March 2024 arXiv papers — page 126

Anton Agafonov, Lihi Zelnik-Manor

In various applications, such as virtual reality and gaming, simulating the deformation of soft tissues in the human body during interactions with external objects is essential. Traditionally, Finite Element Methods (FEM) have been employed for this purpose, but they tend to be slow and resource-intensive. In this paper, we propose a unified representation o

Xu Gan, Chongwen Huang, Zhaohui Yang, Xiaoming Chen

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infras

V. Guzey, M. Strikman

Using the leading twist approach (LTA) to nuclear shadowing, we calculate the ratios of diffractive and usual parton distributions for a heavy nucleus (Pb) and the proton, $R_{A/p}=(f_{i/A}^{D(3)}/f_{i/A})/(f_{i/p}^{D(3)}/f_{i/p})$, for coherent and summed (coherent plus quasi-elastic) nuclear deep-inelastic scattering. We find that $R_{A/p} \approx 0.5-1$ f

Ugo Boscain, Kévin Le Balc'H, Mario Sigalotti

In this paper we investigate when linearly independent eigenfunctions of the Schr\''odinger operator may have the same modulus. General properties are established and the one-dimensional case is treated in full generality. The study is motivated by its application to the bilinear control of the Schr{\"o}dinger equation. By assuming that the potentials of int

Mikhail Martynov, Zhanibek Darush, Aleksey Fedoseev, Dzmitry Tsetserukou

Robots able to run, fly, and grasp have a high potential to solve a wide scope of tasks and navigate in complex environments. Several mechatronic designs of such robots with adaptive morphologies are emerging. However, the task of landing on an uneven surface, traversing rough terrain, and manipulating objects still presents high challenges. This paper intro

Yuan Xu, Chongwen Huang, Li Wei, Zhaohui Yang

In this paper, we investigate the beam training problem in the multi-user millimeter wave (mmWave) communication system, where multiple reconfigurable intelligent surfaces (RISs) are deployed to improve the coverage and the achievable rate. However, existing beam training techniques in mmWave systems suffer from the high complexity (i.e., exponential order)

Maonan Wang, Aoyu Pang, Yuheng Kan, Man-On Pun

Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications. Therefore, effective congestion management is imperative, with traffic signal control (TSC) systems being pivotal in this endeavor. Conventional TSC systems, designed upon rule-based algorithms or reinforcement learn

Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian

Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-dimensional observations. We instead consider a partially observed setting, in which each measurement only provides information about a subset of the underlying causal state. Prior

Louis Fournier, Edouard Oyallon

Training large deep learning models requires parallelization techniques to scale. In existing methods such as Data Parallelism or ZeRO-DP, micro-batches of data are processed in parallel, which creates two drawbacks: the total memory required to store the model's activations peaks at the end of the forward pass, and gradients must be simultaneously averaged

Cheng Huang, Nannan Wang, Ziyan Wang, Siqi Sun

With the growing popularity of modularity in software development comes the rise of package managers and language ecosystems. Among them, npm stands out as the most extensive package manager, hosting more than 2 million third-party open-source packages that greatly simplify the process of building code. However, this openness also brings security risks, as e

Weikai Li, Zhiping Xiao, Xiao Luo, Yizhou Sun

Graph neural networks (GNNs) are widely utilized to capture the information spreading patterns in graphs. While remarkable performance has been achieved, there is a new trending topic of evaluating node influence. We propose a new method of evaluating node influence, which measures the prediction change of a trained GNN model caused by removing a node. A rea

Heejin Do, Yunsu Kim, Gary Geunbae Lee

Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score. However, studies have yet to explore these models in multi-trait AES, possibly due to the inefficiency of replicating BERT-based models for each trait. Breaking away from the existing sole use of encoder, w

Yasunori Taguchi, Hiro Gangi

Optimization of product and system characteristics is required in many fields, including design and control. Bayesian optimization (BO) is often used when there are high observing costs, because BO theoretically guarantees an upper bound on regret. However, computational costs increase exponentially with the number of parameters to be optimized, decreasing s

Cheng Cheng, Hang Wang, Hongbin Sun

The prevalence of convolution neural networks (CNNs) and vision transformers (ViTs) has markedly revolutionized the area of single-image super-resolution (SISR). To further boost the SR performances, several techniques, such as residual learning and attention mechanism, are introduced, which can be largely attributed to a wider range of activated area, that

Didier Henrion, Adrien Le Franc, Victor Magron

We describe a parametric univariate quadratic optimization problem for which the moment-SOS hierarchy has finite but increasingly slow convergence when the parameter tends to its limit value. We estimate the order of finite convergence as a function of the parameter.

