December 2024 arXiv papers — page 177

Talha Azfar, Kaicong Huang, Andrew Tracy, Sandra Misiewicz

Traffic simulations are commonly used to optimize urban traffic flow, with reinforcement learning (RL) showing promising potential for automated traffic signal control, particularly in intelligent transportation systems involving connected automated vehicles. Multi-agent reinforcement learning (MARL) is particularly effective for learning control strategies

Yanming Zhu, Xuefei Yin, Alan Wee-Chung Liew, Hui Tian

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a c

Yuhito Shibaike, Jun Hashimoto, Ruobing Dong, Christoph Mordasini

Gas accreting planets embedded in protoplanetary disks are expected to show dust thermal emission from their circumplanetary disks (CPDs). However, a recently reported gas accreting planet candidate, AB Aurigae b, has not been detected in (sub)millimeter continuum observations. We calculate the evolution of dust in the potential CPD of AB Aurigae b and predi

Sunyoung Jung, Yoonseok Choi, Mohammed A. Al-masni, Minyoung Jung

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in

Siwen Deng, Justin Tzou, Shuangquan Xie

For an activator-inhibitor reaction-diffusion system in a bounded three-dimensional domain $\Omega$ of $O(1)$ volume and small activator diffusivity of $O(\varepsilon^2)$, we employ a hybrid asymptotic-numerical method to investigate two instabilities of a localized one-spot equilibrium that result from Hopf bifurcations: an amplitude instability leading to

Xiachong Feng, Longxu Dou, Ella Li, Qinghao Wang

Game-theoretic scenarios have become pivotal in evaluating the social intelligence of Large Language Model (LLM)-based social agents. While numerous studies have explored these agents in such settings, there is a lack of a comprehensive survey summarizing the current progress. To address this gap, we systematically review existing research on LLM-based socia

Omid Akbarzadeh, MohammadHossein Ashoori, Abolfazl Lavaei

This paper offers a direct data-driven approach for learning robust control barrier certificates (R-CBCs) and robust safety controllers (R-SCs) for discrete-time input-affine polynomial systems with unknown dynamics under unknown-but-bounded disturbances. The proposed method relies on data from input-state observations collected over a finite-time horizon wh

Tonglin Zhang

Selecting interactions from an ultrahigh-dimensional statistical model with $n$ observations and $p$ variables when $p\gg n$ is difficult because the number of candidates for interactions is $p(p-1)/2$ and a selected model should satisfy the strong hierarchical (SH) restriction. A new method called the SHL0 is proposed to overcome the difficulty. The objecti

Xiang-dong Shi, Sheng-bang Qian, Li-ying Zhu, Lin-jia Li

$\beta$ Cephei pulsating variable (BCEP) stars are the most massive pulsating variable stars in the main sequence, exhibiting both p- and g-mode pulsations. In this study, we identified 155 BCEP stars or candidates using data from TESS and Gaia, of which 83 were first confirmed as BCEP stars. They have visual magnitudes ranging from 8 to 12 mag and effective

S. Pratihar, K. C. Sivakumar

This work concerns results on conditions guaranteeing that certain banded $M$-matrices have banded inverses. As a first goal, a graph theoretic characterization for an off-diagonal entry of the inverse of an $M$-matrix to be positive, is presented. This result, in turn, is used in providing alternative graph theoretic proofs of the following: (1) a character

Yuheng Song, Jiayuan Cui, Guohao Liu, Jiabiao Zhao

Terahertz (THz) communications have emerged as a promising technology for 6G networks due to their potential for achieving terabit-per-second data rates. However, the impact of rainfall on THz channel characteristics remains incompletely understood, particularly regarding power attenuation mechanisms and bit error rate (BER) performance. This article present

Alireza Maleki, Mahsa Lavaei, Mohsen Bagheritabar, Salar Beigzad

Deep learning techniques have proven highly effective in image classification, but their deployment in resourceconstrained environments remains challenging due to high computational demands. Furthermore, their interpretability is of high importance which demands even more available resources. In this work, we introduce an approach that combines saliency-guid

Muhammad Mahmudul Hasan, Nezih Pala

This paper proposes a metalens designed to work in 3.3 {\mu}m wavelength which resides in the mid-IR region. The metasurface was created using MoS2 nanopillars taking advantage of high refractive index and low loss. It could be used to make compact optical Methane gas sensors.

