October 2025 arXiv papers — page 130

Fabio Musio, Norman Juchler, Kaiyuan Yang, Suprosanna Shit

The Circle of Willis (CoW) is a critical network of arteries in the brain, often implicated in cerebrovascular pathologies. Voxel-level segmentation is an important first step toward an automated CoW assessment, but a full quantitative analysis requires centerline representations. However, conventional skeletonization techniques often struggle to extract rel

Bovornpratch Vijarnwannaluk, Zhen-Kai Gao, Wei-Hao Wang, Chian-Chou Chen

We investigate the stellar shape and size-mass relationship of X-ray selected Active Galactic Nuclei (AGN) host galaxies using the high-angular resolution and deep sensitivity in the near-infrared of the COSMOS-Web JWST survey field. We present the rest-frame 1-$\mu m$ size, stellar mass, Sersic index, axis-ratio, Gini-$M_{20}$ parameters of 690 moderate lum

Sarah Allred, M. N. Ellingham

Let $Y$ be the subdivided claw, the $7$-vertex tree obtained from a claw $K_{1,3}$ by subdividing each edge exactly once. We characterize the graphs (finite and infinite) that do not have $Y$ as a subgraph, or, equivalently, do not have $Y$ as a minor. This work was motivated by a problem involving VCD minors. A graph $H$ is a vertex contraction-deletion min

Chen Yu Chi, Ming Hsuan Kang, Yu Hsuan Hsieh

We study universal cycles on the Grassmannian $G_q(2,n)$, the set of $2$-dimensional $\mathbb{F}_q$-subspaces of $\mathbb{F}_q^n$. While their existence is known from inductive and Eulerian graph methods, we give a direct algebraic construction when $n$ is odd under the coprimality condition $\gcd(n,\,q(q^2-1))=1$, using a projective-ratio decomposition and

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The first searches for $B^0\to K^+\pi^-\tau^+\tau^-$ and $B^0_s\to K^+K^-\tau^+\tau^-$ decays at the LHCb experiment are conducted with $pp$ collision data corresponding to an integrated luminosity of $5.4\textrm{ fb}^{-1}$. The tau leptons are reconstructed using the $\tau^+\to \mu^+\overline{\nu}_\tau\nu_\mu$ decay and the results are presented in bins of

Dan Jacobellis, Mateen Ulhaq, Fabien Racapé, Hyomin Choi

Video comprises the vast majority of bits that are generated daily, and is the primary signal driving current innovations in robotics, remote sensing, and wearable technology. Yet, the most powerful video understanding models are too expensive for the resource-constrained platforms used in these applications. One approach is to offload inference to the cloud

Christophe Roux, Max Zimmer, Alexandre d'Aspremont, Sebastian Pokutta

Pruning is a common technique to reduce the compute and storage requirements of Neural Networks. While conventional approaches typically retrain the model to recover pruning-induced performance degradation, state-of-the-art Large Language Model (LLM) pruning methods operate layer-wise, minimizing the per-layer pruning error on a small calibration dataset to

Yu Zhou, Hai-Qing Zhang

The imaginary part of the effective action encodes vacuum instability and particle production in the background field. Two standard approaches are commonly used to derive it: the Bogoliubov method and the Green's function method, which are usually expected to agree. However, in de Sitter spacetime they yield different results. We revisit this problem by intr

Shreeharshini Dharanesh Murthy, Victoria Moore, Qiang Du, Angel Jurado

The rapid advancement of Radio Frequency System-onChip (RFSoC) technology from Xilinx (AMD) has enabled the integration of high-speed data converters and programmable logic within a single package. RFSoC platforms are already widely adopted in telecommunications, radar, and satellite communications, where they promise reductions in system footprint and power

Rohan Bose, Jinwei Zhao, Tanya Shreedhar, Jianping Pan

Low Earth Orbit (LEO) satellite ISPs promise universal Internet connectivity, yet their interaction with content delivery remains poorly understood. We present the first comprehensive measurement study decomposing Starlink's web content delivery performance decomposed across Point of Presence (PoP), DNS, and CDN layers. Through two years of measurements comb

Evan Ellis, Vivek Myers, Jens Tuyls, Sergey Levine

Assistive agents should not only take actions on behalf of a human, but also step out of the way and cede control when there are important decisions to be made. However, current methods for building assistive agents, whether via mimicking expert humans or via RL finetuning on an inferred reward, often encourage agents to complete tasks on their own rather th

Georgios Daskalopoulos, Chikako Mese

This survey reviews results on harmonic maps into spaces of non-positive curvature, with a focus on targets that lack smooth structure. More precisely, we consider targets that are complete metric spaces with non-positive curvature in the sense of Alexandrov, commonly referred to as NPC (non-positively curved) or CAT(0) spaces. We discuss applications of har

