December 2024 arXiv papers — page 50

Xiujin Chen, Xiaoping Yang

Let {\Omega} be a bounded domain in R^n with C^{1,1} boundary and let u_{\lambda} be a Neumann Laplace eigenfunction in {\Omega} with eigenvalue {\lambda}. We show that the (n - 1)-dimensional Hausdorff measure of the zero set of u_{\lambda} does not exceed C\sqrt{\lambda}.

A. Biagioni, N. Bourgeois, F. Brandi, K. Cassou

The EuPRAXIA project aims to construct two state-of-the-art accelerator facilities based on plasma accelerator technology. Plasma-based accelerators offer the possibility of a significant reduction in facility size and cost savings over current radio frequency (RF) accelerators. The two facilities - one laser-driven one a beam-driven - are envisioned to prov

Oliver Lueghamer, Stefan Nimmrichter, Clara Conrad-Billroth, Thomas Juffmann

Microscopy gives access to spatially resolved dynamics in different systems, from biological cells to cold atoms. A big challenge is maximizing the information per used probe particle to limit the damage to the probed system. We present a cavity-enhanced continuous-wave microscopy approach that provides enhanced signal-to-noise ratios at fixed damage. Employ

Qijin Song, Weibang Bai

Can robots imagine or generate maps like humans do, especially when only limited information can be perceived like blind people? To address this challenging task, we propose a novel group diffusion model (GDM) based architecture for robots to generate point cloud maps with very limited input information.Inspired from the blind humans' natural capability of i

Quan Dao, Hao Phung, Trung Dao, Dimitris Metaxas

Flow matching has emerged as a promising framework for training generative models, demonstrating impressive empirical performance while offering relative ease of training compared to diffusion-based models. However, this method still requires numerous function evaluations in the sampling process. To address these limitations, we introduce a self-corrected fl

Yuan Ma, Jiankang Wei, Yilun Lyu, Kehao Chen

Machine learning systems are vulnerable to backdoor attacks, where attackers manipulate model behavior through data tampering or architectural modifications. Traditional backdoor attacks involve injecting malicious samples with specific triggers into the training data, causing the model to produce targeted incorrect outputs in the presence of the correspondi

Jiaqi Zhao, Fei Wang, Kun Li, Yanyan Wei

Speech Emotion Recognition (SER) plays a critical role in enhancing user experience within human-computer interaction. However, existing methods are overwhelmed by temporal domain analysis, overlooking the valuable envelope structures of the frequency domain that are equally important for robust emotion recognition. To overcome this limitation, we propose TF

Justin Bloom

We define a property for restricted Lie algebras in terms of cohomological support and tensor-triangular geometry of their categories of representations. By Tannakian reconstruction, the different symmetric tensor category structures on the underlying linear category of representations of a restricted Lie algebra correspond to different cocommutative Hopf al

Weizhu Bao, Ying Ma, Chushan Wang

We prove a nearly optimal error bound on the exponential wave integrator Fourier spectral (EWI-FS) method for the logarithmic Schr\"odinger equation (LogSE) under the assumption of $H^2$-solution, which is theoretically guaranteed. Subject to a CFL-type time step size restriction $\tau |\ln \tau| \lesssim h^2/|\ln h|$ for obtaining the stability of the numer

Qi Deng, Shuaicheng Niu, Ronghao Zhang, Yaofo Chen

Test-time adaptation (TTA) aims to fine-tune a trained model online using unlabeled testing data to adapt to new environments or out-of-distribution data, demonstrating broad application potential in real-world scenarios. However, in this optimization process, unsupervised learning objectives like entropy minimization frequently encounter noisy learning sign

Amir Harati, Elizabeth Shriberg, Tomasz Rutowski, Piotr Chlebek

Speech-based algorithms have gained interest for the management of behavioral health conditions such as depression. We explore a speech-based transfer learning approach that uses a lightweight encoder and that transfers only the encoder weights, enabling a simplified run-time model. Our study uses a large data set containing roughly two orders of magnitude m

Giora Simchoni, Saharon Rosset

Variational Autoencoders (VAE) are widely used for dimensionality reduction of large-scale tabular and image datasets, under the assumption of independence between data observations. In practice, however, datasets are often correlated, with typical sources of correlation including spatial, temporal and clustering structures. Inspired by the literature on lin

