March 2026 arXiv papers — page 33

Ruixing Zhang, Hanzhang Jiang, Leilei Sun, Liangzhe Han

Mobile devices continuously interact with cellular base stations, generating massive volumes of signaling records that provide broad coverage for understanding human mobility. However, such records offer only coarse location cues (e.g., serving-cell identifiers) and therefore limit their direct use in applications that require high-precision GPS trajectories

Tiago F. Silva, Thiago B. Saramela, Willian W. R. A. da Silva, Camilla de S. Codeço

Understanding the chemical stability of Gas Electron Multipliers (GEMs) operated in CO$_2$-based mixtures is essential for improving detector longevity and reliability. In this work, we investigate the interaction between CO$_2$ molecules and the copper electrodes of GEM foils through near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) and compl

Andreas Brandhuber, Paolo Pichini, Gabriele Travaglini, Congkao Wen

We construct the $\mathcal{N}=4$ supersymmetric completion of the recently proposed single-minus gluon amplitudes in $(2,2)$ signature, which are nonvanishing for all multiplicities on a half-collinear kinematic locus. The superamplitude factorises into a permutation-invariant measure $\Delta^{(n-1)}$ with uniform little-group weight that imposes the half-co

Jazmin Collins, Prasanthi Gurumurthy, Eric J. Gonzalez, Mar Gonzalez-Franco

The gaze-and-pinch framework offers a high-fidelity interaction modality for spatial computing in virtual reality (VR), yet it remains vulnerable to coordination errors--timing misalignments between gaze fixation and pinch gestures. These errors are categorized into two types: late triggers (gaze leaves a target before pinch) and early triggers (pinch before

M. Gauding T. Lehmann, T. L. Howarth, L. Berger, M. Rieth

Lean premixed hydrogen-air flames are strongly affected by thermodiffusive (TD) instabilities, which can alter the flame structure and enhance the local reactivity many-fold. Two recent models (Howarth et al. (Combust.~Flame 253, 2023) and Rieth et al. (MSC 2023)) describe the scaling of the stretch factor in turbulent hydrogen flames with the Karlovitz numb

Daniel Burgarth, Paolo Facchi, Giovanni Gramegna, Kazuya Yuasa

We derive a nonperturbative bound on the distance between evolutions of open quantum systems described by time-dependent generators. We show how this result can be employed to provide an explicit upper bound on the error of the rotating-wave approximation in the presence of dissipation and decoherence. We apply the derived bound to the strong-coupling limit

Muhammad R. Andiva, Chuanyin Jiang, Martin Ziegler, Qinghua Lei

Upscaling unsaturated flow in fractured rock remains challenging because fractures and matrix often exhibit sharply contrasting hydraulic behaviors across saturation states. Here, we demonstrate that unsaturated flow undergoes a transition between matrix- and fracture-dominated regimes. Three-dimensional direct numerical simulations reveal that both relative

Sagar Addepalli, Prajita Bhattarai, Abhilasha Dave, Julia Gonski

Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model physics in collider events, along with the potential for unprecedented computational efficiency in future quantum processors. Near-term utilization of these benefits can be achieved b

Marwa Marso, Sabrina Herbst, Jadwiga Wilkens, Vincenzo De Maio

Quantum measurements are slow, while classical processors are fast, yet existing hybrid protocols never exploit this asymmetry. In this work, we propose an alternative formulation of classical estimators as online algorithms that are updated incrementally upon obtaining a new sample. Classical shadows are the natural fit for this approach: designed around th

Constantia Alexandrou, Simone Bacchio, Mathis Bode, Jacob Finkenrath

We present the strange electromagnetic form factors of the nucleon using lattice QCD simulations with degenerate light, a strange, and a charm quark in the sea with masses tuned to their physical values. For the first time, the strange electromagnetic form factors are computed at the continuum limit using only ensembles simulated with physical quark masses,

Yahan Liu, Tao Zhu

Radiative heat transfer (RHT) at the nanoscale can vastly exceed the far-field blackbody limit due to the tunneling of evanescent waves, a phenomenon traditionally described by fluctuational electrodynamics (FE). While FE has been exceptionally successful for systems in local thermal equilibrium, its foundational assumptions break down in the growing number

Yang Liu, Qianqian Xu, Peisong Wen, Siran Dai

Recent studies have made notable progress in video representation learning by transferring image-pretrained models to video tasks, typically with complex temporal modules and video fine-tuning. However, fine-tuning heavy modules may compromise inter-video semantic separability, i.e., the essential ability to distinguish objects across videos. While reducing

