April 2026 arXiv papers — page 138

Saeed Rahmani, Shiva Rasouli, Daphne Cornelisse, Eugene Vinitsky

Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment for evaluating AV performance in varied conditions. However, existing simulation tools mainly focus on graphical realism and rely on simple rule-based models and therefore fail to a

Andrea La Delfa

We focus our attention on the spinor model proposed in an article by J. Magueijo et al. and we analyze it from the point of view of the cosmological background. We show that this model, under some conditions, can well-describe the background behavior of DM and, under other conditions, the behavior of DE. Furthermore, we show that the SET of the spinorial flu

Xuan Wang, Kai Ruan, Jiayi Han, Kaiyue Zhou

Audio-driven bimanual piano motion generation requires precise modeling of complex musical structures and dynamic cross-hand coordination. However, existing methods often rely on acoustic-only representations lacking symbolic priors, employ inflexible interaction mechanisms, and are limited to computationally expensive short-sequence generation. To address t

Lidong Sun, Wentao Zhao, Ye Wang, Huaping Liu

Musculoskeletal robots offer intrinsic compliance and flexibility, providing a promising paradigm for versatile locomotion. However, existing research typically relies on models with fixed muscle physiological parameters. This static physical setting fails to accommodate the diverse dynamic demands of complex tasks, inherently limiting the robot's performanc

Daniele Pranzetti, Domenico Giuseppe Salluce

Celestial symmetries of gravity and gauge theory can be enhanced to a $w_{1+\infty}$ algebra and an $S$-algebra respectively, when restricting to a single graviton/gluon helicity sector. Difficulties in combining both sectors in the full theory have been pointed out in the previous literature. In this work, we face this problem from the covariant phase space

Edward Crane, Alexander E. Holroyd, Erin Russell

Consider time-homogeneous discrete-time Markov chains $X$, $Y$, and $Z$ on countable state spaces, considered as stochastic processes with specified initial distributions. Suppose for maps $f$ and $g$ that $(f(X_t))_{t \ge 0}$ and $(g(Y_t))_{t \ge 0}$ are both equal in law to $Z$. We prove that $X$ and $Y$ can be coupled so that $(X_t, Y_t)_{t \ge 0}$ is a h

Zhihao Cao, Tianxu An, Chenhao Li, Stelian Coros

Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes particularly challenging in complex environments, where interaction signals are difficult to capture and model. We present PAINT, a hierarchical learning framework for partner-agnostic intent-aware collaborativ

Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi, Jing Yao

Despite their global prevalence, many Large Language Models (LLMs) are aligned to a monolithic, often Western-centric set of values. This paper investigates the more challenging task of fine-grained value alignment: examining whether LLMs can emulate the distinct cultural values of demographic subgroups. Using Singapore as a case study and the World Values S

Shanampudi Pranaya Chowdary, Shamik Sural

With increasing emphasis on transparency in digital governance, users expect more than silence when their access requests are denied by a system. However, authorization methods are notorious for their inability to provide any form of meaningful feedback under such situations. This paper shows a direction towards how the problem of explainability can be mitig

Johann Davidov

Motivated by generalized geometry (in the sense of Hitchin), the product bundle ${\mathcal Z}\times_{M} {\mathcal Z}$ of the twistor space ${\mathcal Z}$ of a Riemannian manifold $(M,g)$ is considered. The product twistor space admits a natural family of Riemannian metrics and four compatible almost complex structures, analogs of the Atiyah-Hitchin-Singer an

Yannis Voet, Matthias Möller, Pablo Antolin, Cornelis Vuik

Trimming is a ubiquitous operation in computer-aided-design whereby parts of a geometry are merged, intersected, or simply discarded. While it grants virtually unlimited flexibility in geometric design, it introduces a plethora of other difficulties when such geometries are used within immersed finite element methods. In particular, small cut elements lead t

Yijun Lin, Adrian van Kan, Edgar Knobloch

We study large-scale dynamics in the Ginzburg-Landau equation (GLE) using a reduced description derived from a WKB expansion. Rigorous mathematical results establishing that this reduced equation accurately approximates the full GLE are currently limited to the real GLE (RGLE) and exclude phase-slip dynamics. For the RGLE, we find that the reduced equation h

Andreas Čap, Zhangwen Guo

Path geometries provide a geometric encoding of systems of second order ODE, which serves as a model for the geometric theory of more general systems of ODE and for cone structures. They are an instance of the family of parabolic geometries, thus they are second order structures that are difficult to study using the usual tools of differential geometry. The

