April 2026 arXiv papers — page 43

Timothy Christensen, Silvia Goncalves, Benoit Perron

AI/ML methods are increasingly used in economics to generate binary variables (or labels) via classification algorithms. When these generated variables are included as covariates in regressions, even small misclassification errors can induce large biases in OLS estimators and invalidate standard inference. We study whether the bootstrap can correct this bias

Chandler Moore, Juan Manfredi, Michael Febbraro, Daniel Rutstrom

Pixelated plastic scintillator arrays can serve as high efficiency and high resolution neutron imaging detectors. Manufacturing these arrays is intensive in both time and labor. This work presents a fabrication method based on additive manufacturing for two-dimensional plastic organic scintillator arrays using a custom-built automated assembly machine and a

Saikat Banerjee

The Barnett effect is usually understood through an effective magnetic field generated by mechanical rotation, while its reciprocal Einstein--de Haas effect describes the transfer of spin angular momentum to mechanical motion. We show that this effective-field picture changes qualitatively once the mechanical degree of freedom itself is quantized. To demonst

Carine de Menezes Rebello, Anderson Rapello dos Santos, Idelfonso B. R. Nogueira

Deploying machine learning models across diverse well portfolios requires generalisation to wells with design parameters outside the training distribution. Current data-driven approaches to virtual flow metering (VFM) and bottomhole estimation typically treat each well independently or ignore the influence of well design on operational behaviour. We present

Omer F. Erdem, Dean Price, Paul Seurin, Majdi I. Radaideh

Multiobjective optimization remains challenging for many scientific and engineering problems due to the need to balance convergence, diversity, and computational efficiency across high-dimensional objective landscapes. This work presents the Multiobjective Animorphic Ensemble Optimization (MAEO) framework, a parallelizable ensemble strategy that unifies stat

Duc An Phan

In this paper, we consider the semilinear damped wave equation with Hartree-type nonlinearity $\mathcal{I}_γ\left(|u|^{p_1}\right)|u|^{p_2}$, where $0<γ<n$, with initial data possessing additional negative regularity in $\dot{B}_{2, \infty}^{-β}$, $β\in [0,n/2)$. We first establish decay estimates for the corresponding linear equation in homogeneous Besov sp

Honghao Cai, Xiangyuan Wang, Yunhao Bai, Haohua Chen

Large diffusion transformers (DiTs) follow global editing instructions well but consistently leak local edits into unrelated regions, because joint-attention architectures offer no explicit channel telling the network where to apply the edit. We introduce AdaptEdit, a co-trained, instruction- and region-aware adapter framework that retro-fits a frozen DiT in

Josh Shelton, Adam Rook

We consider the stability of periodic gravity-capillary waves of finite amplitude for small values of the surface tension. Linear stability with respect to both superharmonic and subharmonic perturbations is calculated for each solution, and our methodology obtains the full eigenvalue spectrum consisting of growth rates and temporal frequencies. For small su

Yongen Zhao, Zihao Xu, Wenzhi Lu, Zhen Chu

Wheeled-legged robots combine the energy efficiency of wheeled locomotion with the terrain adaptability of legged systems, making them promising platforms for agile mobility in complex and dynamic environments. However, enabling high-dynamic reflexive evasion against fast-moving obstacles remains challenging due to the hybrid morphology, mode coupling, and n

Yishay Polatov, Oron Sabag

We study the control of finite-state systems driven by exogenous disturbances, and design causal policies that track the performance of a lookahead benchmark controller. This objective is formalized through dynamic regret, so that favorable disturbance sequences are compared against a strong benchmark, while under adverse disturbance sequences the comparison

Petr Červenka, Vít Jakubský

We develop a theoretical framework for designing quantum couplers based on Dirac materials that can modulate the polarization of transmitted quasiparticles without significantly perturbing their propagation. We analyze in detail the conditions required for perfect transmission (Klein tunneling) together with controlled polarization transformation of the inco

Mingze Li, Yu Rong, Songyou Li, Lihong Wang

Artificial intelligence has accelerated materials discovery through high-throughput prediction and generation, yet the decision problem remains a formidable bottleneck. While current AI systems readily propose millions of candidates, navigating the decision regarding a viable experimental target requires resolving multi-dimensional judgments across atomic-sc

Michele Mantovano

Beyond $B$ physics, charm and $\tau$ physics constitute a central part of the Belle and Belle II physics programs. Here, we present recent results on charm baryon decays, including first measurements and observations of several previously unmeasured modes, together with new studies in $\tau$ physics. Particular emphasis is placed on searches for CP violation

