November 2025 arXiv papers — page 163

Martin Kreuzer

The linear code equivalence (LCE) problem is shown to be equivalent to the point set equivalence (PSE) problem, i.e., the problem to check whether two sets of points in a projective space over a finite field differ by a linear change of coordinates. For such a point set $\mathbb{X}$, let $R$ be its homogeneous coordinate ring and $\mathfrak{J}_{\mathbb{X}}$

Lionel Levine, Haniyeh Ehsani Oskouie, Sajjad Ghiasvand, Majid Sarrafzadeh

We introduce MI-to-Mid Distilled Compression (M2M-DC), a two-scale, shape-safe compression framework that interleaves information-guided block pruning with progressive inner slicing and staged knowledge distillation (KD). First, M2M-DC ranks residual (or inverted-residual) blocks by a label-aware mutual information (MI) signal and removes the least informati

Selim Ahmet Iz, Mustafa Unel

Unmanned Aerial Vehicles (UAVs) are widely used for aerial photography and remote sensing applications. One of the main challenges is to stitch together multiple images into a single high-resolution image that covers a large area. Featurebased image stitching algorithms are commonly used but can suffer from errors and ambiguities in feature detection and mat

Qunchao Jin, Yilin Wu, Changhao Chen

Zero-shot object navigation (ZSON) in unseen environments remains a challenging problem for household robots, requiring strong perceptual understanding and decision-making capabilities. While recent methods leverage metric maps and Large Language Models (LLMs), they often depend on depth sensors or prebuilt maps, limiting the spatial reasoning ability of Mul

Selim Ahmet Iz, Mustafa Unel

This paper explores the application of vision-based system identification techniques in quadrotor modeling and control. Through experiments and analysis, we address the complexities and limitations of quadrotor modeling, particularly in relation to thrust and drag coefficients. Grey-box modeling is employed to mitigate uncertainties, and the effectiveness of

Yuzong Chen, Chao Fang, Xilai Dai, Yuheng Wu

The substantial memory bandwidth and computational demands of large language models (LLMs) present critical challenges for efficient inference. To tackle this, the literature has explored heterogeneous systems that combine neural processing units (NPUs) with DRAM-based processing-in-memory (PIM) for LLM acceleration. However, the high-precision PIM compute u

Xiao-Song Yang, Qi Zhou, Xuan Zhou

Determining the minimum width of fully connected neural networks has become a fundamental problem in recent theoretical studies of deep neural networks. In this paper, we study the lower bounds and upper bounds of the minimum width required for fully connected neural networks in order to have universal approximation capability, which is important in network

Wenjiang Zhang, Sifeng Wang, Yuwei Su, Xinyu Li

Visual neural decoding seeks to reconstruct or infer perceived visual stimuli from brain activity patterns, providing critical insights into human cognition and enabling transformative applications in brain-computer interfaces and artificial intelligence. Current approaches, however, remain constrained by the scarcity of high-quality stimulus-brain response

Si-Qi Zhou, Hai Jin, Jin-Min Liang, Shao-Ming Fei

The question of which resources drive the advantages in quantum algorithms has long been a fundamental challenge. While entanglement and coherence are critical to many quantum algorithms, our results indicate that they do not fully explain the quantum advantage achieved by the Grover search algorithm. By introducing a generalized Grover search algorithm, we

Armin Afrough, Pernille Andersen, Tanja Ninette Angelika Weihrauch, Dennis Wilkens Juhl

Lactose is the major component of milk powders and is normally found to be in a glassy/amorphous state. During storage, lactose is known to participate in physicochemical processes, including crystallization on the surface and reaction with proteins such as $\beta$-lactoglobulin. Lactose needs to be mobile to participate in such processes. However, there is

Zhenjie Liu, Jianzhang Lu, Renjie Lu, Cong Liang

Recent advancements in video diffusion models have significantly enhanced audio-driven portrait animation. However, current methods still suffer from flickering, identity drift, and poor audio-visual synchronization. These issues primarily stem from entangled appearance-motion representations and unstable inference strategies. In this paper, we introduce \te

Daniele Ravasio, Danilo Saccani, Marcello Farina, Giancarlo Ferrari-Trecate

This work proposes a two-layered control scheme for constrained nonlinear systems represented by a class of recurrent neural networks and affected by additive disturbances. In particular, a base controller ensures global or regional closed-loop l_p-stability of the error in tracking a desired equilibrium and the satisfaction of input and output constraints w