Xiaofeng Jin, Wentao Huo, Lingwei Ma, Zhenqiu Zhang

This paper investigates homogenization problems for the nonlocal operators with rapidly oscillating coefficients in the cases of periodic and random statistically homogeneous micro-structures. These operators involve the fractional Laplacian and some operators compared to it. Based on the $\Gamma$-convergence method and compactness arguments, we prove the ho

Nicolás Viaux M

Charged lepton-flavor violation is a null-test frontier of the Standard Model and a direct probe of physics beyond it. We present a global effective field theory (EFT) analysis across FCC-ee, ILC, CLIC, HL-LHC, HE-LHC, and muon colliders at 3 and 10 TeV, with operator identification as the primary target rather than exclusion reach alone. The analysis combin

Eliya Habba, Itay Itzhak, Asaf Yehudai, Yotam Perlitz

The rapid release of both language models and benchmarks makes it increasingly costly to evaluate every model on every dataset. In practice, models are often evaluated on different samples, making scores difficult to compare across studies. To address this, we propose a framework based on multidimensional Item Response Theory (IRT) that uses anchor items to

Lucia Bagnoli, Slaven Kožić

Let $\mathcal{V}^c(\mathfrak{gl}_N)$ be Etingof--Kazhdan's quantum affine vertex algebra associated with the trigonometric $R$-matrix. We establish a connection between suitably generalized deformed $\phi$-coordinated $\mathcal{V}^c(\mathfrak{gl}_N)$-modules and the representations of quantized enveloping algebra $U_h(\mathfrak{gl}_N)$ and reflection equatio

Christopher Chamberland, Jan Olle, Muyuan Li, Scott Thornton

Fast, scalable decoding architectures that operate in a block-wise parallel fashion across space and time are essential for real-time fault-tolerant quantum computing. We introduce a scalable AI-based pre-decoder for the surface code that performs local, parallel error correction with low decoding runtimes, removing the majority of physical errors before pas

Jonas Mair, Lukas Schwenkel, Matthias A. Müller, Frank Allgöwer

Operation at steady state is often not optimal when optimizing over an economic cost objective. In many cases, periodic operation yields better performance. Therefore, we derive asymptotic stability guarantees of an economic model predictive control scheme with terminal conditions for systems with optimal periodic operation for a more general setup than exis

Xiaozhou Zou, Lucie Jurkovičová, Anne Weber, Cong Zhao

We experimentally observe a new type of quantum-path interference, in two-dimensions (2D-QPI), in high-harmonic generation (HHG) driven by an orthogonally-polarised highly-bichromatic field. This regime is marked by comparable intensities of the two orthogonal colours. In this highly-bichromatic regime, we demonstrate that 2D-QPI is encoded in the measured h

Yi Liu, Haoxuan Xu, Hongbo Duan, Keyu Fan

Visual SLAM algorithms achieve significant improvements through the exploration of 3D Gaussian Splatting (3DGS) representations, particularly in generating high-fidelity dense maps. However, they depend on a static environment assumption and experience significant performance degradation in dynamic environments. This paper presents GGD-SLAM, a framework that

Huaian Diao, Hongyu Liu, Longyue Tao

Inverse scattering focuses on recovering unknown scatterers from wave measurements. A fundamental challenge is determining whether an inverse obstacle problem can be resolved from a single far-field measurement, a task particularly demanding for non-convex polytope obstacles under generalized impedance boundary conditions and closely linked to the long-stand

Mingxi Zhang, Renjie Xie, Jincheng Wang, Guyue Li

Radio frequency fingerprints (RFFs) enable secure wireless authentication but struggle in open-set scenarios with unknown devices and varying channels. Existing methods face challenges in generalization and incur high computational costs. We propose a lightweight, self-adaptive RFF extraction framework using Low-Rank Adaptation (LoRA). By pretraining LoRA mo

Yingying Zhao, Chengyin Hu, Qike Zhang, Xin Li

Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies focus almost exclusively on the digital setting, leaving physical-world threats largely unexplored. As VLMs are increasingly deployed in real environments, this gap becomes critical, since adversarial perturbatio

Iman Islam, Bram Ruijsink, Andrew J. Reader, Andrew P. King

Deep learning-based medical image segmentation typically relies on ground truth (GT) labels obtained through manual annotation, but these can be prone to random errors or systematic biases. This study examines the robustness of deep learning models to such errors in echocardiography (echo) segmentation and evaluates a novel strategy for detecting and refurbi

