April 2026 arXiv papers — page 62

Paul Keuren, Marc Ponsen, Robert Ayoub Bagheri

Sentence embedding techniques aim to encode key concepts of a sentence's meaning in a vector space. However, the majority of evaluation approaches for sentence embedding quality rely on the use of additional classifiers or downstream tasks. These additional components make it unclear whether good results stem from the embedding itself or from the classifier'

Simon Jarvers, Orestis Papakyriakopoulos

Under the EU AI Act, translating AI governance requirements into software development practice remains challenging. While AI governance frameworks exist at industry and organizational levels, empirical evidence of team-level implementation is scarce. We address this "Last Mile" Challenge through insider action research embedded within an AI startup. We prese

Sara Koljančić, Polona Oblak

In this paper, we investigate the non-symmetric Strong Spectral Property (nSSP) from a combinatorial perspective. To zero-nonzero patterns of matrices we associate directed graphs and study when they require or allow the nSSP, providing a framework that avoids verifying the nSSP for individual matrices. A new combinatorial method is introduced and used to re

Yichen Wang, Mengyu Duan, Dániel Gerbner, Hilal Hama Karim

Given a hypergraph $F$, what is the largest chromatic number that an $F$-free hypergraph can have? In the case of graphs, this question is easy to answer: the chromatic number is unbounded if $F$ contains a cycle, and the largest chromatic number of $F$-free graphs is $k-1$ if $F$ is a forest on $k$ vertices. The situation is more complicated for hypergraphs

Yuxin Yang, Izzah Machfuudzoh, Qiwen Tan, Takumi Sannomiya

The spin angular momentum (SAM) of light has become a cornerstone of numerous photonic applications, including optical communication and chiral photonics. Because SAM is inherently associated with circularly polarized light (CPL), the ability to modulate CPL in a controlled and efficient manner is essential not only for advancing fundamental studies of light

Fridolin Linder, Thomas Leeper, Daniel Haimovich, Niek Tax

Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is fundamental to science, public health, and online trust and safety. Standard approaches correct for known device error rates but assume these rates remain stable across populations. We show this assumption f

Tengyuan Liang

Treatment effect distributions are not identified without restrictions on the joint distribution of potential outcomes. Existing approaches either impose rank preservation -- a strong assumption -- or derive partial identification bounds that are often wide. We show that a single scalar parameter, rank stickiness, suffices for nonparametric point identificat

Wenrui Liu, Hong Chang, Ruibing Hou, Shiguang Shan

Out-of-Distribution (OOD) detection requires sensitivity to subtle shifts without overreacting to natural In-Distribution (ID) diversity. However, from the viewpoint of detection granularity, global representation inevitably suppress local OOD cues, while patch-based methods are unstable due to entangled spurious-correlation and noise. And neither them is ef

Md. Kamrujjaman, Faizunnesa Khondaker, Alvi Ahmed Sarker, Nuzhat Nuari Khan Rivu

\textbf{Background} Measles has resurged globally in the post-pandemic period as routine immunisation recovery remains below the two-dose threshold required to interrupt transmission. Bangladesh, previously nearing measles--rubella elimination, entered 2026 with widening coverage gaps, depleted vaccine stocks, and increasing numbers of missed children. We co

Luisa Ferrari, Maria Franco Villoria, Garritt L. Page, Alex Laini

Clustering multivariate binary data is of interest in many scientific fields, including ecology, biomedicine, and social policy. Beyond heuristic clustering algorithms, such data can be modelled using multivariate Bernoulli mixture models. Many Bayesian implementations of these models involve a trade-off between computational efficiency and full posterior in

Sakshi Dang, Julia Lieb, Pedro Soto, Alex Sprintson

The matrix completion problem provides a unifying lens through which many fundamental problems in coding theory can be viewed. In this paper, we investigate Locally Recoverable Codes (LRCs) with Maximal Recoverability (MR) and Maximum Distance Profile (MDP) convolutional codes in the framework of matrix completion. In particular, we present techniques that a

Aditya Singh, Joseph Samuel, Chien-chia Liu, Luiza Angheluta

We show that surface waves in a draining-bathtub vortex provide a hydrodynamic realization of both Aharonov-Bohm phase shifts and Lense-Thirring frame dragging within a single system. A static time transformation maps the flat (2+1)-dimensional wave equation onto the convected shallow-water equation, yielding an effective vector potential set by the backgrou