Aleksandra Deptuch, Bartosz Sęk, Sebastian Lalik, Mirosława D. Ossowska-Chruściel

Polarizing optical microscopy and differential scanning calorimetry are used to determine the phase sequence of three liquid crystalline 4-pentylphenyl-4'-n-alkyloxythiobenzoates with n = 9, 10, 11. The X-ray diffraction method is applied for structural characterization of the liquid crystalline phases. The smectic layer spacing, tilt angle, average distance

Jessie Levillain, François Alouges, Antonio Desimone, Akash Choudhary

It has been recently shown that it is possible to design simple artificial swimmers at low Reynoldsnumber that possess only one degree of freedom and, nevertheless, can overcome Purcell's celebratedscallop theorem. One of the few examples is given by Montino and DeSimone, Eur. Phys. J. E, vol.38, 2015, who consider the three-sphere Swimmer of Najafi and Gole

Christian Saugbjerg Lange, Thomas Hansen, Lars Bojer Madsen

The spectra produced by high-harmonic generation (HHG) typically exhibit well-defined peaks at odd integers times the laser frequency. However, in recent investigations of HHG from correlated materials, spectra exhibit signals at noninteger harmonics which do not conform to the well-known symmetry-based selection rules for HHG-spectra. Here, we use the Fermi

Viktor D. Zozulia, Anton A. Smirnov, Natalia Ya. Sotnikova

We have translated the results of $N$-body simulations of one barred model into the language of action variables and frequencies. Using this language, we analysed the behaviour of all orbits in the model on a large time scale at the stage of a mature bar. We show that the orbits join the bar while preserving their adiabatic invariant, which takes into accoun

E. S. Pikina, A. R. Muratov, E. I. Kats, V. V. Lebedev

Electro-hydrodynamic phenomena in liquid crystals constitute an old but still very active research area. The reason is that these phenomena play the key role in various applications of liquid crystals and due to the general interest of physical community to out-of-equilibrium systems. Nematic liquid crystals (NLCs) are ideally representative for such investi

Bingrong Huang, Liangxun Li

In this paper, we prove asymptotic formulas of mixed moments of $\rm GL(2)$ and its symmetric square $L$-functions for both Hecke--Maass cusp forms and holomorphic Hecke eigenforms in short intervals. As an application, we prove quantitative simultaneous non-vanishing of central values of these $L$-functions.

Christopher Irwin, Marco Dossena, Giorgio Leonardi, Stefania Montani

Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed. In medical domains, predictive process monitoring can provide valuable decision support in atypical and nontrivial situations. Decision support and quality assessment in medicine cannot

Wang Jie, Zhu Qiuming, Lin Zhipeng, Chen Junting

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources.In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recovery the accurate REM with limited and

Simon Lacan

Datascouting is one of the most known data applications in professional sport, and specifically football. Its objective is to analyze huge database of players in order to detect high potentials that can be then individually considered by human scouts. In this paper, we propose a stacking-based deep learning model to detect high potential football players. Ap

Tran N. Hung, Cao H. Nam

In the context of the generalized (off-shell) free energy, we explore the phase emergence and corresponding phase transitions of charged dilaton $\text{AdS}$ black holes in the gauged Kaluza-Klein (KK) theory where the KK vector field is gauged such that the fermionic fields are charged under the U(1)$_{\text{KK}}$ gauge group. The black hole solutions are a

Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu

Performing language-conditioned robotic manipulation tasks in unstructured environments is highly demanded for general intelligent robots. Conventional robotic manipulation methods usually learn semantic representation of the observation for action prediction, which ignores the scene-level spatiotemporal dynamics for human goal completion. In this paper, we

