March 2026 arXiv papers — page 75

Linkuan Zhou, Yinghao Xia, Yufei Shen, Xiangyu Li

Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments. Existing methods are fundamentally limited, suffering from semantically unaware feature alignment that results in poor distributional fidelity and from pseudo-label validation that disregards global anatomical constraints, thus fa

Horst Lenske

Nuclear isotensor spectroscopy as accessible in nuclear double charge exchange (DCE) reactions is indispensable for quantitative studies of lepton number violation as in double beta decay (DBD). For such studies heavy ion double single charge exchange (DSCE) and direct Majorana double charge exchange (MDCE) reactions are discussed. Isotensor two-body transit

Zhongwei Shen, Jinping Zhuge

In this paper, we consider the elliptic operators $\mathcal{L}_\varepsilon = -\nabla\cdot (A(X/\varepsilon) \nabla )$ with periodic coefficients in a bounded domain $\Omega$ without any local smoothness assumption on $A = A(Y)$, where $\varepsilon \ll \text{diam}(\Omega)$ is a microscopic scale. Due to the irregularity of the coefficients at $\varepsilon$ sc

Qingdong He, Chaoyi Wang, Peng Tang, Yifan Yang

Video subtitle removal aims to distinguish text overlays from background content while preserving temporal coherence. Existing diffusion-based methods necessitate explicit mask sequences during both training and inference phases, which restricts their practical deployment. In this paper, we present CLEAR (Context-aware Learning for End-to-end Adaptive Video

Haroun Elleuch, Ryan Whetten, Salima Mdhaffar, Yannick Estève

We present Ara-BEST-RQ, a family of self-supervised learning (SSL) models specifically designed for multi-dialectal Arabic speech processing. Leveraging 5,640 hours of crawled Creative Commons speech and combining it with publicly available datasets, we pre-train conformer-based BEST-RQ models up to 600M parameters. Our models are evaluated on dialect identi

Kadri-Ann Pankratov, Leonid Zinatullin, Adele Metsniit, Marie Vihmar

Tight matching with the environment is key to effective robot operation in complex settings. Situated robots that build their bodies in situ (e.g. by spinning) are uniquely positioned to exploit their surroundings, yet functionalization of these structures remains an integration challenge - multimaterial spinning requires complex spinneret multiplexing, and

Thomas Bellotti, Tommaso Tenna

We demonstrate how steepest descent arguments and singularity analysis from analytic combinatorics allow for an accurate description of the behavior of linear numerical schemes -- including the notorious leap-frog scheme -- in presence of stable and unstable boundary conditions in the long-time limit.

Kathrin Schnizer

Visualization literacy assessments typically rely on correctness to classify performance, providing little evidence about how readers arrive at their answers. We argue that gaze can address this gap as an implicit process signal that complements standardized tests without sacrificing their scalability. Synthesizing findings from visualization and related res

Kendall Sullivan, Gregory J. Gilbert

Planets in binary-star systems exhibit demographic differences compared to planets in single-star systems. In particular, planets with binary-star hosts have a lower overall occurrence rate compared to their single-star counterparts, as well as a suppressed relative occurrence rate for sub-Neptunes ($R_p=2{-}4R_{\oplus}$) compared to super-Earths ($R_p=1.0{-

Tetiana Berger-Hrynova

This paper examines how the Joint Institute for Nuclear Research (JINR), an international organization formally committed to peaceful science, is deeply embedded in an ecosystem of military-industrial enterprises in the city of Dubna in Russia, contributing to training specialists and developing technologies used in Russia's military operations, including at

Mitja Devetak, Antoine Mandel

A number of recent contributions have put forward the topological structure of production networks as a key determinant of macro-economic dynamics. However, firm-to-firm production networks data is generally not available. Against this background, reconstruction method based on firms' size have been developed. This paper enriches this set of reconstruction m

Shkelqim Sherifi

New technologies, such as blockchain, are designed to address various system weaknesses, particularly those related to security. Blockchain can enhance numerous aspects of traditional banking systems by transforming them into digital, immutable, secure, and anonymous ledger. This paper proposes a new banking application ALBank, which is based on blockchain a

Naihuan Jing, Yinlong Liu, Jian Zhang

In this paper, we introduce the notion of super-immanants for supermatrices over a supercommutative algebra. Using the super Schur-Weyl duality we show that the super immanants play a significant role in covariant tensor representations of the general linear Lie superalgebra. Among various things, we obtain a supertrace formula for super-immanants, which gen

