March 2026 arXiv papers — page 39

Qirui Wu, Yawar Siddiqui, Duncan Frost, Samir Aroudj

Object-centric reconstruction seeks to recover the 3D structure of a scene through composition of independent objects. While this independence can simplify modeling, it discards strong signals that could improve reconstruction, notably repetition where the same object model is seen multiple times in a scene, or across scans. We propose the Joint Reconstructi

Ann Zabludoff, Chen-Yu Chuang, Parker Thomas Johnson, Yichen Liu

We discuss the results of using large language models (LLMs) to conduct original scientific research in an unfamiliar subject area during the Fall 2025 semester. Students in a graduate astronomy and astrophysics course were asked to test whether LLMs could help them complete research tasks faster and at a level of detail and accuracy required for scientific

Yunhui He

In this work, we propose new variants of Anderson acceleration and nonlinear GMRES for general fixed-point iterations, based on modified least-squares problems associated with the methods. To solve the underlying linear systems, we apply these new approaches to accelerate the preconditioned Richardson iteration. We establish connections between the proposed

Mayank Jha

The development of large-scale foundation models, particularly Large Language Models (LLMs), is constrained by significant computational and memory bottlenecks. These challenges elevate throughput optimization from a mere engineering task to a critical strategic lever, directly influencing training time, operational cost, and the feasible scale of next-gener

Jonas Gilg, Johann Fredrik Jadebeck, Mariama Jaiteh, David Kerkmann

Infectious diseases remain a major threat to human societies. During the recent COVID-19 pandemic, mathematical modeling and extensive computer simulations proved highly effective in supporting public health experts and decision makers. Despite these advances, the full potential of modern modeling approaches and digital technologies has not yet been realized

Pietro Grassi

This thesis studies the convergence of finite-population stochastic evolutionary dynamics to their deterministic mean-field limit in continuous-time finite state space games. We first develop refined ergodic theorems for Markov chains with a single positive-recurrent class, guaranteeing the existence of a unique invariant distribution and almost-sure converg

José Niño-Mora

We study restless bandits with binary latent states and imperfect binary feedback, motivated by opportunistic spectrum access with sensing errors. For the associated belief-state model, we develop a partial conservation laws (PCL)-based analytical and computational framework for establishing indexability and evaluating the Whittle index, building on a verifi

Antonio Candelieri, Laurens Bliek

Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-convex objective function. However, the vanilla BO algorithm is not able to exploit possible symmetries characterizing the target problem. An intuitive case is given by optimal location problems, whose decision v

Jia Zhang, Tengfei Ma, Tianle Li, Daojian Zeng

Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives, where early design decisions strongly constrain downstream outcomes. Existing methods typically rely on a single policy or fixed scalarization, which limits their ability to represent diverse trade-offs and to explore multiple promising design traject

Steven Johnson

As AI agents transition from isolated tools to collaborative participants in shared knowledge ecosystems, governing collective knowledge curation becomes a critical challenge. Human platform governance mechanisms do not transfer directly: agent statelessness undermines deterrence-based sanctions, model homogeneity violates independence assumptions underlying

Daniel T. Bonkowsky, Ibrahim Kilinc, Robert W. Heath

Unmanned aerial vehicles (UAVs) fill coverage holes as wireless relays during emergency situations. Fixed-wing UAVs offer longer flight duration and larger coverage in such situations than rotary-wing counterparts. Maximizing the effectiveness of fixed-wing UAV relay systems requires careful tuning of system and flight parameters. This process is challenging

F. Minotti, G. Modanese

We study a scalar-tensor extension of gravity with two scalar fields coupled to the Aharonov-Bohm extension of electrodynamics, where the scalar mode $S\equiv\partial_μA^μ$ is dynamical. In this framework the trace of the electromagnetic energy-momentum tensor is nonvanishing and the scalar $S$ induces an electro-gravitational coupling that can be enhanced b

Vera Schmitt, Niklas Kruse, Premtim Sahitaj, Julius Schöning

Art. 50 II of the EU Artificial Intelligence Act mandates dual transparency for AI-generated content: outputs must be labeled in both human-understandable and machine-readable form for automated verification. This requirement, entering into force in August 2026, collides with fundamental constraints of current generative AI systems. Using synthetic data gene

Daniel Valente, Maurício Matos, Thiago Werlang

The standard Caldeira-Leggett model addresses the problem of Brownian motion in a thermal equilibrium environment. Here, we look for generalizations of the Caldeira-Leggett model to account for thermal gradients in the environment. We devise two types of models, and discuss the advantages and limitations of each one. From both models, we find signatures of t

