November 2025 arXiv papers — page 22

Da Xu

We investigate the spectral properties of the Dirichlet Laplacian on large finite metric balls within irregular infinite graphs of quadratic volume growth. We consider an exhaustion $G_n = B_{R_n}(x_0)$ and the spectral zeta value $Z_n(1) = \operatorname{tr}(L_n^{-1})$ of the killed generator $L_n$. We establish a sharp asymptotic law under the assumptions t

Vadim Safronov, Ionut Bostan, Nicholas Allott, Andrew Martin

Modern networked systems rely on complex software stacks, which often conceal vulnerabilities arising from intricate interdependencies. A Software Bill of Materials (SBOM) is effective for identifying dependencies and mitigating security risks. However, existing SBOM solutions lack precision, particularly in binary analysis and non-package-managed languages

Neel Kaul, Jaehoon Kim, Minseo Kim, David R. Wood

Chung and Graham [J. London Math. Soc. 1983] claimed to prove that there exists an $n$-vertex graph $G$ with $ \frac{5}{2}n \log_2 n + O(n)$ edges that contains every $n$-vertex tree as a subgraph. Frati, Hoffmann and T\'oth [Combin. Probab. Comput. 2023] discovered an error in the proof. By adding more edges to $G$ the error can be corrected, bringing the n

Zhenglin Zhou, Fan Ma, Chengzhuo Gui, Xiaobo Xia

Training-free 3D editing aims to modify 3D shapes based on human instructions without model finetuning. It plays a crucial role in 3D content creation. However, existing approaches often struggle to produce strong or geometrically stable edits, largely due to inconsistent latent anchors introduced by timestep-dependent noise during diffusion sampling. To add

Ethan Berreby, Avner Okun, Shahar Hadar, Amos Ori

We investigate the charging process of a rotating Kerr black hole of mass $M$ and angular momentum $J$ immersed in a stationary, axisymmetric, asymptotically uniform magnetic field of strength $B_{0}$. In Wald's classic analysis (Wald 1974), which was based on the assumption of vanishing injection energy, the black hole was predicted to acquire a universal "

Mengyang Liu, Chenyu Lu, Haodong Tian, Fang Dong

On-device machine learning (ML) has become a fundamental component of emerging mobile applications. Adaptive model deployment delivers efficient inference for heterogeneous device capabilities and performance requirements through customizing neural architectures. SuperNet-based approaches offer a promising solution by generating a large number of model varia

Zheyi Hang, Denghan Xiong, Pengo Xie, Huan Hu

Underwater flow sensing is critical for unmanned underwater vehicles (UUVs) and environmental monitoring, yet existing sensors often suffer from low responsiveness, high detection thresholds, limited directional discrimination, complex packaging, and poor long-term stability, especially for navigation and target perception in turbid and cluttered waters. Pre

Jarne Thys, Davy Vanacken, Gustavo Rovelo Ruiz

AutoML systems targeting novices often prioritize algorithmic automation over usability, leaving gaps in users' understanding, trust, and end-to-end workflow support. To address these issues, we propose an abstract pipeline that covers data intake, guided configuration, training, evaluation, and inference. To examine the abstract pipeline, we report a user s

Anshul Bagaria

The growing realism of AI-generated images produced by recent GAN and diffusion models has intensified concerns over the reliability of visual media. Yet, despite notable progress in deepfake detection, current forensic systems degrade sharply under real-world conditions such as severe downsampling, compression, and cross-domain distribution shifts. Moreover

Xiang Zhou

Variational quantum algorithms (VQAs) are a leading approach for near-term quantum advantage. However, their training is often hindered by barren plateaus (BPs). We present a framework based on observational entropy. The framework separates the coherent part of a quantum state from its incoherent part. We define the coherence fraction $η$ as the ratio of coh

Ankita Mazumdar, Akash Mitra, Shashi C. L. Srivastava

Most quantum batteries studied so far with notable exception of Sachdev-Ye-Kitaev (SYK) batteries are based on integrable models, where superlinear scaling of charging power and hence a quantum advantage can be achieved, but at the cost of unstable stored energy due to integrability. Here, by considering the sunburst quantum Ising battery driven by periodic

Shuao Jia, Zichao Ling, Chen Bai, Kang Zhao

Efficient deployment of Deep Neural Networks (DNNs), such as Large Language Models (LLMs), on tensor accelerators is essential for maximizing computational efficiency in modern AI systems. However, achieving this is challenging due to the enormous and complex design space created by the interaction of intra-layer mapping and inter-layer fusion. In this work,

