October 2025 arXiv papers — page 3

Samuel Degnan-Morgenstern, Alexander E. Cohen, Rajeev Gopal, Megan Gober

Operando microscopy provides direct insight into the dynamic chemical and physical processes that govern functional materials, yet measurement noise limits the effective resolution and undermines quantitative analysis. Here, we present a general framework for integrating unsupervised deep learning-based denoising into quantitative microscopy workflows across

Dong Heon Han, Xiaohao Xu, Yuxi Chen, Yusheng Zhou

Biological systems, such as the octopus, exhibit masterful cross-scale manipulation by adaptively reconfiguring their entire form, a capability that remains elusive in robotics. Conventional soft grippers, while compliant, are mostly constrained by a fixed global morphology, and prior shape-morphing efforts have been largely confined to localized deformation

Louis Thirion, Yorick L. A. Schmerwitz, Max Kroesbergen, Gianluca Levi

Selective configuration interaction methods approximate correlated molecular ground- and excited states by considering only the most relevant Slater determinants in the expansion. While a recently proposed neural-network-assisted approach efficiently identifies such determinants, the procedure typically relies on canonical Hartree-Fock orbitals, which are op

Niloy Saha, Noura Limam, Yang Xiao, Raouf Boutaba

Detecting QoS anomalies in 5G user planes requires fine-grained per-flow visibility, but existing telemetry approaches face a fundamental trade-off. Coarse per-class counters are lightweight but mask transient and per-flow anomalies, while per-packet telemetry postcards provide full visibility at prohibitive cost that grows linearly with line rate. Selective

Xiangjiang Bao, Zheng-da Hu, Lucas Kreiss, Josh Lerner

Quantitative phase imaging (QPI) enables visualization and quantitative extraction of the optical phase information of transparent samples. However, conventional QPI techniques typically rely on multi-frame acquisition or complex interferometric optics. In this work, we introduce Quad-Pixel Phase Gradient Imaging ($QP^{2}GI$), a single-shot quantitative phas

Yunhong Deng, Chaozhen Wei

In this work, we construct a primal-dual forward-backward (PDFB) splitting method for computing a class of cross-diffusion systems that can be formulated as gradient flows under transport distances induced by matrix mobilities. By leveraging their gradient flow structure, we use minimizing movements as the variational formulation and compute these cross-diff

Alireza Saleh Abadi, Leen-Kiat Soh

In the rapidly evolving field of multi-agent reinforcement learning (MARL), understanding the dynamics of open systems is crucial. Openness in MARL refers to the dynam-ic nature of agent populations, tasks, and agent types with-in a system. Specifically, there are three types of openness as reported in (Eck et al. 2023) [2]: agent openness, where agents can

Janghoon Cho, Jungsoo Lee, Munawar Hayat, Kyuwoong Hwang

Recent studies in long video understanding have harnessed the advanced visual-language reasoning capabilities of Large Multimodal Models (LMMs), driving the evolution of video-LMMs specialized for processing extended video sequences. However, the scalability of these models is severely limited by the overwhelming volume of visual tokens generated from extend

Roy Y. He, Ying Liang, Hongkai Zhao, Yimin Zhong

We use elliptic partial differential equations (PDEs) as examples to show various properties and behaviors when shallow neural networks (SNNs) are used to represent the solutions. In particular, we study the numerical ill-conditioning, frequency bias, and the balance between the differential operator and the shallow network representation for different formu

Abhishek Muhuri, Tanoy Kanti Konar, Leela Ganesh Chandra Lakkaraju, Aditi Sen De

Identifying equilibrium criticalities and phases from the dynamics of a system, known as a dynamical quantum phase transition (DQPT), is a challenging task when relying solely on local observables. We exhibit that the experimentally accessible two-body Bell operator, originally designed to detect nonlocal correlations in quantum states, serves as an effectiv

Nandor Licker, Kevin Hu, Vladimir Zaytsev, Lequn Chen

Emerging Large Language Model (LLM) system patterns, such as disaggregated inference, Mixture-of-Experts (MoE) routing, and asynchronous reinforcement fine-tuning, require flexible point-to-point communication beyond simple collectives. Existing implementations are locked to specific Network Interface Controllers (NICs), hindering integration into inference

Ehsan Moradi

Feature-attribution methods (e.g., SHAP, LIME) explain individual predictions but often miss higher-order structure: sets of features that act in concert. We propose Modules of Influence (MoI), a framework that (i) constructs a model explanation graph from per-instance attributions, (ii) applies community detection to find feature modules that jointly affect

