February 2025 arXiv papers — page 33

Qian Wang, Zhenheng Tang, Bingsheng He

Simulation powered by Large Language Models (LLMs) has become a promising method for exploring complex human social behaviors. However, the application of LLMs in simulations presents significant challenges, particularly regarding their capacity to accurately replicate the complexities of human behaviors and societal dynamics, as evidenced by recent studies

Jian Wu, Jiayu Zhang, Dongyuan Li, Linyi Yang

This paper introduces Leaderboard Auto Generation (LAG), a novel and well-organized framework for automatic generation of leaderboards on a given research topic in rapidly evolving fields like Artificial Intelligence (AI). Faced with a large number of AI papers updated daily, it becomes difficult for researchers to track every paper's proposed methods, exper

Alexa Herter, Frieder Lindel, Laura Gabriel, Stefan Yoshi Buhmann

The unique distinction between vacuum-field and source-radiation induced effects in processes such as the Lamb shift, Casimir forces or spontaneous emission, remains unresolved even at the theoretical level, and an experimental approach was never considered feasible [1-4]. In 1932, Fermi introduced the two-atom problem, which is a Gedanken-experiment that ex

Fabian Gundlach, Béranger Seguin

We study the asymptotic distribution of wildly ramified extensions of function fields in characteristic $p > 2$, focusing on (certain) $p$-groups of nilpotency class at most $2$. Rather than the discriminant, we count extensions according to an invariant describing the last jump in the ramification filtration at each place. We prove a local-global principle

Michael J. Walsh

The Kalman filter is ubiquitous for state space models because of its desirable statistical properties, ease of implementation, and generally good performance. However, it can perform poorly in the presence of outliers, or measurements with noise variances much greater than those assumed by the filter. An algorithm that is similar to the Kalman filter but ro

Carlos Gómez-Rodríguez

Large language models, such as the well-known ChatGPT, have brought about an unexpected revolution in the field of artificial intelligence. On the one hand, they have numerous practical applications and enormous potential still to be explored. On the other hand, they are also the subject of debate from scientific, philosophical, and social perspectives: ther

Tanita Permaul, Arijit Dutta, Filippus S. Roux, Thomas Konrad

We present a scheme to teleport both the spatial and number-of-photons degrees of freedom of light. This is achieved by teleportation of each pixel of a target image. We take coherent states as the input and demonstrate the scheme for the cases with ideal and realistic entanglement resources.

Zexuan Ouyang

We study the structure of $\mathfrak{M}_2$, the set of half-dimensional collapsing spaces of hyperk\"ahler metrics on K3 surfaces. We show that $\mathfrak{M}_2$ consists precisely of those underlying metric spaces of integral singular special K\"ahler structures (SKSs) on $ \mathbb{P}^1 $. Furthermore, we establish a bijection between integral singular SKSs

Atik Faysal, Mohammad Rostami, Taha Boushine, Reihaneh Gh. Roshan

We introduce DenoMAE2.0, an enhanced denoising masked autoencoder that integrates a local patch classification objective alongside traditional reconstruction loss to improve representation learning and robustness. Unlike conventional Masked Autoencoders (MAE), which focus solely on reconstructing missing inputs, DenoMAE2.0 introduces position-aware classific

Shutaro Aoyama, Rintaro Chujo, Ari Hautasaari, Takeshi Naemura

The growing prevalence of Large Language Models (LLMs) is reshaping online text-based communication; a transformation that is extensively studied as AI-mediated communication. However, much of the existing research remains bound by traditional communication models, where messages are created and transmitted directly between humans despite LLMs being able to

Jiangjing Hu, Haotian Wu, Wenjing Zhang, Fengyu Wang

Most existing semantic communication (SemCom) systems use deep joint source-channel coding (DeepJSCC) to encode task-specific semantics in a goal-oriented manner. However, their reliance on predefined tasks and datasets significantly limits their flexibility and generalizability in practical deployments. Multi-modal foundation models provide a promising solu

Dmitry D. Lapygin, Georgy Yu. Prokhorov, Oleg V. Teryaev, Valentin I. Zakharov

The Minkowski vacuum in an accelerated frame behaves like a fluid that has not only a finite temperature due to the Unruh effect, but also a finite shear viscosity. Moreover, the ratio of this viscosity to the entropy density exactly satisfies the Kovtun-Son-Starinets (KSS) bound, inspired by the string theory $ \eta/s=1/4\pi $. The origin of this viscosity

