March 2026 arXiv papers — page 91

Dimitra Pinotsi, Michael A. Zazanis

We consider $M/G/\infty$ queues with gated service and obtain results on the distribution of the stage length and the number of customers served in a stage when the system is stationary. The stage length density is expressed as an infinite series of terms, involving the solution of an infinite system of linear equations. The convergence of a sequence of solu

Alejandro Morales-Hernández, Fabrizio De Caroa, Gian Marco Paldino, Pascal Tribel

Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs). These early warnings enable the timely initiation of more detailed system stability assessments and preventi

Wolfgang Hoenig, Christoph Scherer, Khaled Wahba

We describe a novel masters-level projects class that teaches robotics along the traditional robotics pipeline (dynamics, state estimation, controls, planning). One key motivational part is that students have to directly apply the algorithms they learn on a highly constrained compute platform, effectively making a robot fly. We teach nonlinear algorithms as

Lewis Howell, Manisha Waterston, Tze Min Wah, James H. Chandler

Three-dimensional (3D) Ultrasound (US) can facilitate diagnosis, treatment planning, and image-guided therapy. However, current studies rarely provide a comprehensive evaluation of volumetric accuracy and reproducibility, highlighting the need for robust Quality Assurance (QA) frameworks, particularly for tracked 3D US reconstruction using freehand or roboti

Pritom Gogoi, Faris Janjoš, Bin Yang, Andreas Look

Online map generation and trajectory prediction are critical components of the autonomous driving perception-prediction-planning pipeline. While modern vectorized mapping models achieve high geometric accuracy, they typically treat map estimation as a deterministic task, discarding structural uncertainty. Existing probabilistic approaches often rely on diago

Puskal Khadka, KC Santosh

State Space Models (SSMs), especially recent Mamba architecture, have achieved remarkable success in sequence modeling tasks. However, extending SSMs to computer vision remains challenging due to the non-sequential structure of visual data and its complex 2D spatial dependencies. Although several early studies have explored adapting selective SSMs for vision

John Mark P. Martirez

The ST1 diamond color center was experimentally demonstrated to involve a substitutional oxygen atom (O$_C$) and carbon vacancy (V$_C$), has a spin singlet ground-state, and a metastable electron spin ancilla: a triplet. ST1's structure was left unsolved for more than a decade. With embedded multiconfigurational quantum mechanical theory, we investigate O$_C

Shuai Zeng

This paper proposes a hybrid quantum optimization framework for large-scale antenna-array beamforming with jointly optimized discrete phases and continuous amplitudes. The method combines quantum-inspired search with classical gradient refinement to handle mixed discrete-continuous variables efficiently. For phase optimization, a Gray-code and odd-combinatio

Pier Giovanni Bissiri, Chris Holmes, Stephen G. Walker

This paper is concerned with the construction of prior free posterior distributions which rely on the use of one step ahead predictive distribution functions. These are typically more straightforward to motivate than prior distributions. Recent interest has been with Hill's $A_n$ prediction model through what has become known as conformal prediction. This mo

Konstantinos Tsirkas, Leda Wang, Ilias Zadik

Over the last decades, two distinct approaches have been instrumental to our understanding of the computational complexity of statistical estimation. The statistical physics literature predicts algorithmic hardness through local stability and monotonicity properties of the Franz--Parisi (FP) potential \cite{franz1995recipes,franz1997phase}, while the mathema

Tiffany Cai, Philip Greengard, Ben Goodrich, Andrew Gelman

Bayesian inference is often implemented using approximations, which can yield interval estimates that are too narrow, not fully capturing the uncertainty in the posterior distribution. We address the question of how to adjust these approximate posteriors so that they appropriately capture uncertainty. vWe introduce two methods that extend simulation-based ca

Ipek Kuvvetli, Christofer Sundström, Sogol Kharrazi, Erik Frisk

This paper presents a comparative optimization framework for smart charging of electrified vehicle fleets. Using heuristic sequential dynamic programming (SeqDP), the framework minimizes electricity costs while adhering to constraints related to the power grid, charging infrastructure, vehicle availability, and simple considerations of battery aging. Based o

K. Chilikin

A new library VecAmpFit for multidimensional amplitude analyses in high-energy physics has been developed for an ongoing amplitude analysis at Belle II experiment. It includes a fitter performing likelihood calculation and explicitly-vectorized subprograms for amplitude implementation. The fitter supports explicit gradient calculation and simultaneous fittin

