December 2024 arXiv papers — page 71

Théotim Berlioz, Olivier Bénichou, Aurélien Grabsch

Interacting particles diffusing in single-file is a fundamental model of transport in narrow channels where particles cannot bypass each other. An important result has been obtained by Kollmann [Phys. Rev. Lett. 90, 180602 (2003)] for the mean square displacement of a tracer for any single-file model. It applies to any diffusive system, in particular the not

Zijun Chen, Wenbo Hu, Guande He, Zhijie Deng

Multimodal large language models (MLLMs) combine visual and textual data for tasks such as image captioning and visual question answering. Proper uncertainty calibration is crucial, yet challenging, for reliable use in areas like healthcare and autonomous driving. This paper investigates representative MLLMs, focusing on their calibration across various scen

Sumanjit Chakraborty, Dibyendu Chakrabarty, Anil K. Yadav, Gopi K. Seemala

This work shows an anomalously enhanced response of the low-latitude ionosphere over the Indian sector under weak geomagnetic conditions (October 31, 2021) in comparison to a stronger event (November 04, 2021) under the influence of an Interplanetary Coronal Mass Ejection (ICME)-driven Magnetic Cloud (MC)-like and sheath regions respectively. The investigati

Federico Gallo, Alireza Shahedi, Angela Di Febbraro, Mahnam Saeednia

The Physical Internet (PI) paradigm, which has gained attention in research and academia in recent years, leverages advanced logistics and interconnected networks to revolutionize the way goods are transported and delivered, thereby enhancing efficiency, reducing costs and delays, and minimizing environmental impact. Within this system, PI-hubs function simi

Shaohua Yue, Liang Liu, Boya Di

Extremely large-scale multiple-input multiple-output (XL-MIMO) communications, enabled by numerous antenna elements integrated into large antenna surfaces, can provide increased effective degree of freedom (EDoF) to achieve high diversity gain. However, it remains an open problem that how the EDoF is influenced by the directional radiation pattern of antenna

Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong

Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-world applications. Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for prac

Enji Li

At the heart of neural network force fields (NNFFs) is the architecture of neural networks, where the capacity to model complex interactions is typically enhanced through widening or deepening multilayer perceptrons (MLPs) or by increasing layers of graph neural networks (GNNs). These enhancements, while improving the model's performance, often come at the c

Berenike Masing, Niels Lindner, Enrico Bortoletto

Given a public transportation network, which and how many passenger routes can potentially be shortest paths, when all possible timetables are taken into account? This question leads to shortest path problems on graphs with interval costs on their arcs and is closely linked to multi-objective optimization. We introduce a Dijkstra algorithm based on polynomia

Liang-Qi Zhang, Mei-Yu Si, Tong-Pu Yu, Yuan-Jie Bi

We propose a novel scheme for generating and accelerating simultaneously a dozen-GeV isolated attosecond electron bunch from an electron beam-driven hollow-channel plasma target. During the beam-target interaction, transverse oscillations of plasma electrons are induced, and subsequently, a radiative wakefield is generated. Meanwhile, a large number of plasm

Therese Frostad, Anne Louise Kristoffersen, Verena Brehm, Roberto E. Troncoso

Quasiequilibrium magnon Bose-Einstein condensates in ferromagnetic insulators have been a field of great interest, while condensation in antiferromagnetic systems has not yet been explored in detail. We analyze the stability of condensed chiral magnons in two antiferromagnetic insulators: a uniaxial easy-axis system and a biaxial system. We show that two-com

Jake Hyun

Clustering is a key task in machine learning, with $k$-means being widely used for its simplicity and effectiveness. While 1D clustering is common, existing methods often fail to exploit the structure of 1D data, leading to inefficiencies. This thesis introduces optimized algorithms for $k$-means++ initialization and Lloyd's algorithm, leveraging sorted data

Fatemah Alharthi, Ishmael Apachigawo, Dhruvil Solanki, Sazzad Khan

Understanding alterations in structural disorders in tissue or cells or building blocks, such as DNA or chromatin in the human brain, at the nano to submicron level provides us with efficient biomarkers for Alzheimers detection. Here, we report a dual photonics technique to detect nano- to submicron-scale alterations in brain tissues or cells and DNA or chro