Lorenzo Ceragioli, Fabio Gadducci, Giuseppe Lomurno, Gabriele Tedeschi

Reliable verification techniques for quantum communication protocols are of paramount importance, given their high implementation cost and critical contexts of application. Extensions of process calculi have been proposed, together with various notions of behavioural equivalence. However, their standard probabilistic models turn out to introduce some non-det

Shu Li, Jiang Hu

Decentralized optimization provides a fundamental framework for large-scale learning and signal processing with distributed data. We study decentralized optimization with orthogonality constraints on the Stiefel manifold and propose RF-EXTRA, a distributed retraction-free primal-dual method on static undirected networks. The method combines an approximate gr

Nastaran Dab, Raziyeh Zall, Mohammadreza Kangavari

Multimodal affective computing analyzes user-generated social media content to predict emotional states. However, a critical gap remains in understanding how visual content shapes cognitive interpretations and elicits specific affective experiences such as pleasure. This study introduces a novel computational model to infer video-induced pleasure via cogniti

Thomas Budzinski, Victor Dubach, Valentin Féray, Mohamed Slim Kammoun

We study Mallows random permutations conditioned to avoid a given pattern $\alpha$ of length~$3$. When the bias parameter is of the form $e^{\beta/n}$, we prove that these permutations converge to a non-trivial explicit deterministic permuton that depends on the pattern $\alpha$ and on the parameter $\beta$. Along the way, we provide parametrizations for $\a

Shuaizhi Cheng, Xiang Shi, Zhiwei Zhang, Mingwei Li

Hypernetwork-based methods such as Doc-to-LoRA internalize a document into an LLM's weights in a single forward pass, but they fail systematically on conflicts: when the document contradicts pretraining knowledge, accuracy collapses to 46.4% on the deepest facts. We show the failure is a magnitude problem rather than a representational one. The hypernetwork

Bart van Rossum, Rui Chen, Andrea Lodi

We study the fair capacitated vehicle routing problem, in which a fleet of vehicles must serve a set of customers such that the difference between the longest and shortest route, the range, is minimized. A key challenge is that the range objective is non-monotonic: it can be reduced by artificially lengthening routes, leading to solutions that violate TSP-op

Alexis Limozin, Eduard Durech, Torsten Hoefler, Imanol Schlag

Recent mixed-policy optimization methods for LLM reasoning that interleave or blend supervised and reinforcement learning signals report improvements over the standard SFT-then-RL pipeline. We show that numerous recently published research papers rely on a faulty baseline caused by two distinct bugs: a CPU-offloaded optimizer bug in DeepSpeed that silently d

Fabio Pino, Benoit Scheid, Miguel A. Mendez, Demetrios T. Papageorgiou

Liquid films on moving substrates are used in dip-coating processes to form uniform protective layers. Controlling free-surface waves is essential due to the film's inherent linear instability. Therefore, we develop a linear feedback controller to regulate the film toward a desired flat state by modulating the free-surface shear and pressure, with feedback g

Simon Pfreundschuh, Christian D. Kummerow

Current global precipitation estimates from spaceborne precipitation radars are limited by their sensitivity to light and frozen precipitation, leading to systematic underestimation of precipitation at high latitudes. Because passive microwave retrievals (PMW) are commonly trained using these radar observations as reference data, this limitation is propagate

Jun Xue, Zhuolin Yi, Yihuan Huang, Yanzhen Ren

With the rapid advancement of speech generation technologies, the threat posed by speech deepfakes in real-time communication (RTC) scenarios has intensified. However, existing detection studies mainly focus on offline simulations and struggle to cope with the complex distortions introduced during RTC transmission, including unknown speech enhancement proces

K. A. Bronnikov, S. V. Bolokhov, G. S. Nurbakova, B. Tynyshbay

We consider static, spherically symmetric configurations of nonlinear electromagnetic fields with Lagrangians $L(f)$, where $f = F_{\mu\nu} F^{\mu\nu}$, in general relativity (GR) and other metric theories of gravity. The corresponding exact solutions are well known in the framework of GR in cases where only an electric charge ($q_e$) or a magnetic charge ($

Wenyang Jia, Qiankang Xu, Ziwei Yan, Chunhua Kang

Decentralized Federated Learning (DFL) eliminates the central aggregator but introduces a severe 'trust gap': without a trusted coordinator, the system becomes vulnerable to Byzantine and Sybil attacks, while existing solutions treat node selection, aggregation, and consensus as isolated modules, often relying on a trusted root dataset unavailable in truly d