Hector R. Rodriguez, Jiechen Huang, Wenjian Yu

Monte Carlo random walk methods are widely used in capacitance extraction for their mesh free formulation and inherent parallelism. However, modern semiconductor technologies with densely packed structures present significant challenges in unbiasedly sampling transition domains in walk steps with multiple high contrast dielectric materials. We present DeepRW

Qingxiang Xu

Let $\mathcal{L}(H)$ be the set of all adjointable operators on a Hilbert $C^*$-module $H$. For each $T\in\mathcal{L}(H)$, $T^*$ denotes its adjoint operator, and $|T^*|$ is the positive square root of $TT^*$. We establish a simplified formula for the matched projection $m(Q)$ of an idempotent $Q\in\mathcal{L}(H)$ as $$m(Q)=\frac{I+|Q^*|-|I-Q^*|}{2},$$ where

Tianang Chen, Jian Jin, Shilv Cai, Zhuangzi Li

Gaussian Splatting (GS) has recently emerged as a promising technique for 3D object reconstruction, delivering high-quality rendering results with significantly improved reconstruction speed. As variants continue to appear, assessing the perceptual quality of 3D objects reconstructed with different GS-based methods remains an open challenge. To address this

Xiao-Song Yang

This note gives a new elementary proof of Poincar\'e-Miranda theorem based on Sard's theorem and the simple classification of one-dimensional manifolds.

Svitlana Ponomarova, Oleksandr Ponomarov, Yurii Koval

Various techniques can be employed to determine the temperature of magnetic transformation, whether it be the Curie or Neel temperature. The standard procedure typically involves creating alloys with defined compositions and performing measurements experimentally. Alternatively, these temperatures can be predicted based on a material known physical and chemi

Puzhen Su, Haoran Yin, Yongzhu Miao, Jintao Tang

Detecting Alzheimer's disease (AD) from narrative transcripts challenges large language models (LLMs): pre-training rarely covers this out-of-distribution task, and all transcript demos describe the same scene, producing highly homogeneous contexts. These factors cripple both the model's built-in task knowledge (\textbf{task cognition}) and its ability to su

Changlin Liu, Yongtao Du, Xihua Zou, Fang Zou

Integrated radio-frequency (RF) photonics plays a pivotal role in wireless communications, sensing, and radar due to its large intrinsic bandwidth, remote distribution capability, and compact footprint. However, despite significant advances in photonic integrated circuits (PICs), the practical deployment of these systems remains constrained by the bulky natu

Xin Yao, Yang Liu, Jin Jiang, Yesen Chen

Accurate simulation to dynamics of axial piston pump (APP) is essential for its design, manufacture and maintenance. However, limited by computation capacity of CPU device and traditional solvers, conventional iteration methods are inefficient in complicated case with textured surface requiring refined mesh, and could not handle simulation during multiple pe

Ji Li, Chao Wang

Existing plug-and-play image restoration methods typically employ off-the-shelf Gaussian denoisers as proximal operators within classical optimization frameworks based on variable splitting. Recently, denoisers induced by generative priors have been successfully integrated into regularized optimization methods for image restoration under Gaussian noise. Howe

Andrea Marchese

This survey summarizes recent progress on the flat chain conjecture, which asserts the equivalence between metric currents and flat chains with finite mass in the Euclidean space. In particular, we focus on recent work showing that the conjecture is equivalent to a Lipschitz regularity estimate for a certain PDE.

Xiao-Song Yang

In this paper we present a mathematical framework on linking of embeddings of compact topological spaces into Euclidean spaces and separability of linked embeddings under a specific class of dimension reduction maps. As applications of the established theory, we provide some fascinating insights into classification and approximation problems in deep learning

Cheng Xu, Yi Qiao, Baitian Tang, José G. Fernández-Trincado

During its evolution, the Milky Way (MW) incorporated numerous dwarf galaxies, particularly low-mass systems. The surviving dwarf galaxies orbiting the MW serve as exceptional laboratories for studying the unique properties of these systems. Their metal-poor environments and shallow gravitational potentials likely drive significant differences in star format

Ye Jiang, Taihang Wang, Youzheng Liu, Yimin Wang

In-context learning (ICL) for text classification, which uses a few input-label demonstrations to describe a task, has demonstrated impressive performance on large language models (LLMs). However, the selection of in-context demonstrations plays a crucial role and can significantly affect LLMs' performance. Most existing demonstration selection methods prima

