March 2025 arXiv papers — page 173

Dohyun Ahn, Taeho Kim

Given a finite collection of stochastic alternatives, we study the problem of sequentially allocating a fixed sampling budget to identify the optimal alternative with a high probability, where the optimal alternative is defined as the one with the smallest value of extreme tail risk. We particularly consider a situation where these alternatives generate heav

Zeinab Ebrahimi, Mohammad Deghat

Distributed optimization aims to leverage the local computation and communication capabilities of each agent to achieve a desired global objective. This paper addresses the distributed pose graph optimization (PGO) problem under non-convex constraints, with the goal of approximating the rotation and translation of each pose given relevant noisy measurements.

J. D. Zamfirescu-Pereira, Eunice Jun, Michael Terry, Qian Yang

In this work, we explore explicit Large Language Model (LLM)-powered support for the iterative design of computer programs. Program design, like other design activity, is characterized by navigating a space of alternative problem formulations and associated solutions in an iterative fashion. LLMs are potentially powerful tools in helping this exploration; ho

Bruno Klajn, Hrvoje Nikolić

Hidden momentum is a puzzling phenomenon associated with magnetic dipoles and other extended relativistic systems. We point out that the origin of hidden momentum lies in the effective change of individual particle masses of a composite body, during which the total momentum of the system is not equal to the momentum of the center of mass. Defining the hidden

Ashutosh Shukla, Erhan Kutanoglu, John Hasenbein

We present and analyze a three-stage stochastic optimization model that integrates output from a geoscience-based flood model with a power flow model for transmission grid resilience planning against flooding. The proposed model coordinates the decisions made across multiple stages of resilience planning and recommends an optimal allocation of the overall re

Lixia Wang, Ke Ye

This paper concentrates on positive definite functions on finite abelian groups, which are central to harmonic analysis and related fields. By leveraging the group structure and employing Fourier analysis, we establish a lower bound for the second largest value of positive definite functions. For illustrative purposes, we present three applications of our lo

Yajie Wen, Defu Zhang

This paper introduces BSPA, a parallel algorithm that leverages beam search to address the two-dimensional strip packing problem. The study begins with a comprehensive review of existing approaches and methodologies, followed by a detailed presentation of the BSPA algorithm. Experimental results demonstrate the effectiveness of the proposed method. To facili

Ruoying He, Tianning Wu, Shun Mao, Haibo Zong

A 30-year high-resolution Northwestern Atlantic Ocean Reanalysis (NAOR) is presented. NAOR spans from January 1993 to December 2022 with a 4 km horizontal resolution and 50 vertical layers. It provides enhanced resolution and expands the spatial and temporal coverage of existing ocean reanalysis in the region. NAOR was conducted using the Regional Ocean Mode

Mingyu Quan, Kun Xu

This paper presents a comprehensive numerical framework for simulating radiation-plasma systems. The radiative transfer process spans multiple flow regimes due to varying fluid opacity across different regions, necessitating a robust numerical approach. We employ the multiscale unified gas-kinetic scheme (UGKS), which accurately captures photon transport phe

Liming Sun, Juncheng Wei, Wen Yang

In 2023, H.\,Brezis published a list of his ``favorite open problems", which he described as challenges he had ``raised throughout his career and has resisted so far". We provide a complete resolution to the first one--Open Problem 1.1--in Brezis's favorite open problems list: the existence of solutions to the long-standing Brezis-Nirenberg problem on a thre

Hanqing Liu, Shouwei Ruan, Yao Huang, Shiji Zhao

Vision-Language Models (VLMs) have achieved remarkable success in various tasks, yet their robustness to real-world illumination variations remains largely unexplored. To bridge this gap, we propose \textbf{I}llumination \textbf{T}ransformation \textbf{A}ttack (\textbf{ITA}), the first framework to systematically assess VLMs' robustness against illumination

Zhiming Yao, Haoyang Li, Jing Zhang, Cuiping Li

Query optimization is a critical task in database systems, focused on determining the most efficient way to execute a query from an enormous set of possible strategies. Traditional approaches rely on heuristic search methods and cost predictions, but these often struggle with the complexity of the search space and inaccuracies in performance estimation, lead

Chikai Shang, Mengke Li, Yiqun Zhang, Zhen Chen

Visual prompt tuning (VPT), i.e., fine-tuning some lightweight prompt tokens, provides an efficient and effective approach for adapting pre-trained models to various downstream tasks. However, most prior art indiscriminately uses a fixed prompt distribution across different tasks, neglecting the importance of each block varying depending on the task. In this

