March 2026 arXiv papers — page 18

Xinyan Yu, Marius Hoggenmueller, Tram Thi Minh Tran, Martin Tomitsch

Emerging technologies introduce sociotechnical tensions that call for closer collaboration between technology design and policy. In this work, we introduce Design-Policy Adversarial Futuring, a scenario-based workshop method that supports design-policy engagement by structuring contestation between design and policy perspectives. We report on a workshop cond

Chaja Baruch, Gilly Elor, Jared M. Goldberg, Omer Shtaif

Mechanisms of Mesogenesis generate the baryon asymmetry and dark matter of the Universe through late-time decays of Standard Model mesons into baryons and dark matter states. Utilizing the CP violation in the meson systems themselves, the resulting baryon asymmetry is directly controlled by collider observables, the CP asymmetry $A_{CP}$, and the branching f

Kangkang Sun, Jianhua Li, Xiuzhen Chen, Weizhi Meng

Federated Learning (FL) has emerged as a prominent paradigm for privacy-preserving distributed machine learning, yet two fundamental challenges hinder its large-scale adoption. First, gradient inversion attacks can reconstruct sensitive training data from uploaded model updates, so privacy risk persists even when raw data remain local. Second, without adequa

Maciej Grzeszczuk, Kinga Skorupska, Grzegorz M. Wójcik

Digitizing magnetic media containing computer data is only the first step towards the preservation of early home computing era artifacts. The audio tape images must be decoded, verified, repaired if necessary, tested, and documented. If parts of this process could be effectively automated, volunteers could focus on contributing contextual and historical know

Stanisław Knapiński, Maciej Grzeszczuk, Barbara Karpowicz, Pavlo Zinevych

This work aims to establish an end-to-end system for tracking of physical 3D objects for virtual reality (VR) applications. We focus on training applications requiring real-time tracking of the position of small physical objects and their reflection in VR space. Out goal is to perform object tracking in a "plug and play" manner, without using complex systems

Mohammad Nooraiepour, Mohammad Masoudi, Zezhang Song, Helge Hellevang

Accurate prediction of hydrogen sorption in fine-grained geological materials is essential for evaluating underground hydrogen storage capacity, assessing caprock integrity, and characterizing hydrogen migration in subsurface energy systems. Classical isotherm models perform well at the individual-sample level but fail when generalized across heterogeneous p

Michele Fornea

We introduce a new collection of partially global Galois cohomology classes subsuming both plectic Heegner points and mock plectic invariants. The former are recovered as localizations of plectic Heegner classes, while the latter arise as eigenspace projections with respect to a "partial Frobenius"-action. By overcoming some limitations of previous construct

Wannes Tas, Bart Jacobs

The Rust programming language is famous for its strong ownership regime: at each point, each value is either exclusively owned, exclusively borrowed through a mutable reference, or borrowed as read-only through one or more shared references. These rules, known as Rust's pointer-aliasing rules, are exploited by the Rust compiler to generate more efficient mac

Chang Zong, Sicheng Lv, Si-tu Xue, Huilin Zheng

Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, provenance, and quantitative support. Here we present EvidenceNet, a disease-specific dataset of record-level evidence collections and corresponding graph representations derived from full-text biomedical literature. Evi

Sarah Katz, Francesco Romor, Jia-Jie Zhu, Alfonso Caiazzo

We introduce a novel conditional stochastic interpolant framework for generative modeling of three-dimensional shapes. The method builds on a recent LDDMM-based registration approach to learn the conditional drift between geometries. By leveraging the resulting pull-back and push-forward operators, we extend this formulation beyond standard Cartesian grids t

Ján Boldocký, Cary Faulkner, Elad Michael, Martin Gulan

We present a computationally tractable framework for real-time predictive control of multi-chiller plants that involve both discrete and continuous control decisions coupled through nonlinear dynamics, resulting in a mixed-integer optimal control problem. To address this challenge, we extend Differentiable Predictive Control (DPC) -- a self-supervised, model

Raul Ismayilov, Luuk Spreeuwers

Face morphing attacks compromise biometric security by creating document images that verify against multiple identities, posing significant risks from document issuance to border control. Differential Morphing Attack Detection (D-MAD) offers an effective countermeasure, particularly when employing face demorphing to disentangle identities blended in the morp

Yihan Gao, Chenxi Huang, Wen Shi, Ke Sun

Graph condensation (GC) has become a vital strategy for scaling Graph Neural Networks by compressing massive datasets into small, synthetic node sets. While current GC methods effectively maintain predictive accuracy, they are primarily designed for utility and often ignore fairness constraints. Because these techniques are bias-blind, they frequently captur

