March 2024 arXiv papers — page 98

Shima Mohammadi, Yaojun Wu, João Ascenso

Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model through iterative training on large amounts of data. The optimization process typically involves minimizing the distortion b

Ziyun Kan, Mingdong Chen, Haijun Peng, Yizhu Guo

This paper proposes a systematic and novel component level co-rotational (CR) framework, for upgrading existing 3D continuum finite elements to flexible multibody analysis. Without using any model reduction techniques, the high efficiency is achieved through sophisticated operations in both modeling and numerical implementation phrases. In modeling phrase, a

Hao Cheng, Yuan Lu, Zhenliang Lu, Qiang Tang

Recent progresses in asynchronous Byzantine fault-tolerant (BFT) consensus, e.g. Dumbo-NG (CCS' 22) and Tusk (EuroSys' 22), show promising performance through decoupling transaction dissemination and block agreement. However, when executed with a larger number $n$ of nodes, like several hundreds, they would suffer from significant degradation in performance.

Xiaohan Zhang, Bharat Lal Bhatnagar, Sebastian Starke, Ilya Petrov

Interactions between human and objects are influenced not only by the object's pose and shape, but also by physical attributes such as object mass and surface friction. They introduce important motion nuances that are essential for diversity and realism. Despite advancements in recent human-object interaction methods, this aspect has been overlooked. Generat

Mengsha Liu, Daoyuan Chen, Yaliang Li, Guian Fang

Data visualization serves as a critical means for presenting data and mining its valuable insights. The task of chart summarization, through natural language processing techniques, facilitates in-depth data analysis of charts. However, there still are notable deficiencies in terms of visual-language matching and reasoning ability for existing approaches. To

Andrei Diakonov, Konstantin Khrizman, Eliran Zano, Liron Stern

The broad and equidistant spectrum of frequency combs has had a profound impact on spectroscopic studies. Particularly, experiments involving the coupling of frequency combs to cavities have already enabled unprecedented broadband and sensitive spectroscopy on a single-molecule level. The emergence of integrated, compact and broadband Kerr-microcombs holds p

Wenyu Zhang, Qingmu Liu, Felix Ong Wei Cong, Mohamed Ragab

Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this work, we introduce Universal Semi-Supervised Domain Adaptation (UniSSDA), a practical yet challenging setting where the target domain is partially labeled, and the source and target l

Liren Jin, Haofei Kuang, Yue Pan, Cyrill Stachniss

Many autonomous robotic applications require object-level understanding when deployed. Actively reconstructing objects of interest, i.e. objects with specific semantic meanings, is therefore relevant for a robot to perform downstream tasks in an initially unknown environment. In this work, we propose a novel framework for semantic-targeted active reconstruct

Xiaohu Zheng, Zhengxin Liu, Cuiwei Zhang, Huaxue Zhou

The magnetism of Kitaev materials has been widely studied, but their charge properties and the coupling to other degrees of freedom are less known. Here we investigate the charge states of $\alpha$-RuCl$_3$, a promising Kitaev quantum spin liquid candidate, in proximity to graphite. We discover that few-layered $\alpha$-RuCl$_3$ experiences a clear modulatio

Rong Du, Yuhang Zhou

This paper is dedicated to the classification of uniform vector bundles of rank $d+1$ over the Grassmannian $G(d,n)$ ($d\le n-d$) over an algebraically closed field in characteristic $0$. Specifically, we show that all uniform vector bundles with rank $d+1$ over $G(d,n)$ are homogeneous.

Chih-Chung Hsu, Chia-Ming Lee, Yang Fan Chiang, Yi-Shiuan Chou

This study explores the use of deep learning techniques for analyzing lung Computed Tomography (CT) images. Classic deep learning approaches face challenges with varying slice counts and resolutions in CT images, a diversity arising from the utilization of assorted scanning equipment. Typically, predictions are made on single slices which are then combined f

Shumeng Li, Lei Qi, Qian Yu, Jing Huo

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethi

Hong-Wei Guo, Shun-Cai Zhao, Xiao-Jing Wei

Negative refractive index (NRI) of the mescopic dissipative left-handed transmission line (LHTL) is manipulated by the displaced squeezed Fock state (DSFS) and the dissipation presented by the resistance and conductance. Comparing to the classical LHTL, some specific quantum characteristics are shown in the LHTL because of quantum effect, which will be signi

