April 2026 arXiv papers — page 37

Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman Anik

In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error mitigation strategies. We explore transfer learning approaches to apply noise models learned on one quantum device to a different device with the help of a small amount of data. We create a real-hardware dataset

Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan

Vision-Language Models (VLMs) are frequently undermined by object hallucination--generating content that contradicts visual reality--due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free inference framework that intervenes directly in the decoding process to enforce visual fidelity. PND is motivated

Byeonggeuk Lim, JungMin Yun, Junehyoung Kwon, Kyeonghyun Kim

Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary models to construct preference datasets. We identify that this reliance introduces a distributional mismatch between the proprietary and target models that hinders efficient alignment. To address this, we propo

Mufhumudzi Muthivhi, Terence L. van Zyl

There has been growing interest in studying the complexity of Rectified Linear Unit (ReLU) based activation networks. Recent work investigates the evolution of the number of piecewise-linear partitions (linear regions) that are formed during training. However, current research is limited to examining the complexity of models trained in a supervised way. Self

Emmanuel Gobet, Adrien Richou, Charu Shardul

We study backward stochastic differential equations (BSDEs) in infinite horizon and design efficient numerical schemes for solving them. We establish a probabilistic representation of the solution of the BSDE using Malliavin derivative and prove results for contraction of a Picard scheme. We develop three numerical schemes, of which the first two are based o

Zihao Zheng, Xingyue Zhou, Zhihao Mao, Songyu Sun

Vision-Language-Navigation (VLN) models exhibit excellent navigation accuracy but incur high computational overhead. Token caching has emerged as a promising training-free strategy to reduce this cost by reusing token computation results; however, existing token caching approaches rely on visual domain methods for cacheable token selection, leading to challe

Martin Bergerhausen, David J. Prömel

We prove the existence of weak solutions for distribution-dependent stochastic Volterra equations under linear growth and continuity conditions on the coefficients and mild regularity assumptions on the kernels, including singular kernels. To this end, we formulate an associated local martingale problem and establish its connection with weak solutions. Moreo

Georgi Medvedev, Emmanuel Trélat

We construct transported PDEs on self-similar fractal domains from reference equations posed on the unit interval, and derive explicit self-similar interacting particle systems that approximate the resulting dynamics. The construction combines a measure-preserving isometry between $L^2$-spaces on $[0,1]$ and on the fractal \cite{Med2026}, a nonlocal-to-local

Jun Ma

Biological function of living matter is fulfilled by complex motions of biological and soft matter. Unlike general motion is deterministic described by Newton's laws, these motions are mostly random and uncertain for the position in stochastic process, being characterized as irregular trajectories of movement without a defined velocity. Like human fingerprin

Leekyung Kim, Jonghun Park

Automatic chord recognition (ACR) extracts time-aligned chord labels from music audio recordings. Despite recent advances, ACR still struggles with oversegmentation, data scarcity, and imbalance, especially in recognizing complex chords such as non-triads, which are unpopular in existing datasets. To address these challenges, we reformulate ACR as a segment-

Hang Deng, Liang Feng Zhang

A t-private n-server Information-Theoretic Distributed Point Function ((t,n)-ITDPF) allows one to convert any point function f_{alpha,beta}(x): [N] -> G into n shares (secret keys), such that each server can compute an additive share of f_{alpha,beta}(x) with a key while any <= t servers learn absolutely no information about the function. This paper construc

Rakshit Soni, Charles Fox

Automated vehicles (AVs) are commonly programmed to yield unconditionally to pedestrians in the interest of safety. However, this design choice can give rise to the Freezing Robot Problem in which pedestrians learn to assert priority at every interaction, causing vehicles to stall and make no progress. The game theoretic Sequential Chicken model has shown th

Lin-Kun Yan, Zhi-Long Han, Feng-Lan Shao, Fa-Xin Yang

Naturally tiny neutrino masses can be explained by the low scale seesaw with heavy neutral lepton $N$ coupling to the neutrinophilic Higgs doublet $\Phi_\nu$, which obtains a much smaller vacuum expectation value than the standard Higgs doublet $\Phi$. Within this model, the neutrino masses originate from the new Yukawa interaction $y \overline{L}\tilde{\Phi

Brindaban Ojha, Matías P. Grassi, Vassilios Kapaklis

In artificial spin ice (ASI), magnetic interactions between nanomagnets determine both the stable states and the switching pathways under an applied field. Here, first-order reversal curve (FORC) measurements are used to map how these interactions govern magnetization reversal in square arrays as the element shape and spacing are varied. The FORC diagrams sh

