March 2025 arXiv papers — page 52

Istvan Berkes, Ioannis Karatzas, Walter Schachermayer

We show that every sequence $f_1, f_2, \cdots$ of real-valued random variables with $\sup_{n \in \N} \E (f_n^2) < \infty$ contains a subsequence $f_{k_1}, f_{k_2}, \cdots$ converging in \textsc{Ces\`aro} mean to some $\,f_\infty \in \mathbb{L}^2$ {\it completely,} to wit, $ \sum_{N \in \N} \, \P \left( \bigg| \frac{1}{N} \sum_{n=1}^N f_{k_n} - f_\infty \bigg

Nils Lange, Geralf Hütter, Bjoern Kiefer

Numerical homogenization for mechanical multiscale modeling by means of the finite element method (FEM) is an elegant way of obtaining structure-property relations, if the behavior of the constituents of the lower scale is well understood. However, the computational costs of this so-called FE$^2$ method are so high that reduction methods are essential. While

Zhongxin Liu, Zhiwei Wang, Jun Niu, Ying Li

The emergence of large language models (LLMs) has significantly advanced the development of natural language processing (NLP), especially in text generation tasks like question answering. However, model hallucinations remain a major challenge in natural language generation (NLG) tasks due to their complex causes. We systematically expand on the causes of fac

David Valcin, Raul Jimenez, Uroš Seljak, Licia Verde

This is the third article in a series aimed at computing accurate and precise ages of galactic globular clusters from their full color-magnitude diagram in order to estimate the age of the Universe and in turn constrain the cosmological model. We update previous constraints using additional data and an improved methodology which allows us to vary the helium

Shijie Ma, Yuying Ge, Teng Wang, Yuxin Guo

The synergy between generative and discriminative models receives growing attention. While discriminative Contrastive Language-Image Pre-Training (CLIP) excels in high-level semantics, it struggles with perceiving fine-grained visual details. Generally, to enhance representations, generative models take CLIP's visual features as conditions for reconstruction

James M. Shihua, Paul Saves, Rhea P. Liem, Joseph Morlier

Ensuring high accuracy and efficiency of predictive models is paramount in the aerospace industry, particularly in the context of multidisciplinary design and optimization processes. These processes often require numerous evaluations of complex objective functions, which can be computationally expensive and time-consuming. To build efficient and accurate pre

Saverio Cavasin, Pietro Biasetton, Mattia Tamiazzo, Mauro Conti

During criminal investigations, images of persons of interest directly influence the success of identification procedures. However, law enforcement agencies often face challenges related to the scarcity of high-quality images or their obsolescence, which can affect the accuracy and success of people searching processes. This paper introduces a novel forensic

Debismita Naik, Souvick Chakraborty, Akriti Singh, Ayan Mondal

The quasi-one-dimensional Ising spin chain system $\alpha$-CoV$_2$O$_6$ is considered to exhibit fascinating magnetic properties at lower temperatures. We comprehensively study magnetic properties and lattice dynamics using a combination of x-ray diffraction (XRD), DC magnetization, specific heat, temperature-dependent XRD and Raman scattering measurements,

Chuqin Geng, Ziyu Zhao, Zhaoyue Wang, Haolin Ye

Existing rule-based explanations for Graph Neural Networks (GNNs) provide global interpretability but often optimize and assess fidelity in an intermediate, uninterpretable concept space, overlooking grounding quality for end users in the final subgraph explanations. This gap yields explanations that may appear faithful yet be unreliable in practice. To this

Ashis Saha, Anirban Roy Chowdhury, Sunandan Gangopadhyay

The present work delves into probing the importance of mutual information of relevant subsystems in obtaining the correct time-evolution of fine-grained entropy of Hawking radiation, as suggested by Page. This was done by considering a functional renormalization group improved or simply, quantum corrected Schwarzschild black solution which captures the flavo

Yaomin Shen, Xiaojian Lin, Wei Fan

In the domain of multimodal intent recognition (MIR), the objective is to recognize human intent by integrating a variety of modalities, such as language text, body gestures, and tones. However, existing approaches face difficulties adequately capturing the intrinsic connections between the modalities and overlooking the corresponding semantic representation

Yijian Wu, Thomas Philippe, Aymane Graini, Julien Lam

Using state-of-the-art rare-event sampling simulations, we precisely characterize the nucleation of liquid droplets from a supersaturated Lennard-Jones gas and uncover a key physical feature: critical clusters nucleate with a density that differs substantially from that of the macroscopic equilibrium liquid. Our atomistic simulations also reveal a nonclassic