Martin Keller-Ressel, Hannes Nikulski

We investigate the data-driven discovery of parametric representations for implied volatility slices. Using symbolic regression, we search for simple analytic formulas that approximate the total implied variance as a function of log-moneyness and maturity. Our approach generates candidate parametrizations directly from market data without imposing a predefin

Amarnath R

Retinal vessel segmentation methods based on standard overlap losses tend to miss thin peripheral vessels because these structures occupy very few pixels and have low contrast against the background. We propose HMS-VesselNet, a hierarchical multi-scale network that processes fundus images across four parallel branches at different resolutions and combines th

Mingyuan Zhang, Yong Wang, Bettina G. Keller, Hao Wu

We introduce $π$-Girsanov, a new method for constructing Markov state models from biased enhanced-sampling molecular dynamics simulations based on Girsanov reweighting. The key idea behind this new method is to separate the reweighting of the stationary density from the reweighting of the correlation function. We evaluate the effectiveness of this approach o

Hamid Reza Hashempour, Le-Nam Tran, Duy H. N. Nguyen, Hien Quoc Ngo

We consider a reconfigurable intelligent surface (RIS)-assisted heterogeneous network comprising legitimate information-harvesting receivers (IHRs) and untrusted energy-harvesting receivers (UEHRs). A multi-antenna base station (BS) transmits confidential information to IHRs while ensuring sufficient energy transfer to UEHRs that may attempt eavesdropping. T

Xin Guo, Chunrui Zhao, Hong Jia, Ting Dang

Integrating Federated Learning (FL) with self-supervised learning (SSL) enables privacy-preserving fine-tuning for speech tasks. However, federated environments exhibit significant heterogeneity: clients differ in computational capacity, causing straggler effects under unified fine-tuning, while diverse downstream tasks require different representation depth

Wei Zhang, Ping Gong, Yujie Wang, Leilei Yao

Object Goal Navigation (ObjectNav) in temporally changing indoor environments is challenging because object relocation can invalidate historical scene knowledge. To address this issue, we propose a probabilistic planning framework that combines uncertainty-aware scene priors with online target relevance estimates derived from a Vision Language Model (VLM). T

Zhuocheng Zhang, Xingwu Zhang, Kangheng Liang, Guanxuan Li

Recent advances in interactive text-to-image retrieval (I-TIR) use diffusion models to bridge the modality gap between the textual information need and the images to be searched, resulting in increased effectiveness. However, existing frameworks fuse multi-modal views of user feedback by simple embedding addition. In this work, we show that this static and u

Jinhan Guo, Y. Guo, H. Wu, B. Schmieder

Observations show that many solar eruptions remain confined within strong overlying magnetic fields, forming a so-called magnetic cage. While confinement by poloidal overlying fields has been widely investigated, the role of strong external toroidal fields remains unclear. Using three-dimensional magnetohydrodynamic simulations, we study confined eruptions i

Donald Shenaj, Federico Errica, Antonio Carta

Low Rank Adaptation (LoRA) is the de facto fine-tuning strategy to generate personalized images from pre-trained diffusion models. Choosing a good rank is extremely critical, since it trades off performance and memory consumption, but today the decision is often left to the community's consensus, regardless of the personalized subject's complexity. The reaso

Elías Masquil, Thibaud Ehret, Pablo Musé, Gabriele Facciolo

Digital Surface Model generation from satellite imagery is a core task in Earth observation and is commonly addressed using classical stereoscopic matching algorithms in satellite pipelines as in the Satellite Stereo Pipeline (S2P). While recent learning-based stereo matchers achieve state-of-the-art performance on standard benchmarks, their integration into

Andronikos Paliathanasis

We investigate $f\left( Q\right) $-gravity with a matter-gravity coupling as a geometric dark energy candidate for the description of the late-time cosmic acceleration within a spatially flat Friedmann--Lema\^{\i}tre-Robertson-Walker geometry. We select a noncoincident connection that naturally follows from the general framework of cosmological models with n

Nikolas Stavrou, Siamak Mehrkanoon

Weather forecasting supports critical socioeconomic activities and complements environmental protection, yet operational Numerical Weather Prediction (NWP) systems remain computationally intensive, thus being inefficient for certain applications. Meanwhile, recent advances in deep data-driven models have demonstrated promising results in nowcasting tasks. Th

