April 2026 arXiv papers — page 26

Mohamed Taoufik Kaouthar El Idrissi, Edward Zulkoski, Mohammad Hamdaqa

Code understanding models increasingly rely on pretrained language models (PLMs) and graph neural networks (GNNs), which capture complementary semantic and structural information. We conduct a controlled empirical study of PLM-GNN hybrids for code classification and vulnerability detection tasks by systematically pairing three code-specialized PLMs with thre

R. Ganser, M. P. T. Masilamani, B. Geldiyev, M. M. Hirschmann

Moir\'e heterostructures provide a powerful framework for tailoring electronic band structures via controlled long-range periodic superlattice potentials. Beyond widely studied moir\'e-tailored flat bands, folded band structures can host emergent Dirac states, which have recently attracted considerable interest. Direct momentum-resolved observation of gaples

Łukasz Brzozowski, Marek Gagolewski, Grzegorz Siudem

Generating realistic synthetic citation, patent, or component dependency networks is essential for benchmarking community detection, graph visualisation, and network data mining algorithms. We present the first systematic comparison of generators of directed graphs that are nearly acyclic and have a ground-truth community structure. We evaluate 12 methods ac

Wojciech Niewiadomski, Jadwiga Daszyńska-Daszkiewicz, Przemysław Walczak, Wojciech Szewczuk

We present a comprehensive study of the pulsating primary component of the long-period binary system RY Lep. The spectral energy distribution and the absence of detectable lines indicate that the companion is likely a white dwarf. Atmospheric parameters and chemical abundances were determined from a high-resolution spectrum obtained with the the Southern Afr

Puti Dai, Zihua Guo

We prove the following estimate \[ \|{e^{it\partial_x^2}f}\|_{L_{(t,x)\in \mathbb{T}^2}^6}\leq C (\log N)^{{1/6}} \|f\|_{L^2_x(\mathbb{T})}, \] assuming $\mbox{supp} (\hat f)\subset [-N,N]$ for $N>1$. The bound $(\log N)^{{1/6}}$ is sharp in view of the lower bound by Bourgain \cite{Bourgain}.

Song Wang, Kexin Lou, Chen Wei, Zhiyuan Sheng

Non-invasive electrophysiology lacks methods that accurately reconstruct whole-brain spatiotemporal dynamics while incorporating individual cortical geometry, leaving current electroencephalography and magnetoencephalography source imaging limited by simplistic or biologically implausible priors. Here, we show that embedding participant-specific Geometric Ba

Chun-Yi Kuan, Wei-Ping Huang, Hung-yi Lee

Recent audio-aware large language models (ALLMs) have demonstrated strong capabilities across diverse audio understanding and reasoning tasks, but they still frequently produce hallucinated or overly confident outputs. While uncertainty estimation has been extensively studied in text-only LLMs, it remains largely unexplored for ALLMs, where audio-conditioned

W. -J. Kim, A. M. Jacob, D. A. Neufeld, P. Schilke

The HyGAL Stratospheric Observatory for Infrared Astronomy (SOFIA) legacy program aims at characterizing the interstellar medium in the Milky Way using hydrides, [C II], and [O I] absorption lines with the 2.7 m SOFIA telescope toward twenty-five submillimeter-bright Galactic star-forming regions. As part of HyGAL, we investigated correlations among the know

Tin-Yau Tsang

We showed a positive energy theorem for asymptotically flat initial data sets with the concept of spectral PSC by He-Shi-Yu, Bi-Hao-He-Shi-Zhu and Brendle-Wang; and the Jang equation in Schoen-Yau, Eichmair and Jang. Then, we proved a quantitative shielding theorem concerning the causal property of the energy-momentum vector of an asymptotically hyperbolic m

Riccardo Dondi, Mohammad Mehdi Hosseinzadeh

This paper addresses the problem of identifying time interval separators in temporal networks. We introduce d-MinIntSep, a new variant of the temporal separator problem, which models failures as time intervals assigned to vertices and aims to block all temporal paths between a source and a target that can be completed within a given deadline d. We prove that

Ritesh Das, Alvaro Bermejillo-Seco, Herre S. J. van der Zant, Peter G. Steeneken

Magnomechanical systems provide a promising route for exploring coherent hybrid magnon-phonon interactions and hybrid information processing, but their realization has so far been limited by weak magnon-phonon coupling in conventional bulk platforms. We show that a suspended membrane of a two-dimensional van der Waals ferromagnet with in-plane magnetization

Wellington Oliveira

Mobile devices have become ubiquitous tools for communication, entertainment, and productivity, yet battery autonomy remains a constraint. While energy-saving tips exist, they are often generic, anecdotal, or focused on software development rather than end-user behavior, leaving users to rely on grey literature or tacit knowledge to optimize their device ene