Po-Rei Huang, Yue-Tong Jheng, Guo-En Chang

Sensitive and cost-effective Group-IV short-wave infrared (SWIR) photodetectors (PDs), compatible with complementary metal-oxide semiconductor (CMOS) processes, are crucial for various emerging applications. Here, we developed a black GeSn thin-film PD on silicon, optimised for efficient SWIR photodetection and imaging. Incorporating Sn into the Ge layer eff

Shuai Lyu, Rongchen Zhang, Zeqi Ma, Fangjian Liao

Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC research often lacks generalizability due to its focus on specific datasets. Additionally, defect classification heavily relies on contextual information within images, and existing methods fall short of effectively ext

Hsiao-Chih Huang, Kefu Mu, Hui Min Leung, Chen-Ting Liao

Intra-system entanglement occurs between non-separable modes within the same system. For optical systems, the various degrees of freedom of light represent different modes, and the potential use of light to create higher dimensional classical entangle states offers a promising potential to drive new technological developments. In this work, we present experi

Muquan Li, Dongyang Zhang, Qiang Dong, Xiurui Xie

Contemporary deep learning, characterized by the training of cumbersome neural networks on massive datasets, confronts substantial computational hurdles. To alleviate heavy data storage burdens on limited hardware resources, numerous dataset compression methods such as dataset distillation (DD) and coreset selection have emerged to obtain a compact but infor

Charitha Rathnayake, P. R. S. Thilakarathna, Uthpala Nethmini, Rishemjith Kaur

Bilingual lexicons play a crucial role in various Natural Language Processing tasks. However, many low-resource languages (LRLs) do not have such lexicons, and due to the same reason, cannot benefit from the supervised Bilingual Lexicon Induction (BLI) techniques. To address this, unsupervised BLI (UBLI) techniques were introduced. A prominent technique in t

Jialing He, Jiacheng Wang, Ning Wang, Shangwei Guo

Smart grid, through networked smart meters employing the non-intrusive load monitoring (NILM) technique, can considerably discern the usage patterns of residential appliances. However, this technique also incurs privacy leakage. To address this issue, we propose an innovative scheme based on adversarial attack in this paper. The scheme effectively prevents N

Arthur Caetano, Alejandro Aponte, Misha Sra

Physical skill acquisition, from sports techniques to surgical procedures, requires instruction and feedback. In the absence of a human expert, Physical Task Guidance (PTG) systems can offer a promising alternative. These systems integrate Artificial Intelligence (AI) and Mixed Reality (MR) to provide realtime feedback and guidance as users practice and lear

W. B. Rui, Y. X. Zhao, Z. D. Wang

Supersymmetry (SUSY) proposed as an elementary symmetry for physics beyond the Standard Model has found important applications in various areas outside high-energy physics. Here, we systematically implement supersymmetric quantum mechanics -- exhibiting fundamental SUSY properties in the simple setting of quantum mechanics -- into a wide range of topological

Lu Chen, Qiaoqiao Hua, Guozhen Lu, Shuangjie Peng

In this paper, we first establish the quantitative properties for positive solutions to the Moser-Trudinger equations in the two-dimensional Poincar\'e disk $\mathbb{B}^2$: \begin{equation*}\label{mt1} \left\{ \begin{aligned} &-\Delta_{\mathbb{B}^2}u=\lambda ue^{u^2},\ x\in\mathbb{B}^2, &u\to0,\ \text{when}\ \rho(x)\to\infty, &||\nabla_{\mathbb{B}^2} u||_{L^

Shaofei Huang, Zhenwei Shen, Zehao Huang, Yue Liao

In this paper, we focus on the challenging task of monocular 3D lane detection. Previous methods typically adopt inverse perspective mapping (IPM) to transform the Front-Viewed (FV) images or features into the Bird-Eye-Viewed (BEV) space for lane detection. However, IPM's dependence on flat ground assumption and context information loss in BEV representation

Mingyu Huang, Peili Mao, Ke Li

Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the controllability and tuning of the underlying system, yet has long been a dark hole of knowledge due to its black-box na

Jiaozi Wang, Tomaž Prosen, Giulio Casati

We study integrability breaking and transport in a discrete space-time lattice with a local integrability breaking perturbation. We find a singular distribution of the Lyapunov spectrum where the majority of Lyapunov exponents vanish in the thermodynamic limit. The sub-extensive sequence of nonzero exponents, converging in the thermodynamic limit, correspond