SeshaSai Nath Chinagudaba, Darshan Gera, Krishna Kiran Vamsi Dasu, Uma Shankar S

Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. In this context, machine learning (ML) has emerged as a transformative force, providing innovative solutions to the complexities associated with TB treatment.This study explores how machine learning, especially with tabular data, can be used to predict

C. S. Tello Breuer, T. Becker, A. Eckardt

Recently, Nathan and Rudner derived a Gorini-Kossakowski-Sudarshan-Lindblad master equation from the Redfield equation. The claim is that the level of approximation is equal to that of the Redfield equation. Here we benchmark the Nathan-Rudner equation (NRE) against the exact solution of a damped harmonic oscillator and compare its performance to that of the

Rongwu Xu, Zehan Qi, Zhijiang Guo, Cunxiang Wang

This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge. Our focus is on three categories of knowledge conflicts: context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the

Hebeizi Li, Hongyu Yang, Di Huang

Facial Expression Recognition (FER) has consistently been a focal point in the field of facial analysis. In the context of existing methodologies for 3D FER or 2D+3D FER, the extraction of expression features often gets entangled with identity information, compromising the distinctiveness of these features. To tackle this challenge, we introduce the innovati

Michael Zentarra, Julian Ahrens, Lia Ahrens

Learning-based techniques such as artificial intelligence (AI) and machine learning (ML) play an increasingly important role in the development of future communication networks. The success of a learning algorithm depends on the quality and quantity of the available training data. In the physical layer (PHY), channel information data can be obtained either t

Haomin Wen, Zhenjie Wei, Yan Lin, Jiyuan Wang

The rapid development of Large Language Models (LLMs) has facilitated a variety of applications from different domains. In this technical report, we explore the integration of LLMs and the popular academic writing tool, Overleaf, to enhance the efficiency and quality of academic writing. To achieve the above goal, there are three challenges: i) including sea

Marvin Gerlach, Ulrich Nierste, Pascal Reeck, Vladyslav Shtabovenko

This report summarises recent advances made in the calculation of the NNLO QCD corrections to the width difference $\Delta\Gamma_s$ in the $B_s-\overline{B}_s$ system. The inclusion of the effects due to current-current operators leads to an updated prediction of $\Delta\Gamma_s = (0.076\pm 0.017)\,\text{ps}^{-1}$, which narrows the gap between theory and ex

Carlos Romero-Perez, Natalia Fernandez Delgado, Miriam Herrera Collado, Mauricio E. Calvo

Lead halide perovskite nanocrystals are attractive for light emitting devices both as electroluminescent and color converting materials, since they combine intense and narrow emissions with good charge injection and transport properties. However, most perovskite nanocrystals shine at green and red wavelengths, the observation of intense and stable blue emiss

Sweta Agrawal, Amin Farajian, Patrick Fernandes, Ricardo Rei

Despite the recent success of automatic metrics for assessing translation quality, their application in evaluating the quality of machine-translated chats has been limited. Unlike more structured texts like news, chat conversations are often unstructured, short, and heavily reliant on contextual information. This poses questions about the reliability of exis

Duy Hieu Do, Thi Ha Duong Phan

We will present improvements to famous algorithms for community detection, namely Newman's spectral method algorithm and the Louvain algorithm. The Newman algorithm begins by treating the original graph as a single cluster, then repeats the process to split each cluster into two, based on the signs of the eigenvector corresponding to the secondlargest eigenv

Jia-Nan Li, Quan Tu, Cunli Mao, Zhengtao Yu

Standard Large Language Models (LLMs) struggle with handling dialogues with long contexts due to efficiency and consistency issues. According to our observation, dialogue contexts are highly structured, and the special token of \textit{End-of-Utterance} (EoU) in dialogues has the potential to aggregate information. We refer to the EoU tokens as ``conversatio

