November 2025 arXiv papers — page 111

Farook Rahaman, Bikramarka S. Choudhury, Anikul Islam

We study wormhole as the solution of the Wheeler-deWitt (WdW ) equation satisfying Hawking-Page wormhole boundary conditions in Friedmann-Robertson-Walker (FRW) cosmology. The quantum wormholes are formulated with arbitrary factor ordering of the Hamiltonian constraint operators with perfect fluid matter sources as well as minimally coupled scalar fields.

Jiayi Zhu, Yihao Huang, Yue Cao, Xiaojun Jia

Large Visual Language Models (LVLMs) now pose a serious yet overlooked privacy threat, as they can infer a social media user's geolocation directly from shared images, leading to unintended privacy leakage. While adversarial image perturbations provide a potential direction for geo-privacy protection, they require relatively strong distortions to be effectiv

Yuki Minami, Hiroyoshi Nakano, Keiji Saito

We present a symmetry-based formulation of nonlinear fluctuating hydrodynamics (NFH) for one-dimensional many-particle systems with generic homogeneous nearest-neighbor interactions. We derive the hydrodynamic equations solely from symmetry and conservation principles, ensuring full consistency with thermalization. Using the dynamic renormalization group, we

Hyeonji Kim, Sujeong Oh, Sanghack Lee

Reinforcement learning from human feedback (RLHF) is widely used to align large language models (LLMs) with human preferences. However, RLHF-trained reward models often exhibit length bias -- a systematic tendency to favor longer responses by conflating verbosity with quality. We propose a causal framework for analyzing and mitigating length bias in RLHF rew

Bikramarka S Choudhury, Md Khalid Hossain, Farook Rahaman

We unveil a novel class of traversable wormholes exhibiting exact spherical symmetry, geometrically inspired by the minimal surface structure of a catenoid. Introducing the spacetime metric, we rigorously derive its fundamental curvature properties, including the Riemann curvature tensor, and consequently compute the Einstein tensor and stress-energy tensor.

Shubham Jaiswal, Tony J. Puthenpurakal

The main result of this paper is a generalization of the theorem of Chevalley-Shephard-Todd to the rings of invariants of pseudo-reflection groups over regular domains. More precisely, let $A$ be a regular domain and let $K$ be its field of fractions. Let $G\subseteq GL_n(A)$ be a finite group. Let $G$ act linearly on $A[X_1,X_2,\dots, X_n]$ (fixing $A$). As

Mitul Goswami, Romit Chatterjee

This research aims to optimize intricate learning models by implementing quantization and bit-depth optimization techniques. The objective is to significantly cut time complexity while preserving model efficiency, thus addressing the challenge of extended execution times in intricate models. Two medical datasets were utilized as case studies to apply a Logis

Moussa Labbadi, Denis Efimov

A recursive time-varying state feedback is presented for a chain of integrators with unmatched perturbations in continuous and discrete time. In continuous time, it is shown that hyperexponential convergence is achieved for the first state variable \(x_1\), while the second state \(x_2\) remains bounded. For the other states, we establish ISS {\cb property}

Junzhuo Zhou, Ziwen Wang, Haoxuan Xia, Yuxin Yan

Accurate setup/hold time characterization is crucial for modern chip timing closure, but its reliance on potentially millions of SPICE simulations across diverse process-voltagetemperature (PVT) corners creates a major bottleneck, often lasting weeks or months. Existing methods suffer from slow search convergence and inefficient exploration, especially in th

Lingyun Xiang, Chengfu Ou, Xu He, Zhongliang Yang

Existing linguistic steganography methods primarily rely on content transformations to conceal secret messages. However, they often cause subtle yet looking-innocent deviations between normal and stego texts, posing potential security risks in real-world applications. To address this challenge, we propose a content-preserving linguistic steganography paradig

David Denisov, Shlomi Dolev, Dan Felmdan, Michael Segal

We study the $k$-means problem for a set $\mathcal{S} \subseteq \mathbb{R}^d$ of $n$ segments, aiming to find $k$ centers $X \subseteq \mathbb{R}^d$ that minimize $D(\mathcal{S},X) := \sum_{S \in \mathcal{S}} \min_{x \in X} D(S,x)$, where $D(S,x) := \int_{p \in S} |p - x| dp$ measures the total distance from each point along a segment to a center. Variants o

Eljas Linna, Kestutis Baltakys, Alexandros Iosifidis, Juho Kanniainen

Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequency traders to visible order flow. Previous LOB models require cumbersome data representations and lack adaptability outside their original tasks, leading us to introduce LOBERT, a

