March 2026 arXiv papers — page 35

Yi Zhang, Yidong Zhao, Qian Tao

Deformable image registration remains a central challenge in medical image analysis, particularly under multi-modal scenarios where intensity distributions vary significantly across scans. While deep learning methods provide efficient feed-forward predictions, they often fail to generalize robustly under distribution shifts at test time. A straightforward re

Le Mau Hai, Nguyen Van Phu

In this paper, we establish the weak*-convergence of a sequence of the complex Hessian measures $H_m(u_j)$ to the complex Hessian measure $H_m(u)$ in the class $\mathcal{E}(X,\omega,m)$ under hypotheses that $u_j$ is convergent to $u$ in $(\omega,m)$-capacity.

Vincenzo Morinelli, Karl-Hermann Neeb, Gestur Olafsson

Models in Algebraic Quantum Field Theory (AQFT) may be generalized including Lie groups of symmetries whose Lie algebras admit an Euler element $h$, characterized by the property that $ad h$ is diagonalizable with eigenvalues in $\{-1, 0, 1\}$. These elements becomes fundamental to the formal description of wedge localization. In this paper, we extend the ge

Roberto Sprengel Minozzo Tomchak, Oge Marques, Lucas Garcia Pedroso, Luiz Eduardo Oliveira

The Triplet Margin Ranking Loss is one of the most widely used loss functions in Siamese Networks for solving Distance Metric Learning (DML) problems. This loss function depends on a margin parameter {\mu}, which defines the minimum distance that should separate positive and negative pairs during training. In this work, we show that, during training, the eff

Weihua Zhu, Beixiong Zheng, Lipeng Zhu, Jie Tang

Rotatable antenna (RA) provides additional spatial degrees of freedom (DoFs) for communication systems by enabling per-antenna dynamic boresight adjustment, which is attractive for fairness-oriented multicast transmission. This letter investigates an RA-enhanced downlink multi-group multicast system. Specifically, we aim to maximize the minimum signal-to-int

Izaldein Al-Zyoud, Abdulmotaleb El Saddik

Generalization under manipulation and dataset shift remains a core challenge in forged media detection for AI-driven edge sensing systems. Frozen vision foundation models with linear probes are strong baselines, but most pipelines use default backbone outputs without testing conditioning at the frozen feature interface. We present the first controlled probin

Jorge Cervantes-Villanueva, Alberto García-Cristóbal, Davide Sangalli, Alejandro Molina-Sánchez

Monolayer transition metal dichalcogenides are a suitable platform for studying excitonic coherence in the light-matter coupling regime. We present an ab initio time-dependent GW-Bethe-Salpeter equation (GW-BSE) investigation of coherent excitonic dynamics in monolayer WS$_2$. By solving the coherent coupling between the A, A$^{*}$, and B excitons under line

I-Hsiang Chen, Isma Hadji, Enrique Sanchez, Adrian Bulat

Image restoration aims to recover high quality images from inputs degraded by various factors, such as adverse weather, blur, or low light. While recent studies have shown remarkable progress across individual or unified restoration tasks, they still suffer from limited generalization and inefficiency when handling unknown or composite degradations. To addre

Hannes Rox, Feng Liang, Robert Baumann, Mateusz M. Marzec

Understanding how bubbles influence the efficiency of water electrolysis is crucial to achieve economically competitive hydrogen, generated by renewable energy sources, such as wind and solar power. Water electrolysis is typically performed at high pressures to reduce the cost of energy-intensive mechanical compression of the produced H2. Thus, a better unde

Gilderlan Tavares de Araújo, Bruno Sokal, André L. F. de Almeida

Channel estimation is a central bottleneck in BD-RIS-assisted MIMO systems. The richer inter-element coupling that enables large performance gains also makes training and hardware control substantially harder than in diagonal RIS architectures. Existing estimators either target only cascaded channels or require block-by-block reconfiguration of the BD-RIS in

Bojin Zheng, Jingwen Zheng, Weiwu Wang

Traditional Turing machines are semantically poor, they only concern the syntactic manipulation of symbols, discarding the mathematical semantics behind the symbols. This semantic deficiency is considered the root cause of the three major barriers: relativization, natural proofs, and algebrization. This paper proposes the Complex Boolean Turing Machine (CBTM

Akshit Nanda, Ritabrata Thakur

Turbulence in wall-bounded shear flows is increasingly understood through exact coherent structures (ECS) -- invariant solutions of the Navier-Stokes equations that act as organising centres in the high-dimensional state space. Here we report five new ECS of plane Poiseuille flow: two relative periodic orbits (RPOs) and three travelling waves (TWs), that are