Riano E. Giribaldi, Laura Magrini, Martina Rossi, Anish M. Amarsi

The origin of the Galactic halo is one of the fundamental topics linking the study of galaxy formation and evolution to cosmology. We aim at deriving precise and accurate stellar parameters, Mg abundances, and ages for a sample of metal-poor stars with [Fe/H] $<$ -2 dex from high signal-to-noise and high resolution spectra. We derive effective temperatures f

Hongjing Yang, Jennifer C. Yee, Jiyuan Zhang, Chung-Uk Lee

In this work, we continue to apply the updated KMTNet tender-love care (TLC) photometric pipeline to historical microlensing events. We apply the pipeline to a subsample of events from the KMTNet database, which we refer to as the giant source sample. Leveraging the improved photometric data, we conduct a systematic search for anomalies within this sample. T

Mingyang Chen, Linzhuang Sun, Tianpeng Li, Haoze Sun

Large Language Models (LLMs) have shown remarkable capabilities in reasoning, exemplified by the success of OpenAI-o1 and DeepSeek-R1. However, integrating reasoning with external search processes remains challenging, especially for complex multi-hop questions requiring multiple retrieval steps. We propose ReSearch, a novel framework that trains LLMs to Reas

Fred Philippy, Siwen Guo, Cedric Lothritz, Jacques Klein

In NLP, Zero-Shot Classification (ZSC) has become essential for enabling models to classify text into categories unseen during training, particularly in low-resource languages and domains where labeled data is scarce. While pretrained language models (PLMs) have shown promise in ZSC, they often rely on large training datasets or external knowledge, limiting

Nadja Gruber, Johannes Schwab, Markus Haltmeier, Ander Biguri

We propose Noisier2Inverse, a correction-free self-supervised deep learning approach for general inverse problems. The proposed method learns a reconstruction function without the need for ground truth samples and is applicable in cases where measurement noise is statistically correlated. This includes computed tomography, where detector imperfections or pho

Guohui Zheng, Songfen Liu, Huasheng Xie, Hanyue Zhao

Equilibrium reconstruction, which infers internal magnetic fields, plasmas current, and pressure distributions in tokamaks using diagnostic and coil current data, is crucial for controlled magnetic confinement nuclear fusion research. However, traditional numerical methods often fall short of real-time control needs due to time-consuming computations or iter

Leander Kurscheidt, Paolo Morettin, Roberto Sebastiani, Andrea Passerini

In safety-critical applications, guaranteeing the satisfaction of constraints over continuous environments is crucial, e.g., an autonomous agent should never crash into obstacles or go off-road. Neural models struggle in the presence of these constraints, especially when they involve intricate algebraic relationships. To address this, we introduce a differen

Robert Alicki

It is argued the the idea of a single temperature-like variable, introduced in [1], which enters a generalized second law for Markovian open system in non-equilibrium environment is not sufficient for a consistent and useful thermodynamic formalism. The origin of the Second Law and ``local temperatures'' in Markovian theory of Quantum Open Systems is revisit

Deepsikha Das, Sakuntala Chatterjee

We study a persistent exclusion process with time-periodic external potential on a 1d periodic lattice through numerical simulations. A set of run-and-tumble particles move on a lattice of length $L$ and tumbling probability $\gamma \ll 1$ and interact among each other via hardcore exclusion. The effect of the external potential has been modeled as a special

L Bellagamba, G Polesello, N. Valle

The sensitivity of the CERN FCC-ee collider to the production of heavy neutral leptons (HNL) is investigated. The study focuses on a simplified model with a single low-mass HNL mixing with a muon, and addresses the fully leptonic and semileptonic decay modes of the HNL. Complete Monte Carlo analyses of signal and background based on a parametrised detector s

Zhao Wang, Yaping Mao, Sun-Yuan Hsieh, Ralf Klasing

The concept of neighbor connectivity originated from the assessment of the subversion of espionage networks caused by underground resistance movements, and it has now been applied to measure the disruption of networks caused by cascading failures through neighbors. In this paper, we give two necessary and sufficient conditions of the existance of $g$-good-ne

Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu

Diffusion models have achieved remarkable progress in the field of video generation. However, their iterative denoising nature requires a large number of inference steps to generate a video, which is slow and computationally expensive. In this paper, we begin with a detailed analysis of the challenges present in existing diffusion distillation methods and pr

Y. Huang, H. Li, B. Crick, V. Cindro

The ATLAS experiment is planning a complete replacement of its inner detector(ID) with a new all-silicon inner tracker (ITk) for the ATLAS Inner Tracker Phase-2 upgrade. The ATLAS18 silicon strip sensors are designed to operate up to the integrated luminosity of 4000 fb$^{-1}$, which corresponds to the maximum fluence of $1.6 \times 10^{15} \, \text n_{\text

