March 2025 arXiv papers — page 95

Hòa T. Bùi, Minh N. Bùi, Christian Clason

This work is concerned with the convex analysis of functions defined on (not necessarily finite-dimensional) Hilbert spaces whose values depend solely on a certain ``spectrum'' of the arguments, a class we term ``spectral functions.'' We propose a notion of a spectral decomposition system which brings together a wide array of settings underlying important ap

Matthew Low, Arian Prabowo, Hao Xue, Flora Salim

Urban traffic forecasting is a commonly encountered problem, with wide-ranging applications in fields such as urban planning, civil engineering and transport. In this paper, we study the enhancement of traffic forecasting with pre-training, focusing on spatio-temporal graph methods. While various machine learning methods to solve traffic forecasting problems

Yaxiong Chen, Junjian Hu, Chunlei Li, Zixuan Zheng

Video object segmentation is crucial for the efficient analysis of complex medical video data, yet it faces significant challenges in data availability and annotation. We introduce the task of one-shot medical video object segmentation, which requires separating foreground and background pixels throughout a video given only the mask annotation of the first f

Lucas Villanueva, Karine Truffin, Jacques Borée, Marcello Meldi

A Data Assimilation (DA) strategy based on an ensemble Kalman filter (EnKF) is used to enhance the predictive capabilities of scale resolving numerical tools for the analysis of flows exhibiting cyclic behaviour. More precisely, an ensemble of numerical runs using Large Eddy Simulation (LES) for the compressible steady-state flow rig is augmented via the int

Hisato Komatsu

The linear regression (LR) method offers the advantage that optimal parameters can be calculated relatively easily, although its representation capability is limited than that of the deep learning technique. To improve deep reinforcement learning, the Least Squares Deep Q Network (LS-DQN) method was proposed by Levine et al., which combines Deep Q Network (D

Zihan Cao, Yu Zhong, Liang-Jian Deng

Pansharpening, a pivotal task in remote sensing for fusing high-resolution panchromatic and multispectral imagery, has garnered significant research interest. Recent advancements employing diffusion models based on stochastic differential equations (SDEs) have demonstrated state-of-the-art performance. However, the inherent multi-step sampling process of SDE

Yifan Li, Shuai Yang, Jiaying Liu

Image colorization aims to bring colors back to grayscale images. Automatic image colorization methods, which requires no additional guidance, struggle to generate high-quality images due to color ambiguity, and provides limited user controllability. Thanks to the emergency of cross-modality datasets and models, language-based colorization methods are propos

Rishav Rishav, Somjit Nath, Vincent Michalski, Samira Ebrahimi Kahou

Building trust in reinforcement learning (RL) agents requires understanding why they make certain decisions, especially in high-stakes applications like robotics, healthcare, and finance. Existing explainability methods often focus on single states or entire trajectories, either providing only local, step-wise insights or attributing decisions to coarse, epi

Mirosław Werwiński, Andrzej Szajek, Agnieszka Marczyńska, Lesław Smardz

In this work, we investigate the effect of Gd and Co substitutions on the electrochemical and electronic properties of La$_{1.5}$Mg$_{0.5}$Ni$_7$ alloy. Two series of La$_{1.5-x}$Gd$_x$Mg$_{0.5}$Ni$_7$ ($x$ = 0.0, 0.25, 1.0) and La$_{1.5}$Mg$_{0.5}$Ni$_{7-y}$Co$_y$ ($y$ = 0.0, 0.5, 1.5) alloys are produced using mechanical alloying technique. The X-ray diffr

J. Grümbel, Y. Oshima, A. Dubroka, M. Ramsteiner

ScN is an emerging transition metal nitride with unique physical properties arising from the d-electrons of Sc. In this work we present the results of optical characterization techniques spectroscopic ellipsometry, Raman spectroscopy, and photoluminescence measurements of a 40 $\mu$m thick, fully relaxed, and only weakly n-type doped ($n = 1.2 \times 10^{18}

Jonathan E. Moussa

I generalize the well-known classical Metropolis-Hastings algorithm into a quantum algorithm that can equilibrate, measure, and mix a quantum thermal state on a quantum computer. It performs non-symmetric transitions on labels of state preparation and measurement operations and rejects transitions using imprecise energies extracted by Gaussian-filtered quant

Boris Hoi-Lun Ng, Ming-Chung Chu

Recent cosmological measurements suggest the possibility of an anisotropic universe. As a result, the Bianchi Type I model, being the simplest anisotropic extension to the standard Friedmann-Lema\^itre-Robertson-Walker metric has been extensively studied. In this work, we show how the recombination history should be modified in an anisotropic universe and de