Bartosz Bieganowski, Jacopo Schino

Via a new inequality à la Gagliardo--Nirenberg, we prove the existence and nonexistence of solutions to \begin{equation*} \begin{cases} (-Δ)^s u + \fracμ{|y|^{2s}} u + λu = f(u), \quad \mathbb{R}^N \ni x = (y,z) \in \mathbb{R}^K \times \mathbb{R}^{N-K}, \\ \int_{\mathbb{R}^N} u^2 \, \mathrm{d}x = ρ\end{cases} \end{equation*} in the mass-critical and mass-sub

Yiran Huang, Lukas Thede, Massimiliano Mancini, Wenjia Xu

While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment challenges on resource-constrained edge devices. Current parameter reduction techniques primarily involve training LVLMs from small language models, but these methods offer limited flexibility and remain comp

Noémie Cohen, Mélanie Ducoffe, Christophe Gabreau, Claire Pagetti

Safety-critical applications are required to perform as expected in normal operations. Image processing functions are often required to be insensitive to small geometric perturbations such as rotation, scaling, shearing or translation. This paper addresses the formal verification of neural networks against geometric perturbations on their image dataset. Our

Varghese Reji, Joe P. Ninan, Supriyo Ghosh, Devendra K. Ojha

TIFR-ARIES Near-Infrared Spectrometer (TANSPEC) is a spectrograph-cum-imager operating over the wavelength range $0.55 - 2.5~\mu$m. The instrument is mounted on the 3.6-m Devasthal Optical Telescope (3.6-m DOT). It offers two resolution modes: Low Resolution (LR) with $R\sim100-350$ and Cross-Dispersed (XD) via various slits of different widths (0.5", 0.75",

Martina Juhnke, Steffen Schlie

We study the Ehrhart $h^\ast$-polynomial of (the boundary of) a lattice polytope via regular unimodular triangulations and Gr\"obner degenerations of toric ideals. Our main result is a boundary analogue of the well-known Sturmfels correspondence. This allows us to connect the boundary $h^\ast$-polynomial to the $h$-polynomial of any regular unimodular triang

Eliseo Bao, Anxo Perez, David Otero, Javier Parapar

Depression places substantial pressure on mental health services, and many people describe their experiences outside clinical settings in high-volume user-generated text (e.g., online forums and social media). Automatically identifying clinical symptom evidence in such text can therefore complement limited clinical capacity and scale to large populations. We

Jeroen Van Loock, Denise Ahmed-Braun, Jacques Tempere

In a mixture of two Bose-Einstein condensates, the interactions can be tuned such that self-bound objects called quantum droplets appear. Whereas the ground states of such quantum droplets at finite temperature have been studied for three- and one-dimensional configurations, the possible fragmentation of these droplets has so far not been considered in these

Victoria Jackson, Grischa Liebel, Rafael Prikladnicki, Andre van der Hoek

While the use of Large Language Models (LLMs) in programming has been extensively studied, there is limited understanding of how LLMs support collaborative work where creativity plays a central role. Software design, as a collaborative and creative activity, provides a valuable context for exploring the influence of LLMs on creativity. This study investigate

Sichun Luo, Yi Huang, Haochen Luo, Fengyuan Liu

Large Language Model (LLM)-guided evolutionary search is increasingly used for automated algorithm discovery, yet most current methods track search progress primarily through executable programs and scalar fitness. Even when natural-language reasoning is used through heuristic descriptions or reflection, it typically remains transient mutation context or uns

Di Wang, Chupei Tang, Junxiao Kong, Jixiu Zhai

Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed across interacting genes and pathways. We propose PathMoG, a pathway-centric modular graph neural network for multi-omics survival prediction. PathMoG reorganizes genome-scale inputs into 354 KEGG-informed pathw

Matti Hyyppä, Klaara Salolahti, Eric Hyyppä, Xiaowei Yu

The shift from stand-level to individual-tree-level forest assessments supports improved species mapping and biodiversity monitoring, particularly in boreal ecosystems where tree species like aspen (Populus tremula L.) play a keystone role. Airborne laser scanning (ALS) is the standard for such inventories, but a major limitation for developing improved spec