Jonas von Milczewski, Kelly Werker Smith, Susanne F. Yelin

We present a single-photon transduction scheme using 4-wave-mixing and quantum scattering in planar, cooperative Rydberg arrays that is both efficient and highly directional and may allow for terahertz-to-optical transduction. In the 4-wave-mixing scheme, two lasers drive the system, coherently trapping the system in a dark ground-state and coupling a signal

Tahmid Hasan Sakib, Yago Romano Martinez, Carter Brady, Syed Rafay Hasan

This paper presents a proof-of-concept supply chain attack against the Secure ROS 2 (SROS 2) framework, demonstrated on a Quanser QCar2 autonomous vehicle platform. A Trojan-infected Debian package modifies core ROS 2 security commands to exfiltrate newly generated keystore credentials via DNS in base64-encoded chunks to an attacker-controlled nameserver. Po

Nathanael S. Schwindt, Anthony P. Straub, Michael F. Toney, Michael R. Shirts

In reverse osmosis (RO) and nanofiltration (NF) membranes, the polymer structure and interactions with solvent and solutes dictate the permeability and selectivity. However, these interactions have not been fully characterized within hydrated polymer membranes. In this study, we elucidate the local atomic neighborhood around ions within a RO membrane using m

Gheorghe Ivan

The aim of this paper is to describe the definitions and main properties of three generalizations of the group concept, namely: groupoid, generalized group and almost groupoid. Some constructions of these algebraic structures and corresponding examples are presented.

Shuyan Lyu, Zhanzimo Wu, Junliang Du

Modern deep neural networks (DNNs) are typically trained with a global cross-entropy loss in a supervised end-to-end manner: neurons need to store their outgoing weights; training alternates between a forward pass (computation) and a top-down backward pass (learning) which is biologically implausible. Alternatively, greedy layer-wise training eliminates the

Jakob Hackstein, Sidney Bender

Class imbalance poses a fundamental challenge in machine learning, frequently leading to unreliable classification performance. While prior methods focus on data- or loss-reweighting schemes, we view imbalance as a data condition that amplifies Clever Hans (CH) effects by underspecification of minority classes. In a counterfactual explanations-based approach

Ziqian Guan, Xieyi Fu, Pengjun Huang, Hengyuan Zhang

In object detection, a well-defined similarity metric can significantly enhance model performance. Currently, the IoU-based similarity metric is the most commonly preferred choice for detectors. However, detectors using IoU as a similarity metric often perform poorly when detecting small objects because of their sensitivity to minor positional deviations. To

Osama A. Marzouk

In this work, we start with a generic mathematical framework for the equations of motion (EOM) in flight mechanics with six degrees of freedom (6-DOF) for a general (not necessarily symmetric) fixed-wing aircraft. This mathematical framework incorporates (1) body axes (fixed in the airplane at its center of gravity), (2) inertial axes (fixed in the earth/gro

Francesco Moretti, Martin Gorbahn, Sebastian Jaeger

We present the first next-to-leading-logarithmic QCD analysis of the electromagnetic corrections to the semileptonic weak Hamiltonian, including the mixed $\mathcal{O}(\alpha\,\alpha_s^2)$ corrections to the vector coupling $g_V$. The analysis combines the evaluation of three-loop anomalous dimensions and two-loop matching corrections with a consistent facto

Congzhang Shao, Quan Yuan, Guiyang Luo, Yue Hu

Collaborative perception improves task performance by expanding the perception range through information sharing among agents. . Immutable heterogeneity poses a significant challenge in collaborative perception, as participating agents may employ different and fixed perception models. This leads to domain gaps in the intermediate features shared among agents

Cesar H. Comin, Wesley N. Galvão

Semantic segmentation of blood vessels is an important task in medical image analysis, but its progress is often hindered by the scarcity of large annotated datasets and the poor generalization of models across different imaging modalities. A key aspect is the tendency of Convolutional Neural Networks (CNNs) to learn texture-based features, which limits thei

Aron Karakai, Jaap Eising, Andrea Martinelli, Florian Dörfler

We develop a system-theoretic framework for the structured analysis of distributed optimization algorithms with decomposable cost functions. We model such algorithms as a network of interacting dynamical systems and derive tests for convergence based on incremental dissipativity and contraction theory. This approach yields a step-by-step analysis pipeline su

Debosi Mondal, Biswajit Pandey, Amit Mondal

The cosmological principle, asserting large-scale homogeneity and isotropy, underpins the standard model of cosmology. Testing its validity using independent astronomical probes remains crucial for understanding the global structure of the Universe. We investigate the angular distribution of Gamma-Ray Bursts (GRBs) using two of the most comprehensive all-sky