Deepak Kumar, Jared Z. Dans, Keenan E. Avers, Ryan Paxson

The discovery of superconductivity in bilayer nickel-oxides has revived an intense effort to understand the potential of high-temperature superconductivity in these materials and their relation to cuprate superconductors. In this work, we investigate the growth and properties of bilayer La$_3$Ni$_2$O$_7$ thin films as a function of substrate, oxygen treatmen

Helena Löfström, Tuwe Löfström, Anders Hjort, Fatima Rabia Yapicioglu

This paper surveys uncertainty-aware explainable artificial intelligence (UAXAI), examining how uncertainty is incorporated into explanatory pipelines and how such methods are evaluated. Across the literature, three recurring approaches to uncertainty quantification emerge (Bayesian, Monte Carlo, and Conformal methods), alongside distinct strategies for inte

Fumiaki Nakai, Michio Otsuki, Kuniyasu Saitoh, Hiroaki Katsuragi

Vibrational properties of ordered athermal solids near yielding remain poorly understood. We show that yielding in a sheared crystal is governed not by a single localized instability but by directionally extended multimode softening that forms a cross-shaped low-frequency region in wave number space. Near yielding, the acoustic dispersion $ω\sim k$ is replac

Dominik Thy, Jan Diekmann, Uwe Thiele

We investigate compound drops composed of two immiscible nonvolatile partially wetting liquids that slide down an inclined homogeneous smooth solid substrate based on a mesoscopic hydrodynamic two-layer model in full-curvature formulation. First, drops of one liquid stationarily sliding on a layer of the other liquid are briefly investigated with a focus on

Imad Ali Shah, Jiarong Li, Ethan Delaney, Enda Ward

Hyperspectral sensing provides rich spectral information for scene understanding in urban driving, but its high dimensionality poses challenges for interpretation and efficient learning. We introduce Learnable Quantum Efficiency (LQE), a physics-inspired, interpretable dimensionality reduction (DR) method that parameterizes smooth high-order spectral respons

P. Lavvas, R. Liu, G. Tinetti, S. Paraskevaidou

The atmospheric characterisation of temperate exoplanets is becoming accessible with JWST, providing a critical connection between Solar System planets and the more commonly observed hot-Jupiters. K2-18 b, a temperate sub-Neptune orbiting an M dwarf, has emerged as a benchmark case following extensive JWST observations and ongoing debate regarding its atmosp

América Y. Torres-Boy, Anoushka Ghosh, Myles B. T. Osenton, Akash C. Behera

We report on the control and characterization of the isomer population of ions inside superfluid helium nanodroplets, using two-color operation of a dual-oscillator infrared free-electron laser. The timing of both lasers is highly synchronized, and their frequencies (or "colors") can be tuned independently over a wide range. Interaction of the singly

Timm Gödde, Eisso H. Atzema, Bojana Rosić

Neural networks (NNs) have gained significant attention across various engineering disciplines, particularly in design optimization, where they are used to build surrogate models for high-dimensional regression problems. Despite their power as global approximators, NNs often fail to accurately capture local nonlinearities without relying on a large number of

Erik Anders Torsten Svanberg, Daniel Voigt, Vaishali Adya

Squeezed states of light are used for precision metrology and quantum-enhanced measurements, with applications spanning communication and sensing. State-of-the-art squeezed-light sources typically rely on optical cavities to achieve high, usable levels of squeezing. Recently, waveguide-based squeezed-light sources have demonstrated significant improvements i

Johanna I. Hütner-Reisch, Andrea Conti, David Kugler, Florian Mittendorfer

Alumina (Al$_{2}$O$_{3}$) is a key material for thin-film growth and heterogeneous catalysis, where the atomic surface structure critically impacts performance. Using noncontact atomic force microscopy (nc-AFM) combined with density functional theory (DFT) calculations, we challenge the common assumption that the unreconstructed $α$-Al$_{2}$O$_{3}$(0001) sur

Shulin Wang, Jiawei He, Zhiyuan Yang, Stefano Longhi

Non-Hermitian systems exhibit anomalous scaling, a striking departure from conventional bulk laws, rooted in the non-Hermitian skin effect (NHSE). Here, we experimentally uncover this scaling and demonstrate its active control in a temporal photonic lattice. By tracking the real-time evolution of all eigenstates as system size varies, we directly observe sca

Jingchen Pu, Mu Mu, Jie Feng, Hao Li

Predictability analysis, which focuses on perturbation growth dynamic, is a key problem in both weather and climate prediction. Among all perturbations, the conditional nonlinear optimal perturbation (CNOP) leads to maximum uncertainties in forecasts, which is fundamentally important for theoretical studies and applications. Traditionally, CNOPs are solved t

A. Octávio Soares, Nuno M. R. Peres

In this article, we analyze the quantum and topological properties of graphene-based plasmonic systems. We consider the following plasmonic materials: single-layer graphene, twisted bilayer graphene, and other graphene stackings, as well as the following architectures: graphene-based gratings, grids, chains of graphene disks, and the kagomé lattice.

