November 2025 arXiv papers — page 114

Dmytro Valiaiev

The proliferation of SQL for data processing has often occurred without the rigor of traditional software development, leading to siloed efforts, logic replication, and increased risk. This ad-hoc approach hampers data governance and makes validation nearly impossible. Organizations are adopting DataOps, a methodology combining Agile, Lean, and DevOps princi

Boyi Hu, Zhongjian Wang, Jack Xin, Zhiwen Zhang

We develop and analyze a stochastic genetic interacting particle method (SGIP) for reaction-diffusion-advection (RDA) equations. The SGIP method employs operator splitting to approximate the advection-diffusion and reaction processes, treating the former using particle drift-diffusion and the latter via exact or implicit integration of reaction dynamics over

Martin Monperrus

Web3 applications, built on blockchain technology, manage billions of dollars in digital assets through decentralized applications (dApps) and smart contracts. These systems rely on complex, software supply chains that introduce significant security vulnerabilities. This paper examines the software supply chain security challenges unique to the Web3 ecosyste

Mario Bertaina, Pietro Antonio Palmieri, Micol Bargelli, Manuel Dionisio Da Rocha Rolo

The Multi-channel Intergrated Zone-sampling Analogue-memory based Readout (MIZAR) ASIC is a new type of front-end electronics which has been developed for the detection of the optical Cherenkov signals by Extensive Air Showers directly observed from sub-orbital and orbital altitudes. It sets the stage for a new generation of low-power consuming 64-channel Ap

Zhiyu An, Wan Du

Large language models are increasingly influencing human moral decisions, yet current approaches focus primarily on evaluating rather than actively steering their moral decisions. We formulate this as an out-of-distribution moral alignment problem, where LLM agents must learn to apply consistent moral reasoning frameworks to scenarios beyond their training d

Yaxuan Jiao, Qing Xu, Yuxiang Luo, Xiangjian He

Medical image segmentation is essential for clinical diagnosis and treatment planning. Although transformer-based methods have achieved remarkable results, their high computational cost hinders clinical deployment. To address this issue, we propose TM-UNet, a novel lightweight framework that integrates token sequence modeling with an efficient memory mechani

Rupam Mukherjee, Rajkumar Daniel, Soujanya Hazra, Shirin Dasgupta

Cytology is a valuable tool for early detection of oral squamous cell carcinoma (OSCC). However, manual examination of cytology whole slide images (WSIs) is slow, subjective, and depends heavily on expert pathologists. To address this, we introduce the first weakly supervised deep learning framework for patient-level diagnosis of oral cytology whole slide im

Ruixun Liu, Bowen Fu, Jiayi Song, Kaiyu Li

Ultra-high-resolution (UHR) remote sensing (RS) images offer rich fine-grained information but also present challenges in effective processing. Existing dynamic resolution and token pruning methods are constrained by a passive perception paradigm, suffering from increased redundancy when obtaining finer visual inputs. In this work, we explore a new active pe

Richard S. J. Tol

Environmental determinism in the past followed from the belief that the gods bestowed political power and the best possible weather on the sponsors of early scholars. Although later discredited in academia because of the associations with racism and the lack of support for any monocausal explanation of history, environmental determinism in popular culture ha

Anna V. Kononova, Niki van Stein, Olaf Mersmann, Thomas Bäck

Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC isolate algorithmic phenomena but poorly reflect the structure, constraints, and information limitations of continuous and mixed-integer optimization problems in practice. This discon

Jingyao Li, Jingyun Wang, Molin Tan, Haochen Wang

Cross-Video Reasoning (CVR) presents a significant challenge in video understanding, which requires simultaneous understanding of multiple videos to aggregate and compare information across groups of videos. Most existing video understanding benchmarks focus on single-video analysis, failing to assess the ability of multimodal large language models (MLLMs) t

Mario Bertaina, Matteo Battisti, JEM-EUSO collaboration

Mini--EUSO (Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory, known as \emph{UV atmosphere} in the Russian Space Program) is the first mission of the JEM-EUSO program on board the International Space Station. It was launched in August 2019 and it is operating since October 2019 being located in the Russian section (Zvezda mod

Zongxin Shen, Yanyong Huang, Dongjie Wang, Jinyuan Chang

Incomplete multi-view unsupervised feature selection (IMUFS), which aims to identify representative features from unlabeled multi-view data containing missing values, has received growing attention in recent years. Despite their promising performance, existing methods face three key challenges: 1) by focusing solely on the view-missing problem, they are not