Xiaoliang Ju, Hongsheng Li

We present DirectTriGS, a novel framework designed for 3D object generation with Gaussian Splatting (GS). GS-based rendering for 3D content has gained considerable attention recently. However, there has been limited exploration in directly generating 3D Gaussians compared to traditional generative modeling approaches. The main challenge lies in the complex d

Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang

Video-based dialogue systems, such as education assistants, have compelling application value, thereby garnering growing interest. However, the current video-based dialogue systems are limited by their reliance on a single dialogue type, which hinders their versatility in practical applications across a range of scenarios, including question-answering, emoti

S M A Sharif, Abdur Rehman, Zain Ul Abidin, Fayaz Ali Dharejo

Single-shot low-light image enhancement (SLLIE) remains challenging due to the limited availability of diverse, real-world paired datasets. To bridge this gap, we introduce the Low-Light Smartphone Dataset (LSD), a large-scale, high-resolution (4K+) dataset collected in the wild across a wide range of challenging lighting conditions (0.1 to 200 lux). LSD con

Xingzu Zhan, Chen Xie, Honghang Chen, Haoran Sun

Text-to-motion generation sits at the intersection of multimodal learning and computer graphics and is gaining momentum because it can simplify content creation for games, animation, robotics and virtual reality. Most current methods stack spatial and temporal features in a straightforward way, which adds redundancy and still misses subtle joint-level cues.

Xin Liu, Jie Liu, Jie Tang, Gangshan Wu

Transformer-based methods have demonstrated impressive performance in low-level visual tasks such as Image Super-Resolution (SR). However, its computational complexity grows quadratically with the spatial resolution. A series of works attempt to alleviate this problem by dividing Low-Resolution images into local windows, axial stripes, or dilated windows. SR

Yuan-Xing Gao

The photon sphere defines the unstable circular orbit of photons in a black hole spacetime. Photons emitted by a source located inside the photon sphere can be gravitationally lensed by the black hole and have time delays when reaching the observer. These delays may lead to light echoes produced in the light curve if an accretion event in the vicinity of the

Xiaoqian Hu

In Recent Years, Digital Technologies Have Made Significant Strides In Augmenting-Human-Health, Cognition, And Perception, Particularly Within The Field Of Computational-Pathology. This Paper Presents A Novel Approach To Enhancing The Analysis Of Histopathology Images By Leveraging A Mult-modal-Model That Combines Vision Transformers (Vit) With Gpt-2 For Ima

Zhenghai Xue, Lang Feng, Jiacheng Xu, Kang Kang

To learn from data collected in diverse dynamics, Imitation from Observation (IfO) methods leverage expert state trajectories based on the premise that recovering expert state distributions in other dynamics facilitates policy learning in the current one. However, Imitation Learning inherently imposes a performance upper bound of learned policies. Additional

Ike Obi, Vishnunandan L. N. Venkatesh, Weizheng Wang, Ruiqi Wang

Robotics researchers increasingly leverage large language models (LLM) in robotics systems, using them as interfaces to receive task commands, generate task plans, form team coalitions, and allocate tasks among multi-robot and human agents. However, despite their benefits, the growing adoption of LLM in robotics has raised several safety concerns, particular

Xupeng Xie, Ruoyu Geng, Jun Ma, Boyu Zhou

Existing LiDAR-Inertial Odometry (LIO) systems typically use sensor-specific or environment-dependent measurement covariances during state estimation, leading to laborious parameter tuning and suboptimal performance in challenging conditions (e.g., sensor degeneracy and noisy observations). Therefore, we propose an Adaptive Kalman Filter (AKF) framework that

Xiaowei Li, Kuan Xu, Fen Liu, Ruofei Bai

Traditional unmanned aerial vehicle (UAV) swarm missions rely heavily on expensive custom-made drones with onboard perception or external positioning systems, limiting their widespread adoption in research and education. To address this issue, we propose AirSwarm. AirSwarm democratizes multi-drone coordination using low-cost commercially available drones suc

Tianchen Gao, Jingyuan Liu, Rui Pan, Ao Sun

Community detection, which focuses on recovering the group structure within networks, is a crucial and fundamental task in network analysis. However, the detection process can be quite challenging and unstable when community signals are weak. Motivated by a newly collected large-scale academic network dataset from the Web of Science, which includes multi-lay