Dangchan Kim, Chae Young Lim

This paper proposes a method to generate synthetic data for spatial point patterns within the differential privacy (DP) framework. Specifically, we define a differentially private Poisson point synthesizer (PPS) and Cox point synthesizer (CPS) to generate synthetic point patterns with the concept of the $\alpha$-neighborhood that relaxes the original definit

Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida

Consistency Training (CT) has recently emerged as a strong alternative to diffusion models for image generation. However, non-distillation CT often suffers from high variance and instability, motivating ongoing research into its training dynamics. We propose Variational Consistency Training (VCT), a flexible and effective framework compatible with various fo

Hwanjin Kim, Junil Choi, David J. Love

Precise channel state knowledge is crucial in future wireless communication systems, which drives the need for accurate channel prediction without additional pilot overhead. While machine-learning (ML) methods for channel prediction show potential, existing approaches have limitations in their capability to adapt to environmental changes due to their extensi

Anh-Khoa Nguyen Vu, Quoc-Truong Truong, Vinh-Tiep Nguyen, Thanh Duc Ngo

Recent few-shot object detection (FSOD) methods have focused on augmenting synthetic samples for novel classes, show promising results to the rise of diffusion models. However, the diversity of such datasets is often limited in representativeness because they lack awareness of typical and hard samples, especially in the context of foreground and background r

Silvie Illésová, Martin Beseda, Saad Yalouz, Benjamin Lasorne

In the present work, we examine how the recent quantum-computing algorithm known as the state-average orbital-optimized variational quantum eigensolver (SA-OO-VQE), viewed within the context of quantum chemistry as a type of multiconfiguration self-consistent field (MCSCF) electronic-structure approach, exhibits a propensity to produce an ab initio quasi-dia

Ahmed Errahmani, Mounia Magrach, Safae Dahmani, Amine Bouali

In this work, we present a new analysis for $f(R,T)$ gravity by exploring the energy momentum tensor. We demonstrate that $f(R,T)$ gravity with the form $f(R,T)=R+2 \kappa^2 \lambda T-2\Lambda$ is equivalent to Running Vacuum Energy (RVE), which interacts with the components of the cosmic fluid, namely dark matter and radiation. Interestingly, the form of su

Valentina De Renzi, Matteo G. A. Paris, Maria Bondani

Quantum Physics is a cornerstone of modern science and technology, yet a comprehensive approach to integrating it into school curricula and communicating its foundations to policymakers, industrial stakeholders, and the general public has yet to be established. In this paper, we discuss the rationale for introducing entanglement and Bell's inequalities to a

Ovidiu Savin, Hui Yu

We study homogeneous solutions to the Alt-Phillips problem when the exponent $\gamma$ is close to 1. In dimension $d\ge3$, we show that the radial cone is minimizing when $\gamma$ is close to 1. In dimension $d \ge 4$, we construct an axially symmetric cone whose contact set has with positive density. We show that it is a global minimizer. It is analogous to

Thomas D Swinburne, Clovis Lapointe, Mihai-Cosmin Marinica

We present a new method to evaluate vibrational free energies of atomic systems without a priori specification of an interatomic potential. Our model-agnostic approach leverages descriptors, high-dimensional feature vectors of atomic structure. The entropy of a high-dimensional density, the descriptor density of states, is accurately estimated with condition

Chao Wu, Hanbo Sun, Pengqiang Dong, Yin-Zhong Wu

The multiferroic materials, which coexist magnetism, ferroelectric, and ferrovalley, have broad practical application prospects in promoting the miniaturization and integration of spintronic and valleytronic devices. However, it is rare that there are triferroic orders and multiple types of valley polarization in a real material. Here, we propose a mechanism

Sándor P. Fekete, Phillip Keldenich, Michael Perk

We provide a spectrum of new theoretical insights and practical results for finding a Minimum Dilation Triangulation (MDT), a natural geometric optimization problem of considerable previous attention: Given a set $P$ of $n$ points in the plane, find a triangulation $T$, such that a shortest Euclidean path in $T$ between any pair of points increases by the sm

Guanzi Chen, Jiying Zhang, Yang Li

Cross-graph node classification, utilizing the abundant labeled nodes from one graph to help classify unlabeled nodes in another graph, can be viewed as a domain generalization problem of graph neural networks (GNNs) due to the structure shift commonly appearing among various graphs. Nevertheless, current endeavors for cross-graph node classification mainly

Ignatios Antoniadis, Auttakit Chatrabhuti, Jules Cunat, Hiroshi Isono

In a recent paper, we computed the bispectrum of primordial density perturbations in CMB to second order in the slow-roll parameters of single field inflation, and found logarithmic infrared contributions that diverge in both large physical distances and squeezed limit where one momentum vanishes. In this work, we provide an independent test of the result by