Jonas Elsborg, Emma L. Hovmand, Arghya Bhowmik

We approach the search for optimal element ordering in bimetallic alloy nanoparticles (NPs) as a reinforcement learning (RL) problem and have built an RL agent that learns to perform such global optimization using the geometric graph representation of the NPs. To demonstrate the effectiveness, we train an RL agent to perform composition-conserving atomic swa

Puzhen Wu, Hexin Dong, Yi Lin, Yihao Ding

Radiology report generation from chest X-rays is an important task in artificial intelligence with the potential to greatly reduce radiologists' workload and shorten patient wait times. Despite recent advances, existing approaches often lack sufficient disease-awareness in visual representations and adequate vision-language alignment to meet the specialized

Elhadji Cisse Faye, Mame Diarra Fall, Nicolas Dobigeon, Eric Barat

This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which accounts for the underlying non-Euclidean geometry. To address the challenges posed by the Poisson likelihood -- such as non-Lipschitz gradients and positivity constraints -- we derive a Bayesian model which leverages exact a

Tolga Demiroglu, Mehmet Ozan Unal, Metin Ertas, Isa Yildirim

We propose a prompt-conditioned framework built on MedSigLIP that injects textual priors via Feature-wise Linear Modulation (FiLM) and multi-scale pooling. Text prompts condition patch-token features on clinical intent, enabling data-efficient learning and rapid adaptation. The architecture combines global, local, and texture-aware pooling through separate r

Huy M. Le, Dat Tien Nguyen, Phuc Binh Nguyen, Gia Bao Le Tran

The Video Browser Showdown (VBS) challenges systems to deliver accurate results under strict time constraints. To meet this demand, we present Fusionista2.0, a streamlined video retrieval system optimized for speed and usability. All core modules were re-engineered for efficiency: preprocessing now relies on ffmpeg for fast keyframe extraction, optical chara

Yuxiang Zhou, Jichang Li, Yanhao Zhang, Haonan Lu

Mobile agents show immense potential, yet current state-of-the-art (SoTA) agents exhibit inadequate success rates on real-world, long-horizon, cross-application tasks. We attribute this bottleneck to the agents' excessive reliance on static, internal knowledge within MLLMs, which leads to two critical failure points: 1) strategic hallucinations in high-level

Harrison Goldstein, Hila Peleg, Cassia Torczon, Daniel Sainati

Among the biggest challenges in property-based testing (PBT) is the constrained random generation problem: given a predicate on program values, randomly sample from the set of all values satisfying that predicate, and only those values. Efficient solutions to this problem are critical, since the executable specifications used by PBT often have preconditions

Xiaohui Li, Xiaolong Liu, Zhongchen Shi, Wei Chen

Cave Automatic Virtual Environment (CAVE) is one of the virtual reality (VR) immersive devices currently used to present virtual environments. However, the locomotion methods in the CAVE are limited by unnatural interaction methods, severely hindering the user experience and immersion in the CAVE. We proposed a locomotion framework for CAVE environments aime

Doru Sticlet, Romulus Tetean, Coriolan Tiusan

Quantum computation departs from the classical paradigm of deterministic, bit-based processing by exploiting inherently quantum phenomena such as superposition and entanglement. We propose a framework for qubit realization based on skyrmionic states stabilized by the Dzyaloshinskii-Moriya interaction (DMI) in two-dimensional spin lattices. The model incorpor

Khang T. Huynh, Dung H. Nguyen, Binh T. Nguyen

Recent advances in contextualized word embeddings have greatly improved semantic tasks such as Word Sense Disambiguation (WSD) and contextual similarity, but most progress has been limited to high-resource languages like English. Vietnamese, in contrast, still lacks robust models and evaluation resources for fine-grained semantic understanding. In this paper

Kerem Basim, Mehmet Ozan Unal, Metin Ertas, Isa Yildirim

Block-Matching and 3D Filtering (BM3D) exploits non-local self-similarity priors for denoising but relies on fixed parameters. Deep models such as U-Net are more flexible but often lack interpretability and fail to generalize across noise regimes. In this study, we propose Deep Unfolded BM3D (DU-BM3D), a hybrid framework that unrolls BM3D into a trainable ar