Shengding Liu, Qiben Yan

Indoor fire disasters pose severe challenges to autonomous search and rescue due to dense smoke, high temperatures, and dynamically evolving indoor environments. In such time-critical scenarios, multi-agent cooperative navigation is particularly useful, as it enables faster and broader exploration than single-agent approaches. However, existing multi-agent n

Chengyang Bao

In this paper, we apply the Taylor--Wiles--Kisin patching method to the coherent cohomology of modular curves at minimal level. We establish a multiplicity-one result for the patched module by the $q$-expansion principle and show that a certain partial normalization of the crystalline deformation ring is Cohen--Macaulay. As applications, we prove new cases w

Maxence Adly, Alix Chazottes, Emilie Chouzenoux, Jean-Christophe Pesquet

In this work, we introduce a unifying Bregman-based majorization-minimization (MM) framework for solving nonconvex nonsmooth optimization problems. The proposed approach leverages Bregman divergences, possibly varying across iterations, to construct tailored surrogate functions that majorize the objective. We establish the convergence of the iterates of the

Madhav Sharma, Archna Sagdeo, Rajendra S. Dhaka

We report the temperature-dependent synchrotron based X-ray diffraction analysis of NASICON type Na$_3$FeCr(PO$_4$)$_3$ sample, which undergoes a symmetry-lowering structural transition from a monoclinic ($C2/c$) phase with long-range Na-vacancy order to a rhombohedral ($R\bar{3}c$) phase with statistical disordered Na ions. The [FeCr(PO$_4$)$_3$] polyanioni

Taeyoung Kim

We study fixed-design random feature ridge regression beyond the mean-kernel approximation. The expectation is taken over the frozen-feature ensemble, conditional on the training sample. Because the predictor is a nonlinear function of the empirical kernel, its mean training error, test error, and conditional generalization gap depend on centered kernel cova

Deepika Gill, Ruikai Wu, Peter Elliott, Sangeeta Sharma

All-optical generation of pure spin current -- the flow of spin in the absence of a corresponding charge flow -- relies on a symmetry based compensation of valley charge. The 2d $d$-wave altermagnets, ideal spintronics materials due to a very low spin-orbit coupling, possess a magnetic point group and highly anisotropic valley manifolds that would appear to

Iulia Ghiu, Catalina Cirneci, George Alexandru Nemnes

In this article, we find a theorem that gives a relation between the maximal fidelity of teleportation and the concurrence of the inseparable $X$ state used as a quantum channel in this process. Furthermore, we evaluate the concurrence of the output states generated by the local and nonlocal asymmetric broadcasting of entanglement and prove that the concurre

Nikolay Marchuk

This paper proposes SU(2)-gauge-invariant field equations involving interaction with a scalar field. A connection with the Dirac equation is discussed.

Samantha D'Alonzo, Rachel Chen, Weidong Zhang, Melody Yu

Intellectual humility (IH)-a recognition of one's own intellectual limitations-can reduce polarization and foster more understanding across lines of difference. Yet little work explores how IH can be systematically defined, measured, evaluated, and enhanced in spaces that often lack it the most: online political discussions. In this paper, we seek to bridge

Jagadeesh Rachapudi, Pranav Singh, Ritali Vatsi, Praful Hambarde

Large language models (LLMs) inherently absorb harmful knowledge, misinformation, and personal data during pretraining on large-scale web corpora, with no native mechanism for selective removal. While machine unlearning offers a principled solution, existing approaches are provider-centric, requiring retraining pipelines, curated retain datasets, and direct

Paolo Lorenzoni, Zhe Wang

We introduce the notions of generalised (bi-)Hamiltonian structures which generalise naturally the (bi-)Hamiltonian structures of evolutionary partial differential equations. In the hydrodynamic case, these structures are characterised in terms of geometric data. Furthermore, we show that a generalised (bi)-Hamiltonian structure of hydrodynamic type can be a

Michael C. Knaus, Henri Pfleiderer

Difference-in-Differences (DiD) is a widely used research design that often relies on a conditional parallel trends (CPT) assumption. In contrast to settings with unconfoundedness, where causal graphs provide powerful frameworks for reasoning about valid conditioning variables, general-purpose graphical tools for CPT are missing. We introduce transformed Sin

Shaopeng Fu, Di Wang

Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To improve the efficiency of AT for LLMs, recent studies propose continuous AT (CAT) that searches for adversarial inputs within the continuous embedding space of LLMs during AT. While CAT has achieved empirical s