Suhaib Ardah, Francisco J. Profito, Daniele Dini

Accurately depicting multiphysics interactions in interfacial systems requires computational frameworks capable of reconciling geometric adaptability with strict conservation fidelity. However, traditional spatiotemporal discretisation methods often compromise between mesh flexibility and flow conservation enforcement, hence constraining their effectiveness

Alessandro Scirè

This work introduces a new theoretical model for active matter ("complementary-spins" or c-spins), exploring the interplay of positional and orientational order in mobile agents with rotational freedom, divided into two populations with contrasting interactions. The system's behavior depends on its size and a control parameter (circular anisotropy) that spli

Qing Cai, Guihao Yan, Fan Zhang, Cheng Zhang

Ultrasound standard plane recognition is essential for clinical tasks such as disease screening, organ evaluation, and biometric measurement. However, existing methods fail to effectively exploit shallow structural information and struggle to capture fine-grained semantic differences through contrastive samples generated by image augmentations, ultimately re

Lawrence Wang, Stephen J. Roberts

Classical analyses of gradient descent (GD) define a stability threshold based on the largest eigenvalue of the loss Hessian, often termed sharpness. When the learning rate lies below this threshold, training is stable and the loss decreases monotonically. Yet, modern deep networks often achieve their best performance beyond this regime. We demonstrate that

Kamlesh Bora, Lakshmi Pradeep Chitta, Yajie Chen, Damien Przybylski

Small-scale transient jetlet activity and associated upflows from coronal hole plumes are potential sources of the solar wind. To elucidate the magnetic origins and driving mechanisms of such upflows, we perform three-dimensional radiative magnetohydrodynamic simulations using the MURaM code, spanning from the upper convection zone to the low corona. We synt

Jing Liu, Bing Guo, Ren Zhu

This paper pioneers the integration of learning optimization into measurement matrix design for phase retrieval. We introduce the Deep Learning-based Measurement Matrix for Phase Retrieval (DLMMPR) algorithm, which parameterizes the measurement matrix within an end-to-end deep learning architecture. Synergistically augmented with subgradient descent and prox

Hassan Oubba

Let $\mathcal{H}^{a,b}$ be the generalized quaternion algebra over a unitary commutative ring. This paper aims to investigate super-biderivations and local superderivations on the generalized quaternion algebra, which is viewed as a class of Lie superalgebra. It turns out that on generalized quaternion algebras, any local superderivation is a superderivation

Yijie Guo, Dexiang Hong, Weidong Chen, Zihan She

Visual Emotion Analysis (VEA) aims to bridge the affective gap between visual content and human emotional responses. Despite its promise, progress in this field remains limited by the lack of open-source and interpretable datasets. Most existing studies assign a single discrete emotion label to an entire image, offering limited insight into how visual elemen

Reinhild Roden, Tobias Sankowsky-Rothe, Nick Wulbusch, Alexey Chernov

To derive ear canal transfer functions for individualized equalization algorithms of in-ear hearing systems, individual ear canal models are needed. In a one-dimensional approach, this requires the estimation of the individual area function of the ear canal. The area function can be effectively and reproducibly calculated as the inverse solution of Webster's

Federico Taschin, Ozan K. Tonguz

Traffic signal control algorithms are vulnerable to distribution shift, where performance degrades under traffic conditions that differ from those seen during design or training. This paper introduces a principled approach to quantify distribution shift by representing traffic scenarios as demand histograms and comparing them with a GEH-based distance functi

Md Fahad Equbal, S R Hassan, M. A. H. Ahsan

We present an unsupervised learning analysis of correlation hierarchies in the quarter-filled simple and extended Hubbard models by applying principal component analysis (PCA) to exact-diagonalization (ED) data on 3x4 and 4x4 cylindrical clusters. While the non-interacting limit (U=0) provides a finite-size reference, increasing on-site repulsion U induces l

Edward Elliott, C. M. Baugh, Cedric Lacey

The properties of high-redshift sub-millimetre galaxies (SMGs) remain controversial within hierarchical structure formation models. We revisit whether a top-heavy stellar initial mass function (IMF) in starbursts is required to reproduce both SMG observations and local galaxy properties. Using Bayesian optimisation, we perform an extensive search of the 15-d

Guo-Niu Han, Kathy Q. Ji, Huan Xiong

Simsun permutations, Andr\'e I permutations and Andr\'e II permutations are three combinatorial models for Euler numbers. It's known that the descent statistic is equidistributed over the set of Andr\'e I permutations and the set of simsun permutations. In this paper, we prove that the trivariate statistic (ides, des, maj), comprising the inverse descent, de