Manas Kulkarni, Satya N. Majumdar, Gregory Schehr

We compute the full probability distribution of the moment of inertia $I \propto \sum_{i=1}^N \vec{r}_i^2$ of a gas of $N$ noninteracting bosons trapped in a harmonic potential $V(r) = (1/2) m ω^2 r^2$, in all dimensions and at all temperatures. The appropriate thermodynamic limit in a trapped Bose gas consists in taking $N\to\infty$ and $ω\to 0$ with their

Fei Wang, Guoying Liang, Zecheng Zhao, Lin-Yue Luo

In this work, we investigate many-body phase transitions in a one-dimensional anisotropic XY model subject to a complex-valued transverse field. Within the biorthogonal framework, we calculate the ground-state correlation functions and entanglement entropy, confirming that their scaling behavior remains identical to that in the Hermitian XY model. The preser

Xiaobao Liu, Wentao Liu, Shu-Min Wu

Quantum information science has been broadly explored in Einstein gravity and in various modified gravity theories; however, its extension to quantum gravity settings remains largely unexplored. Motivated by this gap, in this paper we investigate the degradation of quantum entanglement of scalar and Dirac fields in the third-type black hole geometry arising

Artur Alho, Claes Uggla

Arguably one can use a canonical scalar field $\varphi$, minimally coupled to gravity, with quadratic potentials $V = \Lambda \pm \frac12 m^2\varphi^2$ to explore some general features of slow-roll and hilltop thawing quintessence, respectively. For each of these two potentials, and pressure-free matter, we introduce a regular unconstrained dynamical system

Ziduo Yang, Yi-Ming Zhao, Xian Wang, Wei Zhuo

Structure optimization, which yields the relaxed structure (minimum-energy state), is essential for reliable materials property calculations, yet traditional ab initio approaches such as density-functional theory (DFT) are computationally intensive. Machine learning (ML) has emerged to alleviate this bottleneck but suffers from two major limitations: (i) exi

Samarth Garg, Divya Singh, Deeksha Varshney, Mamta

The rise of social networks has not only facilitated communication but also allowed the spread of harmful content. Although significant advances have been made in detecting toxic language in textual data, the exploration of concept-based explanations in toxicity detection remains limited. In this study, we leverage various subtype attributes present in toxic

Zhuoran Yu, Armin Schwartzman, Junting Ren, Julia Wrobel

The identification of domain sets whose outcomes belong to predefined subsets can address fundamental risk assessment challenges in climatology and medicine. Existing approaches for inverse domain estimates require restrictive assumptions, including domain density and continuity of function near thresholds, and large-sample guarantees, which limit the applic

Junhyuk Seo, Hyeyoon Moon, Kyu-Hwan Jung, Namkee Oh

Unplanned extubation (UE) remains a critical patient safety concern in intensive care units (ICUs), often leading to severe complications or death. Real-time UE detection has been limited, largely due to the ethical and privacy challenges of obtaining annotated ICU video data. We propose Augmented Unplanned Removal Alert (AURA), a vision-based risk detection

Vishal Joshua Meesala

Modern deep learning systems are typically deployed as open-loop function approximators: they map inputs to outputs in a single pass, without regulating how much computation or explanatory effort is spent on a given case. In safety-critical settings, this is brittle: easy and ambiguous inputs receive identical processing, and uncertainty is only read off ret

Tarun Gupta, Danish Pruthi

World models have garnered substantial interest in the AI community. These are internal representations that simulate aspects of the external world, track entities and states, capture causal relationships, and enable prediction of consequences. This contrasts with representations based solely on statistical correlations. A key motivation behind this research

Bo Tian, Xi Zhang, Ruitao Wu, Yuquan Zhang

Topological transitions are fundamental phenomena in electronics, photonics, and quantum technologies. However, the scalability and tunability of Topological transitions in these systems have still been constrained by their material properties or structural rigidities. Here, we demonstrate that plasmonic Moire superlattices offer a platform for large-range a

Alysson Ribeiro da Silva, Luiz Chaimowicz

Multi-Robot Exploration (MRE) systems with communication constraints have proven efficient in accomplishing a variety of tasks, including search-and-rescue, stealth, and military operations. While some works focus on opportunistic approaches for efficiency, others concentrate on pre-planned trajectories or scheduling for increased interpretability. However,

Raavi Gupta, Pranav Hari Panicker, Sumit Bhatia, Ganesh Ramakrishnan

Large language models (LLMs), despite their remarkable text generation capabilities, often hallucinate and generate text that is factually incorrect and not grounded in real-world knowledge. This poses serious risks in domains like healthcare, finance, and customer support. A typical way to use LLMs is via the APIs provided by LLM vendors where there is no a