Yan-Wu Cao, Ming-Zhu Ai, Long Chen, Juan-Cheng Yang

Flow reversals are rarely observed in low-Prandtl-number liquid metal convection due to the fluid's exceptionally high thermal diffusivity. Here, we demonstrate that an external transverse magnetic field can induce such reversals in a quasi-two-dimensional (Q2D) rectangular cell with an aspect ratio ($\it\Gamma$) of $0.2$. Our experimental observations revea

Dhananjay Ram, Wei Xia, Stefano Soatto

Attention is a core component of transformer architecture, whether encoder-only, decoder-only, or encoder-decoder model. However, the standard softmax attention often produces noisy probability distribution, which can impair effective feature selection at every layer of these models, particularly for long contexts. We propose Focal Attention, a simple yet ef

Rui Wang, Ying Zhou, Hao Wang, Wenwei Zhang

Stereo matching in minimally invasive surgery (MIS) is essential for next-generation navigation and augmented reality. Yet, dense disparity supervision is nearly impossible due to anatomical constraints, typically limiting annotations to only a few image-level labels acquired before the endoscope enters deep body cavities. Teacher-Student Learning (TSL) offe

Bin Wang, Boxiang Tao, Haifeng Jing, Hongbo Dou

Model-based reinforcement learning (MBRL) typically relies on modeling environment dynamics for data efficiency. However, due to the accumulation of model errors over long-horizon rollouts, such methods often face challenges in maintaining modeling stability. To address this, we propose CtrlFlow, a trajectory-level synthetic method using conditional flow mat

Zhenglei Li, Qigang Liang, Xuejun Xu

In this work, we propose and analyze a pointwise a posteriori error estimator for simple eigenvalues of elliptic eigenvalue problems with adaptive finite element methods (AFEMs). We prove the reliability and efficiency of the residual-type a posteriori error estimator in the sense of $L^{\infty}$-norm, up to a logarithmic factor of the mesh size. For theoret

Rui Wang, Wei-Min Gu, Zhi-Xiang Zhang, Tuan Yi

We present 17 cataclysmic variables (CVs) obtained from the crossmatch between the Sloan Digital Sky Survey (SDSS) and eROSITA Final Equatorial Depth Survey (eFEDS), including 8 known CVs before eFEDS and 9 identified from eFEDS. The photometric periods of four CVs are derived from the Zwicky Transient Facility (ZTF) and Catalina Real-Time Transient Survey (

Takahiro Mori, Kei Noba, Toru Sera

We establish large deviation estimates related to the Dynkin--Lamperti arcsine laws for subordinators whose Laplace exponents are either regularly varying or comparable to regularly varying functions. By applying these results to inverse local times, we derive large deviation estimates for the last visit times to the starting point of various Hunt processes

Jean-Pierre Fouque, Mathieu Laurière, Mengrui Zhang

We establish the convergence of the deep actor-critic reinforcement learning algorithm presented in [Angiuli et al., 2023a] in the setting of continuous state and action spaces with an infinite discrete-time horizon. This algorithm provides solutions to Mean Field Game (MFG) or Mean Field Control (MFC) problems depending on the ratio between two learning rat

Bartłomiej Baranowski, Stefano Esposito, Patricia Gschoßmann, Anpei Chen

3D Gaussian Splatting (3DGS) achieves state-of-the-art image quality and real-time performance in novel view synthesis but often suffers from a suboptimal spatial distribution of primitives. This issue stems from cloning-based densification, which propagates Gaussians along existing geometry, limiting exploration and requiring many primitives to adequately c

Feng Wang, Yihong Du, Xueyi Xie, Enrico Calzavarini

Understanding the coupled dynamics of liquid-solid phase change and fluid flows is crucial in a wide range of geophysical and industrial applications. When freezing occurs in saline water, the newly formed ice is mushy, with a porous structure that traps the brine within the ice. In this work, which combines experiments and theoretical analyses, we investiga

Zhiyuan Zhai, Wei Ni, Xin Wang

Edge AI, which brings artificial intelligence to the edge of the network for real-time processing and decision-making, has emerged as a transformative technology across various applications. However, the deployment of Edge AI systems faces significant challenges due to high energy consumption and extended operation time. In this paper, we consider an Edge AI