Wenzhang Du

First-order optimizers are reliable but slow in sharp, anisotropic regions. We study a curvature-adaptive method that periodically sketches a low-rank Hessian subspace via Hessian--vector products and preconditions gradients only in that subspace, leaving the orthogonal complement first-order. For L-smooth non-convex objectives, we recover the standard O(1/T

Zhiguang Lu, Qianqian Xu, Peisong Wen, Siran Dai

Generative diffusion models show promise for data augmentation. However, applying them to fine-grained tasks presents a significant challenge: ensuring synthetic images accurately capture the subtle, category-defining features critical for high fidelity. Standard approaches, such as text-based Classifier-Free Guidance (CFG), often lack the required specifici

Stav Lotan, Hugo Defienne, Ronen Talmon, Guy Bartal

The characterization of high-dimensional quantum entanglement is crucial for advanced quantum computing and quantum information algorithms. Traditional methods require extensive data acquisition and suffer from limited visibility due to experimental noise. Here, we introduce a sparsity-driven framework to enhance the detection and certification of high-dimen

Robin van der Laag, Hao Wang, Thomas Bäck, Yingjie Fan

Stochastic multi-objective optimization (SMOOP) requires ranking multivariate distributions; yet, most empirical studies perform scalarization, which loses information and is unreliable. Based on the optimal transport theory, we introduce the center-outward q-dominance relation and prove it implies strong first-order stochastic dominance (FSD). Also, we deve

Mukul Lokhande, Akash Sankhe, S. V. Jaya Chand, Santosh Kumar Vishvakarma

The growing demand for low-power and area-efficient TinyML inference on AIoT devices necessitates memory architectures that minimise data movement while sustaining high computational efficiency. This paper presents FERMI-ML, a Flexible and Resource-Efficient Memory-In-Situ (MIS) SRAM macro designed for TinyML acceleration. The proposed 9T XNOR-based RX9T bit

Burak Karaduman, Baris Tekin Tezel, Moharram Challenger

The increasing complexity of industrial information-integration systems demands software technologies that enable intelligent behaviour, real-time response, and efficient development. Although many programming languages and frameworks exist, engineers still lack sufficient empirical evidence to guide the choice of tools for advanced industrial applications.

Caixing Gu, Meng Li, Pan Ma

Motivated by the Sarason problem on the products of Hankel and Toeplitz operators on analytic function spaces, we characterize the compactness of products of block Hankel and Toeplitz operators on the vector-valued Hardy space of the unit disk via harmonic extension of the symbols and Douglas algebras generated by the symbols. Additionally, we provide a comp

Olga Avramenko, Volodymyr Naradovyi

This second part of the study develops a geometric and asymptotic description of how surface tension governs the modulational stability of interfacial waves in a two-layer fluid. Extending the analytical framework of Part~I, surface tension is treated as a freely adjustable parameter, enabling one to trace how nonlinear and dispersive properties evolve acros

Katha Ganguly, Bijay Kumar Agarwalla

One of the major advances of quantum technology is the engineering of complex quantum channels in lattice systems that paves the way for a variety of novel non-equilibrium phenomena. For a boundary driven lattice with such engineered quantum channels, the analysis of the full counting statistics of current across boundaries has received limited attention. In

Betzalel Bazak

Small clusters of $^4$He atoms are benchmark systems for universal few-body physics near the unitary limit. We study these systems using a finite-cutoff effective field theory calibrated to low-energy observables from the realistic LM2M2 potential. The chosen two-body cutoff reproduces both the atom--atom scattering length and effective range, while a regula

You-Cai Lv, Yu-Jia Zhu, Zong-Quan Zhou, Chuan-Feng Li

Long-lived storage of single photons is a fundamental requirement for enabling quantum communication and foundational tests of quantum physics over extended distances. While the implementation of a global-scale quantum network requires quantum storage times on the order of seconds to minutes, existing photonic quantum memories have so far been limited to sub

Niket Thakkar

This paper considers large-scale vaccination campaigns, a major platform for vaccine access in a lot of the world, as a recapture estimate of the target population marked by routine immunization. Framing the campaign as a measurement, we learn about its properties, including the campaign's coverage of the target population and some implied sampling propertie

Quoc Thong Le Gia, Ian Hugh Sloan, Holger Wendland

In this paper, we discuss vector-valued Gaussian processes for the approximation of divergence- or rotation-free functions. We establish the theory for such Gaussian processes, then link the theory to multivariate approximation theory, and finally give error estimates for the predictive mean in various situations.