LHC Collaboration

Long-range muons produced in proton-proton collisions at the ATLAS interaction point constitute the primary background for neutrino interaction searches at the SND@LHC experiment. This work presents a comprehensive characterization of the muon flux throughout LHC Run-3, benchmarking Monte Carlo simulations against experimental measurements. Measured and simu

Xinyu Lu, Kaiqi Zhang, Jinglin Yang, Boxi Cao

Reinforcement Learning with Verifiable Rewards (RLVR) enhances Large Language Model (LLM) reasoning but suffers from advantage collapse on ``hard samples'' where all rollouts fail. This lack of variance eliminates crucial learning signals. For these intractable samples, simply scaling up rollout budgets offers limited gains. We introduce Joint Policy and Pro

Xi Xuan, Wenxin Zhang, Zhiyu Li, Jennifer Williams

Speech deepfake source verification systems aims to determine whether two synthetic speech utterances originate from the same source generator, often assuming that the resulting source embeddings are independent of speaker traits. However, this assumption remains unverified. In this paper, we first investigate the impact of speaker factors on source verifica

Ian Crawford, Carl-Emil Pless

We study the associations between everyday economic decision-making quality and people's emotional states. Using high-frequency, highly disaggregated consumer "scanner" data, we show that the cost of poor decision-making is substantial, on average equal to around half of day-to-day consumption budgets. While material circumstances help explain decision-makin

Mingzhe Zheng, Weijie Kong, Yue Wu, Dengyang Jiang

Group Relative Policy Optimization (GRPO) methods for video generation like FlowGRPO remain far less reliable than their counterparts for language models and images. This gap arises because video generation has a complex solution space, and the ODE-to-SDE conversion used for exploration can inject excess noise, lowering rollout quality and making reward esti

Aleksandra Urman, Anikó Hannák, Joachim Baumann

GoogleTrendArchive is a comprehensive archive of Google Trending Now data spanning over one year (from November 28, 2024 to January 3, 2026) across 125 countries and 1,358 locations. Unlike Google Trends, which requires specifying search terms in advance, Trending Now captures search queries experiencing real-time surges, offering a way to inductively discov

Joseph Cho, Mason Pember, Wayne Rossman

We show how permutability of transforms of smooth surfaces with particular characteristics leads to discrete surfaces with discrete analogues of the same characteristics.

Tingkai Xue, Yu Jiao, Te Ba, Jingliang Wang

In this work, we develop a neural-physics solver based on finite volume method (FVM), namely NeuralFVM, for turbulent flows by implementing the standard $k$-$\omega$ model designed for efficient Graphics Processing Unit (GPU) execution. The governing equations for fluid flow and heat transfer are reformulated as local tensor operations using convolution-base

Paweł Borsukiewicz, Daniele Lunghi, Melissa Tessa, Jacques Klein

While the rapid development of facial recognition algorithms has enabled numerous beneficial applications, their widespread deployment has raised significant concerns about the risks of mass surveillance and threats to individual privacy. In this paper, we introduce \textit{Adversarial Camouflage} as a novel solution for protecting users' privacy. This appro

Yingxuan Wen

Authors often struggle to interpret peer review feedback, deriving false hope from polite comments or feeling confused by specific low scores. To investigate this, we construct a dataset of over 30,000 ICLR 2021-2025 submissions and compare acceptance prediction performance using numerical scores versus text reviews. Our experiments reveal a significant perf

Yuyang You, Yongzhi Li, Jiahui Li, Yadong Mu

Video generation has recently emerged as a central task in the field of generative AI. However, the substantial computational cost inherent in video synthesis makes model distillation a critical technique for efficient deployment. Despite its significance, there is a scarcity of methods specifically designed for video diffusion models. Prevailing approaches

Jakob Metson, Ramin Golestanian

The appearance of emergent symmetries in complex systems with components that can form composite units provides us with opportunities for design and control of exotic phase behaviour, for example by exploiting the dynamical symmetry breaking associated with them. We present a novel mechanism for the emergence of non-reciprocal interactions in a single-specie

Weilin Wan, Jingtao Han, Weizhong Zhang, Cheng Jin

Scaling laws for Large Language Models govern macroscopic resource allocation, yet translating them into precise Mixture-of-Experts (MoE) architectural configurations remains an open problem due to the combinatorially vast design space. Existing MoE scaling studies are constrained by experimental budgets to either augment scaling formulas with extra MoE vari