Gérard Ben Arous, Cédric Gerbelot, Vanessa Piccolo

We study the dynamics of gradient flow in high dimensions for the multi-spiked tensor problem, where the goal is to estimate $r$ unknown signal vectors (spikes) from noisy Gaussian tensor observations. Specifically, we analyze the maximum likelihood estimation procedure, which involves optimizing a highly nonconvex random function. We determine the sample co

Benjamin Haïoun

We give a finite presentation of the cobordism symmetric monoidal bicategory of (smooth, oriented) closed manifolds, cobordisms and cobordisms with corners as an extension of the bicategory of closed manifolds, cobordisms and diffeomorphisms. The generators are the standard handle attachments, and the relations are handle cancellations and invariance under r

Donal Patrick Lynch, Mengran Zhao, Aaron M. Graham, Stylianos D. Asimonis

This paper presents a comprehensive study of a compact two-element meandered dipole array aimed at achieving super-realized gain. An optimization was performed using a Genetic Algorithm (GA) implemented in MATLAB, targeting the maximization of the realized gain in the end-fire direction. Three primary excitation schemes were evaluated: the conventional optim

Rui Lin, Han-Sen Zhong, You Li, Zhang-Rui Zhao

Assembling increasingly larger-scale defect-free optical tweezer-trapped atom arrays is essential for quantum computation and quantum simulations based on atoms. Here, we propose an AI-enabled, rapid, constant-time-overhead rearrangement protocol, and we experimentally assemble defect-free 2D and 3D atom arrays with up to 2024 atoms with a constant time cost

Thiemen Siemensma, Bahar Haghighat

Inspection of infrastructure using static sensor nodes has become a well established approach in recent decades. In this work, we present an experimental setup to address a binary inspection task using mobile sensor nodes. The objective is to identify the predominant tile type in a 1mx1m tiled surface composed of vibrating and non-vibrating tiles. A swarm of

Boyang Sun, James M. Lattimer

We develop an analytic method of inverting the Tolman-Oppenheimer-Volkoff (TOV) relations to high accuracy. In principle, a specified $\mathcal{E}\mbox{-}P$ relation gives a unique $M\mbox{-}R$ relation, and vice-versa. Our method is developed from the strong correlations that are shown to exist between the neutron star mass-radius curve and the equation of

Jiachuan Cao, Buyang Li, Katharina Schratz

The regularity of solutions to the stochastic nonlinear wave equation plays a critical role in the accuracy and efficiency of numerical algorithms. Rough or discontinuous initial conditions pose significant challenges, often leading to a loss of accuracy and reduced computational efficiency in existing methods. In this study, we address these challenges by d

Jie Huang, Ruibing Hou, Jiahe Zhao, Hong Chang

Human-centric perceptions play a crucial role in real-world applications. While recent human-centric works have achieved impressive progress, these efforts are often constrained to the visual domain and lack interaction with human instructions, limiting their applicability in broader scenarios such as chatbots and sports analysis. This paper introduces Refer

Farhana Kousar, Maosheng Xiong

In a recent paper Zhang et al. constructed 17 families of permutation pentanomials of the form $x^t+x^{r_1(q-1)+t}+x^{r_2(q-1)+t}+x^{r_3(q-1)+t}+x^{r_4(q-1)+t}$ over $\mathbb{F}_{q^2}$ where $q=2^m$. In this paper for 14 of these 17 families we provide a simple explanation as to why they are permutations. We also extend these 14 families into three general c

Eric Brouwer, Jan Erik van Woerden, Gertjan Burghouts, Matias Valdenegro-Toro

Few-shot, fine-grained classification in computer vision poses significant challenges due to the need to differentiate subtle class distinctions with limited data. This paper presents a novel method that enhances the Contrastive Language-Image Pre-Training (CLIP) model through adaptive prompt tuning, guided by real-time visual inputs. Unlike existing techniq

Iain Burge, Michel Barbeau, Joaquin Garcia-Alfaro

This work focuses on developing efficient post-hoc explanations for quantum AI algorithms. In classical contexts, the cooperative game theory concept of the Shapley value adapts naturally to post-hoc explanations, where it can be used to identify which factors are important in an AI's decision-making process. An interesting question is how to translate Shapl

Anna Franziska Frigge, Lina Uggla, Elena Jiltsova, Markus Fahlström

Objective: The objective of this study is to develop and evaluate a systematic approach to optimize Deep Brain Stimulation (DBS) parameters, addressing the challenge of identifying patient-specific settings and optimal stimulation targets for various neurological and mental disorders. Methods: TuneS, a novel pipeline to predict clinically optimal DBS contact