Jinhao Chen, Zhen Yang, Jianxin Shi, Tianyu Wo

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in vision-language answering tasks. Despite their strengths, these models often encounter challenges in achieving complex reasoning tasks such as mathematical problem-solving. Previous works have focused on fine-tuning on specialized mathematical datasets. However, these datas

Minwoo Jeong, Jeeyun Chang, Yoonjin Yoon

The growing complexity of urban mobility systems has made traffic simulation indispensable for evidence-based transportation planning and policy evaluation. However, despite the analytical capabilities of platforms such as the Simulation of Urban MObility (SUMO), their application remains largely confined to domain experts. Developing realistic simulation sc

Junpeng Zhao, Lin Li, Ming Li, Amran Bhuiyan

Modern data-driven recommendation systems risk memorizing sensitive user behavioral patterns, raising privacy concerns. Existing recommendation unlearning methods, while capable of removing target data influence, suffer from inefficient unlearning speed and degraded performance, failing to meet real-time unlearning demands. Considering the ranking-oriented n

Kianoosh Taghikhani, Yusuke Yamazaki, Jerry Paul Varghese, Markus Apel

We propose a Newton-based scheme, initialized by neural operator predictions, to accelerate the parametric solution of nonlinear problems in computational solid mechanics. First, a physics informed conditional neural field is trained to approximate the nonlinear parametric solutionof the governing equations. This establishes a continuous mapping between the

Praveen Kumar, Tushar Sandhan

The deployment of autonomous service robots in human-centric environments is hindered by a critical gap in perception and planning. Traditional navigation systems rely on expensive LiDARs that, while geometrically precise, are semantically unaware, they cannot distinguish a important document on an office floor from a harmless piece of litter, treating both

Sathiyamoorthy Buvaneswaran, Trilochan Sahoo, Saurabh Ghosh

The bulk photovoltaic effect (BPVE), arising from broken inversion symmetry in ferroelectrics, offers a distinct pathway toward high-efficiency next-generation photovoltaics. We propose and investigate A/A$^\prime$-ordered double perovskites KLaFeMoO$_6$ and NaLaFeMoO$_6$ as promising single-phase ferroelectric photovoltaic (FE-PV) materials. First-principle

Guanxi Li

We generalize Fulton's Residual Intersection Theorem for the Segre class and express the Segre classes of schemes with regularly embedded components in terms of the Chern classes of the normal bundles to the components and their intersections. More specifically, we provide formulas for the following situations: when the components of the scheme intersect tra

Muhammad Sukri Bin Ramli

We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease. The model achieves 0.9375 accuracy and was validated

B. G. Chae

This study introduces the Fast-Weights Homeostatic Reentry Layer (FH-RL), a neural mechanism that integrates fast-weight associative memory, homeostatic regularization, and learned reentrant feedback to approximate self-referential computation in neural networks. Unlike standard transformer architectures that operate in a purely feedforward manner during inf

Mozib Bin Awal, Bidyut Hazarika, Prabwal Phukon

We analyze the impact of ModMax parameter on the dynamics of test particles around black holes and its effect on the characteristics of Quasi-Periodic Oscillations (QPOs). The effect of the ModMax parameter $\eta$ is studied using the effective potential, angular momentum and the energy of the circular orbits of the test particles. Our analysis shows that in

Saroj Kumar Panda, Basabdatta Palit, Sadananda Behera

We propose FedNET, a proactive and privacy-preserving framework for early identification of high-risk links in large-scale communication networks, that leverages a distributed multi-step traffic forecasting method. FedNET employs Federated Learning (FL) to model the temporal evolution of node-level traffic in a distributed manner, enabling accurate multi-ste

MD-Nazmus Sunbeam

Claims that humanoid robots achieve ``human-level'' actuation are common but rarely quantified. Peak torque or speed specifications tell us little about whether a joint can deliver the right combination of torque, power, and endurance at task-relevant postures and rates. We introduce a comprehensive framework that makes ``human-level'' measurable and compara

Neil D. Lawrence

In this paper we introduce the inaccessible game, an information-theoretic dynamical system constructed from four axioms. The first three axioms are known and define \emph{information loss} in the system. The fourth is a novel \emph{information isolation} axiom that assumes our system is isolated from observation, making it observer-independent and exchangea

Lisong He, Yi Yang, Xiangyu Chang

As the right to be forgotten becomes legislated worldwide, machine unlearning mechanisms have emerged to efficiently update models for data deletion and enhance user privacy protection. However, existing machine unlearning algorithms frequently neglect the fact that different data points may contribute unequally to model performance (i.e., heterogeneous data