Zhixian Zhao, Xinfa Zhu, Xinsheng Wang, Shuiyuan Wang

Large-scale audio language models (ALMs), such as Qwen2-Audio, are capable of comprehending diverse audio signal, performing audio analysis and generating textual responses. However, in speech emotion recognition (SER), ALMs often suffer from hallucinations, resulting in misclassifications or irrelevant outputs. To address these challenges, we propose C$^2$S

Adnan Iltaf, Rayan Merghani Ahmed, Zhenxi Zhang, Bin Li

Medical image segmentation is crucial for clinical diagnosis and treatment planning, especially when dealing with complex anatomical structures such as vessels. However, accurately segmenting vessels remains challenging due to their small size, intricate edge structures, and susceptibility to artifacts and imaging noise. In this work, we propose VesselSAM, a

Xukang Zhang, Huanchen Zhang, Xiaofeng Meng

We propose the concept of Intra-Query Runtime Elasticity (IQRE) for cloud-native data analysis. IQRE enables a cloud-native OLAP engine to dynamically adjust a query's Degree of Parallelism (DOP) during execution. This capability allows users to utilize cloud computing resources more cost-effectively. We present Accordion, the first IQRE query engine. Accord

A. Hamadeh, A. Koujok, D. R. Rodrigues, A. Riveros

Magnetic vortices are highly tunable, nonlinear systems with ideal properties for being applied in spin wave emission, data storage, and neuromorphic computing. However, their technological application is impaired by a limited understanding of non conservative forces, that results in the open challenge of attaining precise control over vortex dynamics in cou

Jianyu Wang, Shanzheng Guan, Nicolas Dobigeon, Jingdong Chen

Blind source separation (BSS) refers to the process of recovering multiple source signals from observations recorded by an array of sensors. Common approaches to BSS, including independent vector analysis (IVA), and independent low-rank matrix analysis (ILRMA), typically rely on second-order models to capture the statistical independence of source signals fo

Evgeniy Lokharu, Jörg Weber

A classical and central problem in the theory of water waves is to classify parameter regimes for which non-trivial solitary waves exist. In the two-dimensional, irrotational, pure gravity case, the Froude number $Fr$ (a non-dimensional wave speed) plays the central role. So far, the best analytical result $Fr<\sqrt{2}$ was obtained by Starr (1947 J. Mar. Re

Lei Li, Sen Jia, Jianhao Wang, Zhaochong An

Advancements in Multimodal Large Language Models (MLLMs) have improved human motion understanding. However, these models remain constrained by their "instruct-only" nature, lacking interactivity and adaptability for diverse analytical perspectives. To address these challenges, we introduce ChatMotion, a multimodal multi-agent framework for human motion analy

Gaye Colakoglu, Gürkan Solmaz, Jonathan Fürst

This paper defines and explores the design space for information extraction (IE) from layout-rich documents using large language models (LLMs). The three core challenges of layout-aware IE with LLMs are 1) data structuring, 2) model engagement, and 3) output refinement. Our study investigates the sub-problems and methods within these core challenges, such as

Wen-Hao Zhong, Hai-Qing Lin, Xue-Jia Yu

Quantum entanglement can be an effective diagnostic tool for probing topological phases protected by global symmetries. Recently, the notion of nontrivial topology in critical systems has been proposed and is attracting growing attention. In this work, as a concrete example, we explore the quantum entanglement properties of fermionic symmetry-enriched quantu

Remi Genet

The execution of Volume Weighted Average Price (VWAP) orders remains a critical challenge in modern financial markets, particularly as trading volumes and market complexity continue to increase. In my previous work arXiv:2502.13722, I introduced a novel deep learning approach that demonstrated significant improvements over traditional VWAP execution methods

Mingkun Zhang, Keping Bi, Wei Chen, Jiafeng Guo

In this paper, we aim to build an adversarially robust zero-shot image classifier. We ground our work on CLIP, a vision-language pre-trained encoder model that can perform zero-shot classification by matching an image with text prompts ``a photo of a <class-name>.&#39;&#39;. Purification is the path we choose since it does not require adversarial training on

Víctor Bresó-Pla, Sergio Cruz-Alzaga, Martín González-Alonso, Suraj Prakash

We demonstrate that measurements of Coherent Elastic Neutrino-Nucleus Scattering (CE$\nu$NS) at spallation sources are valuable probes of muon-decay physics. Using COHERENT data we derive the first direct constraint on the Michel parameters governing the $\bar\nu_\mu$ energy distribution. We also discuss future sensitivities, the implications for the Lorentz