Dor Polikar, Alon Cohen

We define the problem of linear Contextual Stochastic Shortest Path (CSSP), where at the beginning of each episode, the learner observes an adversarially chosen context that determines the MDP through a fixed but unknown linear function. The learner's objective is to reach a designated goal state with minimal expected cumulative loss, despite having no prior

Botao 'Amber' Hu, Danlin Huang

The recent more-than-human turn in design calls for attentiveness to nonhuman beings. Yet -- as Thomas Nagel's famous ``What is it like to be a bat?'' thought experiment highlights -- human experience is constrained by our own sensorium and an irreducible gap in phenomenal access to nonhuman \emph{Umwelten}. Grounded in eco-phenomenology and eco-somatics, th

Yogesh Kumar, Anand Mishra

Few-shot Video Object Detection (FSVOD) addresses the challenge of detecting novel objects in videos with limited labeled examples, overcoming the constraints of traditional detection methods that require extensive training data. This task presents key challenges, including maintaining temporal consistency across frames affected by occlusion and appearance v

Camila Franco, Felipe Avila, Armando Bernui, Ulisses Ribeiro

We present a model-independent estimate of the angular homogeneity scale in the Local Universe by analysing data from the Southern Photometric Local Universe Survey (S-PLUS). Two complementary estimators are employed: (i) a parametric approach fitting the power-law of the two-point angular correlation function, which yields the homogeneity scale $\theta_H =

Vu A. Le, Hoa Q. Duong, Tuan A. Nguyen

Motivated by the classical correspondence between short exact sequences and splitting properties in module theory, this paper examines the projective and injective analogues within the category of Lie algebras. We first show that no Lie algebra can serve as a projective or injective object with respect to arbitrary extensions, thereby clarifying the natural

Yuan Zhou, Litao Hua, Shilong Jin, Wentao Huang

Keyframe selection has become essential for video understanding with vision-language models (VLMs) due to limited input tokens and the temporal sparsity of relevant information across video frames. Video understanding often relies on effective keyframes that are not only informative but also causally decisive. To this end, we propose Reinforced Causal Search

Sanchaita Hazra, Doeun Lee, Bodhisattwa Prasad Majumder, Sachin Kumar

Large Language Models have seen expanding application across domains, yet their effectiveness as assistive tools for scientific writing - an endeavor requiring precision, multimodal synthesis, and domain expertise - remains insufficiently understood. We examine the potential of LLMs to support domain experts in scientific writing, with a focus on abstract co

Zheyuan Zhang, Jiwei Zhang, Boyu Zhou, Linzhimeng Duan

Visual Place Recognition (VPR) aims to determine the geographic location of a query image by retrieving its most visually similar counterpart from a geo-tagged reference database. Recently, the emergence of the powerful visual foundation model, DINOv2, trained in a self-supervised manner on massive datasets, has significantly improved VPR performance. This i

Ronaldo F. de Lima, Giuseppe Pipoli

Let $\mathbb Q_{\epsilon_i}^{n_i}$ denote the simply connected space form of dimension $n_i\ge 2$ and constant sectional curvature $\epsilon_i$. We prove that any connected isoparametric hypersurface of $\mathbb Q_{\epsilon_1}^{n_1}\times\mathbb Q_{\epsilon_2}^{n_2}$ has constant angle function. We then use this property to classify the isoparametric and hom

Davide De Benedittis, Giovanni Di Lorenzo, Franco Angelini, Barbara Valle

According to the European Union's Habitat Directive, habitat monitoring plays a critical role in response to the escalating problems posed by biodiversity loss and environmental degradation. Scree habitats, hosting unique and often endangered species, face severe threats from climate change due to their high-altitude nature. Traditionally, their monitoring h

Jing Li, Yifan Wang, Jiafeng Yan, Renlong Zhang

Infrared and visible image fusion aims to integrate complementary multi-modal information into a single fused result. However, existing methods 1) fail to account for the degradation visible images under adverse weather conditions, thereby compromising fusion performance; and 2) rely on fixed network architectures, limiting their adaptability to diverse degr

Adam Dziwoki, Rostislav Horcik

This paper investigates the impact of perturbations on the best-response-based algorithms approximating Nash equilibria in zero-sum games, namely Double Oracle and Fictitious Play. More precisely, we assume that the oracle computing the best responses perturbs the utilities before selecting the best response. We show that using such an oracle reduces the num

