October 2025 arXiv papers — page 128

Rajendra Upadhyay, Al Nahian Bin Emran, Rajendra Paudyal, Lisa Donnan

Uncooperative unmanned aerial vehicles (UAVs) pose emerging threats to critical infrastructure and border protection by operating as rogue user equipment (UE) within cellular networks, consuming resources, creating interference, and potentially violating restricted airspaces. This paper presents minimal features of the operating space, yet an end-to-end simu

Gaoyuan Liu, Joris de Winter, Yuri Durodie, Denis Steckelmacher

Task and motion planning (TAMP) for robotics manipulation necessitates long-horizon reasoning involving versatile actions and skills. While deterministic actions can be crafted by sampling or optimizing with certain constraints, planning actions with uncertainty, i.e., probabilistic actions, remains a challenge for TAMP. On the contrary, Reinforcement Learni

Nan Li, Jiming Ren, Haris Miller, Samuel Coogan

Multi-Agent Task Assignment and Planning (MATP) has attracted growing attention but remains challenging in terms of scalability, spatial reasoning, and adaptability in obstacle-rich environments. To address these challenges, we propose OATH - Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming - which advances MATP by introdu

Călin A. Georgescu, Matthias Möller

As the scope of Computational Fluid Dynamics (CFD) grows to encompass ever larger problem scales, so does the interest in whether quantum computing can provide an advantage. In recent years, Quantum Lattice Gas Automata (QLGA) and Quantum Lattice Boltzmann Methods (QLBM) have emerged as promising candidates for quantum-native implementations of CFD solvers.

Arshpreet Singh Maan, Francisco-Garcia Herrero, Alexandru Paler, Valentin Savin

We introduce a decoding framework for correlated errors in quantum LDPC codes under circuit-level noise. The core of our approach is a graph augmentation and rewiring for interference (GARI) method, which modifies the correlated detector error model by eliminating 4-cycles involving Y-type errors, while preserving the equivalence of the decoding problem. We

Eugenia Hyung, Emma Levy, Loralei Cook, Stein B. Jacobsen

The Australasian strewn field covers more than 15% of Earth's surface, consisting of tektites and microtektites. Australasian tektites from Southeast Asia and Australia, as well as microtektites recovered from deep sea sediments and Antarctica, are established to be derived from upper continental crust sediments. An expedition to retrieve remnants of bolide

Arghyadeep Basu, Benedetta Ciardi, Enrico Garaldi

We investigate the hyperfine transition of $^{3}\mathrm{He}^{+}$ as a promising probe of the IGM during the final stages of helium reionization. Utilising the most recent helium reionization simulation, we generate three-dimensional maps of the 3.5cm ($8.67$ GHz) differential brightness temperature and analyze its evolution. Our results show that the volume-

Demetrious T. Kutzke, Ying-Kun Wu, Elizabeth Terveen, Junaed Sattar

Traditional human activity recognition uses either direct image analysis or data from wearable inertial measurement units (IMUs), but can be ineffective in challenging underwater environments. We introduce a novel hybrid approach that bridges this gap to monitor scuba diver safety. Our method leverages computer vision to generate high-fidelity motion data, e

Yixin Fang, Man Jin

The ICH E9(R1) guideline presents a framework of estimand for clinical trials, proposes five strategies for handling intercurrent events (ICEs), and provides a comprehensive discussion and many real-life clinical examples for quantitative outcomes and categorical outcomes. However, in ICH E9(R1) the discussion is lacking for time-to-event (TTE) outcomes. In

Rui Yang, Jiaming Hu, Jian-Qing Zheng, Yue-Zhen Lu

Artificial intelligence (AI) holds significant promise for enhancing intraoperative perception and decision-making in telesurgery, where physical separation impairs sensory feedback and control. Despite advances in medical AI and surgical robotics, conventional electronic AI architectures remain fundamentally constrained by the compounded latency from serial

Rahma Heni, Andrii Mironchenko, Fabian Wirth, Hanen Damak

We prove that (local) input-to-state stability ((L)ISS) and integral input-to-state stability (iISS) of time-varying infinite-dimensional systems in abstract spaces follows from the existence of a {corresponding} Lyapunov function. In particular, input-to-state stability of linear time-varying control systems in Hilbert spaces with bounded input operators is

Xavier Buffat

The interaction of the two beams in a collider leads to a variety of effects that may limit the performance of the machine. This lecture introduces the basic aspects necessary to understand the design of modern colliders.