Ko Watanabe, Yuki Matsuda, Yugo Nakamura, Yutaka Arakawa

In programming education, fostering self-regulated learning (SRL) skills is essential for both students and teachers. This paper introduces TrackThinkDashboard, an application designed to visualize the learning workflow by integrating web browsing and programming logs into one unified view. The system aims to (1) help students monitor and reflect on their pr

Hayrullah Ayık, Vítor H. Fernandes, Emrah Korkmaz

In this paper, we consider the monoid $\mathcal{PIO}_{n}$, of all partial order-preserving transformations on a chain with $n$ elements whose domains and ranges are intervals, along with its submonoid $\mathcal{PIO}_{n}^-$ of order-decreasing transformations. Our main aim is to give presentations for $\mathcal{PIO}_{n}^-$ and $\mathcal{PIO}_{n}$. Moreover, f

Shujuan Li, Yu-Shen Liu, Zhizhong Han

Reconstructing open surfaces from multi-view images is vital in digitalizing complex objects in daily life. A widely used strategy is to learn unsigned distance functions (UDFs) by checking if their appearance conforms to the image observations through neural rendering. However, it is still hard to learn continuous and implicit UDF representations through 3D

Juntao Jian, Xiuping Liu, Zixuan Chen, Manyi Li

Recent advances in dexterous grasping synthesis have demonstrated significant progress in producing reasonable and plausible grasps for many task purposes. But it remains challenging to generalize to unseen object categories and diverse task instructions. In this paper, we propose G-DexGrasp, a retrieval-augmented generation approach that can produce high-qu

Enze Sun, Zhihao Gavin Tang, Yifan Wang

We study the online stochastic matching problem. Against the offline benchmark, Feldman, Gravin, and Lucier (SODA 2015) designed an optimal $0.5$-competitive algorithm. A recent line of work, initiated by Papadimitriou, Pollner, Saberi, and Wajc (MOR 2024), focuses on designing approximation algorithms against the online optimum. The online benchmark allows

Bo Yan, Zhongjian Zhang, Huabin Sun, Mengmei Zhang

In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns, and all clients jointly train a global graph model by only transmitting model parameters. A pain point of FGL is the heterogeneity problem, where nodes or structures present non-IID properties among clients (e.g., different no

Pierre-A. Vuillermot

In this article we introduce a new scale of weighted Orlicz-Sobolev sequence spaces generated by a class of suitable Orlicz functions and prove various continuity and compactness criteria for them. In a nutshell, continuity is a consequence of pointwise comparison between Orlicz functions while compactness follows from the combination of the existence of a S

Guilherme Ramos, Daniel Silvestre, André M. H. Teixeira, Sérgio Pequito

This paper addresses the challenge of achieving private and resilient average consensus among a group of discrete-time networked agents without compromising accuracy. State-of-the-art solutions to attain privacy and resilient consensus entail an explicit trade-off between the two with an implicit compromise on accuracy. In contrast, in the present work, we p

Yiqing Li, Xuan Wang, Jiawei Wu, Yikun Ma

Synthesizing novel views of large-scale scenes from unconstrained in-the-wild images is an important but challenging task in computer vision. Existing methods, which optimize per-image appearance and transient occlusion through implicit neural networks from dense training views (approximately 1000 images), struggle to perform effectively under sparse input c

Matteo Verzobio

Let $X$ be a smooth projective hypersurface defined over $\mathbb{Q}$. We provide new bounds for rational points of bounded height on $X$. In particular, we show that if $X$ is a smooth projective hypersurface in $\mathbb{P}^n$ with $n\geq 4$ and degree $d\geq 50$, then the set of rational points on $X$ of height bounded by $B$ have cardinality $O_{n,d,\vare

Partha Ghosh

This article explores solutions to a generalised form of the Seiberg--Witten equations in higher dimensions, first introduced by Fine and the author. Starting with an oriented $n$ dimensional Riemannian manifold with a spin$^\mathbb{C}$-structure, we described an elliptic system of equations that recovers the traditional Seiberg-Witten equations in dimension

Zhongchun Zheng, Kan Wu, Long Cheng, Lu Li

Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patterns, often requiring manual hints or domain-specific expertise. Large language models (LLMs), with their ability to capture intricate patterns, provide a promising solution, yet t