Ujjwal Sinha, Vikas Kumar, Shubham Kumar Singh

Routing algorithms play a crucial role in the efficient transmission of data within computer networks by determining the optimal paths for packet forwarding. This paper presents a comprehensive exploration of routing algorithms, focusing on their fundamental principles, classification, challenges, recent advancements, and practical applications. Beginning wi

Shuang-Ying Zhang, Shun-Cai Zhao, Ai-Ling Gong

A approach for two-dimensional(2D) negative permeability in a $\Lambda$-type three-level atomic system interacting with a probe magnetic and the superposition of two orthogonal standing-wave fields is proposed. Through the theoretical analysis and numerical simulation, two equally and tunable peak maxima of negative magnetic responses are observed in the x-y

Anastasia Sandu, Teodor Mihailescu, Sergiu Nisioi

This paper describes the work of the UniBuc Archaeology team for CLPsych's 2024 Shared Task, which involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights (extracting relevant spans within the text) and summaries (aggregating evidence into a synthesis). Our work focuses on evaluati

Shahabedin Nabavi, Kian Anvari Hamedani, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin

Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Be

Shun-Cai Zhao, Qi-Xuan Wu, Kun Ma

2-D(Two-dimensional) isotropic negative refractive index (NRI) is explicitly realized via the orthogonal signal and coupling standing-wave fields coupling the N-type four-level atomic system. Under some key parameters of the dense vapor media, the atomic system exhibits isotropic NRI with simultaneous negative permittivity and permeability (i.e. Left-handedn

Simon Saunders

The Dirac negative energy sea introduced the concept of antimatter, and explained it, not least in its relationship to negative-energy solutions to the wave equation. Post-war, it was largely displaced by what I shall call the 'standard formalism', dependent, among other things, on normal-ordering. A much better explanation is provided by the 'two complex st

Xinyue Liu, Tao Li

Understanding the origin of electron incoherence is believed to be the first step toward the resolution of the mysteries of the high-T$_{c}$ cuprate superconductors. Such electron incoherence manifests itself most evidently in the non-Drude form of the optical absorption spectrum of the system. The spectral weight transfer related to such dissipative respons

Christian Reiher

Erd\H{o}s and Hajnal proved that every graph of uncountable chromatic number contains arbitrarily large finite, complete, bipartite graphs. We extend this result to hypergraphs.

Lin Zhu, Kangmin Jia, Yifan Zhao, Yunshan Qi

Spike cameras, leveraging spike-based integration sampling and high temporal resolution, offer distinct advantages over standard cameras. However, existing approaches reliant on spike cameras often assume optimal illumination, a condition frequently unmet in real-world scenarios. To address this, we introduce SpikeNeRF, the first work that derives a NeRF-bas

Yunlin Li, Jingguang Chen, Xingchao Qi, Langlang Xiong

The systems without symmetries, e.g. the spatial and chiral symmetries, are generally thought to be improper for topological study and no conventional integral topological invariant can be well defined. In this work, with multi-band asymmetric Rice-Mele-like systems as examples, for the first time we show that the topology of all gaps can be reconstructed by

Qiushi Zheng, Zhanhao Zhao, Wei Lu, Chang Yao

Distributed transaction processing often involves multiple rounds of cross-node communications, and therefore tends to be slow. To improve performance, existing approaches convert distributed transactions into single-node transactions by either migrating co-accessed partitions onto the same nodes or establishing a super node housing replicas of the entire da

Yuwei Zhang, Yan Wu, Yanming Liu, Xinyue Peng

Object detection methods under known single degradations have been extensively investigated. However, existing approaches require prior knowledge of the degradation type and train a separate model for each, limiting their practical applications in unpredictable environments. To address this challenge, we propose a chain-of-thought (CoT) prompted adaptive enh