Tran Ky Vi, Bui Duc Tinh, Ngo Quang Duc, Chu Gia Bao

In this paper, we present a unified theoretical study of fluctuation-dominated transport and transverse thermoelectric response in two-dimensional superconducting films subjected to out-of-plane magnetic fields and electric-field drive. Our approach is based on the time-dependent Ginzburg-Landau equation with Langevin thermal noise, in which interaction effe

R. Smolec, O. Ziółkowska, R. Singh Rathour, V. Hocdé

Evolutionary tracks for 2-8M$_\odot$ models, covering a [Fe/H]=$-$1.0 ($Z=0.0014$) to [Fe/H]=+0.2 ($Z=0.02$) metallicity range are computed with Modules for Experiments in Stellar Astrophysics, MESA, to investigate evolutionary and pulsation properties of classical, fundamental mode Cepheids. We examine in detail the effects of convective overshooting from t

Yogesh M Joshi

In this work, we develop a rigorous theoretical framework for the evolution of linear viscoelastic properties across the sol-gel transition. More specifically, we derive general admissible expressions for the relaxation modulus and dynamic moduli as the critical gel state is approached from the pre-gel or the post-gel side. These expressions possess a genera

C. Gieser, W. R. M. Rocha, Y. Chen, K. Slavicinska

Context. The formation and destruction of molecules in the interstellar medium is a complex interplay between gas-phase reactions as well as processes on grain surfaces and within icy mantles. For many decades, the gas-phase composition of the cold material towards star-forming regions could be well characterized using (sub)mm facilities. Prior to the launch

Emma Vancayseele, Erik Schnaubelt, Louis Denis, Christophe Geuzaine

Understanding the thermal behaviour of superconducting accelerator magnets is essential to ensure their stable and reliable operation. This work presents an extension of the Finite Element Quench Simulator (FiQuS) Multipole module to include collar and pole regions of accelerator magnets, which influences the overall thermal response. A thermal thin shell ap

Peng Gao, Liangyi Zhao

We evaluate the first moment of the family of primitive quadratic Hecke $L$-functions in the Gaussian field using the method of double Dirichlet series under the Riemann hypothesis and the Lindelöf hypothesis. We obtain asymptotic formulas with secondary main terms and error terms of size that is one quarter of that of the main term.

Philip Schaten, Moritz Blumenthal, Bernhard Rapp, Christina Unterberg-Buchwald

Purpose: To create modular solutions for interactive real-time MRI using reconstruction algorithms implemented in BART. Methods: A new protocol for streaming of multidimensional arrays is presented and integrated into BART. The new functionality is demonstrated using examples for cardiac interactive real-time MRI based on radial FLASH, where iterative recons

Yi Zheng, Yang Liu, Haoyang Tan, Yanjing Zhao

Soliton optical frequency combs have become key enablers for a wide range of applications, including telecommunications, optical atomic clocks, ultrafast distance measurements, dual-comb spectroscopy, and astrophysical spectrometer calibration, many of which benefit from low repetition rates. However, achieving such low-repetition-rate soliton microcombs is

Margarita Chasapi

This contribution presents a model order reduction strategy for fast parametric modelling of problems with cracks formulated on spline discretizations. In the context of damage detection, parametric reduced order models (ROMs) are well suited for fast computations by establishing an efficient offline/online split of the simulation process. The problems of in

Rihab Gharbi, Wissem Jedidi, Salah Khardani, Frédéric Ouimet

We study nonparametric estimation of univariate cumulative distribution functions (CDFs) pertaining to data missing at random. The proposed estimators smooth the inverse probability weighted (IPW) empirical CDF with the Bernstein operator, yielding monotone, $[0,1]$-valued curves that automatically adapt to bounded supports. We analyze two versions: a pseudo