Ben Bert, Coleridge Faraday, W. A. Horowitz

We present high-$p_T$ $R_{AB}$ and $v_2$ from a perturbative quantum chromodynamics-based energy loss model that includes event-by-event hydrodynamic evolution of the medium and small system size corrections to the energy loss. The model is calibrated on, and describes well, large system $R_{AA}$ and $v_2$ experimental data. The extrapolation of our model to

A. E. Drozdov, N. V. Emelyanov

New Horizons RALPH/LEISA near-infrared spectra allow for regional mapping of Pluto's surface ices and their physical state; however, scan-to-scan artifacts and variable spatial resolution complicate quantitative interpretation. We extend previous LEISA compositional studies (Protopapa et al., 2017(arXiv:2110.00662); Schmitt et al., 2016; Emran et al., 2023(a

María Romero-Colmenares, Katherine Vieira, Jeremy Tregloan-Reed, Yonggi Kim

Context. To better understand the observed brightness of low Earth orbit satellites, we must characterize their reflectivity, which in turn depends importantly on their bus designs. The reflectivity of a body can be described by Lambert's law, in terms of its albedo, cross-sectional area, range (distance), phase angles, and the mixing coefficient between dif

Orlando Luongo, Stefano Mancini, Sebastiano Tomasi

We study entanglement degradation near the horizons of regular, Reissner-Nordstr\"om, and Schwarzschild-de Sitter black holes, considering the Bardeen, Hayward, and generalized Hayward metrics as regular black holes. To this end, we compute the entanglement negativity, $\mathcal{N}$, for two Unruh-like modes of a scalar field shared by Alice, who is inertial

Sulian Thual, Feiyang Cai, Jingjing Wang, Feng Luo

Generative Deep Learning is a powerful tool for modeling of the Madden-Julian oscillation (MJO) in the tropics, yet its relationship to traditional theoretical frameworks remains poorly understood. Here we propose a video diffusion model, trained on atmospheric reanalysis, to synthetize long MJO sequences conditioned on key low-dimensional metrics. The gener

Edoardo Santo, Michele Fumagalli, Seok-Jun Chang, Max Gronke

We use deep MUSE data from the MUDF survey to investigate the cool gas around galaxies at redshifts 0.5 < z < 2. We constructed two samples: one sample for a down-the-barrel analysis, probing outflows via MgII absorption against galaxy continua, and the other sample for projected galaxy pairs to examine the gas around the foreground galaxies in the transvers

Aryan Kasat, Smriti Singh, Aman Chadha, Vinija Jain

Do large language models reason morally, or do they merely sound like they do? We investigate whether LLM responses to moral dilemmas exhibit genuine developmental progression through Kohlberg's stages of moral development, or whether alignment training instead produces reasoning-like outputs that superficially resemble mature moral judgment without the unde

Junhyeok Rui Cha, Woohyun Cha, Jaeyong Shin, Donghyeon Kim

This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Unlike prior methods that typically rely on domain randomization over a fixed finite set of parameters, the proposed approach injects state-dependent perturbations into the input joint torque during forward simulation. These pertu

Andrzej Odrzywołek

A single two-input gate suffices for all of Boolean logic in digital hardware. No comparable primitive has been known for continuous mathematics: computing elementary functions such as sin, cos, sqrt, and log has always required multiple distinct operations. Here I show that a single binary operator, eml(x,y)=exp(x)-ln(y), together with the constant 1, gener

Qi Zhan, Xing Hu, Xin Xia, Shanping Li

Verifying whether two implementations of the same large model are equivalent across frameworks is difficult in practice. Even when they realize the same computation, their graphs may differ substantially in operator decomposition, tensor layout, and the use of fused or opaque kernels, making manual rewrite rules hard to build and maintain. We present Emerge,

Domonkos Varga

Reliable evaluation is essential in machine learning research, yet methodological flaws-particularly data leakage-continue to undermine the validity of reported results. In this work, we investigate whether large language models (LLMs) can act as independent analytical agents capable of identifying such issues in published studies. As a case study, we analyz

Mariella Mischinger, Vahid Ghafouri, Sergio Pastrana, Guillermo Suarez-Tangil

Underground forums play a crucial role in the criminal ecosystem, facilitating the exchange of knowledge and the trade of illegal tools and services. By analyzing the skills, motivations, focus, and operations of cyber-criminals active in these forums, cybersecurity professionals and law enforcement can better understand their tactics, assess the risks they