Jiarou Jin, Yanyong Hong

We develop the theory of nilpotency and the Frattini theory for transposed Poisson algebras. The lower central series is shown to admit a simplified form, and an analogue of Engel's theorem is established: a finite-dimensional transposed Poisson algebra is nilpotent precisely when the left multiplication operators in both the associative and the Lie structur

Afrouz Jabal Ameli, Tomohiro Koana, Jesper Nederlof, Shengzhe Wang

The Strongly Connected Steiner Subgraph (SCSS) problem is a well-studied network design problem that asks for a minimum subgraph that strongly connects a given set of terminals. In this paper, we present several new algorithmic and complexity results for SCSS. As our main result, we show that SCSS can be solved in time $17^{\mathrm{tw}} n^{O(1)}$ on directed

Cennet Oguz, Yasser Hamidullah, Josef van Genabith, Simon Ostermann

We introduce DualFact, a dual-layer, multimodal factuality evaluation framework for procedural video captioning. DualFact separates factual correctness into conceptual facts, capturing abstract semantic roles (e.g., Action, Ingredient, Tool, Location), and contextual facts, capturing their grounded predicate-argument realizations in video. To support complet

Yukun Guo, Xiaodong Liu

The inverse scattering problem from the multi-frequency backscattering data is a long-standing open problem. We advance the theory by proving a local uniqueness result. Moreover, we introduce a direct sampling method for quantitatively reconstructing unknown inhomogeneities. Comprehensive numerical experiments validate the robustness, accuracy, and computati

Tamara Evstafyeva, Antonia Seifert, Ulrich Sperhake, Christopher J. Moore

We explore the gravitational-wave phenomenology of equal-mass inspiralling boson-star binaries using numerical relativity simulations. In particular, we characterise the waveform differences between binary boson-star and black-hole systems across (i) the early inspiral, by matching our waveforms to post-Newtonian expressions, (ii) merger, and (iii) late ring

David Hartmann, Manuel Tonneau, Angelie Kraft, LK Seiling

Perspective API closes at the end of 2026, removing the de facto standard for toxicity measurement and exposing researchers' dependence on a tool they did not control. Drawing on this case, we argue that a research field must build and govern its own measurement infrastructure rather than borrow it. Surveying 241 papers that use or study Perspective, we

Louis-Pierre Arguin, Emma Bailey, Asher Roberts

Assuming the Riemann Hypothesis, we show that for $k>0$ $$ \frac{1}{T}\text{meas}\Big\{t\in [T,2T]:|\zeta(1/2+{\rm i} t)|>(\log T)^k\Big\}\leq C_k \frac{(\log T)^{-k^2}}{\sqrt{\log\log T}}, $$ where $C_k=\exp(e^{ck})$ for some absolute constant $c>0$. This implies that the $2k$-moments of $|\zeta|$ are bounded above by $C_k(\log T)^{k^2}$, recovering the bou

Fan Jiang, Yu Zhao, Chenyang Lyu, Tianqi Shi

We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only around 5\% of the total parameters are activated per input token. This extreme sparsity, combined with upcycling from dense models, enables efficient pre-training on 5T tokens. Our models surpass similarly-s

Chen Xu, Wei Chu, Wenyu Hu, Fengran Mo

Fair re-ranking aims to promote long-tail items and enhance diversity within groups in information retrieval. While previous research on online fairness-aware re-ranking has shown promising outcomes, our comprehensive evaluation of online fair re-ranking methods over 20 settings reveals significant performance disparities among existing methods. To uncover t

Elliot Perviz, Antonio Cammarata

We investigate the nanoscale mechanisms determining lattice thermal conductivity (LTC) of pristine and W-doped MX$_2$-M$^\prime$X$^\prime_2$ transition metal dichalcogenide heterobilayers from first principles, using the exact solution of the linearised Boltzmann transport equation in both phonon and relaxon bases. Pristine heterobilayers exhibit isotropic i

Hasan Yilmaz, Masahiko Isobe, Oliver Clemens, Pascal Puphal

Topochemical fluorination offers a low--temperature route for modifying the anion chemistry and electronic ground states of layered transition-metal oxides, providing access to metastable phases and functionalities that are not able to be achieved through conventional solid--state synthesis. Despite extensive work on polycrystalline samples and thin films, t

Jialong Wu, Yihan Wang, Matthias Rottmann

In autonomous driving, camera-radar fusion offers complementary sensing and low deployment cost. Existing methods perform fusion through input mixing, feature map mixing, or query-based feature sampling. We propose a new fusion paradigm, termed heterogeneous query interaction, and present ConFusion, a camera-radar 3D object detector. ConFusion combines image