Hanhua Long, Wenbin Bi, Jian Sun

Lightweight design, as a key approach to mitigate disparity between computational requirements of deep learning models and hardware performance, plays a pivotal role in advancing application of deep learning technologies on mobile and embedded devices, alongside rapid development of smart home, telemedicine, and autonomous driving. With its outstanding featu

John H. Schwarz

The first quantum string theories were developed around 1970, prior to the discovery of QCD, with the goal of producing a theory of hadrons. Basic physical requirements and mathematical consistency of the string theories known at that time turned out to require the inclusion of gravity and the existence of extra spatial dimensions. This came as a complete su

Mingrong Gong, Chaoqi Chen, Qingqiang Sun, Yue Wang

Out-of-distribution (OOD) detection is a crucial task for deploying deep learning models in the wild. One of the major challenges is that well-trained deep models tend to perform over-confidence on unseen test data. Recent research attempts to leverage real or synthetic outliers to mitigate the issue, which may significantly increase computational costs and

Sudeb Majee, Anuj Abhishek, Thilo Strauss, Taufiquar Khan

In many computational problems, using the Markov Chain Monte Carlo (MCMC) can be prohibitively time-consuming. We propose MCMC-Net, a simple yet efficient way to accelerate MCMC via neural networks. The key idea of our approach is to substitute the true likelihood function of the MCMC method with a neural operator based surrogate. We extensively evaluate the

Huy Vu, Huy Anh Nguyen, Adithya V Ganesan, Swanie Juhng

Artificial intelligence-based language generators are now a part of most people's lives. However, by default, they tend to generate "average" language without reflecting the ways in which people differ. Here, we propose a lightweight modification to the standard language model transformer architecture - "PsychAdapter" - that uses empirically derived trait-la

Dang Nguyen, Sunil Gupta, Kien Do, Svetha Venkatesh

In image classification tasks, deep learning models are vulnerable to image distortions i.e. their accuracy significantly drops if the input images are distorted. An image-classifier is considered "reliable" if its accuracy on distorted images is above a user-specified threshold. For a quality control purpose, it is important to predict if the image-classifi

Shenghai Yuan, Boyang Lou, Thien-Minh Nguyen, Pengyu Yin

Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seaports. The main challenges are calibration and localization in obstructed conditions, which are common in logistics environments. Traditional calibration methods, dependent on line-

Bing Lyu, Xue-Bing Wu, Yuxuan Pang, Huimei Wang

Aims. We reported the discovery that a changing-look AGN SDSS J101152.98+544206.4 (J1011+5442 for short) gradually returns to the type 1 state after a short period between 2014 and 2019 in the faint type 1.9 state. Methods. Motivated by the rebrightening in optical and mid-infrared light curves from ZTF and WISE, we obtained the new spectroscopic observation

Songjun Tu, Jingbo Sun, Qichao Zhang, Xiangyuan Lan

Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences. However, real-time human feedback is hard to obtain in online tasks. Most work suppose there is a "scripted teacher" that utilizes privileged predefined reward to provide preference feedback. In th

Waisullah Yousofi, Pushpak Bhattacharyya

This paper demonstrates that Phrase-Based Statistical Machine Translation (PBSMT) can outperform Transformer-based Neural Machine Translation (NMT) in moderate-resource scenarios, specifically for structurally similar languages, like the Persian-Hindi pair. Despite the Transformer architecture's typical preference for large parallel corpora, our results show

Xu Zheng, Yuanhuiyi Lyu, Lutao Jiang, Jiazhou Zhou

In this paper, we address the challenging modality-agnostic semantic segmentation (MaSS), aiming at centering the value of every modality at every feature granularity. Training with all available visual modalities and effectively fusing an arbitrary combination of them is essential for robust multi-modal fusion in semantic segmentation, especially in real-wo

Tianxin Hu, Shenghai Yuan, Ruofei Bai, Xinghang Xu

Multi-axle autonomous mobile robots (AMRs) are set to revolutionize the future of robotics in logistics. As the backbone of next-generation solutions, these robots face a critical challenge: managing and minimizing the swept volume during turns while maintaining precise control. Traditional systems designed for standard vehicles often struggle with the compl