Amaia Iparragirre, Thomas Lumley, Irantzu Barrio

Complex survey data are usually collected following complex sampling designs. Accounting for the sampling design is essential to obtain unbiased estimates and valid inferences when analyzing complex survey data. The area under the receiver operating characteristic curve (AUC) is routinely used to assess the discriminative ability of predictive models for bin

Luke Palmer, Petar Palasek, Hazem Abdelkawy

Accurately modelling human attention is essential for numerous computer vision applications, particularly in the domain of automotive safety. Existing methods typically collapse gaze into saliency maps or scanpaths, treating gaze dynamics only implicitly. We instead formulate gaze modelling as an autoregressive dynamical system and explicitly unroll raw gaze

Robert Whyte, Manni Cheung, Katharine Childs, Jane Waite

Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms

Yuanqiao Zhang, Tiantian He, Yuan Gao, Yixin Wang

In this paper, we present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communication cost in Federated Learning systems under statistical heterogeneity. Existing second-order optimization methods are often computationally expensive and numerically unstable in di

Yangmei Chen, Zhongyuan Zhang, Xikun Zhang, Xinyu Hao

Thyroid nodule classification using ultrasound imaging is essential for early diagnosis and clinical decision-making; however, despite promising performance on in-distribution data, existing deep learning methods often exhibit limited robustness and generalisation when deployed across different ultrasound devices or clinical environments. This limitation is

Hongjun Wu

Recently, a two-way RFID authentication protocol based on the AM-SUEO-DBLTKM variable matrix encryption algorithm was proposed for low-cost mobile RFID systems. Its design combines adaptive modulus selection, self-updating matrix ordering, and transpose/block-based matrix generation. In this paper, we show that the protocol has structural weaknesses. First,

Yuhui Shen, Zhiyang Tan

Super Weyl group plays an important role in the study of representations of basic classical Lie superalgebras. The Coxeter graphs for super Weyl groups of basis classical Lie superalgebras have been given in \cite{CLS}, where the authors also made a proposal on the Coxeter graphs for the super Weyl groups of exceptional classical Lie superalgebras $D(2,1,\al

Tony Liimatainen, Shubham Jaiswal

We initiate the study of inverse source problems for quasilinear elliptic equations of the form \[ \left\{ \begin{array}{ll} \nabla \cdot (\gamma(x,u,\nabla u) \nabla u) = F & \text{in } \Omega, \\ u = f & \text{on } \partial\Omega, \end{array} \right. \] where $\Omega \subset \mathbb{R}^n$, $n \geq 2$, is a simply connected bounded domain. We consider the s

Denis Fatkhiev, João dos Reis Frazão, Alireza H. Derkani, Kadir Gümüş

We demonstrate the first CV-QKD system featuring a custom-designed monolithic silicon photonic dual-polarisation transceiver. Leveraging PS-64-QAM, we achieved 1.9 Mbit/s secret key rate across 25 km of standard single-mode fibre, highlighting the potential of electronic-photonic integration for practical QKD.

Aymen Lassoued, Nacef Mbarek, Bechir Dardouri, Bassem Ouni

Vulnerability detection in C programs is a critical challenge in software security. Although large language models (LLMs) achieve strong detection performance, their multi-billion-parameter scale makes them impractical for integration into development workflows requiring low latency and continuous analysis. We introduce VULNSCOUT-C, a compact transformer arc

Hugo Roger Paz

Dropout in higher education is commonly analysed through observable academic events such as course failure or repetition. However, these event-based perspectives may obscure the underlying structural dynamics that shape student trajectories. In this study, we adopt a causal computational social science approach to identify the origins of dropout in a constra

Chio Chon Kit

This paper provides a rigorous mathematical analysis of the global regularity problem for the 3D incompressible Navier-Stokes (NS) equations, specifically addressing the conditions under which smooth initial data may lead to a loss of regularity. By departing from traditional phenomenological turbulence models and focusing strictly on the mechanical energy t

Jadwiga Wilkens, Milena Guevara-Bertsch, Marwa Marso, Mederika Zangerl

We experimentally demonstrate local robust shadows on a trapped-ion quantum computing system, a protocol developed to counteract measurement errors. We alternate between a calibration stage and the shadow estimation stage and also introduce Pauli-X-twirling before measurements in both stages to symmetrize error rates. We then demonstrate the protocol on a tr