Binbin Hu, Zhicheng An, Zhengwei Wu, Ke Tu

Estimating individual treatment effects (ITE) from observational data is a critical task across various domains. However, many existing works on ITE estimation overlook the influence of hidden confounders, which remain unobserved at the individual unit level. To address this limitation, researchers have utilized graph neural networks to aggregate neighbors'

Huigaung Zhang

Accurate extraction of multicomponent linear frequency modulation (LFM) signal parameters, such as onset frequency, linear modulation frequency, amplitude, and initial phase, is of great importance in the fields of ISAR, cognitive radio, electronic countermeasures, and star-ground communications. However, the task of accurately extracting the characteristic

Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau

Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information from multiple viewpoints to construct a change-aware 3D Gaussia

Xuesong Li, Jinguang Tong, Jie Hong, Vivien Rolland

Dynamic scene reconstruction from monocular video is essential for real-world applications. We introduce DGNS, a hybrid framework integrating \underline{D}eformable \underline{G}aussian Splatting and Dynamic \underline{N}eural \underline{S}urfaces, effectively addressing dynamic novel-view synthesis and 3D geometry reconstruction simultaneously. During train

Na Zhao, Carlo R Laing, Jian Song, Shenquan Liu

Stochastic resetting has shown promise in enhancing the stability of dynamical systems. Here, we apply this concept to theta neuron networks with partial resetting, where only a fraction of neurons is intermittently reset. We examine both infinite and finite reset rates, using the averaged firing rate as an indicator of network stability. At infinite reset r

Zhizhen Chen, Zhengyu Zhao, Subrat Kishore Dutta, Chenhao Lin

Targeted data poisoning (TDP) aims to compromise the model's prediction on a specific (test) target by perturbing a small subset of training data. Existing work on TDP has focused on an overly ideal threat model in which the same image sample of the target is used during both poisoning and inference stages. However, in the real world, a target object often a

Yizhou Jin, Jiahui Zhu, Guodong Wang, Shiwei Li

Incremental anomaly detection aims to sequentially identify defects in industrial product lines but suffers from catastrophic forgetting, primarily due to knowledge overwriting during parameter updates and feature conflicts between tasks. In this work, We propose ONER (ONline Experience Replay), an end-to-end framework that addresses these issues by synergis

Dennis Wei, Inkit Padhi, Soumya Ghosh, Amit Dhurandhar

Training data attribution (TDA) is concerned with understanding model behavior in terms of the training data. This paper draws attention to the common setting where one has access only to the final trained model, and not the training algorithm or intermediate information from training. We reframe the problem in this "final-model-only" setting as one of measu

Qiong Feng, Xiaotian Ma, Jiayi Sheng, Ziyuan Feng

LLMs have garnered considerable attention for their potential to streamline Automated Program Repair (APR). LLM-based approaches can either insert the correct code or directly generate patches when provided with buggy methods. However, most of LLM-based APR methods rely on a single type of software information, without fully leveraging different software art

Kai Fronsdal, David Lindner

We propose a suite of tasks to evaluate the instrumental self-reasoning ability of large language model (LLM) agents. Instrumental self-reasoning ability could improve adaptability and enable self-modification, but it could also pose significant risks, such as enabling deceptive alignment. Prior work has only evaluated self-reasoning in non-agentic settings

Yucheng Zhang, Koichi Emura, Eiji Watanabe

This paper classifies near-miss traffic videos using the SlowFast deep neural network that mimics the characteristics of the slow and fast visual information processed by two different streams from the M (Magnocellular) and P (Parvocellular) cells of the human brain. The approach significantly improves the accuracy of the traffic near-miss video analysis and

Wei Chao

In this paper, we propose a novel electron-assisted Baryogenesis scenario that does not require explicit B-L violation, which is essential for the traditional Leptogenesis mechanism. This scenario is based on the assumption of high-scale electroweak symmetry restoration, which implies that the electron Yukawa interaction, crucial for the mechanism, does not

Mohsen Aliabadi, Yujia Wu, Sophia Yermolenko

The concept of matchings originated in group theory to address a linear algebra problem related to canonical forms for symmetric tensors. In an abelian group $(G,+)$, a matching is a bijection $f: A \to B$ between two finite subsets $A$ and $B$ of $G$ such that $a + f(a) \notin A$ for all $a \in A$. A group $G$ has the matching property if, for every two fin