Martin Lukarevski, Hans-Peter Schröcker

Among a triangle's exparabolas (parabolas escribed to the triangle), three are distinguished by having locally maximal parameter. They are determined by a simple cubic equation and characterized by having axes that contain the triangle's centroid. More generally, there are three (not necessarily real) exparabolas with axes through a given point $X$. Their fo

Francesco Navarra, Ayesha Asloob Qureshi, Giancarlo Rinaldo

We explore the novel connection between rook placements on collections of cells, also known as pruned chessboards, and the algebraic properties of ideals generated by $2$-minors. We design an algorithm to compute the switching rook polynomial of a collection of cells and show that it coincides with the $h$-polynomial of the associated coordinate ring for all

Yang Chen, Xiaowei Xu, Shuai Wang, Chenhui Zhu

Normalizing Flows (NFs) are a class of generative models distinguished by a mathematically invertible architecture, where the forward pass transforms data into a latent space for density estimation, and the reverse pass generates new samples from this space. This characteristic creates an intrinsic synergy between representation learning and data generation.

Denis Huseljic, Marek Herde, Lukas Rauch, Paul Hahn

Existing active learning (AL) strategies capture fundamentally different notions of data value, e.g., uncertainty or representativeness. Consequently, the effectiveness of strategies can vary substantially across datasets, models, and even AL cycles. Committing to a single strategy risks suboptimal performance, as no single strategy dominates throughout the

Panteleimon Dogoulis, Mohammad Iman Alizadeh, Sylvain Kubler, Maxime Cordy

Power Flow (PF) calculation based on machine learning (ML) techniques offer significant computational advantages over traditional numerical methods but often struggle to maintain full physical consistency. This paper introduces a physics-informed test-time training (PI-TTT) framework that enhances the accuracy and feasibility of ML-based PF surrogates by enf

Arne Geldof, Jan Kopačin, Izidor Straus, Raphael Kriegl

We investigated the physical mechanisms driving directional transport of solid objects by micro-lamellar structures laser-inscribed on the surface of a magnetoactive elastomer (MAE). When subjected to a rotating magnetic field with magnitude of 175 mT and a time period of 0.4 s, the lamellas reorient within a few milliseconds, reaching angular velocities up

Fabio Rosenthal, Sebastian Schmidt, Thorsten Graf, Thorsten Bagodonat

Multimodal Large Language Models (MLLMs) demonstrate strong capabilities in handling image-text inputs. A common way to assess this ability is through multiple-choice Visual Question Answering (VQA). Earlier works have already revealed that these benchmarks are sensitive to answer choice order, a limitation that can be mitigated through careful design. Yet,

Aya Fukami, Richard Buurke

The Replay Protected Memory Block (RPMB) in modern storage systems provides a secure area where data integrity is ensured by authentication. This block is used in digital devices to store pivotal information that must be safeguarded against modification by potential attackers. This paper targets the authentication scheme of the RPMB in three different eMMCs

Antonio Acuaviva

Extending a method developed by Koszmider and Laustsen for constructing $C(K)$-spaces we produce families of $C(K)$-spaces with few operators relative to a partially ordered set $\mathcal{P}$. Using these spaces, we construct new $C(K)$-spaces whose closed operator ideals can be completely classified. Additionally, we use these spaces to resolve some questio

Denghan Xiong, Yanzhe Zhao, Yutong Chen, Zichun Wang

Nonholonomic constraints restrict feasible velocities without reducing configuration-space dimension, which makes collision-free geometric paths generally non-executable for car-like robots. Ackermann steering further imposes curvature bounds and forbids in-place rotation, so escaping from narrow dead ends typically requires tightly sequenced forward and rev

Sachin Kumar Singh, Ko Watanabe, Brian Moser, Shoya Ishimaru

The success of machine learning is deeply linked to the availability of high-quality training data, yet retrieving and manually labeling new data remains a time-consuming and error-prone process. Traditional annotation tools, such as Label Studio, often require post-processing, where users label data after it has been recorded. Post-processing is highly time

Kaifeng Kang, Min Huang, Yang Liu, Jun Lin

A 50-mm balloon-borne white-light coronagraph (BBWLC) to observe whitelight solar corona over the altitude range from 1.08 to 1.50 solar radii has recently been indigenously developed by Yunnan Observatories in collaboration with Shangdong University (in Weihai) and Changchun Institute of Optics, Fine Mechanics and Physics, which will significantly improve t