Changyong He, Jin Zeng, Jiawei Zhang, Jiajie Guo

Time-of-Flight (ToF) sensors efficiently capture scene depth, but the nonlinear depth construction procedure often results in extremely large noise variance or even invalid areas. Recent methods based on deep neural networks (DNNs) achieve enhanced ToF denoising accuracy but tend to struggle when presented with severe noise corruption due to limited prior kn

Jason Zhijingcheng Yu, Aditya Ranjan Jha, Umang Mathur, Trevor E. Carlson

Expressing hardware designs using hardware description languages (HDLs) routinely involves using stateless signals whose values change according to their underlying registers. Unintended behaviours can arise when the stored values in these underlying registers are mutated while their dependent signals are expected to remain constant across multiple cycles. S

Riccardo Zuliani, Efe C. Balta, Alisa Rupenyan, John Lygeros

Iterative learning control (ILC) improves the performance of a repetitive system by learning from previous trials. ILC can be combined with Model Predictive Control (MPC) to mitigate non-repetitive disturbances, thus improving overall system performance. However, existing approaches either assume perfect model knowledge or fail to actively learn system uncer

Yue Yin, Jiangshan He, Runze Li, Yunze Qiu

Dual-atom catalysts supported on nitrogen-doped graphene (DAC/NG) are emerging as a family of promising catalysts that can overcome intrinsic limitations of single-atom catalysts. However, comprehensive assessment of their structural stability is prohibitively demanding due to a vast local configurational space. Here we introduce LOCAL, a locality-based fram

Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang, Hong Yi Lin

With the advancement of AI models, more software systems are adopting AI as a component to facilitate automation. Pre-trained models (PTMs) have become a cornerstone of AI-based software, allowing for rapid integration and development with lower training cost. However, their adoption also introduces failure modes such as data leakage and biased outputs, that

Jiaxin Zhang, Junjun Jiang, Youyu Chen, Kui Jiang

Accurate object segmentation is crucial for high-quality scene understanding in the 3D vision domain. However, 3D segmentation based on 3D Gaussian Splatting (3DGS) struggles with accurately delineating object boundaries, as Gaussian primitives often span across object edges due to their inherent volume and the lack of semantic guidance during training. In o

Benjamin Stölzner, Angus H. Wright, Marika Asgari, Catherine Heymans

We present a cosmic shear consistency analysis of the final data release from the Kilo-Degree Survey (KiDS-Legacy). By adopting three tiers of consistency metrics, we compare cosmological constraints between subsets of the KiDS-Legacy dataset split by redshift, angular scale, galaxy colour and spatial region. We also review a range of two-point cosmic shear

Angus H. Wright, Benjamin Stölzner, Marika Asgari, Maciej Bilicki

We present cosmic shear constraints from the completed Kilo-Degree Survey (KiDS), where the cosmological parameter $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3} = 0.815^{+0.016}_{-0.021}$, is found to be in agreement ($0.73\sigma$) with results from the Planck Legacy cosmic microwave background experiment. The final KiDS footprint spans $1347$ square degrees o

Angus H. Wright, Hendrik Hildebrandt, Jan Luca van den Busch, Maciej Bilicki

We present the redshift calibration methodology and bias estimates for the cosmic shear analysis of the fifth and final data release (DR5) of the Kilo-Degree Survey (KiDS). KiDS-DR5 includes a greatly expanded compilation of calibrating spectra, drawn from $27$ square degrees of dedicated optical and near-IR imaging taken over deep spectroscopic fields. The

Angus H. Wright, Konrad Kuijken, Hendrik Hildebrandt, Mario Radovich

We present the final data release of the Kilo-Degree Survey (KiDS-DR5), a public European Southern Observatory (ESO) wide-field imaging survey optimised for weak gravitational lensing studies. We combined matched-depth multi-wavelength observations from the VLT Survey Telescope and the VISTA Kilo-degree INfrared Galaxy (VIKING) survey to create a nine-band o

Daoyin He, Qianqian Li, Huicheng Yin

For the $2$-D semilinear wave equation with scale-invariant damping $\partial_t^2u-\Delta u+\frac{\mu}{t}\partial_tu=|u|^p$, where $t\ge 1$ and $p>1$, in the paper [T. Imai, M. Kato, H. Takamura, K. Wakasa, The lifespan of solutions of semilinear wave equations with the scale-invariant damping in two space dimensions, J. Differential Equations 269 (2020), no

Tian-Cheng Ding, Jian Huang, Muyang Chen

We construct relativized mock meson states for heavy quarkonium, where the Dirac spinors import kinetic relativistic correction and the dynamic wave functions are the same as those solved from Schr\"odinger equation. We find that the kinetic relativistic correction imported by Dirac spinors is crucial to study the two-photon transition form factors. Using re