D. A. Orlov, Y. Prokazov, E. Turbin, E. Kernen

A plug-and-play PhotonPix single-photon detector with a logical signal output is developed for applications requiring ultimate timing precision down to 10 ps over a wide dynamic photon flux range. The heart of the detector is an Exosens Fast Timing Microchannel Plate Photomultiplier (FT MCP-PMT) with a large 8 mm diameter sensitive area, which can accommodat

Ajan Subramanian, Sumukh Bettadapura, Rohan Sathish

Consumer-grade EEG is entering everyday devices, from earbuds to headbands, raising the question of whether language models can be adapted to individual neural responses. We test this by asking whether frozen LLM representations encode person-specific EEG signals, directions in activation space that predict one person's brain activity but not another's. Usin

Qionghao Huang, Can Hu

Remote sensing scene classification has experienced a paradigmatic transformation from traditional handcrafted feature methods to sophisticated artificial intelligence systems that now form the backbone of modern Earth observation applications. This comprehensive survey examines the complete methodological evolution, systematically tracing development from c

Miquel Colom i Bernadich, Shi Dai, Federico Abbate, Matthew Kerr

Millisecond pulsars (MSPs) are powerful probes of globular clusters (GCs), tracing stellar evolution, cluster dynamics, and the local gravitational potential. We investigate the MSP population in GC Omega Centauri. We perform Fourier-domain acceleration and jerk searches on MeerKAT observations, and carry out pulsar timing using MeerKAT and Parkes Murriyang

Marc Franquesa Monés, Jiaqi Zhang, Caroline Uhler

Learning causal relations from observational data is a fundamental problem with wide-ranging applications across many fields. Constraint-based methods infer the underlying causal structure by performing conditional independence tests. However, existing algorithms such as the prominent PC algorithm need to perform a large number of independence tests, which i

Lewis Wooltorton, Twesh Upadhyaya, Mohsen Razavi

Satellite based quantum key distribution (QKD) aims to establish secure key exchange over long distances despite significant technological challenges. To alleviate some of these challenges, Ghalaii et al. [PRX Quantum 4, 040320 (2023)] proposed that any airborne eavesdropper up to a certain size can be detected by classical monitoring techniques, limiting th

S. Viswanathan, Hao Xing

We study an information acquisition problem in which an informed trader acquires costly information prior to trading in the Kyle equilibrium. The cost of information acquisition is represented by an entropy cost. Regardless of the prior distribution of the asset payoff, continuous signals are optimal. Moreover, any continuously distributed signal, together w

Tomáš Jakl, Luca Reggio

Arboreal categories were introduced as an axiomatic framework for game comonads, which provide a comonadic view on many model-comparison games in logic. We demonstrate the inadequacy of the axiom stating that paths are connected. We then propose the notion of ``tree-connectedness'' to address this deficiency, and show that all the essential properties of arb

Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu, Fabrizio Silvestri

Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly expensive due to the stringent requirement for low-error and high-quality labels necessary for reliable model evaluation. Traditional approaches require annotating entire test sets, leading to substantial resource r

V. B. Mendrot, A. S. de Castro, P. Alberto

We study the Dirac equation in 3+1 dimensions with a general combination of scalar, vector and tensor interactions with arbitrary strengths, all of them described by central Coulomb potentials acting on a particular plane of motion. For the tensor coupling a constant term is also included, since this gives rise to an effective Coulomb potential, which is nec

Jianhong Hu, Hang Yang, Liang Gao

Despite significant progress in cosmological simulations of galaxy formation, the role of subgrid physics in shaping the detailed properties of galaxies remains incompletely understood. In this work, we analyze two sets of zoom-in simulations that share identical initial conditions but adopt distinct implementations of baryonic physics, enabling a controlled

Ezequiel Lopez-Rubio, Mario Pascual-Gonzalez

A fundamental but largely unaddressed obstacle in Symbolic regression (SR) is structural redundancy: every expression DAG with admits many distinct node-numbering schemes that all encode the same expression, each occupying a separate point in the search space and consuming fitness evaluations without adding diversity. We present IsalSR (Instruction Set and L

Bahadur Yadav, Mahaprasad Mohanty, Ratikanta Behera, Sanjay Kumar Mohanty

In this paper, we present a Hybrid Wavelet-based Physics-Informed Neural Networks (HW-PINNs) framework for portfolio management that provides a promising alternative to Physics-Informed Neural Networks (PINNs). Here, we first discuss the generalized framework of the Merton jump diffusion model and the associated HW-PINNs, followed by the one-dimensional case