Thammathip Piumsomboon

Self++ is a design blueprint for human-AI symbiosis in extended reality (XR) that preserves human authorship while still benefiting from increasingly capable AI agents. Because XR can shape both perceptual evidence and action, apparently 'helpful' assistance can drift into over-reliance, covert persuasion, and blurred responsibility. Self++ grounds interacti

Tenghao Cai, Lei Li, Shutao Zhang, Tsung-Hui Chang

Providing guaranteed quality of service for cell-edge users remains a longstanding challenge in wireless networks. While coordinated interference management was proposed decades ago, its potential has been limited by computational complexity and backhaul resource constraints. Distributed user scheduling and coordinated beamforming (D-USCB) offers a scalable

GyeongHyeon Nam

In this paper, we answer the question posed by Goodwin and R\"ohrle for reductive groups and their parabolic subgroups. In addition, we consider an additive analogue of this problem. By studying this additive analogue, we identify similar properties between the Deligne-Lusztig character of a finite reductive group and the Harish-Chandra induction over the co

Zhijie Chen, Houwang Li, Tuoxin Li, Juncheng Wei

We study the blow-up behavior of solutions to the singular Liouville equation \[ \Delta \tilde u+\lambda e^{\tilde u}=4\pi\alpha\delta_0 \quad\text{in }B,\quad \tilde u=0 \quad\text{on }\partial B, \] where $\alpha>0$, $\lambda>0$ and $B\subset\mathbb R^2$ is the unit disk. Our main results give a complete classification of all blow-up solutions and determin

Chanyoung Kim, Minwoo Kim, Minseok Kang, Hyunwoo Kim

Vision-Language-Action (VLA) models achieve strong performance in robotic manipulation by leveraging pre-trained vision-language backbones. However, in downstream robotic settings, they are typically fine-tuned with limited data, leading to overfitting to specific instruction formulations and leaving robustness to paraphrased instructions underexplored. To s

Qing Qing, Huafei Huang, Mingliang Hou, Renqiang Luo

Graph anomaly detection (GAD) aims to identify irregular nodes or structures in attributed graphs. Neighbor information, which reflects both structural connectivity and attribute consistency with surrounding nodes, is essential for distinguishing anomalies from normal patterns. Although recent graph neural network (GNN)-based methods incorporate such informa

Rakhimov Kamoladdin, Sobirov Zarifboy, Jabborov Nasridin

In this work we investigate Cauchy problem and initial boundary value problem for time-fractional Airy equation on the graphs with infinite and finite bonds. We studied properties of potentials for this equation and using these properties found the solutions of the considered problems. The uniqueness theorem is proved using the analogue of Gr\"onwall-Bellman

D. Canillas Martínez, A. González-Parra, D. Miguélez-Caballero, A. Wereszczynski

In contrast to the complex $\phi^4$ model, vortex-antivortex collisions in the complex $\phi^6$ theory reveal a resonant structure due to the existence of a remarkably stable, long-lived, large amplitude oscillon in the broken vacuum. Surprisingly, it persists despite the absence of a mass gap associated with the flat direction in the broken vacuum. We demon

Kun Tang, Xinquan Yang, Mianjie Zheng, Xuefen Liu

The scarcity and high cost of expert annotations in dental imaging present a significant challenge for the development of AI in dentistry. DINOv3, a state-of-the-art, self-supervised vision foundation model pre-trained on 1.7 billion images, offers a promising pathway to mitigate this issue. However, its reliability when transferred to the dental domain, wit

Henning Bahl, Johannes Braathen, Martin Gabelmann, Kateryna Radchenko

A central objective of future collider experiments is to probe the structure of the Higgs potential, which requires access to trilinear scalar couplings, in particular the self-coupling of the observed Higgs boson. While this coupling is fixed in the Standard Model (SM), it can receive sizable modifications in many Beyond the SM (BSM) scenarios, often connec

Thomas Van Mullem, Bart Mesuere, Peter Dawyndt

The rapid emergence of Large Language Models (LLMs) presents both opportunities and challenges for programming education. While students increasingly use generative AI tools, direct access often hinders the learning process by providing complete solutions rather than pedagogical hints. Concurrently, educators face significant workload and scalability challen

Kosuke Ito, Akira Tanji, Hiroshi Yano, Yudai Suzuki

Learning from quantum data using classical machine learning models has emerged as a promising paradigm toward realizing quantum advantages. Despite extensive analyses on their performance, clean and fully labeled quantum data from the target domain are often unavailable in practical scenarios, forcing models to be trained on data collected under conditions t