Vojtech Mrazek, Zdenek Vasicek

The increasing demand for energy-efficient solutions has led to the emergence of an approximate computing paradigm that enables power-efficient implementations in various application areas such as image and data processing. The median filter, widely used in image processing and computer vision, is of immense importance in these domains. We propose a systemat

Ashes Modak, Aditya Singh, Binata Panda

In this paper, we have computed the logarithmic corrections of entropy for the near-extremal Kerr-Newman black holes in $\mathcal{N}=2$ supergravity theory applying the Euclidean path integral approach in the near-horizon geometry. In the near-horizon extremal Kerr geometry, analogous to the $AdS_{2} \times S^2 $ structure, there exists a set of normalizable

Chidre Shravista Kashyap, Pushpak Jagtap, Jishnu Keshavan

The synthesis of a smooth tracking control for Euler-Lagrangian (EL) systems under stringent state, input, and temporal (SIT) constraints is challenging. In contrast to existing methods that utilize prior knowledge of EL model parameters and uncertainty bounds, this study proposes an approximation-free adaptive barrier function-based control policy to ensure

Michael Zhdanov

The goal of this paper is to estimate some parameters of simple social harmonic oscillations modelling U.S. Presidential Approval and Macropartisanship. The harmonic oscillations are simplest solutions of equations of the deterministic social dynamics (Zhdanov M., 2024), relating social bodies positions in the Bayesian space of assessments to noncoercive dri

Matteo Correnti, Francesca Annibali, Michele Bellazzini, Mariarosa Marinelli

We present the results of deep Hubble Space Telescope photometry of the dwarf galaxy DDO 68-C, proposed as possibly associated with the isolated peculiar dwarf DDO 68. The new data resolve for the first time the stars of DDO 68-C down to well below the tip of the Red Giant Branch (RGB), revealing a low mass (M$_{*}$ $\sim$ 1.5 $\times$ 10$^7$ M$_{\odot}$) st

Jayadev Acharya, Abhilash Dharmavarapu, Yuhan Liu, Nengkun Yu

Quantum state tomography is a fundamental problem in quantum computing. Given $n$ copies of an unknown $N$-qubit state $\rho \in \mathbb{C}^{d \times d},d=2^N$, the goal is to learn the state up to an accuracy $\epsilon$ in trace distance, with at least probability 0.99. We are interested in the copy complexity, the minimum number of copies of $\rho$ needed

Noritaka Sakakibara, Tsuyohito Ito, Yukiya Hakuta, Yoshiki Shimizu

The dynamics of solvated electrons were visualized using absorption imaging with sub-picosecond time resolution based on a pump-probe measurement during the early stages of femtosecond laser-induced plasma generation in water. The solvated electrons were generated by the propagation of a femtosecond laser pump pulse. In the area with a pump laser intensity o

Yuxuan Zhang

With the rapid development of large language models (LLMs), fully fine-tuning (FT) these models is becoming increasingly infeasible due to high computational demands. Moreover, FT also increases the risk of catastrophic forgetting. As an alternative, Low-Rank Adaptation (LoRA) has been proposed. By fine-tuning only a small subset of parameters, LoRA achieves

Xiao Shao, Guoqiang Wu

In multi-task learning (MTL) with each task involving graph-dependent data, existing generalization analyses yield a \emph{sub-optimal} risk bound of $O(\frac{1}{\sqrt{n}})$, where $n$ is the number of training samples of each task. However, to improve the risk bound is technically challenging, which is attributed to the lack of a foundational sharper concen

Daniel Werner, Enrico Arrigoni

We present a functional interpolation approach within the auxiliary master equation framework to efficiently and accurately solve correlated impurity problems in nonequilibrium dynamical mean-field theory (DMFT). By leveraging a near-exact auxiliary bath representation, the method estimates corrections via interpolation over a few bath realisations, signific

Jason Behrstock, R. Altar Ciceksiz, Victor Falgas-Ravry

We consider random right-angled Coxeter groups, $W_{\Gamma}$, whose presentation graph $\Gamma$ is taken to be an Erd\H{o}s--R\'enyi random graph, i.e., $\Gamma\sim \mathcal{G}_{n,p}$. We use techniques from probabilistic combinatorics to establish several new results about the geometry of these random groups. We resolve a conjecture of Susse and determine t