V. L. Kalashnikov, E. Sorokin, A. Rudenkov, I. T. Sorokina

We study strongly chirped dissipative solitons of the cubic-quintic complex Ginzburg-Landau equation in normal and anomalous group-delay dispersion. Using a stationary-phase (adiabatic) approximation, we derive analytic spectra and construct master diagrams linking the control-parameter ratios (spectral filtering, dispersion, and cubic-quintic self-phase/sel

Louis Garbe, Peter Rabl

We investigate the dynamics of a lasing system driven by a current of bosonic (quasi-)particles via a dissipative three-mode mixing process. A semi-classical analysis of this system predicts distinct dynamical regimes, where both the cavity mode and the gain medium can undergo lasing transitions. Of particular interest is an intermediate self-pulsing phase t

Ke Ma, Jun Long, Hongxiao Fei, Liujie Hua

Pre-trained Vision-Language Models (VLMs) struggle with Zero-Shot Anomaly Detection (ZSAD) due to a critical adaptation gap: they lack the local inductive biases required for dense prediction and employ inflexible feature fusion paradigms. We address these limitations through an Architectural Co-Design framework that jointly refines feature representation an

Ana Chkhaidze, Reshanne R. Reeder, Connor Gag, Anastasia Kiyonaga

A rapidly alternating red and black display known as Ganzflicker induces visual hallucinations that reflect the generative capacity of the visual system. Individuals vary in their degree of visual imagery, ranging from absent to vivid imagery. Recent proposals suggest that differences in the visual system along this imagery spectrum should also influence the

Allen Tu, Haiyang Ying, Alex Hanson, Yonghan Lee

Dynamic extensions of 3D Gaussian Splatting (3DGS) achieve high-quality reconstructions through neural motion fields, but per-Gaussian neural inference makes these models computationally expensive. Building on DeformableGS, we introduce Speedy Deformable 3D Gaussian Splatting (SpeeDe3DGS), which bridges this efficiency-fidelity gap through three complementar

Klaus Kähler Holst, Andreas Nordland, Julie Funch Furberg, Lars Holm Damgaard

Analysis of data from randomized controlled trials in vulnerable populations requires special attention when assessing treatment effect by a score measuring, e.g., disease stage or activity together with onset of prevalent terminal events. In reality, it is impossible to disentangle a disease score from the terminal event, since the score is not clinically m

Hao Liu, Vikash V. Gayah

This paper proposes a novel perimeter-free regional traffic management strategy for networks under a connected and autonomous vehicle (CAV) environment. The proposed strategy requires a subset of CAVs to temporarily wait at nearby parking facilities when the network is congested. After a designated holding time, these CAVs are allowed to re-enter the network

Adam Artymowicz, Anton Kapustin, Bowen Yang

We show that Hall conductance and its non-abelian and higher-dimensional analogs are obstructions to promoting a symmetry of a state to a gauge symmetry. To do this, we define a local Lie algebra over a Grothendieck site as a pre-cosheaf of Lie algebras with additional properties and propose that a gauge symmetry should be described by such an object. We sho

Madi Yerlanov, Zachary Kilpatrick, Nancy Rodriguez

In populations with community structure, the formation of consensus requires both alignment within and diffusion of beliefs across groups, processes that evolve on distinct time scales. How do modularity, asymmetry, and polarization shape this process? We study a variant of the voter model in which a population is divided into two cliques of sizes $N_1$ and

Joshua D. Ibrahim, Mahdi Taheri, Soon-Jo Chung, Fred Y. Hadaegh

Fault detection and identification (FDI) is critical for maintaining the safety and reliability of systems subject to actuator and sensor faults. In this paper, the problem of FDI for nonlinear control-affine systems under simultaneous actuator and sensor faults is studied. We model fault signatures through the evolution of the probability density flow along

Mohamed Mahdi, Asma Baghdadi

Epileptic seizure prediction from electroencephalographic (EEG) recordings remains challenging due to strong inter-patient variability and the complex temporal structure of neural signals. This paper presents a patient-adaptive transformer framework for short-horizon seizure forecasting. The proposed approach employs a two-stage training strategy: self-super

Amirhosein Chahe, Lifeng Zhou

Navigating to a visually specified goal given natural language instructions remains a fundamental challenge in embodied AI. Existing approaches either rely on reactive policies that struggle with long-horizon planning, or employ world models that suffer from poor action initialization in high-dimensional spaces. We present PiJEPA, a two-stage framework that