Josef Simbrunner, Clemens Krenn, Martin Zach, Andreas Habring

We propose a method for the computation of a consistent system matrix for two- and three-dimensional cone-beam computed tomography (CT). The method relies on the decomposition of the cone-voxel intersection volumes into subvolumes that contribute to distinct detector elements and whose contributions to the system matrix admit exact formulae that can be evalu

Soham Sarkar, Arnab Hazra

Coral bleaching is a major concern for marine ecosystems; more than half of the world's coral reefs have either bleached or died over the past three decades. Increasing sea surface temperatures, along with various spatiotemporal environmental factors, are considered the primary reasons behind coral bleaching. The statistical and machine learning communities

Dongdong Zhao, Qiben Xu, Ranxin Fang, Baogang Song

Deep hashing improves retrieval efficiency through compact binary codes, yet it introduces severe and often overlooked privacy risks. The ability to reconstruct original training data from hash codes could lead to serious threats such as biometric forgery and privacy breaches. However, model inversion attacks specifically targeting deep hashing models remain

Lingfeng Zhang, Erjia Xiao, Xiaoshuai Hao, Haoxiang Fu

Social navigation in densely populated dynamic environments poses a significant challenge for autonomous mobile robots, requiring advanced strategies for safe interaction. Existing reinforcement learning (RL)-based methods require over 2000+ hours of extensive training and often struggle to generalize to unfamiliar environments without additional fine-tuning

Neal E. Young

We give a polynomial-time approximation algorithm for the (not necessarily metric) $k$-Median problem. The algorithm is an $\alpha$-size-approximation algorithm for $\alpha < 1 + 2 \ln(n/k)$. That is, it guarantees a solution having size at most $\alpha\times k$, and cost at most the cost of any size-$k$ solution. This is the first polynomial-time approximat

Zhipeng Xue, Zhipeng Gao, Tongtong Xu, Xing Hu

The use of static analysis tools has gained increasing popularity among developers in the last few years. However, the widespread adoption of static analysis tools is hindered by their high false alarm rates. Previous studies have introduced the concept of actionable warnings and built a machine-learning method to distinguish actionable warnings from false a

Ara Tonoyan, Sushree Subhadarshinee Sahoo, Anahit Gogyan, Oleg Tretiak

We report on measurements of second-order intensity correlations $g^{(2)}(\tau)$ of infrared emission under bichromatic excitation at 589.2\,nm and 569.0\,nm of sodium atoms contained in a buffer-gas-free and uncoated 10-cm-long vapor cell. Directional emissions at $2.34\,\mu$m in the forward direction and $2.21\,\mu$m in both forward and backward directions

Leszek Sliwko, Aleksander Zgrzywa

The paper presents a multi-resource load balancing strategy which can be utilised within an agent-based system. This approach can assist system designers in their attempts to optimise the structure for complex enterprise architectures. In this system, the social behaviour of the agent and its adaptation abilities are applied to determine an optimal setup for

Weibin Ni, Zhijie Li, Guanyu Qu, Asif Equbal

Quantum coherence remains a fundamental challenge for advancing quantum technologies. Although dynamical decoupling can suppress decoherence noise, it frequently misestimates decoherence times due to control errors -- a previously underappreciated issue. Here, we present Hadamard phase cycling, a scalable non-Markovian quantum error mitigation method using g

Anna Florio, Martin Leguil, Alfonso Sorrentino

We investigate rigidity phenomena associated to the stable norm and Mather's $\beta$-function for Riemannian geodesic flows on closed manifolds. Given two metrics $g_1$ and $g_2$, we compare these objects pointwise at individual homology classes. Our main result establishes that if Mather's $\beta$-function (or the stable norm) of $g_2$ at a non-zero homolog

Nishant Vasantkumar Hegde, Suneesh Bare, K B Ramesh, Aamir Ibrahim

Resource-constrained embedded systems demand secure yet lightweight data protection, particularly when data formats must be preserved. This paper introduces eFPE (Enhanced Format-Preserving Encryption), an 8-round Feistel cipher featuring a "novel lightweight Pseudorandom Function (PRF)" specifically designed for this domain. The PRF, architected with an eff