Wenwen Li, Daniel Sanz-Alonso, Ruiyi Yang

This paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points. To ensure tha

Nikhil Krishna, Mariola Klusek-Gawenda, Antoni Szczurek

The production of charged kaon pairs in ultraperipheral heavy-ion collisions can proceed via photoproduction (gamma-Pomeron interaction) or via photon-photon fusion. An important contribution to this process arises from the decays of scalar, tensor and vector mesons. This study provides a consistent description of K+K- production at both the elementary level

Harsh Shah

Large Language Models (LLMs) face significant computational bottlenecks during inference due to the quadratic complexity of self-attention mechanisms, particularly as context lengths increase. We introduce SpecAttn, a novel training-free approach that seamlessly integrates with existing speculative decoding techniques to enable efficient sparse attention in

Luz-Viviana Cobaleda, Julián Carvajal, Paola Vallejo, Andrés López

Modern software systems increasingly integrate machine learning (ML) due to its advancements and ability to enhance data-driven decision-making. However, this integration introduces significant challenges for software engineering, especially in software product lines (SPLs), where managing variability and reuse becomes more complex with the inclusion of ML c

Cynthia S. Froning, Allison Youngblood, David J. Wilson, Girish M. Duvvuri

We present ultraviolet (UV) spectroscopy of the 17 Myr, G0V star, HIP 67522. The UV spectrum is characterized by strong chromospheric and transition region emission lines. There was moderate spectral variability during the observations consisting of 15% stochastic fluctuation and two small flares releasing $E_{UV} \simeq 2-4\times10^{32}$ ergs in each event.

Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu, Brian Lee

Supervised learning is classically formulated as training a model to minimize a fixed loss function over a fixed distribution, or task. However, an emerging paradigm instead views model training as extracting enough information from data so that the model can be used to minimize many losses on many downstream tasks. We formalize a mathematical framework for

Michael T. Jury

We show that when $m>n$, the space of $m\times n$-matrix-valued rational inner functions in the disk is path connected.

Hans-Theo Normann, Nina Rulié, Olaf Stypa, Tobias Werner

We analyze the delegation of pricing by participants, representing firms, to a collusive, self-learning algorithm in a repeated Bertrand experiment. In the baseline treatment, participants set prices themselves. In the other treatments, participants can either delegate pricing to the algorithm at the beginning of each supergame or receive algorithmic recomme

X. Dumusque, K. Al Moulla, M. Cretignier, N. Buchschacher

We recently released 10 years of HARPS-N solar telescope and the goal of this manuscript is to present the different optimisations made to the data reduction, to describe data curation, and to perform some analyses that demonstrate the extreme RV precision of those data. By analysing all the HARPS-N wavelength solutions over 13 years, we bring to light instr

Santosh Karki Chhetri, Rabindra Basnet, Krishna Pandey, Gokul Acharya

Magnetic topological semimetals LnSbTe (Ln = lanthanide elements) provide a platform to study the interplay of structure, magnetism, topology, and electron correlations. Varying Sb and Te compositions in LnSbxTe2-x can effectively control the electronic, magnetic, and transport properties. Here, we report the evolution of transport properties with Sb and Te

Gregory Fletcher Cox, Xiaoxia Shi, Yuya Shimizu

This paper proposes a new test for inequalities that are linear in possibly partially identified nuisance parameters. This type of hypothesis arises in a broad set of problems, including subvector inference for linear unconditional moment (in)equality models, specification testing of such models, and inference for parameters bounded by linear programs. The n

Riccardo Brioschi, Aleksandr Alekseev, Emanuele Nevali, Berkay Döner

Graphic layout generation is a growing research area focusing on generating aesthetically pleasing layouts ranging from poster designs to documents. While recent research has explored ways to incorporate user constraints to guide the layout generation, these constraints often require complex specifications which reduce usability. We introduce an innovative a

T. L. Killestein, M. Pursiainen, R. Kotak, P. Charalampopoulos

Pre-explosion mass loss in supernova (SN) progenitors is a crucial unknown factor in stellar evolution, yet has been illuminated recently by the diverse zoo of interacting transients. We present SN2024cld, a transitional core-collapse SN at a distance of 39 Mpc, straddling the boundary between SN II and SN IIn, showing persistent interaction with circumstell

Dayuan Fu, Yunze Wu, Xiaojie Cai, Lyumanshan Ye

Large Language Model (LLM) agents have recently shown strong potential in domains such as automated coding, deep research, and graphical user interface manipulation. However, training them to succeed on long-horizon, domain-specialized tasks remains challenging. Current methods primarily fall into two categories. The first relies on dense human annotations t