Haorui Liu, Mei Lu, Yan Wang, Yi Zhang

Let $n \in 3\mathbb{Z}$ be sufficiently large. Zhang, Zhao and Lu proved that if $H$ is a 3-uniform hypergraph with $n$ vertices and no isolated vertices, and if $deg(u)+deg(v) > \frac{2}{3}n^2 - \frac{8}{3}n + 2$ for any two vertices $u$ and $v$ that are contained in some edge of $H$, then $ H $ admits a perfect matching. In this paper, we prove that the ra

Semin Oh, Jeong Rye Park, Jongyook Park, Yoshio Sano

In this paper, we completely classify the connected non-bipartite graphs with integral signless Laplacian eigenvalues at most 6.

Tingxiu Chen, Yilei Shi, Zixuan Zheng, Bingcong Yan

Ultrasound video classification enables automated diagnosis and has emerged as an important research area. However, publicly available ultrasound video datasets remain scarce, hindering progress in developing effective video classification models. We propose addressing this shortage by synthesizing plausible ultrasound videos from readily available, abundant

Anjana Arunkumar, Lace Padilla, Chris Bryan

Visualizations are powerful tools for conveying information but often rely on accompanying text for essential context and guidance. This study investigates the impact of annotation patterns on reader preferences and comprehension accuracy among multilingual populations, addressing a gap in visualization research. We conducted experiments with two groups flue

Keith Glennon, Mirian Tsulaia

We consider N=1 supersymmetric systems in d=4, 6 and 10 dimensions which consist of reducible bosonic and fermionic massless representations of the Poincare group. We show in detail how to decompose the corresponding Lagrangians into a sum of Lagrangians for irreducible representations of the Poincare group. We also outline a modification of this procedure i

Xiaohao Liu, Xiaobo Xia, See-Kiong Ng, Tat-Seng Chua

Multimodal Contrastive Learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space. By leveraging contrastive learning across diverse modalities, large-scale multimodal data enhances representational quality. However, a critical yet often overlooked challenge remains: multimodal data is rarely collected

Parin Chaipunya, Thirumulanathan D, Joydeep Dutta

We consider a bilevel optimization problem having a single leader and multiple followers. The followers choose their strategies simultaneously, and are assumed to converge to a Nash equilibrium strategy profile. We begin by providing a practical example of such a problem in an oligopoly setting. We then show the existence of a Nash equilibrium when the objec

Qi Feng, Jun Zhang

We show that there exist infinite-dimensional quasi-flats in the compactly supported Hamiltonian diffeomorphism group of the Liouville domain, with respect to the spectral norm, if and only if the symplectic cohomology of this Liouville domain does not vanish. In particular, there exist infinite-dimensional quasi-flats in the compactly supported Hamiltonian

Seungyeon Cho, Tae-Kyun Kim

Skeleton-based Human Action Recognition (HAR) is a vital technology in robotics and human-robot interaction. However, most existing methods concentrate primarily on full-body movements and often overlook subtle hand motions that are critical for distinguishing fine-grained actions. Recent work leverages a unified graph representation that combines body, hand

J. Iurman, E. Wansart, M. Goffart, B. Donnet

Lightweight Tunnels (LWTs) in the Linux kernel enable efficient per-route tunneling and are widely used by protocols such as In Situ Operations, Administration, and Maintenance (IOAM), Segment Routing over IPv6 (SRv6), and Routing Protocol for Low-Power and Lossy Networks (RPL). However, a performance issue was detected in their implementations, where a doub

Zixuan Zheng, Yilei Shi, Chunlei Li, Jingliang Hu

Few-shot video object segmentation aims to reduce annotation costs; however, existing methods still require abundant dense frame annotations for training, which are scarce in the medical domain. We investigate an extremely low-data regime that utilizes annotations from only a few video frames and leverages existing labeled images to minimize costly video ann

Basura Fernando, Thanh-Son Nguyen, Hong Yang, Tzeh Yuan Neoh

In this work we present Knowledge Module Learning (KML) to understand and reason over procedural tasks that requires models to learn structured and compositional procedural knowledge. KML is a neurosymbolic framework that learns relation categories within a knowledge graph as neural knowledge modules and composes them into executable reasoning programs gener

