April 2026 arXiv papers — page 111

Nicola De Nitti, Yun Wang, Shaoheng Zhang

We consider the three-dimensional incompressible MHD system. Any weak solution satisfying a strong energy inequality is $L^2$-asymptotically stable around a Landau solution. Under an additional integrability assumption on the initial perturbation, we also obtain an explicit algebraic decay rate for the $L^2$-norm of the velocity and magnetic perturbations.

Ullas Chandran S. V., Gabriele Di Stefano, Grahame Erskine, Haritha S

The general position number for graphs ask for largest vertex subsets $S$ such that no three vertices are contained on a common shortest path. We examine this problem in the setting of directed graphs. We provide bounds for the general position number of digraphs, show that the problem is NP-complete for oriented graphs, investigate the problem for some impo

Cecile Monthus

The construction of Quasi-Exactly-Solvable quantum Hamiltonians where only the first two eigenstates $Φ_0(x)$ and $Φ_1(x)$ of energies $E_0$ and $E_1$ are explicit is revisited from the point of view of one-dimensional Markov processes satisfying detailed-balance, whose generators are related to quantum Hamiltonians via similarity transformations. Here, the

D. Revanth Kumar, Santosh Kumar Yadav, S. A. Kadam

In this article, we investigate two phenomenological decaying vacuum cosmological models describing the accelerated expansion of the Universe. We constrain the model parameters using a Markov Chain Monte Carlo (MCMC) technique with recent datasets, including cosmic chronometer (CC), Pantheon+SH0ES (PPS), and DESI BAO data release (DR2). Our analysis provides

Ayodele James Oyejide, Ustaz A. Yaqub, Samir Erturk, Eray A. Baran

Vine-inspired robots achieve large workspace coverage through tip eversion, enabling safe navigation in confined and cluttered environments. However, their deployment in free space is fundamentally limited by low axial stiffness, poor load-bearing capacity, and the inability to retain shape during and after steering. In this work, we propose a reconfigurable

Shungo Aoyagi, Shunsuke Kitou, Yuiga Nakamura, Taka-hisa Arima

Symmetry classification of crystal structures has been central to predicting physical properties of materials. While such structural classification identifies which physical responses are symmetry-allowed, the magnitudes of these responses are governed by the degree of symmetry breaking in the electronic state. However, a well-defined quantitative descriptor

NEXT Collaboration, J. Renner, J. D. Villamil, N. López-March

The NEXT-DEMO++ detector, a high-pressure xenon gas time projection chamber serving as a prototype for the NEXT-100 experiment, was used to measure the electroluminescence (EL) yield as a function of reduced electric field ($E/p$) across pressures from 2.0 to 9.4 bar, utilizing the 41.5 keV de-excitation peak of $^{83m}$Kr. These measurements were made to ex

S. Marcos, N. Sandulescu, R. Niembro

The kink observed in the nuclear charge radius of Sn isotopes around neutron number $N = 82$ is investigated within the relativistic mean-field (RMF) framework using the NL3$^*$ parameter set. It is shown that the small components of the Dirac spinors for the neutron single-particle states near the Fermi level play a crucial role in forming the kink through

Wei Lu, Zi-Yang Bo, Fei-Fei Sang, Yi Liu

Shadows are prevalent in high-resolution aerospace imagery (ASI). They often cause spectral distortion and information loss, which degrade downstream interpretation tasks. While deep learning methods have advanced natural-image shadow removal, their direct application to ASI faces two primary challenges. First, strictly paired training data are severely lack

Elodie Bouzekri, Guillaume Riviere

Conveying environmental data has grown interest in encouraging the adoption of eco-friendly lifestyles through data-driven strategies. This scope appeals to data visualizations representing the environmental purpose. For example, previous work has already proposed nature-inspired counters, gauges, and bitmaps, but data series remains to be explored. Therefor

Keyvan Aghababaiyan, Baldomero Coll-Perales, Javier Gozalvez

Future 6 G networks are envisioned as a network of networks (NoN) ecosystem, integrating communication and computing resources across multiple domains. At the deep edge, IoT and end-user devices will form subnetworks for local communication and distributed task processing. These subnetworks will seamlessly integrate into the NoN ecosystem, creating an IoT-ed

Na Li, Binod Koirala

As energy communities move from policy design to implementation in Switzerland, understanding their performance in practice has become increasingly important. A techno-economic assessment of a regulation-compliant LEC is presented under the new Swiss legal framework in this study. A reference case without local electricity exchange is compared to a LEC scena

Xinke Xiao, Tianle Ma, Lingxuan Shao, Jun Liu

High-Resolution Electron Backscatter Diffraction (HR-EBSD) has advanced rapidly in recent years, significantly improving elastic strain measurements and dislocation density evaluation with submicron spatial resolution. To achieve better accuracy in the measurements, high-quality dynamical simulation patterns are required to be matched with experimental ones.