Boyi Wang, Patrick Pietzonka, Frank Jülicher

Chiral active matter, which breaks both parity symmetry and time-reversal symmetry, is ubiquitous in living systems. Here, we introduce a minimal two-dimensional chiral active lattice gas by incorporating stochastic, biased local rotations. At low temperatures, the system coarsens into condensates with chiral orientations and faceted, crystal-like shapes. Th

Hugo Cazaux, Ralph Rudd, Hlynur Stefánsson, Sverrir Ólafsson

This chapter presents three major reinforcement learning algorithms used for fine-tuning financial forecasters. We propose a clear implementation plan for backpropagating the loss of a reinforcement learning task to a model trained using supervised learning, and compare the performance before and after the fine-tuning. We find an increase in performance afte

Peiying Zhu, Sidi Chang

When a traveler asks an AI search engine to recommend a hotel, which sources get cited -- and does query framing matter? We audit 1,357 grounding citations from Google Gemini across 156 hotel queries in Tokyo and document a systematic pattern we call the Intent-Source Divide. Experiential queries draw 55.9% of their citations from non-OTA sources, compared t

Junhoo Lee, Mijin Koo, Nojun Kwak

Text-to-image models are commercially valuable assets often distributed under restrictive licenses, but such licenses are enforceable only when violations can be detected. Existing methods require pre-deployment watermarking or internal model access, which are unavailable in commercial API deployments. We present Compositional Semantic Fingerprinting (CSF),

Amanda Redlich

This paper analyzes a variation on the well-known "power of two choices" allocation algorithms. Classically, the smallest of $d$ randomly-chosen options is selected. We investigate what happens when the largest of $d$ randomly-chosen options is selected. This process generates a power-law-like distribution: the $i^{th}$-smallest value scales with $i^{d-1}$,

Weidong Bao, Yilin Wang, Ruyu Gao, Fangling Leng

Knowledge Graphs (KGs) are foundational to applications such as search, question answering, and recommendation. Conventional knowledge graph construction methods are predominantly static, rely ing on a single-step construction from a fixed corpus with a prede f ined schema. However, such methods are suboptimal for real-world sce narios where data arrives dyn

L. de A. Gurgel, J. M. de Araújo, L. D. Machado, P. D. S. de Lima

Accurately calculating time delays between signals is pivotal in many modern physics applications. One approach to estimating these delays is computing the cross-spectrum in the time-frequency domain. Linear time-frequency representations, such as the continuous wavelet transform (CWT), are widely used to construct these cross-spectra. However, it is well kn

A. Kovalenko

This review examines multiscale modelling approaches for cellulose nanocrystals (CNCs) and lignocellulosic plant cell walls, with a focus on hemicellulose and lignin interactions in aqueous environments. The three-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) is highlighted as a powerful molecular solvation theor

Xiu Guan, Mingmin Zheng, Dragan Gašević, Wenxin Guo

Artificial intelligence (AI) is increasingly embedded in vocational education systems, yet empirical evidence linking institutional AI readiness to student learning outcomes remains limited. This study develops and tests a 2-2-1 cross-level mediation framework examining how school-level AI readiness is associated with student AI literacy through aggregated t

Yichen Fan, Weitao Yang

The $\Delta$SCF DFT approach defines the system energy as a function of orbital occupancy. Inspired by Landau Fermi liquid theory, we develop an occupancy extrapolation (OE) method that captures excited-state energies via a Taylor expansion of the energy with respect to occupation fluctuation from a reference state. OE retains the physics of $\Delta$SCF whil

Manoy T. Trip

We study minimal del Pezzo surfaces of degree 1 with a conic bundle over a finite field $\mathbb{F}_q$ according to the action of the absolute Galois group on the singular fibers (which is known as their type). We give a lower bound on the size of the field over which they exist, and determine values of $q$ for which certain types cannot exist. In particular

Matt McClinton

In combinatorial representation theory, Kostant's weight multiplicity formula $m(\lambda,\mu)$ is a tool that provides a means of determining the multiplicity of a weight $\mu$ in the adjoint representation of a simple Lie algebra $\mathfrak{g}$, and in this work we consider the case of $\mathfrak{g}=\mathfrak{sl}_{r+1}(\mathbb{C})$. In practice, performing

Z. Štefanič, B. Hribar-Lee

Protein conformational stability and function depend on non-covalent interactions that are strongly influenced by the surrounding environment. To explore protein properties, amino acids are often utilized as model systems. In this study, we determined the densities of seven $\alpha$-amino acids in aqueous solutions between 278.15 K and 308.15 K and calculate