Abraham Itzhak Weinberg, Cristiano Premebida, Diego Resende Faria

Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control,

Tianzong Mao, Mi Jiang

Motivated by the recent findings on the $T$-linear electronic scattering rate in the two-dimensional Hubbard model, we have investigated the three-orbital Emery model and its temperature-dependent electronic and quasiparticle scattering rates by adopting dynamical cluster quantum Monte Carlo simulations. By focusing on two characteristic site energies $\epsi

Jiaxin Wang, YiLong Ma

The discussion on causality in human history dates back to ancient Greece, yet to this day, there is still no consensus. Fundamentally, this stems from the nature of human cognition, as understanding causality requires abstract tools to transcend the limitations of human cognition. In recent decades, the rapid development of mathematical and computational to

Fuchun Ge, Ran Wang, Chen Qu, Peikun Zheng

Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such

Yang Liu, Xiang Yu, Luis Dorfmann

In this paper we present a dimensional reduction to obtain a one-dimensional model to analyze localized necking or bulging in a residually stressed circular cylindrical solid. The nonlinear theory of elasticity is first specialized to obtain the equations governing the homogeneous deformation. Then, to analyze the non-homogeneous part, we include higher orde

Florian le Mardelé, Ivan Mohelský, Jan Wyzula, Milan Orlita

Electric fields represent an ideal means for controlling spins at the nanoscale and, more specifically, for manipulating protected degrees of freedom in multispin systems. Here we perform low-temperature magnetic far-IR spectroscopy on a molecular spin triangle (Fe3) and provide the first experimental evidence of spin-electric transitions in polynuclear comp

John Fernley, Balázs Gerencsér

We find Gaussian cutoff profiles for the total variation distance to stationarity of a random walk on a multiplex network: a finite number of directed configuration models sharing a vertex set, each with its own bounded degree distribution and edge probability. Further we consider the minimal total variation distance over this space of possible doubly stocha

Mojtaba Najafizadeh

The Poincar\'e symmetry can be contracted in two ways to yield the Galilei symmetry and the Carroll symmetry. The well-known Schr\"odinger equation exhibits the Galilei symmetry and is a fundamental equation in Galilean quantum mechanics. However, the question remains: what is the quantum equation that corresponds to the Carroll symmetry? In this paper, we d

Qingrong Sun, Weixiang Zhong, Jie Zhou, Chong Lai

The annotation of digital pathological slide data for renal cell carcinoma is of paramount importance for correct diagnosis of artificial intelligence models due to the heterogeneous nature of the tumor. This process not only facilitates a deeper understanding of renal cell cancer heterogeneity but also aims to minimize noise in the data for more accurate st

Yitian Qian, Shaohua Pan, Shujun Bi, Houduo Qi

This paper focuses on the error bounds for several equivalent rank-one doubly nonnegative (DNN) conic reformulations of the quadratic assignment problem (QAP), a class of challenging combinatorial optimization problems. We provide three equivalent rank-one DNN reformulations of the QAP, including the one proposed in \cite{Jiang21}, and establish the locally

Claudio Pinhanez, Raul Fernandez, Marcelo Grave, Julio Nogima

Representations of AI agents in user interfaces and robotics are predominantly White, not only in terms of facial and skin features, but also in the synthetic voices they use. In this paper we explore some unexpected challenges in the representation of race we found in the process of developing an U.S. English Text-to-Speech (TTS) system aimed to sound like

Qianyang Wu, Ye Shi, Xiaoshui Huang, Jingyi Yu

This paper addresses new methodologies to deal with the challenging task of generating dynamic Human-Object Interactions from textual descriptions (Text2HOI). While most existing works assume interactions with limited body parts or static objects, our task involves addressing the variation in human motion, the diversity of object shapes, and the semantic vag

Paul S. Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva, Reese Kneeland

Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are trained independently per subject where each subject requires dozens of hours of expensive fMRI training data to attain high-quality results. The present work showcases high-quality r

Amir Lukach, Ran Dubin, Amit Dvir, Chen Hajaj

Encrypted network traffic Classification tackles the problem from different approaches and with different goals. One of the common approaches is using Machine learning or Deep Learning-based solutions on a fixed number of classes, leading to misclassification when an unknown class is given as input. One of the solutions for handling unknown classes is to ret