Mahdieh Zaker, David Angeli, Abolfazl Lavaei

Incremental input-to-state stability (delta-ISS) offers a robust framework to ensure that small input variations result in proportionally minor deviations in the state of a nonlinear system. This property is essential in practical applications where input precision cannot be guaranteed. However, analyzing delta-ISS demands precise knowledge of system dynamic

Vyaas Ramakrishnan, Shantanu Desai

We search for transient gamma-ray emission in the energy range from 0.1-10 GeV using data from the Fermi-LAT telescope in coincidence with magnetar flares. For our analysis, we look for coincidence with 15 distinct flares from 11 magnetars using three time windows of $\pm$ 1 day, $\pm$ 7 days, and $\pm$ 15 days. For 14 of these flares from 10 magnetars, we d

Yangsong Chen, Chunlei Wang, Yuhang Zu, Yuto Kobayashi

Single crystals of (Ca$_{1-x}$La$_x$)KFe$_4$As$_4$ (0 <= x <=0.16) have been grown by using the self-flux method, and the evolution of physical properties including the critical current density (Jc) with La-doping has been investigated. Tc decreases monotonically with increasing x, while Jc at the same temperature and magnetic field increases initially and r

Shuang Li, Yibing Wang, Jian Gao, Chulhong Kim

Large-scale dynamic three-dimensional (3D) photoacoustic imaging (PAI) is significantly important in clinical applications. In practical implementations, large-scale 3D real-time PAI systems typically utilize sparse two-dimensional (2D) sensor arrays with certain angular deficiencies, necessitating advanced iterative reconstruction (IR) algorithms to achieve

Zhu Han, Ce Zhang, Lianru Gao, Zhiqiang Zeng

Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of remote sensing. However, existing approaches focus on single-source domain generalization to unseen target domains, and are easily confused by large real-world domain shifts due to

Quang Dang Nguyen, Sheryl L. Chang, Carl J. E. Suster, Rebecca J. Rockett

Computational multi-scale pandemic modelling remains a major and timely challenge. Here we identify specific requirements for a new class of models simulating pandemics across three scales: (1) pathogen evolution, often punctuated by the rapid emergence of new variants, (2) human interactions within a heterogeneous population, and (3) public health responses

Donghoon Ahn, Jiwon Kang, Sanghyun Lee, Jaewon Min

Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free guidance (CFG). Are guidance methods truly necessary? Observing that noise obtained via diffusion inversion can reconstruct high-quality images without guidance, we focus on the init

Madiha Tahreem, Ifrah Andleeb, Bilal Zahid Hussain, Arsalan Hameed

The android operating system is being installed in most of the smart devices. The introduction of intrusions in such operating systems is rising at a tremendous rate. With the introduction of such malicious data streams, the smart devices are being subjected to various attacks like Phishing, Spyware, SMS Fraud, Bots and Banking-Trojans and many such. The app

Zhu Han, Jin Yang, Lianru Gao, Zhiqiang Zeng

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based classification approaches mainly focus on pixel-level spectral and spatial information extraction through complex network arc

Behrad Samari, Mahdieh Zaker, Abolfazl Lavaei

Finite abstractions (a.k.a. symbolic models) offer an effective scheme for approximating the complex continuous-space systems with simpler models in the discrete-space domain. A crucial aspect, however, is to establish a formal relation between the original system and its symbolic model, ensuring that a discrete controller designed for the symbolic model can

Teppei Nakamura

We show that, for a $K_0$-regular projective normal surface $X$ over a perfect field $k$ of positive characteristic and a reduced effective Cartier divisor $D\hookrightarrow X$, the Chow group of zero cycles on $X$ with modulus $D$ coincides with the 0-th Suslin homology of $X\setminus D$. Moreover, we show that this isomorphism also holds for a projective s

Mahendra K. Verma

Fundamental laws of physics are symmetric under time reversal ($T$) symmetry, but the $T$ symmetry is strongly broken in the macroscopic world. In this Perspective, I review $T$ symmetry breaking frameworks: \textit{second law of thermodynamics, multiscale energy transfer}, and \textit{open systems}. In driven dissipative nonequilibrium systems, including tu