Xiyu Wang, Gilberto Berardinelli, Hei Victor Cheng, Petar Popovski

Resource allocation in integrated sensing and communication (ISAC) systems needs to be optimized to balance the requirements of the communication and sensing modules considering complicated cross-layer data traffic and queue status in dynamic multi-user environments. This paper studies the beam allocation for cross-layer ISAC that achieves low-latency commun

Shuo Yang, Zheyu Zhang, Bardh Prenkaj, Gjergji Kasneci

Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domains. Recent approaches leverage LLMs by representing table rows as sequences, yet suffer from two fundamental limitations: (1) they model feature dependencies densely, introducing spurious correlations; and (2

A. S. Rotelli, H. Augustin, D. Bortoletto, A. Brooks

MuPix11 is a High-Voltage Monolithic Active Pixel Sensor (HV-MAPS) developed for the tracking system of the Mu3e experiment. The in-pixel photon response of a MuPix11 sensor thinned to 70 {\mu}m was measured using an 8 keV X-ray beam with a 3 {\mu}m spot size at the B16 beamline at Diamond Light Source, emulating the passage of a minimum ionising particle (M

Zhangliang Chen, Xin-Yue Zhang, Di-Chang Chen, Kejun Wang

Approximately 25--50\% of white dwarfs (WDs) exhibit metal absorption lines in their photospheres, interpreted as evidence of ongoing/recent accretion of planetary debris from remnant systems. Previous theoretical studies have suggested that massive, close-in substellar companion may prevent delivery of larger bodies via dynamical interactions, thereby reduc

Eun Som Jeon, Jisoo Lee, Huisu Lim, Omik M. Save

Wearable sensor-based human gait analysis holds great promise in healthcare, rehabilitation, clinical diagnosis and monitoring, and sports activities. Specifically, ground reaction force (GRF) provides essential insights into the body's interaction with the ground during movement and is typically measured using instrumented treadmills equipped with force pla

Kai Liu, Deguang Han, Omar Nour

Phase retrievability of a quantum channel asks whether pure states can be reconstructed from suitable measurements. In this paper, we study this problem from three complementary viewpoints: quantum information theory, operator-valued frames, and the physical realization through quantum interferometry. We first show that a quantum channel is phase retrievable

Lennart Binkowski

Quantum interior point methods (QIPMs) promise polynomial speed-ups over classical solvers for linear programming by outsourcing the solution of Newton linear systems to quantum linear solvers (QLSAs). However, asymptotic speed-ups do not necessarily translate to practical advantages on realistic problem instances. In this work, I evaluate whether practical

Zekun Yuan, Yangfan Ye, Xiaocheng Feng, Baohang Li

Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorly understood. To bridge this gap, we introduce CanMT, a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation, together with a theoretically grounded, multi-dimensional evaluation framework for

Yi-Cheng Tai, Weijing Wang, Jedd D. Wolchok, Martin T. Wells

Immune checkpoint inhibitor--based therapies often produce heterogeneous survival responses, including early risk, delayed treatment benefit, and durable long-term survival in a subset of patients. In these settings, conventional summary measures such as the hazard ratio may not adequately describe how treatment effects evolve over follow-up. We propose a mi

Andrès E. Piatti

The identification of open cluster (OC) members has been revolutionized by high-precision Gaia astrometry, yet traditional kinematic membership selections remain inherently conservative, often overlooking stars in tidal tails or those with perturbed velocities. This study investigates the reliability of these kinematic probabilities by searching for leaky cl

Olivier Bournez

What do recurrent neural networks, polynomial ODEs, and discrete polynomial maps each bring to computation, and what do they lack? All three operate over the continuum--real-valued states evolved by real-valued dynamics--even when the target functions are discrete. We study them through primitive recursion. We prove that primitive recursion admits equivalent

Mahir Demir, Atahan Cilan, Seyyid Osman Sevgili, Özgün Can Yürütken

This article explores the progress made in the creation of a pilot activated recovery system (PARS) for advanced jet trainers that utilizes artificial intelligence (AI) in an effort to enhance operational efficiency. The PARS model employs an advanced reinforcement learning (RL) architecture, incorporating a cutting-edge soft-actor critic (SAC) model and hyp

Manqing Zhang, Yunwei Dong, Lingru Zhou, Bingxu Xiao

Formal verification using interactive theorem provers ensures high-quality software. However, writing proof scripts for interactive theorem provers is labor-intensive and requires deep expertise. Recent studies have leveraged deep learning to automate theorem proving by learning from manually written proof corpora. Nevertheless, these techniques still achiev