Kento Kawaharazuka, Temma Suzuki, Kei Okada, Masayuki Inaba

We have developed a parallel wire-driven monopedal robot, RAMIEL, which has both speed and power due to the parallel wire mechanism and a long acceleration distance. RAMIEL is capable of jumping high and continuously, and so has high performance in traveling. On the other hand, one of the drawbacks of a minimal parallel wire-driven robot without joint encode

Anirban Mukherjee

Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be used to manipulate public opinion and distort perceptions, resulting in a decline in trust towards digital platforms. This study contributes to marketing literature and practice in t

Anirban Mukherjee

We examine whether Artificial Intelligence (AI) systems generate truly novel ideas rather than merely regurgitating patterns learned during training. Utilizing a novel experimental design, we task an AI with generating project titles for hypothetical crowdfunding campaigns. We compare within AI-generated project titles, measuring repetition and complexity. W

Cevat Volkan Karadağ, Nezih Topaloğlu

The proliferation of extensive neural network architectures, particularly deep learning models, presents a challenge in terms of resource-intensive training. GPU memory constraints have become a notable bottleneck in training such sizable models. Existing strategies, including data parallelism, model parallelism, pipeline parallelism, and fully sharded data

Junbing Yan, Chengyu Wang, Taolin Zhang, Xiaofeng He

KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding. Previous language models facilitated knowledge acquisition by incorporating knowledge-related pre-training tasks learned from relation triples in knowledge graphs. However, these models do not prioritize learning embeddings for entity-related tokens. Moreover, up

Kaiyan Chang, Kun Wang, Nan Yang, Ying Wang

Recent advances in large language models have demonstrated their potential for automated generation of hardware description language (HDL) code from high-level prompts. Researchers have utilized fine-tuning to enhance the ability of these large language models (LLMs) in the field of Chip Design. However, the lack of Verilog data hinders further improvement i

Arijit Mukherjee

In this paper, we describe the entire structure of the vector space $Sym_2^0$ of all symmetric matrices of size $2$ having trace zero. This is motivated by the geometrical interpretation of any arbitrary element of $Sym_2^0$. We further study the orbits and stable sets of these elements. As an application of the obtained structure of $Sym_2^0$, we obtain the

Yurij Salmaniw, Zhongwei Shen, Hao Wang

Habitat loss, driven primarily by anthropogenic activity, significantly threatens ecosystem sustainability. While it is well understood that habitat loss is the leading contributor to declines in biodiversity worldwide, the connection between habitat degradation, destruction, and different locomotion strategies remains unclear. We use a reaction-diffusion fr

Kento Kawaharazuka, Naoaki Kanazawa, Kei Okada, Masayuki Inaba

Wiping behavior is a task of tracing the surface of an object while feeling the force with the palm of the hand. It is necessary to adjust the force and posture appropriately considering the various contact conditions felt by the hand. Several studies have been conducted on the wiping motion, however, these studies have only dealt with a single surface mater

Yasufumi Kawano, Yoshimitsu Aoki

Semantic segmentation is a crucial task in computer vision, where each pixel in an image is classified into a category. However, traditional methods face significant challenges, including the need for pixel-level annotations and extensive training. Furthermore, because supervised learning uses a limited set of predefined categories, models typically struggle

Jitesh P. Mandaliya, Dileep Kumar, Sudhakar Chaudhary

In this article, we propose a fully-discrete scheme for the numerical solution of a nonlinear time-fractional biharmonic problem. This problem is first converted into an equivalent system by introducing a new variable. Then spatial and temporal discretizations are done by the weighted $b$-spline method and $L2$-$1_\sigma$ approximation, respectively. The wei

Luca Maranzana, Naoto Nagaosa, Sergey Artyukhin

In spiral multiferroics, magnetism induces ferroelectricity, thus holding a promise for novel memory devices where an electric field switches magnetic bits. However, such a switching process, in which magnetic domain walls are moved electrically, is still poorly understood. We find multiferroic domain walls containing arrays of meron (half-skyrmion) strings