Michelle Guo, Mia Tang, Hannah Cha, Ruohan Zhang

For designing a wide range of everyday objects, the design process should be aware of both the human body and the underlying semantics of the design specification. However, these two objectives present significant challenges to the current AI-based designing tools. In this work, we present a method to synthesize body-aware 3D objects from a base mesh given a

Okuto Morikawa, Hiroshi Suzuki

We propose a simple numerical method which computes an approximate value of the winding number of a mapping from 3D torus~$T^3$ to the unitary group~$U(N)$, when $T^3$ is approximated by discrete lattice points. Our method consists of a ``tree-level improved'' discretization of the winding number and the gradient flow associated with an ``over-improved'' lat

Hyesu Jang, Wooseong Yang, Hanguen Kim, Dongje Lee

Maritime environmental sensing requires overcoming challenges from complex conditions such as harsh weather, platform perturbations, large dynamic objects, and the requirement for long detection ranges. While cameras and LiDAR are commonly used in ground vehicle navigation, their applicability in maritime settings is limited by range constraints and hardware

Swarnava Sinha Roy, Ayan Kundu

Integrated Gradients is a well-known technique for explaining deep learning models. It calculates feature importance scores by employing a gradient based approach computing gradients of the model output with respect to input features and accumulating them along a linear path. While this works well for continuous features spaces, it may not be the most optima

Tursunali Xamidov, Pankaj Sheoran, Sanjar Shaymatov, Tao Zhu

In this study, we explore the influence of quantum gravitational corrections, derived from Loop Quantum Gravity (LQG), on the efficiency of the magnetic Penrose process (MPP) in black hole (BH) environments. We begin by analyzing the rotating Loop Quantum Black Hole (LQBH) metric, describing the structure of the event horizon and ergosphere as functions of t

Md. Ariful Islam, Md Abrar Jahin, M. F. Mridha, Nilanjan Dey

Explainable Artificial Intelligence (XAI) methods are increasingly used in safety-critical domains, yet there is no unified framework to jointly evaluate fidelity, interpretability, robustness, fairness, and completeness. We address this gap through two contributions. First, we propose a multi-criteria evaluation framework that formalizes these five criteria

Keqi Wang, Sarah W. Harcum, Wei Xie

To advance understanding of cellular metabolism and reduce batch-to-batch variability in cell culture processes, this study introduces a multi-scale hybrid modeling framework designed to simulate and predict the dynamic behavior of CHO cell cultures undergoing metabolic phase transitions. The model captures dependencies across molecular, cellular, and macro-

Wei Dai, Yash Singh, Rui Zhang

The increasing frequency of mass school shootings in the United States has been raised as a critical concern. Active shooters kill innocent students and educators in schools. These tragic events highlight the urgent need for effective strategies to minimize casualties. This study aims to address the challenge of simulating and assessing potential mitigation

Changho Shin, John Cooper, Frederic Sala

The weak-to-strong generalization phenomenon is the driver for important machine learning applications including highly data-efficient learning and, most recently, performing superalignment. While decades of research have resulted in numerous algorithms that produce strong empirical performance, understanding what aspects of data enable weak-to-strong genera

Mingyuan Zhou, Xudong Jian, Ye Xia, Zhilu Lai

Structural health monitoring (SHM) has experienced significant advancements in recent decades, accumulating massive monitoring data. Data anomalies inevitably exist in monitoring data, posing significant challenges to their effective utilization. Recently, deep learning has emerged as an efficient and effective approach for anomaly detection in bridge SHM. D

Jaelyn S. Liang, Rehaan S. Mundy, Shriya Jagwayan

E-commerce is rapidly transforming economies across Africa, offering immense opportunities for economic growth, market expansion, and digital inclusion. This study investigates the effects of e-commerce on select African regions. By utilizing readiness factors, including mobile money deployment, GDP per capita, internet penetration, and digital infrastructur

Sudha Krishnamurthy, Vimal Bhat, Abhinav Jain

The proliferation of several streaming services in recent years has now made it possible for a diverse audience across the world to view the same media content, such as movies or TV shows. While translation and dubbing services are being added to make content accessible to the local audience, the support for making content accessible to people with different

Davor Lauc, Attapol Rutherford, Weerin Wongwarawipatr

This study introduces AyutthayaAlpha, an advanced transformer-based machine learning model designed for the transliteration of Thai proper names into Latin script. Our system achieves state-of-the-art performance with 82.32% first-token accuracy and 95.24% first-three-token accuracy, while maintaining a low character error rate of 0.0047. The complexity of T