Hyukjun Lim, Soojung Yang, Lucas Pinède, Miguel Steiner

Transition states, the first-order saddle points on the potential energy surfaces, govern the kinetics and mechanisms of chemical reactions and conformational changes. Locating them is challenging because transition pathways are topologically complex and can proceed via an ensemble of diverse routes. Existing methods address these challenges by introducing h

Benjamin Pachev, Prateek Arora, Jinpai Zhao, Eirik Valseth

Storm surge is a significant threat to coastal communities across the globe, responsible for loss of life and enormous property damage. Consequently, significant efforts have been expended to develop high-fidelity physics-based models for storm surge prediction. However, such models are often extremely computationally expensive and require supercomputing res

Gustavo Chau Loo Kung, Mohammad Abbasi, Camila Blank, Juze Zhang

Diffusion Magnetic Resonance Imaging (dMRI) plays a critical role in studying microstructural changes in the brain. It is, therefore, widely used in clinical practice; yet progress in learning general-purpose representations from dMRI has been limited. A key challenge is that existing deep learning approaches are not well-suited to capture the unique propert

Mikalai Korbit, Mario Zanon

Second-order methods promise improved stability and faster convergence, yet they remain underused due to implementation overhead, tuning brittleness, and the lack of composable APIs. We introduce Somax, a composable Optax-native stack that treats curvature-aware training as a single JIT-compiled step governed by a static plan. Somax exposes first-class modul

Peter Balogh

We show that they do. Roger Schank's conceptual dependency theory proposed that all human events decompose into primitive operations -- ATRANS (transfer of possession), PTRANS (physical movement), MTRANS (information transfer), and others -- hand-coded from linguistic intuition. We ask: can the same primitives be discovered automatically through compression

B. Li, Y. Ma, K. Ni

Liquid xenon time projection chambers offer a homogeneous detection medium with excellent intrinsic energy resolution, fast scintillation, and true three-dimensional position sensitivity, making them an attractive alternative to crystal-based detectors for positron emission tomography (PET). In this work, we present a new single-phase liquid xenon time proje

Weizhi Zhang, Xiaokai Wei, Wei-Chieh Huang, Zheng Hui

Recent advancements in Large Language Models (LLMs) have expanded context windows to million-token scales, yet benchmarks for evaluating memory remain limited to short-session synthetic dialogues. We introduce \textsc{MemoryCD}, the first large-scale, user-centric, cross-domain memory benchmark derived from lifelong real-world behaviors in the Amazon Review

Hsin-Hsiung Huang, Yuh-Haur Chen, Mahlon Scott

The Global Database of Events, Language and Tone (GDELT) provides geolocated event records that can be aggregated into weekly spatiotemporal panels of event counts across regions, actors, and event types. These panels are typically sparse, bursty, and overdispersed, so calibrated probabilistic forecasting is essential for monitoring rare surges. We propose B

G Abarajithan, Zhenghua Ma, Francesco Restuccia, Ryan Kastner

Hardware-firmware integration is becoming a productivity bottleneck due to the increasing complexity of accelerators, characterized by intricate memory hierarchies and firmware-intensive execution. While numerous verification techniques focus on early-stage, approximate modeling of such systems to speed up initial development, developers still rely heavily o

Zhuoli Zhuang, Yu-Cheng Chang, Yu-Kai Wang, Thomas Do

Recent advancements in computer vision have accelerated the development of autonomous driving. Despite these advancements, training machines to drive in a way that aligns with human expectations remains a significant challenge. Human factors are still essential, as humans possess a sophisticated cognitive system capable of rapidly interpreting scene informat

Janusz Adamus, Rasul Shafikov

We show that an arc-analytic subanalytic function on a complex manifold M, which is holomorphic near one point, is a holomorphic function on M. More generally, an arc-analytic subanalytic function on a real analytic CR-manifold M, which is CR on a nonempty open subset of M, is a CR-function on the whole M.

Fabian Mies

The sample paths of Brownian motion are known to admit the exact Besov-type smoothness exponent 1/2 when measured in the sub-Gaussian Orlicz norm. We extend these regularity results by deriving the exact limit of the sub-Gaussian Orlicz modulus for Brownian motion in Banach spaces, and we provide a rate of convergence towards this limiting value. The central

L. River Spencer, Manuel K. Rausch, Chad M. Landis, Jan N. Fuhg

Magneto-active elastomers exhibit large, nonlinear deformations under combined mechanical loading and magnetic fields, and their effective behavior is strongly governed by microstructural heterogeneity. Predictive modeling of these materials is challenging because their response involves strong magneto-mechanical coupling, large deformations, and the nearly