Mostafa Meliani

We study the local existence of solutions to the magnetohydrodynamics (MHD) system describing the motion of a compressible, viscous, electrically and heat conducting fluid in the $L^p-L^q$ class with inhomogeneous boundary conditions. The open system is allowed to receive incoming matter from the outside through (part of) the boundary which we refer to as an

Adrian Bauske, Arthur Fleig

Overtaking on country roads with possible opposing traffic is a dangerous maneuver and many proposed assistant systems assume car-to-car communication and sensors currently unavailable in cars. To overcome this limitation, we develop an assistant that uses simple in-car sensors to predict the required sight distance for safe overtaking. Our models predict th

Ercai Chen, Tassilo Küpper, Yunxiang Xie

For an expansive homeomorphism, we investigate the relationship among dimension, entropy, and Lyapunov exponents. Motivated by Young's formula for surface diffeomorphisms, which links dimension and measure-theoretic entropy with hyperbolic ergodic measures, we construct the hyperbolic metric with two distinct Lyapunov exponents $\log b>0>-\log a$. We then ex

Pablo Ortega, Eduardo Castelló Ferrer

As robotic systems (RS) become more autonomous, they are becoming increasingly used in small spaces and offices to automate tasks such as cleaning, infrastructure maintenance, or resource management. In this paper, we propose iTrash, an intelligent trashcan that aims to improve recycling rates in small office spaces. For that, we ran a 5 day experiment and f

Neslihan Gügümcü, Hamdi Kayaslan

In this paper, we give a geometric interpretation of virtual knotoids as arcs in thickened surfaces. Then we show that virtual knotoid theory is a generalization of classical knotoid theory. This gives a proof of a conjecture of Kauffman and the first author.

Krzysztof Bartoszek, Bayu Brahmantio, Woodrow Hao Chi Kiang

We present here a thorough study of the first two moments and cross-moments for the pure birth tree's height and for the coalescent time of a randomly sampled pair of tips. We consider also the first two moments of the conditional, on the tree, expectation of this coalescent time.

Mauricio Cruz Reyes, Richard I. Anderson, Susmita Das

We identified a sample of 88 bona fide Population II Cepheids (henceforth referred to as Cepheids) and 44 candidates in Galactic globular clusters (GCs). Seventy-eight of the Cepheids in the sample align within $2\,\sigma$ of the period-luminosity relation for Milky Way Type II Cepheids (T2CEPs). Nine align with the period-luminosity relation for fundamental

Filippo Maria Bianchi, Jakob Grahn

Snow avalanches present significant risks to human life and infrastructure, particularly in mountainous regions, making effective monitoring crucial. Traditional monitoring methods, such as field observations, are limited by accessibility, weather conditions, and cost. Satellite-borne Synthetic Aperture Radar (SAR) data has become an important tool for large

Zahra Dehghanighobadi, Asja Fischer, Muhammad Bilal Zafar

Explanations are an important tool for gaining insights into the behavior of ML models, calibrating user trust and ensuring regulatory compliance. Past few years have seen a flurry of post-hoc methods for generating model explanations, many of which involve computing model gradients or solving specially designed optimization problems. However, owing to the r

David Hartman, Jaroslav Hlinka, Anna Pidnebesna, František Szczepanik

Recently, the influence of potentially present symmetries has begun to be studied in complex networks. A typical way of studying symmetries is via the automorphism group of the corresponding graph. Since complex networks are often subject to uncertainty and automorphisms are very sensitive to small changes, this characterization needs to be modified to an ap

Koushik Dey, Hasina Khatun, Anudeepa Ghosh, Soumik Das

Magnetodielectric (MD) materials are important for their ability to spin-charge conversion, magnetic field control of electric polarization and vice versa. Among these, two-dimensional (2D) van der Waals (vdW) magnetic materials are of particular interest due to the presence of magnetic anisotropy (MA) originating from the interaction between the magnetic mo

Dahun Shin, Dongyeop Lee, Jinseok Chung, Namhoon Lee

Approximate second-order optimization methods often exhibit poorer generalization compared to first-order approaches. In this work, we look into this issue through the lens of the loss landscape and find that existing second-order methods tend to converge to sharper minima compared to SGD. In response, we propose Sassha, a novel second-order method designed

Victoria Clerico, Anirvan Dutta, Donato Francesco Falcone, Wooseok Choi

Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of power-constrained edge training and inference by utilizing analog Resistive Random Access Memory (ReRAM) technology in conjunction with a rea