Pablo Burset, Benjamin Roussel, Michael Moskalets, Christian Flindt

We present a comprehensive Floquet-Nambu theory to describe the time-dependent quantum transport in mesoscopic circuits involving superconductors. The central object of our framework is the first-order correlation function, which accounts for the excitations that are generated by a time-dependent voltage and their coherent scattering off the interface with a

Johan Söderström, Lucas M. Cornetta, Victor Ekholm, Vincenzo Carravetta

Aqueous solutions are crucial in chemistry, biology, environmental science, and technology. The chemistry of solutes is influenced by the surrounding solvation shell of water molecules, which have different chemical properties than bulk water due to their different electronic and geometric structure. It is an experimental challenge to selectively investigate

Yiming Luo, Ting Liu, Patrick Cheong-Iao Pang, Dana McKay

The advent of Large Language Models (LLMs) has profoundly transformed the paradigms of information retrieval and problem-solving, enabling students to access information acquisition more efficiently to support learning. However, there is currently a lack of standardized evaluation frameworks that guide learners in effectively leveraging LLMs. This paper prop

Ofer Neufeld, Matan Even Tzur, Ofer Kfir, Avner Fleischer

The analysis of symmetries is extremely useful across science. In Physics, symmetries are used to derive conservation laws and selection rules for transitions in interacting systems. In the early days of nonlinear optics (NLO), symmetries were used to formulate a set of rules for photonic processes according to the medium's symmetries that are reflected in t

Qiusheng Huang, Xiaohui Zhong, Xu Fan, Lei Chen

Similar to conventional video generation, current deep learning-based weather prediction frameworks often lack explicit physical constraints, leading to unphysical outputs that limit their reliability for operational forecasting. Among various physical processes requiring proper representation, radiation plays a fundamental role as it drives Earth's weather

Wenwen Jian, Yingte Sun

In this paper, we study the interacting random particles with power-law long-rang hopping. Via the multi-scale analysis arguments for the Green's function, we establish the power-law localization for all energy with strong disorder.

Sang-Jin Sin, Yi-Li Wang

We give an understanding how strange metals arise from the spatially random Yukawa-SYK model based on the wormhole picture and find a parallelism between the disorder theory and quantum gravity. We start from the observation that the Gaussian average over the spatial random coupling gives a wormhole, defined as a mechanism for long range interaction without

Jinxue Ding, Jiawen Zhang, Jinfeng Dong, Kimitaka Higuchi

Decreasing thermal conductivity is important for designing efficient thermoelectric devices. Traditional engineering strategies have focused on point defects and interface design. Recently, dislocations as line defects have emerged as a new tool for regulating thermal conductivity. In ceramics-based thermoelectric materials, the key challenge lies in achievi

Masaya Hasegawa, Koji Yasuda

Diffusion models, which have been advancing rapidly in recent years, may generate samples that closely resemble the training data. This phenomenon, known as memorization, may lead to copyright issues. In this study, we propose a method to quantify the ease of reproducing training data in unconditional diffusion models. The average of a sample population foll

Luis A. Anchordoqui, Ignatios Antoniadis, Dieter Lust

We propose a dark energy model in which a quintessence field $\phi$ rolls near the vicinity of a local maximum of its potential characterized by the simplest $S$ self-dual form $V(\phi) = \Lambda \ {\rm sech}(\sqrt{2} \, \phi/M_p)$, where $M_p$ is the reduced Planck mass and $\Lambda \sim 10^{-120} M_p^4$ is the cosmological constant. We confront the model w

Muyi Bao, Shuchang Lyu, Zhaoyang Xu, Qi Zhao

Skin lesion segmentation is a critical challenge in computer vision, and it is essential to separate pathological features from healthy skin for diagnostics accurately. Traditional Convolutional Neural Networks (CNNs) are limited by narrow receptive fields, and Transformers face significant computational burdens. This paper presents a novel skin lesion segme

Suyoung Bae, YunSeok Choi, Jee-Hyong Lee

While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions, leading to a degradation in performance. Existing zero-shot methods are efficient but fail to consider context and prevent bias propagation in the answers. To address this, we prop

Yue Yin, Hai Xiao

The oxidation state (OS) is an essential chemical concept that embodies chemical intuition but cannot be computed with well-defined physical laws. We establish a data-driven paradigm, with its implementation as Tsinghua Oxidation States in Solids (TOSS), to explicitly compute the OSs in crystal structures as the emergent properties from large-sized datasets