Bensaid Bilel

The classical Armijo backtracking algorithm achieves the optimal complexity for smooth functions like gradient descent but without any hyperparameter tuning. However, the smoothness assumption is not suitable for Deep Learning optimization. In this work, we show that some variants of the Armijo optimizer achieves acceleration and optimal complexities under a

Haesung Lee

In this paper, we show that, for a solution to the stationary Fokker-Planck equation with general coefficients, defined as a measure with an $L^2$-density, this density not only exhibits $H^{1,2}$-regularity but also H\"{o}lder continuity. To achieve this, we first construct a reference measure $\mu=\rho dx$ by utilizing existence and elliptic regularity res

Olivier Rahavandrainy

We identify all non-splitting bi-unitary perfect polynomials over the field $\mathbb{F}_4$, which admit at most four irreducible divisors. There is an infinite number of such divisors.

Bartosz Naskręcki, Piotr Pokora

This survey focuses on the geometric problem of log-surfaces, which are pairs consisting of a smooth projective surface and a reduced non-empty boundary divisor. In the first part, we focus on the geography problem for complex log-surfaces associated with pairs of the form $(\mathbb{P}^{2}, C)$, where $C$ is an arrangement of smooth plane curves admitting or

Jie Ji, Jingru Niu

In this paper, we study singular heat flows from a 3-dimensional complete bounded Riemannian manifold without boundary into the hyperbolic space with prescribe singularity along a closed curve. We prove the existence and regularity of the singular heat flows. Furthermore, we prove that the singular heat flows converge to a singular harmonic map at an exponen

Rui Ding, Liang Yong, Sihuan Zhao, Jing Nie

Due to its efficiency, Post-Training Quantization (PTQ) has been widely adopted for compressing Vision Transformers (ViTs). However, when quantized into low-bit representations, there is often a significant performance drop compared to their full-precision counterparts. To address this issue, reconstruction methods have been incorporated into the PTQ framewo

Wanda Hou, Molan Li, Yi-Zhuang You

We propose a data-driven Machine-Learning Symmetry Discovery (MLSD) framework for identifying continuous symmetry generators and their Lie-algebraic structure directly from phase-space trajectory data expressed in canonical coordinates. MLSD parameterizes candidate conserved quantities with neural networks and learns antisymmetric structure coefficients by e

Minglong Xue, Jinhong He, Shivakumara Palaiahnakote, Mingliang Zhou

Image restoration and enhancement are pivotal for numerous computer vision applications, yet unifying these tasks efficiently remains a significant challenge. Inspired by the iterative refinement capabilities of diffusion models, we propose CycleRDM, a novel framework designed to unify restoration and enhancement tasks while achieving high-quality mapping. S

Kexin Li, You-wei Wen, Xu Xiao, Mingchao Zhao

Robust Principal Component Analysis (RPCA) is a fundamental technique for decomposing data into low-rank and sparse components, which plays a critical role for applications such as image processing and anomaly detection. Traditional RPCA methods commonly use $\ell_1$ norm regularization to enforce sparsity, but this approach can introduce bias and result in

Keith G. Mills, Mohammad Salameh, Ruichen Chen, Negar Hassanpour

Diffusion Models (DM) have democratized AI image generation through an iterative denoising process. Quantization is a major technique to alleviate the inference cost and reduce the size of DM denoiser networks. However, as denoisers evolve from variants of convolutional U-Nets toward newer Transformer architectures, it is of growing importance to understand

Cătălin Paşcu Moca, Csanád Hajdú, Balázs Dóra, Gergely Zaránd

A magnetic impurity in a BCS superconductor induces the formation of a Shiba state and drives a local quantum phase transition. We generalize this concept to a one-dimensional superconductor with fractionalized excitations, where the dominant instability is superconducting. In this framework, conduction electrons fractionalize into gapless charge and gapped

Ruochen Li, Liqiang Jing, Chi Han, Jiawei Zhou

Recent advancements in large language models (LLMs) have demonstrated their potential in automating the scientific research ideation. Existing approaches primarily focus on prompting techniques, often producing ideas misaligned with expert standards - novelty, feasibility, and effectiveness, which are widely recognized by the research community as the three