Hojung Jung, Yuki Oto, Oscar M. Mozos, Yumi Iwashita

We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with sy

Katherine Abramski, Giulio Rossetti, Massimo Stella

Implicit biases in both humans and large language models (LLMs) pose significant societal risks. Dual process theories propose that biases arise primarily from associative System 1 thinking, while deliberative System 2 thinking mitigates bias, but the cognitive mechanisms that give rise to this phenomenon remain poorly understood. To better understand what u

Miklós Rásonyi

The so-called SAGA-LD algorithm is used for efficient sampling from high-dimensional distributions in machine learning. Its intricate dynamics resists standard approaches of Markov chain theory. We prove, using a model-specific method, that SAGA-LD converges to a limiting distribution and a law of large numbers holds.

Hao Yan, Yuliang Liu, Xingchen Liu, Yuyi Zhang

Existing Multimodal Large Language Models (MLLMs) suffer from significant performance degradation on the long document understanding task as document length increases. This stems from two fundamental challenges: 1) a low Signal-to-Noise Ratio (SNR), with crucial evidence buried in irrelevant pages; and 2) supervision scarcity, as datasets offering only final

Madhava Gaikwad

Dense Associative Memory (DAM) generalizes Hopfield networks through higher-order interactions and achieves storage capacity that scales as $O(N^{n-1})$ under suitable pattern separation conditions. Existing dynamical analyses primarily study the thermodynamic limit $N\to\infty$ with randomly sampled patterns and therefore do not provide finite-size guarante

F. Zanichelli, A. Veillon, C. Piquard, A. Aassime

Quantum thermodynamics addresses the dynamics of heat flow in quantum devices driven out of equilibrium. Although mesoscopic circuits at low temperatures provide a flexible platform to explore this dynamics, experimental studies are wanting because thermal timescales in nanodevices are often too fast. Here we engineer and investigate with noise thermometry a

Xiaoqi Lei, Zixiang Yan, Jian Gao, Yueheng Lan

Spatiotemporal chaotic systems are difficult to characterize in a model-free manner because of their high dimensionality, strong nonlinearity, and sensitivity to initial conditions. Coupled map lattices, as a representative class of extended nonlinear systems, exhibit diverse regimes such as frozen random pattern, defect chaotic diffusion, and fully develope

Nathaniel Johnston, Vincent Russo

We study the distinguishability of quantum states under local operations with classical communication (LOCC), separable, and positive-partial-transpose (PPT) measurements, focusing on locally diagonal orthogonally invariant (LDOI) states -- those invariant under local diagonal orthogonal twirling. This class includes many important families such as Werner st

Adrien Dorise, Marjorie Bellizzi, Omar Hlimi

Satellite image restoration aims to improve image quality by compensating for degradations (e.g., noise and blur) introduced by the imaging system and acquisition conditions. As a fundamental preprocessing step, restoration directly impacts both ground-based product generation and emerging onboard AI applications. Traditional restoration pipelines based on s

Xiaoxiao Liang, Juyuan Zhang, Liming Pan, Linyuan Lü

Inferring latent interaction structures from observed dynamics is a fundamental inverse problem in many-body interacting systems. Most neural approaches rely on black-box surrogates over trainable graphs, achieving accuracy at the expense of mechanistic interpretability. Symbolic regression offers explicit dynamical equations and stronger inductive biases, b

Feiyu Tan, Heran Yang, Qihong Duan, Kai Ye

Image-to-image translation (I2I) is a fundamental task in computer vision, focused on mapping an input image from a source domain to a corresponding image in a target domain while preserving domain-invariant features and adapting domain-specific attributes. Despite the remarkable success of deep learning-based I2I approaches, the lack of paired data and unsu

Adam T. Müller, Mihai Kocsis, Nicolaj C. Stache

Neuromorphic vision sensors offer low latency and high dynamic range, but their deployment in public spaces raises severe data protection concerns. Recent Event-to-Video (E2V) models can reconstruct high-fidelity intensity images from sparse event streams, inadvertently exposing human identities. Current obfuscation methods, such as masking or scrambling, co

Arefe Boushehrian, Mohammad Reza Badri, Sina Akbari, Negar Kiyavash

Bounding causal effects analytically, rather than numerically, is appealing for its interpretability and conceptual clarity. Existing sharp methods rely on optimization-based approaches such as the Balke-Pearl framework, whose computational complexity grows rapidly. An alternative line of work derives bounds heuristically using probability laws and generic i