Gilberto Recupito, Giammaria Giordano, Filomena Ferrucci, Dario Di Nucci

Context. The adoption of Machine Learning (ML)--enabled systems is steadily increasing. Nevertheless, there is a shortage of ML-specific quality assurance approaches, possibly because of the limited knowledge of how quality-related concerns emerge and evolve in ML-enabled systems. Objective. We aim to investigate the emergence and evolution of specific types

Hongbin Xu, Weitao Chen, Feng Xiao, Baigui Sun

4D style transfer aims at transferring arbitrary visual style to the synthesized novel views of a dynamic 4D scene with varying viewpoints and times. Existing efforts on 3D style transfer can effectively combine the visual features of style images and neural radiance fields (NeRF) but fail to handle the 4D dynamic scenes limited by the static scene assumptio

Ang Li, Qiugen Xiao, Peng Cao, Jian Tang

Reinforcement Learning from AI Feedback (RLAIF) has the advantages of shorter annotation cycles and lower costs over Reinforcement Learning from Human Feedback (RLHF), making it highly efficient during the rapid strategy iteration periods of large language model (LLM) training. Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF tr

Hideto Asashiba, Etienne Gauthier, Enhao Liu

We define two notions. The first one is a $rank\ compression\ system$ $\xi$ for a finite poset $\mathbf{P}$ that assigns each interval subposet $I$ to an order-preserving map $\xi_I \colon I^{\xi} \to \mathbf{P}$ satisfying some conditions, where $I^{\xi}$ is a connected finite poset. An example is given by the $total$ compression system that assigns each $I

Elisa Davoli, Katerina Nik, Ulisse Stefanelli, Giuseppe Tomassetti

We investigate a model for the accretive growth of an elastic solid. The reference configuration of the body is accreted in its normal direction, with space- and deformation-dependent accretion rate. The time-dependent reference configuration is identified via the level sets of the unique viscosity solution of a suitable generalized eikonal equation. After p

Jimin Li, Haonan Li

Let $p_{r+1}-1>n \geq p_r-1$, based on a sequence $\{1,2,3\cdots\ M_r(M_r=p_1p_2\cdots p_r)\}$, we compare the density of coprime numbers and establish a correlation between the proportions of coprime numbers in the ranges from 1 to consecutive square numbers. Then, we derive the relationship between the number of coprimes in the interval of $n^2 \sim {(n+1)

Mingyue Cheng, Hao Zhang, Jiqian Yang, Qi Liu

Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in this area. Despite their effectiveness, these existing works mainly focus on assessing objective questions, overlooking the capability to evaluate subjective questions which is extrem

Haechan Jeong

This paper addresses the increasing significance of UAVs (Unmanned Aerial Vehicles) and the emergence of UAV swarms for collaborative operations in various domains. However, the effectiveness of UAV swarms can be severely compromised by jamming technology, necessitating robust antijamming strategies. While existing methods such as frequency hopping and physi

Daisuke Suyama, Michele Torielli, Shuhei Tsujie

Athanasiadis studied arrangements obtained by adding shifted hyperplanes to the braid arrangement. Similarly, Bailey studied arrangements obtained by adding tilted hyperplanes to the braid arrangement. These two kinds of arrangements are associated with directed graphs and their freeness was characterized in terms of the associated graphs. In addition, there

Matija Bucić, Jacob Fox, Huy Tuan Pham

It is well-known that polynomial versions of theorems of R\"odl and Nikiforov, as conjectured by Fox and Sudakov and Nguyen, Scott and Seymour imply the classical Erd\H{o}s-Hajnal conjecture. In this note, we prove that these three conjectures are in fact equivalent, extending several previous particular results in this direction by Fox, Nguyen, Scott and Se

Seo Wook Han, Maged Iskandar, Jinoh Lee, Min Jun Kim

In this paper, we propose a model predictive control (MPC) that accomplishes interactive robotic tasks, in which multiple contacts may occur at unknown locations. To address such scenarios, we made an explicit contact feedback loop in the MPC framework. An algorithm called Multi-Contact Particle Filter with Exploration Particle (MCP-EP) is employed to establ