Jianyi Zhang

Recent years have witnessed the rapid progression of deep learning, pushing us closer to the realization of AGI (Artificial General Intelligence). Probabilistic modeling is critical to many of these advancements, which provides a foundational framework for capturing data distributions. However, as the scale and complexity of AI applications grow, traditional

Jian Jiang, Long Chen, Lu ke, Bozheng Dou

Chaos is omnipresent in nature, and its understanding provides enormous social and economic benefits. However, the unpredictability of chaotic systems is a textbook concept due to their sensitivity to initial conditions, aperiodic behavior, fractal dimensions, nonlinearity, and strange attractors. In this work, we introduce, for the first time, chaotic learn

Bike Chen, Antti Tikanmäki, Juha Röning

Point cloud segmentation (PCS) aims to separate points into different and meaningful groups. The task plays an important role in robotics because PCS enables robots to understand their physical environments directly. To process sparse and large-scale outdoor point clouds in real time, range image-based models are commonly adopted. However, in a range image,

Francisco Palmí-Perales, Finn Lindgren, Virgilio Gómez-Rubio

The analysis of case-control point pattern data is an important problem in spatial epidemiology. The spatial variation of cases if often compared to that of a set of controls to assess spatial risk variation as well as the detection of risk factors and exposure to putative pollution sources using spatial regression models. The intensities of the point patter

Yang Liu, Wentao Feng, Zhuoyao Liu, Shudong Huang

Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically learn a set of embeddings to find the optimal match for each view's text and compute similarity. However, the visual and text embeddings learned through these approaches have limited information capacity and are

Mirosław Werwiński, Andrzej Szajek, Agnieszka Marczyńska, Lesław Smardz

La-Mg-Ni-based alloys are promising negative electrode materials for 3rd generation of Ni-MH$_x$ batteries. In this work, we investigate the effect of Mg substitution on the electrochemical and electronic properties of La$_{2-x}$Mg$_x$Ni$_7$ materials. The mechanical alloying technique is used to produce a series of La$_{2-x}$Mg$_x$Ni$_7$ alloys ($x$ = 0.00,

Van Duy Tran, Tuan Hai Vu, Vu Trung Duong Le, Hoai Luan Pham

The area of quantum circuit simulation has attracted a lot of attention in recent years. However, due to the exponentially increasing computational costs, assessing and validating these models on large datasets poses significant obstacles. Despite plenty of research in quantum simulation, issues such as memory management, system adaptability, and execution e

Joseph Emmanuel DL Dayo, Prospero C. Naval

The increasing demand for high-accuracy depth estimation in autonomous driving and augmented reality applications necessitates advanced neural architectures capable of effectively leveraging multiple data modalities. In this context, we introduce the Unified Segmentation Attention Mechanism Network (USAM-Net), a novel convolutional neural network that integr

Zhaohua Yang, Yuxing Zhong, Nachuan Yang, Xiaoxu Lyu

This paper focuses on the data-driven optimal structured controller design for discrete-time linear time-invariant (LTI) systems, considering both the $H_2$ performance and the $H_\infty$ performance. Specifically, we consider three scenarios: (i) the model-based structured control, (ii) the data-driven unstructured control, and (iii) the data-driven structu

Hao Liang, Zhipeng Dong, Kaixin Chen, Jiyuan Guo

Surround-view perception has garnered significant attention for its ability to enhance the perception capabilities of autonomous driving vehicles through the exchange of information with surrounding cameras. However, existing surround-view perception systems are limited by inefficiencies in unidirectional interaction pattern with human and distortions in ove

Weijia Huang, Zhongyi Huang, Wenli Yang, Wei Zhu

In this paper, we propose image restoration models using optimal transport (OT) and total variation regularization. We present theoretical results of the proposed models based on the relations between the dual Lipschitz norm from OT and the G-norm introduced by Yves Meyer. We design a numerical method based on the Primal-Dual Hybrid Gradient (PDHG) algorithm

Tuhin G. M. Al Mamun, Ehsanullah, Md. Sharif Hassan, Mohammad Bin Amin

Rising CO$_2$ emissions remain a critical global challenge, particularly in middle-income countries where economic growth drives environmental degradation. This study examines the long-run and short-run relationships between CO$_2$ emissions, energy use, GDP per capita, and population across 106 middle-income countries from 1980 to 2023. Using a Panel Vector

Yanhao Wu, Haoyang Zhang, Tianwei Lin, Lichao Huang

Generative models in Autonomous Driving (AD) enable diverse scene creation, yet existing methods fall short by only capturing a limited range of modalities, restricting the capability of generating controllable scenes for comprehensive evaluation of AD systems. In this paper, we introduce a multimodal generation framework that incorporates four major data mo