Sergei K. Suslov

We revisit the Bohr-Sommerfeld atomic model to explore hydrogen-like ions of Uranium ($Z=92$), Oganesson ($Z=118$), and hypothetical superheavy elements beyond. Although superseded by the Dirac equation and modern quantum electrodynamics, the semiclassical approach offers a historically and pedagogically valuable perspective. Using the Sommerfeld fine struct

Ze Zhou, Guo-Liang Yu, Zhi-Gang Wang, Jie Lu

In this work, we analyze the strong vertices of hadronic molecules $DK$, $D^*K$, $DK^*$ and their bottom analogs within the framework of three-point QCD sum rules. The coupling between interpolating currents and low spin particles is considered in the phenomenological side, and the vacuum condensates $\left\langle \bar qq \right\rangle ,\left\langle g_s^2GG

Fan Chang, Yijia Fang

We prove a support--shattering uncertainty principle for functions on the Boolean cube. Let $\mathbb{F}$ be any field and let $f:\{0,1\}^n\to\mathbb{F}$ be nonzero. If $x^S$ is a maximum-degree monomial in the multilinear representation of $f$, then $\mathrm{supp}(f)$ shatters $S^c$. Consequently, \[ \mathrm{VC}(\mathrm{supp}(f))+\mathrm{deg}_{\mathbb{F}}(f)

Minjung Shin, Hyunin Cho, Sooyeon Go, Jin-Hwa Kim

Multi-view generation with camera pose control and prompt-based customization are both essential elements for achieving controllable generative models. However, existing multi-view generation models do not support customization with geometric consistency, whereas customization models lack explicit viewpoint control, making them challenging to unify. Motivate

Nikola Herceg, Nikola Konjik, A. Naveena Kumara, Andjelo Samsarov

Noncommutative (NC) geometry provides a novel approach to probe quantum gravity effects in black hole spacetimes. This work explores Dirac quasinormal modes (QNMs) of a deformed Reissner-Nordstr\"om black hole, where noncommutativity induces an effective metric with an additional ($ r-\varphi$) component. Employing a semiclassical model equivalent to a NC ga

Nazmul Hasan, Tara Peña, Aditya Dey, Dongyoung Yoon

Two-dimensional (2D) materials with a twist between layers exhibit a moir\'e interference pattern with larger periodicity than any of the constituent layer unit cells. In these systems, a wealth of exotic phases appear that result from moir\'e-dependent many-body electron correlation effects or non-trivial band topology. One problem with using twist to gener

Maksim Sapronov, Evgeniy Glukhov

Repository-level pretraining is commonly used to enable large language models for code to leverage codebase-wide context. This enhances their ability to generate accurate and context-aware code completions. In this work, we investigate how different repository-processing strategies affect in-context learning in OpenCoder, a 1.5B-parameter model. We extend it

Gregor N. C. Simm, Jean Hélie, Hannes Schulz, Yicheng Chen

Polymers are a versatile class of materials with widespread industrial applications. Advanced computational tools could revolutionize their design, but their complex, multi-scale nature poses significant modeling challenges. Conventional force fields often lack the accuracy and transferability required to capture the intricate interactions governing polymer

George A. Elliott, Zhuang Niu

The authors' recent classification of Jesper Villadsen's remarkable generalization (based on a self-reproducing seed space) of Glimm's infinite tensor product (UHF) C*-algebras, by means of the Cuntz semigroup (in the case of a fixed, well-behaved, seed space), is extended to the analogous generalization of Bratteli's approximately finite-dimensional (AF) C*

Yuchun Miao, Liang Ding, Sen Zhang, Rong Bao

Despite the success of Reinforcement Learning from Human Feedback (RLHF) in aligning language models with human values, reward hacking-or reward over-optimization-remains a major challenge. We identify two key obstacles to its mitigation: (1) reward misgeneralization in reward modeling, where reward models overfit to spurious, preference-irrelevant features;

Kenan Alkiek, David Jurgens, Vinod Vydiswaran

Small language models (SLMs) enable low-cost, private, on-device inference, but they often fail on problems that require specialized domain knowledge or multi-step reasoning. Existing approaches for improving reasoning either rely on scale (e.g., chain-of-thought prompting), require task-specific training that limits reuse and generality (e.g., distillation)