Sadman Ali, Roberto De Propris, Chul Chung, Steven Phillipps

We measure the evolution of the rest-frame $NUV-V$ colors for early-type galaxies in clusters at $0<z<1.1$ using data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), CFHT Large Area U-band Deep Survey (CLAUDS) and local SDSS clusters observed with GALEX. Our results show that there is an excess in the ultraviolet spectrum in most quiescent gal

Yue Chang, Shuangai Wan, Shichao Dong, Jie Qin

Field-inhomogeneity-induced relaxation of atomic spins confined in vapor cells with depolarizing walls is studied. In contrast to nuclear spins, such as noble-gas spins, which experience minimal polarization loss at cell walls, atomic spins in uncoated cells undergo randomization at the boundaries. This distinct boundary condition results in a varied depende

Michele Tufano, Anisha Agarwal, Jinu Jang, Roshanak Zilouchian Moghaddam

The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot. However, existing solutions are not leveraging all the potential capabilities available in an IDE such as building, testing, executing code, git operations, etc. Therefore, they are constrained by their limited capabil

Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks

We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is parti

Gabriel Mercier, Emre O. Polat, Shengtai Shi, Shuchi Gupta

Image sensors hold a pivotal role in society due to their ability to capture vast amounts of information. Traditionally, image sensors are opaque due to light absorption in both the pixels and the read-out electronics that are stacked on top of each other. Making image sensors visibly transparent would have a far-reaching impact in numerous areas such as hum

Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both p

Tianyi Chu, Wei Xing, Jiafu Chen, Zhizhong Wang

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the networ

Xiang Hu, Pengyu Ji, Qingyang Zhu, Wei Wu

A syntactic language model (SLM) incrementally generates a sentence with its syntactic tree in a left-to-right manner. We present Generative Pretrained Structured Transformers (GPST), an unsupervised SLM at scale capable of being pre-trained from scratch on raw texts with high parallelism. GPST circumvents the limitations of previous SLMs such as relying on

Liya Guo, Liwei Lu, Zhijun Zeng, Pipi Hu

With the rapid increase of observational, experimental and simulated data for stochastic systems, tremendous efforts have been devoted to identifying governing laws underlying the evolution of these systems. Despite the broad applications of non-Gaussian fluctuations in numerous physical phenomena, the data-driven approaches to extracting stochastic dynamics

Danrui Qi, Zhengjie Miao, Jiannan Wang

Data standardization is a crucial part of the data science life cycle. While tools like Pandas offer robust functionalities, their complexity and the manual effort required for customizing code to diverse column types pose significant challenges. Although large language models (LLMs) like ChatGPT have shown promise in automating this process through natural

Yasuhiro Matsumoto

This paper presents a fast wavefield evaluation method for two-dimensional wave scattering problems. The proposed method is based on a modified version of proxy-surface-accelerated interpolative decomposition, making it effective even if the evaluation points are near the boundary. The commonly known fast multipole method requires the use of direct evaluatio

Kokoro Shikata, Kento Kasahara, Nozomi Morishita Watanabe, Hiroshi Umakoshi

Cholesterol (Chol) plays a crucial role in shaping the intricate physicochemical attributes of biomembranes, exerting considerable influence on water molecules proximal to the membrane interface. In this study, we conducted molecular dynamics simulations on the bilayers of two lipid species, dipalmitoyl phosphatidylcholine (DPPC) and palmitoyl sphingomyelin

Mahsa Haddadi Moghaddam, Zhihao Wang, Daryll J. C Dalayoan, Daehwan Park

Metal thin films on soft polymers provide a unique opportunity for resistance-based strain sensors. A mechanical mismatch between the conductive film and the flexible substrate causes cracks to open and close, changing the electrical resistance as a function of strain. However, the very randomness of the formation, shape, length, orientation, and distance be

Mengsa Wang, Yuzhi Zhou, Han Wang

The rapid development of deep learning techniques has driven the emergence of a neural network-based variational Monte Carlo method (referred to as FermiNet), which has manifested high accuracy and strong predictive power in the electronic structure calculations of atoms, molecules as well as some periodic systems. Recently, the implementation of the effecti