Boyi Wei, Zora Che, Nathaniel Li, Udari Madhushani Sehwag

Open-weight bio-foundation models present a dual-use dilemma. While holding great promise for accelerating scientific research and drug development, they could also enable bad actors to develop more deadly bioweapons. To mitigate the risk posed by these models, current approaches focus on filtering biohazardous data during pre-training. However, the effectiv

Sebastian Benthall, Andrew Clark

While AI agents have long been discussed and studied in computer science, today's Agentic AI systems are something new. We consider other definitions of Agentic AI and propose a new realist definition. Agentic AI is a software delivery mechanism, comparable to software as a service (SaaS), which puts an application to work autonomously in a complex enterpris

Nikos Frantzikinakis, Borys Kuca

We develop a framework for the study of the limiting behavior of multiple ergodic averages with commuting transformations when all iterates are given by the same sparse sequence; this enables us to partially resolve several longstanding problems. First, we address a special case of the joint intersectivity question of Bergelson, Leibman, and Lesigne by givin

A. K. de Almeida, Daniele Mortari

Poincar\'e maps play a fundamental role in nonlinear dynamics and chaos theory, offering a means to reduce the dimensionality of continuous dynamical systems by tracking the intersections of trajectories with lower-dimensional section surfaces. Traditional approaches typically rely on numerical integration and interpolation to detect these crossings, which c

Andrew Leal

Employers are concerned not only with a prospective worker's ability, but also their propensity to avoid shirking. This paper proposes a new experimental framework to study how Principals trade-off measures of ability and prosocial behavior when ranking Agents for independent jobs. Subjects participate in a simulated, incentivized job market. In an initial s

Ethan Abraham

Marcus theory famously predicts that electron-transfer rates decrease once the thermodynamic driving force exceeds the reorganization energy. Yet many systems instead exhibit Rehm-Weller kinetics, in which the rate saturates rather than decreases. Here we show that these apparently contradictory phenomenologies emerge as opposite physical limits of the same

Todd Elder, Matt Landremann, Christoper B. Smiet, Robert Davies

In stellarators, achieving effective divertor configurations is challenging due to the three-dimensional nature of the magnetic fields, which often leads to chaotic field lines and fuzzy separatrices. This work presents a novel approach to directly optimize modular stellarator coils for a sharp X-point divertor topology akin to the Large Helical Device's (LH

Qiusi Zhan, Hyeonjeong Ha, Rui Yang, Sirui Xu

Recent advances in Vision-Language Models (VLMs) have propelled embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. However, such vision-driven embodied agents open a new attack surface: visual backdoor attacks, where the agent behaves normally until a visual trigger appears in the scene, then pers

W. Muneo Okiishi, Caleb Becker, Jesse Belden, Aren Hellum

The water entry of solid and liquid bodies has been studied for over a century, and various researchers have classified the different behaviors that occur when the gas-filled cavity collapses. Although four main cavity collapse regimes have been described and classified for the water entry of small, dense, hydrophobic spheres, only some of these regimes have

Yanhong Li, Tianyang Xu, Kenan Tang, Karen Livescu

Knowledge-intensive question answering is central to large language models (LLMs) and is typically assessed using static benchmarks derived from sources like Wikipedia and textbooks. However, these benchmarks fail to capture evolving knowledge in a dynamic world, and centralized curation struggles to keep pace with rapid LLM advancements. To address these dr

Riccardo Cavuoto, Stefania Scala, Arsenio Cutolo, Giuseppe Mensitieri

This work presents a model for characterizing porous, deformable media embedded with magnetorheological fluids (MRFs). These active fluids exhibit tunable mechanical and rheological properties that can be controlled through the application of a magnetic field, which induces a phase transition from a liquid to a solid-like state. This transition profoundly af

Khaled Kassem, Areeba Fatima, Patrick Cornwall, Muralidhar Madabhushi Balaji

Imaging through dynamic scattering media, such as biological tissue, presents a fundamental challenge due to light scattering and the formation of speckle patterns. These patterns not only degrade image quality but also decorrelate rapidly, limiting the effectiveness of conventional approaches, such as those based on transmission matrix measurements. Here, w

Mariska Hoogkamer, Nathan Rutherford, Daniela Huppenkothen, Benjamin Ricketts

NICER has enabled mass-radius inferences for pulsars using pulse profile modeling (PPM), providing constraints on the equation of state (EOS) of cold, dense matter. To date, PPM and EOS inference have been carried out as two separate steps, with the former using EOS-agnostic priors. This approach has several drawbacks. Ideally, one would perform a fully hier