Benjamin Biaggi, Jan Draisma, Koen de Nooij, Immanuel van Santen

The symmetric subrank of homogeneous polynomial is the largest number of terms in a diagonal form to which it can be specialized by a (typically non-invertible) linear variable substitution. Building on earlier work by Derksen-Makam-Zuiddam and Biaggi-Chang-Draisma-Rupniewski for ordinary tensors, we determine the asymptotic behavior of symmetric subrank and

Leonardo Solidoro, Sebastian H. Völkel, Silke Weinfurtner

The emergence of precision gravity simulators in quantum and fluid systems is opening new avenues for probing curved-spacetime physics and black-hole phenomenology under controlled laboratory conditions. In parallel, advances in understanding how fundamental physics can be probed in the spectral signatures of black holes and exotic compact objects motivate t

Nguyen Kim Thang

The rise of automated bidding strategies in online advertising presents new challenges in designing and analyzing efficient auction mechanisms. In this paper, we focus on proportional mechanisms within the context of auto-bidding and study the efficiency of pure Nash equilibria, specifically the price of anarchy (PoA), under the liquid welfare objective. We

Yupeng Sun, Yanzhao Li, Zhiqiang Zou, Bai Du

FlashAttention-style online softmax enables exact attention computation with linear memory by streaming score tiles through on-chip memory and maintaining a running maximum and normalizer. However, as attention kernels approach peak tensor-core/cube-core throughput on modern accelerators, non-matmul components of online softmax -- especially per-tile rowmax

Javier Minguillón, Fernando Soria, Ana Vargas

We investigate the Sobolev regularity required for almost everywhere convergence to the initial datum of solutions to the linear Schr\"odinger equation along certain tangential curves. In the regime $\alpha<\tfrac12$, we analyze maximal estimates for expressions of the form $e^{it\Delta}f(x+\gamma(t))$ over specific $\alpha$-H\"older curves $\gamma$ and init

Guanyu Pan, Huiyu Yang, Yunpeng Wang, Zikun Xu

Neural operators have been increasingly used as data-driven surrogates for time-marching predictions of turbulent flows. However, long-horizon autoregressive prediction is sensitive to error accumulation and the choice of prediction interval. Excessively small time increments may increase temporal redundancy and lengthen rollouts, which can degrade the stabi

Georges Hattab

Current discourse on Artificial Intelligence (AI) ethics, dominated by "trustworthy" and "responsible" AI, overlooks a more fundamental human-computer interaction (HCI) crisis: the erosion of human agency. This paper argues that the primary challenge of high-stakes AI systems is not trust, but the preservation of human causal control. We posit that "bad AI"

Arturo Tozzi

Glycemic regulation is often described as maintaining glucose levels near a stable baseline. However, continuous glucose monitoring after meals displays intra-individual variability even under controlled conditions, suggesting intrinsic system dynamics beyond sensor noise, measurement error or short-term variability around a fixed set point. Therefore, we es

Mary Nusrat, Sarfuddin Bhuiyan, Gahangir Hossain

The escalating complexity of modern cyber threats demands innovative approaches to security education that transcend traditional pedagogical methods. Conventional training paradigms often fail to engage learners meaningfully or develop the intuitive reasoning necessary for effective threat recognition. This paper introduces an interactive educational framewo

L. Bruno, D. Pelliciari, G. Bernardi, M. Pilia

Persistent radio sources (PRSs) are (sub-)parsec-scale compact non-thermal continuum sources associated with some repeating fast radio bursts (FRBs). Their nature is debated, but their properties provide insights into the FRB environment and progenitors. We measure the spectrum of the recently confirmed PRS associated with FRB 20190417A. Spectral features su

Barbara Niethammer, Lorena Pohl, Juan J. L. Velázquez

In this paper we analyze a model for electroporation, a biological process in which a cell membrane exposed to an external voltage becomes permeable due to the formation and growth of nanoscale membrane pores. We prove a local stability result for asymptotic self-similar solutions with a power-law tail. Our method relies on the analysis of an equation for th

Yiran Du, Jinlong Li, Huimin He, Chenghao Wang

This study investigates Chinese primary school students' acceptance of a social robot for English-as-a-foreign-language (EFL) speaking practice through a sequential explanatory mixed-methods design. Integrating the Technology Acceptance Model (TAM) and the Computers Are Social Actors (CASA) paradigm, the research explores both functional and social factors i