Daniel Fritz, Dimitrios Lagamtzis, Michael Mink, Markus Enzweiler

The continuous advancement of autonomous driving (AD) introduces challenges across multiple disciplines to ensure safe and efficient driving. One such challenge is the generation of High-Definition (HD) maps, which must remain up to date and highly accurate for downstream automotive tasks. One promising approach is the use of crowdsourced data from a vehicle

Zhongjie Duan, Hong Zhang, Yingda Chen

Controllable diffusion methods have substantially expanded the practical utility of diffusion models, but they are typically developed as isolated, backbone-specific systems with incompatible training pipelines, parameter formats, and runtime hooks. This fragmentation makes it difficult to reuse infrastructure across tasks, transfer capabilities across backb

Mengnan Zhao, Lihe Zhang, Tianhang Zheng, Bo Wang

Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial attacks. However, FAT is prone to catastrophic overfitting (CO), wherein models overfit to the specific attack used during training and fail to generalize to others. While existing methods introduce diverse hypo

Zheng Wu, Yi Hua, Zhaoyuan Huang, Chenhao Xue

The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via OS agents navigating complex GUIs. However, the transition of these agents into trustworthy daily partners is hindered by a lack of rigorous evaluation regarding safety, efficiency, and multi-modal robustness.

Yangping Li, Thomas Pinetz, Michael Hölzel, Marieta Toma

In pathology, the spatial distribution and proportions of tissue types are key indicators of disease progression, and are more readily available than fine-grained annotations. However, these assessments are rarely mapped to pixel-wise segmentation. The task is fundamentally underdetermined, as many spatially distinct segmentations can satisfy the same global

Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai

Vision-language models (VLMs) are increasingly deployed as evaluators in tasks requiring nuanced image understanding, yet their reliability in scoring alignment between images and text descriptions remains underexplored. We investigate whether small, open-weight VLMs exhibit \emph{sycophantic} behavior when evaluating image-text alignment: assigning high sco

Taylor Smith, Andreas Morr, Christof Schötz, Niklas Boers

A wide body of work has applied the concept of critical slowing down to estimate the stability of different Earth system components. Most of them -- such as global vegetation -- are inherently non-stationary, for example due to strong seasonal forcing, which complicates the estimation of their resilience to external perturbations. Here, we introduce a new me

Imen Ben Tahar, Dylan Possamaï, Xiaolu Tan

This paper characterises optimal incentive schemes for ESG disclosure in a continuous-time principal-agent setting. We model a risk-averse principal (e.g., a platform or standard-setter) contracting with a team of heterogeneous agents whose disclosure signals are each correlated with a traded climate risk factor. The optimal contract balances incentive provi

W. Sajjad, A. Zahid, M. A. Muawia, M. Azam

We obtained quasinormal modes and greybody factors of the quantum Oppenheimer-Snyder (QOS) black hole with quintessential dark energy and string clouds. We compute the effective potentials of the scalar and the vector perturbation fields and analyze their graphical behavior. For this, we compute quasinormal mode frequencies of the QOS black hole, that is sur

Paweł Rafał Bieliński, Marta Piecyk, Paweł Rzążewski

The complexity of classical computational problems in graph classes defined by forbidding induced subgraphs is one of the central topics of algorithmic graph theory. Recently, there has been a growing interest in the complexity of such problems in ordered graphs, i.e., graphs with a fixed linear ordering of vertices. Such an approach allows us to investigate

Alexios Papadimitriou, Spyridon Zafeiris, George Papadakis

This work presents a high-order Arbitrary-Lagrangian-Eulerian (ALE) Discontinuous Galerkin framework for simulating multi-body Vortex-Induced Vibrations. The ALE formulation extends a Runge-Kutta Interior-Penalty nodal DG solver with minimal additional computational overhead, incorporating discrete enforcement of the Geometric Conservation Law (GCL) to ensur

Zijun Feng, Yuming Feng, Yu Wang, Weizhe Zhang

Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers, resulting in over $2.8 billion in losses due to subtle implementation flaws. Existing defenses, such as bytecode-level static analysis, are ill-equipped to handle the semantic complexity of cross-chain interactions, while LLM-based appro

Andreas Kouloumpris, Georgios L. Stavrinides, Maria K. Michael, Theocharis Theocharides

