandra and XMM-Newton satellites, along with Far-UV absorption lines observed by the Hubble Space Telescope's (HST) Cosmic Origins

Denys Dutykh, Laurent Gosse

Three main topics were raised in this discussion session, which took place on the 19th of June at the NumHyp-2015 meeting: nonlinear resonance for 1D systems of balance laws, dispersive extensions of standard hyperbolic conservation laws, and the validation of weakly dispersive shallow water wave models. An introductory overview with many bibliographic refer

Steven P. Lund, Hari Iyer

Forensic experts use specialized training and knowledge to enable other members of the judicial system to make better informed and more just decisions. Factfinders, in particular, are tasked with judging how much weight to give to experts' reports and opinions. Many references describe assessing evidential weight from the perspective of a forensic expert. So

Yumi Arai

We investigate the integral representations of solutions to the variant of $q$-hypergeometric equation of degree 2 obtained through $q$-middle convolution by using transformation formulas for $q$-hypergeometric series. We show the correspondence between these integral solutions and solutions obtained by other methods. We also show the linear relationships am

Mengting He, Shihao Xia, Boqin Qin, Nobuko Yoshida

The execution of smart contracts on Ethereum, a public blockchain system, incurs a fee called gas fee for its computation and data storage. When programmers develop smart contracts (e.g., in the Solidity programming language), they could unknowingly write code snippets that unnecessarily cause more gas fees. These issues, or what we call gas wastes, can lead

Takashi Goda

We study the multivariate integration problem for periodic functions from the weighted Korobov space in the randomized setting. We introduce a new randomized rank-1 lattice rule with a randomly chosen number of points, which avoids the need for component-by-component construction in the search for good generating vectors while still achieving nearly the opti

Toru Sera

We establish large deviation estimates related to the Darling--Kac theorem and generalized arcsine laws for occupation and waiting times of ergodic transformations preserving an infinite measure, such as non-uniformly expanding interval maps with indifferent fixed points. For the proof, we imitate the study of generalized arcsine laws for occupation times of

Masaki Kawamoto, Hayato Miyazaki

This paper is concerned with nonlinear Schr\"odinger equations with a time-decaying harmonic potential. The nonlinearity is gauge-invariant of the long-range critical order. In [24] and [22], it is proved that the equation admits a nontrivial solution that behaves like a free solution with a logarithmic phase correction in the frameworks of both the final st

Janice van Dam, Guus Avis, Tzula B Propp, Francisco Ferreira da Silva

In blind quantum computing, a user with a simple client device can perform a quantum computation on a remote quantum server such that the server cannot gain knowledge about the computation. Here, we numerically investigate hardware requirements for verifiable blind quantum computing using an ion trap as server and a distant measurement-only client. While the

Zhenyu Zhang, Runjin Chen, Shiwei Liu, Zhewei Yao

This paper aims to overcome the "lost-in-the-middle" challenge of large language models (LLMs). While recent advancements have successfully enabled LLMs to perform stable language modeling with up to 4 million tokens, the persistent difficulty faced by most LLMs in identifying relevant information situated in the middle of the context has not been adequately

Ahmed Farag Ali

Snyder's quantum space-time which is Lorentz invariant is investigated. It is found that the quanta of space-time have a positive mass that is interpreted as a positive real mass gap of space-time. This mass gap is related to the minimal length of measurement which is provided by Snyder's algebra. Several reasons to consider the space-time quanta as a 24-cel

Wei Zhang, Zhenni Wang

The restricted isometry property (RIP) is essential for the linear map to guarantee the successful recovery of low-rank matrices. The existing works show that the linear map generated by the measurement matrices with independent and identically distributed (i.i.d.) entries satisfies RIP with high probability. However, when dealing with non-i.i.d. measurement

Rabin Banerjee, Soumya Bhattacharya, Bibhas Ranjan Majhi

A detailed and systematic formulation of Carrollian relativity is provided. Based on the transformations, first provided by Sengupta [19], we construct a mapping between Lorentz relativistic and Carrollian relativistic vectors. Using this map the Carroll theory is built from the standard Maxwell action. We show that we get self-consistent equations of motion

Ferhat Erata, Arda Goknil, Ivan Kurtev, Bedir Tekinerdogan

We present AlloyInEcore, a tool for specifying metamodels with their static semantics to facilitate automated, formal reasoning on models. Software development projects require that software systems be specified in various models (e.g., requirements models, architecture models, test models, and source code). It is crucial to reason about those models to ensu

Jiarui Xu, Shashank Jere, Yifei Song, Yi-Hung Kao

Integration of artificial intelligence (AI) and machine learning (ML) into the air interface has been envisioned as a key technology for next-generation (NextG) cellular networks. At the air interface, multiple-input multiple-output (MIMO) and its variants such as multi-user MIMO (MU-MIMO) and massive/full-dimension MIMO have been key enablers across success

Liya Jess Kurian, Chithra A.