Toru Kojo, Sakura Itatani

We investigate the properties of neutral and charged mesons in magnetic fields, from weak-field to strong-field regimes. To develop analytic insights, we employ a non-relativistic quark model with a confining potential of the harmonic oscillator type. Short-range correlations, such as Coulomb and color-magnetic interactions, are treated as perturbations. In

Shaojie Zhang, Ozgur B. Akan

Mobile molecular communication (MC) links with counting receivers are sensitive to transmitter--receiver geometry especially when nodes are mobile. We study binary detection from within-symbol count observations with unknown finite-memory inter-symbol interference (ISI) and a block-constant multiplicative geometry gain. Under a mixed-Poisson view mobility an

Dharun Venkateswaran, Felice Francesco Tafuri, Yuanzheng Paul Tan, Bruno Aznar Martinez

Flux-tunable superconducting qubits rely on fast flux control pulses to implement two-qubit entangling quantum gates, a key building block for quantum algorithms. However, distortion effects introduced by non-ideal control electronics, parasitic components, and the cryogenic quantum chip response can all degrade the gate fidelity. We present a digital predis

David Asperó, Curial Gallart

We force the existence of a chain of length $\omega_3$ in $[\omega_1]^{\omega_1}$ increasing modulo finite. The construction involves symmetric systems of models of two types as side conditions, introduced by the second author. This improves previous results of Koszmider and Veli\v{c}kovi\'{c}-Venturi.

Mahmoud Fakhry, Abeer FathAllah Brery

Systolic murmurs are extra heart sounds that occur during the contraction phase of the cardiac cycle, often indicating heart abnormalities caused by turbulent blood flow. Their intensity, pitch, and quality vary, requiring precise identification for the accurate diagnosis of cardiac disorders. This study presents an automatic classification system for systol

Meng Lv, Yapeng Li, Hang Su, Juhua Liu

Intelligent fetal ultrasound (US) interpretation is crucial for prenatal diagnosis, but high annotation costs and operator-induced variance make unsupervised pre-training a highly promising paradigm. However, existing pre-training methods largely ignore US-specific characteristics -- severe data redundancy, fan-shaped locality, and polar coordinate beamformi

Tomoki Ogihara, Yusuke Hiejima, Ayano Chiba

While host-guest co-crystals are well established, and co-amorphous solids are recognized in materials science, the concept of a host-guest co-amorphous structure remains largely unexplored. A potential analogue is seen in SiO2 glass under high pressure with helium as a pressure medium; the drop in compressibility in this system is ascribed to helium atoms o

Lasse Thorup Fallesen, Simon Pauli, Elisabeth Sommer James, Lars Nørvang Andersen

Rooted bifurcating trees are mathematical objects used to model evolutionary relationships and arise naturally in both coalescent theory and phylogenetics. Recent numerical representations of tree topologies, known as F-matrices, allow for summarizing a sample of trees via Fr\'echet means and provide new measures of tree balance. However, the number of ranke

Jakub Takáč

We show that whenever a separable subset $S$ of a complete metric space $X$ admits a $d$-dimensional weak tangent field, the set $S$ is close to being $d$-dimensional in the following sense. Whenever $\mu$ is a Borel finite measure on $X$ supported on $S$, then a typical $1$-Lispchitz map (in the sense of Baire category) into a Euclidean space maps $\mu$-alm

Paul Alphonse, Billel Guelmame, Julien Vovelle

We prove a quantitative averaging lemma for spatially dependent vector fields. Our proof is based on an iteration of the regularizing operator and some elementary considerations about the local inversion theorem.