Anuprabha M, Krishna Gurugubelli, V Kesavaraj, Anil Kumar Vuppala

Automatic detection and severity assessment of dysarthria are crucial for delivering targeted therapeutic interventions to patients. While most existing research focuses primarily on speech modality, this study introduces a novel approach that leverages both speech and text modalities. By employing cross-attention mechanism, our method learns the acoustic an

J. de la Cruz, H. C. Rosu

The factorizations using the general Riccati solution constructed from a given particular solution by means of the Bernoulli ansatz initiated in 1984 by Mielnik and Fernandez C. for the cases of the quantum harmonic oscillator and the radial Hydrogen equation, respectively, are briefy reviewed. The issue of the eigenfunction normalization of the obtained one

Masaki Kawamoto, Haruya Mizutani

We consider the cubic nonlinear Schr\"odinger equation with long-range linear potentials in one space dimension, and prove the modified scattering in the energy space for the associated final state problem with a prescribed small asymptotic profile. Compared with the leading term of the free solution, the asymptotic profile has an additional phase correction

Dian Yu, Yuheng Zhang, Jiahao Xu, Tian Liang

Large language models (LLMs) can refine their responses based on feedback, enabling self-improvement through iterative training or test-time refinement. However, existing methods predominantly focus on refinement within the same reasoning format, which may lead to non-correcting behaviors. We propose CaP, a novel approach that uses external tools to refine c

Naoki Murai, Katsuhiro Suzuki, Masamichi Nakajima, Maiko Kofu

We present a combined experimental and theoretical investigation of the spin dynamics in the iron-based superconductor Ba$_{0.75}$K$_{0.25}$Fe$_2$As$_2$. Time-of-flight inelastic neutron scattering measurements reveal the three-dimensional (3D) nature of the spin fluctuations, manifested as out-of-plane modulations of the low-energy magnetic intensity. As th

Yeyuan Wang, Dehong Gao, Bin Li, Rujiao Long

The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks. However, current MLLM often struggles to effectively address fine-grained multi-modal challenges. We argue that this limitation is closely linked to the models' visual grounding ca

Wenkang Lian, Shunke Ai, He Gao

Gamma-ray bursts (GRBs) have long been proposed as a potential source of high-energy neutrinos. Although no confirmed association between GRBs and neutrinos has been established, meaningful constraints have been placed on GRB prompt emission models. The nondetection of neutrinos, reported by the IceCube Collaboration, from both single and stacked GRB events

Maida Wang, Jinyang Jiang, Peter V. Coveney

Quantum machine learning has gained attention for its potential to address computational challenges. However, whether those algorithms can effectively solve practical problems and outperform their classical counterparts, especially on current quantum hardware, remains a critical question. In this work, we propose a novel quantum machine learning method, call

Emily Tseng, Thomas Ristenpart, Nicola Dell

Researchers increasingly look to understand experiences of pain, harm, and marginalization via qualitative analysis. Such work is needed to understand and address social ills, but poses risks to researchers' well-being: sifting through volumes of data on painful human experiences risks incurring traumatic exposure in the researcher. In this paper, we explore

Weiqi Zhou

We prove that if the zero set of the Fourier transform of $A\subseteq\mathbb Z_n\times\mathbb Z_n$ contains an element of prime power order, then there is an equi-distribution relation in subsets of $A$ with respect to certain hyperplanes. With this we further show that if $A$ is a tiling complement of the subgroup generated by $(p,0)$ and $(0,p)$ in $\mathb

Yin Lin, Cong Yan

Row-level lineage explains what input rows produce an output row through a data processing pipeline, having many applications like data debugging, auditing, data integration, etc. Prior work on lineage falls in two lines: eager lineage tracking and lazy lineage inference. Eager tracking integrates lineage tracing tightly into the operator implementation, ena

Yihao Zhang, Qizhi Qiu, Xiaomin Liu, Jiaping Wu

We propose a novel input refinement methodology incorporating sensitivity analysis and memory-aware weighting for jointly refining numerous diverse inputs. Field trials show ~2.5 dB and ~2.3 dB improvements in Q-factor and power estimation, respectively.