Adesh Kushwaha, Erik M. Gauger, Ivan Kassal

Collective effects, such as superradiance and subradiance are central to emerging quantum technologies -- from sensing to energy storage -- and play an important role in light-harvesting. These effects enhance or suppress rates of dynamic processes (absorption, emission, and transfer) due to the formation of symmetric or antisymmetric collective states. Howe

Pablo Prieto, Pablo Abad

Edge computing processes data where it is generated, enabling faster decisions, lower bandwidth usage, and improved privacy. However, edge devices typically operate under strict constraints on processing power, memory, and energy consumption, making them unsuitable for large language models (LLMs). Fortunately, Small Language Models (SLMs) offer lightweight

Jinjun Yi, Zhixin Zhao, Yitao Hu, Ke Yan

LLM serving is increasingly dominated by decode attention, which is a memory-bound operation due to massive KV cache loading from global memory. Meanwhile, real-world workloads exhibit substantial, hierarchical shared prefixes across requests (e.g., system prompts, tools/templates, RAG). Existing attention implementations fail to fully exploit prefix sharing

Jaewan Chun, Fanchen Bu, Yeongho Kim, Atsushi Miyauchi

Identifying central entities and interactions is a fundamental problem in network science. While well-studied for graphs (pairwise relations), many biological and social systems exhibit higher-order interactions best modeled by hypergraphs. This has led to a proliferation of specialized hypergraph centrality measures, but the field remains fragmented and lac

Rosa Lucia Capurso, Giuseppe Calajó, Simone Montangero, Saverio Pascazio

An array of initially excited emitters coupled to a one-dimensional waveguide exhibits superradiant decay under the Born-Markov approximation, manifested as a coherent burst of photons in the output field. In this work, we employ tensor-network methods to investigate its non-Markovian dynamics induced by finite time delays in photon exchange among the emitte

Silvia Dani, Tiberio Uricchio, Lorenzo Seidenari

Existing video colorization methods struggle with temporal flickering or demand extensive manual input. We propose a novel approach automating high-fidelity video colorization using rich semantic guidance derived from language and segmentation. We employ a language-conditioned diffusion model to colorize grayscale frames. Guidance is provided via automatical

Saeed Mohammadzadeh, Kanapathippillai Cumanan, Zhiguo Ding

In this paper, we consider a flexible-antenna architecture, referred to as a pinching-antenna (PA) system, in which multiple PAs realized by activating small dielectric particles along a dielectric waveguide are jointly employed to serve a single-antenna user. We investigate antenna placement and power allocation optimization in PA-assisted non-orthogonal mu

Shakarim Soltanayev, Odysseas Zisimopoulos, Mohammad Ashraful Anam, Man Cheung Kung

Video-on-demand streaming has benefitted from \textit{content-adaptive encoding} (CAE), i.e., adaptation of resolution and/or quantization parameters for each scene based on convex hull optimization. However, CAE is very challenging to develop and deploy for interactive game streaming (IGS). Commercial IGS services impose ultra-low latency encoding with no l

Moru Song, Kai Chang

Fractional Chern insulators (FCIs) typically emerge in topological flat bands and are regarded as lattice analogs of fractional quantum Hall states. Conventionally, the flat-band wavefunctions that support FCIs are expected to mimic the lowest Landau level, a condition that can be quantified by the quantum-geometric indicators. In realistic systems, however,

Xiaofeng Li, Xiangyi Xiao, Xiaocong Du, Ying Zhang

Urban economic vitality is a crucial indicator of a city's long-term growth potential, comprising key metrics such as the annual number of new companies and the population employed. However, modeling urban economic vitality remains challenging. This study develops ECO-GROW, a multi-graph framework modeling China's inter-city networks (2005-2021) to generate

Kai Zhang

In this note, we present the interior $C^{2,\alpha}$ regularity for viscosity solutions of fully nonlinear uniformly elliptic equations in dimension two.

Baojun Li, Timur Nasybullov, Vyacheslav Zadvornov

A. Smoktunowicz and L. Vendramin conjectured that if $A=(A,\oplus,\odot)$ is a finite skew brace with solvable additive group $A_{\oplus}$, then the multiplicative group $A_{\odot}$ of $A$ is also solvable. Proving or disproving this conjecture is currently an open problem. The interest to the conjecture of A. Smoktunowicz and L. Vendramin is due to the fact

Tobias Meuser, Jannis Weil, Aninda Lahiri, Marius Paraschiv

Quantum networks are becoming increasingly important because of advancements in quantum computing and quantum sensing, such as recent developments in distributed quantum computing and federated quantum machine learning. Routing entanglement in quantum networks poses several fundamental as well as technical challenges, including the high dynamicity of quantum