Kewei Sun, Maxim Gelin, Kaijun Shen, Yang Zhao

We offer a theoretical perspective on simulation and engineering of polaritonic conical-intersection-driven singlet-fission (SF) materials. We begin by examining fundamental models, including Tavis-Cummings and Holstein-Tavis-Cummings Hamiltonians, exploring how disorder, non-Hermitian effects, and finite temperature conditions impact their dynamics, setting

Minjong Cheon, Yo-Hwan Choi, Seon-Yu Kang, Yumi Choi

Deep learning-based, data-driven models are gaining prevalence in climate research, particularly for global weather prediction. However, training the global weather data at high resolution requires massive computational resources. Therefore, we present a new model named KARINA to overcome the substantial computational demands typical of this field. This mode

Cunyuan Jiang, Zihan Zheng, Yangrui Chen, Matteo Baggioli

Unlike crystalline solids, liquids lack long-range order, resulting in diffusive shear fluctuations rather than propagating waves. Simulations predict that liquids exhibit a $k$-gap in wave-vector space, where solid-like transverse waves reappear above this gap. Experimental evidence in classical liquids has been limited, observed only in 2D dusty plasmas. H

Can Liu, Jin Wang

As a new distributed computing framework that can protect data privacy, federated learning (FL) has attracted more and more attention in recent years. It receives gradients from users to train the global model and releases the trained global model to working users. Nonetheless, the gradient inversion (GI) attack reflects the risk of privacy leakage in federa

Masaya Tashiro, Kosuke Ide, Kosei Asano, Satoshi Ishii

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical space, digital twins, autonomous driving, smart cities, and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of availa

Dhruv Toshniwal, Saurabh Loya, Anuj Khot, Yash Marda

In the rapidly evolving landscape of transportation, the proliferation of automobiles has made road traffic more complex, necessitating advanced vision-assisted technologies for enhanced safety and navigation. These technologies are imperative for providing critical traffic sign information, influencing driver behavior, and supporting vehicle control, especi

Zhonghan Zhao, Kewei Chen, Dongxu Guo, Wenhao Chai

Due to the dynamic and unpredictable open-world setting, navigating complex environments in Minecraft poses significant challenges for multi-agent systems. Agents must interact with the environment and coordinate their actions with other agents to achieve common objectives. However, traditional approaches often struggle to efficiently manage inter-agent comm

Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv

Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that strive to achieve high performance across all three domains simultaneously. Achieving a very high level of proficiency for an LLM within a specific domain often requires extensive trai

Katerina Deike-Hofmann, Dorottya Dancs, Daniel Paech, Heinz-Peter Schlemmer

Materials and methods: First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion of also the native T1-weighted images, T2-weighted images, and FLA

Philip Isett, Andrew Ma

Using a new definition for the nonlinear term, we prove that all weak solutions to the SQG equation (and mSQG) conserve the angular momentum. This result is new for the weak solutions of [Resnick, '95] and rules out the possibility of anomalous dissipation of angular momentum. We also prove conservation of the Hamiltonian under conjecturally optimal assumpti

Minsoo Kim, Min-Cheol Sagong, Gi Pyo Nam, Junghyun Cho

Deep learning-based face recognition continues to face challenges due to its reliance on huge datasets obtained from web crawling, which can be costly to gather and raise significant real-world privacy concerns. To address this issue, we propose VIGFace, a novel framework capable of generating synthetic facial images. Our idea originates from pre-assigning v

Lei Liu, Xiao-Chen Sun, Yuan Tian, Xiujuan Zhang

Spin and orbital angular momenta are fundamental physical characteristics described by polarization and spatial degrees of freedom, respectively. Polarization is a feature of vector fields while spatial phase gradient determines the orbital angular momentum ubiquitous to any scalar field. Common wisdom treats these two degrees of freedom as distinct and inde

Mukul Dwivedi, Tanmay Sarkar

In this paper, we present and analyze fully discrete finite difference schemes designed for solving the initial value problem associated with the fractional Korteweg-de Vries (KdV) equation involving the fractional Laplacian. We design the scheme by introducing the discrete fractional Laplacian operator which is consistent with the continuous operator, and p