Da-Shan Jiang

The low frequency vibrational anomaly known as Boson peak (BP) have been studied extensively in various disordered systems, however its origin and theoretical description are still under debate. In this work, as one of the simplest model for describing vibrational properties in disordered systems, inhomogeneous elastic wave equation, is solved in real space

Antoine-Cyrus Becharat, Michael Benzaquen, Jean-Philippe Bouchaud

We analyze the French housing market prices in the period 1970-2022, with high-resolution data from 2018 to 2022. The spatial correlation of the observed price field exhibits logarithmic decay characteristic of the two-dimensional random diffusion equation -- local interactions may create long-range correlations. We introduce a stylized model, used in the pa

Younhun Kim, Myung-Joon Kwon, Wonjun Lee, Changick Kim

Previous Deepfake detection methods perform well within their training domains, but their effectiveness diminishes significantly with new synthesis techniques. Recent studies have revealed that detection models often create decision boundaries based on facial identity rather than synthetic artifacts, resulting in poor performance on cross-domain datasets. To

Luca Marchetti, Edward Wilson-Ewing

We construct relational observables in group field theory (GFT) in terms of covariant positive operator-valued measures (POVMs), using techniques developed in the context of quantum reference frames. We focus on matter quantum reference frames; this can be generalized to other types of frames within the same POVM-based framework. The resulting family of rela

Zhichao Carton Zeng, Annika H. G. Peter, Xiaolong Du, Andrew Benson

Dark matter halos with self-interacting dark matter (SIDM) experience a unique evolutionary phenomenon, in that their central regions eventually collapse to high density through the runaway gravothermal process after initially forming a large and low-density core. When coupled with orbital evolution, this is expected to naturally produce a large diversity in

Bin Chen, Zhiwei Liang, Yi Lei, JingXin Deng

Utilizing the multi-dimensional (MD) space for constellation shaping has been proven to be an effective approach for achieving shaping gains. Despite there exists a variety of MD modulation formats tailored for specific optical transmission scenarios, there remains a notable absence of a dependable comparison method for efficiently and promptly re-evaluating

Gokul Radhakrishnan, Rahul Sundar, Nishant Parashar, Antoine Blanchard

The sparse and spatio-temporally discontinuous nature of precipitation data presents significant challenges for simulation and statistical processing for bias correction and downscaling. These include incorrect representation of intermittency and extreme values (critical for hydrology applications), Gibbs phenomenon upon regridding, and lack of fine scales d

Alexander H. Berger, Laurin Lux, Alexander Weers, Martin Menten

Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuron or vessel segmentation. Despite the recent surge in topology-aware methods addressing this challenge, their real-world applicability is hindered by flawed benchmarking practices.

Nihar Ranjan Ghosh, Malay K. Nandy

The well-known instability of the inner horizon of a Reissner-Nordstr\"om black hole, first suggested by Simpson and Penrose, although studied extensively, has remained illusive so far as several studies led to varied conclusions about the dynamical nature of the inner horizon. In this work, we therefore focus upon the dynamic nature of the inner horizon in

Jacques Savoy

Using large language models (LLMs), computers are able to generate a written text in response to a us er request. As this pervasive technology can be applied in numerous contexts, this study analyses the written style of one LLM called GPT by comparing its generated speeches with those of the recent US presidents. To achieve this objective, the State of the

Sascha Kurz

Additive codes may have better parameters than linear codes. However, still very few cases are known and the explicit construction of such codes is a challenging problem. Here we show that a Griesmer type bound for the length of additive codes can always be attained with equality if the minimum distance is sufficiently large. This solves the problem for the

Yide Yu, Yue Liu, Xiaochen Yuan, Dennis Wong

Partially Observable Markov Decision Process (POMDP) is a mathematical framework for modeling decision-making under uncertainty, where the agent's observations are incomplete and the underlying system dynamics are probabilistic. Solving the POMDP problem within the model-free paradigm is challenging for agents due to the inherent difficulty in accurately ide

Masanari Ohi, Masahiro Kaneko, Naoaki Okazaki, Nakamasa Inoue

Vision-language models (VLMs) have shown impressive abilities across a range of multi-modal tasks. However, existing metrics for evaluating the quality of text generated by VLMs typically focus on an overall evaluation for a specific task, such as image captioning. While the overall evaluation is essential for any task, the criteria prioritized can differ de

Elena Senger, Yuri Campbell, Rob van der Goot, Barbara Plank

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of pr

Andrea Gurioli, Maurizio Gabbrielli, Stefano Zacchiroli

With the increasing popularity of LLM-based code completers, like GitHub Copilot, the interest in automatically detecting AI-generated code is also increasing-in particular in contexts where the use of LLMs to program is forbidden by policy due to security, intellectual property, or ethical concerns.We introduce a novel technique for AI code stylometry, i.e.