Luca Lanzilao, Angela Meyer

The rapid growth of solar energy is reshaping power system operations and increasing the complexity of grid management. As photovoltaic (PV) capacity expands, short-term fluctuations in PV generation introduce substantial operational uncertainty. At the same time, solar power ramp events intensify risks of grid instability and unplanned outages due to sudden

Roope Niemi, Anastasiia Petrovych, Arghya Ranjan Das, Enrico Lupi

PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to environments with strict latency constraints, PQuantML simplifies training of compressed models by providing a unified interface to apply pruning and quantization, either jointly or indi

X. Xu, P. Ravichandran, R. F. Peletier, Junais

We analyze a sample of low surface brightness dwarf galaxies (mu_e,g > 24.2 mag arcsec^-2), detected using interpretable machine learning tools from the DES survey. We use the Tanoglidis et al. (2021) sample, identified with machine learning, supplemented by Thuruthipilly et al. (2024). We focus on the Fornax-Eridanus Supercluster, where our group determined

K. Scott, H. LaBollita, G. A. Pan, X. Yang

The discovery of superconductivity in reduced square-planar nickelates marked a major advance in identifying structural and electronic analogs to the high-$T_c$ cuprates. The more recent observation of superconductivity in parent Ruddlesden-Popper (RP) octahedral nickelates with a clear difference in electron count with respect to cuprates raises new questio

Constantia Alexandrou, Simone Bacchio, Mathis Bode, Jacob Finkenrath

We present the nucleon strange electromagnetic form factors using four lattice QCD ensembles with $N_f=2+1+1$ twisted mass clover-improved fermions and quark masses tuned to approximately their physical values. The four ensembles have similar physical volume and lattice spacings of $a=0.080$ fm, $0.068$ fm, $0.057$ fm and $0.049$ fm allowing us to take the c

Phillip H. Roth

In a world with ever-growing scientific literature, meaningful classifications are vital to keep on top of the latest results. In this Comment, historian and sociologist Phillip Roth traces the history of preprint classification in physics.

Gillian Rosenberg, Skylar Stadhard, Bruce C. Hansen, Michelle R. Greene

What information is sufficient to learn the full richness of human scene understanding? The distributional hypothesis holds that the statistical co-occurrence of language and images captures the conceptual knowledge underlying visual cognition. Vision-language models (VLMs) are trained on massive paired text-image corpora but lack embodied experience, making

Dávid Pukanec, Tibor Kubík, Michal Španěl

We present ToothCraft, a diffusion-based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth. Building upon recent advancements in conditioned diffusion models for 3D shapes, we developed a model capable of an automated tooth crown completion conditioned on local anatomical context. To address the lack of tra

Paul Bontempo

This paper investigates the relationship between utterance sentiment and language choice in English-Tamil code-switched text, using methods from machine learning and statistical modelling. We apply a fine-tuned XLM-RoBERTa model for token-level language identification on 35,650 romanized YouTube comments from the DravidianCodeMix dataset, producing per-utter

Kun Li, Jihao Gu, Fei Wang, Zhiliang Wu

With the rapid development of Multimodal Large Language Models (MLLMs), their potential in Micro-Action understanding, a vital role in human emotion analysis, remains unexplored due to the absence of specialized benchmarks. To tackle this issue, we present MA-Bench, a benchmark comprising 1,000 videos and a three-tier evaluation architecture that progressive

Pankaj K. Agarwal, Matthew J. Katz, Micha Sharir

Let $K$ be a compact, centrally-symmetric, strictly-convex region in ${\mathbb R}^3$, which is a semi-algebraic set of constant complexity, i.e. the unit ball of a corresponding metric, denoted as $\|\cdot\|_K$. Let ${\mathcal{K}}$ be a set of $n$ homothetic copies of $K$. This paper contains two main sets of results: (i) For a storage parameter $s\in[n,n^3]

Tamir Cohen, Leo Segre, Shay Shomer-Chai, Shai Avidan

Reconstructing accurate 3D models of large-scale real-world scenes from unstructured, in-the-wild imagery remains a core challenge in computer vision, especially when the input views have little or no overlap. In such cases, existing reconstruction pipelines often produce multiple disconnected partial reconstructions or erroneously merge non-overlapping regi