Renjie Ding, Dongling Wang, Jun Zou

The Kaczmarz method is widely recognized as an efficient iterative algorithm for solving large-scale linear systems, owing to its simplicity and low memory requirements. However, the development of its nonlinear extensions for solving large-scale nonlinear systems has seen limited progress. In this work, we introduce a new family of momentum-accelerated aver

Yusheng Zhao, Hourun Li, Bohan Wu, Yichun Yin

The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales quadratically with the sequence length, serving as a major bottleneck in long-context language modeling. Sliding window attention restricts the context length for better efficiency at the cost of narrower recepti

Piero Giacomelli

The Bollob\'as--Nikiforov conjecture asserts that for any graph $G \neq K_n$ with $m$ edges and clique number $\omega(G)$, \[ \lambda_1^2(G) + \lambda_2^2(G) \;\leq\; 2\!\left(1 - \frac{1}{\omega(G)}\right)m, \] where $\lambda_1(G) \geq \lambda_2(G) \geq \cdots \geq \lambda_n(G)$ are the adjacency eigenvalues of $G$. We prove the conjecture for all complete

Senura Hansaja Wanasekara, Minh-Duong Nguyen, Xiaochen Liu, Nguyen H. Tran

Generative modeling has become a central paradigm in protein research, extending machine learning beyond structure prediction toward sequence design, backbone generation, inverse folding, and biomolecular interaction modeling. However, the literature remains fragmented across representations, model classes, and task formulations, making it difficult to compa

Mesfin Taye

The product of resting heart rate and maximum lifespan is approximately constant across adult warm-blooded vertebrates, $N^\star = f_H L \approx 10^9$ cardiac cycles, a regularity documented since Rubner (1908) but lacking a thermodynamic derivation. We derive $N^\star$ from the non-equilibrium second law by treating the adult organism as a metabolic non-equ

Pedro Menezes de Araújo, Isobel Claire Gormley, Thomas Brendan Murphy

Age-specific probabilities of death provide a snapshot of population mortality at the country level at a given point in time. Due to the high dimensionality of the data, summarising mortality information is essential for various analyses, such as visualisation and clustering. We propose the use of beta latent variable (BLV) models to summarise mortality info

Barbara Jäger, Ozan Semin

We investigate the sensitivity of current and future hadron-collider experiments to axion-like particles (ALPs) through associated diboson production, focusing on a linear effective field theory framework with bosonic ALP couplings. We analyze the dominant production mechanisms and relevant backgrounds, considering the impact of jet misidentification rates o

Tianhao Liu, Zuyu Ma, Yilin Song, Jiqiang Zheng

We study the following focusing intercritical nonlinear Schr\"odinger equation with partial harmonic confinement: \begin{equation*} \begin{cases} i\partial_t u+\Delta_{z}u-y^2 u =- |u|^{\alpha}u,\quad t\in \mathbb{R},\newline u(0,z)= u_0(z), \ z=(x,y)\in \mathbb{R}^d\times \mathbb{R}, \end{cases} \end{equation*} where $d \geq 1 $ is an integer and the expone

Zhanqiang Bai, Jing Jiang, Rui Wang

Let $\mathfrak{g}$ be a complex simple Lie algebra and $L(\lambda)$ be a highest weight module of $\mathfrak{g}$ with highest weight $\lambda-\rho$, where $\rho$ is half the sum of positive roots. A simple $\mathfrak{g}$-module $L_w:=L(-w\rho)$ is called integral minimal if the associated variety of its annihilator ideal equals the closure of the minimal spe

Peter E. Carstensen, Teddy Groves, Lars K. Nielsen, Ulrich Krühne

Biological systems are governed by coupled interactions between intracellular metabolism and bioreactor operation that span multiple time scales. Constraint-based metabolic models are widely used to describe intracellular metabolism, but repeatedly solving the optimization problem at each time step in dynamic models introduces numerical challenges related to

Lujia Bai, Holger Dette, Zihao Yuan

A crucial assumption to reduce computational complexity in spatial-temporal data analysis is separability, which factors the covariance structure into a purely spatial and a purely temporal component. In this paper, we develop statistical inference tools for validating this assumption for a second-order stationary process under both domain-expanding-infill a