Boaz Carmeli, Yonatan Belinkov, Ron Meir

Artificial agents that learn to communicate in order to accomplish a given task acquire communication protocols that are typically opaque to a human. A large body of work has attempted to evaluate the emergent communication via various evaluation measures, with \emph{compositionality} featuring as a prominent desired trait. However, current evaluation proced

Yasufumi Kawano, Yoshimitsu Aoki

Semantic segmentation is essential in computer vision for various applications, yet traditional approaches face significant challenges, including the high cost of annotation and extensive training for supervised learning. Additionally, due to the limited predefined categories in supervised learning, models typically struggle with infrequent classes and are u

Shun-Cai Zhao, Zheng-Dong Liu

We present the density matrix approach for theoretical description of an asymmetric double quantum dot(QD)system.The results show that the properties of gain,absorption and dispersion of the double QD system ,the population of the state with one hole in one dot and an electron in other dot transferred by tunneling can be manipulated by the laser pulse or gat

Tongfan Guan, Chen Wang, Yun-Hui Liu

Stereo matching is a core task for many computer vision and robotics applications. Despite their dominance in traditional stereo methods, the hand-crafted Markov Random Field (MRF) models lack sufficient modeling accuracy compared to end-to-end deep models. While deep learning representations have greatly improved the unary terms of the MRF models, the overa

Renlong Wu, Zhilu Zhang, Shuohao Zhang, Longfei Gou

Due to the difficulty of collecting real paired data, most existing desmoking methods train the models by synthesizing smoke, generalizing poorly to real surgical scenarios. Although a few works have explored single-image real-world desmoking in unpaired learning manners, they still encounter challenges in handling dense smoke. In this work, we address these

Olivier Brunat, Nathan Chapelier-Laget, Thomas Gerber

We study generalised core partitions arising from affine Grassmannian elements in arbitrary Dynkin type. The corresponding notion of size is given by the atomic length in the sense of [CLG22]. In this paper, we first develop the theory for extended affine Weyl groups. In a series of applications, we give some remarkable parametrisations of the solutions of c

Sumita Datta, James M Rejcek, Rajasee Datta, Maxim Olshanii

We demonstrate that the nonequilibrium spatial density of a one-dimensional interacting Bose gas, following a geometric quench, directly encodes information about the underlying momentum (rapidity) distribution of the system. Starting from the interacting ground state of a Lieb--Liniger gas confined in a hard-wall box of length $L_0$, we study its expansion

Md. Ashraf Uddin, Sunil Aryal, Mohamed Reda Bouadjenek, Muna Al-Hawawreh

In today's digital age, our dependence on IoT (Internet of Things) and IIoT (Industrial IoT) systems has grown immensely, which facilitates sensitive activities such as banking transactions and personal, enterprise data, and legal document exchanges. Cyberattackers consistently exploit weak security measures and tools. The Network Intrusion Detection System

Kun Xia, Le Wang, Sanping Zhou, Gang Hua

The crux of semi-supervised temporal action localization (SS-TAL) lies in excavating valuable information from abundant unlabeled videos. However, current approaches predominantly focus on building models that are robust to the error-prone target class (i.e, the predicted class with the highest confidence) while ignoring informative semantics within non-targ

Michael Kiermaier, Kai-Uwe Schmidt, Alfred Wassermann

Combinatorial designs have been studied for nearly 200 years. Fifty years ago, Cameron, Delsarte, and Ray-Chaudhury started investigating their q-analogs, also known as subspace designs or designs over finite fields. Designs can be defined analogously in finite classical polar spaces, too. The definition includes the m-regular systems from projective geometr

Hang Ruan, Fan Liu

In integrated sensing and communication (ISAC) systems, random signaling is used to convey useful information as well as sense the environment. Such randomness poses challenges in various components in sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems. Unlike quantizers for channel estimation in massive mul

Guangze Zheng, Shijie Lin, Haobo Zuo, Changhong Fu

The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT), often manifested as severe deformations, fast motion, and occlusions. Most methods that solely depend on coarse-grained object cues, such as boxes and the overall appearance of the object, are susceptible to degradation due to distorted internal r

Geoffroy Horel, Thomas Willwacher

Let $\mathsf P$ be an operad acted upon by a group $G$, and let $\mathsf Q=\mathsf P\rtimes G$ be the corresponding framed operad. We relate the homotopy automorphism groups of $\mathsf P$ and $\mathsf Q$. We apply the result to compute the automorphisms of the framed little disks operad.