Yoshikazu Yamagishi

We study the farthest point mapping on (the boundary of) the 4-cube with respect to the intrinsic metric, and its dynamics as a multivalued mapping. It is a piecewise rational map. It is more complicated than the one on the 3-cube, but it is shown that the limit set of the farthest point map on the 4-cube is the union of the diagonals of eight (3-cube) facet

Corin Katzke, Gideon Futerman

This paper examines the strategic dynamics of international competition to develop Artificial Superintelligence (ASI). We argue that the same assumptions that might motivate the US to race to develop ASI also imply that such a race is extremely dangerous. These assumptions--that ASI would provide a decisive military advantage and that states are rational act

Yuhang He, Sangyun Shin, Anoop Cherian, Niki Trigoni

Accurately localizing 3D sound sources and estimating their semantic labels -- where the sources may not be visible, but are assumed to lie on the physical surface of objects in the scene -- have many real applications, including detecting gas leak and machinery malfunction. The audio-visual weak-correlation in such setting poses new challenges in deriving i

Abdullah al Nomaan Nafi, Md. Alamgir Hossain, Rakib Hossain Rifat, Md Mahabub Uz Zaman

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-ge

Jongmin Yu, Zhongtian Sun, Chen Bene Chi, Jinhong Yang

Semantic segmentation requires extensive pixel-level annotation, motivating unsupervised domain adaptation (UDA) to transfer knowledge from labelled source domains to unlabelled or weakly labelled target domains. One of the most efficient strategies involves using synthetic datasets generated within controlled virtual environments, such as video games or tra

Aifeng Li, Zhaodong Lv, Wei Liu, Yiqing Guo

In our previous work, we have investigated Galactic cosmic ray (GCR) spectra and anisotropy from 100 GeV to PeV, under anisotropic propagation model with axisymmetric distributed galactic sources. Numerous observational evidence have indicated that the Milky Way is a typical spiral galaxy. In this work, we further utilize anisotropic propagation models with

Shuyi Li, Yu Zhang, Adrian Bahri, Xiaoliang Zhang

Altermagnetism, a recently discovered class of magnetic order characterized by vanishing net magnetization and spin-splitting band structures, has garnered significant research attention. In this work, we introduce a novel two-dimensional system that exhibits $g$-wave altermagnetism and undergoes a strain-induced transition from $g$-wave to $d$-wave altermag

Abyad Enan, M Sabbir Salek, Mashrur Chowdhury, Gurcan Comert

Virtual Traffic Light (VTL) is a traffic control method that does not require traffic signal-related infrastructure for roadway intersections. Connected vehicles (CVs) are given right-of-way based on prevailing traffic conditions, such as estimated times of arrival (ETAs) of vehicles, the number of CVs in different approaches, and their emissions. These fact

Tingting Liu, Jumin Qiu, Tianbao Yu, Qiegen Liu

Optical metasurfaces have enabled high-speed, low-power image processing within a compact footprint. However, reconfigurable imaging in such flat devices remains a critical challenge for fully harnessing their potential in practical applications. Here, we propose and demonstrate phase-change metasurfaces capable of dynamically switching between edge detectio

Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li

Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a combination of both. Previous work has attempted to adopt multimodal large language models (MLLMs) to realize UMR using only text data. However, our preliminary experiments demonstrate

Jinping Zou, Xiaoge Deng, Tao Sun

Sharpness-Aware Minimization (SAM) has proven highly effective in improving model generalization in machine learning tasks. However, SAM employs a fixed hyperparameter associated with the regularization to characterize the sharpness of the model. Despite its success, research on adaptive regularization methods based on SAM remains scarce. In this paper, we p

Aryaman Shaan, Garvit Banga, Raghav Mantri

Generative models have enabled easy creation and generation of images of all kinds given a single prompt. However, this has also raised ethical concerns about what is an actual piece of content created by humans or cameras compared to model-generated content like images or videos. Watermarking data generated by modern generative models is a popular method to

Li-Yang Gao, Léon V. E. Koopmans, Florent G. Mertens, Satyapan Munshi

Neutral hydrogen (HI) serves as a crucial probe for the Cosmic Dawn and the Epoch of Reionization (EoR). Actual observations of the 21-cm signal often encounter challenges such as thermal noise and various systematic effects. To overcome these challenges, we simulate SKA-Low-depth images in South Celestial Pole (SCP) field and process them with a deep learni