Shan He, Yalong Ma, Tao Song, Yongzhi Jiang

Planning a safe and feasible trajectory for autonomous vehicles in real-time by fully utilizing perceptual information in complex urban environments is challenging. In this paper, we propose a spatio-temporal trajectory planning method based on graph optimization. It efficiently extracts the multi-modal information of the perception module by constructing a

Ayushi Dutta, Marco Pesavento, Marco Volino, Adrian Hilton

Recent approaches to jointly reconstruct 3D humans and objects from a single RGB image represent 3D shapes with template-based or coarse models, which fail to capture details of loose clothing on human bodies. In this paper, we introduce a novel implicit approach for jointly reconstructing realistic 3D clothed humans and objects from a monocular view. For th

Nasrin Altafi, Rodica Dinu, Sara Faridi, Shreedevi K. Masuti

This paper initiates a systematic study for key properties of Artinian Gorenstein \(K\)-algebras having binomial Macaulay dual generators. In codimension 3, we demonstrate that all such algebras satisfy the strong Lefschetz property, can be constructed as a doubling of an appropriate 0-dimensional scheme in \(\mathbb{P}^2\), and we provide an explicit charac

Muhammad Farid Adilazuarda, Musa Izzanardi Wijanarko, Lucky Susanto, Khumaisa Nur'aini

Indonesia is rich in languages and scripts. However, most NLP progress has been made using romanized text. In this paper, we present NusaAksara, a novel public benchmark for Indonesian languages that includes their original scripts. Our benchmark covers both text and image modalities and encompasses diverse tasks such as image segmentation, OCR, transliterat

Lucy Farnik, Tim Lawson, Conor Houghton, Laurence Aitchison

Sparse autoencoders (SAEs) have been successfully used to discover sparse and human-interpretable representations of the latent activations of LLMs. However, we would ultimately like to understand the computations performed by LLMs and not just their representations. The extent to which SAEs can help us understand computations is unclear because they are not

Fernando Micena, Raúl Ures

We study the ergodicity of partially hyperbolic endomorphisms, focusing on skew products where the base dynamics are governed by Anosov endomorphisms. For this family, we establish ergodicity and prove that accessibility holds for an open and dense subset. By analyzing the topological implications of accessibility, we demonstrate that conservative accessible

Ziyue Lin, Siqi Shen, Zichen Cheng, Cheok Lam Lai

Recent interest in human-AI interactions in agent-based modeling and simulation (ABMS) has grown rapidly due to the widespread utilization of large language models (LLMs). ABMS is an intelligent approach that simulates autonomous agents' behaviors within a defined environment to research emergent phenomena. Integrating LLMs into ABMS enables natural language

Yunfeng Li, Bo Wang, Ye Li

Despite great progress in multimodal tracking, these trackers remain too heavy and expensive for resource-constrained devices. To alleviate this problem, we propose LightFC-X, a family of lightweight convolutional RGB-X trackers that explores a unified convolutional architecture for lightweight multimodal tracking. Our core idea is to achieve lightweight cro

Catherine F. Higham, Paul Henderson, Roderick Murray-Smith

Measurement of a physical quantity such as light intensity is an integral part of many reconstruction and decision scenarios but can be costly in terms of acquisition time, invasion of or damage to the environment and storage. Data minimisation and compliance with data protection laws is also an important consideration. Where there are a range of measurement

Tammy Ng, Edward Teo

The Kerr separatrix is a boundary in parameter space that separates bound orbits from plunging orbits in the Kerr black hole space-time. Recently, Stein and Warburton found a polynomial equation for the location of the separatrix, for two different choices of inclination parameter. Following a method of Levin and Perez-Giz developed for the equatorial case,

Jean Van Schaftingen

Trace conjunction integrals are introduced and studied. They appear in trace conjunction inequalities which unify the Hardy inequality on a halfspace and the classical Gagliardo trace inequality. At the endpoint they satisfy a Bourgain-Brezis-Mironescu formula for smooth maps, which raises some new open problems.

Zhuocheng Zhang, Yang Feng, Min Zhang

Retrieval-Augmented Generation (RAG) is a crucial method for mitigating hallucinations in Large Language Models (LLMs) and integrating external knowledge into their responses. Existing RAG methods typically employ query rewriting to clarify the user intent and manage multi-hop logic, while using hybrid retrieval to expand search scope. However, the tight cou

Chenhao Gu, Ling Luo, Zainab Razia Zaidi, Shanika Karunasekera

The rise of echo chambers on social media platforms has heightened concerns about polarization and the reinforcement of existing beliefs. Traditional approaches for simulating echo chamber formation have often relied on predefined rules and numerical simulations, which, while insightful, may lack the nuance needed to capture complex, real-world interactions.