Patrizio Spada, Laura Cappelli, Francesca Cibrario, Christian Mattia

In finance, assessing the creditworthiness of loan applicants requires lenders to cluster borrowers using rating scales. Financial institutions must define the scales in compliance with strict institutional constraints, resulting in solving a complex combinatorial constrained optimization problem. This contribution studies how to solve this problem using a Q

Rosa Barbato, Alba Lia Masiello, Rossano Sannipoli

In this paper, we deal with functionals involving the torsion and the first eigenvalue of the Laplacian with Robin boundary conditions (to which we refer as Robin Torsion and Robin Eigenvalue), with other geometrical quantities, in the class of convex sets. Firstly, we prove an upper bound for the Robin Torsion in terms of the $L^1$ and $L^2$ norms of the di

Bana Singh Sangtan, Anosh Joseph, David Schaich

We present our ongoing work on two-dimensional maximally supersymmetric Yang-Mills (2D MSYM) theory using lattice techniques. The continuum theory is obtained from the dimensional reduction of four-dimensional ${\mathcal N} = 4$ supersymmetric Yang-Mills theory. We construct both the continuum and lattice versions of the 2D MSYM theory. The lattice action pr

Pablo Sánchez-Peralta

We provide a positive answer to an old problem of Jonathan K. Simon: if $K$ and $K'$ are two knots such that there is an epimorphism from the knot group of $K$ to the knot group of $K'$, then the genus of $K$ is greater than or equal to the genus of $K'$. We achieve this by proving a conjecture of Friedl and L\"uck, which states that the existence of a map b

Alexey Elagin

We solve categorical Torelli problem for quartic del Pezzo surfaces. That is, we prove that a del Pezzo surface of degree $4$ can be canonically reconstructed from its Kuznetsov component, which is the orthogonal subcategory to the structure sheaf in the derived category of the surface. Our methods work in equivariant setting and over arbitrary perfect field

Chi Ho Leung, Philip E. Paré

We study port-Hamiltonian systems with energy functions that split into local storage terms. From the interconnection and dissipation structure, we construct a graph on the energy compartments. From this graph, we show that the shortest-path distance from a constrained compartment to the nearest actuated one gives a lower bound on the relative degree of the

M. Esseldeurs, J. Ahuir, L. Amard, S. Mathis

Asteroseismology has become a powerful tool in stellar astrophysics, offering unprecedented insights into the internal structures and dynamics of stars. It enables precise characterization of stellar interiors across a wide range of stellar masses and of evolutionary phases, from the main sequence to the white dwarf phase. At the same time, the number of det

Ghazal Rahimi, Victor Lopez, Marc Clascà, Joan Vinyals Ylla Català

The increasing adoption of heterogeneous platforms that combine CPUs with accelerators such as GPUs in high-performance computing (HPC) introduces new challenges for performance analysis and optimization. Traditional efficiency metrics, such as those proposed by the Performance Optimization and Productivity (POP) Center of Excellence, were designed primarily

Matthias Boeker, Dana Swarbrick, Ulysse T. A. Côté-Allard, Marc T. P. Adam

Climbing is a multifaceted sport that combines physical demands and emotional and cognitive challenges. Ascent styles differ in fall distance with lead climbing involving larger falls than top rope climbing, which may result in different perceived risk and fear. In this study, we investigated the psychophysiological relationship between perceived fear and mu

Jorge Martín-Morales, Wayne Ng Kwing King

We introduce a weighted version of the module of logarithmic derivations of a divisor in weighted projective space, and provide a generalization of Saito's criterion for freeness in terms of weighted multiple eigenschemes (wME-schemes). Freeness of the nonstandard Z-graded module allows one to consider big families of free divisors in affine and standard pro

Zhe Zhang, Martijn Goorden, Michel Reniers

Existing literature on timed opacity uses specific definitions for restricted subclasses of timed automata or limited observation models. This lack of a unified definition makes it difficult to establish formal relationships and compare the expressiveness of different opacity variants. This paper establishes a unified framework for timed opacity by introduci

F. A. Ferreira, M. S. Angelo, J. F. C. Santos, W. J. B. Corradi

We report the discovery of 31 new open clusters (OCs) identified in \textit{Gaia}~DR3 data through a systematic search over 220 adjacent $1^\circ\times1^\circ$ fields towards the Galactic anticentre, in the direction of the Perseus arm gap. Eight of them display low-density structures, possibly indicating open cluster remnants properties. The objects were id