Chuang Li, James Jun He

Nonreciprocity of supercurrents may exist when both spatial inversion and time-reversal symmetries are broken, leading to the supercurrent diode effect (SDE). The spatial inversion symmetry may be broken by chiral structures in nanotubes where the SDE is expected when a magnetic flux passes through the tube. While such an effect has been predicted based on a

J. E. Gómez-Correa, E. Espíndola-Ramos, O. López-Cruz, S. Chávez-Cerda

According to Albert Einstein, gravitation is analogous to an optical medium. Building on this idea, various definitions of the gradient-index (GRIN) medium representing curved spacetime have been proposed; often, these approaches demand advanced knowledge of General Relativity (GR) and its associated mathematical methods. This paper introduces an alternative

Yangluo Zheng

The geometric dimension of a Vector Addition System with States (VASS), emerged in Leroux and Schmitz (2019) and formalized by Fu, Yang, and Zheng (2024), quantifies the dimension of the vector space spanned by cycle effects in the system. This paper explores the VASS reachability problem through the lens of geometric dimension, revealing key differences fro

Paula Boubel, Matthew Colless, Khaled Said, Lister Staveley-Smith

The cosmological principle asserting the large-scale uniformity of the Universe is a testable assumption of the standard cosmological model. We explore the constraints on anisotropic expansion provided by measuring directional variation in the Hubble constant, $H_0$, derived from differential zeropoint measurements of the Tully-Fisher distance estimator. We

Taha Yasseri

This Chapter examines the dynamics of conflict and collaboration in human-machine systems, with a particular focus on large-scale, internet-based collaborative platforms. While these platforms represent successful examples of collective knowledge production, they are also sites of significant conflict, as diverse participants with differing intentions and pe

Bo Hou, Zhanpeng Cui

We establish a bialgebra theory for averaging algebras, called averaging antisymmetric infinitesimal bialgebras by generalizing the study of antisymmetric infinitesimal bialgebras to the context of averaging algebras. They are characterized by double constructions of averaging Frobenius algebras as well as matched pairs of averaging algebras. Antisymmetric s

Mengkun Zhu, Yuting Chen, Jianduo Yu, Chuanzhong Li

In this paper, we {\color{black}study four kinds of polynomials orthogonal with the singularly perturbed Gaussian weight $w_{\rm SPG}(x)$, the deformed Freud weight $w_{\rm DF}(x)$, the jumpy Gaussian weight $w_{\rm JG}(x)$, and the Jacobi-type weight $w_{\rm {\color{black}JC}}(x)$. The second order linear differential equations satisfied by these orthogonal

Zheng Ren, Jingwen Zhou, Wenguan Zhang, Jiapu Yan

Recently, the joint design of optical systems and downstream algorithms is showing significant potential. However, existing rays-described methods are limited to optimizing geometric degradation, making it difficult to fully represent the optical characteristics of complex, miniaturized lenses constrained by wavefront aberration or diffraction effects. In th

Yuxuan Liang, Wentao Zhang, Zeang Sheng, Ling Yang

In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. However, scaling them to large graphs is challenging due to the high computational and storage costs of repeated feature propagation and non-linear transformation during training. One commonly employed approach to address this challenge is model-simplif

Chul-hee Lee, Jian-Rong Li, Euiyong Park

We formulate a positivity conjecture relating the Verlinde ring associated with an untwisted affine Lie algebra at a positive integer level and a subcategory of finite-dimensional representations over the corresponding quantum affine algebra with a cluster algebra structure. Specifically, we consider a ring homomorphism from the Grothendieck ring of this rep

Hongwei Qu, Zeying Zhang, Yuanchang Li

Excitonic insulator remains elusive and there has been a lack of reliable identification methods. In this work, we demonstrate the promise of topological excitonic insulators for identification due to their unique bulk-edge correspondence, as illustrated by the LiFe$X$ ($X$ = S, Se, and Te) family. First-principles Bethe-Salpeter equation calculations reveal