Yuanchen Wu, Xichen Ye, Kequan Yang, Jide Li

Recently, One-stage Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained increasing interest due to simplification over its cumbersome multi-stage counterpart. Limited by the inherent ambiguity of Class Activation Map (CAM), we observe that one-stage pipelines often encounter confirmation bias caused by incorrect CAM pseudo-label

Cenyuan Zhang, Xiaoqing Zheng, Ruicheng Yin, Shujie Geng

Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge. Previous non-invasive decoders either require multiple experiments with identical stimuli to pinpoint cortical regions and enhance signal-to-noise ratios in brain activity, or they are limited to discerning basic linguistic elements such as letters a

Peng-Fu Tian, Ping Zhang, Wen Yang, Wei Wang

Pulsars are known to manifest complex phenomena, such as nulling, sub-pulse drifting, and periodicity. Within the purview of this investigation, we have harnessed the wavelet analysis technique to scrutinize the multifaceted periodicities and sub-pulse drifting characteristics exhibited by PSR J1926-0652, discovered by the Five-hundred-meter Aperture Spheric

Tong Chern

This paper discusses the use of the Riemann-Silberstein vector to solve the source-free Maxwell's equations and obtains novel analytical solutions. The solving process naturally leads to the spinor form of the source-free Maxwell's equations. Several powerful theorems are established to solve this spinor form equation. The Waveguide Solution Theorem provides

Shun-Cai Zhao, Qi-Xuan Wu, Ai-Ling Gong

Two-dimensional (2D) atom localization via the spontaneously generated coherence (SGC) and detunings associated with the probe and standing-wave driving fields in a three-level V-type atomic system are investigated. In the gain process, two equal and tunable peak maxima of position distribution in the plane via the detunings are observed. However, one decrea

Md. Ashraf Uddin, Sunil Aryal, Mohamed Reda Bouadjenek, Muna Al-Hawawreh

The rapid expansion of varied network systems, including the Internet of Things (IoT) and Industrial Internet of Things (IIoT), has led to an increasing range of cyber threats. Ensuring robust protection against these threats necessitates the implementation of an effective Intrusion Detection System (IDS). For more than a decade, researchers have delved into

Eman Leung, Jingjing Guan, Kin On Kwok, CT Hung

Emergency department's (ED) boarding (defined as ED waiting time greater than four hours) has been linked to poor patient outcomes and health system performance. Yet, effective forecasting models is rare before COVID-19, lacking during the peri-COVID era. Here, a hybrid convolutional neural network (CNN)-Long short-term memory (LSTM) model was applied to pub

Hao Wang, Yuan-Chao Gan, Xiang-Rong Chen, Yi-Xian Wang

Constitutive models are essential for describing the mechanical behavior of materials under high temperatures and pressures, among which the Steinberg-Guinan (SG) model is widely adopted. Recent work has discovered a peculiar dual anomaly of compression-induced softening and heating-induced hardening in the elasticity of compressed vanadium [Phys. Rev. B 104

Zhuoqi Liu, Zhaohang Wang, Siying Sun, Shuaibin Gao

This paper focuses on the numerical scheme for multiple-delay stochastic differential equations with partially H\"older continuous drifts and locally H\"older continuous diffusion coefficients. To handle with the superlinear terms in coefficients, the truncated Euler-Maruyama scheme is employed. Under the given conditions, the convergence rates at time $T$ i

M. W. Aslam, A. A. Zafar, M. N. Aslam, A. A. Bhatti

Recent research has indicated that the Standard Model (SM), while historically highly effective, is found to be insufficient due to its prediction of zero mass for neutrinos. With the exception of a few, the majority of the parameters related to neutrinos have been determined by neutrino oscillation experiments with excellent precision. Experiments on neutri

Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini

No-Reference Image Quality Assessment (NR-IQA) focuses on designing methods to measure image quality in alignment with human perception when a high-quality reference image is unavailable. Most state-of-the-art NR-IQA approaches are opinion-aware, i.e. they require human annotations for training. This dependency limits their scalability and broad applicabilit

Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury

Automatic speech recognition (ASR) plays a pivotal role in our daily lives, offering utility not only for interacting with machines but also for facilitating communication for individuals with partial or profound hearing impairments. The process involves receiving the speech signal in analog form, followed by various signal processing algorithms to make it c

Yingru Li, Zhi-Quan Luo

This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. We establish the first prior-dependent Bayesian regret bound for RL with function approximation; and refine the Bayesian regret analysis for posterior sampling reinforcement learning (PSRL), presenting an upper bound of ${\math

Sakshi Shukla, Praveen C. Srivastava, Deepak Patel

In this work, we have performed systematic shell-model calculations for Bi isotopes with $A=$ 204-213 using KHH7B and KHM3Y effective interactions. We have reported yrast and non-yrast shell-model states corresponding to the available experimental data. From the comparison with the experimental data, we could assign spin and parity of several unconfirmed sta

Reinhard Booysen, Anna Sergeevna Bosman

Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of NN architectures and has proven to be successful in automatically finding NN architectures that outperform those manually designed by human experts. N

Zheling Meng, Bo Peng, Jing Dong, Tieniu Tan

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, bring potential security risks to our society. Existing generated image detection methods suffer from performance drop when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN)

Hojung Yang, Suhyeon Lee, Seungjoo Kim

IOTA is a distributed ledger technology that uses a Directed Acyclic Graph (DAG) structure called the Tangle. It is known for its efficiency and is widely used in the Internet of Things (IoT) environment. Tangle can be configured by utilizing the tip selection process. Due to performance issues with light nodes, full nodes are being asked to perform the tip

Xin Liao, Dingding Yu

Denote by $S_n(x,y)$ the length of the longest common substring of $x$ and $y$ with shifts in their first $n$ digits of $b$-ary expansions. We show that the sets of pairs $(x,y)$, for which the growth rate of $S_n(x,y)$ is $\alpha \log n$ with $0\le \alpha \le \infty$, have full Hausdorff dimension.

Xinyi Zhou, Ashish Sharma, Amy X. Zhang, Tim Althoff

Real-world information, often multimodal, can be misinformed or potentially misleading due to factual errors, outdated claims, missing context, misinterpretation, and more. Such "misinformation" is understudied, challenging to address, and harms many social domains -- particularly on social media, where it can spread rapidly. Manual correction that identifie

Ramin Ebrahimi, Alireza Nasr-Isfahani

Let $\Lambda$ be a finite dimensional algebra. In this paper we show that there is a natural bijection between cosilting modules in Mod$\Lambda$ and semibricks in Mod$\Lambda$ satisfying some condition. Also this bijection restricts to a bijection between all semibricks in mod$\Lambda$ and a certain subclass of cosilting modules. These bijections are general

L. E. Czelusniak, I. T. Martins, L. Cabezas-Gómez, N. A. V. Bulgarelli

The pseudopotential and free energy models are two popular extensions of the lattice Boltzmann method for multiphase flows. Until now, they have been developed apart from each other in the literature. However, important questions about whether each method performs better needs to be solved. In this work, we perform a theoretical and numerical comparison betw

Xuanqi Liu, Zhuotao Liu, Qi Li, Ke Xu

The escalating focus on data privacy poses significant challenges for collaborative neural network training, where data ownership and model training/deployment responsibilities reside with distinct entities. Our community has made substantial contributions to addressing this challenge, proposing various approaches such as federated learning (FL) and privacy-

Lai Wei, Li-Li Zhang, Yi-Neng Huang

For relaxor-ferroelectrics and relaxor-ferromagnets, Ehrenfest classification of phase transitions based on the discontinuity of entropy or specific heat etc. with temperature (T) gives no transition that contradicts the measured order parameter, i.e. spontaneous polarization and magnetization, at low temperatures, while Landau classification based on the mi