Ziyue Zeng, Xun Su, Haoyuan Liu, Bingyu Lu

At ultra-low bitrates, high-fidelity reconstruction requires sampling plausible videos from the posterior rather than regressing to oversmoothed conditional means. We propose Generative Video Codebook Codec (GVCC), a zero-shot framework in which a pretrained video generative model serves directly as the decoder, and the transmitted bitstream specifies its ge

Hector Buffière, Yuquan Lin, Jaroslav Nešet{ř}il, Patrice Ossona de Mendez

Tree-ordered weakly sparse models have recently emerged as a robust framework for representing structures in an ``almost sparse'' way, while allowing the structure to be reconstructed through a simple first-order interpretation. A prominent example is given by twin-models, which are bounded twin-width tree-ordered weakly sparse representations of structures

Jingpu Cheng, Ping Liu, Qianxiao Li, Chi Zhang

Forgetting a subset in machine unlearning is rarely an isolated task. Often, retained samples that are closely related to the forget set can be unintentionally affected, particularly when they share correlated features from pretraining or exhibit strong semantic similarities. To address this challenge, we propose a novel two-phase optimization framework spec

Raik Hesse, Christian Schwenzer, Roman Glaznev, Florence Cameron

Fuel-flexible, low-carbon combustion systems need to accommodate methane/hydrogen mixtures with air and exhaust-gas dilution. To develop these, we require accurate and efficient correlations for laminar flame speed (LFS). In this work, we introduce a physics-guided LFS correlation that applies to burners, gas engines, and turbines. Our model uses a core-kine

Yoseph Berhanu Alebachew, Hunter Leary, Swanand Vaishampayan, Chris Brown

Large Language Models (LLMs) have shown impressive capabilities across software engineering tasks, including question answering (QA). However, most studies and benchmarks focus on isolated functions or single-file snippets, overlooking the challenges of real-world program comprehension, which often spans multiple files and system-level dependencies. In this

Nils Lid Hjort

Suppose that the normal model is used for data $Y_1,\ldots,Y_n$, but that the true distribution is a t-distribution with location and scale parameters $\xi$ and $\sigma$ and $m$ degrees of freedom. The normal model corresponds to $m=\infty$. Using a local asymptotic framework where $m$ is allowed to increase with $n$ two classes of estimands are identified.

Yasaman Khorsandmanesh, Emil Bjornson, Joakim Jalden, Bengt Lindoff

This paper considers a wideband millimeter-wave MIMO system with fully digital transceivers at both the base station and the user equipment (UE), focusing on mobile scenarios. To reduce the baseband processing burden at the UE, we propose a two-stage digital combining architecture, where the received signals are compressed from $K$ antennas to dimension $N_{

Valentia Fragkiadaki, Mishko Mitkovski, Cody B. Stockdale

We characterize the boundedness and compactness of dyadic paraproducts on local dyadic fractional Sobolev spaces, $H^s$. We apply this result to establish the algebra property for $H^s$ when $s \in (\frac{1}{2},1)$ and to deduce the boundedness and compactness of commutators with the Haar shift on $H^s$. Our conditions are stated in terms of new dyadic fract

Steffen Borgwardt, Zachary Sorenson

The Traveling Salesman Problem (TSP) is one of the classic and hard problems in combinatorial optimization. We develop a new heuristic that uses a connection between Minimum Cost Flow Problems and the TSP to improve on a given suboptimal tour, such as a local optimum found using a classic heuristic. Minimum Cost Flow Problems can be solved efficiently throug

Claudia Caputo, Daniele Bertacca, Alberto Bassi, Sabino Matarrese

We investigate static, spherically symmetric halo configurations within Unified Dark Matter (UDM) scalar-field models, developing a systematic mapping between standard cold dark matter (CDM) density profiles and their UDM counterparts. Exploiting the equivalence-class structure of UDM models, we show that, in principle, different Lagrangian realisations can

Lilith Zschetzsche, Refik Mansuroglu, Norbert Schuch

The computational complexity of simulating the dynamics of physical quantum systems is a central question at the interface of quantum physics and computer science. In this work, we address this question for the simulation of exponentially large bosonic Hamiltonians with quadratic interactions. We present two results: First, we introduce a broad class of quad

Nobumitsu Yokoi, Pablo D. Mininni, Annick Pouquet, Duane Rosenberg

Large-eddy simulations (LES) with an appropriate subgrid-scale (SGS) model provide a powerful tool for investigating real-world turbulence. The Smagorinsky model, one of the simplest and most used SGS models, often shows an over-dissipative behavior even when using dynamic procedures to adjust the model coefficient. By incorporating the structural or geometr