Ruixiang Chen, Yang Zhao, Haoqin Li, Rui Chen

In the realm of lithography, Optical Proximity Correction (OPC) is a crucial resolution enhancement technique that optimizes the transmission function of photomasks on a pixel-based to effectively counter Optical Proximity Effects (OPE). However, conventional pixel-based OPC methods often generate patterns that pose manufacturing challenges, thereby leading

Lei Su, Xiaochen Ma, Xuekang Zhu, Chaoqun Niu

Non-semantic features or semantic-agnostic features, which are irrelevant to image context but sensitive to image manipulations, are recognized as evidential to Image Manipulation Localization (IML). Since manual labels are impossible, existing works rely on handcrafted methods to extract non-semantic features. Handcrafted non-semantic features jeopardize IM

Qingyue Long, Yuan Yuan, Yong Li

Predicting human mobility is crucial for urban planning, traffic control, and emergency response. Mobility behaviors can be categorized into individual and collective, and these behaviors are recorded by diverse mobility data, such as individual trajectory and crowd flow. As different modalities of mobility data, individual trajectory and crowd flow have a c

Salma Rahmouni, Masaomi Tanaka, Nanae Domoto, Daiji Kato

The observation of the kilonova AT2017gfo and investigations of its light curves and spectra confirmed that neutron star mergers are sites of r-process nucleosynthesis. However, the identification of elements responsible for the spectral features is still challenging, particularly at the near-infrared wavelengths. In this study, we systematically searched fo

Huawen Shen, Gengluo Li, Jinwen Zhong, Yu Zhou

Visual Information Extraction (VIE) plays a crucial role in the comprehension of semi-structured documents, and several pre-trained models have been developed to enhance performance. However, most of these works are monolingual (usually English). Due to the extremely unbalanced quantity and quality of pre-training corpora between English and other languages,

Tomasz Beberok, Leokadia Białas-Cież, Stefano De Marchi

The study of interpolation nodes and their associated Lebesgue constants are central to numerical analysis, impacting the stability and accuracy of polynomial approximations. In this paper, we will explore the Morrow-Patterson points, a set of interpolation nodes introduced to construct cubature formulas of a minimum number of points in the square for a fixe

Mihai Prunescu, Joseph M. Shunia

We present the first fixed-length elementary closed-form expressions for the prime-counting function, $\pi(n)$, and the $n$-th prime number, $p(n)$. These expressions are arithmetic terms, requiring only a finite and fixed number of elementary arithmetic operations from the set: addition, subtraction, multiplication, integer division, and exponentiation. Maz

Sudin Ganguly, Santanu K. Maiti

Non-reciprocal hopping induces a synthetic magnetic flux which leads to the non-Hermitian Aharonov-Bohm effect. Since non-Hermitian Hamiltonians possess both real and imaginary eigenvalues, this effect allows the observation of real and imaginary persistent currents in a ring threaded by the synthetic flux. Motivated by this, we investigate the behavior of p

Eric Yanchenko, Kaoru Irie, Shonosuke Sugasawa

Shrinkage priors are a popular Bayesian paradigm to handle sparsity in high-dimensional regression. Still limited, however, is a flexible class of shrinkage priors to handle grouped sparsity, where covariates exhibit some natural grouping structure. This paper proposes a novel extension of the $R^2$-induced Dirichlet Decomposition (R2D2) prior to accommodate

Wenqiao Li, Bozhong Zheng, Xiaohao Xu, Jinye Gan

Object anomaly detection is essential for industrial quality inspection, yet traditional single-sensor methods face critical limitations. They fail to capture the wide range of anomaly types, as single sensors are often constrained to either external appearance, geometric structure, or internal properties. To overcome these challenges, we introduce MulSen-AD

Md Rysul Kabir, James Mochizuki-Freeman, Zoran Tiganj

The ability to estimate temporal relationships is critical for both animals and artificial agents. Cognitive science and neuroscience provide remarkable insights into behavioral and neural aspects of temporal credit assignment. In particular, scale invariance of learning dynamics, observed in behavior and supported by neural data, is one of the key principle

Dimitris Koutromanos, Dionisis Stefanatos, Emmanuel Paspalakis

Machine learning has been revolutionizing our world over the last few years and is also increasingly exploited in several areas of physics, including quantum dynamics and control.The need for a framework that brings together machine learning models and quantum simulation methods has been quite high within the quantum control field, with the ultimate goal of