Faniry A. Razakason, Daniel Racek, Paul W. Thurner, Göran Kauermann

Timely and accurate conflict event data are essential for real-time monitoring, forecasting, and policy response. Yet near-real-time conflict datasets such as the Armed Conflict Location \& Event Data Project (ACLED) are subject to reporting delays, that is, delays between event occurrence and first inclusion in the database. Such delays can introduce bias i

Shounak Sural, Ragunathan Rajkumar

Localization in GNSS-denied and GNSS-degraded environments is a challenge for the safe widespread deployment of autonomous vehicles. Such GNSS-challenged environments require alternative methods for robust localization. In this work, we propose BEVMapMatch, a framework for robust vehicle re-localization on a known map without the need for GNSS priors. BEVMap

Xabier Feal, Andrey Tarasov, Raju Venugopalan

We derived in arXiv:2206.04188 arXiv:2211.15712 a compact expression for the $N$-th rank QED polarization tensor $\Pi_{\mu_1\cdots \mu_N}(k_1,\cdots,k_N)$ in a $(0+1)$-dimensional worldline framework. This fully off-shell object, a function of $N$ external photon four-momenta, is a key ingredient in high-order computations of cusp anomalous dimensions and le

Binesh Sadanandan, Vahid Behzadan

Large Language Models (LLMs) are increasingly deployed in medical settings, yet their sensitivity to prompt formatting remains poorly characterized. We evaluate MedGemma (4B and 27B parameters) on MedMCQA (4,183 questions) and PubMedQA (1,000 questions) across a broad suite of robustness tests. Our experiments reveal several concerning findings. Chain-of-Tho

Jaidev Gill, Jing Shuang Li

Human and animal brains perform planning to enable complex movements and behaviors. This process can be effectively described using model predictive control (MPC); that is, brains can be thought of as implementing some version of MPC. How is this done? In this work, we translate model predictive controllers into firing rate neural networks, offering insights

Cedric Bernardin, Patricia Gonçalves, João Pedro Mangi

We investigate a boundary-driven Ginzburg-Landau dynamics with long-range interactions. In the hydrodynamic limit, the macroscopic evolution is governed by a fractional heat equation with Dirichlet boundary conditions, while the corresponding stationary profile is characterized by a fractional Laplace equation. We establish a dynamical large deviations princ

Hadi Hojjati, Narges Armanfard

Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are often highly sensitive to localized input corruptions and structured noise. We propose ARTA (Adversarially Robust multivariate Time-series Anomaly detection via sparsity-constrained perturbations), a joint training framewor

Hadi Hojjati, Christopher Roth, Rory Woods, Ken Sills

The progress of Anomaly Detection (AD) in safety-critical domains, such as transportation, is severely constrained by the lack of large-scale, real-world benchmarks. To address this, we introduce EngineAD, a novel, multivariate dataset comprising high-resolution sensor telemetry collected from a fleet of 25 commercial vehicles over a six-month period. Unlike

João Norberto, Ricardo Ferreira, Cláudia Soares

The use of satellite networks has increased significantly in recent years due to their advantages over purely terrestrial systems, such as higher availability and coverage. However, to effectively provide these services, satellite networks must cope with the continuous orbital movement and maneuvering of their nodes and the impact on the network's topolo

Netanel Friedenberg, Kalina Mincheva

In this paper we continue the program to develop the algebraic foundations of tropical (algebraic) geometry. We give strong characterizations of prime congruences containing a given congruence on a toric semiring. We give four applications of this result. (1) We prove an analogue of the strong Nullstellensatz for congruences with finite tropical basis. This

A. Lykholat, G. F. Moreira, I. R. Martins, D. Sousa

This work proposes a scalable framework for topological quantum computing using Matryoshka-type Sine-Cosine chains. These chains support high-dimensional qudit encoding within single systems, reducing the physical resource overhead compared to conventional qubit arrays. We describe how these chains can be used in Y-junction braiding protocols for gate operat

Julia Wolleb, Cristiana Baloescu, Alicia Durrer, Hemant D. Tagare

Implicit neural representations (INRs) have emerged as a powerful framework for continuous image representation learning. In Functa-based approaches, each image is encoded as a latent modulation vector that conditions a shared INR, enabling strong reconstruction performance. However, the structure and interpretability of the corresponding latent spaces remai

Frank Gilson

We introduce a one-step cascade symmetric system whose local symmetry geometry is organized by finite $\rho$-closed windows and one-step stars rather than by rowwise-independent toggles. The resulting symmetric model isolates a new $ZF + DC + \neg \mathrm{BPI}$ geometry in which rank-$1$ hereditarily symmetric reals admit a packet normalization theorem over