Jintao Zhang, Chendong Xiang, Haofeng Huang, Jia Wei

An efficient attention implementation is essential for large models due to its quadratic time complexity. Fortunately, attention commonly exhibits sparsity, i.e., many values in the attention map are near zero, allowing for the omission of corresponding computations. Many studies have utilized the sparse pattern to accelerate attention. However, most existin

Peng-Jie Guo, Xiao-Yao Hou, Ze-Feng Gao, Huan-Cheng Yang

Altermagnetic materials, with real-space antiferromagnetic arrangement and reciprocal-space anisotropic spin splitting, have attracted much attention. However, the spin splitting is small in most altermagnetic materials, which is a disadvantage to their application in electronic devices. In this study, based on symmetry analysis and the first-principles elec

Martin Larsson, Viktor Larsson, Kalle Åström, Magnus Oskarsson

This paper introduces a novel method for solving the single-source localization problem, specifically addressing the case of trilateration. We formulate the problem as a weighted least-squares problem in the squared distances and demonstrate how suitable weights are chosen to accommodate different noise distributions. By transforming this formulation into an

Jingwei Wang, Pengxiang Wang, Chaofan Zhang, Yuntian Chen

Quasi-normal modes (QNMs) and coherent control of light-matter interactions (through synchronized multiple coherent incident waves) are profound and pervasive concepts in and beyond photonics, making accessible photonic manipulations with extreme precision and efficiency. Though each has been playing essential roles in its own, these two sweeping concepts re

Tara Benkel, Mika Lyly, Janne Ruuskanen, Alexandre Halbach

Stellarators present features such as steady-state operation and intrinsic stability that make them more attractive than tokamaks in their scaling to fusion power plants. By leveraging more possible configurations, stellarators can be optimized for better engineering feasibility, e.g., resilience to manufacturing tolerances, reduced mechanical load on conduc

Maximilien Péters de Bonhome, Viviane Pierrard, Fabio Bacchini

Parker Solar Probe (PSP) observations have revealed that most of the solar wind acceleration occurs very close to the Sun. This acceleration is partly due to the global electric potential originating from the mass disparity between electrons and protons, coupled with the constraints of charge quasi-neutrality and zero-current conditions in the solar wind pla

Yan Luo, Kaicheng Sheng

This paper focuses on finding an approximate solution of a kind of Fokker-Planck equation with time-dependent perturbations. A formulation of the approximate solution of the equation is constructed, and then the existence of the formulation is proved. The related Hamiltonian dynamical system explains the estimations. Our work provides a more comprehensive un

Rong-Zhi Sun, Shu-Min Zhao, Ming-Yue Liu, Xing-Yu Han

The N-B-LSSM is an extension of the minimal supersymmetric standard model (MSSM) with the addition of three singlet new Higgs superfields and right-handed neutrinos, whose local gauge group is $SU(3)_C\times SU(2)_L\times U(1)_Y\times U(1)_{B-L}$. In the N-B-LSSM, we study lepton flavor violating decays $l_j\rightarrow l_i\gamma$, $l_j \rightarrow 3l_i$ and

András Haris, Mikko Tuomi, Thomas Hackman

Late-type stars are known to host numerous exoplanets, and their photometric variability, primarily caused by rotational modulation, provides a unique opportunity to study starspots. As exoplanets transit in front of their host stars, they may occult darker, spotted regions on the stellar surfaces. The monitoring of starspots from planetary transits, known a

Janet Flikkema, Maarten Solleveld

Let $G$ be a covering group of a reductive $p$-adic group. We study intertwining operators between parabolically induced representations of $G$ and prove that they satisfy certain adjointness relations. The Harish-Chandra $\mu$-function is defined as a composition of such intertwining operators for opposite parabolic subgroups of $G$. It can be seen as a com

Yun Hao, Che Fan, Beilin Ye, Wenhao Lu

Deep generative models hold great promise for inverse materials design, yet their efficiency and accuracy remain constrained by data scarcity and model architecture. Here, we introduce AlloyGAN, a closed-loop framework that integrates Large Language Model (LLM)-assisted text mining with Conditional Generative Adversarial Networks (CGANs) to enhance data dive

Kengo Matsumoto, Taro Sogabe

Reciprocality in Kirchberg algebras is a duality between strong extension groups and K-theory groups. We describe a construction of the reciprocal dual algebra $\widehat{\mathcal{A}}$ for a Kirchberg algebra $\mathcal{A}$ with finitely generated K-groups via K-theoretic duality for extensions. In particular, we may concretely realize the reciprocal algebra $