Arnaud Brothier, Aidan Sims, Dilshan Wijesena

We develop new techniques for the construction and classification of representations of row-finite and locally convex higher-rank graph C*-algebras O. This class includes Cuntz--Krieger algebras associated to row-finite directed graphs. Our approach relies on the representation theory of a certain non-self-adjoint algebra and a lifting process of representat

Masahiro Suzuki, Hiroki Sakaji

We introduce JFinTEB, the first comprehensive benchmark specifically designed for evaluating Japanese financial text embeddings. Existing embedding benchmarks provide limited coverage of language-specific and domain-specific aspects found in Japanese financial texts. Our benchmark encompasses diverse task categories including retrieval and classification tas

Xia Han, Bin Li

This paper studies optimal insurance design under asymmetric information in a Stackelberg framework, where a monopolistic insurer faces uncertainty about both the insured's risk attitude, captured by a risk-aversion parameter, and the insured's risk type, characterized by the loss distribution. In particular, when the risk type is unobservable, we allow the

Roberto Mossa

We prove a rigidity theorem for the Bergman metric on Hartogs domains over bounded homogeneous domains. Let $\Omega\subset \mathbb C^n$ be a bounded homogeneous domain, let $K_\Omega$ denote its Bergman kernel, and consider $$ \Omega_{m,s}:=\{(z,\zeta)\in \Omega\times \mathbb C^m:\ \|\zeta\|^2<K_\Omega(z,\bar z)^{-s}\}, \qquad m\ge 1,\quad s>-C_\Omega. $$ Fo

Emmanuil H. Georgoulis, Panagiotis Paraschis

We consider the discretization of the $p$-Laplacian equation with an interior penalty discontinuous Galerkin method. We prove novel trace-type inverse estimates, leading to unconditional stability of the method. Further, $hp$-version a priori norm and quasi-norm error estimates are established, subordinate to available polynomial approximation results. The a

Ya-Guang Wang, Yi-Lei Zhao

This paper is devoted to the study of the compressible boundary layer equations in the Gevrey-2 solution space. Compared to the classical Prandtl equation, the additional complexity arises from the strong interaction between viscous layer and thermal layer. By introducing new auxiliary functions and observing the cancellation mechanism to overcome the loss o

Mikhail Shirokikh, Sergey Nikolenko

Prefix caching is a key latency optimization for autoregressive LLM serving, yet existing systems assume dense per-token key/value reuse. State-space models change the structure of the problem: a recurrent layer can resume from a single stored state rather than requiring the entire token history. This asymmetry opens a new design point between no reuse and d

Marco Quantschnig, Yannick Werner, Sonja Wogrin, Thomas Klatzer

Gas infrastructure datasets are essential inputs for energy system planning to support strategic decision-making toward decarbonization. However, relevant data are typically scattered across heterogeneous sources, including geospatial datasets, image-based infrastructure plans, and tabular data, making it complex, time-consuming, and error-prone to create to

Yanming Peng, Shijing Wang, Yaping Huang, Yi Tian

Generalizable gaze estimation methods have garnered increasing attention due to their critical importance in real-world applications and have achieved significant progress. However, they often overlook the effect of label noise, arising from the inherent difficulty of acquiring precise gaze annotations, on model generalization performance. In this paper, we

Sadia Mubashshira, Nazanin Amini, Kevin Desai

We present Cloth-HUGS, a Gaussian Splatting based neural rendering framework for photorealistic clothed human reconstruction that explicitly disentangles body and clothing. Unlike prior methods that absorb clothing into a single body representation and struggle with loose garments and complex deformations, Cloth-HUGS represents the performer using separate G

Kush Kinra

Continuous data assimilation (CDA) techniques, most notably the nudging approach proposed by Azouani, Olson, and Titi (AOT), have been shown to be very successful in deterministic frameworks for achieving long-time synchronization between an approximate state and true state. In this note, we develop and study a CDA scheme for a class of stochastic non-Newton

Judith Sieker, Sina Zarrieß

Large language models (LLMs) are increasingly studied as repositories of linguistic knowledge. In this line of work, models are commonly evaluated both as generators of language and as judges of linguistic output, yet these two roles are rarely examined in direct relation to one another. As a result, it remains unclear whether success in one role aligns with

Xhevahire Tërnava

Feature toggles enable gradual rollouts and experimentation in software systems, yet often persist beyond their intended lifecycle, accumulating as technical debt. Prior research has examined feature toggle interactions and complexity, but no longitudinal study has quantified how toggles evolve over time across different organizational contexts. We analyse o

Lifan Jiang, Tianrun Wu, Yuhang Pei, Chenyang Wang

The evaluation of visual editing models remains fragmented across methods and modalities. Existing benchmarks are often tailored to specific paradigms, making fair cross-paradigm comparisons difficult, while video editing lacks reliable evaluation benchmarks. Furthermore, common automatic metrics often misalign with human preference, yet directly deploying l