Songlin Lyu, Lin Zuo, Rui Peng, Sebastian König

Modern theory approaches for describing atomic nuclei often make use of on an effective theory that constructs the interaction between nucleons systematically based on Quantum Chromodynamics (QCD), exploiting constraints arising from the approximate chiral symmetry of QCD. The tensor nuclear force produced by one-pion exchange is an important feature that ar

Michal R. Wrobel

Mapping discrete and dimensional models of emotion remains a persistent challenge in affective science and computing. This incompatibility hinders the combination of valuable data sets, creating a significant bottleneck for training robust machine learning models. To bridge this gap, this paper presents a novel, human-centric, proxy-based approach that trans

Boyang Zhou, Johan Lindqvist, Lindsey Li

We reproduce the central claims of Test-Time Training on Nearest Neighbors for Large Language Models (Hardt and Sun, 2024), which proposes adapting a language model at inference time by fine-tuning on retrieved nearest-neighbor sequences. Using pretrained RoBERTa embeddings indexed with Faiss, we retrieve 20 neighbors per test input and apply one gradient up

Jie Zhang, Bo Tang, Wanzi Shao, Wenqiang Wei

Retrieval-Augmented Generation (RAG) improves large language models by retrieving external knowledge, often truncated into smaller chunks due to the input context window, which leads to information loss, resulting in response hallucinations and broken reasoning chains. Moreover, traditional RAG retrieves unstructured knowledge, introducing irrelevant details

Aung Phone Maw

We shall investigate and arrive at a certain functional property of the double series \[ \sum\limits_{n,r\geq 1}\frac{1}{\sqrt{x^2n^2+r^2+w^2}\left( e^{2 \pi y\sqrt{x^2n^2+r^2+w^2}}-1\right)}. \]

Linfeng Zhou, Guangrui Zhu

This paper generalizes Llarull's classical scalar curvature rigidity theorem to the setting of weighted manifolds with P-scalar curvature. More precisely, we prove the refinement of Llarull's theorem for P-scalar curvature, which is similar to Listing's work \cite{listing2010scalar}. As an application, we establish a Llarull type theorem in the form of $\mat

R. Vilela Mendes

When four dimensional spacetime R is considered as locally embedded on a larger manifold M, labelled by higher division algebra coordinates, a natural question to ask is how much of the symmetry properties of the larger space are inherited by R. Here this question is studied when M is a quaternion manifold. Of particular relevance is the absence of spinors i

Ziqing Qian, Jiaying Lei, Shengqi Dang, Nan Cao

Social media has fundamentally transformed how people access information and form social connections, with content expression playing a critical role in driving information diffusion. While prior research has focused largely on network structures and tipping point identification, it provides limited tools for automatically generating content tailored for vir

Andrea Sacchetti

In recent years, Winter's nonlinear model has been adopted in theoretical physics as the prototype for the study of quantum resonances and the dynamics of observables in the context of nonlinear Schr\"odinger equations. However, its mathematical treatment still has several important gaps. This article demonstrates a dispersive estimate of the evolution opera

Manish Sahoo, Akash Deshmukh, Yash Kokane, Jayaprakash H M

High-entropy alloys (HEAs) exhibit exceptional properties arising from a combination of thermodynamic, kinetic and structural factors and have found applications in numerous fields such as aerospace, energy, chemical industries, hydrogen storage, and ocean engineering. However, a large compositional space remains to be explored. Unlike conventional approache

Ron Evans, Mark Van Veen

Given a real quadratic integer $u=A+B\sqrt{D}$ with cubic norm, we identify all the classes in a related form class group that represent primes $p$ for which $u$ is a cubic residue mod $p$. A special case of this result was conjectured in a 2025 paper of Evans, Lemmermeyer, Sun, and Van Veen.

Ze Tao, Darui Zhao, Fujun Liu, Ke Xu

Physics-informed neural networks (PINN) face significant challenges from spectral bias, which impedes their ability to model high-frequency phenomena and limits extrapolation performance. To address this, we introduce xLSTM-PINN, a novel architecture that performs representation-level spectral remodeling through memory gating and residual micro-steps. Our me

Jialiang Shen, Jiyang Zheng, Yunqi Xue, Huajie Chen

With growing concerns over image authenticity and digital safety, the field of AI-generated image (AIGI) detection has progressed rapidly. Yet, most AIGI detectors still struggle under real-world degradations, particularly motion blur, which frequently occurs in handheld photography, fast motion, and compressed video. Such blur distorts fine textures and sup