Ruonan Cao, Nianyu Yi

In this paper, we present a second order, linear, fully decoupled, and unconditionally energy stable scheme for solving the Erickson-Leslie model. This approach integrates the pressure correction method with a scalar auxiliary variable technique. We rigorously demonstrate the unconditional energy stability of the proposed scheme. Furthermore, we present seve

Tingting Diao, Xinzhang Wu, Lina Yang, Ling Xiao

Accurate tourism demand forecasting is hindered by limited historical data and complex spatiotemporal dependencies among tourist origins. A novel forecasting framework integrating virtual sample generation and a novel Transformer predictor addresses constraints arising from restricted data availability. A spatiotemporal GAN produces realistic virtual samples

Johann Cigler

We show that the values of the minimal polynomials ${\phi_n}(x)$ of $4{\sin ^2}\left( {\frac{\pi }{n}} \right)$ for $x \in \left\{ {0,1,2,3,4} \right\}$ are intimately related to the prime factorization of $n.$

Tim M. Schwarz, Maïtena Dumont, Victoria Garcia-Giner, Chanwon Jung

Bone structure is generally hierarchically organized into organic (collagen, proteins,...), inorganic (hydroxyapatite (HAP)) components. However, many fundamental mechanisms of the biomineralization processes such as HAP formation, the influence of trace elements, the mineral-collagen arrangement, etc., are not clearly understood. This is partly due to the a

ATLAS Collaboration

Measurements of the total and differential Higgs boson production cross-sections, via $WH$ and $ZH$ associated production using $H\rightarrow WW^\ast\rightarrow\ell\nu\ell\nu$ and $H\rightarrow WW^\ast\rightarrow\ell\nu jj$ decays, are presented. The analysis uses proton-proton events delivered by the Large Hadron Collider at a centre-of-mass energy of 13 Te

Pietro Caputo, Zongchen Chen, Daniel Parisi

We derive entropy factorization estimates for spin systems using the stochastic localization approach proposed by Eldan and Chen-Eldan, which, in this context, is equivalent to the renormalization group approach developed independently by Bauerschmidt, Bodineau, and Dagallier. The method provides approximate Shearer-type inequalities for the corresponding Gi

Xueyao Zhang, Bo Yang, Xuelin Cao, Zhiwen Yu

Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks. This paper considers a vehicular system with multiple self-driving vehicles that is assisted by multi-access edge computing (MEC), where image data collected by the sensors is offloaded from cellular vehicles to the MEC server using v

Yoshihiko Suyama

We study generic conformally flat (local-)hypersurfaces in the Euclidean 4-space $\mathbb{R}^4$. Such a hypersurface $f$ has the dual (hypersurface) $f^*$ in $\mathbb{R}^4$, which is also generic and conformally flat. By repeating the composite action of inversion and the dual transformation on a hypersurface $f$, infinitely many non-equivalent generic confo

Xuli Shen, Hua Cai, Dingding Yu, Weilin Shen

Generating emotion-specific talking head videos from audio input is an important and complex challenge for human-machine interaction. However, emotion is highly abstract concept with ambiguous boundaries, and it necessitates disentangled expression parameters to generate emotionally expressive talking head videos. In this work, we present EmoHead to synthesi

Łukasz Rudnicki

A deep relationship [arXiv:2503.17816v1] between real linear second order ordinary differential equations $u''\left(x\right)+h\left(x\right)u\left(x\right)=0$, with differentiable $h(x)$, and two dimensional hyperbolic geometry is generalized in a multitude of ways. First, I present an equivalent relationship in which the hyperbolic geometry is replaced by a

Hao Jiao, Robert Brandenberger, Vahid Kamali

We study the possibility that parametric resonant excitation of photons in an ultralight dark matter halo could generate the required flux of Lyman-Werner photons to allow the direct collapse formation of supermassive black hole seeds.

Steven Vander Eeckt, Hugo Van hamme

Catastrophic forgetting remains a major challenge when neural networks learn tasks sequentially. Elastic Weight Consolidation (EWC) attempts to address this problem by introducing a Bayesian-inspired regularization loss to preserve knowledge of previously learned tasks. However, EWC relies on a Laplace approximation where the Hessian is simplified to the dia

Aritra Bhowmick, Jin-ichi Itoh, Sachchidanand Prasad

In this article, we prove the stability with respect to the Hausdorff metric $d_H$ of the cut locus $\mathrm{Cut}(p, \mathfrak{g})$ of a point $p$ in a compact Riemannian manifold $(M, \mathfrak{g})$ under $C^2$ perturbation of the metric. Specifically, given a sequence of metrics $\mathfrak{g}_i$ on $M$, converging to $\mathfrak{g}$ in the $C^2$ topology, a