Rijul Sachdeva, Vrinda Mehta, Manpreet Singh Jattana, Kristel Michielsen

We study algorithms inspired by quantum annealing that are suited for the NISQ era. First, we analyze approximate quantum annealing (AQA), which employs a discretized annealing ansatz in which the time step and the number of layers are allowed to deviate from a faithful implementation of quantum annealing. Parameter scans identify regimes that reproduce anne

Erik Lien Bolager, Boumediene Hamzi, Houman Owhadi, Ioannis G. Kevrekidis

Studying nonlinear dynamical systems through their state space behavior can be challenging, and one possible alternative is to analyze them via their associated Koopman operator. This turns the nonlinear problem into a linear, infinite-dimensional one. To approximate the operator in finite dimensions, extended dynamic mode decomposition (EDMD) is a commonly

Jinkun Zhang, Poonam Yadav

Partitioned DNN inference is a promising approach for latency-sensitive intelligent services in edge networks, since it allows different parts of a model to be executed across end devices, edge servers, and the cloud. However, in a multi-hop edge network, partition placement and inference traffic routing are inherently coupled: raw inputs, intermediate featu

Dewei Bai, Hongxiang Peng, Yunyun Zeng, Ziyu Zhang

Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token interactions remain fundamentally misaligned with the sparse and event-driven nature of spiking neural computation. To address this limitation, we propose Vision SmolMamba, an energy-efficient spiking state-space

Haolin Wang, Xianyuan Liu, Anna Jungbluth, Alexandra J. Ramadan

Accurate bandgap prediction is crucial for semiconductor applications, yet machine learning models trained on computational data often struggle to generalize to experimental bandgap measurements. Challenges related to data fidelity, domain generalization, and model interpretability remain insufficiently addressed in existing evaluation frameworks. To bridge

David Zahrádka, David Woller, Denisa Mužíková, Miroslav Kulich

During the execution of Multi-Agent Path Finding (MAPF) plans in real-life applications, the MAPF assumption that the fleet's movement is perfectly synchronized does not apply. Since one or more of the agents may become delayed due to internal or external factors, it is often necessary to use a robust execution method to avoid collisions caused by desynchron

Toshiki Matsusaka, Shin-ichiro Seki

This paper presents various transcendence results in the ring of integers modulo infinitely large primes $\mathcal{A}$. In the ring $\mathcal{A}$, one can consider two notions of transcendence. One is based on the notion of finite algebraic numbers introduced by Rosen, while the other is transcendence in the naive sense. It is known that transcendence in the

Hengjia Fang, Wei Ma

We propose the covariate-balanced-and-adjusted response-adaptive randomization (CBARA) procedure for adaptive design in clinical trials, which integrates the complementary strengths of covariate-adjusted response-adaptive randomization (CARA) and covariate-adaptive randomization (CAR). The CBARA procedure updates the target allocation ratio according to obse

Konstantinos N. Anagnostopoulos, Takehiro Azuma, Mitsuaki Hirasawa, Jun Nishimura

The type IIB matrix model has been proposed as a nonperturbative formulation of superstring theory. While numerical simulations of this model are essential for probing nonperturbative effects, such as the emergence of time and an expanding 3--dimensional space, they are hindered by the sign problem. We address this using the Complex Langevin Method (CLM). Fu

Carson Kohlbrenner, Caleb Escobedo, Sayak Ray, Alexander Dickhans

3D-printed artificial skins are a scalable approach to whole-body tactile and proximity coverage, but prior implementations have been limited to unimodal sensing and rigid materials. To improve the practical usability of 3D-printed artificial skins, we present a hybrid time-of-flight (ToF) and self-capacitance (SC) sensing skin that demonstrates multi-modal

Mengyao Du, Han Fang, Haokai Ma, Jiahao Chen

Web agents have emerged as an effective paradigm for automating interactions with complex web environments, yet remain vulnerable to prompt injection attacks that embed malicious instructions into webpage content to induce unintended actions. This threat is further amplified for screenshot-based web agents, which operate on rendered visual webpages rather th

Surjeet Singh Choudhary, Chong-Wei Liang, Chun-Yen Shen

We study the occurrence of curved three-point configurations in fractal subsets of the real line. We prove that if \(E \subset [0,1]\) is a compact set with sufficiently large Hausdorff dimension, then \(E\) contains a curved three-point progression associated with a broad class of nonlinear functions. Our approach can also show the existence of the curved t

Ricarda S. Beckmann, Rebecca J. Smethurst

Almost every galaxy in the local Universe is observed to have a massive black hole in the centre. The properties of these black holes are observed to tightly correlate with those of their host galaxy which has been interpreted as coevolution regulated by black hole feedback. This coevolution spans most of cosmic history, as the first active black holes, so-c