Yingke He

Do nineteenth-century graphics still work for today's readers? To investigate this question, we conducted a controlled experiment evaluating three canonical historical visualizations- Nightingale's polar area diagram, Playfair's trade balance chart, and Minard's campaign map-against modern redesigns. Fifty-four participants completed structured question-answ

Ruoyi Guo, Xinyi Yuan

In this paper, we compute the nef cone and the pseudo-effective cone of $C\times J$ for a smooth projective curve $C$ and its Jacobian variety $J$ such that $C\times J$ has the minimal Picard number. As a consequence, we also compute the successive minima of a height function for the relative setting $C\times J\to J$, and our result shows that Zhang's theore

Yanhao Wang, Lei Wang, Jie Wang, Yimin Liu

Complex electromagnetic environments, often containing multiple jammers with different jamming patterns, produce non-uniform jamming power across the frequency spectrum. This spectral non-uniformity directly induces severe distortion in the target's HRRP, consequently compromising the performance and reliability of conventional HRRP-based target recognition

Jingtian Ma, Jingyuan Wang, Leong Hou U

Road networks are critical infrastructures underpinning intelligent transportation systems and their related applications. Effective representation learning of road networks remains challenging due to the complex interplay between spatial structures and frequency characteristics in traffic patterns. Existing graph neural networks for modeling road networks p

Levente Bodnár, Wanfang Chen, Jinghua Deng, Jianfeng Hou

The famous Tetrahedron Conjecture of Tur\'an from the 1940s asserts that the number of edges in an $n$-vertex $3$-graph without the tetrahedron, the complete $3$-graph on four vertices, cannot exceed that of the balanced complete cyclic $3$-partite $3$-graph, whose edges are of types $V_1 V_2 V_3$, $V_1 V_1 V_2$, $V_2 V_2 V_3$, and $V_3 V_3 V_1$. A recent su

Allan Lo, Klas Markström, Dhruv Mubayi, Katherine Staden

An edge-colouring of a graph $G$ can fail to be rainbow for two reasons: either it contains a monochromatic cherry (a pair of incident edges), or a monochromatic matching of size two. A colouring is a proper colouring if it forbids the first structure, and a star-colouring if it forbids the second structure. In this paper, we study rainbow subgraphs in star-

Maria Tseytlin, Paul Roit, Omri Abend, Ido Dagan

Decomposing sentences into fine-grained meaning units is increasingly used to model semantic alignment. While QA-based semantic approaches have shown effectiveness for representing predicate-argument relations, they have so far left noun-centered semantics largely unaddressed. We introduce QA-Noun, a QA-based framework for capturing noun-centered semantic re

Fereidoon Zangeneh, Leonard Bruns, Amit Dekel, Alessandro Pieropan

Accurate camera pose estimation from an image observation in a previously mapped environment is commonly done through structure-based methods: by finding correspondences between 2D keypoints on the image and 3D structure points in the map. In order to make this correspondence search tractable in large scenes, existing pipelines either rely on search heuristi

Yu Liang, Yu Yang, Wenjie Wei, Ammar Belatreche

Binary Spiking Neural Networks (BSNNs) offer promising efficiency advantages for resource-constrained computing. However, their training algorithms often require substantial memory overhead due to latent weights storage and temporal processing requirements. To address this issue, we propose Binary Spiking Online (BSO) optimization algorithm, a novel online t

Muhammad Awad, Muhammad Osama, Brandon Potter

Multi-GPU programming traditionally requires developers to navigate complex trade-offs between performance and programmability. High-performance implementations typically rely on low-level HIP/CUDA communication libraries that demand substantial engineering effort for even basic overlap patterns, while simpler abstractions often sacrifice performance. We pre

Toru Hasunuma

The class of cographs is one of the most well-known graph classes, which is also known to be equivalent to the class of $P_4$-free graphs. We show that Mader's conjecture is true if we restrict ourselves to cographs, that is, for any tree $T$ of order $m$, every $k$-connected cograph $G$ with $\delta(G) \geq \left\lfloor \frac{3k}{2} \right\rfloor +m-1$ cont

Jongseong Bae, Junwoo Ha, Jinnyeong Heo, Yeongin Lee

Recent camera-based 3D semantic scene completion (SSC) methods have increasingly explored leveraging temporal cues to enrich the features of the current frame. However, while these approaches primarily focus on enhancing in-frame regions, they often struggle to reconstruct critical out-of-frame areas near the sides of the ego-vehicle, although previous frame