Nettuno Nadalini, Tarannom Mehri, Anne H Hoekman, Katerina Kagialari

Writing discharge summaries to transfer medical information is an important but time-consuming process that can be assisted by Large Language Models (LLMs). This prospective mixed methods pilot study evaluated an Electronic Health Record (EHR)-integrated LLM to generate discharge summaries drafts. In total, 379 discharge summaries were generated in clinical

Ali Mokhtari, Anselm W. Stark, Dominik Obrist, Marius R. Bigler

Background: Right anomalous aortic origin of coronary arteries (R-AAOCA) involves fixed compression, assessable with adenosine-derived fractional flow reserve (FFRAdnosine), and additional stress-induced dynamic compression captured by dobutamine-derived FFR (FFRDobutamine). We hypothesized that coronary CT angiography (CCTA)-derived fluid dynamics-informed

Lucky Gangwar

This paper develops an analytical framework for the retarding forces on macroscopic spherical probes travelling through the interstellar medium (ISM) at relativistic speeds (0.1c to 0.99c). Integrating the aberrated momentum flux of both baryonic and radiative fields yields scaling laws that expose what this work calls the Magnitude Paradox: relativistic ine

Juno Kim, Eshaan Nichani, Denny Wu, Alberto Bietti

Spectral optimizers such as Muon have recently shown strong empirical performance in large-scale language model training, but the source and extent of their advantage remain poorly understood. We study this question through the linear associative memory problem, a tractable model for factual recall in transformer-based models. In particular, we go beyond ort

László Csató, Sándor Bozóki

Incomplete pairwise comparison matrices are increasingly employed to save resources and reduce cognitive load by collecting only a subset of all possible pairwise comparisons. We present their graph representation and some completion algorithms, including the incomplete eigenvector and incomplete logarithmic least squares methods, as well as a lexicographica

Ryota Shimada, Lucy O. McNeill, Vishnu Varma, Keiichi Maeda

Rotation and magnetic fields in the cores of evolved massive stars in their final phase are thought to play an important role in the subsequent supernova explosion and the formation of a compact object, especially in hyperenergetic explosions. However, the interplay between rotation, magnetic fields, and convection up to the final collapse is a nonlinear, mu

Nour E. H. Chetoui, Jonas Grumm, Robert Lemke, Andreas Knorr

Transient absorption spectroscopy is routinely used to study the electron dynamics in plasmonic gold nanoparticles. Typically, the transient absorption bleach is analyzed as measure for the electron temperature. However, the implicitly assumed linear dependence between bleach intensity and temperature has not been systematically studied. Similarly, the influ

Charly Andral, Laetitia Leduc, Guillaume Matheron, Yukihide Nakada

This study examines the impact of residential energy retrofits on household energy consumption in France using smart meter data from nearly 2,500 Hello Watt users, using a two-period difference-in-differences design. The dataset combines daily electricity and gas consumption collected through smart meters, hourly temperatures from M\'et\'eo France, and user-

D. Revanth Kumar, Santosh Kumar Yadav

In this article, we investigate a new parametrization of the dark energy equation of state (EoS) with a single parameter for a barotropic fluid that deviates from the standard $\Lambda$CDM cosmology. We derive observational constraints on the model parameters using recent datasets including Observational Hubble Data (OHD), Pantheon+SH0ES (PPS), and Dark Ener

Rongjin Li, Zichen Tang, Xianghe Wang, Xinyi Hu

With the rapid progress of multimodal large language models (MLLMs), AI already performs well at literature retrieval and certain reasoning tasks, serving as a capable assistant to human researchers, yet it remains far from autonomous research. The fundamental reason is that current work on academic paper reasoning is largely confined to a search-oriented pa

Tor Lattimore

We adapt the analysis of policy gradient for continuous time $k$-armed stochastic bandits by Lattimore (2026) to the standard discrete time setup. As in continuous time, we prove that with learning rate $\eta = O(\Delta_{\min}^2/(\Delta_{\max} \log(n)))$ the regret is $O(k \log(k) \log(n) / \eta)$ where $n$ is the horizon and $\Delta_{\min}$ and $\Delta_{\ma

Tianyu Liu, Weitao Xiong, Kunming Luo, Manyuan Zhang

Generative video models have significantly advanced the photorealistic synthesis of adverse weather for autonomous driving; however, they consistently demand massive datasets to learn rare weather scenarios. While 3D-aware editing methods alleviate these data constraints by augmenting existing video footage, they are fundamentally bottlenecked by costly per-