Heng Ping, Arijit Bhattacharjee, Peiyu Zhang, Shixuan Li

Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show promise for Hardware Description Language (HDL) generation, but face challenges due to limited parametric knowledge and domain-specific constraints. While prompt engineering and fine-tuning have limitations in knowle

Riccardo Zinelli, Zijia Wu, Christian A. Nijhuis, Qianqi Lin

Self-oscillators are intriguing due to their ability to sustain periodic motion without periodic stimulus. They remain rare as achieving such behavior requires a balance of energy input, dissipation and non-linear feedback mechanism. Here, we report a molecular-scale optoelectronic self-oscillatory system based on electrically excited plasmons. This system g

Liyao Lyu, Huan Lei

We propose a stochastic branching particle-based method for solving nonlinear non-conservative advection-diffusion-reaction equations. The method splits the evolution into an advection-diffusion step, based on a linearized Kolmogorov forward equation and approximated by stochastic particle transport, and a reaction step implemented through a branching birth-

Pedro Silva Gomes, Carlos Baquero

Set reconciliation protocols typically make two critical assumptions: they are designed for fixed-sized elements and they are optimized for when the difference cardinality, d, is very small. When adapting to variable-sized elements, the current practice is to synchronize fixed-size element digests. However, when the number of differences is considerable, suc

Yaoju Tarn, Yidi Liu, Florian Theuss, Jiarui Li

The remarkable discovery of high temperature superconductivity in bulk bilayer nickelates under high pressure has prompted the conjecture that epitaxial compressive strain might mimic essential aspects of hydrostatic pressure. The successful realization of superconductivity in films on SrLaAlO4 (001) (SLAO) supports this correspondence, yet it remains unclea

Timo Jakobs, Marco Garofalo, Tobias Hartung, Karl Jansen

A representation of Lattice Gauge Theories (LGT) suitable for simulations with tensor network state methods or with quantum computers requires a truncation of the Hilbert space to a finite dimensional approximation. In particular for U(1) LGTs, several such truncation schemes are known, which we compare with each other using tensor network states. We show th

Zhuohan Wang, Ziwei Zhu, Ziniu Li, Congliang Chen

Formulating optimization problems for industrial applications demands significant manual effort and domain expertise. While Large Language Models (LLMs) show promise in automating this process, evaluating their performance remains difficult due to the absence of robust metrics. Existing solver-based approaches often face inconsistency, infeasibility issues,

Elena Erben, Ivan Saraev, Weida Liao, Fan Nan

High-precision micromanipulation techniques, including optical tweezers and hydrodynamic trapping, have garnered wide-spread interest. Recent advances in optofluidic multiplexed assembly and microrobotics demonstrate significant progress, particularly by iteratively applying laser-induced, localized flow fields to manipulate microparticles in viscous solutio

Chaithanya Purushottam Bhat, Joy Mukherjee, Antara Banerjee, Debashis Bandyopadhyay

This study investigates into the adsorption sensing capabilities of single-walled (5,5) boron nitride nanotubes (BNNTs) towards environmental pollutant gas molecules, including CH2, SO2, NH3, H2Se, CO2 and CS2. Employing a linear combination of atomic orbital density functional theory (DFT) and spin-polarized generalized gradient approximation (GGA), the inv

Yuhong Liu, Beichen Zhang, Yuhang Zang, Yuhang Cao

Spatial understanding remains a weakness of Large Vision-Language Models (LVLMs). Existing supervised fine-tuning (SFT) and recent reinforcement learning with verifiable rewards (RLVR) pipelines depend on costly supervision, specialized tools, or constrained environments that limit scale. We introduce Spatial-SSRL, a self-supervised RL paradigm that derives

Raagini Patki, Nicholas Battaglia, Rachel Bean

We present a new method for measuring the $E_G$ statistic that combines two CMB secondaries -- the kinematic Sunyaev-Zeldovich (kSZ) effect and CMB lensing -- for the first time to probe gravity on linear scales. The $E_G$ statistic is a discriminating tool for modified gravity theories, which leave imprints in lensing observables and peculiar velocities. Ex

Alex Alarcon, Andrew P. Hearin, Matthew R. Becker, Gillian Beltz-Mohrmann

We present DiffstarPop, a differentiable forward model of cosmological populations of galaxy star formation histories (SFH). In the model, individual galaxy SFH is parametrized by Diffstar, which has parameters $\theta_{\rm SFH}$ that have a direct interpretation in terms of galaxy formation physics, such as star formation efficiency and quenching. DiffstarP