Zhen Zheng, Xiaonan Song, Chuanjie Liu

Quantization has become one of the most effective methodologies to compress LLMs into smaller size. However, the existing quantization solutions still show limitations of either non-negligible accuracy drop or low system efficiency. In this paper, we propose MixLLM that explores the optimization space of mixed-precision quantization between output features,

Sen Yang, Xiaofeng Li

The GEO satellite maintains good synchronization with the ground, reducing the priority of acquisition time in the establishment of the optical link. Whereas energy is an important resource for the satellite to execute space missions, the consumption during the acquisition process rises to the primary optimization objective. However, no previous studies have

Kepu Zhang, Haoyue Yang, Xu Tang, Weijie Yu

In legal practice, judges apply the trichotomous dogmatics of criminal law, sequentially assessing the elements of the offense, unlawfulness, and culpability to determine whether an individual's conduct constitutes a crime. Although current legal large language models (LLMs) show promising accuracy in judgment prediction, they lack trichotomous reasoning cap

Zhenxin Lei, Man Yao, Jiakui Hu, Xinhao Luo

Spiking Neural Networks (SNNs) have a low-power advantage but perform poorly in image segmentation tasks. The reason is that directly converting neural networks with complex architectural designs for segmentation tasks into spiking versions leads to performance degradation and non-convergence. To address this challenge, we first identify the modules in the a

Kun Chen

A universal numerical method is developed for the investigation of magnetic neutron scattering. By applying the pseudospectral-time-domain (PSTD) algorithm to the spinor version of the Schr\"odinger equation, the evolution of the spin-state of the scattered wave can be solved in full space and time. This extra spin degree of freedom brings some unique new fe

Chenxiao Yu, Jinyi Ye, Yuangang Li, Zheng Li

While LLMs have demonstrated remarkable capabilities in text generation and reasoning, their ability to simulate human decision-making -- particularly in political contexts -- remains an open question. However, modeling voter behavior presents unique challenges due to limited voter-level data, evolving political landscapes, and the complexity of human reason

Minkuk Kim, Hyeon Bae Kim, Jinyoung Moon, Jinwoo Choi

With the growing demand for solutions to real-world video challenges, interest in dense video captioning (DVC) has been on the rise. DVC involves the automatic captioning and localization of untrimmed videos. Several studies highlight the challenges of DVC and introduce improved methods utilizing prior knowledge, such as pre-training and external memory. In

Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu

Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems

Takehiro Ito, Naonori Kakimura, Naoyuki Kamiyama, Yusuke Kobayashi

We study the electrical distribution network reconfiguration problem, defined as follows. We are given an undirected graph with a root vertex, demand at each non-root vertex, and resistance on each edge. Then, we want to find a spanning tree of the graph that specifies the routing of power from the root to each vertex so that all the demands are satisfied an

Miaomiao Guo, Yuanchang Li

The crystal structure, stability, electronic and optical properties of the Ta$_2$NiSe$_5$ monolayer have been investigated using first-principles calculations in combination with the Bethe-Salpeter equation. The results show that it is feasible to directly exfoliate a Ta$_2$NiSe$_5$ monolayer from the low-temperature monoclinic phase. The monolayer is stable

Youngwon Lee, Seung-won Hwang, Daniel Campos, Filip Graliński

With the adoption of retrieval-augmented generation (RAG), large language models (LLMs) are expected to ground their generation to the retrieved contexts. Yet, this is hindered by position bias of LLMs, failing to evenly attend to all contexts. Previous work has addressed this by synthesizing contexts with perturbed positions of gold segment, creating a posi

Yiren Song, Xiaokang Liu, Mike Zheng Shou

Diffusion models have fundamentally transformed the field of generative models, making the assessment of similarity between customized model outputs and reference inputs critically important. However, traditional perceptual similarity metrics operate primarily at the pixel and patch levels, comparing low-level colors and textures but failing to capture mid-l

Qianpu Sun, Changyong Shu, Sifan Zhou, Runxi Cheng

Weakly-supervised 3D occupancy perception is crucial for vision-based autonomous driving in outdoor environments. Previous methods based on NeRF often face a challenge in balancing the number of samples used. Too many samples can decrease efficiency, while too few can compromise accuracy, leading to variations in the mean Intersection over Union (mIoU) by 5-