Fernando Albiac, José L. Ansorena, Miguel Berasategui, Pablo M. Berná

We construct two counterexamples that resolve long-standing open problems on greedy approximation theory with respect to bases, posed in [F. Albiac et al., Dissertationes Math. 560 (2021)] and restated in [F. Albiac, J. L. Ansorena, V. Temlyakov, J. Approx. Theory 307 (2025)]. Our first result exhibits a quasi-Banach space $\mathbb{X}$ with an almost greedy

Deepak A. Cherian

I take inspiration from the property-testing literature, particularly the work of Prof. John Hughes, and explore how such ideas might be applied to numerical models of the ocean. Specifically, I ask whether geophysical fluid dynamics (GFD) theory, expressed as property tests, might be used to address the oracle problem of testing the correctness of ocean mod

Cecilia Di Florio, Huimin Dong, Antonino Rotolo

Logic-based models can be used to build verification tools for machine learning classifiers employed in the legal field. ML classifiers predict the outcomes of new cases based on previous ones, thereby performing a form of case-based reasoning (CBR). In this paper, we introduce a modal logic of classifiers designed to formally capture legal CBR. We incorpora

Henrique Borrin

In this paper, we obtain quantitative estimates of regular Lagrangian flows associated to vector fields whose derivative can be written as convolution of a fundamental singular kernel satisfying the ``H\"ormander'' condition convoluted with summable function in spacetime.

Zhiyuan He, Yi Xu, Cheng Luo, Lili Qiu

Satellite communication offers Internet connectivity to remote locations, such as villages, deserts, mountains, and at sea. However, transmitting content over satellite networks is significantly more expensive than traditional Internet. To address this issue, we propose placing content replica servers within satellite networks and optimizing replica placemen

Xiaohui Gao, Haoran Yang, Yue Cheng, Mengfei Zuo

In recent years, the alignment between artificial neural network (ANN) embeddings and blood oxygenation level dependent (BOLD) responses in functional magnetic resonance imaging (fMRI) via neural encoding models has significantly advanced research on neural representation mechanisms and interpretability in the brain. However, these approaches remain limited

Aravind Asok, Morgan Opie, Brian Shin, Tariq Syed

Assume $k$ is a field and $R$ is a smooth $k$-algebra of dimension $d$. If $P$ is a projective module of rank $r$, then it is well-known that $P$ can be generated by $r+d$-elements (Forster--Swan). Under suitable assumptions on $r$ and $d$, we investigate obstructions to generation of $P$ by fewer than $r+d$ elements using motivic homotopy theory. For exampl

Miana Smith, Paul Arthur Richard, Alexander Htet Kyaw, Neil Gershenfeld

Although digital fabrication processes at the desktop scale have become proficient and prolific, systems aimed at producing larger-scale structures are still typically complex, expensive, and unreliable. In this work, we present an approach for the fabrication of scalable macroscale structures using simple robots and interlocking lattice building blocks. A t

Russelle Guadalupe

We study certain arithmetic properties of an analogue $B(n)$ of Lin's restricted partition function that counts the number of partition triples $\pi=(\pi_1,\pi_2,\pi_3)$ of $n$ such that $\pi_1$ and $\pi_2$ comprise distinct odd parts and $\pi_3$ consists of parts divisible by $4$. With the help of elementary $q$-series techniques and modular functions, we e

Ana Lawry Aguila, Peirong Liu, Marina Crespo Aguirre, Juan Eugenio Iglesias

Generating healthy counterfactuals from pathological images holds significant promise in medical imaging, e.g., in anomaly detection or for application of analysis tools that are designed for healthy scans. These counterfactuals should represent what a patient's scan would plausibly look like in the absence of pathology, preserving individual anatomical char

Morris Ang, Xin Sun, Pu Yu

In the mating-of-trees approach to Schramm-Loewner evolution (SLE) and Liouville quantum gravity (LQG), it is natural to consider two pairs of correlated Brownian motions coupled together. This arises in the scaling limit of bipolar-orientation-decorated planar maps (Gwynne-Holden-Sun, 2016) and in the related skew Brownian permuton studied by Borga et al. T

Jiayang Li, Qingyu Zhang, Sohmyung Ha, Andreas Demosthenous

This paper presents a fast impedance measurement IC for large-scale piezo-resistive sensor array. It features a unified differential time-to-digital demodulation architecture that readout impedance directly through the excitation circuit. The proposed pre-saturation adaptive bias technique further improves power efficiency. The chip scans 253 sensors in 12.2

Matthieu Dubois, François Yvon, Pablo Piantanida

As texts generated by Large Language Models (LLMs) are ever more common and often indistinguishable from human-written content, research on automatic text detection has attracted growing attention. Many recent detectors report near-perfect accuracy, often boasting AUROC scores above 99\%. However, these claims typically assume fixed generation settings, leav

Bingbin Liu, Rachit Bansal, Depen Morwani, Nikhil Vyas

Diagonal preconditioners are computationally feasible approximate to second-order optimizers, which have shown significant promise in accelerating training of deep learning models. Two predominant approaches are based on Adam and Gauss-Newton (GN) methods: the former leverages statistics of current gradients and is the de-factor optimizers for neural network

Stefan Schreieder

We survey recent developments on rationality problems for algebraic varieties, with a particular emphasis on cycle-theoretic and combinatorial methods and their applications to hypersurfaces.