Emerging IoT-enabled cyber-physical applications demand low-latency, energy-efficient, and reliable execution across resource-constrained edge devices with heterogeneous multicore processors and diverse sensing and actuating capabilities, in collaboration with a hub device and a cloud server. These workflow-based applications comprise interdependent tasks th

Zhiheng Wu, Tong Wang, Shuning Wang, Naiming Liu

Recent advances in Vision-Language Models (VLMs) have benefited from Reinforcement Learning (RL) for enhanced reasoning. However, existing methods still face critical limitations, including the lack of low-level visual information and effective visual feedback. To address these problems, this paper proposes a unified multimodal interleaved reasoning framewor

Haye Hinrichsen, Arsim Kastrati

We investigate whether retarded scalar propagators on causal sets can be expressed in terms of the link matrix $\mathbf{L}$. For Poisson sprinklings into $1+1$ dimensional Minkowski spacetime, we show by asymptotic analysis and supporting numerical simulations that the averaged massless retarded propagator is naturally associated with a normalized exponentia

Atahan Cilan, Mahir Demir, Özgün Can Yürütken, Seyyid Osman Sevgili

This paper evaluates an advanced jet trainer's utilization of artificial intelligence (AI)-based aircraft aerobatic maneuvers with the intention of developing an AI-assisted pilot training module for specific aircraft maneuvers. A multitude of aircraft maneuvers have been simulated using reinforcement learning (RL) agents, which will serve as a training tool

Stefano Lombardi, Nurfatima Jandarova, Kristina Zguro, Jarkko Harju

Education is a major source of inequality in income and health. Polygenic indices for educational attainment (EA-PGI) capture both direct and indirect genetic influences on education, but their effects on income and health remain unclear. Using Finnish registry data on 51,056 graduates followed annually since graduation for up to 25 years, we report three fi

Truman Welling, Onur Günlü, Aylin Yener

Integrated sensing and communication (ISAC) combines sensing and communication within a shared system framework by using the same transmitted signal for both objectives. ISAC can improve the efficiency of spectrum and hardware use but also gives rise to new security challenges, as users associated with one function may need to be prevented from inferring inf

Xiao-Gang He, Jusak Tandean, German Valencia

The LHCb collaboration has recently observed the rare hyperon decay $Σ^+\to pμ^+μ^-$. It can also measure the corresponding antihyperon channel with comparable precision and is thus in a position to extract information on $CP$ violation in this mode. Interestingly, the long-distance contributions that dominate it within the standard model provide large absor

Daria Berdyugina, Anaëlle Cohen, Yohann Rioual

Standard Retrieval-Augmented Generation (RAG) chunking methods often create excessive redundancy, increasing storage costs and slowing retrieval. This study explores chunk filtering strategies, such as semantic, topic-based, and named-entity-based methods in order to reduce the indexed corpus while preserving retrieval quality. Experiments are conducted on m

Qiuxia Wu, Yaqiang Wang, Huabing Ke

The nonlinear synergy between global warming and urbanization is amplifying extreme climate risks in cities worldwide. While observations and simulations confirm these compounding effects, two fundamental bottlenecks impede predictive understanding: (1) fragmented, case-specific perspectives that hinder the discovery of universal mechanisms, and (2) a method

Mengnan Zhao, Lihe Zhang, Bo Wang, Tianhang Zheng

Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representations. However, FAT often suffers from catastrophic overfitting (CO), where the model overfits to the training attack and fails to generalize to unseen ones. Moreover, robustness oriented optimization typically

Alexander Zimmer, Yasmeen Abdrabou, Enkelejda Kasneci

Research on video-based eye-tracking has long explored stereo and glint-based methods, yet existing wearable eye trackers - both commercial and open-source - offer limited flexibility for algorithm development and comparative evaluation. We present an affordable, wearable stereo eye-tracking platform built from off-the-shelf and 3D-printable components that

Xue-Ting Zhang, Jonathan Gair, Chris Messenger, Natalia Korsakova

The space-borne gravitational-wave (GW) detectors will open a new mass and redshift regime, allowing us to observe massive black hole binaries (MBHBs) throughout the Universe. A subset of these systems is expected to produce electromagnetic (EM) counterparts, offering a unique opportunity to follow the continuous evolution of massive black holes through join

Panrui Ni, Jun Yan

We study the Lyapunov stability of stationary solutions to contact-type Hamilton-Jacobi equations on a compact manifold. Previous works typically assume $C^3$ Tonelli Hamiltonians and characterize stability in terms of Mather measures. In this paper, we consider continuous, convex and coercive Hamiltonians and establish verifiable PDE-type criteria for both

Qianlei Wang, Kexun Chen, Shaolin Zhang, Hongli Gao

Monocular depth estimation (MDE) has witnessed remarkable progress driven by Convolutional Neural Networks and transformer-based architectures. However, these approaches typically treat the problem as a generic image-to-image regression on Euclidean grids, thereby overlooking the intrinsic algebraic and geometric structures induced by perspective projection.