Zihan Cao, Yu Zhong, Ziqi Wang, Liang-Jian Deng

Image fusion, a fundamental low-level vision task, aims to integrate multiple image sequences into a single output while preserving as much information as possible from the input. However, existing methods face several significant limitations: 1) requiring task- or dataset-specific models; 2) neglecting real-world image degradations (\textit{e.g.}, noise), w

Yifan Wang, Ivan Molodetskikh, Ondrej Texler, Dimitar Dinev

As the digital and physical worlds become more intertwined, there has been a lot of interest in digital avatars that closely resemble their real-world counterparts. Current digitization methods used in 3D production pipelines require costly capture setups, making them impractical for mass usage among common consumers. Recent academic literature has found suc

Sung-Soo Byun, Kohei Noda

We investigate the real eigenvalues of asymmetric Wishart matrices of size $N$, indexed by the rectangular parameter $\nu \in \mathbb{N}$ and the non-Hermiticity parameter $\tau \in [0,1]$. The rectangular parameter $\nu$ is either fixed or proportional to $N$. The non-Hermiticity parameter $\tau$ is either fixed or $\tau = 1 - O(1/N)$, corresponding to the

Qihui Zhang, Munan Ning, Zheyuan Liu, Yanbo Wang

Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations of these models. Existing evaluation methods face limitations due to the significant human workload required to design Q&A pairs for visual images, which inherently restricts the

Andrei Voronin

Sharp bounds on partially identified parameters are often given by the values of linear programs (LPs). This paper introduces a novel estimator of the LP value. Unlike existing procedures, our estimator is root-n-consistent, pointwise in the probability measure, whenever the population LP is feasible and finite. Our estimator is valid under point-identificat

Tengjin Weng, Jingyi Wang, Wenhao Jiang, Zhong Ming

Can Multimodal Large Language Models (MLLMs) develop an intuitive number sense similar to humans? Targeting this problem, we introduce Visual Number Benchmark (VisNumBench) to evaluate the number sense abilities of MLLMs across a wide range of visual numerical tasks. VisNumBench consists of about 1,900 multiple-choice question-answer pairs derived from both

Zhong Ji, Ci Liu, Jingren Liu, Chen Tang

Few-Shot Remote Sensing Scene Classification (FS-RSSC) presents the challenge of classifying remote sensing images with limited labeled samples. Existing methods typically emphasize single-modal feature learning, neglecting the potential benefits of optimizing multi-modal representations. To address this limitation, we propose a novel Optimal Transport Adapt

Alexander I. Zenchuk, Wentao Qi, Junde Wu

We propose the variational quantum singular value decomposition based on encoding the elements of the considered { $N\times N$} matrix into the state of a quantum system of appropriate dimension. This method doesn't use the expansion of this matrix in terms of the unitary matrices. Controlled measurement is involved to avoid small success probability in anci

Nicola Bena, Claudia Diamantini, Michela Natilli, Luigi Romano

Proceedings of the 3rd Italian Conference on Big Data and Data Science (ITADATA2024), held in Pisa, Italy, September 17-19, 2024. The Italian Conference on Big Data and Data Science (ITADATA2024) is the annual event supported by the CINI Big Data National Laboratory and ISTI CNR that aims to put together Italian researchers and professionals from academia, i

Yifan Zhang, Chen Huang, Zachary Karas, Dung Thuy Nguyen

Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost of collecting eye-tracking data, there has also been limited progress in systematically using these signals for code LLM training. To address both issues, we propose a cohesive pipe

Chongjun Tu, Lin Zhang, Pengtao Chen, Peng Ye

Multimodal Large Language Models (MLLMs) have shown remarkable capabilities in video content understanding but still struggle with fine-grained motion comprehension. To comprehensively assess the motion understanding ability of existing MLLMs, we introduce FAVOR-Bench, comprising 1,776 videos with structured manual annotations of various motions. Our benchma

José Alejandro Butanda Mejía, Daniel Castañon Quiroz, Raffaele Folino, Luis Fernando Lopez Ríos

The goal of this paper is to describe the metastable dynamics of the solutions to the reaction-diffusion equation with nonlinear phase-dependent diffusion $u_t=\varepsilon^2(D(u)u_x)_x-f(u)$, where $D$ is a strictly positive function and $f$ is a bistable reaction term. We derive a system of ordinary differential equations describing the slow evolution of th