Huadong Liao

Despite the Transformer's dominance across machine learning, its architecture remains largely heuristic and lacks a unified theoretical foundation. We introduce Score-based Variational Flow (SVFlow), a continuous-time dynamical system for representation learning in which the state evolves according to a variational posterior-weighted average of conditional l

Wen Cui, Dao-yu Zhang, Chuang Huang, Wei-jie Fu

We study the kaon quasi-distribution amplitude (quasi-DA) and distribution amplitude (DA) within the large-momentum effective theory (LaMET) combined with the first-principles functional QCD. Using quark correlation functions and the kaon Bethe-Salpeter amplitude in the Euclidean space from the 2+1 flavour functional QCD [1] as inputs, we obtain the kaon qua

Tom Sanders

We study modular analogues of Schur numbers for systems of linear equations. We show that these only depend on the number of equations, not their coefficients and in the case of one equation show stronger bounds.

Rui Kuang, Bing Li, Yuanfen Xiao

Let $ 1<\beta< 2 $, the sequence $\alpha(\beta)=\alpha(\beta)_1\alpha(\beta)_2\dotsb $ be the quasi-greedy $ \beta $-expansion of $ 1 $, and $ t\in [0,1) $ be a bifurcation parameter. The $\beta$-transformation is defined to be $T_{\beta}(x)=\beta x (mod 1) $ for $x\in [0,1)$. The Hausdorff dimension of the survivor set $K(t)=\{x\in [0,1)\colon T_{\beta}^k(x

Takuya Masuda, Toshio Tagawa

This study derives a scaling law connecting the Nusselt (Nu) and Bejan (Be) numbers in natural convection. Combining entropy generation analysis with boundary-layer scaling, the relation Be^-1 - 1 = a Nu^b naturally emerges without explicit dependence on geometry or boundary conditions. This is achieved within the present scaling framework when transport is

Fei Jiang, Xiao Ren, Yi Zhou

In 1965 Kraichnan pointed out that a sufficiently strong background magnetic field, i.e. the case of small Alfv\'en number, will reduce the nonlinear interaction and inhibit the formation of strong gradients in the magnetohydrodynamic (abbr. MHD) system with $\mu=\nu\geqslant 0$, where ${\mu}$ and $\nu $ are the coefficients of kinematic viscosity and resist

Dun Zhang

Modern retrieval pipelines increasingly serve downstream consumers like retrieval-augmented generation (RAG) and autonomous agents that need more than a scalar relevance score. A reranker that only tells the caller "how relevant" forces the agent to dump entire documents into the language-model context, wasting tokens on tangential passages and boilerplate.

Yating Wu, William Rudman, Venkata S Govindarajan, Alexandros G. Dimakis

Discourse comprehension in complex documents often involves continuously posing and resolving Questions Under Discussion (QUDs). While QUD frameworks have so far focused on text, scientific literature is inherently multimodal: figures convey discourse goals distinct from their textual counterparts, thus invoking implicit questions that the surrounding text a

Shao-Qun Zhang, Zhi-Hua Zhou

Spiking Neural Networks (SNNs) have garnered increasing attention as one of bio-inspired models due to their great potential in neuromorphic computing and sparse computation. Many practical algorithms and techniques have been developed; however, theoretical understandings of the generalization, that is, the extent to which SNNs perform well on unseen data, a

Ramandeep Kaur, Shaina Dhamija, Garima Bhutani, Amit Kumar

Vibrational wavepacket dynamics in the ground (X) and excited (B) electronic states of iodine under impulsive-pump/broadband-probe excitation are revisited. A method for accurate chirp correction, necessary to determine the zero time for each component of spectrally dispersed data and thereby separate coherent vibrational dynamics from coherent artifacts and

Jungmin Choi, Keisuke Sakaguchi, Hiroaki Yamada

Large language models (LLMs) have shown strong performance on legal benchmarks, including multiple-choice components of bar exams. However, their capacity for generating open-ended legal reasoning in realistic scenarios remains insufficiently explored. Notably, to our best knowledge, there are no prior studies or datasets addressing this issue in the Japanes

Takanori Kotama, Yang Huang

High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic configurations. In this paper, we propose crystal fractional graph neural network for predicting the energy of high-entropy alloys by explicitly integrating both local atomic environments and global compositional inf

Takahiro Arai, Toshio Aoyagi, Yoji Kawamura

Building on the phase reduction theory formulated for reaction-diffusion systems with spatial translational symmetry, we develop a data-driven method that reconstructs the spatiotemporal phase dynamics of traveling and oscillating patterns. Spatiotemporal phase dynamics are described by spatial and temporal phases that represent the position and oscillation