Chiun-Yan Lin, Da-We Weng, Chih-Wei Chiu, Godfrey Gumbs

This study delves into the magneto-electronic and magneto-optical properties of stacking-modulated bilayer graphene. By manipulating domain walls (DWs) across AB-BA domains periodically, we unveil oscillatory Landau subbands and the associated optical excitations. The DWs act as periodic potentials, yielding fascinating 1D spectral features. Our exploration

Yukun Ma, Zikun Mao

In daily life and industrial production, it is crucial to accurately detect changes in liquid level in containers. Traditional contact measurement methods have some limitations, while emerging non-contact image processing technology shows good application prospects. This paper proposes a container dynamic liquid level detection model based on U^2-Net. This m

Jieun Han, Haneul Yoo, Junho Myung, Minsun Kim

The integration of generative AI in education is expanding, yet empirical analyses of large-scale and real-world interactions between students and AI systems still remain limited. Addressing this gap, we present RECIPE4U (RECIPE for University), a dataset sourced from a semester-long experiment with 212 college students in English as Foreign Language (EFL) w

Long Lan, Fengxiang Wang, Xiangtao Zheng, Zengmao Wang

Fine-grained ship classification in remote sensing (RS-FGSC) poses a significant challenge due to the high similarity between classes and the limited availability of labeled data, limiting the effectiveness of traditional supervised classification methods. Recent advancements in large pre-trained Vision-Language Models (VLMs) have demonstrated impressive cap

Peini Guo, Mengyuan Liu, Hong Liu, Ruijia Fan

Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify the target person in more realistic surveillance scenarios, where pedestrians usually change their clothing. Despite great progress, limited cloth-changing training samples in existing CC-ReID datasets still prevent the model from adequately learning cloth-irrelevant features. In a

Sumit Mahajan, Arbaz Khan

This paper explores the residual based a posteriori error estimations for the generalized Burgers-Huxley equation (GBHE) featuring weakly singular kernels. Initially, we present a reliable and efficient error estimator for both the stationary GBHE and the semi-discrete GBHE with memory, utilizing the discontinuous Galerkin finite element method (DGFEM) in sp

Gokul Puthumanaillam, Manav Vora, Pranay Thangeda, Melkior Ornik

This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs). Superalignment is a theoretical framework that aspires to ensure that superintelligent AI systems act in accordance with human values and goals. Despite its promising vision, we argue that achieving superalignment re

Yue Ma, Yingqing He, Hongfa Wang, Andong Wang

Despite recent advances in image-to-video generation, better controllability and local animation are less explored. Most existing image-to-video methods are not locally aware and tend to move the entire scene. However, human artists may need to control the movement of different objects or regions. Additionally, current I2V methods require users not only to d

Zhangquan Chen, Chunjiang Liu, Haobin Duan

In this paper, we introduce CodingTeachLLM, a large language model (LLM) designed for coding teaching. Specially, we aim to enhance the coding ability of LLM and lead it to better teaching mode in education context. Thus, we propose an end-to-end prior-based three-phases supervised fine-tuned model, which is proved more competitive than traditional fine-tuni

Harshit Saurabh, Anupam Golder, Samarth Shivakumar Titti, Suparna Kundu

This paper presents SNOW-SCA, the first power side-channel analysis (SCA) attack of a 5G mobile communication security standard candidate, SNOW-V, running on a 32-bit ARM Cortex-M4 microcontroller. First, we perform a generic known-key correlation (KKC) analysis to identify the leakage points. Next, a correlation power analysis (CPA) attack is performed, whi

Jian Lin, Xueting Liu, Chengze Li, Minshan Xie

While manga is a popular entertainment form, creating manga is tedious, especially adding screentones to the created sketch, namely manga screening. Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty of generating high-quality shaded high-frequency screentones. The classic manga screening app

Rezsa Farahani

Sparse neural networks have shown similar or better generalization performance than their dense counterparts while having higher parameter efficiency. This has motivated a number of works to learn or search for high performing sparse networks. While reports of task performance or efficiency gains are impressive, standard baselines are lacking leading to poor