Xinyang Li, Tengfei Wang, Zixiao Gu, Shengchuan Zhang

We propose FlashWorld, a generative model that produces 3D scenes from a single image or text prompt in seconds, 10~100$\times$ faster than previous works while possessing superior rendering quality. Our approach shifts from the conventional multi-view-oriented (MV-oriented) paradigm, which generates multi-view images for subsequent 3D reconstruction, to a 3

Matteo Scialpi, Francesco Di Clemente, Leigh Smith, Michał Bejger

Physics-Informed Neural Networks (PINNs) embed the partial differential equations (PDEs) governing the system under study directly into the training of Neural Networks, ensuring solutions that respect physical laws. While effective for single-system problems, standard PINNs scale poorly to datasets containing many realizations of the same underlying physics

Natalia Tsilevich, Yahel Manor

We introduce the notion of $GL(n)$-dependence of matrices, which is a generalization of linear dependence taking into account the matrix structure. Then we prove a theorem, which generalizes, on the one hand, the fact that $n+1$ vectors in an $n$-dimensional vector space are linearly dependent and, on the other hand, the fact that the natural action of the g

Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka, Stefan Schmid

Open-domain visual entity recognition aims to identify and link entities depicted in images to a vast and evolving set of real-world concepts, such as those found in Wikidata. Unlike conventional classification tasks with fixed label sets, it operates under open-set conditions, where most target entities are unseen during training and exhibit long-tail distr

Isobel C. Clarke, Virginia Ciriano-Tejel, David J. Ibberson, Grayson M. Noah

Constructing a quantum computer capable of broad and important applications is likely to require millions of addressable physical qubits, posing the challenge of large-scale integration of quantum systems with classical electronics. Fully depleted silicon-on-insulator CMOS technology has been used to develop a range of cryogenic electronic components for the

Gal Porat

The theory of locally analytic representations of $p$-adic Lie groups with $\mathbf{Q}_p$-coefficients is a powerful tool in $p$-adic Hodge theory and in the $p$-adic Langlands program. This perspective reveals important differential structures, such as the Sen and Casimir operators. Rodr\'iguez Camargo and Rodrigues Jacinto developed in \cite{RJRC22} a soli

Marcílio Ferreira dos Santos, Andreza dos Santos Rodrigues de Melo

Dengue remains one of Brazil's major epidemiological challenges, marked by strong intra-urban inequalities and the influence of climatic and socio-environmental factors. This study analyzed confirmed dengue cases in Recife from 2015 to 2024 using a Bayesian hierarchical spatio-temporal model implemented in R-INLA, combining a BYM2 spatial structure with an R

Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu, Hailong Yan

This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of the challenge is to identify effective networks capable of producing brighter, clearer, and visually compelling images under diverse and challenging conditions. A remarkable total

Zian Li, Muhan Zhang

Masked autoregressive models (MAR) have emerged as a powerful paradigm for image and video generation, combining the flexibility of masked modeling with the expressiveness of continuous tokenizers. However, when sampling individual frames, video MAR models often produce highly distorted outputs due to the lack of a structured global prior, especially when us

Zhibin Wang, Zetao Hong, Xue Li, Zibo Wang

Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly for long-output reasoning tasks. Existing systems, such as PD disaggregation architectures, rely on static prefill-to-decode scheduling, which often results in SLO violations and OO

Jiawei Yan, Jonas B. Profe, Yuta Murakami, Philipp Werner

We develop a nonequilibrium multi-orbital extension of the two-particle self-consistent theory and apply it to the bilayer Hubbard model as a minimal platform to investigate correlation effects in the presence of interlayer interactions and tunneling. The method determines vertex corrections in the spin and charge channels self-consistently at the two-partic

Hyunsu Kim, Jonggeon Park, Joan Bruna, Hongseok Yang

The advent of foundation models in AI has significantly advanced general-purpose learning, enabling remarkable capabilities in zero-shot inference and in-context learning. However, training such models on physics data, including solutions to partial differential equations (PDEs), poses a unique challenge due to varying dimensionalities across different syste