Tingting Liu, Meibao Qin, Jumin Qiu, Xu Tu

Recent advancements in nonlinear nanophotonics are driven by the exploration of sharp resonances within high-index dielectric metasurfaces. In this work, we leverage doubly degenerate quasi-bound states in the continuum (quasi-BICs) to demonstrate robust enhancement of third-harmonic generation (THG) in silicon metasurfaces. These quasi-BICs are governed by

Linta Joseph, Wynter Alford, Chandrasekhar Ramanathan

Dense spin ensembles in solids present a natural platform for studying quantum many-body dynamics. Multiple-pulse coherent control can be used to manipulate the magnetic dipolar interaction between the spins to engineer their dynamics. Here, we investigate the performance of a series of well-known pulse sequences that aim to suppress inter-spin dipolar coupl

Yun Chen-Shue, Yukun Li, Jiongmin Yong

We introduce a model for limit order book of a certain security with two main features: First, both the limit orders and market orders for the given asset are allowed to appear and interact with each other. Second, the high frequency trading activities are allowed and described by the scaling limit of nearly-unstable multi-dimensional Hawkes processes with p

Yuxiang Zhang, Yuqi Yang, Jiangming Shu, Yuhang Wang

OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple pattern imitation. This technical report presents \emph{OpenRFT}, our attempt to fine-tune generalist reasoning models for domain-specific tasks under the same settings as RFT. OpenRFT a

Kun Wu, Yinuo Zhao, Zhiyuan Xu, Zhengping Che

Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a safe and promising control policy. The prevailing methods typically learn a conservative policy to mitigate the problem of Q-value overestimation, but it is prone to overdo it, leadin

Kourosh Kakhi, Senthil Kumar Jagatheesaperumal, Abbas Khosravi, Roohallah Alizadehsani

Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of wearable technologies, such as fitness trackers and smartwatches, has made it possible to continuously analyze physiological signals in real-time to determine a person level of exhaustion. This has allowed for ti

Kangxiang Xia, Xinfa Zhu, Jixun Yao, Wenjie Tian

We introduce KALL-E, a novel autoregressive (AR) language model for text-to-speech (TTS) synthesis that operates by predicting the next distribution of continuous speech frames. Unlike existing methods, KALL-E directly models the continuous speech distribution conditioned on text, eliminating the need for any diffusion-based components. Specifically, we util

Hui Li

If the circle acts in a Hamiltonian way on a compact symplectic manifold of dimension $2n$, then there are at least $n+1$ fixed points. The case that there are exactly $n+1$ isolated fixed points has its importance due to various reasons. Besides dimension 2 with 2 fixed points, and dimension 4 with 3 fixed points, which are known, the next interesting case

Zhigang Pu, Wenpei Long, Kun Xu

The Gas-Kinetic Scheme (GKS), widely used in computational fluid dynamics for simulating hypersonic and other complicated flow phenomena, is extended in this work to electromagnetic problems by solving Maxwell's equations. In contrast to the classical GKS formulation, the proposed scheme employs a discrete rather than a continuous velocity space. By evaluati

Zirong Chen, Elizabeth Chason, Noah Mladenovski, Erin Wilson

Emergency response services are vital for enhancing public safety by safeguarding the environment, property, and human lives. As frontline members of these services, 9-1-1 dispatchers have a direct impact on response times and the overall effectiveness of emergency operations. However, traditional dispatcher training methods, which rely on role-playing by ex

Reed L. Brown, Shahab Mirjalili, Makrand A. Khanwale, Ali Mani

A mass-conserving contact line treatment for second-order conservative phase field methods is presented and applied to the conservative diffuse interface (CDI) model. The treatment centers on a no-flux boundary condition for the phase field along with a slip boundary condition for the velocity that is based on the generalized Navier boundary condition (GNBC)

Edwin Salcedo

Accurate and timely prediction of heavy rainfall events is crucial for effective flood risk management and disaster preparedness. By monitoring, analysing, and evaluating rainfall data at a local level, it is not only possible to take effective actions to prevent any severe climate variation but also to improve the planning of surface and underground hydrolo

Zhihua Wang, Lijing Zhou, Wenqiang Zhang, Xiaorong Wang

A phase-field model for three-phase flows is established by combining the Navier-Stokes (NS) and the energy equations, with the Allen-Cahn (AC) and Cahn-Hilliard (CH) equations and is demonstrated analytically to satisfy the energy dissipation law. A finite difference scheme is then established to discretize the model and this numerical scheme is proved to b