Jannis Kurtz, Bart P. G. van Parys

Optimization problems routinely depend on uncertain parameters that must be predicted before a decision is made. Classical robust and regret formulations are designed to handle erroneous predictions and can provide statistical error bounds in simple settings. However, when predictions lack rigorous error bounds (as is typical of modern machine learning metho

Hsin-Hsiung Huang, Bulent Soykan

Digital twins for radiation-based imaging and therapy are most useful when they assimilate patient data, quantify predictive uncertainty, and support clinically constrained decisions. This paper presents a modular framework for actionable digital twins in radiation-based imaging and therapy and instantiates its reproducible open-data component using the \ope

Jesús García-Gamborino, Laura Fuster, Daniel de la Prida, Luis A. Azpicueta-Ruiz

This paper presents UPV_RIR_DB, a structured database of measured room impulse responses (RIRs) designed to provide acoustic data with explicit spatial metadata and traceable acquisition parameters. The dataset currently contains 166 multichannel RIR files measured in three rooms of the Universitat Polit\`ecnica de Val\`encia (UPV). Each multichannel RIR fil

Alex Koran, Dimitrios Sinodinos, Hadi Hojjati, Takuya Nanri

High infraction rates remain the primary bottleneck for end-to-end (E2E) autonomous driving, as evidenced by the low driving scores on the CARLA Leaderboard. Despite collision-related infractions being the dominant failure mode in closed-loop evaluations, collision-aware representation learning has received limited attention. To address this gap, we first de

Guoping Xu, Jayaram K. Udupa, Yubing Tong, Xin Long

Accurate lesion segmentation is essential in medical image analysis, yet most existing methods are designed for specific anatomical sites or imaging modalities, limiting their generalizability. Recent vision-language foundation models enable concept-driven segmentation in natural images, offering a promising direction for more flexible medical image analysis

Snehit Vaddi

Large language models show promise for legal applications, but deploying frontier models raises concerns about cost, latency, and data privacy. We evaluate whether sub-10B parameter models can serve as practical alternatives by testing nine models across three legal benchmarks (ContractNLI, CaseHOLD, and ECtHR) using five prompting strategies (direct, chain-

David Andrade, Marcelo V. Flamarion

The conformal mapping approach is a well established technique for solving the Euler equations for potential flows with one spatial dimension. In this work, we extend this framework to problems with a weakly transversal dependence and, by means of asymptotic expansions, obtain a Kadomtsev-Petviashvili type equation formulated in conformal variables as a mode

Farshad Rostami Ghadi, Kai-Kit Wong, Masoud Kaveh, Mohammad Javad Ahmadi

Enormous fluid antenna systems (E-FAS) have recently emerged as a surface-wave (SW)-enabled architecture that can induce controllable large-scale channel gains through guided electromagnetic routing. This paper develops a secrecy analysis framework for E-FAS-assisted downlink transmission with practical pilot-based channel estimation. We consider a multiple-

Peiyao Wang, Haotian Xu, Noranart Vesdapunt, Rui Hou

Multi-modal Large Language Models (MLLMs) show promise in video understanding. However, their reasoning often suffers from thinking drift and weak temporal comprehension, even when enhanced by Reinforcement Learning (RL) techniques like Group Relative Policy Optimization (GRPO). Moreover, existing RL methods usually depend on Supervised Fine-Tuning (SFT), wh

Dan-Dan Cheng, Minghui Ding, Daniele Binosi, Craig D. Roberts

Using a symmetry preserving treatment of a vector*vector contact interaction (SCI), results are delivered for the four kaon transverse momentum dependent parton distribution functions (TMDs), viz. helicity-independent (HI) and Boer-Mulders (BM) TMDs for the kaon's $u$, $s$ valence degrees of freedom. In completing this analysis, we are able to deliver insigh

Jean Ruel, Frédéric Legoll, Arthur Lebée, Ludovic Chamoin

Effective models for slender structures derived from well-known plate (or shell) theories are justified within the limit of a small thickness, and may therefore prove limited for intermediate slenderness. On the other hand, direct 3D simulation of such structures is sub-optimal because it does not take advantage of the presence of small dimensions in some di

Robert Fulsche

In this paper, we discuss three short topics related to the parity operator and his role in quantum harmonic analysis. We derive results for the Fredholm index of even and odd operators, discuss operators on which the modulation action acts continuous in operator norm and show that the parity operator plays a natural role in the operator-to-operator Fourier