Jihoon Jeong

We introduce M-CARE (Model Clinical Assessment and Reporting for Evaluation), a clinical case report framework for AI model behavioral disorders adapted from human medicine. M-CARE provides a 13-section report format, a 4-axis diagnostic assessment system, and a nosological classification of AI behavioral conditions. We present 20 cases from three source cat

Ehud Meir

Functor morphing provides a method to translate complex representations of automorphism groups of finite modules over finite rings to representations of automorphism groups of functors in some abelian category. In this paper we give an explicit criterion for a representation to be in the image of functor morphing using the action of parabolic subgroups. We t

Tomer Raviv, Nir Shlezinger

Foundation models learn transferable representations, motivating growing interest in their application to wireless systems. Existing wireless foundation models are predominantly based on transformer architectures, whose quadratic computational and memory complexity can hinder practical deployment for large-scale channels. In this work, we introduce WiMamba,

Shida Wang, YongXiang Hua, Zhou Tao, Haoyu Cao

Multimodal Large Language Models have demonstrated remarkable capabilities in video understanding, yet face prohibitive computational costs and performance degradation from ''context rot'' due to massive visual token redundancy. Existing compression strategies typically rely on heuristics or fixed transformations that are often decoupled from the downstream

Xiaoyu Fan, Huizhi Xie, Wei Zou, Yunzhang Chen

Large language model (LLM)-based text-to-speech (TTS) systems achieve remarkable naturalness via autoregressive (AR) decoding, but require N sequential steps to generate N speech tokens. We present LLaDA-TTS, which replaces the AR LLM with a masked diffusion model that completes generation in a fixed number of parallel steps, decoupling inference latency fro

Steffen Herbold, Florian Lemmerich

The generation of texts using Large Language Models (LLMs) is inherently uncertain, with sources of uncertainty being not only the generation of texts, but also the prompt used and the downstream interpretation. Within this work, we provide a formal framework for the measurement of uncertainty that takes these different aspects into account. Our framework mo

MD Khalequzzaman Chowdhury Sayem, Mubarrat Tajoar Chowdhury, Yihalem Yimolal Tiruneh, Muneeb A. Khan

Understanding the fine-grained articulation of human hands is critical in high-stakes settings such as robot-assisted surgery, chip manufacturing, and AR/VR-based human-AI interaction. Despite achieving near-human performance on general vision-language benchmarks, current vision-language models (VLMs) struggle with fine-grained spatial reasoning, especially

Shayan Eskandari, Leid Zejnilovic, Jeremy Clark

Blockchain technology introduces asset types and custody mechanisms that fundamentally break traditional financial auditing paradigms. This paper presents an autoethnographic analysis of cryptoasset auditing challenges, build on top of prior research on a comprehensive framework addressing existence, ownership, valuation, and internal control verification. D

Chen Yang, Yucheng Hu, Yunchao Ma, Yunhuan Yang

In deployment of the VLA models to real-world robotic tasks, execution speed matters. In previous work arXiv:2510.26742 we analyze how to make neural computation of VLAs on GPU fast. However, we leave the question of how to actually deploy the VLA system on the real robots open. In this report we describe a set of practical techniques to achieve the end-to-e

Fabian Finger, Frederic Rapp, Pranav Kalidindi, Kerry He

Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesis, which utilises large language models to drive a highly distributed evolutionary process for discovering new algorithms. We focus on the ground state problem

Kejin Wu, Raanju R. Sundararajan, Michel F. C. Haddad, Luiza S. C. Piancastelli

Accurate real-time monitoring of disease transmission is crucial for epidemic control, which has conventionally relied on reported cases or hospital admissions. Such metrics are frequently susceptible to delays in reporting, various forms of bias, and under-ascertainment. Cycle threshold values obtained from reverse transcription quantitative polymerase chai

Quan Dao, Dimitris Metaxas

Transformer architectures, particularly Diffusion Transformers (DiTs), have become widely used in diffusion and flow-matching models due to their strong performance compared to convolutional UNets. However, the isotropic design of DiTs processes the same number of patchified tokens in every block, leading to relatively heavy computation during training proce

Zhenghao Xu, Mengning Yang

As plots play a critical role in modern data visualization and analysis, Plot2API is launched to help non-experts and beginners create their desired plots by directly recommending graphical APIs from reference plot images by neural networks. However, previous works on Plot2API have primarily focused on the recommendation for standard plot images, while overl