Kunhao Li, Wenhao Li, Di Wu, Lei Yang

Multimodal Large Language Models (MLLMs) extend foundation models to real-world applications by integrating inputs such as text and vision. However, their broad knowledge capacity raises growing concerns about privacy leakage, toxicity mitigation, and intellectual property violations. Machine Unlearning (MU) offers a practical solution by selectively forgett

Yunfei Su, Lei Yao

We investigate the hydrodynamic limit of weak solutions to compressible Navier-Stokes-Vlasov-Poisson equations with local alignment force in three-dimensional torus domain. Due to the absence of dissipation terms in particle equation, it is difficult to study this problem. Based on the relative entropy method, it is shown that the global weak solutions of th

Beichen Zhang, Mohammed T. Zaki, Hanna Breunig, Newsha K. Ajami

Transitioning to sustainable and resilient energy systems requires navigating complex and interdependent trade-offs across environmental, social, and resource dimensions. Neglecting these trade-offs can lead to unintended consequences across sectors. However, existing assessments often evaluate emerging energy pathways and their impacts in silos, overlooking

Zidong Wang, Xi Lin, Chuchao He, Xiaoguang Gao

Robust causal discovery from observational data under imperfect prior knowledge remains a significant and largely unresolved challenge. Existing methods typically presuppose perfect priors or can only handle specific, pre-identified error types. And their performance degrades substantially when confronted with flawed constraints of unknown location and type.

Jingkun Qiu

Higher criticism is a large-scale testing procedure that can attain the optimal detection boundary for sparse and faint signals. However, there has been a lack of knowledge in most existing works about its asymptotic distribution for more realistic settings other than the independent Gaussian assumption while maintaining the power performance as much as poss

Tianyang Chu, Xiaoying Dai, Shengyue Wang, Aihui Zhou

Solving large-scale eigenvalue problems poses a significant challenge due to the computational complexity and limitations on the parallel scalability of the orthogonalization operation, when many eigenpairs are required. In this paper, we propose an intrinsic orthogonality-preserving model, formulated as an evolution equation, and a corresponding numerical m

Arvish Dabra, Aparajita Dasgupta, Prerna Gulia

In this article, we establish several fundamental uncertainty principles for the Strichartz Fourier transform on the Heisenberg group, including Benedicks' theorem, the Donoho-Stark principle, the local uncertainty principle of Price, and a weak form of Beurling's theorem. The Strichartz Fourier transform, introduced by Thangavelu (2023), provides a scalar-v

Boyang Zhang, Daning Cheng, Yunquan Zhang

Modern deep models have massive parameter sizes, leading to high inference-time memory usage that limits practical deployment. Parameter sharing, a form of structured compression, effectively reduces redundancy, but existing approaches remain heuristic-restricted to adjacent layers and lacking a systematic analysis for cross-layer sharing. However, extending

Lejun Ai, Yulong Li, Haodong Yi, Jixuan Xie

Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resour

A. V. Parafilo

We study the thermal transport properties of a mesoscopic device comprising two metallic islands embedded in a two-dimensional electron gas in the integer quantum Hall regime. It is shown that the $2M$ ballistic edge channels connecting the islands to the external reservoirs and the $N$ inter-island channels play a central role in the phenomenon of heat Coul

Qiang Wang, Liying Yang, Jiayun Song, Yifan Bai

Cross-subject electroencephalography (EEG) emotion recognition remains a major challenge in brain-computer interfaces (BCIs) due to substantial inter-subject variability. Multi-Source Domain Adaptation (MSDA) offers a potential solution, but existing MSDA frameworks typically assume equal source quality, leading to negative transfer from low-reliability doma

Tung-Long Vuong, Julien Monteil, Hien Dang, Volodymyr Vaskovych

Variational Autoencoders (VAEs) are a powerful alternative to matrix factorization for recommendation. A common technique in VAE-based collaborative filtering (CF) consists in applying binary input masking to user interaction vectors, which improves performance but remains underexplored theoretically. In this work, we analyze how collaboration arises in VAE-

Songhui Yue, Yang Shao, Sean Hayes

Query optimization has been studied using machine learning, reinforcement learning, and, more recently, graph-based convolutional networks. Ontology, as a structured, information-rich knowledge representation, can provide context, particularly in learning problems. This paper presents OntoTune, an ontology-based platform for enhancing learning for query opti