Yuta Sasahara

We study isoparametric hypersurfaces, whose principal curvatures are all constant, in the pseudo-Riemannian space forms. In this paper, we investigate three topics.Firstly, according to Petrov's classification theorem, we give a classification of hypersurfaces of index 2 with respect to a pair of a shape operator and a metric. Therefore, we can define types

Samangi Wadinambiarachchi, Ryan M. Kelly, Saumya Pareek, Qiushi Zhou

Generative AI systems have been heralded as tools for augmenting human creativity and inspiring divergent thinking, though with little empirical evidence for these claims. This paper explores the effects of exposure to AI-generated images on measures of design fixation and divergent thinking in a visual ideation task. Through a between-participants experimen

Xuetong Li, Yuan Gao, Hong Chang, Danyang Huang

This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades. In this work, we focus on three categories of statistical computation methods: (1) distributed computing, (2) subsampling methods, and (3) minib

Xiaoyu Wu, Yang Hua, Chumeng Liang, Jiaru Zhang

Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot generation where a pretrained model is fine-tuned on a small set of images to capture a specific style or object. Despite their success, concerns exist about potential copyright violations stemming from the use of unauthorized data in this process. In response,

I. A. Taimanov

Spectral (Bloch) varieties of multidimensional differential operators on non-simply connected manifolds are defined. In their terms it is given a description of the analytical dependence of the spectra of magnetic Laplacians on non-simply connected manifolds on the values of the Aharonov-Bohm fluxes and a construction of analogues of spectral curves for two-

Irene Ada Picatoste, Rafael Sánchez

Few-level systems coupled to thermal baths provide useful models for quantum thermodynamics and to understand the role of heat currents in quantum information settings. Useful operations such as cooling or thermal masers have been proposed in autonomous three-level systems. In this work, we propose the coherent coupling of two qutrits as a simultaneous refri

Eliad Shem-Tov, Achiya Elyasaf

We present a novel multi-parent crossover operator in genetic algorithms (GAs) called ``Deep Neural Crossover'' (DNC). Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of deep reinforcement learning (DRL) and an encoder-decoder architecture to select the genes. Specifically, we use D

Yuxiang Guo, Xiaopeng Gao, Bo Jiang

Previous works on Just-In-Time (JIT) defect prediction tasks have primarily applied pre-trained models directly, neglecting the configurations of their fine-tuning process. In this study, we perform a systematic empirical study to understand the impact of the settings of the fine-tuning process on BERT-style pre-trained model for JIT defect prediction. Speci

Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang

Universal image restoration is a practical and potential computer vision task for real-world applications. The main challenge of this task is handling the different degradation distributions at once. Existing methods mainly utilize task-specific conditions (e.g., prompt) to guide the model to learn different distributions separately, named multi-partite mapp

Haofeng Huang, Li Yi

This report presents the development and results of an advanced SPH (Smoothed Particle Hydrodynamics) simulation framework, designed for high fidelity fluid dynamics modeling. Our framework, accessible at https://github.com/jason-huang03/SPH_Project, integrates various SPH algorithms including WCSPH, PCISPH, and DFSPH, alongside techniques for rigid-fluid co

Dheeraj Shukla, Sudhaker Upadhyay

In this article, we explore the inconsistencies in the physics of fermionic oscillators and propose potential solutions to address them. By rigorously deriving the Hamiltonian and Lagrangian from first principles, we aim to provide a comprehensive and fundamental understanding of the system. Furthermore, we calculate the partition function for a system of fe

Yinfeng Li, Fengkui Ju

Coalition Logic is an important logic in logical studies of strategic reasoning, whose models are concurrent game models. In this paper, first, we systematically discuss three assumptions of concurrent game models and argue that they are too strong. The first is seriality; that is, every coalition always has an available joint action. The second is the indep

Yixiang Mao, Liyang Sun, Yong Liu, Yao Wang

For $360^{\circ}$ video streaming, FoV-adaptive coding that allocates more bits for the predicted user's field of view (FoV) is an effective way to maximize the rendered video quality under the limited bandwidth. We develop a low-latency FoV-adaptive coding and streaming system for interactive applications that is robust to bandwidth variations and FoV predi