Yi Luo, Hamed Hooshangnejad, Xue Feng, Gaofeng Huang

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence(AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potenti

Ziyao Wang, Yexiao He, Zheyu Shen, Yu Li

In recent years, Large Language Models (LLMs) have demonstrated remarkable abilities in various natural language processing tasks. However, adapting these models to specialized domains using private datasets stored on resource-constrained edge devices, such as smartphones and personal computers, remains challenging due to significant privacy concerns and lim

Ehud Nahum, Yael Edan, Tal Oron-Gilad

Deploying robots in human environments requires effective social robot navigation. This article focuses on proxemics, proposing a new taxonomy and suggesting future directions through an analysis of state-of-the-art studies and the identification of research gaps. The various factors that affect the dynamic properties of proxemics patterns in human-robot int

Giuseppe Dattoli, Subuhi Khan, Ujair Ahmad

The operational calculus associated with Hermite numbers has been shown to be an effective tool for simplifying the study of special functions. Within this context, Hermite polynomials have been viewed as Newton binomials, with the consequent possibility of establishing previously unknown properties. In this article, this method is extended to study the lacu

Shijing Chen, Shoaib Jameel, Mohamed Reda Bouadjenek, Feilong Tang

Traditional Multi-level Hierarchical Classification (MLHC) classifiers often rely on backbone models with $n$ independent output layers. This structure tends to overlook the hierarchical relationships between classes, leading to inconsistent predictions that violate the underlying taxonomy. Additionally, once a backbone architecture for an MLHC classifier is

Yufan Sheng, Xin Cao, Kaiqi Zhao, Yixiang Fang

Cardinality estimation is a fundamental functionality in database systems. Most existing cardinality estimators focus on handling predicates over numeric or categorical data. They have largely omitted an important data type, set-valued data, which frequently occur in contemporary applications such as information retrieval and recommender systems. The few exi

Jiaxin Ye, Hongming Shan

Vision-guided speech generation aims to produce authentic speech from facial appearance or lip motions without relying on auditory signals, offering significant potential for applications such as dubbing in filmmaking and assisting individuals with aphonia. Despite recent progress, existing methods struggle to achieve unified cross-modal alignment across sem

Yidan Wu, Yu Yu, Jianan Zhang, Li Jin

We consider the traffic control problem of dynamic routing over parallel servers, which arises in a variety of engineering systems such as transportation and data transmission. We propose a semi-gradient, on-policy algorithm that learns an approximate optimal routing policy. The algorithm uses generic basis functions with flexible weights to approximate the

Minkyoo Song, Eugene Jang, Jaehan Kim, Seungwon Shin

In light of rising drug-related concerns and the increasing role of social media, sales and discussions of illicit drugs have become commonplace online. Social media platforms hosting user-generated content must therefore perform content moderation, which is a difficult task due to the vast amount of jargon used in drug discussions. Previous works on drug ja

Haoyu Lei, Shizhan Gong, Qi Dou, Farzan Farnia

Federated learning (FL) algorithms commonly aim to maximize clients' accuracy by training a model on their collective data. However, in several FL applications, the model's decisions should meet a group fairness constraint to be independent of sensitive attributes such as gender or race. While such group fairness constraints can be incorporated into the obje

Shanto Rahman, Sachit Kuhar, Berk Cirisci, Pranav Garg

Software updates, including bug repair and feature additions, are frequent in modern applications but they often leave test suites outdated, resulting in undetected bugs and increased chances of system failures. A recent study by Meta revealed that 14%-22% of software failures stem from outdated tests that fail to reflect changes in the codebase. This highli

Donghuo Zeng, Roberto Legaspi, Yuewen Sun, Xinshuai Dong

Tailoring persuasive conversations to users leads to more effective persuasion. However, existing dialogue systems often struggle to adapt to dynamically evolving user states. This paper presents a novel method that leverages causal discovery and counterfactual reasoning for optimizing system persuasion capability and outcomes. We employ the Greedy Relaxatio

Changxin Dong, Samya Sen, Zhennan Ru, Athena Kolli

The 2025 Los Angeles wildfires caused widespread urban destruction and displacement, and severe economic losses, highlighting the urgent need for better fire retardants. Current fire suppression strategies rely heavily on water, chemical fire retardants, and water-enhancing gels, which use superabsorbent polymers to retain water and adhere to substrates, off

Jiazhu Dai, Haoyu Sun

Graph Convolutional Networks (GCNs) have shown excellent performance in graph-structured tasks such as node classification and graph classification. However, recent research has shown that GCNs are vulnerable to a new type of threat called the backdoor attack, where the adversary can inject a hidden backdoor into the GCNs so that the backdoored model perform

Qiliang Luo, Vladimir Marković

We prove that affine maps are uniquely extremal quasiconformal maps on the complement of a well distribute set in the complex plane answering a conjecture from \cite{markovic}. We construct the required Reich sequence using Bergman projections, and meromorphic partitions of unity.