Kateryna Buryachenko, Annegret Glitzky, Matthias Liero, Barbara Zwicknagl

We study the finite deformation of a thin, elastically heterogeneous sheet subject to electrostatic coupling. The interaction between mechanics and electrostatics is formulated as a saddle-point problem involving the deformation and the electrostatic potential. Starting from a three-dimensional electro-elastic model with prestrain in the elastic energy, we r

Angie Veronica, Thomas H. Reiprich, Florian Pacaud, Marcus Brüggen

The mechanical feedback from the central AGNs can be crucial for balancing the radiative cooling of the intracluster medium at the cluster centre. We aim to understand the relationship between the power of AGN feedback and the cooling of gas in the centres of galaxy clusters by correlating the radio properties of the brightest cluster galaxies (BCGs) with th

Markus Funke, Patricia Lago

Sustainability has evolved from an emerging concern into a fundamental responsibility in software design, development, and operation. Research increasingly explores how sustainability can be systematically integrated into existing software engineering practices. Building on an industry-academia collaboration, we contribute to this discourse by conducting a m

Yuki Origane, Koya Cho, Hideyuki Tsukagoshi

Aiming to achieve both high functionality and flexibility in soft robot system, this paper presents a soft urethane sheet-like valve with an amplifier that can perform logical operations using only pneumatic signals. When the control chamber in the valve is pressurized, the main path is compressed along its central axis, buckling and being pressed,resulting

Giovanni Maria Cristiano, Salvatore D'Antonio, Jonah Giglio, Giovanni Mazzeo

The growing scalability demand of public Blockchains led to the rise of Layer-2 solutions, such as Rollups. Rollups improve transaction throughput by processing operations off-chain and posting the results on-chain. A critical component in Rollups is the Sequencer, responsible for receiving, ordering and batching transactions before they are submitted to the

Jinying Xiao, Bin Ji, Shasha Li, Xiaodong Liu

Large Language Models (LLMs) quantization facilitates deploying LLMs in resource-limited settings, but existing methods that combine incompatible gradient optimization and quantization truncation lead to serious convergence pathology. This prolongs quantization time and degrades LLMs' task performance. Our studies confirm that Straight-Through Estimator (STE

Bakhtawar Abdalla, Rebwar Mala Nabi, Hassan Eshkiki, Fabio Caraffini

This work contributes towards balancing the inclusivity and global applicability of natural language processing techniques by proposing the first 'name entity recognition' dataset for Kurdish Sorani, a low-resource and under-represented language, that consists of 64,563 annotated tokens. It also provides a tool for facilitating this task in this and many oth

Boris Kriuk, Logic Ng

Multi-agent Large Language Model (LLM) systems face a critical bottleneck: redundant transmission of contextual information between agents consumes excessive bandwidth and computational resources. Traditional approaches discard internal semantic representations and transmit raw text, forcing receiving agents to recompute similar representations from scratch.

Wenbin Wu, Kejiang Qian, Alexis Lui, Christopher Jack

We curate the DeXposure dataset, the first large-scale dataset for inter-protocol credit exposure in decentralized financial networks, covering global markets of 43.7 million entries across 4.3 thousand protocols, 602 blockchains, and 24.3 thousand tokens, from 2020 to 2025. A new measure, value-linked credit exposure between protocols, is defined as the inf

Zahri Aksa Dautd, Aviv Yuniar Rahman

The rapid growth of digital commerce has led to the accumulation of a massive number of consumer reviews on online platforms. Shopee, as one of the largest e-commerce platforms in Southeast Asia, receives millions of product reviews every day containing valuable information regarding customer satisfaction and preferences. Manual analysis of these reviews is

Anjaneya Praharaj, Jaykumar Kasundra

This paper addresses the critical challenge of deriving interpretable confidence scores from generative language models (LLMs) when applied to multi-label content safety classification. While models like LLaMA Guard are effective for identifying unsafe content and its categories, their generative architecture inherently lacks direct class-level probabilities

Fiona Y. Wang, Di Sheng Lee, David L. Kaplan, Markus J. Buehler

Designing proteins de novo with tailored structural, physicochemical, and functional properties remains a grand challenge in biotechnology, medicine, and materials science, due to the vastness of sequence space and the complex coupling between sequence, structure, and function. Current state-of-the-art generative methods, such as protein language models (PLM