Sara Hahner, Lorenzo Zampieri, Jean-Raymond Bidlot, Philip Browne

Machine-learning (ML) models, such as the AIFS at the ECMWF, have revolutionised weather forecasting in recent years. We present an extension of the AIFS that jointly models the atmosphere and surface ocean, including ocean waves and sea ice. The primary objective of this extension is to enhance machine-learning medium-range forecasting and enable new use ca

Anna Kilian, Paweł Bilski, Małgorzata Sankowska

Background Remover (BGR) is a novel software tool developed as a plugin to the well-known ImageJ program and designed to address the challenges of analysing fluorescent microscopy images characterized by low signal-to-noise ratios and heterogeneous backgrounds. The used algorithm effectively differentiates between signal and noise pixels, preserving the sign

Erdong Guo, Paul Jackson, Jin Min Yang, Pengxuan Zhu

High-energy physics phenomenology often requires linking multiple computational tools to evaluate observables, likelihoods, and experimental constraints across nontrivial parameter spaces. In this work, we introduce Jarvis-HEP, a lightweight Python framework for workflow composition and parameter scans in high-energy physics. The framework provides YAML-base

Georgios Stamoulis

We study the rainbow matching (RM) problem: given an edge-colored graph, find a maximum matching with at most one edge of each color. Rainbow matchings correspond to stable sets in the \emph{augmented} graph $H$ obtained from the line graph by completing each color class into a clique. For a hereditary graph class $\mathcal{X}$, we introduce the parameter $\

James Flora, Kowshik Thopalli, Akshay R. Kulkarni, Weng-Keen Wong

We present LatentDiff, a scalable framework for semantic dataset comparison that operates directly in the latent space of pretrained vision encoders. By combining sparse autoencoder-based divergence testing with density ratio estimation, LatentDiff identifies interpretable semantic differences between datasets at a fraction of the computational cost of capti

Jiaming Yang, Chenwei Tang, Liangli Zhen, Jiancheng Lv

Key-Value (KV) caching is essential for large language model inference, yet its memory overhead poses a critical bottleneck for long-context generation. Existing eviction policies predominantly rely on empirical heuristics, lacking a rigorous theoretical foundation. This work rethinks KV cache eviction through the lens of the Information Bottleneck principle

Ignacio Peyrano

Enterprise software engineering is shifting away from deterministic CRUD/REST architectures toward AI-native systems where large language models act as cognitive orchestrators. This transition introduces a critical security tension: probabilistic LLMs weaken classical mechanisms for validation, access control, and formal testing. This paper proposes the desi

Carson Kohlbrenner, Niraj Pudasaini, William Xie, Naren Sivagnanadasan

Collision-free motion is often aided by tactile and proximity sensors distributed on the body of the robot due to their resistance to occlusion as opposed to external cameras. However, how to shape the sensor's properties, such as sensing coverage; type; and range, to enable avoidant behavior remains unclear. In this work, we present a reinforcement learning

Grzegorz Krasucki, Artur O. Slobodeniuk, Kacper Walczyk, Katarzyna Olkowska-Pucko

Optically dark excitonic states play a critical role in the valleytronic, electronic, and optical properties of monolayer semiconducting transition metal dichalcogenides. Here, we investigate how electrostatic doping affects the in-plane magnetic-field-induced activation of dark excitonic complexes in a gated WSe$_2$ monolayer. By continuously tuning the car

Yadong Jiao, Xiaoyan Cheng, Yuansheng Tang, Ming Xu

For the polar codes introduced by Arikan in 2009, the first code family achieving the capacity of binary-input discrete memoryless channels (BIDMCs) with low-complexity encoding and decoding, it is crucial to evaluate the reliability of the synthetic channels resulted in the code construction. Since the synthetic channels have an output alphabet that grows e

Jeroen Bollen, Stijn Vansummeren

Recurrent Graph Neural Networks (RGNNs) extend standard GNNs by iterating message-passing until some stopping condition is met. Various RGNN models have been proposed in the literature. In this paper, we study three such models: converging RGNNs, where all vertex representations must stabilise; output-converging RGNNs, where only the output classifications m

Haoran Chen, Wentao Wang

SignSGD compresses each stochastic gradient coordinate to a single bit, offering substantial memory and communication savings, but its 1-bit quantization removes magnitude information and is known to leave a generalization gap relative to well-tuned SGD. We revisit SignSGD from a 1-bit quantization and dithering perspective and contribute three improvements.