Hiroo Azuma

We study implementing a heralded single-photon source with second-order nonlinear photonic crystals. Injecting pump and signal light beams into a one-dimensional photonic crystal composed of a material with a large second-order nonlinear optical susceptibility $\chi^{(2)}$, we can transform the coherent incident signal light into squeezed light. Preparing tw

Junyu Shi, Lijiang Liu, Yong Sun, Zhiyuan Zhang

Scaling up motion datasets is crucial to enhance motion generation capabilities. However, training on large-scale multi-source datasets introduces data heterogeneity challenges due to variations in motion content. To address this, we propose Generative Pretrained Multi-path Motion Model (GenM\(^3\)), a comprehensive framework designed to learn unified motion

Matija Bucić, Vanshika Jain, Varun Sivashankar

In their famous 1974 paper introducing the local lemma, Erd\H{o}s and Lov\'asz posed a question-later referred by Erd\H{o}s as one of his three favorite open problems: What is the minimum number of edges in an $r$-uniform, intersecting hypergraph with cover number $r$? This question was solved up to a constant factor in Kahn's remarkable 1994 paper. More rec

Jiazheng Li, Lu Yu, Qing Cui, Zhiqiang Zhang

High-quality data plays a critical role in the pretraining and fine-tuning of large language models (LLMs), even determining their performance ceiling to some degree. Consequently, numerous data selection methods have been proposed to identify subsets of data that can effectively and efficiently enhance model performance. However, most of these methods focus

Teruhiro Ikeuchi, Takashi Mori

It is a central problem in various fields of physics to elucidate the behavior of quantum many-body systems subjected to bulk dissipation. In this context, several microscopic derivations of the Lindblad quantum master equation for many-body systems have been proposed so far. Among them, the universal Lindblad equation derived by Nathan and Rudner is fascina

Samya Sen, Changxin Dong, Carolyn K. Jons, Wencke Reineking

Hydrogels are crosslinked polymer networks with high water content, widely employed in biomedical applications such as drug delivery, tissue engineering, and regenerative medicine. Injectable, depot-forming hydrogels enable sustained release of therapeutic agents by modulating macromolecular diffusion through dynamic polymer networks. However, achieving reli

Hongyu Liu, Catharine W. K. Lo, Shen Zhang

In this book, we present a curated collection of existing results on inverse problems for Mean Field Games (MFGs), a cutting-edge and rapidly evolving field of research. Our aim is to provide fresh insights, novel perspectives, and a comprehensive foundation for future investigations into this fascinating area. MFGs, a class of differential games involving a

Xiao Chen, Yixin Luo, Jingrun Chen

In this paper, we propose a hybrid method that combines finite element method (FEM) and physics-informed neural network (PINN) for solving linear elliptic problems. This method contains three steps: (1) train a PINN and obtain an approximate solution $u_{\theta}$; (2) enrich the finite element space with $u_{\theta}$; (3) obtain the final solution by FEM in

Gahye Lee, Hyejeong Yoon, Jungeon Kim, Seungyong Lee

This paper presents a novel framework for compactly representing a 3D indoor scene using a set of polycuboids through a deep learning-based fitting method. Indoor scenes mainly consist of man-made objects, such as furniture, which often exhibit rectilinear geometry. This property allows indoor scenes to be represented using combinations of polycuboids, provi

Siyuan Yan, Ming Hu, Yiwen Jiang, Xieji Li

The emergence of vision-language models has transformed medical AI, enabling unprecedented advances in diagnostic capability and clinical applications. However, progress in dermatology has lagged behind other medical domains due to the lack of standard image-text pairs. Existing dermatological datasets are limited in both scale and depth, offering only singl

Jingyi Liao, Xun Xu, Yongyi Su, Rong-Cheng Tu

Anomaly detection plays a crucial role in quality control for industrial applications. However, ensuring robustness under unseen domain shifts such as lighting variations or sensor drift remains a significant challenge. Existing methods attempt to address domain shifts by training generalizable models but often rely on prior knowledge of target distributions