JoonHo Lee, HyeonMin Cho, Jaewoong Yun, Hyunjae Lee

We present SGuard-v1, a lightweight safety guardrail for Large Language Models (LLMs), which comprises two specialized models to detect harmful content and screen adversarial prompts in human-AI conversational settings. The first component, ContentFilter, is trained to identify safety risks in LLM prompts and responses in accordance with the MLCommons hazard

Jayanand Maurya, Yu Zhang, Hubiao Niu

The extended Main Sequence Turn-off (eMSTO) in the open cluster NGC 2355 is investigated using precise astrometry and photometry from Gaia DR3 and spectroscopic data from the Gaia-ESO Survey. We find a clear positive correlation between the rotational velocity (v sin i) and color of eMSTO stars, supporting the role of stellar rotation and gravity darkening i

Zhen Tao, Xinke Jiang, Qingshuai Feng, Haoyu Zhang

Dynamic recommendation systems aim to provide personalized suggestions by modeling temporal user-item interactions across time-series behavioral data. Recent studies have leveraged pre-trained dynamic graph neural networks (GNNs) to learn user-item representations over temporal snapshot graphs. However, fine-tuning GNNs on these graphs often results in gener

Jiecheng Jiang, Jiawei Tang, Jiahao Jiang, Hui Liu

Label distribution learning (LDL) is a novel paradigm that describe the samples by label distribution of a sample. However, acquiring LDL dataset is costly and time-consuming, which leads to the birth of incomplete label distribution learning (IncomLDL). All the previous IncomLDL methods set the description degrees of "missing" labels in an instance to 0, bu

Sungjun Seo, Kooktae Lee

The growing scale of modern farms has increased the need for efficient and adaptive multi-agent coverage strategies for pest, weed, and disease management. Traditional methods such as manual inspection and blanket pesticide spraying often lead to excessive chemical use, resource waste, and environmental impact. While unmanned aerial vehicles (UAVs) offer a p

Ponhvoan Srey, Yaxin Shi, Hangwei Qian, Jing Li

Fully Test-Time Adaptation (FTTA) addresses domain shifts without access to source data and training protocols of the pre-trained models. Traditional strategies that align source and target feature distributions are infeasible in FTTA due to the absence of training data and unpredictable target domains. In this work, we exploit a dual perspective on FTTA, an

Mainak Singha

We document a high-performing cross-sectional equity factor that achieves out-of-sample Sharpe ratios above 13 through regime-conditional signal activation. The strategy combines value and short-term reversal signals only during stock-specific drift regimes, defined as periods when individual stocks show more than 60 percent positive days in trailing 63-day

Qingsong Zhong, Haomin Yu, Yan Lin, Wangmeng Shen

Structure-Based drug design (SBDD) has emerged as a popular approach in drug discovery, leveraging three-dimensional protein structures to generate drug ligands. However, existing generative models encounter several key challenges: (1) incorporating boundary condition constraints, (2) integrating hierarchical structural conditions, and (3) ensuring spatial m

Yuan Shi, Pengjie Zhang, Zhao Chen, Jian Qin

Weak lensing mass-mapping from shear catalogs faces systematic challenges from survey masks and spatially varying noise. To overcome these issues and reconstruct unbiased convergence $\kappa$ maps, we have constructed the AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free and maximum-likelihood based analytical method. It has been validated for moc

Duneesha Fernando, Maria A. Rodriguez, Rajkumar Buyya

Edge computing environments host increasingly complex microservice-based IoT applications, which are prone to performance anomalies that can propagate across dependent services. Identifying the true source of such anomalies, known as Root Cause Localization (RCL), is essential for timely mitigation. However, existing RCL approaches are designed for cloud env

Pengze Li, Jiaqi Liu, Junchi Yu, Lihao Liu

Large language models (LLMs) are increasingly used in scientific domains. While they can produce reasoning-like content via methods such as chain-of-thought prompting, these outputs are typically unstructured and informal, obscuring whether models truly understand the fundamental reasoning paradigms that underpin scientific inference. To address this, we int

Xu Yang, Chenhui Lin, Haotian Liu, Qi Wang

With the integration of massive distributed energy resources and the widespread participation of novel market entities, the operation of active distribution networks (ADNs) is progressively evolving into a complex, multi-scenario, and multi-objective problem. Although expert engineers have developed numerous domain specific models (DSMs) to address distinct

Zhao-Jun Yan, Jun Yin, Lei Hao, Shi-Yin Shen

The Chinese Space Station Survey Telescope (CSST) is a next-generation Stage-IV facility renowned for its wide field of view, high image quality, and multi-band observational capabilities. Among the five instruments onboard the CSST, the Integral Field Spectrograph (IFS) offers the unique ability to simultaneously capture spatial and spectral information acr