Ramin Emadi, Domenico Ribezzo, Giulia Guarda, Davide Bacco

High-visibility Franson interference at telecom C-band wavelengths is achieved using a cascaded periodically poled lithium niobate (PPLN) waveguide photon-pair source combined with fully passive, path-imbalanced Mach-Zehnder interferometers implemented on photonic integrated circuits (PICs). The interferometers require neither on-chip phase shifters nor acti

Nazia Aslam, Abhisek Ray, Thomas B. Moeslund, Kamal Nasrollahi

Video anomaly detection (VAD) systems are increasingly deployed in safety critical environments and require a large amount of data for accurate detection. However, such data may contain personally identifiable information (PII), including facial cues and sensitive demographic attributes, creating compliance challenges under the EU General Data Protection Reg

Qurat Ul Ain, Alptekin Temizel, Soyiba Jawed

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains challenging due to the lack of reliable imaging-based biomarkers, particularly anatomical markers. Structural MRI (sMRI) provides a non-invasive modality for investigating brain alterations associated with ADHD; n

Guangyan Zhu

Let $a, b$ and $n$ be positive integers and let $S=\{x_1, \cdots, x_n\}$ be a set of $n$ distinct positive integers. We denote by $(S^a)$ (resp. $[S^a]$) the $n\times n$ matrix having the $a$th power of the greatest common divisor (resp. the least common multiple) of $x_i$ and $x_j$ as its $(i,j)$-entry. For any $x\in S$, let $G_{S}(x) :=\{d\in S: d<x, d|x \

Xinyu Tian, Xiaotong Shen

Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse sup

Shuai Lv, Chang Liu, Feng Tang, Yujie Yuan

Multimodal Large Language Models (MLLMs) achieve strong multimodal reasoning performance, yet we identify a recurring failure mode in long-form generation: as outputs grow longer, models progressively drift away from image evidence and fall back on textual priors, resulting in ungrounded reasoning and hallucinations. Interestingly, Based on attention analysi

Camilla Celli, Andrea Bini, Valerio Novelli, Alessandro Filippeschi

In haptics, guaranteeing stability is essential to ensure safe interaction with remote or virtual environments. One of the most relevant methods at the state-of-the-art is the Time Domain Passivity Approach (TDPA). However, its high conservatism leads to a significant degradation of transparency. Moreover, the stabilizing action may conflict with the device'

Jiyuan An, Jiachen Zhao, Fan Chen, Liner Yang

The construction of CAD models has traditionally relied on labor-intensive manual operations and specialized expertise. Recent advances in large language models (LLMs) have inspired research into text-to-CAD generation. However, existing approaches typically treat generation and editing as disjoint tasks, limiting their practicality. We propose PR-CAD, a pro

Matheus Vinícius Todescato, Joel Luís Carbonera

Learning discriminative representations is a central goal of supervised deep learning. While cross-entropy (CE) remains the dominant objective for classification, it does not explicitly enforce desirable geometric properties in the embedding space, such as intra-class compactness and inter-class separation. Existing metric learning approaches, including supe

Walter Rieck, Ariadna Soro, Anton Frisk Kockum, Guangze Chen

Giant atoms are quantum emitters coupled to waveguides at multiple, spatially separated points, enabling interference effects that fundamentally change their light-matter interactions. A notable consequence of the interference is the emergence of decoherence-free interaction (DFI), which allows coherent excitation exchange between giant atoms via the wavegui

Toru Nakashika

The complex Gaussian distribution has been widely used as a fundamental spectral and noise model in signal processing and communication. However, its Gaussian structure often limits its ability to represent the diverse amplitude characteristics observed in individual source signals. On the other hand, many existing non-Gaussian amplitude distributions derive

Munawar Hasan, Apostol Vassilev, Edward Griffor, Thoshitha Gamage

The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk-SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that

J. J. Font, S. Macario, M. Sanchis

In this paper we provide a general setting to deal with level continuous fuzzy-valued functions. Namely, we embed such functions into a product of spaces of real-valued functions of two variables satisfying certain types of left-continuity, right-continuity and monotonicity.