Ruiwen Dong, Doron Shafrir

We show that the Skolem Problem is decidable in finitely generated commutative rings of positive characteristic. More precisely, we show that there exists an algorithm which, given a finite presentation of a (unitary) commutative ring $\mathcal{R} = \mathbb{Z}_{/T}[X_1, \ldots, X_n]/I$ of characteristic $T > 0$, and a linear recurrence sequence $(\gamma_n)_{

Simone Bonechi, Paolo Andreini, Barbara Toniella Corradini

The rapid rise of generative models has yielded synthetic images of striking realism, blurring the line between real and fake content. As novel models proliferate, detectors must go beyond mere fake identification to robustly generalise across unseen generators and synthetic content. We introduce FRIDA (Fake image Recognition and source Identification via Di

Gabriel Soares, Nicolò Crescini, Giovanni Carugno, Giuseppe Ruoso

We present here an optimisation and demonstration of a wide band instrument capable of measuring localised and directionally alternated magnetic fields below pT in the very high frequency (VHF) range. We take advantage of the magnon-photon hybridization between a yttrium iron garnet (YIG) sphere and a copper resonant cavity to employ a resonant heterodyne de

Juan David Marmolejo Lozano, Nikola Popovic, Ramon Grima

Simplified stochastic models are widely used in the study of frequency-resolved noise propagation in biochemical reaction networks, a common measure being the coherence between random fluctuations in molecule number trajectories. Such models have also found widespread application in the quantification of how information is transmitted in reaction networks vi

Samarup Bhattacharya, Anubhab Bhattacharya, Abir Chakraborty

Neural networks have changed the way machines interpret the world. At their core, they learn by following gradients, adjusting their parameters step by step until they identify the most discriminant patterns in the data. This process gives them their strength, yet it also opens the door to a hidden flaw. The very gradients that help a model learn can also be

Yunze Wu, Dayuan Fu, Weiye Si, Zhen Huang

AI agents could accelerate scientific discovery by automating hypothesis formation, experiment design, coding, execution, and analysis, yet existing benchmarks probe narrow skills in simplified settings. To address this gap, we introduce InnovatorBench, a benchmark-platform pair for realistic, end-to-end assessment of agents performing Large Language Model (

Yongzhao Yao, Yoshiyuki Tsusaka, Yukari Ishikawa

Dislocations in a thick ammonothermal GaN substrate were investigated using synchrotron-radiation X-ray topography (SR-XRT) under six-beam diffraction conditions. The high brilliance of the synchrotron source enabled the observation of the super-Borrmann effect, which markedly enhanced the anomalous transmission of X-rays through the 350~$\mu$m-thick crystal

Karin Olthof, Theo Ruers, Tiziano Natali, Lisanne Venix

Purpose: This proof-of-concept study evaluates feasibility and accuracy of an ultrasound-based navigation system for open liver surgery. Unlike most conventional systems that rely on registration to preoperative imaging, the proposed system provides navigation-guided resection using 3D models generated from intraoperative ultrasound. Methods: A pilot study w

Ruibo Shang, Matthew Thompson, Matthew D. Carson, Eric J. Seibel

Near-infrared fluorescence (NIRF) can deliver high-contrast, video-rate, non-contact imaging of tumor-targeted contrast agents with the potential to guide surgeries excising solid tumors. However, it has been met with skepticism for wide-margin excision due to sensitivity and resolution limitations at depths larger than ~5 mm in tissue. To address this limit

L. De Lillo, Z. Duan, M. Frau, F. Galvagno

We present a detailed analysis of integrated correlators for an $\mathcal{N}=2$ superconformal field theory on a squashed sphere with SU$(N)$ gauge group and fundamental/anti-symmetric matter. Employing the matrix model arising from supersymmetric localisation, we compute derivatives of the partition function $\mathcal{Z}$ with respect to the fundamental mas

Derik T. Boonstra, Rakheon Kim, Dean M. Young

Sufficient dimension reduction (SDR) methods aim to identify a dimension reduction subspace (DRS) that preserves all the information about the conditional distribution of a response given its predictor. Traditional SDR methods determine the DRS by solving a method-specific generalized eigenvalue problem and selecting the eigenvectors corresponding to the lar

M. Abello, J. Drevon, A. Meilland, A. Domiciano de Souza

3 Puppis is the brightest known B[e] star. Recent work classifies this A-type object as a supergiant, yet the impact of its binarity on the circumstellar environment (CE) remains hard to characterize. To resolve its dusty region at 5-10 mas, we obtained mid-IR interferometric observations with VLTI/MATISSE over 3-12 {\mu}m. Because the (u,v) coverage support