Yifan Chen, Hongjun An, Zhe Sun, Tong Tian

Ghost imaging (GI) achieves 2D image reconstruction through high-order correlation of 1D bucket signals and 2D light field information, particularly demonstrating enhanced detection sensitivity and high-quality image reconstruction via efficient photon collection in scattering media. Recent investigations have established that deep learning (DL) can substant

Dan Sun, Jacky He, Hanlu Zhang, Zhen Qi

This paper proposes a medical text summarization method based on LongFormer, aimed at addressing the challenges faced by existing models when processing long medical texts. Traditional summarization methods are often limited by short-term memory, leading to information loss or reduced summary quality in long texts. LongFormer, by introducing long-range self-

Nasla Saleem, Talukder Zaki Jubery, Aditya Balu, Yan Zhou

High-density planting is a widely adopted strategy to enhance maize productivity, yet it introduces challenges such as increased interplant competition and shading, which can limit light capture and overall yield potential. In response, some maize plants naturally reorient their canopies to optimize light capture, a process known as canopy reorientation. Und

Chenghan Li, Mingchen Li, Ruisheng Diao

With the recent development and advancement of Transformer and MLP architectures, significant strides have been made in time series analysis. Conversely, the performance of Convolutional Neural Networks (CNNs) in time series analysis has fallen short of expectations, diminishing their potential for future applications. Our research aims to enhance the repres

Sotirios Karamitsos

The Palatini formulation has been successful in the development of several alternative theories of gravity. It is well understood that the Palatini and metric formulations are equivalent in minimally coupled scalar-tensor models, but nonminimal scalar-tensor models can lead to physically distinct theories depending on the underlying formulation. Once a model

Yan Yang, Dongxu Li, Haoning Wu, Bei Chen

Solving expert-level multimodal tasks is a key milestone towards general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to improve, evaluation of such advanced multimodal intelligence becomes necessary yet challenging. In this work, we introduce ProBench, a benchmark of open-ended user queries that require professional

Xuyang Guo, Jiayan Huo, Yingyu Liang, Zhenmei Shi

Generative modeling is widely regarded as one of the most essential problems in today's AI community, with text-to-image generation having gained unprecedented real-world impacts. Among various approaches, diffusion models have achieved remarkable success and have become the de facto solution for text-to-image generation. However, despite their impressive pe

Zhiyuan Xi, Kun Zhu, Yuanyuan Xu

Semantic communication has emerged as a transformative paradigm in next-generation communication systems, leveraging advanced artificial intelligence (AI) models to extract and transmit semantic representations for efficient information exchange. Nevertheless, the presence of unpredictable semantic noise, such as ambiguity and distortions in transmitted repr

Tingyang Chen, Cong Fu, Kun Wang, Xiangyu Ke

Maximum Inner Product Search (MIPS) for high-dimensional vectors is pivotal across databases, information retrieval, and artificial intelligence. Existing methods either reduce MIPS to Nearest Neighbor Search (NNS) while suffering from harmful vector space transformations, or attempt to tackle MIPS directly but struggle to mitigate redundant computations due

Mengting Ai, Tianxin Wei, Yifan Chen, Zhichen Zeng

Mixture-of-Experts (MoE) Transformer, the backbone architecture of multiple phenomenal language models, leverages sparsity by activating only a fraction of model parameters for each input token. The sparse structure, while allowing constant time costs, results in space inefficiency: we still need to load all the model parameters during inference. We introduc

Tianyi Zhou

In this paper we survey known results of characterizations of reflexive Banach spaces, which are based on convergence of usual and generalized arithmetic mean (or Ces\`aro sum), weakly compact subsets, affine sets in a Banach space or its dual and an unbounded bi-orthogonal system generalized from the one in a finite-dimensional Banach space. We also include

Vikranth Udandarao, Noel Abraham Tiju, Muthuraj Vairamuthu, Harsh Mistry

In this paper, we present Roamify, an Artificial Intelligence powered travel assistant that aims to ease the process of travel planning. We have tested and used multiple Large Language Models like Llama and T5 to generate personalised itineraries per user preferences. Results from user surveys highlight the preference for AI powered mediums over existing met

Ding Lin, Han Guo, Jianhui Wang, Meng Yue

With the increasing penetration of renewable energy sources, growing demand variability, and evolving grid control strategies, accurate and efficient load modeling has become a critical yet challenging task. Traditional methods, such as fixed-form parametric models and data-driven approaches, often struggle to balance accuracy, computational efficiency, and

Xiaoyou Chen, Mark L. Lewis

Let $G$ be a finite group and $p$ be a prime. We prove that if $G$ has three codegrees, then $G$ is an $M$-group. We prove for some prime $p$ that if every irreducible Brauer character of $G$ is a prime, then for every normal subgroup $N$ of $G$ either $G/N$ or $N$ is an $M_p$-group.