Jonas Bodingbauer, Márton Hajdu, Laura Kovács, Axel Polaczek

Vampire proves theorems completely automatically in first- and higher-order logic extended with theories. Proof checking is increasingly demanded to consolidate user trust in Vampires output. We describe ongoing efforts in reconstructing Vampire proofs as trusted proofs in Lean

Mingyu Zhang, Zixu Li, Zhiwei Chen, Zhiheng Fu

Composed Image Retrieval (CIR) is a challenging image retrieval paradigm. It aims to retrieve target images from large-scale image databases that are consistent with the modification semantics, based on a multimodal query composed of a reference image and modification text. Although existing methods have made significant progress in cross-modal alignment and

Arup Ratan Jana, Animesh Goswami, Malay Kanti Dey

The K130 Room Temperature Cyclotron (RTC) at the Variable Energy Cyclotron Centre (VECC), Kolkata has delivered a variety of ion beams for experimental programs with stable performance since the 1970s. The extracted beams are transported to experimental stations through four beamlines. In this work, a two-step semi-numerical optimization framework is present

Ajith Anil Meera, Wouter Kouw

We propose an Expected Free Energy-based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e., optimize and learn the underlying function simultaneously. We show that, under specific assumptions, Expected Free Energy reduces to Upper Confidence Bound, Lower Confidence Bound, and Expected Information Gain.

Federico Pieroni

In this work, we want to show several properties of an unnodal, complex Coble surface $X$ with irreducible boundary curve $C \in |-2 K_X|$. Namely, we show that every isotropic sequence ${\cal E}_1, \ldots, {\cal E}_r$ with $r \le 8$ and ${\cal E}_i {\cal E}_j = 1 - \delta_{i, j}$ can be extended to a sequence of length $10$. Moreover, such a surface admits

Nazia Aslam, Abhisek Ray, Joakim Bruslund Haurum, Lukas Esterle

Recent advances in large-scale video models have significantly improved video understanding across domains such as surveillance, healthcare, and entertainment. However, these models also amplify privacy risks by encoding sensitive attributes, including facial identity, race, and gender. While image anonymization has been extensively studied, video anonymizat

Bingfang Li, Songhao Yang, Pu Cheng, Zhiguo Hao

Integrating grid-forming converters (GFMCs) into grid-following converter (GFLC)-dominated power systems enhances the grid strength, but GFMCs' current-limiting characteristic triggers dynamic switching between constant voltage control (CVC) and current limit control (CLC). This switching feature poses critical transient stability risks to GFLCs, requiring u

Teemu Härkönen, Hui Chen, Erik Vartiainen

We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrapolating measurements, facilitating statistical integration of Kramers-Kronig relations to estimate the whole complex refractive index. Additionally, we statistically model the anch

S. Adarsh, P. N. Bala Subramanian, Sreeraj T. P

Simultaneous decoherence of conjugate observables of an open quantum system leads to a classical statistical mechanical description with constant phase space probability density in terms of a uniform ensemble. We investigate a scenario where this may be realized by measurement of basic conjugate observables of a quantum system by the environment.

T. Wienand, B. Magdalou, R. Nock, P. Hufe

We develop an axiomatic framework to evaluate income distributions from the perspective of an opportunity-egalitarian social planner. Building on a formal link with the literature on decision theory under ambiguity, we characterize a class of opportunity-sensitive social welfare functions based on a two-stage evaluation: the planner first computes the expect

JiHyeok Jung, TaeYoung Yoon, HyunSouk Cho

Legal reasoning requires not only the application of legal rules but also an understanding of the context in which those rules operate. However, existing legal benchmarks primarily evaluate rule application under the assumption of fixed norms, and thus fail to capture situations where legal judgments shift or where multiple norms interact. In this work, we p

Kayran Schmidt

In this thesis, an approximation for the full (3+1)D dynamics of the Glasma is presented, which breaks boost-invariance on the level of the nuclear fields and leads to rapidity dependence in the final results. For this treatment, the Yang-Mills equations are linearized in covariant gauge, where lower-order, nonlinear contributions are neglected and the dynam

Vanessa Kosoy

We propose novel algorithms for sequence prediction based on ideas from stringology. These algorithms are time and space efficient and satisfy mistake bounds related to particular stringological complexity measures of the sequence. In this work (the first in a series) we focus on two such measures: (i) the size of the smallest straight-line program that prod

Yiming Ren, Yujiu Yang, Junjie Wang

Supervised fine-tuning (SFT) on visual instruction data often improves perceptual capabilities in vision-language models (VLMs) while degrading reasoning performance, creating a persistent reasoning tax during post-training. We investigate whether this degradation is related to disrupted access to depth-wise representations, and find that even fixed cross-de

Sebin Lee, Jumin Lee, Taeyeon Kim, Younju Na

Rapidly-exploring random trees (RRTs) have been widely adopted for robot motion planning due to their robustness and theoretical guarantees. However, existing RRT-based planners require explicit goal configurations specified as numerical joint angles, while many practical applications provide goal specifications through visual observations such as images or