Ondřej Zindulka, Piotr Nowakowski

Let $S$ be a family of sequences of positive numbers that decrease to 0, let $X$ be a metric space and $A \subset X$. $A$ is said to be $S$-dominated if, for every $s\in S$, a countable cover $\{E_n\}$ of $E$ can be found such that $diam E_n < s_n$ for all $n$. We examine the family of all $S$-dominated sets, denoted by $\mathcal{D}(S)$. In particular, we ex

Daniel J. Long, Edmund Tarleton, Alan C. F. Cocks, Felix Hofmann

Thermomigration is the driving force for hydrogen transport due to a temperature gradient. It can compete with hydrogen transport induced by stress gradients. While stress-driven hydrogen migration is well established, thermomigration remains comparatively underexplored, largely due to limited mechanistic understanding and a scarcity of experimental data. In

Salmane Naoumi, Mehdi Bennis, Marwa Chafii

We introduce a self-supervised framework for learning predictive and structured representations of wireless channels by modeling the temporal evolution of channel state information (CSI) in a compact latent space. Our method casts the problem as a world modeling task and leverages the Joint Embedding Predictive Architecture (JEPA) to learn action-conditioned

Francisco Albergaria, Francisco J. Botella, G. C. Branco, José Filipe Bastos

We deduce pertinent relations between the elements of the CKM matrix, and find that not all of these are totally compatible with experiment and/or the assumption of the $3 \times 3$ unitarity. We identify complex phases in the CKM-elements which may signal deviations from unitary (DU). We focus on DUs induced by VLQ-singlets, and the possibility of having si

Wenjian Zhang, Kongcheng Zhang, Jiaxin Qi, Baisheng Lai

Reinforcement Learning (RL) with rubric-based rewards has recently shown remarkable progress in enhancing general reasoning capabilities of Large Language Models (LLMs), yet still suffers from ineffective exploration confined to curent policy distribution. In fact, RL optimization can be viewed as steering the policy toward an ideal distribution that maximiz

Jan Emily Mangulabnan, Akshat Chauhan, Laura Fleig, Lalithkumar Seenivasan

In endoscopic surgery, surgeons continuously locate the endoscopic view relative to the anatomy by interpreting the evolving visual appearance of the intraoperative scene in the context of their prior knowledge. Vision-based navigation systems seek to replicate this capability by recovering camera pose directly from endoscopic video, but most approaches do n

Yiting Li, Michael R. Meyer, Skylar D&#39;Angiolillo, Stephen R. Kane

At a distance of 5.1 pc, the 70 Oph AB binary star system is one of the most favorable targets for future direct imaging and astrometry missions surveying mature, terrestrial planets. We present new radial velocities (RVs) obtained with the Planet Finder Spectrograph (PFS) on the 6.5\,m Magellan II Clay Telescope in Chile. We collected 499 measurements of 70

Ł. Baran, D. Tarasewicz, W. Rżysko

Computer simulations are employed to investigate the adsorption mechanisms of ethane on both homogeneous and inhomogeneous substrates. For homogeneous surfaces, the full range of surface phase transitions - from incomplete to complete wetting - can be accessed by tuning the strength of the surface potential. The resulting layering transition temperatures sho

Jianan Chen, Xiaoxue Gao, Tatsuya Kawahara, Nancy F. Chen

Large language models (LLMs) have driven substantial advances in speech language models (SpeechLMs), yielding strong performance in automatic speech recognition (ASR) under high-resource conditions. However, existing benchmarks predominantly focus on high-resource languages, leaving the ASR behavior of SpeechLMs in low-resource languages insufficiently under

Enrique P. Cital, Viktor Holubec

Thermodynamic uncertainty relations (TURs) impose a universal trade-off between current precision and entropy production in autonomous steady states, constraining in particular the power, efficiency, and constancy of heat engines. We demonstrate strong violations of the long-time TUR in a minimal autonomous heat engine composed of a discrete ratchet generati

M. Aguilar, L. Pusztai, O. Pizio

We explore the pressure dependence of a set of properties of simple monohydric alcohols, namely of methanol, ethanol and 1-propanol, by using isobaric-isothermal molecular dynamics computer simulations. A recently proposed united atom, non-polarizable force field for each of alcohols [V. Garc\'{i}a-Melgarejo et al., J. Mol. Liq., 323, 114576 (2021)] is appli