The corona of hypergraphs is an extension of the corona operation applied to graphs. The corona $G_0^* \odot_1^n G_1^*$ of two hypergraphs is obtained by taking $n$ copies of $G_1^*$ (where $n$ is the order of $G_0^*$) and by joining the $i$-th vertex of $G_0^*$ with the $i$-th copy of $G_1^*$. In this paper, we estimate the complete spectrum(adjacency and S

Zhongqi Yue, Pan Zhou, Richang Hong, Hanwang Zhang

Even when using large multi-modal foundation models, few-shot learning is still challenging -- if there is no proper inductive bias, it is nearly impossible to keep the nuanced class attributes while removing the visually prominent attributes that spuriously correlate with class labels. To this end, we find an inductive bias that the time-steps of a Diffusio

Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horvath

Adaptive methods are extremely popular in machine learning as they make learning rate tuning less expensive. This paper introduces a novel optimization algorithm named KATE, which presents a scale-invariant adaptation of the well-known AdaGrad algorithm. We prove the scale-invariance of KATE for the case of Generalized Linear Models. Moreover, for general sm

Xiangyu Li, Xinjie Shen, Yawen Zeng, Xiaofen Xing

The task of stock earnings forecasting has received considerable attention due to the demand investors in real-world scenarios. However, compared with financial institutions, it is not easy for ordinary investors to mine factors and analyze news. On the other hand, although large language models in the financial field can serve users in the form of dialogue

Ankita Ghosh, Bhaswati Bhattacharyya, Andrew Lyne, David L. Kaplan

We present the timing solution for the 5.31-ms spider millisecond pulsar (MSP) J1242-4712, discovered with the GMRT. PSR J1242-4712 orbits a companion of minimum mass 0.08 M$_{\odot}$ with an orbital period of 7.7 hrs and occupies a relatively unexplored region in the orbital period versus companion mass space. We did not detect gamma-ray pulsations for this

Ghazal Asemian, Mohammadreza Amini, Burak Kantarci, Melike Erol-Kantarci

The Synchronization Signal Block (SSB) is a fundamental component of the 5G New Radio (NR) air interface, crucial for the initial access procedure of Connected and Automated Vehicles (CAVs), and serves several key purposes in the network's operation. However, due to the predictable nature of SSB transmission, including the Primary and Secondary Synchronizati

Wolfgang Gatterbauer

Query formulation is increasingly performed by systems that need to guess a user's intent (e.g. via spoken word interfaces). But how can a user know that the computational agent is returning answers to the "right" query? More generally, given that relational queries can become pretty complicated, how can we help users understand relational queries, whether h

Phillip Broadbridge, Roman Cherniha, Joanna Goard

New classes of conditionally integrable systems of nonlinear reaction-diffusion equations are introduced. They are obtained by extending a well known nonclassical symmetry of a scalar partial differential equation to a vector equation. New exact solutions of nonlinear predator-prey systems, related to the diffusive Lotka-Volterra system, are constructed. An

Kun Zhou

A modular tensor category is a non-degenerate ribbon finite tensor category. And a ribbon factorizable Hopf algebra is exactly the Hopf algebra whose finite-dimensional representations form a modular tensor category. The goal of this paper is to construct both semisimple and non-semisimple modular categories with Hopf algebras. In particular, we study centra

Ohn Kim, Junwon Seo, Seongyong Ahn, Chong Hui Kim

Autonomous off-road navigation requires an accurate semantic understanding of the environment, often converted into a bird's-eye view (BEV) representation for various downstream tasks. While learning-based methods have shown success in generating local semantic terrain maps directly from sensor data, their efficacy in off-road environments is hindered by cha

Ye Wang, Yanyan Song

For graphs $G$ and $H$, the Ramsey number $R(G,H)$ is the smallest $r$ such that any red-blue edge coloring of $K_r$ contains a red $G$ or a blue $H$. The path-critical Ramsey number $R_{\pi}(G,H)$ is the largest $n$ such that any red-blue edge coloring of $K_r \setminus P_{n}$ contains a red $G$ or a blue $H$, where $r=R(G,H)$ and $P_{n}$ is a path of order