Vitali Vougalter, Vitaly Volpert

The article is devoted to the solvability of a system of integro-differential equations in the case of the difference of the standard Laplacian and the bi-Laplacian in the diffusion terms. The proof of the existence of solutions is based on a fixed point technique. We use the solvability conditions for the elliptic operators without the Fredholm property in

Poushali Sengupta, Sabita Maharjan, Frank Eliassen, Yan Zhang

The decentralization of modern energy systems is transforming consumers into prosumers who continuously exchange data with aggregators, peers, and market operators. While such data is essential for peer-to-peer trading, demand response, and distributed forecasting, it can reveal sensitive household patterns and introduce privacy risks. Existing data sharing

Petr A. Golovach, Laure Morelle, Daniël Paulusma

An identification of two vertices $u$ and $v$ in a graph replaces them with a new vertex whose neighborhood is the union of the neighborhoods of $u$ and $v$. We study the {\sc ${\cal H}$-Identification} problem, which is to decide whether a given graph $G$ can be transformed (``identified'') to a graph in ${\cal H}$ by applying at most $k$ vertex identificat

Luca Becchetti, Andrea Clementi, Luciano Gualà, Emanuele Natale

The Approximate Near Neighbor (ANN) problem is a cornerstone of high-dimensional data analysis. While Locality Sensitive Hashing (LSH) has been the classical paradigm, recent work has investigated Locality Sensitive Filtering (LSF), which can afford greater expressivity by allowing asymmetric regions to independently control queries and data updates. In its

Patrick Krüger, Hanno Gottschalk, Werner Krebs, Bastian Werdelmann

The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion system to ensure stable operation without any flashback. Since all engine frames featuring a power range from 4 MW up to 600 MW are affected, a huge design effort is expected. To reduce this effort, especially to

Syrine Salouhou, Edgar Dubourg, Maxwell Scott-Slade, Hugo Spiers

Citizen science is transforming how cognitive scientists study the human mind, and video games are at the heart of this shift. By embedding experimental tasks into engaging, game-like experiences, researchers can reach large, diverse populations while collecting rich behavioral data outside the lab. In this review, we explore how citizen science video games

Junshuo Zhang, Chengrui Huang, Feng Guo, Zihan Li

Large language model (LLM) agents that follow the sequential "reason-then-act" paradigm have achieved superior performance in many complex tasks.However, these methods suffer from limited exploration and incomplete environmental understanding, as they interact with only a single environment per step. In this paper, we first introduce a novel paradigm that en

Yan Ding, Sizhou Wu, Ying Zhang

We study the temporal-spatial regularity properties of tamed Euler approximations for L\'evy-driven SDEs with superlinearly growing drift and diffusion coefficients. We first introduce a novel tamed Euler-type scheme and establish its strong convergence. We then derive temporal-spatial regularity estimates with respect to the initial value, the initial time,

Yuki Tsukamoto

We study the fast reaction limit for a two-component reaction-diffusion system with asymmetric reaction terms, where only one component diffuses. For nonnegative and mutually segregated initial data, we prove that the initial interface vanishes instantaneously. More precisely, the diffusive component converges uniformly to the solution of the heat equation,

Dibyadip Chatterjee, Zhanzhong Pang, Fadime Sener, Yale Song

Streaming video models should respond the moment an event unfolds, not after the moment has passed. Yet existing online VideoQA benchmarks remain largely retrospective. They pause the video at fixed timestamps, pose questions about current or past events, and score models only at those moments. This protocol leaves streaming predictions untested. To close th

Giovanni Siclari, Stefano Vita

We study local regularity properties of solutions to stationary anisotropic magnetic Schr\"odinger equations in $\mathbb{R}^d$, $d \ge 2$, arising from singular magnetic potentials concentrated along manifolds of general codimension $2 \le n \le d$. The magnetic interaction is modeled through a covariant gradient of the form \[ \nabla_m u = (iM\nabla + A)u,