Christian Döding

We study energy minimizers of the Ginzburg-Landau (GL) free energy, a fundamental model of superconductivity. We address the high-$κ$ regime, the regime of a large GL parameter, in which energy minimizers exhibit vortex structures whose finite element approximations require a fine mesh resolution. This difficulty is reflected in the error analysis of discret

Samuel González-Castillo, Joon Hyung Lee, Alfons Laarman

We study PRODSAT-QSAT($k$): given rank-one $k$-local projectors, determine whether a quantum $k$-SAT instance admits a satisfying product state. We present a CDCL-style refutation framework that searches a finite partition of each qubit's Bloch sphere while a sound theory solver checks region feasibility using a geometric overapproximation of the projection

Nikita Zeulin, Olga Galinina, Nageen Himayat, Sergey Andreev

In the Industrial Internet of Things (IIoT) systems, edge devices often operate under strict constraints in memory, compute capability, and wireless bandwidth. These limitations challenge the deployment of advanced data analytics tasks, such as predictive and prescriptive maintenance. In this work, we explore hyperdimensional computing (HDC) as a lightweight

Henry J. Kobs

Continual learning methods usually preserve old behavior by regularizing parameters, matching old outputs, or replaying previous examples. These strategies can reduce forgetting, but they do not directly specify how the latent representation should evolve. We study a narrower geometric alternative for the regime where old and new data should remain on the sa

Kaushiki Mukherjee

In the last decade research of quantum nonlocality has moved beyond the regime of standard Bell nonlocality to consider network-based experimental set-ups involving multiple independent sources. Notion of full network nonlocality has emerged as some truly network phenomena that cannot be realized in traditional Bell experiments. Present work manifests utilit

Saron Samuel, Andrew Yates, Dawn Lawrie, Ian Soboroff

We wish to measure the information coverage of an ad hoc retrieval algorithm, that is, how much of the range of available relevant information is covered by the search results. Information coverage is a central aspect for retrieval, especially when the retrieval system is integrated with generative models in a retrieval-augmented generation (RAG) system. The

Zachary Burr, Mario Damiano, Vincent Kofman, Renyu Hu

The detection and characterization of potentially habitable exoplanets is one of the chief goals of astrophysics for the coming decades. Imaging in reflected light is well suited for characterizing Earth-like planets, as much can be learned about these planets in this wavelength range (i.e., ~0.3-2 {\mu}m). Several studies have been conducted to determine th

T. Staszewski, M. Borówko

We study the behavior of aqueous surfactant solutions in the bulk phase and in slit-like pores by molecular dynamics. Adsorption and self-assembly of nonionic surfactants C$_7$E$_3$ that mimic alkyl poly(ethylene oxide) molecules are investigated. We consider pores with the same walls and Janus-like slits. The individual walls are inert, hydrophilic, or hydr

Tom Colin, Sashwat Tanay, Laura Bernard

Recent gravitational wave (GW) detections showing signatures of eccentricity and spin precession underscore the need to model binary black holes (BBHs) possessing these features simultaneously. Most efforts over the past fifteen years to model spinning BBHs and their corresponding GWs have relied on heuristically twisting waveforms from nonprecessing systems

Søren Fuglede Jørgensen, Rafael Emilio Barfknecht, Patrick Ettenhuber, Nikolaj Thomas Zinner

Accurately estimating expectation values of quantum observables with as few measurements as possible is crucial to many quantum computing applications. We introduce a framework that covers many of existing measurement strategies and introduce heuristics that can be used to enhance randomized schemes, including those based on Pauli grouping with inverse proba

Maximiliano Armesto, Christophe Kolb

Evidence on AI in software engineering still leans heavily toward individual task completion, while evidence on team-level delivery remains scarce. We report a retrospective longitudinal field study of Chiron, an industrial platform that coordinates humans and AI agents across four delivery stages: analysis, planning, implementation, and validation. The stud

Andreas Katsanikakis, Nikolaos Bekiaris-Liberis, Delphine Bresch-Pietri

We develop a predictor-feedback control design for a class of linear systems with state-dependent switching. The main ingredient of our design is a novel construction of an exact predictor state. Such a construction is possible as for a given, state-dependent switching rule, an implementable formula for the predictor state can be derived in a way analogous t

M. Aguilar, E. Núñez-Rojas, O. Pizio

We explore the dependence of a wide set of properties of monohydric alcohols on temperature by using the isobaric-isothermal molecular dynamics computer simulations. Namely, methanol (MeOH), ethanol (EtOH) and 1-propanol (PrOH) alcohols are studied. The recently proposed united atom, non-polarizable force field for each of alcohols [V. Garc\'{i}a-Melgarejo e