D. Veerababu, Prasanta K. Ghosh

The study of sound propagation in a uniform duct having a mean flow has many applications, such as in the design of gas turbines, heating, ventilation and air conditioning ducts, automotive intake and exhaust systems, and in the modeling of speech. In this paper, the convective effects of the mean flow on the plane wave acoustic field inside a uniform duct w

Zhihan Jiang, Yujie Huang, Guangba Yu, Junjie Huang

Large Language Models (LLMs) have revolutionized numerous domains, driving the rise of Language-Model-as-a-Service (LMaaS) platforms that process millions of queries daily. These platforms must minimize latency and meet Service Level Objectives (SLOs) while optimizing resource usage. However, conventional cloud service management techniques, designed for tra

Eun-Kyung Cho, Ilkyoo Choi, Boram Park, Mark Siggers

For a graph $H$, an $H$-colouring of a graph $G$ is a vertex map $\phi:V(G) \to V(H)$ such that adjacent vertices are mapped to adjacent vertices. A graph $G$ is $C_{2k+1}$-critical if $G$ has no $C_{2k+1}$-colouring but every proper subgraph of $G$ has a $C_{2k+1}$-colouring. We prove a structural characterisation of $C_{2k+1}$-critical graphs when $k \geq

Takuma Kuno, Takeru Utsugi, Andrew J. Ramsay, Normann Mertig

Silicon quantum dots are one of the most promising candidates for practical quantum computers because of their scalability and compatibility with the well-established complementary metal-oxide-semiconductor technology. However, the coherence time is limited in industry-standard natural silicon because of the $^{29}$Si isotopes, which have non-zero nuclear sp

Mickaël Latocca, Huy Q. Nguyen

We consider the free boundary incompressible porous media equation which describes the dynamics of a density transported by a Darcy flow in the field of gravity, with a free boundary between the fluid region and the dry region above it. For any stratified density state, we identify a stability condition for the initial free boundary. Under this condition, we

Hyeokjun Heo, Jeongha An, Junyoung Kwon, Kwangrae Kim

Sr2IrO4 is one of the prime candidates for realizing exotic quantum spin orders owing to the subtle combination of spin-orbit coupling and electron correlation. Sensitive local magnetization measurement can serve as a powerful tool to study these kinds of systems with multiple competing spin orders since the comprehensive study of the spatially-varying magne

Bingjian Yao, Weiping Lin, Yan He, Zheng Wang

The fine-grained annotations in whole slide images (WSIs) show the boundaries of various pathological regions. However, generating such detailed annotation is often costly, whereas the coarse annotations are relatively simpler to produce. Existing methods for refining coarse annotations often rely on extensive training samples or clean datasets, and fail to

Ziyuan Liu, Jiawei Zhang, Wenyu Wang, Yuantao Gu

Most existing change detection (CD) methods focus on optical images captured at different times, and deep learning (DL) has achieved remarkable success in this domain. However, in extreme scenarios such as disaster response, synthetic aperture radar (SAR), with its active imaging capability, is more suitable for providing post-event data. This introduces new

Mingxiao Tu, Hoijoon Jung, Alireza Moghadam, Jineel Raythatha

In perioperative care, precise in-bed 3D patient pose and shape estimation (PSE) can be vital in optimizing patient positioning in preoperative planning, enabling accurate overlay of medical images for augmented reality-based surgical navigation, and mitigating risks of prolonged immobility during recovery. Conventional PSE methods relying on modalities such

Jiaqi Liao, Yuwei Niu, Fanqing Meng, Hao Li

Recent years have witnessed remarkable advances in Large Vision-Language Models (LVLMs), which have achieved human-level performance across various complex vision-language tasks. Following LLaVA's paradigm, mainstream LVLMs typically employ a shallow MLP for visual-language alignment through a two-stage training process: pretraining for cross-modal alignment

Junbin Li, Jinhua Wang

We prove the local existence theorem and establish an extension principle for the spherically symmetric Einstein Yang--Mills system (SSEYM) with $H^1$ data. This in addition implies Cauchy stability for the system. In contrast to a massless scalar field, the purely magnetic Yang--Mills field in spherical symmetry satisfies a wave-type equation with a singula

Kian Kai Ang, Damith C. Ranasinghe

Network applications are routinely under attack. We consider the problem of developing an effective and efficient fuzzer for the recently ratified QUIC network protocol to uncover security vulnerabilities. QUIC offers a unified transport layer for low latency, reliable transport streams that is inherently secure, ultimately representing a complex protocol de