Gopal Sharma, Sampat Sharma

In this article, we prove the following results:\\ \noindent \text{(1).} Let $R$ be a smooth affine algebra of dimension $3$ over an algebraically closed field $K$ with $3!\in K$, then we show that $\Um_4(R)=e_1\Sp_4(R)$ and $\Um_4(R [X])=e_1\Sp_4(R[X])$. \noindent \text{(2).} We also show that if $R$ is a smooth affine algebra of dimension $4$ over an algeb

David V. Svintradze

We develop a geometric formulation of stochastic dynamics in which noise, diffusion, path probabilities, fluctuation theorems, and entropy production arise from the intrinsic geometry of an evolving manifold rather than from externally imposed randomness. Within the theory of moving manifolds, we establish a curvature-noise correspondence: fluctuations are g

Matthieu Elineau, Lucille Kuhler, Alexandre Chabory

The problem of electromagnetic scattering by cylinders is an old problem that has been studied in many configurations. The present publication provides a theoretical study on a not yet investigated general case: the set of finite metallic circular cylinders. A model which takes into account both the finiteness of the cylinders and their electromagnetic coupl

Sijie Fei, Grace Li Zhang, Bing Li, Ulf Schlichtmann

Distributed learning is widely used for training large models on large datasets by distributing parts of the model or dataset across multiple devices and aggregating the computed results for subsequent computations or parameter updates. Existing communication algorithms for distributed learning such as ring all-reduce result in heavy communication overhead b

Petr Machovec, Lukáš Horák, Milan Dopita, Neda Neykova

Mixed-halide perovskites (MHPs) offer good band gap tunability by stoichiometry changes, which is an essential property for the creation of multijunction solar cells. However, under illumination, halide ions in MHP segregate and create I- and Br-rich regions, which decreases the efficiency of potential solar cells. In this work, a method for a detailed inves

Gnankan Landry Regis N'guessan

Kolmogorov-Arnold Networks (KAN) employ B-spline bases on a fixed grid, providing no intrinsic multi-scale decomposition for non-smooth function approximation. We introduce Fractal Interpolation KAN (FI-KAN), which incorporates learnable fractal interpolation function (FIF) bases from iterated function system (IFS) theory into KAN. Two variants are presented

Mattia D'Urso, Yuxi Hu, Christian Sormann, Mattia Rossi

Despite the growing need for data of more and more sophisticated 3D reconstruction pipelines, we can still observe a scarcity of suitable public datasets. Existing 3D datasets are either low resolution, limited to a small amount of scenes, based on images of varying quality because retrieved from the internet, or limited to specific capturing scenarios. Moti

Wytze de Vries, Erik van den Eshof, Jorn van Kampen, Mauro Salazar

Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race-winning policies therefore becomes a fundamentally multi-agent, game-theoretic problem. These policies must jointly govern low-level driver inputs as well as high-level strategic decisions, including energy management and charging. Th

Emanuela Penitente, Urszula Foryś, Burcu Gürbüz

We analyse the global dynamics of a Susceptible--Vaccinated--Exposed--Infected--Recovered (SVEIR) epidemic model with demographic turnover, imperfect vaccination, and two transmission routes: direct host-to-host contagion and indirect transmission via contaminated fomites. Indirect transmission is described through an environmental pathogen concentration and

Saronath Halder, Suchetana Goswami

In this work, we construct small sets of bipartite orthogonal pure states that cannot be perfectly distinguished by local operations and classical communication (LOCC). We mention that not all the states within the constructed sets are necessarily entangled. However, such a set contains at least one entangled state which cannot be conclusively identified by

Tenghao Cai, Lei Li, Tsung-Hui Chang

Distributed scheduling is essential for open radio access network (O-RAN) employing advanced physical-layer techniques such as multi-user MIMO (MU-MIMO), carrier aggregation (CA), and joint transmission (JT). This work investigates the multi-component-carrier (multi-CC) resource block group (RBG) scheduling in MU-MIMO O-RAN with both JT and non-JT users. We

Mathieu Fauvel

Whittaker smoother is a widely adopted solution to pre-process satellite image time series. Yet, two key limitations remain: the smoothing parameter must be tuned individually for each pixel, and the standard formulation assumes homoscedastic noise, imposing uniform smoothing across the temporal dimension. This paper addresses both limitations by casting the

Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban, Adish Singla

We consider robustness against data corruption in offline multi-agent reinforcement learning from human feedback (MARLHF) under a strong-contamination model: given a dataset $D$ of trajectory-preference tuples (each preference being an $n$-dimensional binary label vector representing each of the $n$ agents' preferences), an $\epsilon$-fraction of the samples