Panagiota Birmpa, Eric Joseph Hall

We study adversarial learning when the target distribution factorizes according to a known Bayesian network. For interpolative divergences, including $(f,\Gamma)$-divergences, we prove a new infimal subadditivity principle showing that, under suitable conditions, a global variational discrepancy is controlled by an average of family-level discrepancies align

Natacha Kuete Meli, Jovita Lukasik, Vladislav Golyanik, Michael Moeller

We introduce layered Quantum Architecture Search (layered-QAS), a strategy inspired by classical network morphism that designs Parametrised Quantum Circuit (PQC) architectures by progressively growing and adapting them. PQCs offer strong expressiveness with relatively few parameters, yet they lack standard architectural layers (e.g., convolution, attention)

A. Patrykiejew

Symmetric mixtures characterized by high negative geometric and energetic non-additivity do not exhibit phase separation in the bulk. However, the phase separation occurs when such mixtures are confined in slit pores with selective walls. It is demonstrated that the wall selectivity affects the pore filling. When the difference of the interaction energies be

Anand Choudhary, Xiaowu Sun, Thabo Mahendiran, Ortal Senouf

Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains subjective and prone to intra- and inter-operator variability. In this work, we introduce ODySSeI: an Open-source end-to-end framework for automated Detection, Segmentation, and Severity estimation of lesions in

Bo Yuan, Hexuan Deng, Xuebo Liu, Min Zhang

Knowledge graph question answering (KGQA) is a promising approach for mitigating LLM hallucination by grounding reasoning in structured and verifiable knowledge graphs. Existing approaches fall into two paradigms: retrieval-based methods utilize small specialized models, which are efficient but often produce unreachable paths and miss implicit constraints, w

Tianling Liu, Hongying Liu, Fanhua Shang, Lequan Yu

In clinical practice, crossmodal information including medical images and tabular data is essential for disease diagnosis. There exists a significant modality gap between these data types, which obstructs advancements in crossmodal diagnostic accuracy. Most existing crossmodal learning (CML) methods primarily focus on exploring relationships among high-level

Peng Yang, Li Wang, Ying Yuan

Recently, the U.S. Food and Drug Administration (FDA) released draft guidance \citep{FDA2026} signaling a paradigm shift that facilitates the use of Bayesian methodology as the primary analysis and decision framework for drug approval. The cornerstone and fundamental challenge of this framework is the specification and calibration of Bayesian success criteri

Yun Chen, Moyu Zhang, Jinxin Hu, Yu Zhang

Neural Architecture Search (NAS) deployment in industrial production systems faces a fundamental validation bottleneck: verifying a single candidate architecture pi requires evaluating the deployed ensemble of M models, incurring prohibitive O(M) computational cost per candidate. This cost barrier severely limits architecture iteration frequency in real-worl

Francisco Rego

This paper addresses the synthesis of an optimal fixed-gain distributed observer for discrete-time linear systems over wireless sensor networks. The proposed approach targets the steady-state estimation regime and computes fixed observer gains offline from the asymptotic error covariance of the global distributed BLUE estimator. Each node then runs a local o

Zheng Gao, Debin Meng, Yunqi Miao, Zhensong Zhang

Current diffusion-based makeup transfer methods commonly use the makeup information encoded by off-the-shelf foundation models (e.g., CLIP) as condition to preserve the makeup style of reference image in the generation. Although effective, these works mainly have two limitations: (1) foundation models pre-trained for generic tasks struggle to capture makeup

Hong-Bae Jeon, Kai-Kit Wong, Chan-Byoung Chae

This paper introduces a unified analytical and optimization framework for fluid antenna system-active reconfigurable intelligent surface (FAS-ARIS) communications in 6G. By combining the port reconfigurability of FAS with the signal amplification of ARIS, the proposed design enables more flexible control of the propagation environment and enhanced link relia

Assem Afanah, Bernd Rosenow

We analyze the soft committee machine with Rectified Linear Unit (ReLU) activation by means of the replica method. In a realizable teacher--student setting, we compute the quenched free energy within a replica-symmetric ansatz and obtain the typical generalization behavior from the saddle-point equations for the macroscopic order parameters. The system exhib

Renjith Nelson Joseph

Modern web test automation frameworks rely heavily on CSS selectors, XPath expressions, and visible text labels to locate UI elements. These locators are inherently brittle -- when web applications update their DOM structure or class names, test suites fail at scale. Existing self-healing approaches increasingly delegate element discovery to Large Language M