Peng Yang, Yunfeng Zhu, Chao Chang, Shengcheng Yu

The rapid integration of Large Language Models (LLMs) into software engineering practice is reshaping how software testing activities are performed. LLMs are increasingly used to support software testing. Consequently, software testing education must evolve to prepare students for this new paradigm. However, while students have already begun to use LLMs in a

Zidong Zhao, Yihao Huang, Qing Guo, Tianlin Li

As Text-to-Image (T2I) generation becomes widespread, third-party platforms increasingly integrate multiple model APIs for convenient image creation. However, false claims of using official models can mislead users and harm model owners' reputations, making model verification essential to confirm whether an API's underlying model matches its claim. Existing

Bailey Andrew, David R. Westhead, Luisa Cutillo

In this paper we develop a graph-learning algorithm, MED-MAGMA, to fit multi-axis (Kronecker-sum-structured) models corrupted by multiplicative noise. This type of noise is natural in many application domains, such as that of single-cell RNA sequencing, in which it naturally captures technical biases of RNA sequencing platforms. Our work is evaluated against

Bo-Tao Li, Wei Wang, Zhuo Li

Recently, the GeV--sub-PeV spectrum of supernova remnant (SNR) Cassiopeia A (Cas A), one of the youngest and most well-studied SNRs in our Galaxy, has been updated by observations of Fermi-LAT and LHAASO. We revisit Cas A with our previous shell-plus-jet asymmetric model and investigate its particle acceleration ability. The broadband fitting results suggest

Yaqing Ma, Junwei Cao, Huaze Zhu, Yijian Song

Heteroepitaxy conventionally relies on rigid crystalline substrates, implicitly assuming that lattice and thermal mismatch must be accommodated within the epitaxial layer, leading to residual strain and defects that worsen with increasing substrate size. Here we demonstrate a substrate-mediated strain-partitioning regime in which lattice and thermal mismatch

Eugenio Rodrigo Zimmer Neves, Amanda Vanon Correa, Camila Campioni, Gabielli Pare Guglielmi

Most existing approaches to AI in pharmacy collapse three epistemologically distinct operations into a single technical layer: document preservation, semantic interpretation, and contextual presentation. This conflation is a root cause of recurring fragilities including loss of provenance, interpretive opacity, alert fatigue, and erosion of accountability. T

Jiyuan An, Liner Yang, Mengyan Wang, Luming Lu

As spatial intelligence becomes an increasingly important capability for foundation models, it remains unclear whether large language models' (LLMs) performance on spatial reasoning benchmarks reflects structured internal spatial representations or reliance on linguistic heuristics. We address this question from a mechanistic perspective by examining how spa

Iana Zhura, Yara Mahmoud, Jeffrin Sam, Hung Khang Nguyen

Efficiently predicting motion plans directly from vision remains a fundamental challenge in robotics, where planning typically requires explicit goal specification and task-specific design. Recent vision-language-action (VLA) models infer actions directly from visual input but demand massive computational resources, extensive training data, and fail zero-sho

Swapan Jana, Sourav Pal

The isometric dilation of a pair of commuting contractions due to And\^{o} is not minimal. We modify And\^{o}'s dilation and construct a minimal isometric dilation on $\mathcal H \oplus_2 \ell_2(\mathcal H \oplus_2 \mathcal H)$ for a commuting pair of strict contractions on a Hilbert space $\mathcal H$. In the same spirit, we construct under certain conditio

Dominic Gribben, Carolina Allende, Alba Villarino, Aser Cortines

We develop a tensor-network surrogate for option pricing, targeting large-scale portfolio revaluation problems arising in market risk management (e.g., VaR and Expected Shortfall computations). The method involves representing high-dimensional price surfaces in tensor-train (TT) form using TT-cross approximation, constructing the surrogate directly from blac

Wonyoung Lee, Wooseong Jeong, Kuk-Jin Yoon

Model merging combines independently fine-tuned checkpoints without joint multi-task training. In the era of foundation-model, fine-tuning with Low-Rank Adaptation (LoRA) is prevalent, making LoRA merging a promising target. Existing approaches can work in homogeneous settings where all target tasks are classification but often fail when tasks span classific

Cai Selvas-Sala, Lei Kang, Lluis Gomez

As multimodal models like CLIP become integral to downstream systems, the need to remove sensitive information is critical. However, machine unlearning for contrastively-trained encoders remains underexplored, and existing evaluations fail to diagnose fine-grained, association-level forgetting. We introduce SALMUBench (Sensitive Association-Level Multimodal