Yi Liu, Chengxin Li, Xiaohui Dong, Lei Li

Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only preliminary research treats them as two contradictory tasks, training models on large-scale labeled data alternately for each task and assessing them i

Wenhang Shi, Yiren Chen, Shuqing Bian, Xinyi Zhang

Knowledge stored in large language models requires timely updates to reflect the dynamic nature of real-world information. To update the knowledge, most knowledge editing methods focus on the low layers, since recent probes into the knowledge recall process reveal that the answer information is enriched in low layers. However, these probes only and could onl

Changjian Chen, Fei Lv, Yalong Guan, Pengcheng Wang

The performance of computer vision models in certain real-world applications (e.g., rare wildlife observation) is limited by the small number of available images. Expanding datasets using pre-trained generative models is an effective way to address this limitation. However, since the automatic generation process is uncontrollable, the generated images are us

Hang Li, Tianlong Xu, Kaiqi Yang, Yucheng Chu

The rise of large language models (LLMs) offers new opportunities for automatic error detection in education, particularly for math word problems (MWPs). While prior studies demonstrate the promise of LLMs as error detectors, they overlook the presence of multiple valid solutions for a single MWP. Our preliminary analysis reveals a significant performance ga

Qi Sun, Yayun Xue, Zhijun Song

This study introduces an adaptive user interface generation technology, emphasizing the role of Human-Computer Interaction (HCI) in optimizing user experience. By focusing on enhancing the interaction between users and intelligent systems, this approach aims to automatically adjust interface layouts and configurations based on user feedback, streamlining the

Tianjiao Wang, Xiang Xu, Yue Zhao

In this paper, we study the inverse random source scattering problem for the biharmonic Schrodinger equation in two and three dimensions. The driven source is assumed to be a generalized microlocally isotropic Gaussian random function whose covariance operator is a classical pseudodifferential operator. We examine the meromorphic continuation and estimates f

Shugang Hao, Lingjie Duan

Reinforcement learning from human feedback (RLHF) has become an essential step in fine-tuning large language models (LLMs) to align them with human preferences. However, human labelers are selfish and have diverse preferences. They may strategically misreport their online feedback to influence the system's aggregation towards their own preferences. Current p

Kaiwen Zuo, Yirui Jiang, Fan Mo, Pietro Lio

Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise. We present KG4Diagnosis, a novel hierarchical multi-agent framework that combines LLMs with automated knowledge graph construction, encompassing 362 common diseases across medical sp

Zhaoyang Sun, Fei Du, Weihua Chen, Fan Wang

Recently, the success of text-to-image synthesis has greatly advanced the development of identity customization techniques, whose main goal is to produce realistic identity-specific photographs based on text prompts and reference face images. However, it is difficult for existing identity customization methods to simultaneously meet the various requirements

Vitali Vougalter

We address the persistence under a perturbation of stationary pulse solutions of some reaction-diffusion type equations in dimensions d=2,3 and evaluate the asymptotic approximations of such pulses to the leading order in the parameter of the perturbation.

Shugang Hao, Lingjie Duan

Continual learning (CL) is a new online learning technique over sequentially generated streaming data from different tasks, aiming to maintain a small forgetting loss on previously-learned tasks. Existing work focuses on reducing the forgetting loss under a given task sequence. However, if similar tasks continuously appear to the end time, the forgetting los

Peitong Duan, Chin-Yi Cheng, Bjoern Hartmann, Yang Li

Feedback is crucial for every design process, such as user interface (UI) design, and automating design critiques can significantly improve the efficiency of the design workflow. Although existing multimodal large language models (LLMs) excel in many tasks, they often struggle with generating high-quality design critiques -- a complex task that requires prod

Yu Ren, Xu Zhan, Yunqiao Hu, Xiangdong Ma

Array synthetic aperture radar (Array-SAR), also known as tomographic SAR (TomoSAR), has demonstrated significant potential for high-quality 3D mapping, particularly in urban areas.While deep learning (DL) methods have recently shown strengths in reconstruction, most studies rely on pixel-by-pixel reconstruction, neglecting spatial features like building str

Zhen Qin, Zhihui Zhu

As intelligent reflecting surface (IRS) has emerged as a new and promising technology capable of configuring the wireless environment favorably, channel estimation for IRS-assisted multiple-input multiple-output (MIMO) systems has garnered extensive attention in recent years. Despite the development of numerous algorithms to address this challenge, a compreh