Tomasz Gogacz, Filip Murlak, Marcin Przybyłko, Alexandra Rogova

Aiming to harmonise finite and infinite model reasoning, we initiate the study of partially finite models, where the reasoning task comes with a formula that specifies a part of the model that must be finite. We focus on the problem of partially finite query entailment in description logics (DLs): given a knowledge base (KB), a query, and a distinguished con

János Ádám, Andrew J. Winter, Deniz Aybas, Dmitry Budker

Macroscopic spin ensembles in solids are powerful platforms for quantum sensing and precision metrology. A key challenge is controlling the nuclear spin population relaxation time $T_1$, which can become prohibitively long at cryogenic temperatures due to phonon freeze-out. We demonstrate optical control of the $T_1$ relaxation time of the $^{207}$Pb nuclear

Federica Gregorio, Chiara Spina, Cristian Tacelli

In this paper we consider higher order Schr\"odinger operators $$\mathcal L u=Lu+Vu,$$ where $L$ denotes a fourth order operator and $V\geq 0$ a suitable potential. We initiate our analysis by considering the constant coefficients differential operator $L=\Delta^2$. Subsequently, we extend our results to more general operators $L$ featuring suitable variable

Zichen Li, Hao Liang, Yuan Gu, Jiaye Zhang

The generation of vortex matter waves carrying quantized orbital angular momentum is challenging and relies heavily on the material nanofabrication methods due to their extremely small de-Broglie wavelengths. Here, we introduce an all-optical method for generating an electron vortex by diffraction through a grating made of light. We realize the orbital angul

Fuchen Zheng, Chengpei Xu, Long Ma, Weixuan Li

Visual state-space models (SSMs) have shown strong potential for medical image segmentation, yet their effectiveness is often limited by two practical issues: axis-biased scan ordering weakens the modeling of oblique and curved structures, and naive multi-branch fusion tends to amplify redundant responses. We present TopoMamba, a topology-aware scan-and-fuse

Luyao Wang

Deploying an intrusion detector trained in one industrial plant to another remains difficult because Industrial Control System (ICS) traffic is highly site-dependent, labels are scarce, and unseen attacks often appear after deployment. To address this challenge, this paper introduces a medoid prototype alignment framework for cross-plant unknown attack detec

Muhammad Luqman Hakeem Musa, Zamri Zainal Abidin, Masatoshi Imanishi, Yoshiaki Hagiwara

We analyse Compton-thick active galactic nuclei (CT AGNs), a heavily obscured subclass that challenges traditional X-ray diagnostics. Using 243 sources from the 70-Month \textit{SWIFT}/BAT catalogue (26 CT, 217 non-CT), we investigate their properties across radio, infrared (IR), optical, and X-ray bands. VLASS data reveals slightly higher 2--3~GHz mean lumi

Paul Schwarzbach

Mobile robots that move between outdoor and indoor environments still struggle with consistent positioning. Satellite-based and terrestrial ranging each work well in their home domains, but combining them at the raw measurement level has received little attention, and the building boundary is precisely where both classes degrade. This paper reports prelimina

P. Bechtle, O. Freyermuth, M. Geffers, M. Giffels

The rising share of abundant renewable energy inevitably increases volatility in the electricity production. The concept of sector coupling means that the volatility of electricity production to a large degree can be absorbed by dispatching electricity consumption whenever excess renewable energy is available. A system that is dynamically operated based on t

Yuki Yanagi, Michi-To Suzuki

We present a first-principles investigation of the electronic structure of the inversion-symmetry-broken spin-orbit-coupled metal candidate PbRe$_2$O$_6$. Our calculations reveal that the Fermi surfaces derived from the $d_{yz}$ and $d_{zx}$ orbitals exhibit pronounced one-dimensional characteristics, which naturally account for the highly anisotropic charge

CMS Collaboration

A simultaneous measurement of 25 substructure observables is presented using large-radius jets with high transverse momentum from proton-proton collisions at $\sqrt{s}$ = 13 TeV. The measurement is carried out on dijet events and $\mathrm{t\bar{t}}$ events enriched in Lorentz-boosted W bosons and top quarks decaying hadronically. The three data samples consi

Xiaolin Liu, Zhoujian Cao

In this work, we developed analytic asymptotic methods for computing the Fourier modes of gravitational waves from post-Newtonian binary systems in the quasi-Keplerian parametrization in the high eccentricity regime. We have also derived the large-eccentricity asymptotic expansion of the eccentricity enhancement function appearing in the tail contributions t

Walid Hachem

A generalization of the Sherrington-Kirkpatrick (SK) model for spin glasses is considered, in which the interaction matrix is endowed with a variance profile that has no particular structure an may be sparse. In the first part of this paper, an asymptotic equivalent of the free energy is derived at sufficiently high temperatures, regardless of the signature