Alon Gat

Modern democracies face an existential crisis of waning public trust in election results. While End-to-End Verifiable (E2E-V) voting systems promise mathematically secure elections, their reliance on complex cryptography creates a ``black box'' that forces blind trust in opaque software or external experts, ultimately failing to build genuine public confiden

Mohammad Moulaeifard, Philip J. Aston, Peter H. Charlton, Nils Strodthoff

Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based algorithms are typically trained on small-scale datasets of uncertain quality, which hinders meaningful algorithm comparisons. We present a comprehensive benchmark for PPG-based clinical prediction using the \dbname~dataset, establishing base

Sharifa Sultana, Zinnat Sultana, Jeffrey M. Rzeszotarski, Syed Ishtiaque Ahmed

There is an increasing interest in telling serious stories with data. Designers organize information, construct narratives, and present findings to inform audiences. However, many of these practices emerge from modern information visualization rhetoric and ethical frameworks which may marginalize communities with low digital and media literacy. In a ten-mont

Tom Maus, Stephan Frank, Tobias Glasmachers

Reinforcement learning (RL) is still rarely applied in industrial control, partly due to the difficulty of training reliable agents for real-world conditions. This work investigates how evolution strategies can support RL in such settings by introducing a continuous-control adaptation of an industrial sorting benchmark. The CMA-ES algorithm is used to genera

Stella Suge, Sarah W. Spencer, Nyalleng Moorosi, Helen McElhinney

Across the Global North, calls for participatory artificial intelligence (AI) to improve the responsible, safe, and ethical use of AI have increased, particularly efforts that engage citizens and communities whose well-being and safety may be directly impacted by AI and other algorithmic tools. These initiatives include surveys, community consultations, citi

Xiaochan Yuan, Pai Zeng

Accurate segmentation of coronary arteries from computed tomography angiography (CTA) images is of paramount clinical importance for the diagnosis and treatment planning of cardiovascular diseases. However, coronary artery segmentation remains challenging due to the inherent multi-branching and slender tubular morphology of the vasculature, compounded by sev

Hanyin Cheng, Xingjian Wu, Yang Shu, Zhongwen Rao

Most existing Time Series Foundation Models (TSFMs) use channel independent modeling and focus on capturing and generalizing temporal dependencies, while neglecting the correlations among channels or overlooking the different aspects of correlations. However, these correlations play a vital role in Multivariate time series forecasting. To address this, we pr

Shengping Zhang, Haiou Zeng, Ningran Wu, Guodong Xue

Ion channels regulate many essential properties of biological cells, especially the membrane potential. Despite decades of efforts on artificial channels, it remains a great challenge to mimic the dipole potential-an indispensable constituent of the membrane potential, due to its angstrom-scale characteristic length. Here, we explore nanopores in monolayer m

Jian Guan, Huolong Wu, Zhenzhong Wang, Gary G. Yen

Dynamic multiobjective optimization problems (DMOPs) feature time-varying objectives, which cause the Pareto optimal solution (POS) set to drift over time and make it difficult to maintain both convergence and diversity under limited response time. Many existing prediction-based dynamic multiobjective evolutionary algorithms (DMOEAs) either depend on learned

Foivos Fioravantes, Harmender Gahlawat, Nikolaos Melissinos, Šimon Schierreich

Hedonic games are an archetypal problem in coalition formation, where a set of selfish agents want to partition themselves into stable coalitions. In this work, we focus on two natural constraints on the possible outcomes. First, we require that exactly k coalitions are created. Then, loosely following the model of Bil\`o et al. (AAAI 2022), we assume that e

Taizhou Chen, Kai Chen, Xingyu Liu, Pingchuan Ke

Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present BadminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing. Through interviews with experienced badminton players, we identified four system design requirements with three implementatio

Shuxian Zhao, Jie Gui, Baosheng Yu, Dacheng Tao

Steel surface defect analysis is critical for industrial quality control, yet existing benchmarks rely primarily on label-only annotations, limiting fine-grained semantic understanding and systematic evaluation of vision-language models. To address this gap, we introduce SteelDefectX, a vision-language dataset with multi-form textual annotations for steel su

Bros Victor, Barbini Matilde, Gerard Patrick, Gatica-Perez Daniel

Interrogatives in news discourse have been examined in linguistics and conversation analysis, but mostly in broadcast interviews and relatively small, often English-language corpora, while large-scale computational studies of news rarely distinguish interrogatives from declaratives or differentiate their functions. This paper brings these strands together th