Jyoti Garg, Sugandha Maheshwary, Himanshu Setia

This article determines the structure of the group ring $\mathbb{Z}_nG$, where $G$ is a finite group and $\mathbb{Z}_n$ is the ring of integers modulo $n$, such that $n$ is relatively prime to the order of $G$. The decomposition of $\mathbb{Z}_nG$ is given as a direct sum of matrix rings over Galois rings, thereby extending the structural theory of group rin

Ruofei Bai, Shenghai Yuan, Xinhang Xu, Xingyu Ji

Multi-robot systems rely on underlying connectivity to ensure reliable communication and timely coordination. This paper studies the line-of-sight (LoS) connectivity maintenance problem in multi-robot navigation with unknown obstacles. Prior works typically assume known environment maps to formulate LoS constraints between robots, which hinders their practic

Mengjia Wu, Yi Zhang, Robin Haunschild, Lutz Bornmann

Assessing the quality of scientific research is essential for scholarly communication, yet widely used approaches face limitations in scalability, subjectivity, and time delay. Recent advances in large language models (LLMs) offer new opportunities for automated research evaluation based on textual content. This study examines whether LLMs can support post-p

Shay Mozes, Daniel Prigan

We prove that, up to subpolynomial or polylogarithmic factors, there is no tradeoff between preprocessing time, query time, and size of exact distance oracles for planar graphs. Namely, we show how given an $n$-vertex weighted directed planar graph $G$, one can compute in $n^{1+o(1)}$ time and space a representation of $G$ from which one can extract the exac

Sourav Pal, Nitin Tomar

The structured singular value $\mu_E$ for a linear subspace $E$ of $M_n(\mathbb C)$ is defined by \[ \mu_E(A)=1 / \inf\{\|X\| \ : \ X \in E, \ \det(I_n-AX)=0 \} \quad (A \in M_n(\mathbb{C})), \] and $\mu_E(A)=0$ if there is no $X \in E$ with $\det(I_n-AX)=0$. It is well-known that $\mu_E(A)$ coincides with the spectral radius $r(A)$ when $E=\{cI_n: c \in \ma

F. Pastore, O. Sauter, F. Felici, D. Kropackova

Real-time control of tokamak plasmas encompasses sustaining a high-performance stationary state, avoiding disruptions, and managing ramp-up and ramp-down phases. Real-time estimation and control of electron density is fundamental for monitoring and controlling particle confinement, heating efficiency, exhaust conditions, impurity concentration, fusion power,

Xue Liu, Xin Ma, Yuxin Ma, Yongchang Peng

As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks characterizing genuine expert-level cognition. Existing frameworks suffer from narrow domain coverage, reliance on generalist tasks, or self-evaluation biases. To bridge this gap, w

Victor Medina-Olivares, Wangzhen Xia, Stefan Lessmann, Nadja Klein

We propose a semi-structured discrete-time multi-state model to analyse mortgage delinquency transitions. This model combines an easy-to-understand structured additive predictor, which includes linear effects and smooth functions of time and covariates, with a flexible neural network component that captures complex nonlinearities and higher-order interaction

Qurat Ul Ain, Alptekin Temizel, Soyiba Jawed

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due to complex time-varying disruptions in brain connectivity. Functional MRI (fMRI) provides a powerful non-invasive modality for identifying functional alterations. Existing deep learning (DL) studies employ dive

Benjamin Gess, Max Sauerbrey, Zhengyan Wu

We establish a solution theory for the incompressible Navier--Stokes--Fourier system with thermal noise, posed on the three-dimensional torus. While in the incompressible deterministic setting the equation for the velocity can be solved independently of the temperature, the inclusion of the effects of thermal fluctuations by means of the GENERIC framework le

André Romão, Francisco Faria, João R. Matos, Emanuel Nunes

Enterprise adoption of permissioned blockchains remains limited due to the complexity and cost of integrating legacy systems. We present a modular adapter architecture that bridges enterprise applications with blockchain networks, designed to support small and medium-sized enterprises with limited technical resources. The architecture provides five key modul

Mohammadmehdi Torkzadeh, Mattia Iannetti, Mathieu Lizée, Amitayhush Thakur

The one-third monolayer Sn layer on Si(111) has long been considered a benchmark system for exploring two-dimensional Mott physics, owing to its narrow bandwidth and sizable on-site Coulomb repulsion. Previous experiments suggested the emergence of a low-temperature Mott insulating phase with an energy gap of only a few tens of meV, while theory predicted a