James K. Ruffle, Samia Mohinta, Chris Foulon, Mohamad Zeina

Despite there now being more than 1,000 FDA-authorised AI medical devices, formal equity assessments -- whether model performance is uniform across patient subgroups -- are rare. Here, we evaluate the equity of 18 open-source brain tumour segmentation models across 648 glioma patients from two independent datasets (n = 11,664 model inferences) along distinct

James Price, Guillermo Valenzuela-Venegas, Oskar Vågerö, Marianne Zeyringer

The large-scale deployment of wind power is central to Europe`s energy transition but faces challenges due to its social and environmental impacts on communities. Here we assess how the tolerance of local stakeholders to such impacts translates across spatial scales to shape the cost and design of the continent`s net-zero electricity system using a soft-link

Sergei N. Gninenko, N. V. Krasnikov, I. V. Voronchikhin, D. V. Kirpichnikov

Some extensions of the Standard Model consider inelastic dark matter (iDM) as an attractive candidate for sub-GeV DM of thermal origin that could be detected at modern accelerators. In the present paper, we calculate the production rate of iDM pairs $\chi_{1} \bar{\chi}_0$ interacting with the ordinary photon via dipole magnetic moment in the reaction of hig

Rajeev Gangwar, Ujjwal Sen

Understanding whether the features of open quantum dynamics are genuinely quantum remains a central challenge in quantum dynamics. Even though the non-Markovian behavior of quantum dynamics has been widely investigated across different settings, there is still no consensus on which properties of a dynamics reflect genuine quantum features and which arise fro

M. Cappelli, A. Wallach, H. Abele, G. Angloher

Phonon-mediated cryogenic calorimeters find application in rare event searches due to their intrinsically low energy threshold. Achieving the best sensitivity for this kind of detectors is crucial for signal identification, leading to various optimization techniques. In this work, we present two complementary methods to increase the sensitivity of cryogenic

Paweł Jucha, Antoni Szczurek

We discuss several possible extensions of present studies of $\gamma \gamma \to \gamma \gamma$ scattering in ultraperipheral heavy ion collisions. One of the possible extensions are studies for lower diphoton invariant masses (smaller transverse momenta). There new mechanisms may show up. This includes possible studies with future FOCAL and ALICE 3 detectors

Antoine Soetewey

Statistics 101, 201, and 202 are three open-source interactive web applications built with R \citep{R} and Shiny \citep{shiny} to support the teaching of introductory statistics and probability. The apps help students carry out common statistical computations -- computing probabilities from standard probability distributions, constructing confidence interval

Catherine Matias

Modeling higher-order interactions (HOI) has emerged as a crucial challenge in complex systems analysis, as many phenomena cannot be fully captured by pairwise relationships alone. Hypergraphs, which generalize graphs by allowing interactions among more than two entities, provide a powerful framework for representing such intricate dependencies. Adopting a s

Yongqi Zhang, Jiajie Zhang, Chengqian Li, Fujing Xie

The deployment of mobile robots in large-scale, multi-floor environments demands navigation systems that achieve spatial scalability without compromising local kinematic precision. Traditional navigation stacks, reliant on monolithic occupancy grid maps, face severe bottlenecks in storage efficiency, cross-floor reasoning, and long-horizon planning. To addre

Sakthikumaran Ravichandran, Piotr Kulik, Krzysztof Jachymski

Polar molecules represent a promising platform for quantum simulation and computation protocols. Highly controllable arrays of optical tweezers are now accessible in experiments, allowing for unprecedented control of individual molecules. Motional dephasing is typically seen as an obstacle in quantum computing scenarios. Here, we instead consider using the t

Ilayda Bulunur, Osman Ergec, Oguzhan Kasikci, Mehmet Ozkan

Magnetic Carrollian theories provide a natural setting for field theories with nontrivial spatial structure in the Carroll limit and are therefore natural candidates for flat-space holographic duals. Embedding such boundary theories into a top-down framework requires a consistent supersymmetric completion and, in particular, an understanding of the relativis

Vincent Cohen-Addad, Karthik C. S., David Saulpic, Chris Schwiegelshohn

The $k$-means problem is a classic objective for modeling clustering in a metric space. Given a set of points in a metric space, the goal is to find $k$ representative points so as to minimize the sum of the squared distances from each point to its closest representative. In this work, we study the approximability of $k$-means in Euclidean spaces parameteriz