V. E. Didenko, A. V. Korybut

Higher-spin symmetry is known to mix lower-spin fields with higher-spin fields, creating a complex interaction picture where no closed finite field sector is expected to exist for dimensions greater than three. By studying the self-dual part of higher-spin interaction vertices in four dimensions, we show that gauge fields of spins greater than two can be con

Jan Grošelj, Ada Šadl Praprotnik, Hendrik Speleers

In this paper, we consider $C^1$ cubic Powell-Sabin splines for the numerical solution of boundary value problems on planar and spatial surface domains. We first review the construction and basic properties of polynomial and rational $C^1$ cubic Powell-Sabin spline representations on unstructured triangulations. Then, we discuss how these flexible representa

Yanglin Deng, Tianyang Xu, Chunyang Cheng, Hui Li

Infrared and visible image fusion(IVIF) combines complementary modalities while preserving natural textures and salient thermal signatures. Existing solutions predominantly rely on extensive sets of rigidly aligned image pairs for training. However, acquiring such data is often impractical due to the costly and labour-intensive alignment process. Besides, ma

Jesper B. Christensen, Ciaran Bench, Spencer A. Thomas, Hüsnü Aslan

We introduce ControlAugment (Ctrl-A), an automated data augmentation algorithm for image-vision tasks, which incorporates principles from control theory for online adjustment of augmentation strength distributions during model training. Ctrl-A eliminates the need for initialization of individual augmentation strengths. Instead, augmentation strength distribu

Ciro Ciliberto, Rick Miranda

A finite, normal cover $f: X\longrightarrow \bbP^2$ of degree $m\geq 3$ (the case $m=2$ is well known and we do not consider it in this paper) is called \emph{simple}, if there is a pencil $\mathcal P$ of rational curves of $\bbP^2$ such that the pull back via $f$ of $\mathcal P$ is a pencil of rational curves on $X$. Up to Cremona equivalence $\mathcal P$ c

Benedikt Jahnel, Jonas Köppl

We consider interacting particle systems with unbounded interaction range on general countably infinite graphs $S$ and prove explicit non-asymptotic error bounds for approximations of the infinite-volume dynamics by systems of finitely many interacting particles. Moreover, we also provide non-asymptotic quantitative bounds on the spatial decay of correlation

Chi Luo, Jiaxin Song, Yuhao Zhang, Kai Wang

Bipartite graphs serve as a natural model for representing relationships between two different types of entities. When analyzing bipartite graphs, butterfly counting is a fundamental research problem that aims to count the number of butterflies (i.e., 2x2 bicliques) in a given bipartite graph. While this problem has been extensively studied in the literature

Emre Akusta

This study analyses the potential of renewable energy to reduce inflationary pressures arising from energy imports in Turkiye. Annual data for the period 1980-2022 are used in the analysis. In this study, unit root properties are examined using the Zivot-Andrews and Lee-Strazicich tests, both of which explicitly account for structural breaks. Cointegration i

Hasret Ozan Sevim, Christof Ferreira Torres

Decentralized finance introduces new business models and use cases as part of digital finance. Restaking has recently emerged as a transformative mechanism in DeFi, promising extra yields but introducing complex and interconnected risks. The paper monitors the current restaking landscape, empirically analyzes the revenue drivers of a liquid restaking protoco

Mitsuyoshi Takahata, Jameesh Keloth, Takashi Yamamoto, Ken-ichi Harada

Integrating the scalability of individually addressable arrays of optical-tweezer-trapped single atoms with the efficient light-matter interface provided by nanophotonic waveguides has been a long-standing challenge in quantum technologies based on atoms and photons. Here we realize a quantum interface between photons guided in an optical nanofiber with a di

Turki Bin Mohaya, Peter Seiler

Attention mechanisms excel at learning sequential patterns by discriminating data based on relevance and importance. This provides state-of-the-art performance in advanced generative artificial intelligence models. This paper applies this concept of an attention mechanism for multi-agent safe control. We specifically consider the design of a neural network t

Lei Yang, Yi He, Fei Wu, Shilin Wang

Chinese mandarin visual speech recognition (VSR) is a task that has advanced in recent years, yet still lags behind the performance on non-tonal languages such as English. One primary challenge arises from the tonal nature of Mandarin, which limits the effectiveness of conventional sequence-to-sequence modeling approaches. To alleviate this issue, existing C