Olavi Kiuru

Quantum electrodynamics (QED) becomes nonlinear when the magnetic field strength surpasses the critical Schwinger limit $B_Q \approx 4.41\cdot 10^{13}$ G. This limit is surpassed, for example, in the magnetospheres of a specific class of neutron stars known as magnetars, which has important consequences for magnetospheric plasma dynamics due to modifications

Maria Kefala, Jeffery L. Painter, Syed Tauhid Bukhari, Maurizio Sessa

Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. This s

Mario Andres Chavarria, Santiago Price Torrendell, Aude Billard, Samia Hurst

Robotic wheelchairs (RWs) offer significant potential to enhance autonomy and participation for people with mobility impairments, yet many systems have failed to achieve sustained real-world adoption. This narrative literature review examined the extent and quality of end-user involvement in RW design, development, and evaluation over the past decade (2015--

Zilong Deng, Federico Tombari, Marc Pollefeys, Johanna Wald

Incremental open-vocabulary 3D instance-semantic mapping is essential for autonomous agents operating in complex everyday environments. However, it remains challenging due to the need for robust instance segmentation, real-time processing, and flexible open-set reasoning. Existing methods often rely on the closed-set assumption or dense per-pixel language fu

Haojie Shen, Jie Chen, Xiaoqun Wang

We study diagnostics of thermalization in quantum many-body systems with global SU(2) symmetry, where the standard eigenstate thermalization hypothesis (ETH) is generalized to its non-Abelian form. As an eigenstate-level probe, we introduce a symmetry-resolved trace distance constructed from the block structure of the reduced density matrix. This block struc

Ulrich Vogl, Markus Siegle

Directed Acylic Graphs with a single entry vertex and a single exit vertex (st-DAGs) have many applications. For instance, they are frequently used for modelling flow problems or precedence conditions among tasks, work packages, etc.. This paper presents an algorithm for finding special types of subgraphs in such st-DAGs, called clusters. Knowing the cluster

Florian Suerhoff, Andreas Morr, Sebastian Bathiany, Niklas Boers

There is growing interest in anticipating critical transitions in natural systems, often pursued through statistical detection of early warning signals associated with dynamical bifurcations. In stochastic dynamical systems, such signals commonly rely on manifestations of critical slowing down. However, we still need additional development for the underlying

J. B. Rodríguez-González, R. Orozco-Duarte, J. A. Toalá, M. M. Miller Bertolami

We present the first radiation-hydrodynamical simulations of the formation of a born-again planetary nebula (PN) triggered by a late thermal pulse (LTP). The 2D radiation-hydrodynamic simulations, performed with the {\sc pluto} code, have been consistently coupled to stellar evolution calculations using the Modules for Experiments in Stellar Astrophysics ({\

Zelin Tan, Zhouliang Yu, Bohan Lin, Zijie Geng

We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, treating all correct responses identically r

Yonghui Zhou, Xiaowan Li, Shuguan Ji

This paper is concerned with the local well-posedness, wave breaking, blow-up rate for a Camassa-Holm type equation with time-dependent weak dissipation. Firstly, we obtain the local well-posedness of solutions by using Kato's theory. Secondly, by using energy estimates, characteristic methods, and comparison principles, we derive two blowup criteria involvi

Min Zhiwei, Xiao Xu, Jiang Zhujun, Xiao Liang

Lightcone observations are the natural data format of galaxy surveys, but their evolving geometry breaks the translational symmetry assumed by standard convolutional neural networks (CNNs). In particular, applying CNNs to 3D gridded lightcone data implicitly treats the line-of-sight direction as translationally invariant, despite encoding cosmic time evoluti

Huiyun Xia, Yijie Mao, Sai Xu, Shuai Han

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) enable full-space coverage but also expose wireless transmissions to security from multiple spatial directions. This paper investigates a STAR-RIS-assisted secure RSMA system where both internal and external eavesdroppers may coexist in the transmission and reflection

Lei Zhu, Tengfei Wu, Bernhard Rauer, Hilton B. de Aguiar

Fluorescence imaging is an essential diagnostic tool in many fields, but diffraction-limited optical imaging at depth is limited by scattering. Here, we present a method based on multiple random illuminations, combined with a computational framework that retrieves high-resolution images by aligning local speckle replicas and computing their pixel-wise varian