Wenbang Wang, Neng Li, Wei Ren

This paper addresses characterizations of Integral Input-to-State Stability (iISS) for hybrid systems with memory. Based on the Krasovskii approach, a novel Lyapunov characterization of iISS is established to extend the hybrid system theory to the time-delay case. In particular, we introduce the notions of dissipativity, detectability and storage functional

Yan Ning, Xingzhou Chen, Delong Li, Hao Zhang

Wheel-legged robots combine the efficiency of wheeled locomotion with the versatility of legged systems, enabling rapid traversal over both continuous and discrete terrains. However, conventional designs typically employ fixed wheels as feet and limited degrees of freedom (DoFs) at the hips, resulting in reduced stability and mobility during legged locomotio

Nihad Abuawwad, Samir Lounis

Chiral spin textures such as skyrmions have attracted considerable attention due to their nontrivial topology, chirality, stability at the nanoscale, and potential for low-power spintronic devices. The recent discovery of intrinsic magnetism in van der Waals (vdW) materials and the ability to engineer their heterostructures has opened a new platform to study

Utku Erdogan, Gabriel J. Lord, Joaquin Miguez

Particle filters (PFs) are recursive Monte Carlo algorithms for Bayesian tracking and prediction in state space models. This paper addresses continuous-discrete filtering problems, where the hidden state evolves as an It\^o stochastic differential equation (SDE) and observations arrive at discrete times. We propose a novel class of constrained PFs that enfor

Sebastiano A. Piccolo, Andrea Tagarelli

Identifying critical nodes in complex networks is a fundamental task in graph mining. Yet, methods addressing an all-or-nothing coverage mechanics in a bipartite dependency network, a graph with two types of nodes where edges represent dependency relationships across the two groups only, remain largely unexplored. We formalize the CriticalSet problem: given

Nikita Severin, Danil Kartushov, Vladislav Urzhumov, Vladislav Kulikov

Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present opportunities to enhance user understanding with their reasoning capabilities, yet existing integration approaches create prohibitive inferenc

Stephon Alexander, Pisin Chen, Jinglong Liu, Antonino Marciano

Fermion condensate inflation, where inflation emerges from four-fermion interactions induced by spacetime torsion, removes the need for additional scalar fields beyond the Standard Model. In this framework, the fermion field can be decomposed into two distinguished sectors, each giving rise to bound states. After integrating out fermions, the bound fields pl

Wim Nijsink, Bruno Endres Forlin, Amirreza Yousefzadeh, Marco Ottavi

Neuromorphic, or spiking, processors are increasingly being considered for use in harsh, radiation-prone environments such as space and avionics, where energy efficiency and graceful degradation are essential. In this study, we propose and experimentally validate a radiation-testing methodology specifically designed for neuromorphic processors that employ on

Haris Turkmanović, David Vukoje, Aleksandra Lekić, Milan Prokin

The aim of this paper is to demonstrate some interesting and useful approaches for writing a program in the assembly language. In order to demonstrate the possibilities of the assembly language, a project called "Arkanoid" was created. This project is written in assembly language and it presents few interesting algorithms. Assembly language, which is used fo

Ishay Haviv

We study the kernel complexity of constraint satisfaction problems over a finite domain, parameterized by the number of variables, whose constraint language consists of two relations: the non-equality relation and an additional permutation-invariant relation $R$. We establish a conditional lower bound on the kernel size in terms of the largest arity of an OR

Laura Valeria Perez-Herrera, M. J. Garcia-Gonzalez, Karen Lopez-Linares

Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indicators of prognosis and can influence treatment decisions. Common deep learning approaches to determine the predominant pattern rely on patch-level classification or segmentation, requiring extensive annotations. This study proposes an attention-based multiple

Emilie Frost, Astrid Nieße

Applying the concept of controlled self-organization in agent-based Cyber-Physical Energy Systems (CPES) is a promising approach to ensure system robustness. By introducing an observer/controller architecture to the system, this concept allows for self-organization while still enabling intervention when disturbances occur. Thus, it is possible to respond to

Tina Šfiligoj, Oded Cats

In this study, we take a systematic look at the unrealised part of public transport networks (PTNs) with functional connections. We consider their complement graphs and study their structure. The complement graph $\bar G$ of an unweighted graph $G$ is a straightforward concept, yielding a graph on the same set of nodes, and an edge exists in $\bar G$ if and