Arunn Suntharalingam, Lucas Fernández-Alcázar, Pablo Fabián Wagner-Boián, Mattis Reisner

Sublinear resonant deviations from an exceptional point degeneracy (EPD) has been recently promoted as a sensing scheme. However, there is still an ongoing debate whether the sensitivity advantage is negated by an increase in fundamental noise - especially when active elements induce self-oscillations. In this case, nonlinearities are crucial in stabilizing

Haoyu Chen, Xiaojie Xu, Wenbo Li, Jingjing Ren

Poster design is a critical medium for visual communication. Prior work has explored automatic poster design using deep learning techniques, but these approaches lack text accuracy, user customization, and aesthetic appeal, limiting their applicability in artistic domains such as movies and exhibitions, where both clear content delivery and visual impact are

T. A. Rector, L. Prato, R. Kerr, E. Papraniku

We report the results of a spatially compete, high-sensitivity survey for Herbig-Haro (HH) outflows in the Western and Eastern Circinus molecular clouds. We have detected 28 new HH objects in Circinus West, doubling the number known in this dark nebula. We have also discovered 9 outflows in Circinus East, the first to be identified here. Although both Circin

Yaofei Duan, Tao Tan, Zhiyuan Zhu, Yuhao Huang

Fetal ultrasound (US) examinations require the acquisition of multiple planes, each providing unique diagnostic information to evaluate fetal development and screening for congenital anomalies. However, obtaining a comprehensive, multi-plane annotated fetal US dataset remains challenging, particularly for rare or complex anomalies owing to their low incidenc

Siwei Wen, Junyan Ye, Peilin Feng, Hengrui Kang

With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challenges for authenticity assessment and detection. Despite the effectiveness of existing methods in evaluating image authenticity and locating forgeries, these approaches often lack h

John Joseph Carrasco, Renata Kallosh, Andrei Linde, Diederik Roest

The $SL(2,\mathbb{Z})$ invariant $\alpha$-attractor models have plateau potentials with respect to the inflaton and axion fields. The potential in the axion direction is almost exactly flat during inflation, hence, the axion field remains nearly massless. In this paper, we develop a generalized class of such models, where the $SL(2,\mathbb{Z})$ symmetry is p

Yoshinori Shiihara, Takuya Iwashita, Nozomu Adachi, Yoshikazu Todaka

Elucidating mechanical deformation in glassy materials at the atomic level is challenging due to their disordered atomic structure. Using our novel "frozen atom analysis," we reveal that anelastic deformation in CuZr metallic glasses is fundamentally driven by cooperative atomic motions of approximately 40 elastically linked atoms, forming trigger groups. Th

Kiyoharu Kawana

We study phases and propagation of closed $p$-brane within the framework of effective field theory with higher-form global symmetries, i.e., {\it brane-field theory}. We extend our previous studies by including the kinetic term of the center-of-mass motion as well as the kinetic term for the relative motions constructed by the area derivatives. This inclusio

Hussein Naeem Hasan

Communication is essential feature in human communities. For some reasons, deaf-mute disabled people lose their ability to hear, speak, or both which makes them suffer to communicate and convey their ideas, especially with normal people. Sign language is the solution for communication in the deaf-mute societies, but it is difficult for the rest of people to

Estrid He, Tabinda Sarwar, Ibrahim Khalil, Xun Yi

The past a few years have witnessed the great success of large language models, demonstrating powerful capabilities in comprehending textual data and generating human-like languages. Large language models achieve success by being trained on vast amounts of textual data, including online sources with copyrighted content and user-generated knowledge. However,

Jia Li, Xinyu Zhang, Wei Wu

Understanding the loss of non-equilibrium capacitance in the electric double layer capacitor (EDLC) during continuous charging compared to equilibrium capacitance is crucial for the design of high-rate energy storage devices. Due to three major challenges, including strong ion correlations induced by high concentrations at the interface, the difficulty in re

Takeru Goto, Kosuke Toda, Takayasu Kumano

This study presents a robust optimization algorithm for automated highway merge. The merging scenario is one of the challenging scenes in automated driving, because it requires adjusting ego vehicle's speed to match other vehicles before reaching the end point. Then, we model the speed planning problem as a deterministic Markov decision process. The proposed