Da Huo, Marc A. Kastner, Tingwei Liu, Yasutomo Kawanishi

Object detection is the task of detecting objects in an image. In this task, the detection of small objects is particularly difficult. Other than the small size, it is also accompanied by difficulties due to blur, occlusion, and so on. Current small object detection methods are tailored to small and dense situations, such as pedestrians in a crowd or far obj

Lisa McBride, Adélie Gorce, Marian Douspis, Romain Meriot

High-precision measurements of the Cosmic Microwave Background (CMB) will soon allow for the unprecedented detection of small-scale secondary anisotropies, such as the kinetic Sunyaev-Zel'dovich (kSZ) effect. Linking the kSZ power spectrum to the properties of ionising sources would provide an opportunity to use such observations to access astrophysical and

Inhyo Lee, Junhyeong Lee, Jongwon Park, KyungTae Lim

Double perovskites (DPs) are promising candidates for sustainable energy technologies due to their compositional tunability and compatibility with low-energy fabrication, yet their vast design space poses a major challenge for conditional materials discovery. This work introduces a multi-agent, text gradient-driven framework that performs DP composition gene

Dario Fenoglio, Mohan Li, Pietro Barbiero, Nicholas D. Lane

Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy. Traditional FL methods often use a global model to fit all clients, assuming that clients' data are independent and identically distributed (IID). However, when this assumption does not hold, the global model accuracy may drop significantly, li

Djawad Bekkoucha, Lamine Diop, Abdelkader Ouali, Bruno Crémilleux

Pattern sampling has emerged as a promising approach for information discovery in large databases, allowing analysts to focus on a manageable subset of patterns. In this approach, patterns are randomly drawn based on an interestingness measure, such as frequency or hyper-volume. This paper presents the first sampling approach designed to handle interval patt

Domenico Caparello, Lorenzo Pareschi, Thomas Rey

In this work we construct a high-order Asymptotic-Preserving (AP) Implicit-Explicit (IMEX) scheme for the ES-BGK model for gas mixtures introduced in [Brull, Commun. Math. Sci., 2015]. The time discretization is based on the IMEX strategy proposed in [Filbet, Jin, J. Sci. Comput., 2011] for the single-species BGK model and is here extended to the multi-speci

Ahmad Tarraf, Koutaiba Kassem-Manthey, Seyed Ali Mohammadi, Philipp Martin

Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more efficiently. However, the growing scale of simulations demands substantial expert knowledge, computational resources, and time. A key challenge is identifying input parameter

Włodzimierz Zwonek

In the paper we discuss the problem of existence, uniqueness and extension through the boundary of left inverses to complex geodesics in Lempert domains. We concentrate on special left inverses (so called Lempert left inverses) characterized by the fact that their fibers are intersections of affine hyperplanes with the domain.

Takahiro Yokoyama, Yicheng Qiang, David Zwicker, Arash Nikoubashman

Phase separation within polymer networks plays a central role in shaping the structure and mechanics of both synthetic materials and living cells, including the formation of biomolecular condensates within cytoskeletal networks. Previous experiments and theoretical studies indicate that network elasticity can regulate demixing and stabilize finite-sized doma

Adarsh Bathula, M. A. Krishnakumar, S. Jena

In high precision pulsar timing, the accurate recovery of intrinsic pulsar profiles and their associated scattering parameters is of paramount importance. In this paper, we present a comprehensive study focused on the retrieval of intrinsic pulsar profiles through the utilization of a CLEAN-based algorithm as described in Bhat et al. (2003). The primary obje

Yuta Takahashi, Hiraku Sakamoto, Shin-ichiro Sakai

Electromagnetic formation flight (EMFF) uses the electromagnetic force to control the relative positions of multiple satellites without using conventional fuel-based propulsion. To compensate for the electromagnetic torque generated alongside the electromagnetic force, in most previous studies, all satellites were assumed to have reaction wheels (RWs) beside

Konstantin A. Beyer, Igor A. Valuev, Zoia A. Mandrykina, Zewen Sun

The long-standing fine-structure anomaly in muonic $^{90}$Zr is resolved through a rigorous treatment of the relativistic-recoil effect. From a fit of ab initio QED calculations of the muonic $^{90}$Zr spectrum to precision measurements performed four decades ago, we extract a significantly more precise root-mean-square (rms) charge radius with 6-fold improv

Debargha Dutta, Kerim Ture, Fabio Olivieri, Alberto Gomez-Saiz

We present a modeling approach based on the BSIM-IMG compact model to capture the deep-cryogenic behavior of MOSFET devices in a 22-nm FDSOI technology. The modeling flow is based on DC measurements to extract static parameters including variability and RF measurements to extract dynamic parameters. Modifications to the mobility equations are introduced to e