Arindam Sengupta, Tony Bush, Ben Marner, Jose Miguel Pérez

Low-cost air quality sensors (LCS) provide a practical alternative to expensive regulatory-grade instruments, making dense urban monitoring networks possible. Yet their adoption is limited by calibration challenges, including sensor drift, environmental cross-sensitivity, and variability in performance from device to device. This work presents a deep learnin

Tugal Zhanlav, Lkhamsuren Altangerel, Khuder Otgondorj

In this paper, we propose a class of super-schemes for efficiently solving nonlinear unconstrained optimization problems. The proposed approach introduces two novel choices of step-size parameters, leading to efficient descent directions without requiring second-order information. We develop one-step, two-step, and three-step iterative schemes (denoted by SS

Guojing Li, Zichuan Fu, Junyi Li, Faxue Liu

Span-level skill extraction from job advertisements underpins candidate-job matching and labor-market analytics, yet generative large language models (LLMs) often yield malformed spans, boundary drift, and hallucinations, especially with long-tail terms and cross-domain shift. We present SRICL, an LLM-centric framework that combines semantic retrieval (SR),

Igor F. Herbut, Samson C. H. Ling

The internal low-energy symmetry of the massless Lorentz-invariant Dirac Hamiltonian in $2+1$ dimensions is known to be $O(2N)$, where $N$ is the number of two-component Dirac fermions. Here we point out that there exists an analogous internal symmetry of the single-particle quadratic-band-touching Hamiltonian in two spatial dimensions, and it is the unitary

Mohammed Safi Ur Rahman Khan, Sanjay Suryanarayanan, Tushar Anand, Mitesh M. Khapra

Large Vision-Language Models (VLMs) are increasingly used to evaluate outputs of other models, for image-to-text (I2T) tasks such as visual question answering, and text-to-image (T2I) generation tasks. Despite this growing reliance, the reliability of these Evaluator VLMs remains under explored. In this work, we systematically evaluate the reliability of Eva

Donato Bini, Thibault Damour, Andrea Geralico

We study the quadrupolar part of the gravitational waveform $h_{ij}$ (encoded in the helicity-($-2)$ radiative quadrupole moment $U_2 = \frac{1}{2!} \bar m^{i} \bar m^{j } U_{i j} \in\frac{R}{4G} \bar m^{i} \bar m^{j } h_{i j}\equiv W $) emitted during the scattering of two masses. Working within the Multipolar Post-Minkowskian (MPM) formalism, we compute th

Sebastian Heller, Franz Pedit, Charles Ouyang

We construct the first smooth embedded compact special Legendrian surfaces in \(\mathbb S^5\) of genus greater than one. More precisely, for every sufficiently large integer \(k\), we construct an embedded special Legendrian surface whose conformal structure is the Fermat curve of degree \(k\) and genus \(\tfrac12(k-1)(k-2)\). Our approach combines an elemen

Luis E. Padilla, Tomohiro Harada, Ethan Milligan, David Mulryne

We demonstrate that discrete self-similarity (DSS), originally discovered in the collapse of a massless scalar field in an asymptotically flat system, survives in primordial black hole (PBH) formation within an expanding cosmological background. Using fully relativistic numerical simulations of massless scalar-field collapse in an Friedmann-Lema\^{i}tre-Robe

Meijun Xiong, Zhenguo Shi, Xinyu Zhou, Yuhe Zhang

In the automatic reassembly of fragments acquired using laser scanners to reconstruct objects, a crucial step is the matching of fractured surfaces. In this paper, we propose a novel local descriptor that uses the Gaussian Mixture Model (GMM) to fit the distribution of points, allowing for the description and matching of fractured surfaces of fragments. Our

Shiyan Su, Ruyi Zha, Danli Shi, Hongdong Li

Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from severe artifacts under sparse-view settings. To address this, we propose DiffNR, a novel framework that enhances NR optimization with diffusion priors. At its core is SliceFixer, a single-step diffusion model des

Chien-Chih Chen, Yitian Wang, Emma Nasseri, Yebo Feng

The rapid expansion of blockchain and digital asset ecosystems has intensified the challenge of translating academic research into deployable systems and regulatory frameworks. While advances in cryptography, consensus, digital assets, and governance are substantial, institutional mechanisms that sustain research-to-deployment translation at ecosystem scale