Theophile Gaichies, Anniina Salonen, Arnaud Antkowiak, Emmanuelle Rio

We report on the shape taken by the interface of a liquid bath when hit by a smooth oblique steady jet. When the angle between the jet and the bath decreases below $50^\circ$, a cavity is formed in front of the jet. In the inertial regime we explore, the jet boundary layer detaches in the impact region, thereby delimiting a core jet region outside of which t

Tiberiu Harko

The observed unusual behaviors of the orbits of Trans-Neptunian objects as well as the gravitational anomalies detected by the Optical Gravitational Lensing Experiment can be explained by assuming the existence of a ninth planet in the Solar System, having a mass of the order of $5-10M_{\oplus}$, and located at the distance of 300-1000 AU from the Sun. Since

Xilai Bao, Oleksandr V. Pylypovskyi, Huali Yang, Yali Xie

Prospective spintronic memory and logic devices will benefit from the negligible stray field and ultrafast magnetic dynamics inherent to antiferromagnets [1]. However, realizing isothermal, nonvolatile, and deterministic switching of antiferromagnetic states remains a key challenge [2, 3]. Here, we propose a piezomagnetic writing scheme in triangular Mn3Ir-b

Alexander Altland, Julian Sonner

Recent years have witnessed tremendous progress in developing a fine-grained low-dimensional holographic correspondence, specifically the construction of quantum mechanical boundary theories as holographic duals of two-dimensional gravity. In these developments, quantum chaos played a crucial role, both as source of universality and as a guiding principle fo

Johannes Bleher, Claudia Tarantola

When variable selection methods are applied to bootstrapped and multiply imputed datasets, the set of selected variables typically varies across iterations. Aggregating results via the union rule can lead to overly dense models. We propose a sequential evidence aggregation procedure that models detection outcomes across perturbation iterations as Bernoulli t

Zhiyuan Zhang, Yanzhao Li, Zhiqiang Zou, Bai Du

While 4-bit quantization is essential for high-throughput deployment of Large Language Models, activation outliers often lead to significant accuracy degradation due to the restricted dynamic range of low-bit formats. In this paper, we systematically investigate the spatial distribution of outliers and demonstrate a token-persistent structural clustering eff

Haoyang Jiang, Mingyang Yi, Shaolei Zhang, Junxian Cai

Recently, detecting AI-generated images produced by diffusion-based models has attracted increasing attention due to their potential threat to safety. Among existing approaches, reconstruction-based methods have emerged as a prominent paradigm for this task. However, we find that such methods exhibit severe security vulnerabilities to adversarial perturbatio

Wenyun Li, Zheng Zhang, Dongmei Jiang, Yaowei Wang

Vision Transformer (ViT) models have achieved remarkable performance across various vision tasks, with scalability being a key advantage when applied to large datasets. This scalability enables ViT models to exhibit strong generalization capabilities. However, as the number of parameters increases, the robustness of ViT models to adversarial examples does no

Fan Xiao, Nikolaos Delopoulos, Niklas Wahl, Lennart Volz

Purpose: Accurate dose calculation is essential in radiotherapy for precise tumor irradiation while sparing healthy tissue. With the growing adoption of MRI-guided and real-time adaptive radiotherapy, fast and accurate dose calculation on CT and MRI is increasingly needed. The DoseRAD2026 dataset and challenge provide a public benchmark of paired CT and MRI

Huanzhen Wang, Ziheng Zhou, Zeng Tao, Aoxing Li

The human brain constructs emotional percepts not by processing facial expressions in isolation, but through a dynamic, hierarchical integration of sensory input with semantic and contextual knowledge. However, existing vision-based dynamic emotion modeling approaches often neglect emotion perception and cognitive theories. To bridge this gap between machine

Shiyu He, Minchi Kuang, Mengxian Wang, Bin Hu

Realizing endogenous narrative evolution in LLM-based multi-agent systems is hindered by the inherent stochasticity of generative emergence. In particular, long-horizon simulations suffer from social memory stacking, where conflicting relational states accumulate without resolution, and narrative-spatial dissonance, where spatial logic detaches from the evol

Shao-Qin Wu, Jing-Rui Zhang, Rong-Gen Cai, Bing Zhang

The temporal and spatial coincidence between the gravitational wave (GW) event GW190425 and the fast radio burst (FRB) event FRB 20190425A raises the intriguing possibility of a physical connection between the two. The widely discussed possibility invoking the collapse of a supermassive neutron star as the merger product suffers the inconsistency between the