Raza Nowrozy, Khandakar Ahmed, Hua Wang

As digital healthcare evolves, the security of electronic health records (EHR) becomes increasingly crucial. This study presents the GPT-Onto-CAABAC framework, integrating Generative Pretrained Transformer (GPT), medical-legal ontologies and Context-Aware Attribute-Based Access Control (CAABAC) to enhance EHR access security. Unlike traditional models, GPT-O

Parth Shah, Gauranga C. Samanta, Kazuharu Bamba, R. Myrzakulov

We explore an autonomous system analysis of dark energy models with interactions between dark energy and cold dark matter in a general systematic approach to cosmological fluids. We investigate two types of models such as local and non-local ones. In particular, a local form of interaction is directly proportional to only the energy density, while a non-loca

Minje Kim, Tae-Kyun Kim

Creating personalized hand avatars is important to offer a realistic experience to users on AR / VR platforms. While most prior studies focused on reconstructing 3D hand shapes, some recent work has tackled the reconstruction of hand textures on top of shapes. However, these methods are often limited to capturing pixels on the visible side of a hand, requiri

Aman Kumar, Khushboo Anand, Shubham Mandloi, Ashutosh Mishra

Generative Adversarial Networks (GANs) have proven to exhibit remarkable performance and are widely used across many generative computer vision applications. However, the unprecedented demand for the deployment of GANs on resource-constrained edge devices still poses a challenge due to huge number of parameters involved in the generation process. This has le

Arlen Fan, Fan Lei, Michelle Mancenido, Alan MacEachren

Maps are crucial in conveying geospatial data in diverse contexts such as news and scientific reports. This research, utilizing thematic maps, probes deeper into the underexplored intersection of text framing and map types in influencing map interpretation. In this work, we conducted experiments to evaluate how textual detail and semantic content variations

Dingbang Li, Wenzhou Chen, Xin Lin

Zero-shot navigation is a critical challenge in Vision-Language Navigation (VLN) tasks, where the ability to adapt to unfamiliar instructions and to act in unknown environments is essential. Existing supervised learning-based models, trained using annotated data through reinforcement learning, exhibit limitations in generalization capabilities. Large Languag

Tamás Darvas, Mingchen Xia

We introduce the trace operator for quasi-plurisubharmonic functions on compact K\"ahler manifolds, allowing to study the singularities of such functions along submanifolds where their generic Lelong numbers vanish. Using this construction we obtain novel $L^2$ extension theorems and give applications to restricted volumes of big line bundles.

Wenjing Zhu, Sining Sun, Changhao Shan, Peng Fan

Conformer-based attention models have become the de facto backbone model for Automatic Speech Recognition tasks. A blank symbol is usually introduced to align the input and output sequences for CTC or RNN-T models. Unfortunately, the long input length overloads computational budget and memory consumption quadratically by attention mechanism. In this work, we

Yilin Xia, Shawn Bowers, Lan Li, Bertram Ludäscher

We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal

Minsoo Kim, Gi Pyo Nam, Haksub Kim, Haesol Park

In the realm of face image quality assesment (FIQA), method based on sample relative classification have shown impressive performance. However, the quality scores used as pseudo-labels assigned from images of classes with low intra-class variance could be unrelated to the actual quality in this method. To address this issue, we present IG-FIQA, a novel appro

Qing Lin, Jingfeng Zhang, Yew-Soon Ong, Mengmi Zhang

Despite the rapid progress in image generation, emotional image editing remains under-explored. The semantics, context, and structure of an image can evoke emotional responses, making emotional image editing techniques valuable for various real-world applications, including treatment of psychological disorders, commercialization of products, and artistic des

Na Li, Chunyi Zhou, Yansong Gao, Hui Chen

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the right to be forgotten of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user r

Jiaxi Gu, Xinjuan Chen, Jae-Hun Jung

The aim of this paper is to design the explicit radial basis function (RBF) Runge-Kutta methods for the initial value problem. We construct the two-, three- and four-stage RBF Runge-Kutta methods based on the Gaussian RBF Euler method with the shape parameter, where the analysis of the local truncation error shows that the s-stage RBF Runge-Kutta method coul