Anand Bhojan

This paper presents an expanded account of the Holistic Cognitive Development (HCD) framework for reflective and creative learning in computing education. The HCD framework integrates design thinking, experiential learning, and reflective practice into a unified constructivist pedagogy emphasizing autonomy, ownership, and scaffolding. It is applied across co

Ruiheng Liu, XiaoBing Chen, Jinyu Zhang, Qiongwen Zhang

The rapid advancement of Large Language Models (LLMs) has driven significant progress in Natural Language Interface to Database (NLIDB). However, the widespread adoption of LLMs has raised critical privacy and security concerns. During interactions, LLMs may unintentionally expose confidential database contents or be manipulated by attackers to exfiltrate da

Shiwei Zhu, Xinyuan Guan, Zhen Sun, Qiuyao Zhang

We uncover a universal two-stage dynamics during skyrmion formation and establish its connection to equilibrium phases through the introduction of a chiral correlation $\chi$. Stage I involves stripe coarsening governed by the exchange-to-DMI ratio $J'$, while stage II entails stripe contraction driven by the synergy between $J'$ and the anisotropy-to-DMI ra

Zhicheng Zhou, Jing Li, Suming Qiu, Junjie Huang

General-purpose large language models (LLMs) are increasingly deployed in verticals such as telecommunications, where adaptation is hindered by scarce, low-information-density corpora and tight mobile/edge constraints. We propose Data Trajectory Alignment (DTA), a two-phase, model-agnostic data curation framework that treats solution processes - not only fin

Scott Wilkinson, Toby Brown, Chiara Circosta, Sara L. Ellison

Star-forming galaxies form tight relations between their stellar mass, star-formation rate, and molecular gas reservoir on global and resolved scales. On the path to quiescence, the exchange between gas and stars must inevitably be broken. Understanding the mechanisms governing star formation and quenching therefore requires observations of both the stellar

Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts, Matthias J. Ehrhardt

Bilevel optimization is a central tool in machine learning for high-dimensional hyperparameter tuning. Its applications are vast; for instance, in imaging it can be used for learning data-adaptive regularizers and optimizing forward operators in variational regularization. These problems are large in many ways: a lot of data is usually available to train a l

Junyan Cao, Ya Deng, Christopher D. Hacon, Mihai Paun

In this article, we pursue two main objectives. The first is to show that the fundamental results of Green-Lazarsfeld (1987, 1991) on generic vanishing theorems, and works of Budur-Wang (2015, 2020) on cohomology jumping loci, can be established within a unified framework based on suitable versions of the $\partial\bar{\partial}$-lemma. Our second-and primar

Zhen Su, Tong-Hui Shou, Huan Yang, Jin Cao

We report the observation of a broad magnetic Feshbach resonance with a large background scattering length in an ultracold fermionic mixture of $^{23}$Na$^{40}$K molecules and $^{40}$K atoms, with both species prepared in their lowest hyperfine states. The Feshbach resonance is characterized by measuring resonantly enhanced loss rates and elastic scattering

M. P. Telenkov, Yu. A. Mityagin

Scattering processes by the interface roughness in a quantum well in a quantizing magnetic field are considered. An expression for the scattering rate is derived for a magnetic field tilted relative to the quantum well layers. By analyzing this expression, trends in the behavior of the scattering rate are established with variation in the magnetic field stre

Tamoghno Das, Khanh Phan Vu, Hanning Chen, Hyunwoo Oh

The integration of spiking neural networks (SNNs) with transformer-based architectures has opened new opportunities for bio-inspired low-power, event-driven visual reasoning on edge devices. However, the high temporal resolution and binary nature of spike-driven computation introduce architectural mismatches with conventional digital hardware (CPU/GPU). Prio

Ridoy Chandra Shil, Ragib Abid, Tasnia Binte Mamun, Samiul Based Shuvo

Background and Objective: Colorectal cancer prevention relies on early detection of polyps during colonoscopy. Existing public datasets, such as CVC-ClinicDB and Kvasir-SEG, provide valuable benchmarks but are limited by small sample sizes, curated image selection, or lack of real-world artifacts. There remains a need for datasets that capture the complexity