Zuyi Chen, Daniel P. Stark, Charlotte A. Mason, Adele Plat

JWST has revealed a population of super-luminous early galaxies with a volume density in excess of most expectations. The spectra reveal diverse properties: while some reveal strong emission lines characteristic of galaxies in the midst of strong bursts, others show weak emission lines that could reflect old stellar populations, large escape fractions, or po

Marcos Cramer, Tom Friese

ASPIC-style structured argumentation frameworks provide a formal basis for reasoning in artificial intelligence by combining internal argument structure with abstract argumentation semantics. A key challenge in these frameworks is ensuring compliance with five critical rationality postulates: closure, direct consistency, indirect consistency, non-interferenc

Mario Gauvrit

We study bubbling for sequences of Yang-Mills connections on closed four-manifolds and we derive a compatibility of Pohozaev type between the weak limit connection and the bubble formed at a concentration point, involving the Weyl tensor of the background metric. This yields obstruCtions to bubbling extending earlier results of Yin beyond the locally conform

Xinyu Zhang, Boxuan Zhang, Yuchen Wan, Lingling Zhang

While Large Language Models (LLMs) demonstrate remarkable reasoning, complex optimization tasks remain challenging, requiring domain knowledge and robust implementation. However, existing benchmarks focus narrowly on Mathematical Programming and Combinatorial Optimization, hindering comprehensive evaluation. To address this, we introduce OptiVerse, a compreh

Ali Can Günhan, Onur Pusuluk, Thomas Oikonomou, G. Baris Bagci

The generalized second laws of quantum thermodynamics are usually formulated in terms of R\'enyi divergences and the associated family of generalized free energies. In catalytic thermal transformations, this framework typically certifies the existence of a suitable catalyst but does not make the catalytic contribution explicit in the resulting system-level i

Jiaxian Yan, Jintao Zhu, Yuhang Yang, Qi Liu

Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction remains challenging because it requires not only interpreting biochemical semantics distributed across text, tables, and figures, but also reconstructing che

Nikhil Raghav

Speaker diarization (SD) is the task of answering "who spoke when" in a multi-speaker audio stream. Classically, an SD system clusters segments of speech belonging to an individual speaker's identity. Recent years have seen substantial progress in SD through end-to-end neural diarization (EEND) approaches. DiariZen, a hybrid SD pipeline built upon a structur

Manzi Huang, Panu Lahti, Jiang Li, Zhuang Wang

For $0<\delta,\tau<1$ and $1\le s\le \frac{n}{n-\delta}$, we prove that for a given $s$-John domain $\Omega\subset \mathbb{R}^n$, the following Boxing inequality holds for every Lebesgue measurable set $U\subset\Omega$ with $|U|/|\Omega|\le\gamma<1$: \[ \mathcal{H}^{s(n-\delta)}_{\infty}(U\setminus\mathcal{N}_U)\le C(1-\delta)\int_\Omega\int_{|x-y|<\tau\oper

Di Yang, Xinou Xie, Xiuwen Yang, Ming Hu

Software requirement ambiguity is ubiquitous in real-world development, stemming from the inherent imprecision of natural language and the varying interpretations of stakeholders. While Large Language Models (LLMs) have demonstrated impressive capabilities in generating code from precise specifications, such ambiguity poses a significant obstacle to reliable

Gianlorenzo D'Angelo, Riccardo Michielan

We study the efficient generation of random graphs with a prescribed expected degree sequence, focusing on rank-1 inhomogeneous models in which vertices are assigned weights and edges are drawn independently with probabilities proportional to the product of endpoint weights. We adopt a temporal viewpoint, adding edges to the graph one at a time up to a fixed

D. S. Akerib, A. K. Al Musalhi, F. Alder, B. J. Almquist

The radioisotope $^{125}\text{Xe}$ is a short-lived ($T_{1/2}\sim16.9 h$) activation product of the neutron calibrations performed in the LUX-ZEPLIN experiment. Subsequently, $^{125}$Xe decays primarily ($>99\%$) via electron capture, but positron emission has been confirmed by direct measurement to at least the 243 keV level of $^{125}\text{I}$. An addition

Aladin Djuhera, Haris Gacanin, Holger Boche

Recent works have demonstrated that attention-based transformer and large language model (LLM) architectures can achieve strong channel state prediction (CSP) performance by capturing long-range temporal dependencies across channel state information (CSI) sequences. However, these models suffer from quadratic scaling in sequence length, leading to substantia