Hongtai Wang, Ming Xu, Yanpei Guo, Weili Han

The real-time demand for system security leads to the detection rules becoming an integral part of the intrusion detection life-cycle. Rule-based detection often identifies malicious logs based on the predefined grammar logic, requiring experts with deep domain knowledge for rule generation. Therefore, automation of rule generation can result in significant

Anshul Bagaria

Antimicrobial resistance (AMR) poses a mounting global health crisis, requiring rapid and reliable prediction frameworks that capture its complex evolutionary dynamics. Traditional antimicrobial susceptibility testing (AST), while accurate, remains laborious and time-consuming, limiting its clinical scalability. Existing computational approaches, primarily r

Hangshuo Tian

Particle filters (PFs) are often combined with swarm intelligence (SI) algorithms, such as Chicken Swarm Optimization (CSO), for particle rejuvenation. Separately, Kullback--Leibler divergence (KLD) sampling is a common strategy for adaptively sizing the particle set. However, the theoretical interaction between SI-based rejuvenation kernels and KLD-based ad

Qin-Sheng Zhu, Geng Chen, Lian-Hui Yu, Xiaodong Xing

We present a channel-constrained Markovian quantum diffusion (CCMQD) model that prepares quantum states by rigorously framing the generative process within the dynamics of open quantum systems. Our model interprets the forward diffusion process as natural decoherence using quantum master equations, whereas the reverse denoising is achieved by learning invers

Ameen Ali, Tamim Zoabi, Lior Wolf

Vision-language models (VLMs) frequently produce hallucinations in the form of descriptions of objects, attributes, or relations that do not exist in the image due to over-reliance on language priors and imprecise cross-modal grounding. We introduce Spectral Representation Filtering (SRF), a lightweight, training-free method to suppress such hallucinations b

Osafu Augustine Egbon, Asrat Mekonnen Belachew, Ezra Gayawan, Francisco Louzada

Fatalities resulting from violence in armed conflict have long been a significant public health issue in Ethiopia. Despite the severity of this problem, more comprehensive quantitative scientific studies need to be conducted to elucidate the sequence and dynamics of these occurrences. In response, this study introduces a spatio-temporal statistical method de

Lazaros Kanellopoulos

In this paper we investigate continuity properties for ruin probability in the classical risk model. Properties of contractive integral operators are used to derive continuity estimates for the deficit at ruin. These results are also applied to obtain desired continuity inequalities in the setting of continuous time surplus process perturbed by diffusion. In

Pierre-Olivier Goffard, Hansjoerg Albrecher, Jean-Pierre Fouque

Mining blocks in a blockchain using the \textit{Proof-of-Work} consensus protocol involves significant risk, as network participants face continuous operational costs while earning infrequent capital gains upon successfully mining a block. A common risk mitigation strategy is to join a mining pool, which combines the computing resources of multiple miners to

Gil Goren, Shahar Katz, Lior Wolf

Large Language Models (LLMs) are vulnerable to adversarial attacks that bypass safety guidelines and generate harmful content. Mitigating these vulnerabilities requires defense mechanisms that are both robust and computationally efficient. However, existing approaches either incur high computational costs or rely on lightweight defenses that can be easily ci

Van Ho-Long, Nguyen Ho, Anh-Vu Dinh-Duc, Ha Manh Tran

The explosive growth of IoT-enabled sensors is producing enormous amounts of time series data across many domains, offering valuable opportunities to extract insights through temporal pattern mining. Among these patterns, an important class exhibits periodic occurrences, referred to as \textit{seasonal temporal patterns} (STPs). However, mining STPs poses ch

Peng Zhang, Zhihui Lai, Wenting Chen, Xu Wu

Medical vision-language pre-training (VLP) offers significant potential for advancing medical image understanding by leveraging paired image-report data. However, existing methods are limited by Fa}lse Negatives (FaNe) induced by semantically similar texts and insufficient fine-grained cross-modal alignment. To address these limitations, we propose FaNe, a s

Ruochen Li, Zhanxing Zhu, Tanqiu Qiao, Hubert P. H. Shum

Pedestrian trajectory prediction is critical for ensuring safety in autonomous driving, surveillance systems, and urban planning applications. While early approaches primarily focus on one-hop pairwise relationships, recent studies attempt to capture high-order interactions by stacking multiple Graph Neural Network (GNN) layers. However, these approaches fac