Tara Kemp, James G. Lefevre

Given a partition $h_1+h_2+\dots+h_k = n$, a latin square of order $n$ with pairwise disjoint subsquares of orders $h_1,\dots ,h_k$ is called a realization. When the values $h_i$ are of at most two sizes, the existence of a realization has been completely determined. However, the existence of a latin cube with pairwise disjoint subcubes of two orders is only

Zhixiong Zhao, Haomin Li, Fangxin Liu, Yuncheng Lu

Transformer-based models have revolutionized computer vision (CV) and natural language processing (NLP) by achieving state-of-the-art performance across a range of benchmarks. However, nonlinear operations in models significantly contribute to inference latency, presenting unique challenges for efficient hardware acceleration. To this end, we propose QUARK,

Dhrubajyoti Biswas, Arpan Banerjee

Dynamics of complex systems are often driven by interactions that extend beyond pairwise links, underscoring the need to establish a correspondence between interpretable system parameters and emergent phenomena in hypergraph-based networks. The current work formulates an adaptive Kuramoto model that incorporates hyperedges of arbitrary order and explores the

Meijun Guo, Yongliang Shi, Caiyun Liu, Yixiao Feng

3D Gaussian Splatting (3DGS) has emerged as a key rendering pipeline for digital asset creation due to its balance between efficiency and visual quality. To address the issues of unstable pose estimation and scene representation distortion caused by geometric texture inconsistency in large outdoor scenes with weak or repetitive textures, we approach the prob

Pu Wang, Shuning Sun, Jialang Lu, Chen Wu

Purple flare, a diffuse chromatic aberration artifact commonly found around highlight areas, severely degrades the tone transition and color of the image. Existing traditional methods are based on hand-crafted features, which lack flexibility and rely entirely on fixed priors, while the scarcity of paired training data critically hampers deep learning. To ad

Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani

Small Language Models (SLMs) are increasingly being deployed in resource-constrained environments, yet their behavioral robustness to data contamination during instruction tuning remains poorly understood. We systematically investigate the contamination sensitivity of 23 SLMs (270M to 4B parameters) across multiple model families by measuring susceptibility

Rui Lu, Lyuye Zhang, Kaixuan Li, Min Zhang

Re-using open-source software (OSS) can avoid reinventing the wheel, but failing to keep it up-to-date can lead to missing new features and persistent bugs or vulnerabilities that have already been resolved. The use of outdated OSS libraries introduces technical lag, necessitating timely upgrades. However, maintaining up-to-date libraries is challenging, as

Fei Yang

Human prowess in intuitive physics remains unmatched by machines. To bridge this gap, we argue for a fundamental shift towards brain-inspired computational principles. This paper introduces the Spatiotemporal Relational Neural Network (SRNN), a model that establishes a unified neural representation for object attributes, relations, and timeline, with computa

Kengo Matsumoto, Taro Sogabe

Reciprocality in Kirchberg algebras with finitely generated K-groups is regarded as a K-theoretic duality through K-groups and strong extension groups. We will prove that the reciprocal Kirchberg algebra has a universal property with respect to some generating C*-subalgebra and a family of generating partial isometries. By using the universal property, we wi

Lin-Yue Bai, René Meyer, Zhen-Hua Zhou

We construct a simple holographic model incorporating higher-order coupling terms for electron self-interactions. It can exhibit typical behavior of a Mott insulator, including a metal-insulator transition and a decrease in DC conductivity with the increase of charge density. In the analysis of AC conductivity, a soft gap is generally observed. Notably, when

Alexey Rosaev, Eva Plavalova, Pavel Nesterov

The goal of this paper is to obtain an approximate solution of the restricted three-body problem in the case of small perturbations in the vicinity of, but not in exact resonance. In this paper, we study the restricted threebody problem known as planetary type (i.e., when the eccentricity of the test particle is small). A method of linearizing the equation o

Dongcheng Li, Junhan Chen, Aoxiang Zhou, Chunpei Li

As large language models continue to develop and expand, the extensive public data they rely on faces the risk of depletion. Consequently, leveraging private data within organizations to enhance the performance of large models has emerged as a key challenge. The federated learning paradigm, combined with model fine-tuning techniques, effectively reduces the

Xin He, Yili Wang, Yiwei Dai, Xin Wang

Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to become indistinguishable. While existing solutions, such as residual connections and skip layers, alleviate this issue to some extent, they fail to explicitly model how node representations evolve in a node-specif