Jiangyuan Qian, Jintao Wu, Jianhong Ruan

The advent of multi-messenger astronomy has provided abundant information for understanding the acceleration and particle-production mechanisms of cosmic rays. In this work, we present a unified study of cosmic gamma-ray and neutrino spectra within the Gluon Condensation (GC) model. Derived from Quantum Chromodynamics (QCD), the GC model predicts that, in hi

Shima Esfandiari, Seyed Mostafa Fakhrahmad

In recent years, e-commerce platforms have become one of the most prominent examples of large-scale interaction networks, where understanding influence dynamics among users, products, and digital entities is essential for applications such as online marketing, recommendation systems, and customer behavior analysis. A key challenge in these platforms is that

Hye Jin Lee, Ho Sub Lee, PooGyeon Park

In multi-agent systems, heterogeneous time delays exist for all agents because of the difference in communication environments. Therefore, the consensus analysis of a system considering a homogeneous time-varying delay among all agents results in conservatism. In this study, an individual-delay-reflected generalized consensus is proposed for multi-agent syst

Luis Espath, Eliot Fried

We formulate a thermodynamically consistent continuum theory for compressible, viscous, heat-conducting fluids in which the velocity entering the balance of mass is distinguished from the specific linear momentum entering the balances of linear momentum and energy. Starting from balances of mass, linear momentum, angular momentum, and internal energy, togeth

Jian-Peng Zhang, Yu Zhang, Li Han

In this paper, we investigate the photon regions, shadow, and observational constraints of a Kerr-Newman-like black hole in Bumblebee gravity within a plasma medium. By employing a specific non-homogeneous power-law plasma model to ensure the separability of the Hamilton-Jacobi equation, we derive the null geodesic equations, analyze the photon regions, and

Viet-Hoang Tran, An Nguyen, Benoît Guérand, Thieu N. Vo

Metanetworks are neural architectures designed to operate directly on pretrained weights to perform downstream tasks. However, the parameter space serves only as a proxy for the underlying function class, and the parameter-function mapping is inherently non-injective: distinct parameter configurations may yield identical input-output behaviors. As a result,

Michael Keeman

Mechanistic interpretability research on emotion in large language models -- linear probing, activation patching, sparse autoencoder (SAE) feature analysis, causal ablation, steering vector extraction -- depends on stimuli that contain the words for the emotions they test. When a probe fires on "I am furious", it is unclear whether the model has detected ang

Xuefen Liu, Xinquan Yang, Mianjie Zheng, Kun Tang

As dental caries appear as subtle, low-contrast lesions in intraoral imaging, existing deep learning models face significant challenges in the early detection of caries. While recent Transformer-based detectors have shown promising results in natural images, they often fail to capture the domain-specific anatomical priors crucial for dental caries detection.

Peize He, Yaodi Luo, Xiaoqian Liu, Xuyang Liu

Recent large audio language models (LALMs) demonstrate remarkable capabilities in processing extended multi-modal sequences, yet incur high inference costs. Token compression is an effective method that directly reduces redundant tokens in the sequence. Existing compression methods usually assume that all attention heads in LALMs contribute equally to variou

Danny E. P. Vanpoucke

Among the group-IV vacancy color centers in diamond, the SnV holds promise for photonics based quantum applications. In this work, the Tin-Vacancy (SnV) zero-phonon line (ZPL) and its pressure coefficient are calculated using first principles approaches. The predicted absolute ZPL position is shown to be strongly influenced by the method and supercell size u

Fragkiskos Papadopoulos

Principled prediction of when and where links form in complex networks is a fundamental problem. We derive a closed-form non-Markovian expression for next-step connection probabilities that unifies latent hyperbolic geometry with long-range memory of past interactions. This expression yields interpretable forecasts governed by a small set of parameters. Appl

Yujian Yuan, Jie Liu, He Gao, Jiamin Guo

The erratic non-Hermitian skin effect (ENHSE), emerging from the interplay between disorders and locally nonreciprocal yet globally reciprocal couplings, has reshaped the conventional bulk-boundary correspondence through its disorder-dependent localization properties. Here, we experimentally observe the dynamical phenomena of ENHSE in phononic crystals with

Chenyi Li, Yanchen Nie, Zhenyu Ming, Gong Zhang

Recent advances in formal theorem proving have focused on Olympiad-level mathematics, leaving undergraduate domains largely unexplored. Optimization, fundamental to machine learning, operations research, and scientific computing, remains underserved by existing provers. Its reliance on domain-specific formalisms (convexity, optimality conditions, and algorit