Shaoxiong Chen, Vishvesh Kumar, Zhipeng Yang, Xi Zhang

In this paper, we study the mass-constrained fractional Choquard equation \( (-\Delta)^s u = \lambda u + \alpha (I_\mu * |u|^{\frac{2N-\mu}{N}})|u|^{\frac{2N-\mu}{N}-2}u + (I_\mu * |u|^p)|u|^{p-2}u \) in \( \mathbb{R}^N \), under the constraint \( \int_{\mathbb{R}^N} |u|^2 \, dx = c^2 > 0 \), where \( N > 2s \), \( s \in (0,1) \), \( \mu \in (0,N) \), \( \al

Randall Powers, Wendy Martinez

The linked micromaps approach was originally developed as an improvement to choropleth maps for displaying statistical summaries connected with spatial areal units, such as countries, states, and counties. Two R packages to create linked micromaps were published in 2015. These are the micromap and micromapST packages. The latter was originally for data index

Ivan Hejný, Giovanni Bonaccolto, Philipp Kremer, Sandra Paterlini

This paper studies Graphical SLOPE for precision matrix estimation, with emphasis on its ability to recover both sparsity and clusters of edges with equal or similar strength. In a fixed-dimensional regime, we establish that the root-$n$ scaled estimation error converges to the unique minimizer of a strictly convex optimization problem defined through the di

Timon Ziegenbein, Maja Stahl, Henning Wachsmuth

Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one's arguments more appropriate for a discussion. Comparing human to LLM-generated edits, however, we observe a mismatch in editing strategies: While LLMs often perform multiple scattered edits and tend to change meaning notably, humans rather enc

Wei Chen, Zhen Liu

This paper presents a pressure-robust and element-wise divergence-free nonconforming finite element method for the Stokes problem on curved domains. The discrete element is constructed by mapping the Fortin-Soulie element from a reference triangle using an isoparametric mapping for the geometry and a Piola transform for the function space. The inf-sup condit

Li Shen, Yan Sun, Dacheng Tao

Federated learning (FL) is a distributed paradigm that coordinates massive local clients to collaboratively train a global model via stage-wise local training processes on the heterogeneous dataset. Previous works have implicitly studied that FL suffers from the ``client-drift'' problem, which is caused by the inconsistent optimum across local clients. Howev

Yunkai Dang, Yizhu Jiang, Yifan Jiang, Qi Fan

Multimodal Large Language Models (MLLMs) suffer from substantial computational overhead due to the high redundancy in visual token sequences. Existing approaches typically address this issue using single-layer Vision Transformer (ViT) features and static pruning strategies. However, such fixed configurations are often brittle under diverse instructions. To o

Yeeun Park, Miqdad Naduthodi, Suryansh Kumar

Marker-based motion capture (MoCap) systems have long been the gold standard for accurate 4D human modeling, yet their reliance on specialized hardware and markers limits scalability and real-world deployment. Advancing reliable markerless 4D human motion capture requires datasets that reflect the complexity of real-world human interactions. Yet, existing be

Frederik Laust Durhuus, Andrea Roberto Insinga, Rasmus Bjørk

The magnetic field from a uniformly magnetised, rectangular prism is known exactly, which is the basis for a large number of micromagnetic simulations. Here we derive an analytical solution for the field from a periodically repeating infinite array of prisms aligned end-to-end, which becomes exact on the center axis in the limit of infinitesimally thin prism

Francis J. Headley, Mahdi RouhbakhshNabati, Henry Harper-Gardner, Daniel Braun

We present a real-time path-integral formulation of the quantum Fisher information for dynamical parameter estimation. For pure states undergoing unitary evolution, we show that the quantum Fisher information can be expressed as a connected symmetrized covariance of a time-integrated action deformation, equivalently as an integrated insertion of $\partial_\l

Myungchul Kim, Kwanyong Park, Junmo Kim, In So Kweon

Existing person search methods assume access to complete visual queries or exhaustive tracking, yet real-world witness accounts are vague, partial, and spread across cameras and time. We introduce ARGOS (Agentic Retrieval with Grounded Observational Search), a benchmark and agent framework that recasts multi-camera person search from one-shot retrieval on a

Subramanya Bhat K. N., Amita Das, V Ravishankar, Bhooshan Paradkar

Yang-Mills theory has extended well beyond its original role in describing the strong force and now emerges as an effective theory in condensed matter, ultracold atomic, and photonic systems. In these systems, the theory has been successful in explaining phenomena such as the spin-Hall effect, spin transport, and controlling the polarisation of light. Moreov