Mingzhang Yang, Yuxi Wang, Jun Deng, Tianping Ying

Superatomic compounds, composed of atomic clusters interwoven by weak chemical bonds exhibit large anharmonicity vibrations, are excellent candidates for ultralow thermal conductivity (\k{appa}) materials. However, growing bulk superatomic single crystals is challenging due to complex chemical composition and chemical bonds, and studies on their intrinsic th

Christian Kaspers

$k$th-order sum-free functions are a natural generalization of APN functions using the concept of (non)vanishing flats. In this paper, we introduce a new combinatorial technique to study the nonvanishing flats of Boolean functions. This approach allows us to determine the number of nonvanishing flats for an infinite family of Boolean functions. We moreover u

Lotte Blank, Karl Bringmann, Parinya Chalermsook, Karthik C. S.

In the (continuous) Euclidean $k$-center problem, given $n$ points in $\mathbb{R}^d$ and an integer $k$, the goal is to find $k$ center points in $\mathbb{R}^d$ that minimize the maximum Euclidean distance from any input point to its closest center. In this paper, we establish conditional lower bounds for this problem in constant dimensions in two settings.

Ata Khalili, Brikena Kaziu, Vasilis K. Papanikolaou, Robert Schober

We investigate a novel integrated sensing and communication (ISAC) system enabled by pinching antennas (PAs), which are dynamically activated along a dielectric waveguide. Unlike prior designs, the PAs are organized into multiple clusters of movable antennas. The movement of the antennas within each cluster enables transmit beamforming, while the spatial sep

Eneko Valero, Maria Ribalta i Albado, Oscar Sainz, Naiara Perez

Large Language Models (LLMs) remain heavily centered on English, with limited performance in low-resource languages. Existing adaptation approaches, such as continual pre-training, demand significant computational resources. In the case of instructed models, high-quality instruction data is also required, both of which are often inaccessible for low-resource

Ludovica Pannitto, Sylvain Kahane, Kaja Dobrovoljc, Elena Battaglia

The paper proposes annotation guidelines for syntactic dependencies that span across speaker turns - including collaborative coconstructions proper, wh-question answers, and backchannels - in spoken language treebanks within the Universal Dependencies framework. Two representations are proposed: a speaker-based representation following the segmentation into

Jérémie Chalopin, Yi-Jun Chang, Giuseppe Antonio Di Luna, Haoran Zhou

In the content-oblivious (CO) model (proposed by Censor-Hillel et al.), processes inhabit an asynchronous network and communicate only by exchanging pulses. A series of works has clarified the computational power of this model. In particular, it was shown that, when a leader is present and the network is 2-edge-connected, content-oblivious communication can

Jon-Paul Cacioli

Categorical perception (CP) -- enhanced discriminability at category boundaries -- is among the most studied phenomena in perceptual psychology. This paper reports that analogous geometric warping occurs in the hidden-state representations of large language models (LLMs) processing Arabic numerals. Using representational similarity analysis across six models

Yukihiro Tsuzuki

We study the upper hedging price for contingent claims in market models with strong types of arbitrage: increasing profit, strong arbitrage, and arbitrage of the first kind. The existence of arbitrage may make the price smaller than if it did not exist. For example, when the asset price process has a reflecting boundary, which introduces increasing profit in

Iztok Fister, Žan Hozjan, Iztok Fister,, Damjan Strnad

The domain of metaheuristic optimization has become vibrant due to a flood of new algorithms using a new nature-inspired metaphor but lacking clear methodological novelty. The Criticism behind the development of these algorithms has reached such an extent that the critics started to assert that all novel algorithms are only copies of already developed ones.

Da Chang, Qiankun Shi, Lvgang Zhang, Yu Li

Orthogonalized-update optimizers such as Muon improve training of matrix-valued parameters, but existing extensions typically either rescale updates after orthogonalization or use heavier whitening-based preconditioners before it. We introduce {\method}, a lightweight family of pre-orthogonalization equilibration schemes for Muon with three forms: two-sided

Xianyong Xu, Yuanjun Zuo, Zhihong Huang, Yihan Qin

Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modeling. We propose MR-CDM, a framework combining hierarchical multi-resolution trend decomposition, an adaptive embedding mechanism for variable-length inputs, and a multi-scale conditional diffusion process. Evaluations on

Qizhe Wen, Yan Wang, Linlin Yang, Yiqing Wang

The instability characteristics of lean hydrogen/air flames have attracted considerable research attention, yet the effect of gravity remains insufficiently understood. In this study, time-resolved two-dimensional simulations with detailed chemistry and transport are conducted to investigate the influence of gravity-induced Rayleigh-Taylor (RT) instability o