Said Laaroua

We present a full-sky, covariance-weighted analysis of redshift-frame transformations in the Pantheon+ Type Ia supernova sample to assess their impact on local measurements of the Hubble constant. Using 1,543 supernovae with heliocentric and CMB-frame redshifts, we study the residual field delta z = zCMB - zHEL, which traces the Solar System's kinematic corr

Taisei Hanyu, Nhat Chung, Huy Le, Toan Nguyen

Inspired by how humans reason over discrete objects and their relationships, we explore whether compact object-centric and object-relation representations can form a foundation for multitask robotic manipulation. Most existing robotic multitask models rely on dense embeddings that entangle both object and background cues, raising concerns about both efficien

Cong Xu, Tao Li, Ruonan Ren, Ming-Jing Zhao

Nonclassical correlations are significant physical resources with extensive applications in quantum information processing. We introduce the modified Wigner-Yanase-Dyson skew information of a quantum state relative to a quantum channel, and a quantitative measure of quantum correlations. Their basic properties are explored in detail. Through a specific examp

You-Kyoung Na, Yeong-Jun Cho

Understanding symptom-image associations is crucial for clinical reasoning. However, existing medical multimodal models often rely on simple one-to-one hard labeling, oversimplifying clinical reality where symptoms relate to multiple organs. In addition, they mainly use single-slice 2D features without incorporating 3D information, limiting their ability to

Tao Lv, Daoming Zhou, Chenglong Huang, Chongde Zi

Computational spectral imaging (CSI) achieves real-time hyperspectral imaging through co-designed optics and algorithms, but typical CSI methods suffer from a bulky footprint and limited fidelity. Therefore, Spectral Deconvolution imaging (SDI) methods based on PSF engineering have been proposed to achieve high-fidelity compact CSI design recently. However,

Yichi Xu, Hanmeng Zhan

We study a transport phenomenon in certain coined quantum walks where a subspace of states localized at a vertex gets transferred to another vertex. We first develop characterizations for perfect and pretty good subspace state transfer using the spectral properties of a Hermitian weighted digraph obtained from the underlying graph. We then provide a polynomi

Weining Lu, Deer Bin, Lian Ma, Ming Ma

Efficient, accurate, and flexible relative localization is crucial in air-ground collaborative tasks. However, current approaches for robot relative localization are primarily realized in the form of distributed multi-robot SLAM systems with the same sensor configuration, which are tightly coupled with the state estimation of all robots, limiting both flexib

Ji Li, Chao Wang

Regularized optimization has been a classical approach to solving imaging inverse problems, where the regularization term enforces desirable properties of the unknown image. Recently, the integration of flow matching generative models into image restoration has garnered significant attention, owing to their powerful prior modeling capabilities. In this work,

Anu Kuncheria, Joan L. Walker, Jane Macfarlane

The structure of road networks plays a pivotal role in shaping transportation dynamics. It also provides insights into how drivers experience city streets and helps uncover each urban environment's unique characteristics and challenges. Consequently, characterizing cities based on their road network patterns can facilitate the identification of similarities

Zhaohui Yang, Dawei Ding, Qi Ye, Cupjin Huang

The performance of current quantum hardware is severely limited. While expanding the quantum ISA with high-fidelity, expressive basis gates is a key path forward, it imposes significant gate calibration overhead and complicates compiler optimization. As a result, even though more powerful ISAs have been designed, their use remains largely conceptual rather t

Lan Thi Ha Nguyen, Kien Ton Manh, Anh Do Duc, Nam Pham Hai

Self-supervised goal-conditioned reinforcement learning enables robots to autonomously acquire diverse skills without human supervision. However, a central challenge is the goal setting problem: robots must propose feasible and diverse goals that are achievable in their current environment. Existing methods like RIG (Visual Reinforcement Learning with Imagin

Da-Yeong Kim, Yeong-Jun Cho

In this paper, we propose PointCubeNet, a novel multi-modal 3D understanding framework that achieves part-level reasoning without requiring any part annotations. PointCubeNet comprises global and local branches. The proposed local branch, structured into 3x3x3 local blocks, enables part-level analysis of point cloud sub-regions with the corresponding local t

Tomoki Wada

Magnetars provide natural laboratories for strong-field quantum electrodynamics processes, such as vacuum polarization, which gives rise to vacuum resonance together with the plasma response. We develop a general framework to describe vacuum resonance in a three-component plasma consisting of ions, electrons, and positrons, as expected in baryon-loaded magne