Mehwish Nasim, Syed Muslim Gilani, Amin Qasmi, Usman Naseem

This paper introduces a simulator designed for opinion dynamics researchers to model competing influences within social networks in the presence of LLM-based agents. By integrating established opinion dynamics principles with state-of-the-art LLMs, this tool enables the study of influence propagation and counter-misinformation strategies. The simulator is pa

Shenglong Hu, Ke Ye

In applications, a substantial number of problems can be formulated as non-linear least squares problems over smooth varieties. Unlike the usual least squares problem over a Euclidean space, the non-linear least squares problem over a variety can be challenging to solve and analyze, even if the variety itself is simple. Geometrically, this problem is equival

Zi-Fan Zhu, Yu Su, Yao Wang, Rui-Xue Xu

Understanding the effects of vibrations in electron transfer (ET) dynamics and optical spectroscopies is essential to precisely interpret the role of decoherence, especially for systems embedded in solvents. In this work, we study the correlated Duschinsky and solvent effects on ET and spectroscopy. Exploited is a novel extended dissipaton-equation-of motion

Yang Ma, Shengqian Han, Chenyang Yang

This paper studies distributed resource block (RB) allocation in wideband orthogonal frequency-division multiplexing (OFDM) cell-free systems. We propose a novel distributed sequential algorithm and its two variants, which optimize RB allocation based on the information obtained through over-the-air (OTA) transmissions between access points (APs) and user eq

Houan Teng, Xin Liu, Nianjia Zhang, Haihao Fan

The creation and manipulation of photonic skyrmions provide a novel degree of freedom for light-matter interactions, optical communication and nanometrology. Since the localized vortex within skyrmions arises from the twist and curl of the phase structure, the orbital angular momentum of light is essential for their construction. While numerous skyrmionic te

Jingyi Zheng, Junfeng Wang, Zhen Sun, Wenhan Dong

As Large Language Models (LLMs) advance, Machine-Generated Texts (MGTs) have become increasingly fluent, high-quality, and informative. Existing wide-range MGT detectors are designed to identify MGTs to prevent the spread of plagiarism and misinformation. However, adversaries attempt to humanize MGTs to evade detection (named evading attacks), which requires

Ta Duc Huy, Sen Kim Tran, Phan Nguyen, Nguyen Hoang Tran

The ability to interpret and intervene model decisions is important for the adoption of computer-aided diagnosis methods in clinical workflows. Recent concept-based methods link the model predictions with interpretable concepts and modify their activation scores to interact with the model. However, these concepts are at the image level, which hinders the mod

Holly G. Stemp, Mark R. van Blankenstein, Serwan Asaad, Mateusz T. Mądzik

The use of nuclear spins for quantum computation is limited by the difficulty in creating genuine quantum entanglement between distant nuclei. Current demonstrations of nuclear entanglement in semiconductors rely upon coupling the nuclei to a common electron, which is not a scalable strategy. Here we demonstrate a two-qubit Control-Z logic operation between

Wenqi Li, Zhenyi Tang, Pengyi Zhang, Jun Wang

The development of digital humanities necessitates scholars to adopt more data-intensive methods and engage in multidisciplinary collaborations. Understanding their collaborative data behaviors becomes essential for providing more curated data, tailored tools, and a collaborative research environment. This study explores how interdisciplinary researchers col

Kyle Broder, Jan Nienhaus, Peter Petersen, James Stanfield

It is shown that a compact $n$-dimensional K\"ahler manifold with $\frac{n}{2}$-positive Calabi curvature operator has the rational cohomology of complex projective space. For even $n,$ this is sharp in the sense that the complex quadric with its symmetric metric has $\frac{n}{2}$-nonnegative Calabi curvature operator, yet $b_n =2.$ Furthermore, the compact

Kai Li, Zhao Ma, Liang Li, Shiyu Zhao

In this paper, we propose a framework, collective behavioral cloning (CBC), to learn the underlying interaction mechanism and control policy of a swarm system. Given the trajectory data of a swarm system, we propose a graph variational autoencoder (GVAE) to learn the local interaction graph. Based on the interaction graph and swarm trajectory, we use behavio