Fangyuan Luo, Yankai Chen, Jun Wu, Tong Li

With the explosive growth of users and items, Recommender Systems are facing unprecedented challenges in terms of retrieval efficiency and storage overhead. Learning to Hash techniques have emerged as a promising solution to these issues by encoding high-dimensional data into compact hash codes. As a result, hashing-based recommendation methods (HashRec) hav

Yaoyu Li, Chaosheng Huang, Dongsheng Yang, Wenbo Liu

In this paper, a learning based Model Predictive Control (MPC) using a low dimensional residual model is proposed for autonomous driving. One of the critical challenge in autonomous driving is the complexity of vehicle dynamics, which impedes the formulation of accurate vehicle model. Inaccurate vehicle model can significantly impact the performance of MPC c

Shuhao Chen, Chengyi Tu

The rapid expansion of the electric vehicle (EV) industry has highlighted the importance of user feedback in improving product design and charging infrastructure. Traditional sentiment analysis methods often oversimplify the complexity of user emotions, limiting their effectiveness in capturing nuanced sentiments and emotional intensities. This study propose

Sana Amairi-Pyka, Christoph Fischer, Konstantin Kravtsov, Gianluca De Santis

With the growing number of satellite-based Quantum Key Distribution (QKD) payload launches, it becomes essential to ensure compatibility across different platforms for satellite tracking and quantum signal acquisition. In this paper, the Technology Innovation Institute (TII) presents the development of the Abu Dhabi Quantum Optical Ground Station (ADQOGS) fo

Chu Myaet Thwal, Ye Lin Tun, Minh N. H. Nguyen, Eui-Nam Huh

Beyond the success of Contrastive Language-Image Pre-training (CLIP), recent trends mark a shift toward exploring the applicability of lightweight vision-language models for resource-constrained scenarios. These models often deliver suboptimal performance when relying solely on a single image-text contrastive learning objective, spotlighting the need for mor

Adriana Mancini Pires, Christian Motch, Axel Schwope, Iris Traulsen

The discovery of radio-quiet, X-ray thermally emitting isolated neutron stars (XINSs) in the ROSAT All-Sky Survey revealed a previously overlooked component of the neutron star population. Advancements in X-ray instrumentation and the availability of deep, wide-area optical surveys now enable us to explore XINSs at fainter X-ray fluxes and greater distances.

Hanna Krasowski, Eric Palanques-Tost, Calin Belta, Murat Arcak

Modeling dynamical biological systems is key for understanding, predicting, and controlling complex biological behaviors. Traditional methods for identifying governing equations, such as ordinary differential equations (ODEs), typically require extensive quantitative data, which is often scarce in biological systems due to experimental limitations. To addres

Kun Cheng, Yurui Tang, Xingzhi Zhan

A connected graph is called fragile if it contains an independent vertex cut. In 2002 Chen and Yu proved that every connected graph of order $n$ and size at most $2n-4$ is fragile, and in 2013 Le and Pfender characterized the non-fragile graphs of order $n$ and size $2n-3.$ It is natural to consider minimum vertex cuts. We prove two results. (1) Every connec

Li Li, Weinan Wang

In this paper, we are interested in an inverse problem for the active scalar equations with fractional dissipation on the torus. We perform a second order linearization to relate our model to the linear fractional diffusion equation. Our approach to solving the inverse problem relies on nonlocal phenomena such as the unique continuation property of the fract

Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad, H. Vincent Poor

Second-order methods are widely adopted to improve the convergence rate of learning algorithms. In federated learning (FL), these methods require the clients to share their local Hessian matrices with the parameter server (PS), which comes at a prohibitive communication cost. A classical solution to this issue is to approximate the global Hessian matrix from

Xue Zhang, Jose Reina-Gálvez, Di'an Wu, Jan Martinek

Recent advances in scanning tunneling microscopy have enabled quantum-coherent control of single surface spins via all-electric electron spin resonance (ESR). Such control requires magnetoelectric coupling, since spin resonance is a magnetic effect. We show that a magnetic tip induces a bias-dependent exchange field on a localized Anderson impurity via virtu