Cong Ma, Lei Qiao, Chengkai Zhu, Kai Liu

Vehicle-to-everything (V2X) is a popular topic in the field of Autonomous Driving in recent years. Vehicle-infrastructure cooperation (VIC) becomes one of the important research area. Due to the complexity of traffic conditions such as blind spots and occlusion, it greatly limits the perception capabilities of single-view roadside sensing systems. To further

Jiyong Oh, Junhaeng Lee, Woongchan Byun, Minsang Kong

Recent studies have focused on enhancing the performance of 3D object detection models. Among various approaches, ground-truth sampling has been proposed as an augmentation technique to address the challenges posed by limited ground-truth data. However, an inherent issue with ground-truth sampling is its tendency to increase false positives. Therefore, this

J. Seo, N. Carlin, D. F. F. S. Cavalcante, J. S. Chung

Cosmic ray muons prove valuable across various fields, from particle physics experiments to non-invasive tomography, thanks to their high flux and exceptional penetrating capability. Utilizing a scintillator detector, one can effectively study the topography of mountains situated above tunnels and underground spaces. The Hankuk Atmospheric-muon Wide Landscap

Yu Chen, Liyan Ma, Liping Jing, Jian Yu

Humans can easily distinguish the known and unknown categories and can recognize the unknown object by learning it once instead of repeating it many times without forgetting the learned object. Hence, we aim to make deep learning models simulate the way people learn. We refer to such a learning manner as OnLine Open World Object Detection(OLOWOD). Existing O

Diptarama Hendrian, Dominik Köppl, Ryo Yoshinaka, Ayumi Shinohara

Lyndon words are extensively studied in combinatorics on words -- they play a crucial role on upper bounding the number of runs a word can have [Bannai+, SIAM J. Comput.'17]. We can determine Lyndon words, factorize a word into Lyndon words in lexicographically non-increasing order, and find the Lyndon rotation of a word, all in linear time within constant a

Ke Zhang, DanDan Zhu, Qiuhan Xu, Hao Zhou

Training for multi-agent reinforcement learning(MARL) is a time-consuming process caused by distribution shift of each agent. One drawback is that strategy of each agent in MARL is independent but actually in cooperation. Thus, a vertical issue in multi-agent reinforcement learning is how to efficiently accelerate training process. To address this problem, c

Karol Łukanowski

In optical communication the transmitter encodes information into a set of light states defined by the modulation format, selected to accommodate specific channel conditions and to remain sufficiently distinguishable at the output. Various receiver architectures have been designed to improve the demodulation performance, ultimately limited by quantum theory.

Xinyu Zhang, Vincent CS Lee, Duo Xu, Jun Chen

A learning management system streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an e-learning system plays a crucial role in rule regulation, policy establishment, and system development. However, e

Anzheng Tang, Jun-Bo Wang, Yijin Pan, Wence Zhang

In this paper, channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of the spherical wavefront effect and the spatially non-stationary (SnS) property. Due to the diversities of SnS characteristics among different propagation paths, the concurrent channel estimation of multiple

Cunyi Yin, Xiren Miao, Jing Chen, Hao Jiang

Low-resolution infrared-based human activity recognition (HAR) attracted enormous interests due to its low-cost and private. In this paper, a novel semi-supervised crossdomain neural network (SCDNN) based on 8 $\times$ 8 low-resolution infrared sensor is proposed for accurately identifying human activity despite changes in the environment at a low-cost. The

Yongqiang Wang

With the increasing awareness of privacy and the deployment of legislations in various multi-agent system application domains such as power systems and intelligent transportation, the privacy protection problem for multi-agent systems is gaining increased traction in recent years. This article discusses some of the representative advancements in the filed.

Hongyu Zhang, Dongyi Zheng, Lin Zhong, Xu Yang

In recent years, Cross-Domain Recommendation (CDR) has drawn significant attention, which utilizes user data from multiple domains to enhance the recommendation performance. However, current CDR methods require sharing user data across domains, thereby violating the General Data Protection Regulation (GDPR). Consequently, numerous approaches have been propos

Xizhi Wang, Yaxiong Wang, Mengjian Li

This paper presents a Geometric-Photometric Joint Alignment~(GPJA) method, which aligns discrete human expressions at pixel-level accuracy by combining geometric and photometric information. Common practices for registering human heads typically involve aligning landmarks with facial template meshes using geometry processing approaches, but often overlook de