Gary R. W. Greaves, Haoran Zhu

We establish a sharp lower bound on the spectral gap of the biased adjacent-transposition Markov chain on the symmetric group. As a consequence, we resolve a longstanding conjecture of Fill, proving that among all regular probability vectors, the minimum spectral gap of the transition matrix is attained by the uniform probability vector. We also characterise

Christian Berg, Ryszard Szwarc

For an N-extremal solution $\mu$ to an indeterminate moment problem it is known by a theorem of M. Riesz that the measure $(1+x^2)^{-1}d\mu(x)$ is determinate. For $0<\alpha<1$ we show by contradiction that there exist indeterminate N-extremal solutions $\mu$ such that $(1+x^2)^{-\alpha}d\mu(x)$ is determinate, and there exist also indeterminate N-extremal s

Leihao Chen, Joris M. Mooij

Many causal discovery algorithms, including the celebrated FCI algorithm, output a Partial Ancestral Graph (PAG). PAGs serve as an abstract graphical representation of the underlying causal structure, modeled by directed acyclic graphs with latent and selection variables. This paper develops a characterization of the set of extended-type conditional independ

Maximilian R. Becker, Ruben Cruz, Kenichi Ataka, Joachim Heberle

Per- and polyfluoroalkyl substances are a class of synthetic chemical compounds widely used as coatings to lower surface energies. Yet the microscopic mechanisms of their weak interaction with water and organic compounds remain poorly understood. Here, we perform large-scale density-functional-theory molecular dynamics simulations to investigate water at sel

Wooseong Jeong, Wonyoung Lee, Kuk-Jin Yoon

Merging multiple Low-Rank Adaptation (LoRA) modules is promising for constructing general-purpose systems, yet challenging because LoRA update directions span different subspaces and contribute unevenly. When merged naively, such mismatches can weaken the directions most critical to certain task losses while overemphasizing relatively less important ones, ul

Chao Chang, Yuning Yang

Power iteration can improve the accuracy of randomized SVD, but requires multiple data passes, making it impractical in streaming or memory-constrained settings. We introduce a lightweight yet effective sketch-power iteration, allowing power-like iterations with only a single pass of the data, which can be incorporated into one-pass algorithms for low-rank a

Adam Nie, Yanrong Yang, Han Lin Shang, Yi He

This article explores a general factor structure for high-dimensional nonstationary functional time series, encompassing a wide range of factor models studied in the existing literature. We investigate the asymptotic spectral behaviors of the sample covariance operator under this general data structure. A novel fundamental sufficient condition, formulated in

Tugba Coskun Aslan, Gulser Uncular, Hasan Durmus, Yasin Kavla

This study aimed to adapt the Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models (LLM-D12) into Turkish and evaluate its psychometric properties among regular LLM users. A sample of 387 participants (68.5% female; mean age = 25.22 +/- 7.13) completed the translated scale, which underwent cultural-linguistic validation

Yongchul G. Chung, Myoung Soo Lah

Digital reticular chemistry relies on accurate crystal structures to power computational screening, data-driven discovery, and structure-property analysis, yet recent studies reveal that more than half of the top-performing candidates in major computational screening campaigns are chemically invalid. In experimental MOF databases, structural errors arise whe

Lawson T. Lloyd, Tommaso Pincelli, Mohamed Amine Wahada, Alessandro De Vita

Van der Waals (vdW) layered materials with long-range magnetic order have the potential to enable novel optoelectronic and spintronic applications. Among these, CrSBr is an air-stable, direct band gap semiconductor that hosts interlayer antiferromagnetic order, a highly anisotropic electronic structure, and strongly bound excitons. In particular, excitons in

Cian Lalor, Matthew Marshall, Antonio Russo

Bitcoin's limited programmability and transaction throughput have historically prevented native Bitcoin from participating in decentralized finance (DeFi) applications. Existing solutions depend on honest-majority thresholds, or centralized custodial entities that introduce significant trust requirements. This paper introduces Bitcoin Smart Accounts (BSA), a

A. A. Araújo Filho, A. F. Santos, J. A. A. S. Reis, L. Lisboa-Santos

We investigate the electromagnetic dynamics of spin-nondegenerate classical particle models arising from Lorentz-violating sectors of the Standard-Model Extension, focusing on the $b_\mu$ background. Starting from the type-2 relativistic Lagrangian, we introduce minimal electromagnetic coupling and derive the exact Hamiltonian dynamics associated with each s