Erik D. Demaine, Tonan Kamata, Ryuhei Uehara

In 1907, Henry Ernest Dudeney posed a puzzle: ``cut any equilateral triangle \dots\ into as few pieces as possible that will fit together and form a perfect square'' (without overlap, via translation and rotation). Four weeks later, Dudeney demonstrated a beautiful four-piece solution, which today remains perhaps the most famous example of dissection. In thi

Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye

Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive applications, such as real-time financial fraud detection. To address this limitation, recent studies have proposed distilling knowledge from t

Saintan Wu

The Union-Closed Sets Conjecture, also known as Frankl's conjecture, asks whether, for any union-closed set family $\mathcal{F}$ with $m$ sets, there is an element that lies in at least $\frac{1}{2}\cdot m$ sets in $\mathcal{F}$. In 2022, Nagel posed a stronger conjecture that within any union-closed family whose ground set size is at least $k$, there are al

Shagnik Das, Saintan Wu

The Union-Closed Sets Conjecture asks whether every union-closed set family $\mathcal{F}$ has an element contained in half of its sets. In 2022, Nagel posed a generalisation of this problem, suggesting that the $k$th-most popular element in a union-closed set family must be contained in at least $\frac{1}{2^{k-1} + 1} |\mathcal{F}|$ sets. We combine the entr

Baohong Guo, Hemaditya Malla, Alejandro Malagon-Romero, Jannis Teunissen

We study how the choice of input data affects simulations of positive streamers in humid air, focusing on H2O cross sections, photoionization models, and chemistry sets. Simulations are performed in air with a mole fraction of 0%, 3% or 10% H2O using an axisymmetric fluid model. Five H2O cross section sets are considered, which lead to significant difference

Shuchi Chawla, Dimitris Christou, Amit Harlev, Ziv Scully

We consider a class of optimization problems over stochastic variables where the algorithm can learn information about the value of any variable through a series of costly steps; we model this information acquisition process as a Markov Decision Process (MDP). The algorithm's goal is to minimize the cost of its solution plus the cost of information acquisiti

Angxuan Chen, Yuang Wei, Huixiao Le, Yan Zhang

This study investigates the potential of using ChatGPT as a teachable agent to support students' learning by teaching process, specifically in programming education. While learning by teaching is an effective pedagogical strategy for promoting active learning, traditional teachable agents have limitations, particularly in facilitating natural language dialog

Hui Zhang, Dexiang Hong, Yitong Wang, Jie Shao

Diffusion models have been recognized for their ability to generate images that are not only visually appealing but also of high artistic quality. As a result, Layout-to-Image (L2I) generation has been proposed to leverage region-specific positions and descriptions to enable more precise and controllable generation. However, previous methods primarily focus

Hao Hao, Xiaoqun Zhang, Aimin Zhou

Expensive optimization problems (EOPs) are prevalent in real-world applications, where the evaluation of a single solution requires a significant amount of resources. In our study of surrogate-assisted evolutionary algorithms (SAEAs) in EOPs, we discovered an intriguing phenomenon. Because only a limited number of solutions are evaluated in each iteration, r

Patrick Ocheja, Brendan Flanagan, Yiling Dai, Hiroaki Ogata

E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the system identifies and appends the most sali

Xiang-dong Shi, Sheng-bang Qian, Li-ying Zhu, Lin-jia Li

This paper reports the discovery of new slowly pulsating B-type stars. Based on the photometric, spectral, and astrometric data of TESS, LAMOST, and Gaia surveys, we have found 286 new slowly pulsating B-type stars (SPB stars) and 21 candidates. Among these, 20 are Be stars or candidates with emission line profiles. It is shown that these SPB stars have lumi

Edward Raff, Michel Benaroch, Sagar Samtani, Andrew L. Farris

The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a "reproducibility crisis" has spurred significant research in the past few years. Yet with each paper, it is often unclear what someone means by "reproducibility". Our work attempts to clarify the scope of "reproducibility" as displayed by the community at large. In doing s

Jayaprakash Sundararaj, Akhil Vyas, Benjamin Gonzalez-Maldonado

Transforming mathematical expressions into LaTeX poses a significant challenge. In this paper, we examine the application of advanced transformer-based architectures to address the task of converting handwritten or digital mathematical expression images into corresponding LaTeX code. As a baseline, we utilize the current state-of-the-art CNN encoder and LSTM