Wonyong Cho, Taemin Kim, Jungmin Kim, Jeong-Rae Kim

Training large-scale deep neural networks effectively and stably is essential for applying deep learning across various fields. However, conventional methods, which rely on training a single large network, often encounter challenges such as gradient vanishing, overfitting and unstable learning. To overcome these limitations, we introduce Self-Abstraction Lea

Xiaolin Qin, Qianlei Wang, Jiacen Liu, Chaoning Zhang

Recovering 3D human pose from multi-view imagery typically relies on precise camera calibration, which is often unavailable in real-world scenarios, thereby severely limiting the applicability of existing methods. To overcome this challenge, we propose an unconstrained framework that synergizes deep neural networks, algebraic priors, and temporal dynamics fo

Mahdi Chamseddine, Fabian Kaufmann, Marius Schellen, Christian Glock

Automatic generation of Building Information Models (BIM) from building scans is a key challenge in architecture and construction. We present a modular pipeline for generating IFC-compliant BIM from 3D point clouds. The hybrid approach combines learning-based semantic segmentation with topology-aware geometric reconstruction to model structural elements accu

Xin-Yu Lu, De-Yi Lin, Tong Zhu, Bin Ren

Retrieving or generating two-dimensional molecular structures on the basis of vibrational spectra has been well demonstrated via deep learning models. However, deciphering three-dimensional molecular conformations is still challenging, primarily due to spectral ambiguities caused by conformational heterogeneity, which are difficult to resolve. To address thi

Hao Yan, Zhen-hui Qin, Zhi-Wen Wang, Shu-Mao Wu

We report a structurally simple double-layer lithium tantalate (LiTaO3) bulk acoustic resonator that enables symmetry-selective excitation of the second-order thickness-shear (SH2) mode with ultrahigh electromechanical coupling. Two 31 deg Y-oriented single-crystal LiTaO3 films are rotation-bonded with complementary polarization (+X/-X) and driven by a longi

Niclas Boehmer, Luca Kreisel, Jannik Peters

In approval-based multiwinner voting, voters express approval preferences over a set of candidates, and the goal is to return a winning committee. This model captures a broad range of subset selection problems under preferences. Prior work has focused on the study of binary proportionality axioms that certify whether a given committee is proportionally repre

Ankan Basu, Jyotiraditya Roy, Aditya Datta, Prayas Sanyal

Accurate forecasting of solar power output is essential for efficient integration of renewable energy into the grid. In this study, an attention-based deep learning model, inspired by transformer architecture, is used for short-term solar power forecasting. Our proposed model, "SolarTformer", is designed to predict solar power output from meteorological data

Dileep K, S Murugesh

We investigate the dynamics of a binary mixture of Bose-Einstein condensates in the impurity limit -- where one component is dilute enough to be treated like an impurity -- and confined to two dimensions. Using the mean-field coupled Gross-Pitaevskii equations, we find that the binary mixture supports the formation of stable symbiotic dark-bright solitons wh

Xiaohua Zhou, Tianyu Fang, Yijie Mao, Bruno Clerckx

Microwave linear analog computer (MiLAC)-aided transmit beamforming, which processes transmitted symbols entirely in the analog domain, has recently emerged as a promising alternative to fully digital or hybrid beamforming architectures for single-user multi-antenna systems. However, recent studies have shown that deploying a single lossless and reciprocal M

Shun Shao, Binxu Wang, Shay B. Cohen, Anna Korhonen

Mechanistic interpretability has made it possible to localize circuits underlying specific behaviors in language models, but existing methods are expensive, model-specific, and difficult to scale to larger architectures. We introduce \textbf{Differentiable Faithfulness Alignment (DFA)}, a framework that transfers circuit information from a smaller source mod

Sebastian Chenery

A gyration is an operation on Poincar\'{e} Duality complexes that arises from a certain surgery on the product of a given complex $N$ and a sphere, parametrised by a chosen twisting. Of particular recent interest is the notion of gyration stability; that is, $N$ is gyration stable when all of its gyrations have the same homotopy type, regardless of the twist

Yiming Zhang, Jiacheng Chen, Jiaqi Tan, Yongsen Mao

Current evaluations of spatial intelligence can be systematically invalid under modern vision-language model (VLM) settings. First, many benchmarks derive question-answer (QA) pairs from point-cloud-based 3D annotations originally curated for traditional 3D perception. When such annotations are treated as ground truth for video-based evaluation, reconstructi