Yuecai Han, Yuhang Li

In this paper, we study the discrete time filtering problems for linear systems driven by fractional noises. The main difficulty comes from the non-Markovian of the noises. We construct the difference equation of the covariance process through the properties of the noises and transform the filtering problem to an optimal control problem. We obtain the necess

Onyinye Dibia, Prianka Bhattacharjee, Brad Stenger, Steven Baldasty

Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis, yet its practical adoption is often hindered by challenges in the implementation and communication of DP. This paper presents a comprehensive systematization of existing research studies around the usability of DP, synthesizing insights from stud

Kyle Drury

STRINGS is a Monte Carlo (MC) event generator for simulating the production and decay of first and second string resonances in proton-proton collisions. STRINGS can also interface with other programs such as Pythia using the Les Houches Accord to produce more accurate data. In this paper, we validate STRINGS for the simulation of 2-parton $\rightarrow$ $\gam

Yufei Yang, Timothy S. Horbury, Domenico Trotta, Lorenzo Matteini

We investigate a class of ion-scale magnetic solitary structures in the solar wind, characterized by distinct magnetic field enhancements and bipolar rotations over spatial scales of several proton inertial lengths. These structures are revisited using high-resolution data from the Solar Orbiter and Parker Solar Probe missions. Using a machine learning-based

Tingting Wang, Tianrui Wang, Meng Ge, Qiquan Zhang

Time-frequency (T-F) domain methods for monaural speech enhancement have benefited from the success of deep learning. Recently, focus has been put on designing two-stream network models to predict amplitude mask and phase separately, or, coupling the amplitude and phase into Cartesian coordinates and constructing real and imaginary pairs. However, most metho

Haoran You, Connelly Barnes, Yuqian Zhou, Yan Kang

Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) image generation quality but suffer from high latency and memory inefficiency, making them difficult to deploy on resource-constrained devices. One major efficiency bottleneck is that existing DiTs apply equal computation across all regions of an image. However, not all image tokens are equa

Yuecai Han, Yuhang Li

In this paper, the optimal control for discrete-time systems driven by fractional noises is studied. A stochastic maximum principle is obtained by introducing a backward stochastic difference equation contains both fractional noises and the constructed white noises. The solution of the backward stochastic difference equations is also investigated. As an appl

Qian Xiong, Weiliang Ma, Xuanhua Shi, Yongluan Zhou

Elliptic Curve Cryptography (ECC) is an encryption method that provides security comparable to traditional techniques like Rivest-Shamir-Adleman (RSA) but with lower computational complexity and smaller key sizes, making it a competitive option for applications such as blockchain, secure multi-party computation, and database security. However, the throughput

Portia X. Anderson, Esther Banaian, Melanie J. Ferreri, Owen C. Goff

For integral weights $\lambda$ and $\mu$ of a classical simple Lie algebra $\mathfrak{g}$, Kostant's weight multiplicity formula gives the multiplicity of the weight $\mu$ in the irreducible representation with highest weight $\lambda$, which we denote by $m(\lambda,\mu)$. Kostant's weight multiplicity formula is an alternating sum over the Weyl group of the

Xianchao Xiu, Chenyi Huang, Pan Shang, Wanquan Liu

To deal with high-dimensional unlabeled datasets in many areas, principal component analysis (PCA) has become a rising technique for unsupervised feature selection (UFS). However, most existing PCA-based methods only consider the structure of datasets by embedding a single sparse regularization or constraint on the transformation matrix. In this paper, we in

Michael Shalyt, Uri Seligmann, Itay Beit Halachmi, Ofir David

Ongoing efforts that span over decades show a rise of AI methods for accelerating scientific discovery, yet accelerating discovery in mathematics remains a persistent challenge for AI. Specifically, AI methods were not effective in creation of formulas for mathematical constants because each such formula must be correct for infinite digits of precision, with

Farnoosh Hadizadeh, Wrik Mallik, Rajeev K. Jaiman

This article presents a graph neural network (GNN) based surrogate modeling approach for fluid-acoustic shape optimization. The GNN model transforms mesh-based simulations into a computational graph, enabling global prediction of pressure and velocity flow fields around solid boundaries. We employ signed distance functions to implicitly represent geometries