Liang Xue, Haoyu Liu, Yajun Tian, Xinyu Zhong

Fine-grained entity recognition is crucial for reasoning and decision-making in task-oriented dialogues, yet current large language models (LLMs) continue to face challenges in domain adaptation and retrieval controllability. We introduce MME-RAG, a Multi-Manager-Expert Retrieval-Augmented Generation framework that decomposes entity recognition into two coor

Petr Boriskov, Kirill Rudkovskii, Andrei Velichko

This paper studies the removal of salt-and-pepper noise from images using median filter (MF) and simple three-layer autoencoder (AE) within recursive threshold algorithm. The performance of denoising is assessed with two metrics: the standard Structural Similarity Index SSIMImg of restored and clean images and a newly applied metric SSIMMap - the SSIM of ent

Shu Shen, Jianqing Yu

We give a new differential-geometric proof of Grauert's theorem on the coherence of the higher direct image of a coherent sheaf under a proper holomorphic morphism between complex analytic spaces. In the smooth case, our approach is based on the antiholomorphic superconnection introduced by Block and further developed by Bismut-Shen-Wei. The required finiten

Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi, Jacco Th. van Loon

Differences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects the performance of machine learning models in classifying dusty stellar sources. The five stellar classes studied include young stellar objects (YSOs), red supergiants (RSGs), post-

Hefei Xu, Le Wu, Chen Cheng, Hao Liu

With the rapid advancement of large language models (LLMs), aligning them with human values for safety and ethics has become a critical challenge. This problem is especially challenging when multiple, potentially conflicting human values must be considered and balanced. Although several variants of existing alignment methods (such as Reinforcement Learning f

Akshay Chandran, Praveen Kumar Kolluru, Berni J. Alder, Sauro Succi

We present a two-level (fluid-kinetic) coupling procedure for the simulation of wall-bounded flows at Reynolds numbers up to thousands. The method combines a kinetic Direct Simulation Monte Carlo (DSMC) treatment of the near-wall layer, with a high-order Lattice-Boltzmann (HOLB) scheme as a fluid solver in the bulk flow. Given the kinetic nature of HOLB, thi

Jilong Liu, Pengyang Shao, Wei Qin, Fei Liu

Knowledge Graph Question Answering (KGQA) aims to improve factual accuracy by leveraging structured knowledge. However, real-world Knowledge Graphs (KGs) are often incomplete, leading to the problem of Incomplete KGQA (IKGQA). A common solution is to incorporate external data to fill knowledge gaps, but existing methods lack the capacity to adaptively and co

Haozhe Liu, Ding Liu, Mingchen Zhuge, Zijian Zhou

We introduce MoS (Mixture of States), a novel fusion paradigm for multimodal diffusion models that merges modalities using flexible, state-based interactions. The core of MoS is a learnable, token-wise router that creates denoising timestep- and input-dependent interactions between modalities' hidden states, precisely aligning token-level features with the d

Nishant Vasantkumar Hegde, Aditi Agarwal, Minal Moharir

Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8

Jiaqi Wu, Yaosen Chen, Shuyuan Zhu

Multi-view image generation holds significant application value in computer vision, particularly in domains like 3D reconstruction, virtual reality, and augmented reality. Most existing methods, which rely on extending single images, face notable computational challenges in maintaining cross-view consistency and generating high-resolution outputs. To address

Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto

We present a unified approach for constraint displacement problems in which a robot finds a feasible path by displacing constraints or obstacles. To this end, we propose a two stage process that returns locally optimal obstacle displacements to enable a feasible path for the robot. The first stage proceeds by computing a trajectory through the obstacles whil

Zhuojiang Cai, Yiheng Zhang, Meitong Guo, Mingdao Wang

Recently, multi-view diffusion-based 3D generation methods have gained significant attention. However, these methods often suffer from shape and texture misalignment across generated multi-view images, leading to low-quality 3D generation results, such as incomplete geometric details and textural ghosting. Some methods are mainly optimized for the frontal pe

Feng Chen, Yefei He, Shaoxuan He, Yuanyu He

Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge the training-inference gap and lack the capacity for fine-grained token selection across multiple dimensions such as queries, key-values (KV), and heads, leading to suboptimal perfo

Sujun Sun, Haowen Gu, Cheng Xie, Yanxu Ren

Cross-domain Few-shot Segmentation (CD-FSS) aims to segment novel classes from target domains that are not involved in training and have significantly different data distributions from the source domain, using only a few annotated samples, and recent years have witnessed significant progress on this task. However, existing CD-FSS methods primarily focus on s