Raul Moreno, Dale Durran

Accurately representing surface precipitation is crucial for the operational use of weather and climate models. Presently, global numerical weather prediction (NWP) models struggle to accurately generate precipitation due to their parametrization of unresolved deep convective clouds and, in regions of grid-resolved ascent, inadequate parameterizations of clo

Shah Saood, Saddam Hussain Khan

This work presents a new Hybrid Dense SwinV2, a two-branch framework that jointly leverages densely connected convolutional features and hierarchical customized Swin Transformer V2 (SwinV2) representations for cassava disease classification. The proposed framework captures high resolution local features through its DenseNet branch, preserving the fine struct

Yinan Shen, Jinchi Lv

In this work, we investigate how to develop sharp concentration inequalities for sub-Weibull random variables, including sub-Gaussian and sub-exponential distributions. Although the random variables may not be sub-Guassian, the tail probability around the origin behaves as if they were sub-Gaussian, and the tail probability decays align with the Orlicz $\Psi

Yimeng Wang, Yinzhou Wang, Alicia Hong, Yixuan Zhang

Social anxiety (SA) is a prevalent mental health challenge that significantly impacts daily social interactions. Imaginal Exposure (IE), a Cognitive Behavioral Therapy (CBT) technique involving imagined anxiety-provoking scenarios, is effective but underutilized, in part because traditional IE homework requires clients to construct and sustain clinically rel

Jie Gao, Jiayi Zhang, Dan Chen

While Generative AI (GenAI) rapidly integrated into higher education, existing research has primarily focused on regulating student use. As a result, student perspectives on faculty adoption of GenAI remained unexplored. In this study, we analyzed survey responses from 156 undergraduate and graduate students to examine their attitudes toward both student and

Abolfazl Meyarian, Amin Karimi Monsefi, Rajiv Ramnath, Ser-Nam Lim

Flow-matching video generators produce temporally coherent, high-fidelity outputs yet routinely violate elementary physics because their reconstruction objectives penalize per-frame deviations without distinguishing physically consistent dynamics from impossible ones. Contrastive flow matching offers a principled remedy by pushing apart velocity-field trajec

Fatih Bulut, Carlo DePaolis, Raghav Batta, Anjali Mangal

With the rapid advancement of AI in code generation, cybersecurity detection engineering faces new opportunities to automate traditionally manual processes. Detection authoring - the practice of creating executable logic that identifies malicious activities from security telemetry - is hindered by fragmented code across repositories, duplication, and limited

Henrique Nogueira Bastos

We prove uniqueness results for capillary disks in three-dimensional domains that are modeled by an elliptic PDE, under the assumption that the domain admits a family of surfaces with suitable properties. Our main theorem generalizes Nitsche's result for capillary constant mean curvature disks in the Euclidean ball and is inspired by the extension of Hopf's

Hitesh Poddar, Jianhua Zhang, Ximan Liu, Mansoor Shafi

This document provides comprehensive details of the Measurement Campaigns, Datasets, and Curve Fitting Officially Used by 3GPP in Release 19 for Channel Modeling in TR 38.901 for 7-24 GHz

Yijiong Yu, Shuai Yuan, Jie Zheng, Huazheng Wang

Soft context compression reduces the computational workload of processing long contexts in LLMs by encoding long context into a smaller number of latent tokens. However, existing frameworks apply uniform compression ratios, failing to account for the extreme variance in natural language information density. While adopting a density-aware dynamic compression

Jie Gao, Adam K. Dubé

Game-based learning (GBL) is widely adopted in mathematics education. It enhances learners' engagement and critical thinking throughout the mathematics learning process. However, enabling players to learn intrinsically through mathematical games still presents challenges. In particular, effective GBL systems require dozens of high-quality game levels and mec

Vasundra Srinivasan

Multimodal AI systems are evaluated by downstream task accuracy, but high accuracy does not mean the underlying data is coherent. A model can score well on Visual Question Answering (VQA) while its inputs contradict each other. We introduce the Multimodal Coherence Score (MCS), a metric that evaluates fusion quality independent of any downstream model. MCS d

Yixin Zhou, Zhixiang Liu, Vladimir I. Zadorozhny, Jonathan Elmer

Deep learning models have shown promise in EEG-based outcome prediction for comatose patients after cardiac arrest, but their reliability is often compromised by subtle forms of data leakage. In particular, when long EEG recordings are segmented into short windows and reused across multiple training stages, models may implicitly encode and propagate label in