Simone Murari, Celeste Veronese, Daniele Meli

Deep Reinforcement Learning (DRL) algorithms often require a large amount of data and struggle in sparse-reward domains with long planning horizons and multiple sub-goals. In this paper, we propose a neuro-symbolic extension of Proximal Policy Optimization (PPO) that transfers partial logical policy specifications learned in easier instances to guide learnin

Junchao Cui, Wenqi Shi, Shaoyong Du, Hang He

Worldwide image geo-localization aims to infer the geographic location of an image captured anywhere on Earth, spanning street, city, regional, national, and continental scales. Existing methods rely on visual features that are sensitive to environmental variations (e.g., lighting, season, and weather) and lack effective post-processing to filter outlier can

Rubén Darío Guerrero

We identify a missing local-refinement stage in the cotengra tensor-network contraction pipeline and show that its impact grows monotonically with bond dimension on the \emph{connectivity graph} of Sycamore-like topologies. Appending a nearest-neighbor interchange (NNI) search to the \cotengra{} output at matched 8-s wallclock yields a median \emph{predicted

Maximilian Zorn, Melinda Braun, Michael Ertl, Tommy Kiss

This paper studies quantum optimization baselines for the Generalized Traveling Salesman Problem (GTSP), a clustered routing problem that naturally models variant selection and sequencing problems under discrete alternatives. We propose a novel GTSP QUBO formulation focused on maintaining feasible solutions for quantum annealing, as well as a hardware-execut

Muhammad Ali, Kevin Alexander Laube, Madan Ravi Ganesh, Lukas Schott

Recent knowledge distillation (KD) methods for semantic segmentation introduce increasingly complex hand-crafted objectives, yet are typically evaluated under fixed iteration schedules. These objectives substantially increase per-iteration cost, meaning equal iteration counts do not correspond to equal training budgets. It is therefore unclear whether report

Bernardo Cornejo Avila, Sofia Bisero, Mickäel Costa, Antoine Ciric

Time-domain astrophysics is a rapidly growing field focused on the study of transient phenomena such as Gamma-Ray Bursts (GRBs), Fast Radio Bursts (FRBs), supernovae, novae, and AGN flares. Their characterization increasingly relies on a multi-messenger and multi-wavelength approach, combining gravitational waves, high-energy neutrinos, and electromagnetic o

Jishan Fan, Yu Jiang, Sei Nagayasu, Gen Nakamura

This paper concerns an inverse problem for the initial boundary value problem of the two-dimensional Navier-Stokes system defined in a bounded simply connected domain with slip, vorticity boundary conditions, and a global vorticity invariant constraint. We establish conditional Lipschitz stability and a local recovery for this inverse problem, where the velo

Antoine Thibault Vié, Leonid Fridman, Roberto Galeazzi, Dimitrios Papageorgiou

In digital sliding mode control implementations, discretization-induced chattering and inter-sample blindness can severely degrade the closed-loop performance, especially in case of fast perturbations. This paper addresses these challenges for a discrete-time implementation of the super-twisting sliding mode controller. Building upon recent results on barrie

Siyuan Ji

AI tools are being deployed over MBSE models today, and those models were not designed for this kind of consumption. The problem is not simply that tools hallucinate: well-prompted frontier models produce competent, useful output over a conformant SysML model, but the reasoning they produce is drawn from training rather than retrieved from the model itself,

GunSik Min, Yujin Kang, Jun Heo

Defect-adaptive surface-code methods have substantially advanced the construction of valid logical patches on imperfect hardware, but fault-tolerant computation also requires executable logical oper ations on the resulting irregular geometries. We formulate the seam-boundary defect problem: how to perform a lattice-surgery merge when the intended seam inters

Rachid El Maaouy, Hanane Ouberka

Let $\mathcal{Q}$ be a class of objects in an abelian category $\mathcal{A}$ which need not have enough projective or injective objects. In this paper, we prove that if $\mathcal{Q}$ is the first class of a Hovey triple $(\mathcal{Q},\mathcal{W},\mathcal{R})$ in $\mathcal{A}$ satisfying certain assumptions-weaker than those required in the recent literature-

Pietro Biroli, Nicolau Martin-Bassols, Andries T. Marees, Hans van Kippersluis

The start of a human's life can be characterized by two lotteries: that of your genes (nature) and the family you were born into (nurture). These set in motion a trajectory, from birth onward, in health and human capital. Leveraging three longitudinal social-science data sets, we systematically analyze the relationship between an individual's genotype, the s