Muhammad Haseeb, Jinkun Geng, Radhika Mittal, Aurojit Panda

A growing class of applications demands \emph{fair ordering} of events, which ensures that events generated earlier are processed before later events. However, achieving such sequencing is challenging due to the inherent errors in clock synchronization: two events at two clients generated close together may have timestamps that cannot be compared confidently

Ali Sait Demir

Let $\text{Ham(M,L)}$ denote the group of Hamiltonian diffeomorphisms on a symplectic manifold $M$, leaving a Lagrangian submanifold $L\subset M$ invariant. In this paper, we show that $\text{Ham(M,L)}$ has the fragmentation property, using relative versions of the techniques developed by Thurston and Banyaga.

Mahdi Esmailoghli, Matthias Weidlich

Over the past decade, the proliferation of public and enterprise data lakes has fueled intensive research into data discovery, aiming to identify the most relevant data from vast and complex corpora to support diverse user tasks. Significant progress has been made through the development of innovative index structures, similarity measures, and querying infra

Hongyu Qu, Jianan Wei, Xiangbo Shu, Yazhou Yao

Current 3D gaze estimation methods struggle to generalize across diverse data domains, primarily due to i) the scarcity of annotated datasets, and ii) the insufficient diversity of labeled data. In this work, we present OmniGaze, a semi-supervised framework for 3D gaze estimation, which utilizes large-scale unlabeled data collected from diverse and unconstra

Sylvester Eriksson-Bique, Mathav Murugan

We affirmatively resolve the energy image density conjecture of Bouleau and Hirsch (1986). Beyond the original framework of Dirichlet structures, we establish the energy image density property in several related settings. In particular, we formulate a version of the property that encompasses strongly local, regular Dirichlet forms, Sobolev spaces defined via

Haotian Feng, Ke Sheng

We develop and validate a novel spherical radiomics framework for predicting key molecular biomarkers using multiparametric MRI. Conventional Cartesian radiomics extract tumor features on orthogonal grids, which do not fully capture the tumor's radial growth patterns and can be insensitive to evolving molecular signatures. In this study, we analyzed GBM radi

Arega Getaneh Abate, Xiao-Bing Zhang, Xiufeng Liu, Ruyu Liu

Sequential intraday electricity trading allows photovoltaic (PV) operators to reduce imbalance settlement costs as forecasts improve throughout the day. Yet deployable trading policies must jointly handle forecast uncertainty, intraday prices, liquidity, and the asymmetric economics of PV imbalance exposure. This paper proposes a feature-driven reinforcement

Di Wu, Shuang-Qing Wu

We construct, for the first time, new static non-extremal five-dimensional black hole solutions (without or with squashed horizons) endowing with four different electric charge parameters in the $D = 5$, $\mathcal{N} = 2$ supergravity coupled to three vector multiplets with a specific pre-potential $\mathcal{V} = STU -W^2U \equiv 1$. When the fourth charge p

Marcel Meyer, Sascha Kaltenpoth, Kevin Zalipski, Oliver Müller

Time Series Foundation Models (TSFMs) represent a new paradigm for time-series forecasting, promising zero-shot predictions without the need for task-specific training or fine-tuning. However, similar to Large Language Models (LLMs), the evaluation of TSFMs is challenging: as training corpora grow increasingly large, it becomes difficult to ensure the integr

Yoshua Bengio, Stephen Clare, Carina Prunkl, Shalaleh Rismani

Since the publication of the first International AI Safety Report, AI capabilities have continued to improve across key domains. New training techniques that teach AI systems to reason step-by-step and inference-time enhancements have primarily driven these advances, rather than simply training larger models. As a result, general-purpose AI systems can solve

Damek Davis, Benjamin Recht

We show that several popular algorithms for reinforcement learning in large language models with binary rewards can be viewed as stochastic gradient ascent on a monotone transform of the probability of a correct answer given a prompt. In particular, the transformation associated with rejection sampling algorithms is the logarithm and that associated with the

Hong-Qian Sun, Shou-Feng Shen, Zuo-Nong Zhu

In this paper, we primarily construct Darboux transformation(DT) of the nonlocal coupled modified complex integrable dispersionless (cm-CID) equation, which is first proposed by the connection with a nonlocal coupled modified complex short pulse(cm-CSP) equation. Utilizing DT, we present soliton-type solutions for the nonlocal cm-CID equation under vanishing

Maurício F. C. Martins Quintela, Guilherme J. Inacio, Miguel Sá, Giovanni Cistaro