Leonardo Kuffo, Sven Hepkema, Peter Boncz

We present SuperKMeans: a k-means variant designed for clustering collections of high-dimensional vector embeddings. SuperKMeans' clustering is up to 7x faster than FAISS and Scikit-Learn on modern CPUs and up to 4x faster than cuVS on GPUs (Figure 1), while maintaining the quality of the resulting centroids for vector similarity search tasks. SuperKMeans ac

Carsten van de Bruck, C. P. Burgess, Adam Smith

Mechanisms that dynamically relax the vacuum energy offer a concrete way to approach the cosmological constant problem, but because relaxation is not confined to the vacuum energy alone it can have consequences for the rest of low-energy physics. We explore this issue using the recently proposed 'yoga' relaxation models as an explicit framework and show how

Yang Zhong, Xiwen Li, Xingao Gong, Hongjun Xiang

Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern physics in polar crystals and heterostructures. We derive closed-form long-range Hamiltonian matrix elements in a nonorthogonal atomic-orbital basis through variational decomposition

Cédric Ledoux, Fernanda Muñoz-Olivares, L. Felipe Barrientos, Nicolas Tejos

We present the first spatially resolved characterisation of the cool intragroup medium (IGrM) in a spectroscopically confirmed galaxy group at z=1.167. Using 30 independent sightlines towards the gravitationally lensed galaxy SGAS J0033+02, we combine background light from an extended gravitational arc and various sources in the field to map the distribution

Haoyue Liu, Jinghan Xu, Luxin Feng, Hanyu Zhou

High-quality imaging of dynamic scenes in extremely low-light conditions is highly challenging. Photon scarcity induces severe noise and texture loss, causing significant image degradation. Event cameras, featuring a high dynamic range (120 dB) and high sensitivity to motion, serve as powerful complements to conventional cameras by offering crucial cues for

Yuxuan Zhu, Tengjun Jin, Yoojin Choi, Daniel Kang

Translating natural language questions to SQL queries (Text-to-SQL) is a long-standing problem in database research. Recent efforts have focused on improving accuracy by building increasingly complex multi-stage large LLM pipelines, layering task decomposition, schema linking, and model-based query selection on top of an LLM. Despite this growing complexity,

Yifan He, David Martens

Explainable AI (XAI) research has experienced substantial growth in recent years. Existing XAI methods, however, have been criticized for being technical and expert-oriented, motivating the development of more interpretable and accessible explanations. In response, large language model (LLM)-generated XAI narratives have been proposed as a promising approach

Jeremy Wood Natalie Cummins

In this work, we used numerical integration of the 4-body problem to study 3-body resonance chains (two planets and an asteroid in the innermost orbit) as a possible mechanism for white dwarf pollution. Two 3-body resonance chains were selected for study: the 6:3:2 and the 4:2:1. Asteroids in both a dynamically colder initial orbit in the 6:3:2 resonance and

Hongyi Ling, Thibault Dardinier, Ellen Arlt, Peter Müller

Automated program verifiers are often organized into a front-end, which encodes an input program into an intermediate verification language (IVL), and a back-end, which proves that the IVL program is correct. Soundness of such translational verifiers requires that the back-end verification is sound and that correctness of the IVL program implies correctness

Alex Walls, James Barry, Devina Mohan, Anna M. M. Scaife

Dramatically increasing data volumes are forcing astronomers to adopt automated methods for the identification and classification of astronomical objects. Although deep-learning models are often well-suited to this task, obtaining a measure of uncertainty on their predictions is challenging. Here we consider the suitability of Monte Carlo conformal predictio

Özlem Tuğfe Demir, Ozan Alp Topal, Cicek Cavdar, Emil Björnson

This paper investigates the fundamental tradeoff between reconfigurable intelligent surfaces (RISs) and network-controlled repeaters (NCRs) in terms of achievable signal-to-noise ratio (SNR). Considering an uplink system with a multi-antenna base station (BS) and a single-antenna user equipment (UE), we derive closed-form SNR expressions for passive RIS-, ac

Mattia Talpo

We show exactness of the homotopy sequence for the logarithmic fundamental group in the case of log smooth, finitely presented, proper and saturated morphisms of fs log schemes over a field. This generalizes earlier results of Hoshi in the log regular case. In passing, we also construct a "log Stein factorization" in some particular cases.