Leonidas Daskalakis

We establish a pointwise convergence result for ergodic averages modeled along orbits of the form $(n\lfloor n\sqrt{k}\rfloor)_{n\in\mathbb{N}}$, where $k$ is an arbitrary positive rational number with $\sqrt{k}\not\in\mathbb{Q}$. Namely, we prove that for every such $k$, every measure-preserving system $(X,\mathcal{B},\mu,T)$ and every $f\in L^{\infty}_{\mu

Yu Cui, Yujian Zhang, Lina Tao, Yang Li

Achieving human-like dexterous manipulation remains a major challenge for general-purpose robots. While Vision-Language-Action (VLA) models show potential in learning skills from demonstrations, their scalability is limited by scarce high-quality training data. Existing data collection methods face inherent constraints: manual teleoperation overloads human o

Manan Bhatia

We consider Brownian last passage percolation evolving dynamically via a discrete resampling procedure. Using $\Gamma_{(0,0)}^{(n,n),r}$ to denote a geodesic from $(0,0)$ to $(n,n)$ at time $r$, we prove that the expected total number of coarse-grained changes (or "switches") accumulated by $\Gamma_{(0,0)}^{(n,n),r}$ away from its endpoints during a time int

Anish Ghoshal, Paweł Kozów, Marek Olechowski, Stefan Pokorski

A systematic approach is presented for using CMB observables and reheating temperature for discriminating between various models of inflation and certain freeze-in dark matter scenarios. It is applied to several classes of $\alpha$-attractor models as an illustrative example. In the first step, all independent parameters of the inflationary potential are exp

Khursid Alam, Koushik Dutta, Ahamadullah Khan

We analyze GW production during preheating for an $\alpha$-attractor potential terminating in the positive-curvature regime, with energy transfer via $\phi\chi^{2}$. Linear Floquet analysis and nonlinear simulations show that $\phi$ fluctuations grow by parametric resonance, while $\chi$ undergoes tachyonic bursts. The GW spectrum features two peaks: a domin

Jiawen Zhang, Yongjun Zhang, Yuxin Chen, Zhaoyang Shan

Single crystals of $R$Rh$_6$Ge$_4$ ($R$ = Pr, Nd, Sm, Gd - Er) were synthesized using a Bi flux and their physical properties were characterized by magnetization, resistivity, and specific heat measurements. These compounds crystallize in the noncentrosymmetric LiCo$_6$P$_4$-type structure (space group $P\bar{6}m2$), where rare-earth atoms form a triangular

Ilyass Moummad, Kawtar Zaher, Hervé Goëau, Alexis Joly

Efficient large-scale retrieval requires representations that are both compact and discriminative. Foundation models provide powerful visual and multimodal embeddings, but nearest neighbor search in these high-dimensional spaces is computationally expensive. Hashing offers an efficient alternative by enabling fast Hamming distance search with binary codes, y

Giulia de Meijere, Hugo Martin

In recent years, numerous advances have been made in understanding how epidemic dynamics is affected by changes in individual behaviours. We propose an SIS-based compartmental model to tackle the simultaneous and coupled evolution of an outbreak and of the adoption by individuals of the isolation measure. The compliance with self-isolation is described with

Chandrish Ambati, Trung Diep

The rapid growth of large language models (LLMs) has driven the need for high-performance, scalable GPU hardware capable of efficiently serving models with hundreds of billions of parameters. While NVIDIA GPUs have traditionally dominated LLM deployments due to their mature CUDA software stack and state-of the-art accelerators, AMD's latest MI300X GPUs offer

Richard Karl, Meissa Diouf, Aleksandr Shlykov, Mikolaj Roguski

Modern experiments with cold molecular ions have reached a high degree of complexity requiring frequent sample preparation, state initialization and protocol execution while demanding precise control over multiple devices and laser sources. To maintain a high experimental duty cycle and robust measurement conditions, automation becomes essential. We present

Michael Doerfler, Wenhao Mao, Lin Ge, Yuzi Zhang

In this paper, we expand upon and refine a monitoring strategy proposed for surveillance of diseases in finite, closed populations. This monitoring strategy consists of augmenting an arbitrarily non-representative data stream (such as a voluntary flu testing program) with a random sample (referred to as an "anchor stream"). This design allows for the use of

Aleksandr Chatrchyan, Florian Niedermann, Phoebe Richman-Taylor

False vacuum decay typically proceeds via the nucleation of spherical bubbles of true vacuum, described by $O(4)$ symmetric field configurations in Euclidean time. In this work, we investigate how the presence of cosmic strings can catalyze the decay process. To this end, we consider a complex scalar field charged under a global or local $U(1)$ symmetry. Ass