Suyog Chandramouli, George Kachergis, Akshay Jagadish

Cognitive science often evaluates theories through narrow paradigms and local model comparisons, limiting the integration of evidence across tasks and realizations. We introduce an automated adversarial collaboration framework for adjudicating among competing theories even when the candidate models and experiments must be discovered during the adjudication p

G. Pini, F. Santilli

This paper deals with the variational analysis, for every $s \in (0,1)$ and $p \in [1,+\infty)$, of $(s,p)$-Gagliardo seminorms in a periodic setting. First, we consider the space of $L^p$, $T$-periodic functions and define the energy functional $\mathcal{F}_p^s$ as the density of the \(d\)-dimensional $(s,p)$-Gagliardo seminorm over the periodic cell. Our g

Elena Rozas, Wojciech Bukalski, Yannik Brune, Adbhut Gupta

Polariton condensates provide a versatile platform for exploring non-equilibrium phase transitions and collective phenomena in open quantum systems. Near the condensation threshold, these systems are particularly sensitive to fluctuations and instabilities, which can strongly influence the condensate formation. Using optical trapping and homodyne detection,

Chandrasekhar Bhamidipati, Parashar Chatterjee, Sudipta Mukherji, Yogesh Kumar Srivastava

We study the trajectories of massive particles in spherically symmetric black holes in arbitrary dimensions, and find certain universal features based on the topological classification of the fixed points. If the system admits a center, we find two possible outcomes: regardless of the value of the angular momentum, the center always survives, which is realiz

Chang-Xian Song, Yong-Bo Tang

We report relativistic many-body calculations of the interelectronic-interaction correction to the Land\'{e} $g$-factor of the $2s_{1/2}$, $2p_{1/2}$, $2p_{3/2}$, and $3s_{1/2}$ states in lithium-like ions with nuclear charge $Z = 4-20$. Starting from the Dirac-Coulomb-Breit Hamiltonian, we treat positive-energy contributions using the coupled-cluster method

Tianci Luo, Yong Wei, Rong Zhou

We study contracting curvature flows of compact hypersurfaces with positive sectional curvature in hyperbolic space $\mathbb{H}^{n+1}$. The speed is assumed to be homogeneous of degree one in the principal curvatures and to satisfy certain conditions. This class of flows includes the $k$th mean curvature flow as a special case. We show that if the initial hy

Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti

Phishing detection systems are predominantly rely on statistical machine learning models, which often lack contextual reasoning and are vulnerable to adversarial manipulation. In this work, we propose a hybrid framework that integrates machine learning classifiers with non-monotonic reasoning using Answer Set Programming (ASP) to enable context-aware decisio

Shunya Saito

We extend the classification results for torsion classes and torsion-free classes in the category of finitely generated modules over a commutative noetherian ring to suitable symmetric monoidal closed noetherian abelian categories. Our main tool is the orbit atom spectrum, defined as the quotient of Kanda's atom spectrum by the action induced by tensoring wi

Weijie Huang, Xinran Ruan

We propose a sharp-interface model for solid-state dewetting of thin films with wetting potential, where the wetting effect is incorporated through a thickness-dependent surface energy. The model is governed by surface diffusion together with natural boundary conditions, and describes the morphological evolution of the film-vapor interface. For its numerical

Anina Köhler, Jakob Murauer, Tim Heine, Stefan Rosemann

Simon's algorithm is a polynomial period-finding algorithm that has been used to exploit the algebraic structure of specific symmetric ciphers, showing that exponential speedups in their cryptanalysis are theoretically possible. While the theoretical framework for an attack using Simon's algorithm on the Even-Mansour cipher is well-established, practical imp

Artur Eisele, Bernd Frauenknecht, Friedrich Solowjow, Sebastian Trimpe

Safety remains an open problem in reinforcement learning (RL), especially during training. While safety filters are promising to address safe exploration, they are generally poorly suited for high-dimensional systems with unknown dynamics. We propose Dyna-style Safety Augmented Reinforcement Learning (Dyna-SAuR), a novel algorithm that learns both a scalable

Samuele Centorrino, Frédérique Fève, Jean-Pierre Florens

We develop a nonparametric approach to identify and estimate consumer preferences and unobserved heterogeneity under nonlinear price schedules. Leveraging variation across multiple price schedules, we show that both the utility function and the distribution of preference types can be nonparametrically identified. The quantile function of unobserved types bec

Pratyush Sahu, Rahul Bothra, Venkat Arun, Brighten Godfrey

Designing the architecture of modern networked systems requires navigating a large, combinatorial space of hardware, systems, and configuration choices with complex cross-layer interactions. Architects must balance competing objectives such as performance, cost, and deployability while satisfying compatibility and resource constraints, often relying on scatt