Eñaut Mendiluze Usandizaga, Thomas Laurent, Paolo Arcaini, Shaukat Ali

Quantum software testing has attracted interest in recent years, prompting the development of various techniques to automate the testing of quantum software. These techniques generate test cases that must be assessed for their effectiveness in detecting faults. Such an assessment requires benchmarks of faulty programs. However, there is a lack of benchmarks

Xuanming Huang, Chi Ding, Yujie Sun, Shidi Huang

This study presents a systematic validation and comparative assessment of computational fluid dynamics (CFD) strategies for centrifugal blood pump simulations using the U.S. Food and Drug Administration benchmark model. A scale-resolving large eddy simulation (LES) with transient sliding-interface (SI) coupling is evaluated and compared against Reynolds-aver

Gustavo Banegas, Kim Youngbeom, Seo Seog Chung, Vredendaal Christine Van

We present a low-stack implementation of the module-lattice signature scheme HAETAE, targeting microcontrollers with 8 kB-16 kB of available SRAM. On such devices, peak stack usage is often the binding constraint, and HAETAE's hyperball-based sampler, large transient polynomial vectors, and variable-length signature payloads (hint and high-bits arrays) pose

Hiroshi Ohno

The Hadamard test is a standard quantum primitive for estimating inner products and expectation values, but in data-processing settings its practical utility is often limited by the cost of preparing amplitude-encoded quantum states. In this study, we investigate an angle-encoding variant of the Hadamard test for estimating cosine similarity between normaliz

Siun Kim, Hyung-Jin Yoon

Large language models (LLMs) have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER), yet their generative outputs still show persistent and systematic errors. Despite progress through instruction fine-tuning, zero-shot NER still lags far behind supervised systems. These recurring errors mirror inconsistenc

Lu Chen, Jiali Lan, Yong Wu

In this paper, we study the regularity of the free boundary for minimizers of the Alt-Phillips functional with negative powers \[\mathcal{E}_{\gamma}(u)=\int_{\Omega}\frac{1}{2}|\nabla u|^2+\frac{1}{\gamma}u^{-\gamma}\chi_{\{u>0\}}dx,\quad\gamma\in(0,2).\] We proved that the free boundaries are $C^{\infty}$ at regular points. A key technical tool is the line

Yijia Guo, Wenkai Huang, Tong Hu, Gaolei Li

3D Gaussian Splatting (3DGS) has recently redefined the paradigm of 3D reconstruction, striking an unprecedented balance between visual fidelity and computational efficiency. As its adoption proliferates, safeguarding the copyright of explicit 3DGS assets has become paramount. However, existing invisible message embedding frameworks struggle to reconcile sec

Ahana Pradhan, Srinivas Karthik, Imtiyazuddin Shaik, Srinivas Vivek

Growing privacy regulations and internal governance mandates are driving demand for fine-grained, context-sensitive access control in data management systems. Among competing approaches, content-based access control -- where access decisions depend on the data values referenced by a query -- is becoming particularly prominent, and is supported directly in mo

Angela Capocefalo, Francesco Brasili, Javier Pérez, Edouard Chauveau

Plasmonic nanoparticles (NPs) integrated within thermoresponsive polymeric microgels provide a versatile platform for the realization of stimuli-responsive optical materials, where the microgel volume phase transition enables dynamic control of plasmon coupling. This study uncovers a counter-intuitive re-entrant behavior with increasing NP loading in which p

Jeremy Qin, Maksym Andriushchenko

Forecasting has become a natural benchmark for reasoning under uncertainty. Yet existing evaluations of large language models remain limited to judgmental tasks in simple formats, such as binary or multiple-choice questions. In practice, however, forecasting spans a far broader scope. Across domains such as economics, public health, and social demographics,

Ming-Xing Luo

Weber's conjecture (1886) governs three aspects of lattice-based cryptography: the solvability of the Principal Ideal Problem, the freeness of modules over rings of integers, and the tightness of worst-case-to-average-case reductions in Ring-LWE (R-LWE) and Module-LWE (MLWE). Existing verifications for $k \ge 9$ rely on Generalized Riemann Hypothesis (GRH).