Hongyun Wang, Shannon E. Foley, Hong Zhou

As exposure to electromagnetic waves becomes increasingly widespread, it is important to quantify how incident fields couple into biological tissue and where absorbed energy is deposited. This work presents an analytical, physics based framework derived from Maxwell's equations to model the propagation of a normally incident electromagnetic plane wave within

Martin Lorenz, Niko Konzack, Alexander Lingler, Philipp Wintersberger

Designing mobile and interactive technologies requires understanding how users sample dynamic environments to acquire information and make decisions under time pressure. However, existing computational user models either rely on hand-crafted task representations or are limited to static or non-interactive visual inputs, restricting their applicability to rea

Néstor Armesto, Fabio Domínguez, Adrián Romero

We compute non-eikonal corrections to dijet production in deep inelastic scattering off a nucleus. Such corrections are expected to be quantitatively important at the energies of the future Electron Ion Collider. We focus on those corrections stemming solely from the finite longitudinal size of the nucleus. For both longitudinally and transversely polarized

Guillermo Prieto-Viertel, Carsten Källner, Elma Dervic, Ola Ali

Political discourse attributes the pressure on European welfare systems to foreign nationals. Yet projections of service demand rarely disaggregate service demand by citizenship status. We develop a structural demographic model and project healthcare, education, and housing demand in Austria through 2050, disaggregated by citizenship status and regions acros

Tanya Klowden, Terence Tao

Artificial intelligence (AI) is the name popularly given to a broad spectrum of computer tools designed to perform increasingly complex cognitive tasks, including many that used to solely be the province of humans. As these tools become exponentially sophisticated and pervasive, the justifications for their rapid development and integration into society are

Boyin Yang, Jun Zhao

Children's agency plays a critical role in shaping children's autonomy, participation, and well-being in their interactions with digital systems, particularly in emerging child-AI contexts. However, how designers currently understand and reason about children's agency in practice remains underexplored. In this paper, we examine designers's engagement with ch

Md Touhidul Islam, Mahir Akgun, Syed Billah

Generative AI (GenAI) is increasingly used as a knowledge partner in higher education, raising the need for instructional designs that emphasize AI literacy practices such as evaluating output credibility and maintaining human accountability. Existing AI literacy frameworks focus more on what learners should do than on how these practices are enacted in rout

Parthapratim Mahapatra, Jonathan E. Thompson, Edward Fauchon-Jones, Mark Hannam

Binary black hole (BBH) mergers detected via gravitational waves are addressing key open questions in astrophysics, cosmology, and fundamental physics. Our scientific conclusions rely on extracting accurate source parameters, for which we require accurate signal modelling. It is well known that current BBH waveform models need to be improved for high-mass-ra

Jiamin Zheng, Yue Deng, Jessica Chen, Shujun Li

A new form of human trafficking has emerged across Chinese borders, where individuals are lured to Southeast Asia with fraudulent job offers and then coerced into operating online scams. Despite its massive economic and human toll, this scam-driven trafficking remains underexplored in academic research. Through qualitative analysis of 158 RedNote posts, we e

Hua-Yu Bai, Yang Chen, Guang-Can Guo, Ming Gong

It is well known that Hermitian and non-Hermitian models exhibit distinct physics and require different theoretical tools. In this work, we propose a unified generating-function framework for both classes with generic boundary conditions and local impurities. Within this framework, any finite lattice model can be mapped to a generating function of the form G

Colton Magnant, Thor Whalen

This paper was originally written by the authors circa 2005 but was never submitted for publication. The present version corrects minor errors, adds references to work published since the original draft, and includes a section discussing further research directions. The core framework -- the property function $\psi_G$, the density function $\varphi^\psi_\mu$

Jan Hůla, David Adamczyk, Tomáš Filip, Martin Pavlíček

We introduce a two-dimensional (2D) early exit strategy that coordinates layer-wise and sentence-wise exiting for classification tasks in large language models. By processing input incrementally sentence-by-sentence while progressively activating deeper layers, our method achieves multiplicative computational savings that exceed those from optimizing either

Francesco Regazzoni

Since the earliest stages of human civilization, advances in technology have been tightly linked to our ability to understand and predict the mechanical behavior of materials. In recent years, this challenge has increasingly been framed within the broader paradigm of data-driven scientific discovery, where governing laws are inferred directly from observatio