Javier Lopez-Santiago, Luca Martino, Joaquin Miguez, Gonzalo Vazquez-Vilar

Bayesian computational strategies for inference can be inefficient in approximating the posterior distribution in models that exhibit some form of periodicity. This is because the probability mass of the marginal posterior distribution of the parameter representing the period is usually highly concentrated in a very small region of the parameter space. There

Sonia Laguna, Andrea Agostini, Alain Ryser, Samuel Ruiperez-Campillo

Building robust medical machine learning systems requires pretraining strategies that exploit the intrinsic structure present in clinical data. We introduce Multiview Masked Autoencoder (MVMAE), a self-supervised framework that leverages the natural multi-view organization of radiology studies to learn view-invariant and disease-relevant representations. MVM

Teysir Baoueb, Xiaoyu Bie, Mathieu Fontaine, Gaël Richard

Recent advances in diffusion models have positioned them as powerful generative frameworks for speech synthesis, demonstrating substantial improvements in audio quality and stability. Nevertheless, their effectiveness in vocoders conditioned on mel spectrograms remains constrained, particularly when the conditioning diverges from the training distribution. T

Kavya Subramanian, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar

Forecasting tumor growth is critical for optimizing treatment. Classical growth models such as the Gompertz and Bertalanffy equations capture general tumor dynamics but may fail to adapt to patient-specific variability, particularly with limited data available. In this study, we leverage Neural Ordinary Differential Equations (Neural ODEs) and Universal Diff

Marco Mussi, Marcello Restelli

Traditional pricing paradigms, once dominated by static models and rule-based heuristics, are increasingly being replaced by dynamic, data-driven approaches powered by machine learning algorithms. Despite their growing sophistication, most dynamic pricing algorithms focus on optimizing the price of each product independently, disregarding potential interacti

Alexandru Paler

We present a systematic method for translating fault-tolerant quantum circuits between their braiding and lattice surgery (LS) representations within the surface code. Our approach employs the ZX calculus to establish an equivalence between these two paradigms, enabling verified, bidirectional conversion of arbitrary surface-code-level circuits. We show that

Christine Awofeso, Pål Grønås Drange, Patrick Greaves, Oded Lachish

Understanding how a vertex relates to a set of vertices is a fundamental task in graph analysis. Given a graph $G$ and a vertex set $X \subseteq V(G)$, consider the collection of subsets of the form $N(u) \cap X$ where $u$ ranges over all vertices outside $X$. These intersections, which we call the traces of $X$, capture all ways vertices in $G$ connect to $

Despoina Antonakaki, Sotiris Ioannidis

The Israeli-Palestinian conflict remains one of the most polarizing geopolitical issues, with the October 2023 escalation intensifying online debate. Social media platforms, particularly Telegram, have become central to real-time news sharing, advocacy, and propaganda. In this study, we analyze Telegram, Twitter/X, and Reddit to examine how conflict narrativ

Zhaorui Meng, Lu Yin, Yangqing Hou, Anjun Chen

Sparse Inertial Measurement Units (IMUs) based human motion capture has gained significant momentum, driven by the adaptation of fundamental AI tools such as recurrent neural networks (RNNs) and transformers that are tailored for temporal and spatial modeling. Despite these achievements, current research predominantly focuses on pipeline and architectural de

Kate Feingold, Omri Kaduri, Tali Dekel

We present Match-and-Fuse - a zero-shot, training-free method for consistent controlled generation of unstructured image sets - collections that share a common visual element, yet differ in viewpoint, time of capture, and surrounding content. Unlike existing methods that operate on individual images or densely sampled videos, our framework performs set-to-se

Teerawat Chalermpusitarak, Kai Schwennicke, Ivan Kassal, Ting Rei Tan

Harmonic oscillators are promising continuous-variable (CV) quantum resources because their infinite-dimensional Hilbert spaces allow for resource-efficient quantum computing and simulation. To reach their full potential, CV platforms need to be able to efficiently implement non-Gaussian operations. Bosonic quantum-signal-processing schemes have emerged as a

Houssem Rezgui, Chuang Zhang, Clivia Sotomayor-Torres

Ultrafast thermal transport in low-dimensional materials challenges traditional diffusive models due to reduced scattering, strong electron-phonon coupling, and pronounced non-equilibrium effects. To address these complexities, we extend the macroscopic multi-temperature model by incorporating non-diffusive and non-local phenomena, treating electrons, optica