Runhao Jiang, Chengzhi Jiang, Rui Yan, Huajin Tang

The surrogate gradient (SG) method has shown significant promise in enhancing the performance of deep spiking neural networks (SNNs), but it also introduces vulnerabilities to adversarial attacks. Although spike coding strategies and neural dynamics parameters have been extensively studied for their impact on robustness, the critical role of gradient magnitu

G. Furioli, A. Pulvirenti, E. Terraneo, G. Toscani

We consider new connections between the problem of trend to equilibrium for the n-dimensional Fokker--Planck equation of statistical physics, and weighted Poincar\'e inequality. To this aim we consider a class of n-dimensional Fokker--Planck equations with variable isotropic coefficient of diffusion and drift, inspired by the analogous one-dimensional Fokker

Aditi Bhalla, Christian Hellert, Enkelejda Kasneci

Driver distraction remains a leading cause of road traffic accidents, contributing to thousands of fatalities annually across the globe. While deep learning-based driver activity recognition methods have shown promise in detecting such distractions, their effectiveness in real-world deployments is hindered by two critical challenges: variations in camera vie

Johannes Buchner

In the last decades, scientific software has graduated from a hidden side-product to a first-class member of the astrophysics literature. We aim to quantify the activity and impact of software development for astronomy, using a systematic survey. Starting from the Astrophysics Source Code Library and the Journal of Open Source Software, we analyse 3432 publi

Zixing Jia, Jiawei Li, Ziping Chen, Xin Li

We propose a multimodal fusion network (MFN) for precise micro-displacement measurement using a modified Michelson interferometer. The model resolves the intrinsic half-wave displacement ambiguity that limits conventional single-wavelength interferometry by introducing a dual-head learning mechanism: one head performs sub-half-wave displacement regression, a

Szymon Wojciechowski, Michał Woźniak

Many machine learning tasks aim to find models that work well not for a single, but for a group of criteria, often opposing ones. One such example is imbalanced data classification, where, on the one hand, we want to achieve the best possible classification quality for data from the minority class without degrading the classification quality of the majority

Sanghee Lee, Engin Bahar, Hakan Volkan Şenavcı, Emre Işık

Latitude distribution of stellar magnetic activity is not well constrained by observations, despite its importance for a better understanding of stellar dynamos. We aim to obtain an accurate reconstruction of the surface spot distribution on the young, rapidly rotating K2 star PW And by combining spectroscopic and photometric diagnostics. In particular, we s

Yongsheng Zhang

We verify the spiral minimal product structure through the Takahashi Theorem with full computational details which were omitted in [LZ].

Camilo Chacón Sartori, Christian Blum

Automatic algorithm configuration tools such as irace efficiently tune parameter values but leave algorithmic code unchanged. This paper introduces a first version of irace-evo, an extension of irace that integrates code evolution through large language models (LLMs) to jointly explore parameter and code spaces. The proposed framework enables multi-language

Nelson H. T. Lemes, José Claudinei Ferreira, Higor V. M. Ferreira

The interference of fluorescence signals and noise remains a significant challenge in Raman spectrum analysis, often obscuring subtle spectral features that are critical for accurate analysis. Inspired by variational methods similar to those used in image denoising, our approach minimizes a functional involving fractional derivatives to balance noise suppres

Xuanyu Chen, Nan Yang, Shuai Wang, Dong Yuan

The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about the depletion of high-quality, well-curated training data. This has led practitioners to explore training approaches like Federated Learning (FL), which can leverage the abundant da

Walter Schneller

Edgeworth expansions of first and second order are established for general linear rank statistics under the null hypothesis with asymptotically ''sufficiently'' small remainder terms. The methods used are the Stein method combined with an extension of a combinatorial method of Bolthausen (1984). The conditions obtained for the validity of these Edgeworth exp

Jun Huo, Jian Huang, Jie Zuo, Bo Yang

Supernumerary robotic limbs (SRLs) offer substantial potential in both the rehabilitation of hemiplegic patients and the enhancement of functional capabilities for healthy individuals. Designing a general-purpose SRL device is inherently challenging, particularly when developing a unified theoretical framework that meets the diverse functional requirements o

Mills Staylor, Arup Kumar Sarker, Gregor von Laszewski, Geoffrey Fox

Data is found everywhere, from health and human infrastructure to the surge of sensors and the proliferation of internet-connected devices. To meet this challenge, the data engineering field has expanded significantly in recent years in both research and industry. Traditionally, data engineering, Machine Learning, and AI workloads have been run on large clus