Taewoong Kang, Hyojin Jang, Sohyun Jeong, Seunggi Moon

Recent digital media advancements have created increasing demands for sophisticated portrait manipulation techniques, particularly head swapping, where one's head is seamlessly integrated with another's body. However, current approaches predominantly rely on face-centered cropped data with limited view angles, significantly restricting their real-world appli

Irem Ulku, Erdem Akagündüz, Ömer Özgür Tanrıöver

Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unavailable due to sensor failures, acquisition issues, or challenging atmospheric conditions. Existing multimodal segmentation models typically address missing modalities by learning a shared representation across

Jun-Chao Liang, Yin-Bi Li, A-Li Luo, Shuo Li

To improve the accuracy and efficiency of high-dimensional stellar parameter inference in large spectroscopic datasets, we propose a projection-assisted parameter-inference framework -- Projected-Space Inference of Stellar Parameters (PISP). PISP constructs an orthonormal basis and optimizes in the projected space, reducing the impact of parameter correlatio

Jed R Muff, Karine Miras, A. E. Eiben

Lamarckian inheritance has been shown to be a powerful accelerator in systems where the joint evolution of robot morphologies and controllers is enhanced with individual learning. Its defining advantage lies in the offspring inheriting controllers learned by their parents. The efficacy of this option, however, relies on morphological similarity between paren

Liwen Yu, Chi Liu, Xiaotong Han, Congcong Zhu

Automated Aesthetic Quality Assessment (AQA) treats images primarily as static pixel vectors, aligning predictions with human-rating scores largely through semantic perception. However, this paradigm diverges from human aesthetic cognition, which arises from dynamic visual exploration shaped by scanning paths, processing fluency, and the interplay between bo

Yu. D. Fomin

Molecular dynamics is a powerful tool to investigate the properties of fluid systems. However, a correct interpretation of the results of simulations is required. In particular, some simulations show appearance of large voids in liquids, which contradicts our common sense on what is liquid. In the present paper we discuss the origin of large cavities liquids

D. A. Christie, M. Zamyatina, E. Hébrard, T. M. Evans-Soma

Chemical kinetics is becoming an increasingly vital component of hot Jupiter general circulation models (GCMs). Here we simulate the hot Jupiter WASP-96b using two chemical networks, a reduced chemical network frequently used in the GCM literature (which we refer to as V19) and a more recent effective network making use of tables of net reactions (MiniCHEM),

Alvin K. Y. Li, Otto A. Hannuksela

Accurate sky localization is essential for gravitational-wave (GW) astronomy, particularly for multimessenger follow-up and host galaxy identification. For strongly lensed GW events, achieving localization at the level of $\sim 10~\mathrm{deg}^2$ is critical for associating signals with their lensing structures and enabling targeted searches for additional f

Xiquan Li, Aurian Quelennec, Slim Essid

Music understanding and reasoning are central challenges in the Music Information Research field, with applications ranging from retrieval and recommendation to music agents and virtual assistants. Recent Large Audio-Language Models (LALMs) have shown remarkable progress in answering music-related questions by following user instructions. However, their mass

Tobias Abilock Mikkelsen, Cristian Placinta, Jesper Glückstad, Mirza Karamehmedović

We present a phase-only time-reversal framework for steering photonic nanojets without mechanical motion or amplitude modulation. Time-reversed radiation by a synthetic source placed at the target PNJ location helps define a phase-only modulation on a control line, compatible with a spatial light modulator, that produces the desired PNJ. Full-wave finite-dif

Junyi Li, Yongqiang Chen, Ningning Ding

Machine unlearning has gained increasing attention in recent years, as a promising technique to selectively remove unwanted privacy or copyrighted information from Large Language Models that are trained on a massive scale of human data. However, the emergence of Large Reasoning Models (LRMs), which emphasize long chain-of-thought (CoT) reasoning to address c

Behzad Azmi, Stefan Frei, Felix Sauer

This paper investigates the local exponential stabilization of the two-dimensional Navier--Stokes equations to a given reference trajectory by means of receding horizon control (RHC). The control is realized as a linear combination of finitely many actuators, represented by indicator functions supported on subsets of a prescribed control subdomain. We establ

Alessandro Lotto, Francesco Marchiori, Mauro Conti

Modern computing systems implicitly trust human input devices, allowing USB Human Interface Device (HID) emulators, such as the USB Rubber Ducky, to inject arbitrary keystrokes while bypassing conventional defenses. Speed- and regularity-based heuristics are easily evaded through slower or randomized timing. Keystroke dynamics offers a behavioral alternative