Natalia Bila, Kata Naszádi, Alexandra Mayn, Christof Monz

We investigate the separation of literal interpretation from contextual inference in a collaborative block-building tasks, where an agent must resolve underspecified instructions using context. We adapt an existing two-speaker psycholinguistic paradigm into an interactive benchmark called Build What I Mean (BWIM). This setup contrasts a pragmatically coopera

Yujie Xu, Shutong Chen, Nan Li, Yansha Deng

Efficient multi-user multi-task video transmission is an important research topic within the realm of current wireless communication systems. To reduce the transmission burden and save communication resources, we propose a goal-oriented semantic communication framework for optical flow-based multi-user multi-task video transmission (OF-GSC). At the transmitt

John Turnbull, Shivam Grover, Amin Jalali, Ali Etemad

Deep learning models often struggle under natural distribution shifts, a common challenge in real-world deployments. Test-Time Adaptation (TTA) addresses this by adapting models during inference without labeled source data. We present the first evaluation of TTA methods for FER under natural domain shifts, performing cross-dataset experiments with widely use

Rozain Shakeel, Abdul Rahman Mohammad Ali, Muneeb Mushtaq, Tausifa Jan Saleem

Despite the rapid progress of Multimodal Large Language Models (MLLMs), their ability to perform reliable visual grounding in high-stakes clinical software environments remains underexplored. Existing GUI benchmarks largely focus on isolated, single-step grounding queries, overlooking the sequential, workflow-driven reasoning required in real-world medical i

Curtis DeWitt, Marta De Simone, Eleonora Bianchi, Cecilia Ceccarelli

Water and methanol are key components of interstellar ices and gas in star- and planet-forming regions, but direct observations of water in low-mass protostars are challenging due to atmospheric absorption. We present high-resolution (R = 70,500) mid-infrared spectroscopy of the Class I protostar SVS13-A with EXES on board SOFIA at 26 $\mu$m, targeting both

Davi Lima, Rafael Lucena

The robust statistical description of dynamical systems under perturbations is a central problem in ergodic theory. In this paper, we investigate the statistical properties of skew-product maps driven by a subshift of finite type with contracting fiber maps, a setting that naturally encompasses Iterated Function Systems (IFS) and Random Dynamical Systems (RD

Samuele Fiorini, Guillaume T. Bokman, Anunay Prasanna, Stefanos Nikolaou

Localized cavitation in liquids and soft tissues, typically initiated by the rarefaction phase of high-amplitude ultrasound waves, is leveraged in several biomedical applications such as ablation techniques and drug delivery with vaporizing agents. However, safety considerations aimed at avoiding unwanted bubble activity outside the targeted region pose a li

Johannes Kistner, Julian Jüchter, Norman Wildmann

The SWUF-3D drone fleet is used in the atmospheric boundary layer (ABL) for in situ turbulence measurements of complex flows, such as in mountainous terrain or wind turbine wakes. Previous calibrations for measuring vertical wind speed $w$ using the drones' avionics data were performed on field data, limiting applicability to low winds ($\leq 8~\mathrm{m}\,\

Nicolas Eschenbaum, Nicolas Greber

This paper studies how platform design shapes strategic behavior in decentralized electricity trading. We develop a finite-horizon dynamic game in which photovoltaic- and battery-equipped players ("prosumers") trade on a platform that maps aggregate imports and exports into internal buy and sell prices. We establish existence of a perfect conditional epsilon

Yurun Yuan, Tengyang Xie

Reinforcement learning (RL) has become a standard paradigm for post-training and aligning Large Language Models (LLMs), yet recent evidence suggests it faces a persistent "capability ceiling": unlike classical RL systems that discover novel strategies, RL for LLMs often acts as a mere refiner of patterns already latent in pre-trained weights. In this work, w

Gregor Zens, Zoe Sigman

Estimating the number of migrants who die or go missing along dangerous routes such as the Central Mediterranean remains challenging as available records are incomplete. Some incidents are never documented, and fatalities associated with such unobserved incidents are absent from observed totals. We propose a Bayesian approach for probabilistic estimation of

Pierre-Alain Jacqmin, Jean Liénardy

Arbitrated quantum signature (AQS) schemes aim at ensuring the authenticity of a message with the help of an arbitrator. Moreover, they aim at preventing repudiation, both from a sender that denies the origin of a message, and from a receiver who disavows its reception. Such protocols use quantum communication and are often designed to protect quantum messag

Luna Rigby, Rüdiger Frey, Erik Schlögl

Model risk arises from the misspecification of probabilistic models used for pricing and hedging derivatives. While model risk for European-style claims has been widely studied, much less attention has been given to American-style derivatives and the associated optimal stopping problems. This paper analyzes model risk in the optimal exercise of an American p