Pete Rigas

We initiate a new study on the correspondence between the 20-vertex model and a SOS (Solid-on-Solid) model. In comparison to two previous works of the author in 2024 which characterized properties of the transfer, and quantum monodromy, matrices of the 20-vertex model from the perspective of the quantum inverse scattering method, in addition to the structure

Rongzhong Xiao

In this paper, we build some ergodic theorems involving function $\Omega$, where $\Omega(n)$ denotes the number of prime factors of a natural number $n$ counted with multiplicities. As a combinatorial application, it is shown that for any $k\in \mathbb{N}$ and every $A\subset \mathbb{N}$ with positive upper Banach density, there are $a,d\in \mathbb{N}$ such

Jiechao Gao, Yuangang Li

Personalized medication aims to tailor healthcare to individual patient characteristics. However, the heterogeneity of patient data across healthcare systems presents significant challenges to achieving accurate and effective personalized treatments. Ethical concerns further complicate the aggregation of large volumes of data from diverse institutions. Feder

Zhaoyang Liu, Xijun Wang, Chenyuan Feng, Xinghua Sun

This paper focuses on spectrum sharing in heterogeneous wireless networks, where nodes with different Media Access Control (MAC) protocols to transmit data packets to a common access point over a shared wireless channel. While previous studies have proposed Deep Reinforcement Learning (DRL)-based multiple access protocols tailored to specific scenarios, thes

Keisuke Himeno, Masakazu Teragaito

Twisted right-angled Artin groups are defined through presentation based on mixed graphs. Each vertex corresponds to a generator, each undirected edge yields a commuting relation and each directed edge gives a Klein bottle relation. If there is no directed edge, then this reduces to an ordinary right-angled Artin group. There is a characterization of right-a

Omar Elezabi, Marcos V. Conde, Zongwei Wu, Radu Timofte

Professional photo editing remains challenging, requiring extensive knowledge of imaging pipelines and significant expertise. While recent deep learning approaches, particularly style transfer methods, have attempted to automate this process, they often struggle with output fidelity, editing control, and complex retouching capabilities. We propose a novel re

Shiwen Ni, Min Yang

Intelligent dialogue systems are increasingly used in modern education and psychological counseling fields, but most existing systems are limited to a single domain, cannot deal with both educational and psychological issues, and often lack accuracy and professionalism when dealing with complex issues. To address these problems, this paper proposes an intell

Naoki Kitazawa

As a topic of mathematics, "arrangements", systems of hyperplanes, circles, and general (regular) submanifolds, attract us strongly. We present a natural elementary study of arrangements of circles. It is also a kind of new studies. Our study is closely related to geometry and singularity theory of Morse(-Bott) functions. Regions surrounded by circles are re

J. S. Chung, O. Gileva, C. Ha, J. A Jeon

We present the development of a decay energy spectroscopy (DES) method for the analysis of radioactive impurities using magnetic microcalorimeters (MMCs). The DES system was designed to analyze radionuclides, such as Ra-226, Th-228, and their daughter nuclides, in materials like copper, commonly used in rare-event search experiments. We tested the DES system

Jingyu Lin, Jiaqi Gu, Lubin Fan, Bojian Wu

Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes featuring transient objects is challenging. We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per image and maintaining traditional 3D Gaussians for the whole static scenes. Note that, the 3DGS itself is better suited f

Hossein Yousefizadeh, Shenghui Gu, Lionel C. Briand, Ali Nasr

Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios highlights the need for rigorous system-level testing to ensure safety, a task usually performed with a simulator in the loop. Though one important goal of such testing is to detect safety

Ketong Shang, Jiangnan Lin, Yu Qin, Muyan Shen

Confidential Computing has emerged to address data security challenges in cloud-centric deployments by protecting data in use through hardware-level isolation. However, reliance on a single hardware root of trust (RoT) limits user confidence in cloud platforms, especially for high-performance AI services, where end-to-end protection of sensitive models and d

Yuan Xue, Qi Zhang, Chuanmin Jia, Shiqi Wang

Image Compression for Machines (ICM) aims to compress images for machine vision tasks rather than human viewing. Current works predominantly concentrate on high-level tasks like object detection and semantic segmentation. However, the quality of original images is usually not guaranteed in the real world, leading to even worse perceptual quality or downstrea