Ragib Shahariar Ayon, Shibbir Ahmed

Automatically generating formal specifications could reduce the effort needed to improve program correctness, but in practice, this is still challenging. Many developers avoid writing contracts by hand, which limits the use of automated verification tools. Recent large language models (LLMs) can generate specifications from code, but these specifications oft

Rachit Anand Srivastava, Vikram Bhattacharjee, Will Arnold, Toufeeq Pasha

Light clients are essential for scalable blockchain systems because they verify data availability without downloading full blocks. In data availability sampling based systems, sampled cells are retrieved from a peer-to-peer network and verified against cryptographic commitments. A common deployment pattern associates each sampled cell with an independent Kat

Dariusz Kosz, Jakub Niksiński, Błażej Wróbel

We prove a uniform upper and lower bound for Delannoy numbers. This is achieved by using the representation of Delannoy numbers as the number of lattice points in high-dimensional cross-polytopes (also known as hyper-octahedrons or $\ell^1$ balls) and proving a uniform (dimension-free) count for these lattice points. Using this count, we establish dimension-

Tanja Baeumel, Josef van Genabith, Simon Ostermann

Large language models (LLMs) have demonstrated impressive capabilities, yet their internal mechanisms for handling reasoning-intensive tasks remain underexplored. To advance the understanding of model-internal processing mechanisms, we present an investigation of how LLMs perform arithmetic operations by examining internal mechanisms during task execution. U

Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li

While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative subcultural language in the Chinese internet context, remains largely unexplored. In this paper, we introduce Mouse, a specialized benchmark designed to evaluate the capabilities of LLMs on NLP tasks involvi

Shidong Yang, Ziyu Ma, Tongwen Huang, Yiming Hu

Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent's evolving behavior and leads to poor coverage of complex environment interactions. To address these challenges, we propose CoEvolve, an agent-data mutual evolution framework that enables LLM agents to improve through closed-loop, int

Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie

Most ATP benchmarks embed the final answer within the formal statement -- a convention we call "Easy Mode" -- a design that simplifies the task relative to what human competitors face and may lead to optimistic estimates of model capability. We call the stricter, more realistic setting "Hard Mode": the system must independently discover the answer before con

Thomas Landais, Olivier Goudet, Adrien Goëffon, Frédéric Saubion

Combinatorial black-box optimization in high-dimensional settings demands a careful trade-off between exploiting promising regions of the search space and preserving sufficient exploration to identify multiple optima. Although Estimation-of-Distribution Algorithms (EDAs) provide a powerful model-based framework, they often concentrate on a single region of i

Zhixiong Yang, Long Jing, Yao Li, Shuli Cheng

AIGC has shown remarkable success in CV and NLP, and has recently demonstrated promising potential in the wireless domain. However, significant data imbalance exists across RF modalities, with abundant WiFi data but scarce mmWave and RFID data due to high acquisition cost. This makes it difficult to train high-quality generative models for these data-scarce

Zengyu Pang, Hua Xiang

Semi-quantum signature (SQS) schemes aim to enable quantum signature functionality in scenarios where only a subset of participants possess full quantum capabilities, thereby improving practical deployability while preserving quantum security advantages. Within this framework, we present a practical SQS protocol based on Bell states. The protocol is designed

Zhaobo Hu, Vincent Gauthier, Mehdi Naima

Spatiotemporal modeling has evolved beyond simple time series analysis to become fundamental in structural time series analysis. While current research extensively employs graph neural networks (GNNs) for spatial feature extraction with notable success, these networks are limited to capturing only pairwise relationships, despite real-world networks containin

Ashleigh Ratcliffe, Tho Nguyen Xuan

In Section 6.6 of the book {\it Number Theory, Volume I: Tools and Diophantine Equations, Graduate Texts in Mathematics, Volume 239, Springer (2007)}, Cohen investigated the solubility of the equation $n=x^4+y^4$ in the rational numbers $x,y$ for all positive integers $n \leq 10000$. Motivated by this, we investigate the equation $n=x^4-y^4$ and obtain the c

Taki Eddine Djidjekh, Alexandru Takacs, Gaël Loubet, Lamoussa Sanogo

This paper presents a lightweight, protocol-agnostic security enhancement for Simultaneous Wireless Information and Power Transfer (SWIPT) in Internet of Things (IoT) applications. Building on a backscatter-based identification mechanism, the proposed approach introduces a secure, energy-efficient layer that operates independently of communication protocols

Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin

Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy problems can cause overfitting and pass@k degradation, while training on hard problems often results in sparse rewards. Recent question augmentation methods address this by prepending partial solutions as hints