Yu Cui, Ruiqing Yue, Hang Fu, Sicheng Pan

With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM) agents has become a critical issue. Existing privacy attacks against LLMs primarily target training data, while research on inference-time contextual privacy risks in LLM agent memory remains limited. Moreover, prior

Xinheng Li, Minghao Chen, Mengqing Wu, Yan Liu

Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency. While efficient, CNN networks struggle to learn robust priors for dense and non-homogeneous haze. Conversely, diffusion models provide strong generative priors but suffer from severe inference latency and sampling instability. To address these

Margherita Disertori, Yan V. Fyodorov

Non-Hermitian random matrices provide a useful framework for understanding universal characteristics of dissipative quantum chaotic systems with loss or gain. We consider a model of two such system represented by two independent $N\times N$ complex Ginibre matrices interacting via a deterministic matrix $c{\bf 1}_N$, where $c$ is the complex coupling paramet

Taha El Hajji, Aleksandr Nadkin, Stefan Skoog, Lars Sjöberg

Variable flux memory motors, which employ Low Coercive Force (LCF) magnets, achieve extended high-efficiency operation through controllable magnetization states. To address the need for a unified approach to defining and comparing the magnetization state (MS) across material and motor levels, this paper proposes four MS definitions: two based on intrinsic ma

Adam Kukučka, Ondřej Fabián, Vít Musil, Tomáš Brázdil

Histologic assessment of ulcerative colitis (UC) activity is an important endpoint in clinical trials and routine care, but manual grading with indices such as the Nancy histological index (NHI) is time-consuming and prone to observer variability. While computational pathology methods can automate scoring, many approaches depend on dense region-level annotat

Antonij Mijoski, Marko Karbevski

We study when a skip connection around a single-hidden-layer MLP can be absorbed into a residual-free MLP of the same width. We first show that for any architecture whose skip branch is an invertible linear map (including Hyper-Connections and their manifold-constrained variants), the problem reduces to the identity skip case. For homogeneous activations of

Shunkun Liang, Banglei Guan, Bin Li, Qifeng Yu

Multi-camera systems offer rich observation capabilities for visual navigation and 3D scene reconstruction; however, the resulting feature redundancy often compromises computational efficiency. This challenge is particularly pronounced during bundle adjustment, where the non-linear optimization of both system poses and scene points incurs substantial computa

Hanze Hu, Luying Feng, Silu Chen, Tianjiang Zheng

Humanoid robots operating in human-centered environments (e.g., homes, hospitals, and offices) must mitigate foot--ground impact transients, as impact-induced vibration and noise degrade user experience and repeated impacts accelerate hardware wear. However, existing low-noise locomotion training often relies on kinematic proxy objectives or fragile force se

Wentao Zhang, Qi Zhang, Mingkun Xu, Mu You

Crop disease diagnosis from field photographs faces two recurring problems: models that score well on benchmarks frequently hallucinate species names, and when predictions are correct, the reasoning behind them is typically inaccessible to the practitioner. This paper describes Agri-CPJ (Caption-Prompt-Judge), a training-free few-shot framework in which a la

Yuval Idan, Eitan Zahavi, Elad Mentovich, Eliahu Cohen

The current noisy intermediate-scale quantum (NISQ) era is characterized by substantial errors and noise, which limit the practical feasibility of deep, many-qubit circuits. To address these constraints, quantum circuit cutting has emerged as a promising tool. Recently, there has been significant research on methods for performing such cutting effectively. I

Seongjin Choi

We present a semantic-structural atlas of transportation research built from 120{,}323 papers across 34 peer-reviewed journals published between 1967 and 2025, roughly an order of magnitude larger than and a decade beyond Sun and Rahwan's~(2017) coauthorship study. We use OpenAlex and Crossref as open, CC0-licensed data sources, resolve author identity throu

Yifan Zhang, Hsiao-Dong Chiang, Yitong Li, Yang Wu

The increasing penetration of inverter-based resources (IBRs) has fundamentally altered the transient stability characteristics of modern power systems. IBRs typically rely on proportional--integral (PI) controllers for synchronization and regulation, resulting in nonlinear swing equations that differ significantly from those of synchronous generators (SGs)

Walid Shaker, Mustafa Suphi Erden

Haptic feedback has been a long-missed feature in robotic-assisted surgery, one that would allow surgeons to perceive tissue properties and apply controlled forces during delicate procedures. Although commercial robotic systems have begun to integrate haptic technologies, their high costs limit accessibility for training and research purposes. To address thi