Oleksandr Pastukh, Piotr Graczyk, Mateusz Zelent, Lukasz Laskowski

Miniaturization is an essential element in the development of information processing technologies and is also one of the main determinants of the usability of the tested artificial neural networks. It is also a key element and one of the main challenges in the development of magnonic neuromorphic systems. In this work, we propose a new platform for the devel

Fabian H. Kreten, Ludger Santen, Reza Shaebani

Tracking the movement of tracer particles has long been a strategy for uncovering complex structures. Here, we study discrete-time random walks on finite Cayley trees to infer key parameters such as tree depth and geometric bias toward the root or leaves. By analyzing first passage properties, we show that the first two first-passage-time factorial moments (

Wei Guo, Shunqi Mao, Zhuonan Liang, Xuanhua Yin

Observing certain patches in an image reduces the uncertainty of others. Their realization lowers the distribution entropy of each remaining patch feature, analogous to collapsing a particle's wave function in quantum mechanics. This phenomenon can intuitively be called patch collapse. To identify which patches are most relied on during a target region&#

Ningxin Kong, Haojie Wang, Mingsheng Tian, Yilun Xu

A central challenge in quantum metrology is to effectively harness quantum resources to surpass classical precision bounds. Although recent studies suggest that the indefinite causal order may enable sensitivities to attain the super-Heisenberg scaling, the physical origins of such enhancements remain elusive. Here, we introduce the nilpotency index $\mathca

Hector Buffière, Rutger Campbell, Kevin Hendrey, Sang-il Oum

We investigate a pursuit-evasion game on an undirected graph in which a robber, moving at a fixed constant speed, attempts to evade a team of cops who are blind to the robber's location and can quickly travel between any pair of vertices in the graph. The blind cop-width is the minimum number of cops needed to catch the robber on a given graph. We link it wi

Henrijs Princis, Arindam Sharma, Cristina David

Large language models (LLMs) have shown remarkable ability to generate code, yet their outputs often violate syntactic or semantic constraints when guided only through natural language prompts. We introduce TreeCoder, the most general and flexible framework to date for exploring decoding strategies, constraints, and hyperparameters in LLMs, and use it in cod

De-Chang Dai

We study the retrograde second caustics of extremal Kerr black holes, where the intensity of the light beam is infinitely magnified. We find that the caustics of different polarized beams are split by as much as $10^{-3}$rad by an external black hole for a suitable range of parameters. A lensing black hole at several lys away separates the polarized beams ab

Mengfan Li, Xuanhua Shi, Yang Deng

Large Language models are revolutionizing the conversational recommender systems through their impressive capabilities in instruction comprehension, reasoning, and human interaction. A core factor underlying effective recommendation dialogue is the ability to infer and reason about users' mental states (such as desire, intention, and belief), a cognitive cap

Chenxiao Dai, Feiyu Jiang, Dong Li, Xiaofeng Shao

Wasserstein autoregression provides a robust framework for modeling serial dependence among probability distributions, with wide-ranging applications in economics, finance, and climate science. In this paper, we develop portmanteau-type diagnostic tests for assessing the adequacy of Wasserstein autoregressive models. By defining autocorrelation functions for

Zaile Li, Weiwei Fan, L. Jeff Hong

Selecting the best alternative from a finite set is the central objective of ranking and selection (R&S) and best arm identification (BAI). Traditional R&S or BAI approaches have predominantly relied on Gaussian or sub-Gaussian assumptions on the performance distributions of all alternatives, which limit their applicability to non-sub-Gaussian---especially h

Hansjoerg Albrecher, Jan Beirlant

We provide a survey of how techniques developed for the modelling of extremes naturally matter in insurance, and how they need to and can be adapted for the insurance applications. Topics covered include truncation, tempering, censoring and regression techniques. The discussed techniques are illustrated on concrete data sets.

Zhongjie Shi, Puyu Wang, Chenyang Zhang, Yuan Cao

Modern deep learning techniques focus on extracting intricate information from data to achieve accurate predictions. However, the training datasets may be crowdsourced and include sensitive information, such as personal contact details, financial data, and medical records. As a result, there is a growing emphasis on developing privacy-preserving training alg

Ruoyu Wen, Xiaoli Wu, Kunal Gupta, Simon Hoermann

Young people's mental well-being is a global concern, with peer support playing a key role in daily emotional regulation. Conversational agents are increasingly viewed as promising tools for delivering accessible, personalised peer support, particularly where professional counselling is limited. However, existing systems often suffer from rigid input formats