Junhao Zhang, Richong Zhang, Fanshuang Kong, Ziyang Miao

Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored. As input length increases, LLMs increasingly overlook information in the middle of the context--a limitation known as the "lost-in-the-middle" phenomenon--leading to inconsistent and incoherent outputs. To addre

Jianqi Zhang, Yuchan Liu, Zeen Song, Yuefei Li

Time series forecasting (TSF) is important in real-world applications. Recently, Transformer-based methods have achieved strong performance by modeling token dependencies through attention mechanisms. However, existing methods are usually trained mainly with prediction error losses, which may cause models to exploit both critical and redundant token dependen

Wanjing Huang, Tongjie Pan, Yalan Ye

Recent advancements in large language models (LLMs) have expanded their role in robotic task planning. However, while LLMs have been explored for generating feasible task sequences, their ability to ensure safe task execution remains underdeveloped. Existing methods struggle with structured risk perception, making them inadequate for safety-critical applicat

Yamile Godoy, Marcos Salvai

Given a quadratically convex compact connected oriented hypersurface $N$ of the complex hyperbolic plane, we prove that the characteristic rays of the symplectic form restricted to $N$ determine a double geodesic foliation of the exterior $U$ of $N$. This induces an outer billiard map $B$ on $U$. We prove that $B$ is a diffeomorphism (notice that weaker noti

Chenyin Gao, Xiang Zhang, Shu Yang

Externally controlled trials are crucial in clinical development when randomized controlled trials are unethical or impractical. These trials consist of a full treatment arm with the experimental treatment and a full external control arm. However, they present significant challenges in learning the treatment effect due to the lack of randomization and a para

Shufang Zhang, Tao Jiang, Jiazheng Wu, Ziyu Meng

3D point cloud mapping plays a essential role in localization and autonomous navigation. However, dynamic objects often leave residual traces during the map construction process, which undermine the performance of subsequent tasks. Therefore, dynamic object removal has become a critical challenge in point cloud based map construction within dynamic scenarios

Gunho Park, Hyeokjun Kwon, Jiwoo Kim, Jeongin Bae

Weight-only quantization has emerged as a promising solution to the deployment challenges of large language models (LLMs). However, it necessitates FP-INT operations, which make implementation on general-purpose hardware like GPUs difficult. In this paper, we propose FIGLUT, an efficient look-up table (LUT)-based GEMM accelerator architecture. Instead of per

Mengzhe Hei, Zhouran Zhang, Qingbao Liu, Yan Pan

Extracting high-quality structured information from scientific literature is crucial for advancing material design through data-driven methods. Despite the considerable research in natural language processing for dataset extraction, effective approaches for multi-tuple extraction in scientific literature remain scarce due to the complex interrelations of tup

Cagri Gungor, Derek Eppinger, Adriana Kovashka

Tactile sensing, which relies on direct physical contact, is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning. Because tactile data is often scarce and costly to acquire, generating synthetic tactile images provides a scalable solution to augment real-world measurements. However, ensuring robust g

Konstantinos D. Polyzos, Athanasios Bacharis, Saketh Madhuvarasu, Nikos Papanikolopoulos

Gaussian splatting (GS) along with its extensions and variants provides outstanding performance in real-time scene rendering while meeting reduced storage demands and computational efficiency. While the selection of 2D images capturing the scene of interest is crucial for the proper initialization and training of GS, hence markedly affecting the rendering pe

Subhendra Mohanty, Sukanta Panda, Archit Vidyarthi

We show that the Starobinsky inflation model based on $R^2$ gravity has a special feature that it provides a unique scalaron-two-graviton vertex with a coupling proportional to $1/M_P$. In this model stochastic gravitational waves are produced when the scalaron - which is the massive scalar mode of the metric - decays into gravitons during reheating. This de

Adrian Dumitrescu

Given a set of points in the plane, the \textsc{General Position Subset Selection} problem is that of finding a maximum-size subset of points in general position, i.e., with no three points collinear. The problem is known to be ${\rm NP}$-complete and ${\rm APX}$-hard, and the best approximation ratio known is $\Omega\left({\rm OPT}^{-1/2}\right) =\Omega(n^{

Steven Campbell, Georgy Gaitsgori, Richard Groenewald, Ioannis Karatzas

We study a sequential estimation problem for an unknown reward in the presence of a random horizon. The reward takes one of two predetermined values that can be inferred from the drift of a Wiener process, which serves as a signal. The objective is to use the information in the signal to estimate the reward which is made available until a stochastic deadline