Sirui Huang, Hanqian Li, Yanggan Gu, Xuming Hu

Given that substantial amounts of domain-specific knowledge are stored in structured formats, such as web data organized through HTML, Large Language Models (LLMs) are expected to fully comprehend this structured information to broaden their applications in various real-world downstream tasks. Current approaches for applying LLMs to structured data fall into

Gopal Hazra

The review aims to give an overview of atmospheric escape processes from exoplanets. I briefly discuss the physics of various escape processes responsible for atmospheric escape across different types of exoplanets. Transmission spectroscopy is one of the major workhorses to observe the escaping atmosphere from exoplanets. I discuss recent observations that

Yuhu Feng, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

Egocentric video gaze estimation requires models to capture individual gaze patterns while adapting to diverse user data. Our approach leverages a transformer-based architecture, integrating it into a PFL framework where only the most significant parameters, those exhibiting the highest rate of change during training, are selected and frozen for personalizat

B. Sun, P. Liò

In this study, we propose MHEX+, a framework adaptable to any U-Net architecture. Built upon MHEX+, we introduce novel U-Net variants, EU-Nets, which enhance explainability and uncertainty estimation, addressing the limitations of traditional U-Net models while improving performance and stability. A key innovation is the Equivalent Convolutional Kernel, whic

Ryo Takizawa, Izumi Karino, Koki Nakagawa, Yoshiyuki Ohmura

Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning have made it increasingly feasible to replicate the dexterity of human teleoperation in robots, generalizing these acquired skills to previously unseen scenarios remains a significan

Yubin Choi, Jeanne Choi, Joseph Seering

Creating games together is both a playful and effective way to develop skills in computational thinking, collaboration, and more. However, game development can be challenging for younger developers who lack formal training. While teenage developers frequently turn to online communities for peer support, their experiences may vary. To better understand the be

Honghong Lin, Yun Shang

Large-scale eigenvalue problems pose a significant challenge to classical computers. While there are efficient quantum algorithms for unitary or Hermitian matrices, eigenvalue problems for non-normal matrices remain open in quantum computing. In this work, we propose a quantum algorithm that given a non-normal matrix, outputs an estimate of an eigenvalue to

Changyuan Zhao, Jiacheng Wang, Ruichen Zhang, Dusit Niyato

Low-Altitude Economy Networks (LAENets) have emerged as significant enablers of social activities, offering low-altitude services such as the transportation of packages, groceries, and medical supplies. Owing to their control mechanisms and ever-changing operational factors, LAENets are inherently more complex and vulnerable to security threats than traditio

Shasha Huo, Bo Sun

While Moore's Law has approached its physical limits lately, the high integration and miniaturisation of electronics have also brought another thermal failure obstacle. Previous studies on single-phase flow demanded significant pump power to achieve higher CHF, but this approach risked exceeding the chip's mechanical limits and complicating packaging. The el

Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang

In the rapidly evolving field of image generation, achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. To address these challenges, we propose BayesGenie, an off-the-shelf approach that integrates Large Langu

Alexander Hock

Let $F_g$ be the free energy derived from Topological Recursion for a given spectral curve on a compact Riemann surface, and let $F_g^\vee$ be its $x$-$y$ dual, that is, the free energy derived from the same spectral curve with the roles of $x$ and $y$ interchanged. $F_g$ is sometimes called a symplectic invariant due to its invariance under certain symplect

Bence Bécsy, Neil J. Cornish, Polina Petrov, Xavier Siemens

The recent discovery of the stochastic gravitational-wave background via pulsar timing arrays will likely be followed by the detection of individual black hole binaries that stand out above the background. However, to confidently claim the detection of an individual binary, we need not only more and better data, but also more sophisticated analysis technique

Mengzhao Wang, Haotian Wu, Xiangyu Ke, Yunjun Gao

In high-dimensional vector spaces, Approximate Nearest Neighbor Search (ANNS) is a key component in database and artificial intelligence infrastructures. Graph-based methods, particularly HNSW, have emerged as leading solutions among various ANNS approaches, offering an impressive trade-off between search efficiency and accuracy. Many modern vector databases

Athul Dileep, Santosh Joshi, Sneh Lata, Patricia Lampens

Pulsating variables are prevalent in the classical {\delta} Scuti instability strip of intermediate-age open star clusters. The cluster membership of these stars facilitates a comparative analysis of their evolution in analogous environments. In this study, we integrate ground-based observations, TESS Full Frame Images (FFIs), and Gaia DR3 data to investigat