Andrey R. Chekhlov, Peter V. Danchev, Patrick W. Keef

We consider the so-called {\it strongly co-Hopfian} and {\it uniformly strongly co-Hopfian} Abelian groups, significantly generalizing some important results due to Abdelalim in the J. Math. Analysis (2015). Specifically, we prove that any strongly co-Hopfian group is a direct sum of an sp-group and a divisible group, both of which are strongly co-Hopfian. W

Yuyang Zou, Youwei Xiao, Chenyun Yin, Yansong Xu

Application-Specific Instruction-Set Processors (ASIPs) built on the RISC-V architecture offer specialization opportunities for various applications. Existing frameworks are largely designed around fixed instruction extension interfaces and rely on manual software adaptation. However, as emerging domains scale up in complexity, two major challenges arise. Fi

Yuxuan Shi, A. A. Araújo Filho

We investigate how the newly obtained static black hole in bumblebee gravity affects the behavior of accreting matter and its observable signatures. The Lorentz-violating parameter that characterizes this geometry modifies photon trajectories and shifts the location of the critical curve that defines the shadow. Using ray tracing, we examine light deflection

Yuan Yao, Lixu Wang, Jiaqi Wu, Jin Song

Federated learning (FL) enables collaborative training across clients while preserving privacy. While most existing FL methods assume homogeneous model architectures, client heterogeneity in both data and resources makes this assumption impractical, thus motivating model-heterogeneous FL. To address this problem, we propose Federated Representation Entanglem

Xiaosong Jia, Yanhao Liu, Yu Hong, Renqiu Xia

Feed-forward reconstruction has been progressed rapidly, with the Visual Geometry Grounded Transformer (VGGT) being a notable baseline. However, directly applying VGGT to autonomous driving (AD) fails to capture three domain-specific priors: (i) Sparse Spatial Overlap: the overlap among mutli-view cameras is minimal due to $360^{\circ}$ coverage requirements

Taeryun Won, Tae Kwan Lee, Hiun Kim, Hyemin Lee

This paper presents a comprehensive comparison of BM25, SPLADE, and Expanded-SPLADE models in the context of large-scale web document retrieval. We evaluate the effectiveness and efficiency of these models on datasets spanning from tens of millions to billions of web document titles. SPLADE and Expanded-SPLADE, which utilize sparse lexical representations, d

Wenkai Huang, Yijia Guo, Gaolei Li, Lei Ma

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership verification. However, can existing 3D Ga

Andrea Amoretti, Matteo Anselmi, Daniel K. Brattan

We explore new infrared dualities in $(2+1)$-dimensional quantum field theories involving Majorana fermions. Building on the recently proposed operator-deformation approach to bosonization dualities, we incorporate the bosonization of neutral fermions into the established web of Dirac dualities. Starting from the decomposition of a Dirac fermion into two Maj

Clifford E. Woodward, David Ribar, Jan Forsman

Ion clustering has been proposed as a mechanism leading to the peculiar 'anomalous underscreening' phenomenon seen for electrostatic interactions between charge surfaces immersed in concentrated electrolytes. These interactions have been measured using the Surface Force Apparatus, according to which there are strong repulsive interactions between like-charge

Christoph Weissenborn, Steffen Wendzel

A Covert Channel (CC) exploits legitimate communication mechanisms to stealthily transmit information, often bypassing traditional security controls. Among these, a novel paradigm called History Covert Channels (HCC) leverages past network events as reference points to embed covert messages. Unlike traditional timing- or storage-based CCs, which directly man

Guifeng Wang, Yuanfeng Song, Meng Yang, Tao Zhu

Text-to-SQL, a pivotal natural language processing (NLP) task that converts textual queries into executable SQL, has seen substantial progress in recent years. However, existing evaluation and reward mechanisms used to train and assess the text-to-SQL models remain a critical bottleneck. Current approaches heavily rely on manually annotated gold SQL queries,

Winnie Ossete Ingoba, Souhaibou Sambou, Salomon Sambou

In this paper, we consider the problem of solving the $\partial \bar{\partial}$ with prescribed support for forms or currents in a domain $\Omega$ of an complex manifold $X$.

Dengbo Chen, Ziwei Zhao, Kexin Zhang, Shishuang Zhao

Despite significant strides in medical foundation models, the ultrasound domain lacks a comprehensive solution capable of bridging low-level Ultrasound Grounded Perception (e.g., segmentation, localization) and high-level Ultrasound Comprehensive Interpretation (e.g., diagnosis, reasoning). To bridge this gap, we propose UMind-VL, a unified foundation model