Jan Nordström

We formulate a well posed interface formulation for canonical one-dimensional evaporation two-phase model problems (the Stefan and Sucking problems) commonly used to validate production codes. We focus on the interface between the vapor and the liquid and derive conditions leading to an energy bound and well-posedness. Next, by mimicking the continuous analy

Amina Boulkaboul, Alessandro Sozzetti, Caroline Soubiran, Yassine Damerdji

We analyze periodicities in radial velocity (RV) measurements and magnetic activity indicators (S-index and BIS) for 767 Gaia RV standard stars to distinguish between stellar activity and planetary signals. Significant RV periods were detected in only 359 of these stars. Rotation and magnetic cycle periods are identified through iterative periodogram analysi

Yuxiang Li, Kun Chen, Jiancheng Wang, Shihao Fang

Heterogeneous multi-robot systems feature significant adaptability for complex environments. However, effective collaboration that fully exploits the robots' potential remains a core challenge. This paper proposes a decentralized collaborative framework for heterogeneous multi-robot systems to autonomously explore indoor and outdoor 3D environments. First, a

Xuangeng Chu, Yu Han, Wei Mao, Shih-En Wei

Audio-driven facial animation is essential for immersive digital interaction, yet existing frameworks fail to reconcile real-time streaming with high-fidelity personalization. Current methods often rely on latency-inducing audio look-ahead, or require high user compliance to pre-encode static embeddings that fails to capture dynamic idiosyncrasies. We presen

Peiwen Jiang, Fangyu Liu, Jiajia Guo, Chao-Kai Wen

Smart glasses are emerging as a promising interface between humans and artificial intelligence (AI) agents, enabling first-person perception, contextual awareness, and real-time assistance. However, continuous offloading of visual data from wearable devices to cloud-based vision-language models (VLMs) is fundamentally constrained by limited wireless bandwidt

Andrey Yurkov

Let $\mathbb F$ be a field and $P \in \mathbb F [x_1,\ldots, x_n]$ be a homogeneous polynomial such that $|\mathbb F| > \deg(P)$ and $\phi, \psi\colon \mathbb F^n \to \mathbb F^n$ be two maps such that $P(\mathbf{x} + \lambda\mathbf{y}) = P(\phi(\mathbf{x}) + \lambda \psi (\mathbf{y}))$ for all $\lambda \in \mathbb F$ and $\mathbf{x}, \mathbf{y} \in \mathbb

L. Ya. Glozman

We review recent developments on the QCD phase diagram at small chemical potentials and increasing temperature. There are three regimes/phases in QCD which differ by symmetries, degrees of freedom and N_c scaling: the hadron gas below the chiral restoration temperature T_ch, the stringy fluid between T_ch and the deconfinement temperature T_d and the quark-g

Ruiqing Sun, Xingshan Yao, Zhijing Wu, Tian Lan

Proactive defense methods protect portrait images from unauthorized editing or talking face generation (TFG) by introducing pixel-level protective perturbations, and have attracted increasing attention for privacy protection. In real-world use, images inevitably undergo sequences of benign operations during display and dissemination, such as resizing and col

Xuanshuo Fu, Lei Kang, Ernest Valveny, Dimosthenis Karatzas

Accurate text recognition in low-light environments is essential for intelligent systems in applications ranging from autonomous vehicles to smart surveillance. However, challenges such as poor illumination and noise interference remain underexplored. To address this gap, we introduce LSTR, a large-scale Low-light Scene Text Recognition dataset comprising 11

Shibing Chen, Yuanyuan Li, Xianduo Wang

We study the free-boundary equation \[ \Delta u=\chi_{\{|\nabla u|>0\}} \] near the origin. We prove that, at a singular point of \(\partial\{|\nabla u|>0\}\), the quadratic blow-up is unique. As noted in \cite[Notes to Chapter 7]{PSU2012}, little is known about the singular set for this problem. The usual Weiss--Monneau monotonicity argument does not seem t

Giansalvo Cirrincione

A widely cited result by Dong et al. (2021) showed that Transformers built from self-attention alone, without skip connections or feed-forward layers, suffer from rapid rank collapse: all token representations converge to a single direction. The proposed remedy was the MLP. We show that this picture, while correct in the regime studied by Dong, is incomplete