Jun Li, Lizhi Xiong, Ziqiang Li, Weiwei Jiang

Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in large-scale training datasets. Existing concept erasure methods, whether text-only or image-assisted, face trade-offs: textual approaches often fail to fully suppress concepts, while n

Alexander Musiat, Nikolas Ebert, Oliver Wasenmüller

Hyperspectral imaging enables fine-grained recognition of materials by capturing rich spectral signatures, but learning robust classifiers is challenging due to high dimensionality, spectral redundancy, limited labeled data, and strong domain shifts. Beyond earth observation, labeled HSI data is often scarce and imbalanced, motivating compact models for gene

Tobias Schimanski, Stefanie Lewandowski, Christian Woerle, Nicola Reichenau

Conventional information retrieval is concerned with identifying the relevance of texts for a given query. Yet, the conventional definition of relevance is dominated by aspects of similarity in texts, leaving unobserved whether the text is truly useful for addressing the query. For instance, when answering whether Paris is larger than Berlin, texts about Par

Vito Seinen, Peter G. Bolhuis, Daan Crommelin, Sara Jabbari Farouji

We present a theoretical framework that enables investigating rare transitions in a general model of an active particle in an external potential, with the thermal Active Ornstein-Uhlenbeck Particle (AOUP) appearing as a special case. Using a projection-operator formalism, we compute transition rates perturbatively in two distinct asymptotic regimes. In the r

Kevin Michael Frick

Artificial intelligence algorithms are increasingly used by firms to set prices. Previous research shows that they can exhibit collusive behaviour, but how quickly they can do so has so far remained an open question. I show that a modern deep reinforcement learning model deployed to price goods in a repeated oligopolistic competition game with continuous pri

Mikio Kano, Shun-ichi Maezawa, Kenta Ozeki

An {\em odd subgraph} of a graph is a subgraph in which every vertex has odd degree. A graph $G$ is said to be {\em odd $k$-edge-colorable} if there exists an edge-coloring $E(G) \rightarrow \{1,2, \ldots, k\}$ such that each non-empty color class induces an odd subgraph of $G$. The {\em odd chromatic index} of $G$, denoted by $\chi'_o(G)$, is the minimum $k

Ze Dong, Hao Shi, Zejia Gao, Zhonghua Yi

Embodied robotic agents often perceive movies through an egocentric screen-view interface rather than native cinematic footage, introducing domain shifts such as viewpoint distortion, scale variation, illumination changes, and environmental interference. However, existing research on movie emotion understanding is almost exclusively conducted on cinematic fo

Saloni Garg, Ukant Jadia, Amit Sagtani, Kamal Kant Hiran

Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compares three traditional machine learning algorithms (Decision Tree Classifier, Random Forest Classifier, and Logistic Regression) and three deep learning models (Simple Convolutional Neural Network (CNN), Long Shor

Yuanchang Zhou, Hongyu Wang, Yiming Du, Yan Wang

Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire periodic table, serve as foundational models for quantum-accurate physical simulations. However, uMLIP training requires second-order derivatives, which lack corresponding parallel trai

Thea Budde, Marina Kristć Marinković, Joao C. Pinto Barros

The Hamiltonian formulation of lattice gauge theories plays a central role in quantum simulations of gauge theories, and understanding their spectrum and other properties is expected to become crucial in the upcoming years. The relevant Hamiltonians in this framework possess local symmetry at each lattice site and may exhibit higher-form symmetries. There ar

Kevin Michael Frick, Jonas Gathen

We propose a new approach to estimate government worker skills, a setting where output is hard to observe and wages may be uninformative about skills. The approach uses wages in comparable jobs in the private sector and machine learning tools to link skills to skill-related observables. We apply the approach to rich Indonesian household-level panel data from

Jamal Drewlo

We show a continuity result for the Weyl pseudometric on subshifts which are generated by model sets. This fact is then used for multiple constructions of subshifts that exhibit different behavior regarding entropy, amorphic complexity and their maximal equicontinuous factor.