Jun Huo, Kehan Xu, Chengyao Li, Yu Cao

In human-robot systems, ensuring safety during force control in the presence of both internal and external disturbances is crucial. As a typical loosely coupled floating-base robot system, the supernumerary robotic leg (SRL) system is particularly susceptible to strong internal disturbances. To address the challenge posed by floating base, we investigated th

John Herrick

A correlation between karyotype diversity and species richness was first observed in mammals in 1980, and subsequently confirmed after controlling for phylogenetic signal. The correlation was attributed to submicroscopic factors, presumably operating at the level of the genome. At the same time, an unexpected association between mutation rates and substituti

Yifei Zhu, Jiahui Zhang, Jiawei Peng, Mengge Li

Obtaining 3D conformations of realistic polyatomic molecules at the quantum chemistry level remains challenging, and although recent machine learning advances offer promise, predicting large-molecule structures still requires substantial computational effort. Here, we introduce StoL, a diffusion model-based framework that enables rapid and knowledge-free gen

Jinyuan Hu, Jiayou Zhang, Shaobo Cui, Kun Zhang

Autoregressive (AR) approaches, which represent images as sequences of discrete tokens from a finite codebook, have achieved remarkable success in image generation. However, the quantization process and the limited codebook size inevitably discard fine-grained information, placing bottlenecks on fidelity. Motivated by this limitation, recent studies have exp

Ge Cheng, Shuo Wang, Yun Zhang

Contrastive learning has emerged as a cornerstone of unsupervised representation learning across vision, language, and graph domains, with InfoNCE as its dominant objective. Despite its empirical success, the theoretical underpinnings of InfoNCE remain limited. In this work, we introduce an explicit feature space to model augmented views of samples and a tra

Ismaila A. Jallow, Samya Tajmouati

This study examines the determinants of financial and digital inclusion in West and Central Africa using the World Bank Findex 2021 data. Unlike prior works that rely solely on traditional logit and probit models, we combine country-by-country analysis with robustness checks including K-fold cross-validation and Vuong test. Three samples were considered : a

B. Feigin, M. Jimbo, E. Mukhin

We give a realization $\mathcal{A}_0$ of quantum toroidal algebra associated to $\mathfrak{gl}_2$ which can be viewed as an affinization of the Drinfeld new realization of quantum affine $\mathfrak{gl}_2$. We use this realization to define an affinization $\mathcal{A}_N$, $N\in{\mathbb Z}$, of shifted quantum affine $\mathfrak{gl}_2$. We construct a large fa

Manish Kataria, Kanak Saha

We present a detailed kinematic and stellar population analysis of the inner disk of Malin 1, a giant low surface brightness (GLSB) galaxy with a prominent SB0-type central morphology. AstroSat far-UV imaging reveals clumpy emission features indicating recent star formation. Using MUSE integral field spectroscopy, we identify four star-forming complexes (SFC

Xiaobin Song, Siyuan Bai, Da-Wei Wang, Hanxiao Tao

Charging optimization is a key challenge to the implementation of quantum batteries, particularly under inhomogeneity and partial observability. This paper employs reinforcement learning to optimize piecewise-constant charging policies for an inhomogeneous Dicke battery. We systematically compare policies across four observability regimes, from full-state ac

Koushik Ahmed Kushal, Florimond Gueniat

This study presents an AI enhanced IoT framework for predictive maintenance and affordability optimization in smart microgrids using a Digital Twin modeling approach. The proposed system integrates real time sensor data, machine learning based fault prediction, and cost aware operational analytics to improve reliability and energy efficiency in distributed m

Lifeng Shen, Xuyang Li, Lele Long

Diffusion models have shown great promise in data generation, yet generating time series data remains challenging due to the need to capture complex temporal dependencies and structural patterns. In this paper, we present \textit{TSGDiff}, a novel framework that rethinks time series generation from a graph-based perspective. Specifically, we represent time s

Shah Fahad, Muzamil Shah, Gao Xianlong

Non-Hermitian cavity magnomechanics (CMM), which incorporates the magnon-photon and magnon-phonon interactions simultaneously, enables rich physical phenomena, including exceptional-point-enhanced sensing, and offers pathways toward topological transitions and nonreciprocal quantum transformation. These interactions exert a pivotal influence on the optical r