Kang Zhou

In this paper, we propose a universal diagrammatic interpretation of hidden zeros and $2$-splits of tree-level amplitudes. Originally developed for ${\rm Tr}(\phi^3)$ amplitudes in our previous work, this interpretation is now extended to tree-level amplitudes in Nonlinear sigma model (NLSM) and Yang-Mills (YM) theories. The interpretation is based on a cert

Hao Pan, Peipei Wang, Yajie Chang, Bingyi Lu

Tongue squamous cell carcinoma (TSCC) is an aggressive malignancy with marked biological heterogeneity and variable clinical outcomes. Although molecular profiling has improved understanding of TSCC heterogeneity, its clinical use remains constrained by invasive tissue sampling and limited representation of whole-tumor spatial complexity. Meanwhile, most rad

Jinming Yang, Shaoyu Huang, Zongyuan Huang, Yaohui Jin

Accurate modeling of human mobility is critical for tackling urban planning and public health challenges. In undeveloped regions, the absence of comprehensive travel surveys necessitates reconstructing mobility networks from publicly available data. Here we develop neuroGravity, a physics-informed deep learning model that reliably reconstructs mobility flows

Halfdan Nordahl Fundal, Yuri Bizzoni

We investigate narrative agency in human-LLM creative co-writing, asking who drives story development in turn-based collaboration. Using a new corpus of 87 human-LLM co-written stories, we apply sentiment and semantic modeling to quantify affective alignment and semantic novelty in turn-taking, and directional measures to assess which agent shapes narrative

Jingjing Jiang

This work presents GS-DOT, a novel image reconstruction framework based on Gaussian Splatting (GS) for diffuse optical tomography (DOT). Inspired by GS for rendering applications, absorption coefficients are represented as a sparse sum of anisotropic Gaussian primitives optimized to fit measured time-resolved point-spread functions through analytic gradients

Zihao Wu, Steven Xu, Bowen Chen, Shaowen Wan

With the emergence of large language models (LLMs) and AI agent frameworks, the human-AI co-work paradigm known as Vibe Coding is changing how people code, making it more accessible and productive. In scientific research, where workflows are more complex and the burden of specialized labor limits independent researchers and those in low-resource areas, the p

Facundo Arreyes, Federico Escudero, Arián Gorza, Sebastián Ardenghi

We investigate the momentum-space entanglement between two Dirac quasiparticles in a double-layer honeycomb lattice coupled via a planar electromagnetic cavity. We model the low-energy excitations as massive Dirac fermions in $(1+2)$ dimensions and derive the Bethe-Salpeter equation using the ladder approximation. We use a Born-level approximation around a f

Y. S. Liu, X. Z. Zhang

We study a one-dimensional non-Hermitian Stark chain in which nonreciprocal hopping, a linear potential, and linearly graded hopping act simultaneously. The central question is how boundary pumping and field-induced confinement are reorganized when the hopping amplitude itself grows with position. We show that the graded term separates the two localization c

Souvik Mandal, Ankur Sarkar

Game-theoretic characterizations of selection principles provide a powerful framework for analyzing covering properties through strategic interactions. For a Tychonoff space $X$ and a non-trivial metrizable arc-connected topological group $G$, we prove that Player~II has a winning strategy in the $\Omega$-Menger game on $X$ if and only if Player~II has a win

Andreas Feik, Nicolas Lanzetti, Saverio Bolognani, Florian Dörfler

In many multiagent settings, such as electric vehicle charging and traffic routing, agents must make decisions in the face of uncertain behavior exhibited by others. Often, this uncertainty arises from multiple sources, such as incomplete information, limited computation, or bounded rationality, ultimately impacting the aggregate behavior. To tackle this cha

Semih Çağlar, Şükrü Eren Gökırmak, Eray Tüzün

Context: Code reviews are essential for maintaining software quality, yet many human review comments suffer from issues such as redundancy, vagueness, or lack of constructiveness. These types of comments may slow down feedback and obscure important insights. Prior work on code review comments mostly explore the detection and categorization of useful comments

Xueyuan Huang, Yuheng Wang, Yuanzhi He, Siqi Gou

Liver cirrhosis is a major global health problem causing millions of deaths annually, and timely detection with aggressive treatment can significantly improve patients' quality of life. Modelling complex diseases from biomedical data is computationally challenging due to high dimensionality, strong feature correlations, noise, and limited labelled samples. C

Ahmad Mohammad Saber, Ahmed Saber Refae, Davor Svetinovic, Hatem Zeineldin

Line current differential relays (LCDRs) are measurement-driven relays that rely on time-synchronized multi-phase current waveforms to infer internal faults in AC and DC power networks. In inverter-based microgrids, however, the increasing reliance on digitally communicated measurements exposes LCDRs to false-data injection attacks (FDIAs), in which adversar