D. O. R. Azevedo, F. M. Guedes, M. S. Guimaraes, I. Roditi

Unitary transformations are employed to enhance the violations of the Bell-CHSH inequality in relativistic Quantum Field Theory. The case of the scalar field in $1+1$ Minkowski space-time is scrutinized by relying on the Tomita-Takesaki modular theory. The example of the bounded Hermitian operator $sign(\varphi(f))$, where $\varphi(f)$ stands for the smeared

Tuo Wu, Kangda Zhi, Junteng Yao, Xiazhi Lai

Fluid antenna system (FAS) as a new version of reconfigurable antenna technologies promoting shape and position flexibility, has emerged as an exciting and possibly transformative technology for wireless communications systems. FAS represents any software-controlled fluidic, conductive or dielectric structure that can dynamically alter antenna's shape and po

José Andrés Rodríguez Migueles

We show that for at most three closed geodesics with linearly independent directions, the homeomorphism type of its complement in the 3-torus is determine by the orbit of their direction vectors subspaces under the action of $PSL_3(\mathbb{Z}).$ Moreover, we provide asymptotically sharp volume bounds for a family of closed geodesics complements. The bounds d

Abul Ehtesham, Saket Kumar, Aditi Singh, Tala Talaei Khoei

Generative AI is reshaping the media landscape, enabling unprecedented capabilities in video creation, personalization, and scalability. This paper presents a comprehensive SWOT analysis of Metas Movie Gen, a cutting-edge generative AI foundation model designed to produce 1080p HD videos with synchronized audio from simple text prompts. We explore its streng

Jian-Ying Wu

We address in this review unified analysis of phase-field models for cohesive fracture. Aiming to regularize the Barenblatt (1959) cohesive zone model, all the discussed models are distinguished by three characteristic functions, i.e., the geometric function dictating the crack profile, the degradation function for the constitutive relation and the dissipati

Shinto Eguchi

In recent years, biodiversity measures have gained prominence as essential tools for ecological and environmental assessments, particularly in the context of increasingly complex and large-scale datasets. We provide a comprehensive review of diversity measures, including the Gini-Simpson index, Hill numbers, and Rao's quadratic entropy, examining their roles

Shilei Fan

This study explores the properties of the function which can tile the field $\mathbb{Q}_p$ of $p$-adic numbers by translation. It is established that functions capable of tiling $\mathbb{Q}_p$ is by translation uniformly locally constancy. As an application, in the field $\mathbb{Q}_p$, we addressed the question posed by H. Leptin and D. M\"uller, providing

John Park, Yunpeng Zhao, Ning Hao

In this short note, we address the identifiability issues inherent in the Degree-Corrected Stochastic Block Model (DCSBM). We provide a rigorous proof demonstrating that the parameters of the DCSBM are identifiable up to a scaling factor and a permutation of the community labels, under a mild condition.

Akshay Prasadan, Matey Neykov

We obtain the minimax rate for a mean location model with a bounded star-shaped set $K \subseteq \mathbb{R}^n$ constraint on the mean, in an adversarially corrupted data setting with Gaussian noise. We assume an unknown fraction $\epsilon \le 1/2-\kappa$ for some fixed $\kappa\in(0,1/2]$ of $N$ observations are arbitrarily corrupted. We obtain a minimax risk

Xiao Zhu, Srinivasan S. Iyengar

Computing high-dimensional potential energy surfaces for molecular systems and materials is considered to be a great challenge in computational chemistry with potential impact in a range of areas including the fundamental prediction of reaction rates. In this paper, we design and discuss an algorithm that has similarities to large language models in generati

Yiyan Fan, Qinghua Zhang, Ting Lin, He Bai

Creating a heterostructure by combining two magnetically and structurally distinct ruthenium oxides is a crucial approach for investigating their emergent magnetic states and interactions. Previously, research has predominantly concentrated on the intrinsic properties of the ferromagnet SrRuO3 and recently discovered altermagnet RuO2 solely. Here, we enginee

Zuo Zuo, Jiahao Dong, Yao Wu, Yanyun Qu

Industrial anomaly classification (AC) is an indispensable task in industrial manufacturing, which guarantees quality and safety of various product. To address the scarcity of data in industrial scenarios, lots of few-shot anomaly detection methods emerge recently. In this paper, we propose an effective few-shot anomaly classification (FSAC) framework with o