Cheng Chen, Daniela Paz Iglesias, James M. Miley, C. J. Nixon

Observations of the binary system DX Cha (HD 104237) reveal a compact, asymmetric ring structure with a radius of 0.43\,au. This ring is just outside the binary orbit, which has semi-major axis $a_{\rm b} = 0.22$\,au and eccentricity $e_{\rm b} = 0.665$; placing the ring at $\approx 1.2$ times the binary apocenter distance. The inner regions of circumbinary

Ji-il Park, Inwook Shim, Chong Hui Kim

The automation system for Course of Action (CoA) planning is an essential element in future warfare. As maneuver speeds increase, surveillance ranges extend, and weapon ranges grow, the operational area expands, making traditional manned-based CoA planning increasingly challenging. Consequently, the development of an AI-based automated CoA planning system is

Pablo Rodriguez-Lopez, Jian-Sheng Wang, Mauro Antezza

In the Comment by Bordag et al. [Phys. Rev. B 113, 207401 (2026) and ArXiv:2506.10792], concerns are raised regarding the validity of the results presented in [Phys. Rev. B 111, 115428 (2025)], where the theoretical descriptions of the electric conductivity of graphene obtained from the Kubo formula and from quantum field theory via the polarization tensor a

Vicenç Torra, Maria Bras-Amorós

Memory poisoning attacks for Agentic AI and multi-agent systems (MAS) have recently caught attention. It is partially due to the fact that Large Language Models (LLMs) facilitate the construction and deployment of agents. Different memory systems are being used nowadays in this context, including semantic, episodic, and short-term memory. This distinction be

David Fernández-Duque, Milan Morreel, Andreas Weiermann

In arXiv:2508.14768, a variant of Goodstein's original process was recently introduced which, given a set $B\subseteq \mathbb{N}$ of bases, writes each $n\in\mathbb{N}$ in $B$-normal form, namely $n=b^ea+r$, where $b\in B$ the greatest base below $n$. The numbers $e$ and $r$ are then recursively written in $B$-normal form, and finally each base of $B$ is rep

Shuai Zeng

The Quantum Approximate Optimization Algorithm (QAOA) and its advanced variant, the Quantum Alternating Operator Ansatz (QAOA), are major research topics in the current era of Noisy Intermediate-Scale Quantum (NISQ) computing. However, the problem of initializing their parameters remains unresolved. Motivated by the combinatorial optimization task in the 6th

Chaoda Zheng, Sean Li, Jinhao Deng, Zhennan Wang

Scalable and reliable evaluation is increasingly critical in the end-to-end era of autonomous driving, where vision--language--action (VLA) policies directly map raw sensor streams to driving actions. Yet, current evaluation pipelines still rely heavily on real-world road testing, which is costly, biased toward limited scenario coverage, and difficult to rep

Judith Korth, Azib Norazman, Raphäel Bendahan-West, Grant Kennedy

Active small bodies in extrasolar systems, the extrasolar analogues of Solar System comets, provide insights into the orbital evolution and physical processes shaping planetary systems. Since the discovery of exocomets around $\beta$ Pictoris, these small, icy bodies have shown the potential to become key probes for understanding planetary formation and migr

Nicolas Bianco, Nadja Klein

Gaussian processes provide a flexible framework for spatial prediction, but their computational cost limits applicability to large-scale data with large sample size $n$. Predictive processes (PPs), a popular low-rank approximation, mitigate this burden by projecting the original process onto a reduced set of $m\ll n$ inducing points. However, existing theory

Angshul Majumdar

We study the computational complexity of constrained nonnegative Gram feasibility. Given a partially specified symmetric matrix together with affine relations among selected entries, the problem asks whether there exists a nonnegative matrix $H \in \mathbb{R}_+^{n\times r}$ such that $W = HH^\top$ satisfies all specified entries and affine constraints. Such

Thomas Şerban von Davier, Hao-Ping Lee, Jodi Forlizzi, Sauvik Das

The evidence on the effects of generative AI (GenAI) on critical thinking is mixed, with studies suggesting both potential harms and benefits depending on its implementation. Some argue that AI-driven provocations, such as questions asking for human clarification and justification, are beneficial for eliciting critical thinking. Drawing on our experience des

Fazhong Liu, Zhuoyan Chen, Tu Lan, Haozhen Tan

Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to active system interaction and environment management. OpenClaw, a representative platform in this emerging paradigm, introduces an extensible skill ecosystem that allows third-party developers to inject behavio