David K. Johansson

Single-shot neural decoders commit to answers without iterative refinement, while chain-of-thought methods introduce discrete intermediate steps but lack a scalar measure of reasoning progress. We propose Energy-Based Reasoning via Structured Latent Planning (EBRM), which models reasoning as gradient-based optimization of a multi-step latent trajectory $z_{1

Delio Jaramillo-Velez, Hiram H. López, Rodrigo San-José

In this work, we investigate the connections between dominating sets, private neighbors, and perfect codes in graphs, and their relationships with commutative algebra. In particular, we estimate the $\mathtt{v}$-number of closed neighborhood ideals in terms of minimal dominating sets and private neighbors. We show how the $\mathtt{v}$-number is related to ot

Elias Goller, Gordon Fraser, Isabella Graßl

Block-based programming environments like Scratch have become widely adopted in Computer Science Education, but the mouse-based drag-and-drop interface can challenge users with disabilities. While prior work has provided solutions supporting children with visual impairment, these solutions tend to focus on making content perceivable and do not address the ph

Yulun Wu, Ziwei Wang, Faqian Chong, Hua Shao

Topological photonics has emerged as a powerful platform for terahertz on-chip systems due to its robust waveguiding capabilities. However, directly extracting topological valley-locked edge states into directional free-space radiation without auxiliary couplers while preserving guided-wave functionality remains a fundamental challenge. In this work, we prop

J. Kluson

In this short note we show that any action for $N$ interacting particles can be made invariant under gauged Galilean transformations. While resulting Lagrangian is generally very complicated its Hamiltonian has simple form with first class constraints which are generators of corresponding gauge transformations included.

David Ramos, Lucas Lacasa, Fermín Gutiérrez, Eusebio Valero

Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query applications. In recent years deep learning (DL) has been used to construct data-driven fluid-dynamic surrogate models. In this work we consider a different learning paradigm and embrace generative modelling as a framework

Wenqi Cai, Kyriakos G. Vamvoudakis, Sébastien Gros, Anthony Tzes

In this paper, we propose a cost-matching approach for optimal humanoid locomotion within a Model Predictive Control (MPC)-based Reinforcement Learning (RL) framework. A parameterized MPC formulation with centroidal dynamics is trained to approximate the action-value function obtained from high-fidelity closed-loop data. Specifically, the MPC cost-to-go is e

Lucas Pouillart

The cyclic sieving phenomenon was introduced by Reiner, Stanton and White in 2004 as a generalization of Stembridge's $q=-1$ phenomenon. In a paper from 2008, Eu and Fu studied many occurrences of this phenomenon on the faces of the generalized cluster complex with the action of the Fomin-Reading rotation in the classical types $A_n$, $B_n$, $D_n$ and $I_2(k

A. F. Borghesani, P. Lamp

We report measurements of the electron drift mobility in dense argon gas over an extended range of densities, temperatures, and electric fields, supplementing our earlier work. The measurements confirm the validity of the heuristic model we previously developed by introducing multiple scattering effects in the classical kinetic theory description of the elec

Federico Mariano, Elena De Momi, Giovanni Berselli, Jovana Jovanova

This paper presents an off-axis, monolithic compliant Remote Center of Motion (RCM) joint for neuroendoscopic manipulation, combining near-isotropic stiffness with minimal parasitic motion. Based on the Tetra II concept, the end-effector is placed outside the tetrahedral flexure to improve line of sight, facilitate sterilization, and allow rapid tool release

E. Nagel, J. Köhler, M. Zechmeister, A. D. Rains

We present the first planet mass measurement obtained with CRIRES+ radial velocity (RV) observations using the K-band gas cell. Our target, LHS 3844 b (TOI-136), is a transiting super-Earth with radius $R_b=1.286^{+0.043}_{-0.044}R_\oplus$ and an orbital period of $P_b = 0.462929709^{+0.000000044}_{-0.000000042}$d, placing it in the class of ultra-short-peri

Alejandro Hacker, Nicola Astudillo-Defru, Rodrigo F. Díaz, Caroline Dorn

Context: LHS 3844 b (TOI-136 b) is a ultra short-period, Earth-size exoplanet detected by TESS. It is one of the most favourable object for atmospheric characterisation and the study of its surface with the James Webb Space Telescope. However, the dynamical mass of this planet has not been measured yet. Aims: We aim to determine the mass of LHS 3844 b using