Andronikos Paliathanasis, Amlan Halder

We perform a detailed Lie symmetry analysis for the hyperbolic system of partial differential equations that describe the one-dimensional Shallow Water magnetohydrodynamics equations within a rotating reference frame. We consider a relaxing condition $\mathbf{\mathbf{\nabla }}\left( h\mathbf{B} \right) \neq 0$ for the one-dimensional problem, which has been

Eduard Feireisl, Piotr Gwiazda, Agnieszka Świerczewska-Gwiazda

We show that certain radially symmetric steady states of compressible viscous fluids in domains with inflow/outflow boundary conditions are unconditionally stable. This means that any not necessarily radially symmetric solution of the associated evolutionary problem converges to a single radially symmetric steady state.

Kunpeng Wang, Keke Chen, Chenglong Li, Zhengzheng Tu

Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the labor-intensive process of collecting and annotating image pairs limits the scale of existing b

Subuhi Khan, Ujair Ahmad, Mehnaz Haneef

The umbral approach provides methods for comprehending and redefining special functions. This approach is employed efficiently in order to uncover intricacies and introduce new families of special functions. In this article, the umbral perspective is adopted to introduce a hybrid family of hypergeometric and Mittag-Leffler functions. The umbral-operational p

Wenhan Liu, Xinyu Ma, Yutao Zhu, Ziliang Zhao

Large Language Models (LLMs) have shown exciting performance in listwise passage ranking. Due to the limited input length, existing methods often adopt the sliding window strategy. Such a strategy, though effective, is inefficient as it involves repetitive and serialized processing, which usually re-evaluates relevant passages multiple times. As a result, it

Yanghong Yu

Given a one-parameter family of flows over a parameter interval $\Lambda$, assuming there is a continuation of Morse decompositions over $\Lambda$, Reineck defined a singular transition matrix to show the existence of a connection orbit between some Morse sets at some parameter points in $\Lambda$. This paper aims to extend the definition of a singular trans

Diego Renner, Georgios Kissas

One of the goals of personalized medicine is to tailor diagnostics to individual patients. Diagnostics are performed in practice by measuring quantities, called biomarkers, that indicate the existence and progress of a disease. In common cardiovascular diseases, such as hypertension, biomarkers that are closely related to the clinical representation of a pat

Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka, Md. Al Mehedi Hasan

Parkinson's disease (PD) is a progressive neurological disorder that impairs movement control, leading to symptoms such as tremors, stiffness, and bradykinesia. Many researchers analyzing handwriting data for PD detection typically rely on computing statistical features over the entirety of the handwriting task. While this method can capture broad patterns,

Ruoyu Xu, Zhiyu Xiang, Chenwei Zhang, Hanzhi Zhong

3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds similar to Lidar while maintaining robust measurements under adverse weather. However, due to the high sparsity and noise associated with the rada

Emery Cooper, Caspar Oesterheld, Vincent Conitzer

In Tennenholtz's program equilibrium, players of a game submit programs to play on their behalf. Each program receives the other programs' source code and outputs an action. This can model interactions involving AI agents, mutually transparent institutions, or commitments. Tennenholtz (2004) proves a folk theorem for program games, but the equilibria constru

Chaoqun Liu, Xuanpeng Li, Chen Gong, Guangyu Li

Traffic prediction is an indispensable component of urban planning and traffic management. Achieving accurate traffic prediction hinges on the ability to capture the potential spatio-temporal relationships among road sensors. However, the majority of existing works focus on local short-term spatio-temporal correlations, failing to fully consider the interact

Yongsung Kim, Minjun Park, Jooyoung Choi, Sungroh Yoon

Recent learning-based Multi-View Stereo models have demonstrated state-of-the-art performance in sparse-view 3D reconstruction. However, directly applying 3D Gaussian Splatting (3DGS) as a refinement step following these models presents challenges. We hypothesize that the excessive positional degrees of freedom (DoFs) in Gaussians induce geometry distortion,

Jianyun Guan, Kefeng Liu, Yong Wang

Using the Liu's method, we prove a new Witten rigidity theorem of elliptic genus of twisted Dirac operators in even dimensional spin manifolds under the circle action. Combined with the Han-Yu's method, we prove the Witten rigidity theorems of elliptic genus of twisted Toplitz operators of odd-dimensional spin manifolds under the circle action. Moreover, we