Thomas Brun, Javier Rial, Lucia Risoli, Johanna Fischer

Miniaturized, ultra-sensitive and easily integrable magnetometers are needed for many applications, like space exploration or health monitoring. Achieving this goal requires a magnetic sensor with high sensitivity and low noise. High sensitivity (>1000 %/mT) can be obtained by integrating high gain permalloy flux concentrators (FC). And reducing the magnetic

Xin-Sheng Gao, Qun Wang, Ya-Xun He, Xing-Jian Liu

The intermediate state of type-I superconductors presents a classic paradigm of modulated pattern formation, arising from the competition between short-range attractive and long-range repulsive vortex-vortex interactions. However, direct visualization and, more importantly, active control over the topology and dynamics of these flux structures have remained

Mario Francisco-Fernández, Andrea Meilán-Vila

Spatial orientation is a fundamental cognitive skill that relies on sensory information to update perceived direction. Understanding how sensory conditions influence directional accuracy is important for both cognitive science and the design of assistive technologies. We analyze experimental data in which blind, low-vision, and sighted participants performed

Shinya Yamada, Natalie Hell, Elisa Costantini, Oluwashina Adegoke

We present the first high-resolution X-ray spectral analysis of Cygnus X-1 using XRISM. The observation was carried out from April 7 to 10, 2024, covering the orbital phase range 0.65--0.17 during its low/hard state. Taking advantage of the exceptional energy resolution of the Resolve instrument, we examined highly ionized iron absorption lines and character

Aleksander Szewczyk, Leonardo Medrano Sandonas, David Bodesheim, Bohayra Mortazavi

Covalent Organic Frameworks (COFs) are versatile two-dimensional (2D) materials for flexible electronics, catalysis, and sensing, owing to their tunable architectures and large surface areas. However, like most materials, COFs inevitably contain synthesis-induced defects, which-similar to graphene-can strongly influence intrinsic properties, such as thermal

Debajyoti Choudhury, Jaydeb Das, Tripurari Srivastava

We study a minimal framework that naturally yields viable Dark Matter, a strong first-order electroweak phase transition and low-scale resonant leptogenesis. Augmenting the Standard Model with three heavy Majorana neutrinos, we study the corresponding neutrino-extended Standard Model Effective Field Theory, including operators upto mass-dimension six. The pu

Keita Fukuyama, Yukiko Mori, Tomohiro Kuroda, Hiroaki Kikuchi

Differential privacy (DP) is a mathematical framework that guarantees individual privacy; however, systematic evaluation of its impact on statistical utility in survival analyses remains limited. In this study, we systematically evaluated the impact of DP mechanisms (Laplace mechanism and Randomized Response) with data-driven clipping bounds on the Cox propo

Yining Xing, Zehong Ke, Yiqian Tu, Zhiyuan Liu

Multi-modal trajectory generation is essential for safe autonomous driving, yet existing diffusion-based planners suffer from high inference latency due to iterative neural function evaluations. This paper presents MISTY (Mixer-based Inference for Single-step Trajectory-drifting Yield), a high-throughput generative motion planner that achieves state-of-the-a

Shu-Xiang Qiao, Ya-Ping Li, Jie Zhang, Yi Wan

These years, kagome materials with 1:1 stoichiometry have garnered increasing attention, among which FeSn, CoSn, and FeGe have been the focus of current studies. However, all of them are antiferromagnetic, thereby hindering the observation of superconductivity and other novel physical properties. Here, we predict a novel 1:1 kagome metal VSn, which is an int

Jorik Jooken, Denys Lohvynov

A $k$-regular graph of girth $g$ is called vertex-girth-regular if every vertex is contained in the same number of cycles of length $g$. For integers $n, k, g$ and $\lambda$, we denote such a graph on $n$ vertices in which every vertex lies on exactly $\lambda$ cycles of length $g$ by a $\text{vgr}(n,k,g,\lambda)$-graph. It is well-known that any vertex-girt

Bereneice Sephton, Rakhi Thomas, Carlo Schiano, Francesco Reda

Interferometry provides highly sensitive access to optical phase and is central to much of modern metrology and phase imaging methods. Conventional implementations, however, often face trade-offs between mechanical stability and experimental or computational complexity. Here, we present a general framework for designing custom interferometers within a single