Jonathan DeWitt, Dmitry Dolgopyat

We show that the generator of a conservative IID random system whose dynamics expands on average codimension $1$ planes has an essential spectral radius strictly smaller than $1$ on Sobolev spaces of small positive index index. Consequently, such a system has finitely many ergodic components. If there is only one component for each power of the random system

Ruy Coimbra Charão, Ryo Ikehata

We consider the system of elastic waves with critical space dependent damping $V(x)$. We study the Cauchy problem for this model in the $2$-dimensional Euclidean space ${\bf R}^{2}$, and we obtain faster decay rates of the total energy as time goes to infinity. In the $2$-D case we do not have any suitable Hardy type inequality, so generally one has no idea

Takaaki V. Joya, Takuto Kawakami, Mikito Koshino

We calculate the shift current response in twisted double bilayer graphenes (TDBG) by applying the perturbative approach to the effective continuum Hamiltonian. We have performed a systematic study of the shift current in AB-AB and AB-BA stacked TDBG, where we have investigated the dependence of the signal on the twist angle, the vertical bias voltage and th

Yihong Leng, Jiaojiao Li, Haitao Xu, Rui Song

Current hyperspectral image (HSI) reconstruction methods primarily rely on image-level approaches, which are time-consuming to form abundant high-quality HSIs through imagers. In contrast, spectrometers offer a more efficient alternative by capturing high-fidelity point spectra, enabling pixel-level HSI reconstruction that balances accuracy and label efficie

Masaki Tsukamoto

Rate distortion dimension describes the theoretical limit of lossy data compression methods as the distortion bound goes to zero. It was originally introduced in the context of information theory, and recently it was discovered that it has an intimate connection to Gromov's theory of mean dimension of dynamical systems. This paper studies the behavior of rat

Woochan Jung, Ethan G Arnault, Bevin Huang, Jinho Park

The thermal response of proximity Josephson junctions (JJs) is governed by the temperature ($T$)-dependent occupation of Andreev bound states (ABS), making them promising candidates for sensitive thermal detection. In this study, we systematically engineer ABS to enhance the thermal sensitivity of the critical current ($I_c$) of proximity JJs, quantified as

K. Abe, S. Abe, R. Akutsu, H. Alarakia-Charles

Since its first observation in the 1970s, neutrino-induced neutral-current single positive pion production (NC1$\pi^+$) has remained an elusive and poorly understood interaction channel. This process is a significant background in neutrino oscillation experiments and studying it further is critical for the physics program of next-generation accelerator-based

Zhenran Tang, Ruixuan Liu, Changliu Liu

Manipulation and insertion of small and tight-toleranced objects in robotic assembly remain a critical challenge for vision-based robotics systems due to the required precision and cluttered environment. Conventional global or wrist-mounted cameras often suffer from occlusions when either assembling or disassembling from an existing structure. To address the

Xiangyan Qu, Jing Yu, Jiamin Zhuang, Gaopeng Gou

Zero-shot learning (ZSL) aims to train a model on seen classes and recognize unseen classes by knowledge transfer through shared auxiliary information. Recent studies reveal that documents from encyclopedias provide helpful auxiliary information. However, existing methods align noisy documents, entangled in visual and non-visual descriptions, with image regi

Claire M. Miller, Michael J. Lydeamore, Lee Berger, Lee F. Skerratt

The fungal disease Chytridiomycosis poses a threat to frog populations worldwide. It has driven over 90 amphibian species to extinction and severely affected hundreds more. Difficulties in disease management have shown a need for novel conservation approaches. We present a novel mathematical model for chytridiomycosis transmission in frogs that includes the

Kexin Li, Hu Zheng, Kexin Huang, Yinying Chai

Biomedical research increasingly relies on heterogeneous, high-dimensional datasets, yet effective visualization remains hindered by fragmented code resources, steep programming barriers, and limited domain-specific guidance. Bizard is an open-source visualization code repository engineered to streamline data analysis in biomedical research. It aggregates a

Chaoran Xiong, Fangyu Jiang, Kehui Ma, Zhen Sun

Recently, multi-node inertial measurement unit (IMU)-based odometry for legged robots has gained attention due to its cost-effectiveness, power efficiency, and high accuracy. However, the spatial and temporal misalignment between foot-end motion derived from forward kinematics and foot IMU measurements can introduce inconsistent constraints, resulting in odo