Ravi Trivedi, Chaithanya Purushottam Bhat, Shakti S. Ray, Debashis Bandyopadhyay

We present a first--principles density functional theory (DFT) study of transition metal (TM = Ti, Cr, Mn, Fe, Co, Ni) functionalized two--dimensional polyaramid (2DPA) to explore their structural, electronic, and magnetic properties. Mechanical parameters, such as bulk modulus, shear modulus, Young's modulus, Poisson's ratio, and Pugh ratio, together with p

T. Matsuura, S. Saijo

The built-in potential of p-n junctions plays a pivotal role in charge separation, a fundamental process underlying the photovoltaic effect.However, conventional classical theories of photovoltaic behavior in p-n junctions often neglect its quantitative influence. In this work, we revisit the classical framework and derive an improved analytical expression f

Leatile Marata, Mariona Jaramillo-Civill, Tales Imbiriba, Petri Välisuo

The global navigation satellite system (GNSS) continues to evolve in order to meet the demands of emerging applications such as autonomous driving and smart environmental monitoring. However, these advancements are accompanied by a rise in interference threats, which can significantly compromise the reliability and safety of GNSS. Such interference problems

Senne Van Loon, Maria Rugenstein

Marine low clouds play a crucial role in Earth's radiation budget. These clouds efficiently reflect sunlight and drive the magnitude and sign of the global cloud feedback. Nevertheless, the evolution of shallow cloud decks over the last decades is not well understood. A dominant control of this low cloud cover is the lower tropospheric stability, quantified

Camilo Barreto, Melissa Beerbower, Jennifer Elder, Pamela E. Harris

We study lucky cars in subsets of parking functions, called Fubini rankings and unit Fubini rankings. A Fubini ranking is a sequence of nonnegative integers that encodes a valid ranking of competitors, where ties are allowed. A car (or competitor) is said to be lucky if it is the first instance of that rank appearing in the sequence. We present combinatorial

Aleksandra Kowalska

Green showed that, conditional on GRH, a subset $A \subseteq [N]$ with $\mid A \mid \gg_{\epsilon} N^{\frac{11}{12}+\epsilon}$ must contain two elements whose difference is $p-1$ for $p$ a prime. We prove an analogous unconditional result for $\mathbf{F}_2[x]$, improving the exponent to $\frac{7}{8}+\epsilon$.

Likitha Tadakala, Muskan Saraf, Sajjad Rezvani Boroujeni, Hossein Abedi

Effective business intelligence (BI) dashboards evolve through iterative refinement rather than single-pass design. Addressing the lack of structured improvement frameworks in BI practice, this study documents the four-stage evolution of a Power BI dashboard analyzing profitability decline in a fictional retail firm, Global Superstore. Using a dataset of \$1

Zhuoning Guo, Mingxin Li, Yanzhao Zhang, Dingkun Long

The prevailing video retrieval paradigm is structurally misaligned, as narrow benchmarks incentivize correspondingly limited data and single-task training. Therefore, universal capability is suppressed due to the absence of a diagnostic evaluation that defines and demands multi-dimensional generalization. To break this cycle, we introduce a framework built o

Andrew Akerson, Aakila Rajan, Daniel Casem, Kaushik Bhattacharya

We continue the development of a method to accurately and efficiently identify the constitutive behavior of complex materials through full-field observations that we started in Akerson, Rajan and Bhattacharya (2024). We formulate the problem of inferring constitutive relations from experiments as an indirect inverse problem that is constrained by the balance

Qi Luo, Xiaonan Li, Yuxin Wang, Tingshuo Fan

Large Language Models (LLMs) excel at reasoning and generation but are inherently limited by static pretraining data, resulting in factual inaccuracies and weak adaptability to new information. Retrieval-Augmented Generation (RAG) addresses this issue by grounding LLMs in external knowledge; However, the effectiveness of RAG critically depends on whether the

Einan Gardi, Zehao Zhu

We determine the complete three-loop QCD soft anomalous dimension for multileg amplitudes involving a single massive coloured particle and any number of massless ones. This is achieved by applying a novel strategy based on a lightcone expansion of correlators of semi-infinite Wilson lines using the method of regions. The resulting region integrals depend exc

Yulong Hui, Chao Chen, Zhihang Fu, Yihao Liu

Retrieval-Augmented Generation (RAG) has significantly enhanced LLMs by incorporating external information. However, prevailing agentic RAG approaches are constrained by a critical limitation: they treat the retrieval process as a black-box querying operation. This confines agents' actions to query issuing, hindering its ability to tackle complex information