Wei Xing

Jasso-K\"{u}lshammer introduced the class of $d$-Nakayama algebras as a higher dimensional analogue of Nakayama algebras. In particular, they are endowed with a distinguished $d\mathbb{Z}$-cluster tilting subcategory. In this paper, we investigate which $d$-Nakayama algebras admit an $nd\mathbb{Z}$-cluster tilting subcategory for $n>1$. The radical square ze

A. V. Ivanov, I. V. Korenev

In this paper, we study the properties of averaged fundamental solutions of a special type for Laplace operators in the Euclidean space of an arbitrary dimension. We consider a class of kernels suitable for probabilistic averaging, and propose new representations for the deformed fundamental solutions and their values at zero. In addition, we give examples r

Gaku Kawashima, Shiu-Hang Lee, Keiichi Maeda, Daniel Patnaude

Core-collapse supernova remnants (CCSNRs) are crucial for understanding the final stages of massive star evolution, as they reflect the imprints of their progenitors' pre-explosion activities. However, the evolution of CCSNRs, particularly those originating from progenitors with high mass-loss rates -- known as stripped-envelope SNRs (SESNRs) -- remains poor

Abdelghani Maddi, Chongjun Xi, Xiaoting Chen, Isabelle Dorsch

Scientific research is a key input into technological innovation, yet not all scientific knowledge is equally mobilized in patents. This paper examines how different scientific publishing models shape both the selection of scientific publications cited in patents and their cognitive alignment with patented technologies. Using large-scale data on non-patent r

Minh-Khoi Do, Huy Che, Dinh-Duy Phan, Duc-Khai Lam

Accurate and efficient perception is essential for autonomous driving, where segmentation tasks such as drivable-area and lane segmentation provide critical cues for motion planning and control. However, achieving high segmentation accuracy while maintaining real-time performance on low-cost hardware remains a challenging problem. To address this issue, we i

Bayu Dharmaputra, Klejsi Curumi, Nicolas Noiray

Thermoacoustic instabilities remain a major challenge in the operation and development of modern gas turbine combustors for power generation and propulsion. In laboratory environments, such instabilities can also hinder the accurate characterization of key thermoacoustic properties of the flames. Many modern combustors therefore employ wall-mounted acoustic

Ryohei Kobayashi

The 2D $\mathbb{Z}_2$ toric code admits a global symmetry exchanging electric and magnetic quasiparticles, known as electromagnetic duality. Known realizations include lattice translation symmetry, an exact $\mathbb{Z}_4$ symmetry generated by a Clifford circuit, and an exact $\mathbb{Z}_2$ symmetry generated by a non-Clifford circuit. We show that a Cliffor

Stefan Neuwirth

This constant is the maximum of the sum $|c_0|+|c_1|+|c_2|+|c_3|$ of the moduli of the coefficients of a trigonometric polynomial $c_0+c_1e^{it}+c_2e^{2it}+c_3e^{3it}$ bounded by 1. Its value is still unknown, but I will present some ideas on how to compute it and describe a distinguished torus of extremal functions.

Anna Cascioli, Martín Gilabert Vio, Eduardo Silva

We give a sufficient condition for a countable group $G$ to possess a probability measure $\mu$ that admits a non-trivial $\mu$-boundary modeled in the space $\mathrm{Sub}_{\mathrm{am}}(G)$ of amenable subgroups of $G$. In particular, for such $\mu$ the space $\mathrm{Sub}_{\mathrm{am}}(G)$ is not uniquely $\mu$-stationary. This contrasts with a theorem of H

Stefan Neuwirth

We study the relationship between the growth rate of an integer sequence and harmonic and functional properties of the corresponding sequence of characters. In particular we show that every polynomial sequence contains a set that is Lamba(p) for all p but is not a Rosenthal set. This holds also for the sequence of primes.

Mahdi Hormozi, Jie-Xiang Zhu

Let $(\mathcal F_n)_{n\ge 1}$ be a filtration and let $f\ge0$ belong to $L^1(\mathcal F_\infty)$. For the martingale $f_n=\mathbb E[f\mid \mathcal F_n]$ and each $\lambda>0$ we prove a Gundy--Stein decomposition \[ f=g+h+k \] with explicit numerical constants. In the positive closed case the three parts satisfy explicit bounds, and the bounded part is bounde

Rahul Jaiswal, Joakim Hellum, Halvor Heiberg

Bridges are critical components of national infrastructure and smart cities. Therefore, smart bridge monitoring is essential for ensuring public safety and preventing catastrophic failures or accidents. Traditional bridge monitoring methods rely heavily on human visual inspections, which are time-consuming and prone to subjectivity and error. This paper prop