Eleonora Giovannetti

In this work, we address the question about the fate of chaos in the Mixmaster model when we promote the system at a quantum level. We consider Deformed Commutation Relations for the Misner anisotropic variables, whose Deformed Algebras are related to two different Quantum Gravity approaches, i.e. Loop Quantum Gravity and String Theory. Also, this approach n

Mykyta Kliapets, Pablo Huijse, Jeroen Audenaert, Andrew Tkachenko

The PLAnetary Transits and Oscillations of stars (PLATO) mission is expected to launch in January 2027. A total of 8\% of its data rate will be dedicated to complementary science targets selected from approved Guest Observer proposals. We seek to provide an open-source catalogue of variable stars in PLATO's first long-term observing field, LOPS2. We want to

Arya Shah, Kaveri Visavadiya, Manisha Padala

Adversarial robustness is essential for deploying neural networks in safety-critical applications, yet standard evaluation methods either require expensive adversarial attacks or report only a single aggregate score that obscures how robustness is distributed across classes. We introduce the \emph{GF-Score} (GREAT-Fairness Score), a framework that decomposes

Claudio Gutierrez, Daniel Hernández

This text advances the hypothesis that the meaning of the Web as an object of study has diluted as a clear research domain. One example of this phenomenon is the identity crisis of the Web Conference and the International Semantic Web Conference. At its root is the Web's evolution from a focused technological object into a universal digital environment, a tr

D. T. Binh, V. H. Binh, H. T. Hung, Duong Van Loi

We extend the $SU(3)_C \times SU(3)_L \times U(1)_X$ model with neutral leptons (331LHN) by introducing scalar leptoquarks. We determine the particle content of the leptoquark multiplets and their Yukawa interactions with fermions. We find that a singlet leptoquark can fully account for the $4.2\sigma$ discrepancy in the muon anomalous magnetic moment $\Delt

Patrick Adelhardt, Sean R. Muleady, Kai P. Schmidt, Alexey V. Gorshkov

Long-range interactions can fundamentally reshape the low-energy properties of low-dimensional quantum matter, altering both continuous symmetry breaking and topological phenomena. However, their impact on the quantum criticality separating these regimes remains poorly understood. We determine the ground-state phase diagram and critical properties of the spi

Shang-En Tsai, Wei-Cheng Sun

Specular glare on reflective floors, glass boundaries, and glossy indoor surfaces can corrupt active-stereo RGB-D measurements, producing holes and spikes that persist as phantom obstacles in navigation costmaps. This article presents a glare-resilient RGB-D sensor-fusion method based on explicit per-pixel depth reliability. A lightweight Depth Reliability M

T. Camaret Ndir, Marco Reisert, Robin T. Schirrmeister

In-context learning (ICL) enables medical image segmentation models to adapt to new anatomical structures from limited examples, reducing the clinical annotation burden. However, standard ICL methods typically rely on dense, global cross-attention, which scales poorly with image resolution. While recent approaches have introduced localized attention mechanis

Yu Zhou, Mengmou Li, Masaaki Nagahara

In this paper, we develop a scaled gradient-momentum framework for continuous-time optimization that achieves global finite-time convergence. A state-dependent scaling mechanism is introduced to enable classical dynamics, such as Heavy-Ball-type and proportional-integral (PI)-type flows, to attain finite-time convergence. We establish explicit conditions tha

Yiheng Zhao, Jun Yan

Although large language models (LLMs) excel in complex reasoning tasks, they suffer from severe causal hallucination in event causality identification (ECI), particularly in smaller models ($\leq$1.5B parameters). A promising approach to address this issue is to fine-tune them with Chain-of-Thought (CoT) traces. However, there is currently a lack of CoT trac

Arshad Beg, Diarmuid O'Donoghue, Rosemary Monahan

Artificial intelligence systems have achieved remarkable capability in natural language processing, perception and decision-making tasks. However, their behaviour often remains opaque and difficult to verify, limiting their applicability in safety-critical systems. Formal methods provide mathematically rigorous mechanisms for specifying and verifying system

Oscar Martinez Mozos, Virginia Sandulescu, Sally Andrews, David Ellis

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector

Juan-Diego Urbina, Klaus Richter

In quantum systems with a classical limit, advanced semiclassical methods provide the crucial link between phase-space structures, reflecting the distinction between chaotic, mixed or integrable classical dynamics, and the corresponding quantum properties. Well established techniques dealing with ergodic wave interference in the usual semiclassical limit $\h