Hiroyuki Sagawa

We explore unsolved nuclear structure problems related with the spin and isospin degree of freedom by using microscopic models which accommodate realistic isoscalar and isovector pairing interactions, and also tensor correlations. For the attempt of universal theoretical framework for both nuclear and astrophysical phenomena, we adopt a self-consistent Hartr

A. Peláez-Torres, F. J. Pozuelos, G. Morello, M. Dévora-Pajares

The Transiting Exoplanet Survey Satellite (TESS) has delivered a large number of transiting planet candidates around nearby stars by identifying periodic decreases in stellar brightness. Establishing the planetary nature of these signals and determining their fundamental properties is a necessary step toward detailed studies of their internal structure, atmo

P. J. P. Kersbergen, J. van de Kraats, D. J. M. Ahmed-Braun, S. J. J. M. F. Kokkelmans

We introduce a novel coupled-channels method for elastic three-body scattering in systems of identical bosonic alkali-metal atoms. The approach relies on the numerically exact two-body off-the-energy-shell transition matrix, constructed from realistic multichannel molecular interaction potentials that support many bound states. By rigorously accounting for t

Fazeng Li, Gan Sun, Chenxi Liu, Yao He

Hand-eye calibration through visual localization is a critical capability for robotic manipulation in open-world environments. However, most deep learning-based calibration models suffer from catastrophic forgetting when adapting into unseen data amongst open-world scene changes, while simple rehearsal-based continual learning strategy cannot well mitigate t

Tianqi Luo, Leixian Shen, Yuyu Luo

Agentic visual analytics (VA) represents an emerging class of systems in which large language model (LLM)-driven agents autonomously plan, execute, evaluate, and iterate across the full visual analytics pipeline. By shifting users from low-level tool operations to high-level analytical goals expressed through natural language, these systems are fundamentally

Trong Duy Tran, Maxime Ferreira Da Costa, Salah Eddine Elayoubi, Nguyen Linh Trung

In this paper, we consider resource allocation for a collaborative integrated sensing and communication (ISAC) scenario, in which distributed smart devices can be scheduled to perform sensing and transmit their sensing features to a fusion center. The fusion center aims to perform classification tasks on the environment based on received features. A scalable

Kim Christensen, Wenjing Liu, Zhi Liu, Yoann Potiron

We study a new measure of codependency in the second moment of a continuous-time multivariate asset price process, which we name the realized copula of volatility. The statistic is based on local volatility estimates constructed from high-frequency asset returns and affords a nonparametric estimator of the empirical copula of the latent stochastic volatility

Florian Lehn, Pascal Ahr, Hans D. Schotten

Static Random Access Memory (SRAM) Physically Unclonable Functions (PUFs) make use of intrinsic manufacturing variations in memory cells to derive device-unique responses. Employing such hardware-rooted fingerprints for authentication, this work demonstrates a threshold-based authentication proof of concept for constrained Industrial Internet of Things (IIoT

Laura Alonso Bartolomé, Jean-Loup Faulon, Xavier Hinaut

Biological systems are promising substrates for computation because they naturally process environmental information through complex internal dynamics. In this study, we investigate whether bacterial metabolic models can act as physical reservoirs and whether their computational performance can be predicted from dynamical properties linked to separability an

Chengxin Liu, Wonseok Choi, Chenshuang Zhang, Tae-Hyun Oh

Vision-Language Models (VLMs) have demonstrated strong capability in a wide range of tasks such as visual recognition, document parsing, and visual grounding. Nevertheless, recent work shows that while VLMs often manage to capture the correct image region corresponding to the question, they do not necessarily produce the correct answers. In this work, we dem

Lama Moukheiber, Caleb M. Yeung, Haotian Xue, Alec Helbling

Spatial reasoning and visual grounding are core capabilities for vision-language models (VLMs), yet most medical VLMs produce predictions without transparent reasoning or spatial evidence. Existing benchmarks also evaluate VLMs on isolated 2D images, overlooking the volumetric nature of clinical imaging, where findings can span multiple frames or appear on o

P. Tanga, K. Tsiganis, D. Souami, R. Anderson

The DART spacecraft impacted Dimorphos, the satellite of (65803) Didymos, in September 2022. Evidence of crater formation and possible global reshaping has been obtained indirectly from spacecraft and ground-based data. Since the impact, several stellar occultations by Didymos have been observed, but only one in particular, on January 21, 2023, can provide u

Ping Hu, Ting Lan

The Tur\'an number of a graph $F$, $ex(n,F)$, is the maximum number of edges in a graph on $n$ vertices which does not contain $F$ as a subgraph. Let $S_{a,b}$ denote a double star with a central edge $uv$, $a$ leaves connected to $u$ and $b$ leaves connected to $v$. The function $ex(n,S_{a,b})$ has been studied for $a=1,2$, their extremal graphs are disjoin