Feiyang Yin, Isidro Butaslac, Patrick Gebhard, Monica Perusquia-Hernandez

Micro-expressions are brief and subtle facial movements that convey nuanced affective information but often remain imperceptible during natural social interaction. Although prior research has primarily focused on computational recognition and spotting of micro-expressions, their application in human-centered contexts remains limited. From the perspective of

Amos Lapidoth, Baohua Ni, Ligong Wang

State-dependent bipartite networks with state-cognizant receivers and state-informed transmitters are studied. Such networks have no nodes that both transmit and receive. Examples are the multi-access channel, the broadcast channel, and the interference channel. Without computing the capacity region of the network, it is shown that if the state sequence is e

Haoran Zhou, Zhaohui Fu, Yangshuai Wang, Xinlong Feng

We study a discrete-time random feature method for nonlinear, time-dependent partial differential equations. In contrast to continuous-time formulations that treat time as an additional input variable, the method advances the solution step by step, with each time level computed from previously available states. The spatial solution at each step is represente

Matteo Acclavio, Lutz Straßburger, Vladimir Zamdzhiev

BV-categories are a recent development that aims to give categorical semantics to proofs in the logic BV. However, due to the absence of a coherence theorem on one side and a well-defined notion of proof identity for BV on the other side, the precise relation between BV-categories and the logic BV is still not clear. To improve on this situation, we define i

Lan-Lin Du, Yang Yang, Shiqi Hu, Sheng Meng

Hydrogenation is known to tune superconductivity in a wide range of materials. While its microscopic role has been clarified in phonon-mediated superconductors such as hydrogenated MgB2, LaH10, and H3S, much less is known for hydrogenated cuprates and iron-based superconductors, where even the underlying structural motifs remain elusive. Using hydrogenated F

Xuanhao Yang, Bing Xue, Mengjie Zhang

Time series classification is an important analytical task across diverse domains. However, its practical application is often hindered by the scarcity of labeled data and the requirement for substantial computational resources. To address these challenges, this paper proposes EvoTSC, a novel genetic programming approach designed to automatically evolve ligh

Johanna Weidelt, Jijeesh Ravi Nair, Diddo Diddens, Wentao Zhang

Solid polymer electrolytes (SPEs) based on cross-linked poly(ethylene oxide) (PEO) encompassing lithium salts have gained significant attention as separators in solid-state lithium metal batteries. Here, we employ terahertz time-domain spectroscopy (THz-TDS), as a noninvasive contact-free technique, to investigate the conduction properties of these cross-lin

Mozib Bin Awal, Prabwal Phukon

We probe the thermodynamic phase structure of four dimensional Horava Lifshitz black holes by Lyapunov exponent analysis. For both massless and massive test particles, the Lyapunov exponent exhibits a multivalued dependence on temperature in regimes with a first-order phase transition, with distinct branches corresponding to small, intermediate, and large bl

Takiko Sasaki, Tetsuji Tokihiro

We study finite-size adiabatic state preparation on Boolean hypercubes using graph-local drivers built from sector/path coordinates related to monotone Gray-code representatives. The construction is not presented as a new all-$n$ Gray-code existence theorem; rather, it provides finite representatives, explicitly checked through the cases used in the numerica

Bowy M. La Riviere, Natalia Chepiga

We investigate the effect of a single-ion anisotropy on the bilinear-biquadratic spin-1 J1-J2 chain, focusing on the quantum phase transitions out of the trimerized phase. Using large-scale density matrix renormalization group simulations, we uncover a rich phase diagram comprising five gapped phases and, remarkably, two critical floating phases. These incom

Feiyan Guo, Jianlin Zhou, Lin Qi, Ying Fan

Localized shocks arising from climate extremes, geopolitical conflicts, and trade protectionism cascade through trade networks, triggering global food crises. Cross-product substitution, a critical response strategy, induces cross-product cascading effects that remain underexplored. Here, we develop a multilayer network model that simulates the short-term re

Terry Generet, Joey Huston, Kyle Lee, Ian Moult

Jet production at the LHC is a powerful probe of QCD, making it ideal for precision tests and determinations of QCD parameters such as parton distribution functions and the strong coupling constant. To make the most of the abundant jet production data collected at the LHC, precise calculations are required. While state-of-the-art calculations reach next-to-n

Ritz Ann Aguilar, Maxwell LaBerge, Andreas Doepp, Alexander Debus

Coherent transition radiation (CTR) spectroscopy is a critical diagnostic for characterizing the longitudinal structure of relativistic electron bunches in laser-plasma and conventional accelerators. In practice, recovering the bunch profile from a measured CTR spectrum is an ill-posed phase-retrieval problem. Traditionally, this is addressed using Gerchberg