Ke Ma, Feng Wang, Lihui Lei, Shu Tan

Deep learning-based channel estimation has been recognized as a promising technique for sixth-generation wireless systems. However, most existing approaches rely solely on least-squares estimates obtained from demodulation reference signals, which fail to explicitly exploit channel time-frequency correlation parameters. Inspired by the independent channel pa

Mohan Liyanage, Arnova Abdullah, Eldiyar Zhantileuov, Rolf Schuster

We present a lightweight and interpretable decision framework for dynamic edge server selection in latency-critical applications that explicitly accounts for tail risk and switching stability. Each candidate server is characterised by predictive mean and uncertainty summaries of network latency, which are used to estimate the risk of service-level objective

Minghui Ma, Rui Shi, Shanshan Yang

An operator $T$ in a separable factor $\mathcal{M}$ is said to be irreducible in $\mathcal{M}$ if the von Neumann subalgebra $W^*(T)$ generated by $T$ is an irreducible subfactor of $\mathcal{M}$, i.e., $W^*(T)'\cap\mathcal{M}=\mathbb{C}I$. We say that $T$ is a single generator of $\mathcal{M}$ if $W^*(T)=\mathcal{M}$. In this paper, we study generators of s

Itay Nakash, George Kour, Ateret Anaby-Tavor

Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate success. However, this approach is computationally inefficient, rep

Yanjiao Liu, Jiawei Liu, Xun Gong, Zifei Nie

Large language models (LLMs) have recently demonstrated strong reasoning capabilities and attracted increasing research attention in the field of autonomous driving (AD). However, safe application of LLMs on AD perception and prediction still requires a thorough understanding of both the dynamic traffic agents and the static road infrastructure. To this end,

Yuhan Luo, Tao Chen, Decheng Liu

Nowadays, visual data forgery detection plays an increasingly important role in social and economic security with the rapid development of generative models. Existing face forgery detectors still can't achieve satisfactory performance because of poor generalization ability across datasets. The key factor that led to this phenomenon is the lack of suitable me

Hervé Dutrieux, Robert G. Edwards, Joe Karpie, Cédric Mezrag

We propose a reconstruction of the full $(x, \xi, t)$ dependence of unpolarized isovector proton generalized parton distributions (GPDs) $H^{u-d}$ and $E^{u-d}$ from lattice QCD data in the pseudo-distribution formalism. For the first time, we extract double distributions (DDs) directly from lattice data, enforcing therefore an important property of GPDs lin

Sebastian Celis Sierra, Meruyert Khamitova, Ran Zhao, Sadeed Bin Sayed

A thin-sheet (TS) volume integral equation (VIE) formulation incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) bianisotropic metasurfaces. The metasurface is represented as an equivalent TS, with its constitutive tensors derived from the GSTC susceptibility tensors. Invoking the TS approxi

Jiyan Song, Wenyang Wang, Chengcheng Yan, Zhiquan Han

In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast, combination drug therapies can significantly improve therapeutic outcomes through synergistic effects. However, experimentally validating all possible drug combinations is prohibitively expensive, underscorin

Simon Schäfer, Lucas Hegerath, Marius Molz, Massimo Marcon

Infrastructure-based localization enhances road safety and traffic management by providing state estimates of road users. Development is hindered by fragmented, application-specific stacks that tightly couple perception, tracking, and middleware. We introduce Ufil, a Unified Framework for Infrastructure-Based Localization with a standardized object model and

Fariz Ikhwantri, Dusica Marijan

Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one regulation often fail to generalise to others. This limitation underscores the need for principled methods to improve cross-domain transfer. We study data selection as a strategy to mitigate negative transfe

Arnaud Spiwack, Csongor Kiss, Jean-Philippe Bernardy, Nicolas Wu

Linear constraints are the linear counterpart of Haskell's class constraints. Linearly typed parameters allow the programmer to control resources such as file handles and manually managed memory as linear arguments. Indeed, a linear type system can verify that these resources are used safely. However, writing code with explicit linear arguments requires bure

Junyan Luo, Peipeng Yu, Jianwei Fei, Shiya Zeng

Deepfake technologies have rapidly advanced with modern generative AI, and face swapping in particular poses serious threats to privacy and digital security. Existing proactive defenses mostly rely on pixel-level perturbations, which are ineffective against contemporary swapping models that extract robust high-level identity embeddings. We propose ID-Eraser,