K. Abe, S. Abe, R. Akutsu, H. Alarakia-Charles

This article presents a study of single $\pi^+$ production in neutrino neutral-current interactions (NC1$\pi^+$) using the FGD1 hydrocarbon target of the ND280 detector of the T2K experiment. We report the largest sample of such events selected by any experiment, providing the first new data for this channel in over four decades and the first using a sub-GeV

Jimmy Xuekai Li, Thomas Flottmann, Max Millen, Shuai Chen

This study presents a novel sonic mapping method applied to coal samples, verified by CT scan imaging. Cubic coal samples with side lengths of 50-70 mm were subjected to non-destructive sonic tests, measuring both P-wave (Vp) and S-wave (Vs) velocities. Each of the three orthogonal directions (X, Y, and Z) of the cube was divided into 9 regions, resulting in

Tianci Miao, Qihang Zheng, Yangyang Hu, Xiaoyu Cheng

As the technology node continues to shrink, nanosheet field effect transistors (NSFETs) and complementary FETs (CFETs) become valid candidates for the 3nm and sub-nanometre nodes. However, due to the shrinking device size, self-heating and inter-device thermal crosstalk of NSFETs and CFETs become more severe. It is important to accurately calculate the self-

Yu-Yuan Chen, Zhihui Peng, Yu-xi Liu

Surface acoustic wave (SAW) devices are key components of classical communication systems and recently studied for quantum information processing. We here propose and study a hybrid quantum system composed of skyrmion qubit and a SAW cavity, which supports a number of long-lived phononic modes. The results show that the system allows for strong coupling betw

Somayeh Hussaini, Tobias Fischer, Michael Milford

In visual place recognition (VPR), filtering and sequence-based matching approaches can improve performance by integrating temporal information across image sequences, especially in challenging conditions. While these methods are commonly applied, their effects on system behavior can be unpredictable and can actually make performance worse in certain situati

Zhuowen Zheng, Yain-Whar Si, Xiaochen Yuan, Junwei Duan

Nowadays, with the advancement of deep neural networks (DNNs) and the availability of large-scale datasets, the face recognition (FR) model has achieved exceptional performance. However, since the parameter magnitude of the fully connected (FC) layer directly depends on the number of identities in the dataset. If training the FR model on large-scale datasets

Yoshiko Hayashi

This paper investigates a robust empirical Bayes correction for Bayesian modeling. We show the application of the model on income distribution. Income shock includes temporal and permanent shocks. We aim to eliminate temporal shock and permanent shock using two-step local empirical correction method. Our results show that only 6.7% of the observed income sho

Mikiya Masuda

Motivated by a work of Fu-So-Song, we associate a symmetric matrix $A$ to a plane vector sequence $v$ and give a formula to find the signature of $A$ in terms of the sequence $v$. When $A$ is nonsingular, we interpret the relation between $A$ and $A^{-1}$ from a topological viewpoint. Finally, we associate an omnioriented quasitoric orbifold $X$ of real dime

Akito Sakai, Yosuke Matsumoto, Mingxuan Fu, Takachika Isomae

Multipolar moments entail a new route to tackle frontier problems in superconductivity (SC). A key progress in the search for multipolar SC is the discovery of Pr$Tr_2$Al$_{20}$ ($Tr =$ Ti, V), which possesses quadrupolar and octupolar but no magnetic dipolar moments. The Kondo entanglement of these multipolar moments with conduction electrons leads to exoti

Shintaro Takada, Giorgos Georgiou, Junliang Wang, Yuma Okazaki

Coherent manipulation of plasmon wavepackets in solid-state systems is crucial for advancing nanoscale electronic devices, offering a unique platform for quantum information processing based on propagating quantum bits. Controlling the eigenstate of plasmon wavepackets is essential, as it determines its propagation speed and hence the number of quantum opera

Dongfang Zhao

The Hausdorff distance is a fundamental measure for comparing sets of vectors, widely used in database theory and geometric algorithms. However, its exact computation is computationally expensive, often making it impractical for large-scale applications such as multi-vector databases. In this paper, we introduce an approximation framework that efficiently es

Ramon H- Ruiz-Medina, Victor M. Lara-Gómez

For a group $G$ acting over a set $X$, the set of all the $G$-equivariant functions, i.e., the set of functions which conmute with the action, ($g\cdot f(x)=g\cdot f(x), \forall g\in G, \forall x\in X$), is a monoid with the composition. The Green Relations are powerful tools to comprehend the structure of a semigroup. We study the case where $X$ is a finite