Recently, the isolation of 2D magnetic materials has opened several avenues for possible new ap- plications in spintronics. Among these materials, CrSBr has sparked interest due to its relatively high Curie temperature, highly anisotropic lattice structure, and high structural stability. These properties ran along others shared by any atomically thin materia

Mengyang Cui, Hongxing Qi, Chengduo Hu, Qing Li

We investigated the nonlinear phenomena observed in the dark current of BIB (blocked-impurity-band) infrared detectors, including negative differential resistance (NDR) and current oscillations. Our analysis systematically elucidated the intrinsic transport mechanisms in optimized devices, revealing that these anomalies arise from current path clustering ind

Sangjoon Lee, Haris Moazam Sheikh

Effective airfoil geometry optimization requires exploring a diverse range of designs using as few design variables as possible. This study introduces AirDbM, a Design-by-Morphing (DbM) approach specialized for airfoil optimization that systematically reduces design-space dimensionality. AirDbM selects an optimal set of 12 baseline airfoils from the UIUC air

Michael Bosello, Flavio Pinzarrone, Sara Kiade, Davide Aguiari

Drone technology is proliferating in many industries, including agriculture, logistics, defense, infrastructure, and environmental monitoring. Vision-based autonomy is one of its key enablers, particularly for real-world applications. This is essential for operating in novel, unstructured environments where traditional navigation methods may be unavailable.

Akib Mohammed Khan, Bartosz Krawczyk

Vision foundation models such as DINOv2 enable strong few-shot anomaly detection (FSAD) through simple non-parametric k-nearest-neighbor (k-NN) scoring over frozen patch features. Existing robust anomaly detection methods assume large normal-class training sets and adversarial training of the feature extractor. The few-shot regime, where the detector consist

Yuval Waschitz, Ady Stern, Yuval Oreg

We develop a theoretical framework for probing superconductivity with momentum resolution using the quantum twisting microscope (QTM), a planar tunneling device where a graphene tip is rotated relative to a two-dimensional sample. Because of in-plane momentum conservation, the QTM directly measures the superconducting spectral function along well-defined tra

Xavier Erny

We establish an analogue of the first fundamental theorem of calculus for functions defined on the Wasserstein space of probability measures. Precisely, we show that if a function on the Wasserstein space is sufficiently regular in the sense of the linear functional derivative, then its integral is differentiable and the derivative coincides with the integra

J. Thomas Beale, Svetlana Tlupova

Solutions of partial differential equations can often be written as surface integrals having a kernel related to a singular fundamental solution. Special methods are needed to evaluate the integral accurately at points on or near the surface. Here we derive formulas to regularize the integrals with high accuracy, using analysis from Beale and Tlupova (Adv. C

Chun Wai Chin, Haniza Yazid, Hoi Leong Lee

Medical image enhancement is crucial for improving the quality and interpretability of diagnostic images, ultimately supporting early detection, accurate diagnosis, and effective treatment planning. Despite advancements in imaging technologies such as X-ray, CT, MRI, and ultrasound, medical images often suffer from challenges like noise, artifacts, and low c

Giuseppe E. Lio, Giulio Carotta, Lorenzo Lavista, Andrea Camposeo

Thin dielectric films are known to show distinct colors, responsible for the iridescence of various natural and artificial objects such as insect wings and soap bubbles. In the present article we show that a specialized thin film Fabry-P\'erot resonator, that we name conductor-dielectric-conductor (CDC) matched cavity, appears instead completely grey when ob

Félix Almendra-Hernández, Miles Bakenhus, Vishesh Karwa, Mitsunori Ogawa

A valued stochastic blockmodel (SBM) is a general way to view networked data in which nodes are grouped into blocks and links between them are measured by counts or labels. This family allows for varying dyad sampling schemes, thereby including the classical, Poisson, and labeled SBMs, as well as those in which some edge observations are censored. This paper

M. M. Piva, T. Helm, J. C. Souza, K. R. Pakuszewski

The Weyl semimetal CeAlSi crystallises in the noncentrosymmetric tetragonal space group $I4_1md$ and exhibits ferromagnetic order below 8 K, thereby breaking both spatial inversion and time-reversal symmetries. This unique combination of properties establishes CeAlSi as a model system for studying the interplay between non-trivial topological states and stro

Pooja Kulkarni, Ruta Mehta, Vishnu V. Narayan, Tomasz Ponitka

We study the problem of fairly allocating $m$ indivisible items arriving online, among $n$ (offline) agents. Although envy-freeness has emerged as the archetypal fairness notion, envy-free (EF) allocations need not exist with indivisible items. To bypass this, a prominent line of research demonstrates that there exist allocations that can be made envy-free b