Sukesh Subaharan

Standard reinforcement learning (RL) optimizes policies for reward but imposes few constraints on how decisions evolve over time. As a result, policies may achieve high performance while exhibiting temporally incoherent behavior such as abrupt confidence shifts, oscillations, or degenerate inactivity. We introduce Dynamical Prior Reinforcement Learning (DP-R

Michal Valko, Hamed Valizadegan, Branislav Kveton, Gregory F. Cooper

Timely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response, such as the omission of an important lab test. We develop a new non-parametric approach for conditional anomaly detection based on the soft h

Michal Valko, Branislav Kveton, Hamed Valizadegan, Gregory F. Cooper

In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further r

Chentao Li, Zirui Gao, Mingze Gao, Yinglian Ren

Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite advancements in Multimodal Large Language Models (MLLMs), current systems often fail to precisely ground the spatial semantics of pointing. Instead, they rely on spurious correlations with visual proximity o

K. D. Temmink, S. Justham, O. R. Pols

Non-conservative mass transfer plays a central role in close-binary evolution, yet its effects on mass-transfer stability are uncertain. One widely adopted prescription, isotropic re-emission, is often assumed to promote stability compared to conservative mass transfer. We investigate the impact of isotropic re-emission on the stability of mass transfer in b

Jun Ye

Closed-loop superconducting-qubit calibration has matured into DAG-orchestrated protocol chains, yet published frameworks treat the bath via a Markovian master equation or a phenomenological likelihood, absorbing bath structure into fit residuals instead of reporting it as a diagnostic. We integrate a QuTiP 5.x hierarchical-equations-of-motion (HEOM) solver

Heng Yang

We propose a sampling-based framework for finite-horizon trajectory and policy optimization under differentiable dynamics by casting controller design as inference. Specifically, we minimize a KL-regularized expected trajectory cost, which yields an optimal "Boltzmann-tilted" distribution over controller parameters that concentrates on low-cost solutions as

Vincent Freiberger

The current "notice and consent" paradigm is broken: consent dialogues are often manipulative, and users cannot realistically read or understand every privacy policy. While recent LLM-based tools empower users seeking active control, many with limited time or motivation prefer full automation. However, fully autonomous solutions risk hallucinations and opaqu

Haowei Sun, Kai Zhou, Hao Gao, Shiteng Zhang

Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space, which is critical for applications like drone navigation and security surveillance. However, it faces two key bottlenecks in real-world deployment: confusion from visually similar distractors caused by insufficient instance-level discrimination and severe failure under occlu

Fernando Mário de Oliveira Filho, Andreas Spomer, Frank Vallentin

We determine putative optimal packings of regular spherical polygons via optimization on smooth manifolds. For several cases, we establish maximality by extending the Lov\'asz theta number to Cayley graphs on the special orthogonal group ${\rm SO}(3)$. To this end, we introduce an algebraic criterion characterizing when congruent regular spherical polygons h

Yixuan Zhu, Shilin Ma, Haolin Wang, Ao Li

Recent advancements in visual autoregressive models (VAR) have demonstrated their effectiveness in image generation, highlighting their potential for real-world image super-resolution (Real-ISR). However, adapting VAR for ISR presents critical challenges. The next-scale prediction mechanism, constrained by causal attention, fails to fully exploit global low-

Ivan Borodii, Halyna Osukhivska

The paper presents a study of the efficiency of loading and storing data in the three most common Data Lakehouse systems, including Apache Hudi, Apache Iceberg, and Delta Lake, using Apache Spark as a distributed data processing platform. The study analyzes the behavior of each system when processing structured (CSV) and semi-structured (JSON) data of differ

Umut Gürsoy, Pedro Vicente Marto, Edwan Préau

We construct asymptotically AdS$_5$ black brane solutions in a theory of gravity with an infinite series of curvature corrections. The action is based on an $O(d,d)$ symmetric ansatz which has been argued to describe the classical NSNS sector of string theories. We find that, for this general class of theories, the singularity behind the horizon is not resol

Andrew Shin

We present AI-Gram, a fully deployed, continuously operating social platform where every participant is an autonomous LLM-driven agent generating and responding to visual content. Unlike prior multi-agent simulations, AI-Gram operates as a live, AI-native social network with genuine visual perception: agents observe each other's images, generate new images i

Yuyang Zhang, Zhuoya Zhu, Xin Zeng, Shuai Zhang

Exciton-polaritons provide a great platform for developing ultrafast all-optical logic gates for quantum and optical chips. However, progress toward practical polariton logic remains limited due to incomplete logical functionality on a single device. Herein, we present a single-device perovskite polariton platform enabling reconfigurable, ultrafast logic gat