Siqi Dai, Tian-Cheng Yi, Xingbo Wei, Yunbo Zhang

We study the many-body localization (MBL) transition in interacting fermionic systems on disordered one-dimensional lattices using a physics-informed machine-learning framework. Instead of feeding full many-body wave functions into the model, we construct a compact feature representation based on four physically motivated observables: the inverse participati

Lev Ayzenberg, Shady Abu-Hussein, Raja Giryes, Hayit Greenspan

Full data acquisition in MRI is inherently slow, which limits clinical throughput and increases patient discomfort. Compressed Sensing MRI (CS-MRI) seeks to accelerate acquisition by reconstructing images from under-sampled k-space data, requiring both an optimal sampling trajectory and a high-fidelity reconstruction model. In this work, we propose a novel a

Antonio Arnal, Borbala Gerhat, Julien Royer, Petr Siegl

We study the damped wave equation with a damping coefficient which is possibly singular and unbounded at infinity. In general, zero belongs to the spectrum of the corresponding generator, which prevents a uniform (exponential) decay for the energy. However, for initial conditions in a suitable subspace, a detailed analysis of the resolvent norm for low frequ

Jingyi Zhao, Wenxuan Wang, Weijun Zhou, Yongxin Wu

This paper studies coordinated trajectory planning and tracking control for multiple unmanned surface vessels (USVs) under strict privacy requirements. To avoid the privacy risks associated with direct position sharing in conventional cooperative methods, the proposed approach adopts an estimated fleet centroid as the only shared variable, preventing individ

Yaelle Zribi, Florian Cafiero, Vincent Lépinay, Chahan Vidal-Gorène

Stand-up comedy, and humor in general, are often studied through their verbal content. Yet live performance relies just as much on embodied presence and audience feedback. We introduce TIC-TALK, a multimodal resource with 5,400+ temporally aligned topic segments capturing language, gesture, and audience response across 90 professionally filmed stand-up comed

Niels Voorneveld, Peeter Laud

We propose a method for reasoning about trust in multi-agent systems, specifying a language for describing communication protocols and making trust assumptions and derivations. This is given an interpretation in a modal logic for describing the beliefs and communications of agents in a network. We define how information in the network can be shared via forwa

Filip Novkoski, Loïc Fache, Félicien Bonnefoy, Guillaume Ducrozet

Solitons are localized nonlinear wave packets that propagate without spreading because nonlinearity balances dispersion. Their robustness is well understood in effectively one-dimensional systems, but introducing additional spatial dimensions is generally expected to destabilize them or destroy their coherent character. Here we experimentally investigate how

Zipeng Chen, Song Liu, Zhaoyang Yin

Recently, Coiculescu and Palasek \cite{Coiculescu2025} shows the non-uniqueness of solutions for the 3D incompressible Navier-Stokes equations with initial data in $BMO^{-1}$. Inspired by their breakthrough work, we develop their schemes for the incompressible magnetohydrodynamic equations and obtain a similar result in 5 dimensional case. More precisely, we

David Vokrouhlický

The sequence of eclipses of binary stars is subject to inequalities for various reasons. The presence of a third component in the system causes periodic motion of the binary's center of mass along the line of sight of an observer. The finite value of the light velocity implies that the epochs of eclipses periodically advance and delay with respect to the exa

Abhishek Kumar Maurya, Dinkar Mishra, Bhupesh Kumar, Ramesh C Sharma

In the present study, wakefield amplification via coherent resonant excitation using two co propagating laser pulses in a homogeneous plasma is investigated. The proposed scheme is based on linearly polarized leading seed pulse followed by a trailing pulse with identical or controlled parameters, enabling phase synchronized energy transfer to the plasma wave

Hasret Ozan Sevim

This paper emphasizes the critical role of interoperability in enabling efficient and secure communication for the fragmented distributed ledger ecosystem, particularly within on-chain finance. The purpose of this study is to streamline and accelerate empirical research on the intersection of cross-chain interoperability solutions and their impact within on-

Anne-Laure Dalibard, Thierry Gallay

As a model for vortex-wall interactions, we consider the two-dimensional incompressible Navier--Stokes equations in the half-plane $R^2_+$ with no-slip boundary condition and point vortices as initial data. We focus on the paradigmatic example of a single vortex in an otherwise stagnant fluid, which is already quite challenging from a mathematical point of v