Santiago Cuervo, Skyler Seto, Maureen de Seyssel, Richard He Bai

Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counterparts--and even cascaded pipelines--on language understanding tasks. We term this shortfall the text-speech understanding gap: the performance drop observed when a speech-adapted

Sumit Srivastav, Sylvain Maclot, Alicja Domaracka, Sergio Díaz-Tendero

We investigate two nitrogen-containing isomers of polycyclic aromatic hydrocarbons (PAHs), quinoline (Q) and isoquinoline (IQ), of composition C$_9$H$_7$N in collisions with 7~keV O$^+$ and 48~keV O$^{6+}$ projectile ions. Employing ion-ion coincidence mass spectrometry, we determine branching ratios for H-loss, C$_2$H$_2$-loss, and HCN-loss dissociation cha

Amjid Ali, Zulfiqar Ahmad Khan, Altaf Hussain, Muhammad Munsif

Anomaly recognition plays a vital role in surveillance, transportation, healthcare, and public safety. However, most existing approaches rely solely on visual data, making them unreliable under challenging conditions such as occlusion, low illumination, and adverse weather. Moreover, the absence of large-scale synchronized audio-visual datasets has hindered

Michael J. Barlow

Supernova 1987A was the closest supernova event to be observed in nearly 400 years. The outflowing ejecta from the explosion continues to interact with extended circumstellar material and with the equatorial ring (ER) in the triple ring system, while observations of the system have continued across the whole electromagnetic spectrum. This review mainly focus

Jianfei Xu

As pointed out in recent research, the near extremal black hole entropy with one-loop effect exhibits universal $\log T$ behavior at sufficiently low temperature. In this paper, we discuss the low-temperature quantum corrections to the thermodynamics of four-dimensional accelerating black holes with rotation and charges by using the method of Euclidean path

Viviana Centritto, Ama Bandara, Heqi Deng, Masoud Babaie

Scaling quantum computers from a few qubits to large numbers remains one of the critical challenges in realizing practical quantum advantage. Multi-core quantum architectures have emerged as a promising solution, enabling scalability through distributed quantum processing units (QPUs) interconnected via classical and quantum links. However, the bottleneck of

Senyu Fei, Siyin Wang, Junhao Shi, Zihao Dai

Visual-Language-Action (VLA) models report impressive success rates on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. We perform a systematic vulnerability analysis by introducing controlled perturbations across seven dimensions: objects layout, camera viewpoints, robot initial states, language instructions,

Nicolas Pottier, Meng Cheng Lau

Within the field of robotics, computer vision remains a significant barrier to progress, with many tasks hindered by inefficient vision systems. This research proposes a generalized vision module leveraging YOLOv9, a state-of-the-art framework optimized for computationally constrained environments like robots. The model is trained on a dataset tailored to th

Stefan Lenz, Lakisha Ortiz Rosario, Georg Vollmar, Arsenij Ustjanzew

Accurate coding of tumor diagnoses with ICD-10-GM and ICD-O-3 is essential for structured cancer documentation in Germany. Smaller open-weight LLMs are appealing for privacy-preserving automation but often struggle with coding accuracy in German-language contexts. This study investigates whether instruction-based fine-tuning on public datasets improves the c

Qing-Hua Shen, Jun-Xu Lu, Li-Sheng Geng, Xiang Liu

We present a theoretical investigation of the radiative decay process $\Omega(2012) \to \gamma \Omega$, where the $\Omega(2012)$ resonance with spin-parity $J^P=\frac{3}{2}^-$, is treated as a dynamically generated state from $\bar{K}\Xi(1530)$ and $\eta \Omega$ in $s$-wave and $\bar{K}\Xi$ in $d$-wave. The radiative decay width of the $\Omega(2012)$ is calc

Riddhish Thakare, Kingdom Mutala Akugri

Classical nonlinear dimensionality reduction (NLDR) techniques like t-SNE, Isomap, and LLE excel at creating low-dimensional embeddings for data visualization but fundamentally lack the ability to map these embeddings back to the original high-dimensional space. This one-way transformation limits their use in generative applications. This paper addresses thi

Yuexing Hao, Yue Huang, Haoran Zhang, Chenyang Zhao

Cutting-edge research in Artificial Intelligence (AI) requires considerable resources, including Graphics Processing Units (GPUs), data, and human resources. In this paper, we evaluate of the relationship between these resources and the scientific advancement of foundation models (FM). We reviewed 6517 FM papers published between 2022 to 2024, and surveyed 2