Francesco Dibitonto, Cigdem Beyan, Vittorio Murino

Recent advances in representation learning have shown that hyperbolic geometry can offer a more expressive alternative to the Euclidean embeddings used in CLIP models, capturing hierarchical structures and leading to better-organized representations. However, current hyperbolic CLIP variants are trained entirely from scratch, which is computationally expensi

Angsuman Das, Khyati Sharma

In this paper, we provide new criteria for the solvability and supersolvability of a finite group based on its number of cyclic subgroups. A finite group G is called n-cyclic if it contains n cyclic subgroups. This paper also partially extends the classification of n-cyclic groups for n\geq 13.

Yaxuan Chen, Guangchi Zhang, Miao Cui, Hao Fu

A core challenge in physical-layer security is the difficulty of obtaining the channel state information (CSI) of potential eavesdroppers. The inherent sensing functionality of integrated sensing and communication (ISAC) systems offers a promising solution by enabling the estimation of key parameters, such as the eavesdropper's angles of departure (AoDs). Ca

Misbah Ijaz, Saif Ur Rehman Khan, Abd Ur Rehman, Arooj Zaib

The increasing global deployment of solar photovoltaic (PV) systems needs robust, scalable, and automated inspection technologies capable of detecting a wide range of panel flaws under a variety of operating situations. The lack of large-scale, multi-modal, publicly available annotated datasets is a major obstacle preventing advancement in this field. We int

Haisum Hayat, Harry M. Quiney

Recent developments have seen the application of finite Gaussian basis sets to the $\alpha(Z\alpha)^{n\geq3}$ vacuum polarization. The energy shift for $s$ and $p$ electron states have been tabulated and their convergence investigated. In this work, we extend this problem to the multi-electron case. Hartee-Fock potentials obtained self-consistently are used

Jianlong Fu, Hoi Chun Po

Ergodicity breaking in isolated systems has emerged as an important frontier in the study of quantum many-body physics. While generic Hamiltonians are expected to obey the eigenstate thermalization hypothesis (ETH), recent studies on Hilbert space fragmentation (HSF) have revealed possible robust nonthermal behavior even in disorder-free systems. Although nu

Hyunsoo Kim, Makariy A. Tanatar, Cedomir Petrovic, Ruslan Prozorov

The temperature and magnetic field dependent magnetic penetration depth, $\lambda_m(T,H)$, was measured in a single crystal of a heavy fermion superconductor CeCoIn$_5$ using a frequency-domain tunnel diode resonator. In addition to the London penetration depth, which yields the superfluid density, measurements in a finite DC magnetic field provide Campbell

Hanwu Li, Peng Luo, Mengbo Zhu

In this paper, we study the doubly reflected backward stochastic differential equations driven by $G$-Brownian motion ($G$-BSDEs for short) when the generator has quadratic growth in the $z$-component. Based on the theory of $G$-BMO martingale and $G$-Girsanov theorem, we establish the existence and uniqueness result when the upper obstacle is almost a gener

Guoxi Huang, Ruirui Lin, Yini Li, David R. Bull

Videos captured in low-light and underwater conditions often suffer from distortions such as noise, low contrast, color imbalance, and blur. These issues not only limit visibility but also degrade automatic tasks like detection. Post-processing is typically required but can be time-consuming. AI-based tools for video enhancement also demand significantly mor

Rabee Al-Qasem

Reliable agricultural data is essential for food security, land-use planning, and economic resilience, yet in Palestine, such data remains difficult to collect at scale because of fragmented landscapes, limited field access, and restrictions on aerial monitoring. This paper presents ResAF-Net, a satellite-based tree detection framework designed for large-sca

Xiangdi Huang, Muxi Lei, Huitao Zhou

Since the pioneering work of Korteweg (1901) and the subsequent refinement of capillary fluid models by Dunn and Serrin (1985), the global existence of strong solutions to the multi-dimensional compressible Navier-Stokes-Korteweg (NSK) system with arbitrarily large initial data has stood as a formidable open problem in fluid mechanics. This challenge was rec

Navid Aslankhani Khameneh, Marco Carletti, Cigdem Beyan

Robust in-bed human pose estimation under blanket occlusion remains challenging due to the scarcity of reliable labeled training data for heavily covered poses. Existing approaches rely on multi-modal sensing or image-to-image translation frameworks that remain conditioned on visible source imagery, limiting scalability and pose diversity. In this work, we r