Shivanshu Tripathi, Abed AlRahman Al Makdah, Fabio Pasqualetti

In this paper, we study optimization problems where the cost function contains time-varying parameters that are unmeasurable and evolve according to linear, yet unknown, dynamics. We propose a solution that leverages control theoretic tools to identify the dynamics of the parameters, predict their evolution, and ultimately compute a solution to the optimizat

Vaibhav Rathore, Shubhranil B, Saikat Dutta, Sarthak Mehrotra

Generalized Class Discovery (GCD) clusters base and novel classes in a target domain using supervision from a source domain with only base classes. Current methods often falter with distribution shifts and typically require access to target data during training, which can sometimes be impractical. To address this issue, we introduce the novel paradigm of Dom

Bumhoo Lim, Masateru Ishiguro, Jun Takahashi, Hiroshi Akitakya

We conducted contemporaneous optical and near-infrared polarimetric and spectroscopic observations of C/2023 A3 (Tsuchinshan-ATLAS, hereafter T-A) from 2024 October 16 to December 17, covering a wide range of phase angles (20-123 deg) and wavelength (0.5-2.3 um). We paid special attention to gas contamination in the dust polarization using these data. As a r

Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan

Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model's over-susceptibility to specific image frequency features in detec

Yuya Maeda, Yasunari Suzuki, Toshiki Kobayashi, Takashi Yamamoto

Sharing logical entangled pairs between distant quantum nodes is a key process to achieve fault tolerant quantum computation and communication. However, there is a gap between current experimental specifications and theoretical requirements for sharing logical entangled states while improving experimental techniques. Here, we propose an efficient logical ent

Zachary Englhardt, Felix Hähnlein, Yuxuan Mei, Tong Lin

Life cycle assessment (LCA) is a methodology for holistically measuring the environmental impact of a product from initial manufacturing to end-of-life disposal. However, the extent to which LCA informs the design of computing devices remains unclear. To understand how this information is collected and applied, we interviewed 17 industry professionals with e

He Huang, Yong Chen, Yujun Guo, Wei He

Hyperspectral image (HSI) fusion is an efficient technique that combines low-resolution HSI (LR-HSI) and high-resolution multispectral images (HR-MSI) to generate high-resolution HSI (HR-HSI). Existing supervised learning methods (SLMs) can yield promising results when test data degradation matches the training ones, but they face challenges in generalizing

Honglin Lin, Zhuoshi Pan, Yu Li, Qizhi Pei

Large Language Models (LLMs) have demonstrated promising capabilities in solving mathematical reasoning tasks, leveraging Chain-of-Thought (CoT) data as a vital component in guiding answer generation. Current paradigms typically generate CoT and answers directly for a given problem, diverging from human problem-solving strategies to some extent. Humans often

Zhengze Xin

We study perverse-Hodge complexes for Lagrangian fibrations on holomorphic symplectic varieties. We prove the symplectic Hard Lefschetz type theorem and the symmetry of perverse-Hodge complexes when the symplectic variety admits symplectic resolutions, therefore generalize the previous result by Schnell in the smooth case verifying a conjecture by Shen-Yin.

Kenjiro Ishizuka

We consider the damped nonlinear Klein-Gordon equation: \begin{align*} \partial_{t}^2u-\Delta u+2\alpha \partial_{t}u+u-|u|^{p-1}u=0, \ & (t,x) \in \mathbb{R} \times \mathbb{R}^d, \end{align*} where $\alpha>0$, $1\leq d\leq 5$ and energy sub-critical exponents $p>2$. In this paper, we prove that 3-solitary waves behave as if the three solitons are on a line.

Shijun Zheng

We give an exposition on the $L^2$ theory of the perturbed Fourier transform associated with a Schr\"odinger operator $H=-d^2/dx^2 +V$ on the real line, where $V$ is a real-valued \mbox{finite} measure. In the case $V\in L^1\cap L^2$, we explicitly define the perturbed Fourier transform $\mathcal{F}$ for $H$ and obtain an eigenfunction expansion theorem for

Hang Li, Xiao Wang, Bevan Koopman, Guido Zuccon

Scaling dense retrievers to larger large language model (LLM) backbones has been a dominant strategy for improving their retrieval effectiveness. However, this has substantial cost implications: larger backbones require more expensive hardware (e.g. GPUs with more memory) and lead to higher indexing and querying costs (latency, energy consumption). In this p

Shangkun Weng

The existence and stability of a spherical transonic shock in a hemispherical shell under the three dimensional perturbations of the incoming flows and the exit pressure is established without any further restrictions on the background transonic shock solutions. The perturbed transonic shock are completely free and its strength and position are uniquely dete