Lajos Molnár, Peter Šemrl

A map on a group into itself is called a local automorphism if at any two points of the group, it can be interpolated by an automorphism of that group. In this paper we investigate the question of how local automorphisms of some classical groups are related to automorphisms. In some cases it turns out that the local automorphisms are in fact automorphisms. I

Ian D R Mackinnon, Jose A Alarco

Electronic band structures (EBSs) for orthorhombic beta FeSe1-x at less than 16 K and up to 23 GPa using experimentally determined cell dimensions are evaluated for cosine-shaped bands near, or crossing, EF. Cosine shaped bands are present in reciprocal directions parallel to the c axis at all pressures. Calculations using a P1 cell derived from Cmma symmetr

Marvin Schwiering, Timo Ziegler, Lennart Binkowski, Benjamin Sambale

This paper introduces the concept of exhaustively parametrised, feasibility-respecting quantum circuits for constrained combinatorial optimisation problems. Such circuits can reach, given the right parameter values, every feasible solution with certainty -- including the optimum -- with a fixed number of parameters, while avoiding infeasible solutions altoge

Benhard H. Haak, Peer Chr. Kunstmann

We study $C_0$-semigroups on UMD Banach spaces under the assumption that a single semigroup operator admits a lower bound. We establish boundedness of $H^\infty$ functional calculi for the negative generator of such semigroups. Our approach is based on a dilation argument: combining a recent construction due to Madani with transference results for groups on

Xiaohua Zhao, Zhaowei Huang, Chen Chen, Haiyi Yang

Inefficient driving behaviors, such as overly conservative yielding, remain a key obstacle to deployment of autonomous vehicles (AVs). Instantaneous driving efficiency metrics are crucial for self-driving decision-making because they affect real-time performance evaluation and control optimization. However, commonly used indicators, including speed, relative

Hidir Selcuk Nogay

Artificial intelligence is increasingly described as a candidate next generation general purpose technology (GPT). However, existing interpretations predominantly emphasize performance scaling rather than structural transformation. This paper introduces a formal framework for evaluating AI as a systemic infrastructural transition rather than merely a computa

Qinhan Hou, Jing Tang

Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adaptive long-range propagation, we show that Graph ODEs with strictly positive irreducible mixing operators face an inherent \emph{monostability trap}: in the long-time regime, inform

Gert Aarts, Dénes Sexty

The sign problem obstructs the determination of the QCD phase diagram in the temperature-baryon chemical potential plane using lattice QCD. We review the sign problem in QCD and related field theories, including applications to real-time dynamics. We focus on approaches where the sign problem can potentially be solved or controlled, irrespective of its sever

Jiayu Liang, Shaowei He, Wenlong Liao, Tan Shi

The radiation tolerance of gallium nitride under extreme conditions is critical for its deployment in next-generation electronic and optoelectronic devices, yet the microscopic mechanisms governing swift heavy ion induced damage at elevated temperatures remain poorly understood. Therefore, this study employs a coupled approach including the two-temperature m

Lavi Jain, Venkata Kalyan Tavva

The Rowhammer vulnerability poses an increasing challenge with newer generations of DRAM and aggressive technology scaling. Existing mitigation techniques, such as Graphene, Twice, and Hydra, primarily rely on tracking activation counts for each row and issuing refreshes when a row reaches a predefined tracking threshold. However, these methods have inherent

Nguyen Khoa Tran, Lin Mu, Martin Papenberg, Gunnar W. Klau

The $k$-Maximum Dispersion Problem with Cardinality Constraints ($k$-MDCC) asks for a partition of a given item set with pairwise dissimilarities into $k$ cardinality-constrained groups such that the minimum pairwise intra-group dissimilarity, which is also known as the dispersion, is maximized. The problem arises in the context of anticlustering, where the

Kota Otsuka, Kazuki Yokomizo

Nonlinear eigenvalue problems arise in a wide range of physical systems, in which system parameters depend on the eigenvalue. Such systems have been proposed to exhibit an extreme sensitivity of their spectra to boundary conditions, which leads to the breakdown of conventional topological characterizations. In this work, we establish a non-Bloch framework fo

A. A. Molavi Choobini, M. Shahmansouri

Hybrid laser plasma radiofrequency (RF) acceleration architectures signify a promising advancement in addressing the stability challenges associated with traditional laser wakefield accelerators. A thorough theoretical and numerical analysis of the three-dimensional dynamics of ultra-relativistic electron bunches in these hybrid systems is presented, clearly