Chen Chen, Kangcheng Bin, Ting Hu, Jiahao Qi

Unmanned aerial vehicles (UAV)-based object detection with visible (RGB) and infrared (IR) images facilitates robust around-the-clock detection, driven by advancements in deep learning techniques and the availability of high-quality dataset. However, the existing dataset struggles to fully capture real-world complexity for limited imaging conditions. To this

Daniel Choate, Jason Rife

In this paper we introduce a visualization methodology to aid a human analyst in classifying adversity modes that impact lidar scan matching. Our methodology is intended for offline rather than real-time analysis. The method generates a vector-field plot that characterizes local discrepancies between a pair of registered point clouds. The vector field plot r

Álvaro Buendía, Nuno M. R. Peres

Coreshell nanoparticles can combine optical features of different materials in a single nanostructure, which makes them interesting for many applications from biomedicine to energy harvesting. On the other hand, periodic arrays of plasmonic nanoparticles can exhibit topological phenomena such as topological edge states. Here we study periodic chains of Si@Ag

Akifumi Chitose, Masahiro Ibe, Satoshi Shirai

Supersymmetry beyond the TeV scale offers several theoretical and phenomenological advantages, such as accommodating the observed Higgs mass and alleviating the flavor and CP problems. However, flavor and CP observables still impose stringent constraints even at the PeV scale, motivating a systematic study of flavor symmetries in this regime. In this work, w

Preston Fairchild, Claudia Chen, Xiaobo Tan

Properly handling delicate produce with robotic manipulators is a major part of the future role of automation in agricultural harvesting and processing. Grasping with the correct amount of force is crucial in not only ensuring proper grip on the object, but also to avoid damaging or bruising the product. In this work, a flexible pressure sensor that is both

Pablo Barceló, Fabian Jogl, Alexander Kozachinskiy, Matthias Lanzinger

Graph neural networks (GNNs) are widely used in graph learning and most architectures propagate information by passing messages between vertices. In this work, we shift our attention to GNNs that perform message passing on edges and introduce EB-1WL, an edge-based color-refinement test, and a corresponding architecture, EB-GNN. Our EB-GNN architecture is ins

Jaume de Haro, Emilio Elizalde

The geometric foundations of General Relativity are revisited, with particular attention to its gauge invariance, as a key to understanding the true nature of spacetime. Beyond the common image of spacetime as a deformable 'fabric' filling the Universe, curvature is interpreted as the dynamic interplay between matter and interacting fields; a view already em

Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu

Large Language Models (LLMs) have achieved impressive reasoning abilities, but struggle with temporal understanding, especially when questions involve multiple entities, compound operators, and evolving event sequences. Temporal Knowledge Graphs (TKGs), which capture vast amounts of temporal facts in a structured format, offer a reliable source for temporal

S. A. Mane, N. V. Shinde

An important question in the study of quasi-perfect codes is whether such codes can be constructed for all possible lengths $n$. In this paper, we address this question for specific values of $n$. First, we investigate the existence of quasi-perfect codes in the Cartesian product of a graph $G$ and a path (or cycle), assuming that $G$ admits a perfect code.

J. I. Farrán, J. C. Rosales, R. Tapia-Ramos, A. Vigneron-Tenorio

This work introduces a new kind of affine semigroups called $P$-semigroups. Within the framework of $\mathcal C$-semigroups, we define a finite-state automaton associated to them. Moreover, this automaton determines whether a $\mathcal C$-semigroup is a $P$-semigroup, which represents a bridge between affine semigroups and Discrete Mathematics. Furthermore,

Erroxe Etxabarri-Alberdi, James Matthew Jones, Theodoros Stylianos Papazachariou

We explicitly fully describe the K-moduli space of Fano threefold family number 3.3. We first show that K-semistable Fano varieties with volume greater than 18 are Gorenstein canonical and admit general elephants, decreasing the bound on a result by Liu and Zhao. Combining this with the moduli-continuity method via lattice-polarized K3 surfaces, we identify

N. Gopalswamy, P. Mäkela, S. Akiyama, S. Yashiro

The sustained gamma ray emission (SGRE) from the Sun is one of the fascinating high energy phenomena closely related to the acceleration of protons to energies >300 MeV. Here we report on the solar cycle variation of SGRE events based on observations from Fermi's Large Area Telescope (LAT). This report covers solar cycles (SCs) 24 and 25 during which Fermi h

Raghavendra Dheeraj Peddinti, Stefano Pisoni, Narsimha Rapaka, Yacine Addad

The curse of dimensionality is ubiquitous in both numerical and data-driven methods. This is particularly severe for space-time methods, which treat the combined space-time domain simultaneously. We investigate the effectiveness of a quantum-inspired approach in alleviating this curse, both for solving PDEs and making data-driven predictions. We achieve this