Yuehan Zhu, Jingqi Zhao, Jiawen Zhao, Xudong Mao

Long-form video understanding remains fundamentally challenged by pervasive spatiotemporal redundancy and intricate narrative dependencies that span extended temporal horizons. While recent structured representations compress visual information effectively, they frequently sacrifice temporal coherence, which is critical for causal reasoning. Meanwhile, exist

Yisheng He, Steven Hoi

We introduce MeshLAM, a feed-forward framework for one-shot animatable mesh head reconstruction that generates high-fidelity, animatable 3D head avatars from a single image. Unlike previous work that relies on time-consuming test-time optimization or extensive multi-view data, our method produces complete mesh representations with inherent animatability from

Shurui Wang, Yuhe Zhang, Ruizhe Guo, Yaning Zhang

The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby presenting significant challenges in point cloud processing. Fast neighboring points search is one of the most common problems, which is frequently used in model reconstruction, classification, retrieval and fe

Lukas Müllender, Berk Hess, Erik Lindahl

Neural network potentials (NNPs) are rapidly changing the landscape of state-of-the-art molecular dynamics (MD) simulations. To make full use of this development, the community needs flexible, easy-to-use interfaces firmly integrated with existing methodologies. To address this, we here present an interface for hybrid machine learning/molecular mechanics (ML

R. Zamanov, L. Dankova, M. Minev, D. Boneva

We present optical B and V band photometry of GP Com - an ultracompact binary consisting of an accreting white dwarf and helium secondary component. Our data set contains 7.7 hours observations in V band with the 2.0m telescope and 2.9 hours simultaneous observations in B and V bands with the 1.5m telescope of the Rozhen National Astronomical Obsevatory, Bul

Yongliang Song

White-light flares (WLFs) are usually tend to be those very large flares. Nevertheless, several small and compact WLFs have been reported and thought to be produced by low-height magnetic reconnection. However, whether low-height magnetic reconnection can efficiently produce WLFs remains unclear. For the first time, we conduct a statistical study of the WL e

Martin Hendrick, Maximilian Trique, Gabriele Manoli

Urban expansion fronts display a robust local roughness exponent together with strongly dispersed growth and nonuniversal dynamic exponents. We show that this coexistence can arise from a disorder-controlled crossover in projected-front growth. Introducing a minimal Eden model, in which geographic constraints act as quenched dilution and coalescence as quenc

Willie Kouam, Stefan Rass, Zahra Seyedi, Shahzad Ahmad

Similar to a strategic interaction between rational and intelligent agents, cryptography problems can be examined through the prism of game theory. In this setting, the agent aiming to protect a message is called the defender, while the one attempting to decrypt it, generally for malicious purposes, is the attacker. To strengthen security in cryptography, va

Jingfang Li, Haoran Zhu, Wen Yang, Jinrui Zhang

Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small objects in such vast scenes presents a critical dilemma: retaining the original resolution for small objects causes prohibitive memory bottlenecks. Conversely, conventional compromises like image downsampling o

Dong-Hao Guan, Licheng Wang, Yuan Zhou, Ai-Lei He

We develop a general algebraic scheme to decompose fractional quantum Hall (FQH) wave functions based on the operator contraction multiplication. By introducing fermionic and bosonic operators and establishing three fundamental contraction rules, we achieve an exact decomposition of Laughlin states. This approach naturally extends to multi-component systems

Sebastian Lehner, Alejandro Lopez-Lira

Frozen large language model (LLM) checkpoints extract information from pre-cutoff public text that is associated with future fundamentals and equity returns beyond standard contemporaneous valuation measures. Because each frozen checkpoint has a fixed knowledge cutoff, it can be interpreted as a compressed representation of publicly available textual informa

Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko

In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over time. This decay is either caused by the actions executed in the past (e.g., a user may get bored when songs of the same genre are recommended over and over) or by an external factor (e.g., content becomes outdate

James Hipperson, Jonathan Hargreaves, Trevor Cox

Engineering structures are increasingly designed using numerical optimisation. However, traditional optimisation methods can be challenging with multiple objectives and many parameters. In machine learning, stable training of artificial neural networks with millions or billions of parameters is achieved using automatic differentiation frameworks such as JAX