Inês Vieira, Inês Calvo, Iago Paulo, James Furtado

As Large Language Models (LLMs) expand across multilingual domains, evaluating their performance in under-represented languages becomes increasingly important. European Portuguese (pt-PT) is particularly affected, as existing training data and benchmarks are mainly in Brazilian Portuguese (pt-BR). To address this, we introduce ALBA, a linguistically grounded

Guangzhao Yang, Yu Pan, Shi Qiu, Ningjie Bai

Despite recent advances, efficient and robust turn-taking detection remains a significant challenge in industrial-grade Voice AI agent deployments. Many existing systems rely solely on acoustic or semantic cues, leading to suboptimal accuracy and stability, while recent attempts to endow large language models with full-duplex capabilities require costly full

Dongsheng Yang, Yinfeng Yu, Liejun Wang

Vision-and-Language Navigation (VLN) requires an agent to navigate through complex unseen environments based on natural language instructions. However, existing methods often struggle to effectively capture key semantic cues and accurately align them with visual observations. To address this limitation, we propose Beyond Textual Knowledge (BTK), a VLN framew

Roberto Daluiso, Héctor Folgar-Cameán, Andrea Pallavicini, Carlos Vázquez

In this paper, we develop a general rough volatility model for commodities that provides an automatic calibration of the initial term structure of the futures prices and an appropriate treatment of the Samuelson effect. After the theoretical analysis of this general model, we focus on the rBergomi and rHeston models and their calibration to market data of va

Rayan Moussa, Karsten Kahl

Algebraic multigrid (AMG) methods derive their optimal efficiency from the interplay between a relaxation process and a corresponding coarse grid correction. In many standard formulations, relaxation and coarse-graining are analyzed and treated as largely separate of one another. Here we propose an alternative theoretical approach centered entirely on the re

Jesse Barkley, Rumi Loghmani, Amir Barati Farimani

Existing methods for text-to-CAD generation either operate in a single pass with no geometric verification or rely on lossy visual feedback that cannot resolve dimensional errors. We present CADSmith, a multi-agent pipeline that generates CadQuery code from natural language. It then undergoes an iterative refinement process through two nested correction loop

Afonso Simplício, Gonçalo Vinagre, Miguel Moura Ramos, Diogo Tavares

Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant's linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data durin

Vinicius Anjos de Almeida, Sandro Saorin da Silva, Josimar Chire, Leonardo Vicenzi

Clinical notes contain valuable unstructured information. Named entity recognition (NER) enables the automatic extraction of medical concepts; however, benchmarks for Portuguese remain scarce. In this study, we aimed to evaluate BERT-based models and large language models (LLMs) for clinical NER in Portuguese and to test strategies for addressing multilabel

Martin Rath, Morteza Ghahremani, Yitong Li, Ashkan Taghipour

Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited availability. Standard chest X-rays are cost-effective and widely accessible, but provide only 2D projections with limited pathological information. Reconstructing 3D CT volumes from 2D X-rays could markedly increase

Maximilien Gadouleau, Marianne Johnson

The semiring of discrete dynamical systems is a simple algebraic model for modularity in deterministic systems. The objects of the semiring are finite transformations (viewed as directed graphs and regarded up to isomorphism), the sum of two transformations corresponds to applying them independently on distinct sets, and the product corresponds to applying b

Bertrand Duplantier, Véronique Gayrard, Eero Saksman

We study the integral means spectrum associated with the analytic function whose derivative is the so-called randomized Riemann zeta-function, introduced some time ago by Bagchi. The randomized $\zeta$-function, ${\zeta}_{\mathrm{rand}}(\sigma+ih)$, is known to represent the asymptotic statistical behaviour of the random vertical shifts of the actual $\zeta$

Allan Borodin, Tristan Lueger

We study online temporal voting, where a group of voters submit 0/1 approvals on sets of alternatives that arrive online over multiple rounds and a single alternative is chosen in each round. We introduce online variants of two well-known game theoretic properties, strategyproofness (SP) and independence of irrelevant alternatives. We show that online indepe

Jérôme Bolte, Edouard Pauwels, Cheik Traoré

We establish that nonconvex definable parametric optimization problems with possibly nonsmooth objectives, inequality constraints, conic constraint systems, and non-unique primal and dual solutions admit an adjoint state formula under a mere qualification condition. The adjoint construction yields a selection of a conservative field for the value function, p

Matthew Pryce, Karla Diaz-Ordaz, Ruth H. Keogh, Stijn Vansteelandt

In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Estimation approaches have been proposed which attenuate the inherent regularisation bias in machine learning predictions, with each of these estimators addressing measured confoundin