Sungsik Kim, Janghyun Baek, Jinkyu Kim, Jaekoo Lee

While Large Language Models (LLMs) have recently shown impressive results in reasoning tasks, their application to pedestrian trajectory prediction remains challenging due to two key limitations: insufficient use of visual information and the difficulty of predicting entire trajectories. To address these challenges, we propose Goal-driven and User-Informed D

Yilong Wang, Edward Johns

We introduce One-Shot Dual-Arm Imitation Learning (ODIL), which enables dual-arm robots to learn precise and coordinated everyday tasks from just a single demonstration of the task. ODIL uses a new three-stage visual servoing (3-VS) method for precise alignment between the end-effector and target object, after which replay of the demonstration trajectory is

Koji Imamura, Shinya Kawabuchi, Keisuke Shiromoto

In this paper we introduce a $q$-analogue of the single-element extensions of matroids for $q$-matroids, which we call one-dimensional extensions. To enumerate such extensions, we define a $q$-analogue of modular cuts and define a certain function which we call a modular cut selector. It assigns each newly appearing one-dimensional subspace to a modular cut.

Andrea Erdas

In this paper I study the Casimir effect caused by a charged and massive scalar field that breaks Lorentz invariance in a CPT-even, aether-like manner. The breaking of Lorentz invariance is implemented by a constant space-like vector directly coupled to higher order derivatives of the field. I take this vector to be space-like to avoid non-causality problems

Rajat Vashistha, Arif Gulzar, Parveen Kundu, Punit Sharma

India faces a significant cancer burden, with an incidence-to-mortality ratio indicating that nearly three out of five individuals diagnosed with cancer succumb to the disease. While the limitations of physical healthcare infrastructure are widely acknowledged as a primary challenge, concerted efforts by government and healthcare agencies are underway to mit

Somayeh Farahani, Marjaneh Hejazi, Antonio Di Ieva, Emad Fatemizadeh

Accurate, noninvasive glioma characterization is crucial for effective clinical management. Traditional methods, dependent on invasive tissue sampling, often fail to capture the spatial heterogeneity of the tumor. While deep learning has improved segmentation and molecular profiling, few approaches simultaneously integrate tumor morphology and molecular feat

S M A Sharif, Rizwan Ali Naqvi, Woong-Kee Loh

Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often generate visual artifacts when applied to heterogeneous medical images. This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (A

Michael Krivelevich, Rajko Nenadov

We study large minors in small-set expanders. More precisely, we consider graphs with $n$ vertices and the property that every set of size at most $\alpha n / t$ expands by a factor of $t$, for some (constant) $\alpha > 0$ and large $t = t(n)$. We obtain the following: * Improving results of Krivelevich and Sudakov, we show that a small-set expander contains

Krishan Mohan Nagpal

This paper describes recursive algorithms for state estimation of linear dynamical systems when measurements are noisy with unknown bias and/or outliers. For situations with noisy and biased measurements, algorithms are proposed that minimize $\epsilon$ insensitive loss function. In this approach which is often used in Support Vector Machines, small errors a

Dong LT Tran, Thanh C Vo, Hoang T Tran, Minh T Nguyen

Various control methods have been studied to control the position and attitude of quadrotors. There are some differences in the mathematical equations between the two types of quadrotor configurations that lead to different control efficiency in disturbance environments. This paper described the nonlinear back stepping approach based on the Lyapunov function

Suraiya Tairin, Shohaib Mahmud, Haiying Shen, Anand Iyer

In recent years, Mixture-of-Experts (MoE) has emerged as an effective approach for enhancing the capacity of deep neural network (DNN) with sub-linear computational costs. However, storing all experts on GPUs incurs significant memory overhead, increasing the monetary cost of MoE-based inference. To address this, we propose eMoE, a memory efficient inference

Haomin Li, Daniel K. Sewell

Clustering is a fundamental task in network analysis, essential for uncovering hidden structures within complex systems. Edge clustering, which focuses on relationships between nodes rather than the nodes themselves, has gained increased attention in recent years. However, existing edge clustering algorithms often overlook the significance of edge weights, w

Siyu Li, Yihong Cao, Hao Shi, Yongsheng Zang

The exploration of Bird's-Eye View (BEV) mapping technology has driven significant innovation in visual perception technology for autonomous driving. BEV mapping models need to be applied to the unlabeled real world, making the study of unsupervised domain adaptation models an essential path. However, research on unsupervised domain adaptation for BEV ma