Rakibul Islam Kanak, Taslima Hossain Sanjana, Apurba Sarker, Sourav Saha

Supplying spare parts from Earth for in space repair is economically prohibitive and logistically slow, posing a major barrier to sustainable space operations. As lunar and Martian missions accelerate in the coming decades, the feasibility of in-situ repair methods, particularly laser based welding, must be rigorously evaluated. The micro scale physics gover

Zhao-Jun Yan, Huan-Yuan Shan, Zhen-Ya Zheng, Xi-Yan Peng

The Chinese Space Station Survey Telescope (CSST), a two-meter aperture astronomical space telescope under China's manned space program, is equipped with multiple back-end scientific instruments. As an astronomical precision measurement module of the CSST, the Multi-Channel Imager (MCI) can cover a wide wavelength range from ultraviolet to near-infrared with

Jingshan Hong, Haigen Hu, Huihuang Zhang, Qianwei Zhou

In supervised learning, traditional image masking faces two key issues: (i) discarded pixels are underutilized, leading to a loss of valuable contextual information; (ii) masking may remove small or critical features, especially in fine-grained tasks. In contrast, masked image modeling (MIM) has demonstrated that masked regions can be reconstructed from part

Navin Sriram Ravie, Keerthi Vasan M, Bijo Sebastian

Dense clutter removal for target object retrieval presents a challenging problem, especially when targets are embedded deep within densely-packed configurations. It requires foresight to minimize overall changes to the clutter configuration while accessing target objects, avoiding stack destabilization and reducing the number of object removals required. Rul

Mahdi Pirayesh Shirazi Nejad, David Hicks, Matt Valentine, Ki H. Chon

Electrocardiogram (ECG) signals are often degraded by various noise sources such as baseline wander, motion artifacts, and electromyographic interference, posing a major challenge in clinical settings. This paper presents a lightweight deep learning-based denoising framework, forming a compact autoencoder architecture. The model was trained under severe nois

Yanan Wang, Zikun Lin, Linhui Wu, Weihua Lei

Theories and simulations predict that intense spacetime curvature near black holes bends the trajectories of light and matter, driving disk and jet precession under relativistic torques. However, direct observational evidence of disk-jet co-precession remains elusive. Here, we report the most compelling case to date: a tidal disruption event (TDE) exhibiting

Bhathiya Divelgama, Nancy Asare Nyarko, Naa Sackley Dromo Aryee, Abootaleb Shirvani

Investing in Asian markets through exchange-traded funds (ETFs) provides investors with access to rapidly expanding economies and valuable diversification opportunities. This study examines the advantages and challenges of investing in Asian ETFs by conducting comprehensive risk assessments, portfolio analyses, and performance comparisons. The dataset compri

Hong-Hao Fan, Lie-Juan Li, Zhi-Hang Yao, Orkash Amat

We investigate the multiphoton pair production in circularly polarized field via two level model. There appears obvious discrete ring structures in the momentum distribution of the created particles, in which the ring radius is mainly controlled by the number of the photons absorbed in the creation with the energy conservation and could also be modulated by

Chucheng Xiang, Ruchao Bao, Biyin Feng, Wenzheng Wu

We present a novel framework for automated interior design that combines large language models (LLMs) with grid-based integer programming to jointly optimize room layout and furniture placement. Given a textual prompt, the LLM-driven agent workflow extracts structured design constraints related to room configurations and furniture arrangements. These constra

Yunling Chen, John Erik Fornæss, Song-Yan Xie

Let \(X\) be a compact complex manifold possessing the \emph{Runge approximation property on discs}, meaning that every holomorphic map from a closed disc into \(X\) is approximable by a global holomorphic map from \(\mathbb{C}\). We construct an entire curve \(F : \mathbb{C} \to X\) such that the associated family of concentric holomorphic discs \(\{F|_{\ov

Mengying Wang, Chenhui Ma, Ao Jiao, Tuo Liang

Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predictable answers. A missing yet desired capacity is to exploit LLMs to suggest surprise and novel ("serendipitious") answers. In this paper, we formally define the serendipity-aware KG

Youming Chen, Zhaoqiang Liu

Diffusion models (DMs) have demonstrated to be powerful priors for signal recovery, but their application to 1-bit quantization tasks, such as 1-bit compressed sensing and logistic regression, remains a challenge. This difficulty stems from the inherent non-linear link function in these tasks, which is either non-differentiable or lacks an explicit character