Cinthia Altamirano, Ricardo Bustos, Raul A. Monsalve, Silvia E. Restrepo

Radio experiments trying to detect the global $21$~cm signal from the early Universe are very sensitive to the electrical properties of their environment. For ground-based experiments with the antenna above the soil it is critical to characterize the effect from the soil on the sky observations. This characterization requires estimating the soil's electrical

Ning-Ning Gao, Hui-Li Han, Ting-Yun Shi

We investigate the elastic and inelastic scattering of positrons by sodium atoms in both the ground state, Na($3s$), and excited states, Na*($3p$, $4s$, $3d$), using the hyperspherical coordinate method with a model potential to represent the atomic core. The threshold behavior of positronium (Ps) formation cross sections is analyzed as the positron impact e

Hirakjyoti Das, Saikat Maity, Manjil P. Saikia

We prove several congruences satisfied by the generalized cubic and generalized overcubic partition functions, recently introduced by Amdeberhan, Sellers, and Singh. We also prove infinite families of congruences modulo powers of $2$ and modulo $12$ satisfied by the generalized overcubic partitions, as well as some density results that they satisfy. We use b

Mengyao Guo, Yu Nie, Jinda Han, Zongxing Li

This paper introduces CyanKitten, an interactive virtual companion system tailored for elderly users, integrating advanced posture recognition, behavior recognition, and multimodal interaction capabilities. The system utilizes a three-tier architecture to process and interpret user movements and gestures, leveraging a dual-camera setup and a convolutional ne

Chenghao Li, Razvan Beuran, Nak Young Chong

Vision-guided robot grasping methods based on Deep Neural Networks (DNNs) have achieved remarkable success in handling unknown objects, attributable to their powerful generalizability. However, these methods with this generalizability tend to recognize the human hand and its adjacent objects as graspable targets, compromising safety during Human-Robot Intera

Arunangshu Bora, Anirban Mandal, Shreya Mittal, Mihir N Pandey

This study presents the analysis of data related to the two-point function of kaon generated from lattice QCD simulations. Using gauge configurations of twisted-mass fermions, we obtain the correlation functions for 6 values of momentum for the kaon between 0 and 2GeV (both inclusive), we use statistical techniques such as jackknife resampling to derive the

Yuqi Li, Han Zhang, Xiaofan Gui, Zhao Chen

Battery degradation is governed by complex and randomized cyclic conditions, yet existing modeling and prediction frameworks usually rely on rigid, unchanging protocols that fail to capture real-world dynamics. The stochastic electrical signals make such prediction extremely challenging, while, on the other hand, they provide abundant additional information,

Akihiro Haga, Yoshi-hide Sato, Hana Fujiwara, Dousatsu Sakata

This study evaluates the accuracy of nuclear fragmentation simulations using a quantum molecular dynamics (QMD) model based on relativistic mean field (RMF) theory for an energy range of 50-400 MeV/u, relevant to hadron therapy. A total of 16 parameter sets within the RMF framework are assessed based on their ability to reproduce ground-state properties such

Mehul Shetty, Connor Jordan

Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions. In this work, we move beyond correlation to investigate the causal influence of key symptoms-specifically "chest pain" on diagnostic predictions. Le

Mingming Cao, Weiyi Kong, Dachun Yang, Wen Yuan

In this article, we establish the parabolic version of the celebrated Rubio de Francia extrapolation theorem. As applications, we obtain new characterizations of parabolic BMO-type spaces in terms of various commutators of parabolic fractional operators with time lag. The key tools to achieve these include to establish the appropriate form in the parabolic s

K. Mahesh Krishna

We formulate p-adic versions of following three: (1) Grothendieck Inequality, (2) Johnson-Lindenstrauss Flattening Lemma, (3) Bourgain-Tzafriri Restricted Invertibility Theorem.

F. -X. Zhou, C. -W. Lian, R. Yan, Y. Ji

The absolute growth of Stimulated Raman side scattering (SRSS) predicted by previous theories appeared to be surprisingly absent in the recent ignition-scale direct-drive experiments with the absence attributed to different reasons. We present evidence from simulations that the linear SRSS modes are naturally all convective (i.e., absolute SRSS does not exis

Zhiying Song, Lei Yang, Fuxi Wen, Jun Li

Cooperative perception presents significant potential for enhancing the sensing capabilities of individual vehicles, however, inter-agent latency remains a critical challenge. Latencies cause misalignments in both spatial and semantic features, complicating the fusion of real-time observations from the ego vehicle with delayed data from others. To address th