David Kepplinger, Anand N. Vidyashankar

Reliable inference from complex survey samples can be derailed by outliers and high-leverage observations induced by unequal inclusion probabilities and calibration. We develop a minimum Hellinger distance estimator (MHDE) for parametric superpopulation models under complex designs, including Poisson PPS and fixed-size SRS/PPS without replacement, with possi

Fatih Ilhan, Selim Furkan Tekin, Tiansheng Huang, Gaowen Liu

Fine-tuning pre-trained large language models (LLMs) has become a common practice for personalized natural language understanding (NLU) applications on downstream tasks and domain-specific datasets. However, there are two main challenges: (i) limited and/or heterogeneous data for fine-tuning due to proprietary data confidentiality or privacy requirements, an

Shriyash Upadhyay, Chaithanya Bandi, Narmeen Oozeer, Philip Quirke

Recent proposals for regulating frontier AI models have sparked concerns about the cost of safety regulation, and most such regulations have been shelved due to the safety-innovation tradeoff. This paper argues for an alternative regulatory approach that ensures AI safety while actively promoting innovation: mandating that large AI laboratories release small

Xixing Xue, Dong Shen, Steven X. Ding, Dong Zhao

Anomaly detection plays a vital role in the security and safety of cyber-physical control systems, and accurately distinguishing between different anomaly types is crucial for system recovery and mitigation. This study proposes a dual detection framework for anomaly detection and discrimination. By leveraging the dynamic characteristics of control loops and

Avihai Naaman, Ron Shapira Weber, Oren Freifeld

Synchronizing videos captured simultaneously from multiple cameras in the same scene is often easy and typically requires only simple time shifts. However, synchronizing videos from different scenes or, more recently, generative AI videos, poses a far more complex challenge due to diverse subjects, backgrounds, and nonlinear temporal misalignment. We propose

Michael Mandulak, Sayan Ghosh, S M Ferdous, Mahantesh Halappanavar

Large-scale network sensing plays a vital role in network traffic analysis and characterization. As network packet data grows increasingly large, parallel methods have become mainstream for network analytics. While effective, GPU-based implementations still face start-up challenges in host-device memory management and porting complex workloads on devices, am

Guangyi Chen, Yunlong Deng, Peiyuan Zhu, Yan Li

Causal Representation Learning (CRL) aims to uncover the data-generating process and identify the underlying causal variables and relations, whose evaluation remains inherently challenging due to the requirement of known ground-truth causal variables and causal structure. Existing evaluations often rely on either simplistic synthetic datasets or downstream p

Shashi B. Mishra, Zhe Liu, Sabyasachi Tiwari, Feliciano Giustino

We present a systematic first-principles study of phonon-limited transport in the TaAs family of Weyl semimetals using the ab initio Boltzmann transport equation. The calculated electrical conductivities show excellent agreement with experimental data for high-quality samples, confirming that transport in these systems is predominantly limited by phonon scat

David Alonso-Gutiérrez, Silouanos Brazitikos, Giorgos Chasapis

We revisit an ingenious argument of K. Ball to provide sharp estimates for the volume of sections of a convex body in John's position. Our technique combines the geometric Brascamp-Lieb inequality with a generalised Parseval-type identity. This lets us complement some earlier results of the first two named authors, as well as generalise the classical estimat

Apekshya Ghimire, Chandralekha Singh

Many early career educators, such as teaching assistants (TAs) in college courses, as well as pre-college educators, need help both with content and pedagogical knowledge to effectively help their students learn. One pedagogical approach that has been found effective in prior studies is collaboration with peers. Collaborative learning not only has the potent

Qinghua Ma, Reetam Sen Biswas, Denis Osipov, Guannan Qu

Existing or planned power grids need to evaluate survivability under extreme events, like a number of peak load overloading conditions, which could possibly cause system collapses (i.e. blackouts). For realistic extreme events that are correlated or share similar patterns, it is reasonable to expect that the dominant vulnerability or failure sources behind t

Santanu Acharjee, Ripunjoy Choudhury

Due to the exponential growth of big data in this digital era, an advanced method for effective information retrieval becomes essential. The basic objective of this paper is to propose a topology-based method for cognitive information retrieval (CIR) in big data environments. By using concepts such as cognitive similarity distances, metric spaces, retrieval

Younghoon Kim, Zachary F. Fisher, Vladas Pipiras

The multiple-subject vector autoregression (multi-VAR) model captures heterogeneous network Granger causality across subjects by decomposing individual sparse VAR transition matrices into commonly shared and subject-unique paths. The model has been applied to characterize hidden shared and unique paths among subjects and has demonstrated performance compared

Shimiao Li, Guannan Qu, Bryan Hooi, Vyas Sekar

Power grids increasingly need real-time situational awareness under the ever-evolving cyberthreat landscape. Advances in snapshot-based system identification approaches have enabled accurately estimating states and topology from a snapshot of measurement data, under random bad data and topology errors. However, modern interactive, targeted false data can sta

Apekshya Ghimire, Chandralekha Singh

Collaboration with peers both inside and outside the classroom can be an invaluable tool for helping students learn physics. We investigated the impact of peer collaboration on learning physics by examining the characteristics of women and men who typically worked alone versus those who typically collaborated with peers in their algebra-based introductory ph

Valeriy I. Kondratyev, Vanik Shahnazaryan, Mikhail Tyugaev, Tatyana V. Ivanova

Layered van der Waals materials offer novel opportunities for on-chip waveguiding and development of integrated photonic circuits. In the strong light-matter coupling regime, their nonlinear response can be significantly enhanced, which is crucial for developing active photonic devices. However, probing the nonlinearity of waveguide modes in subwavelength-th

George Flint, Kaustubh Kislay

Language is, as commonly theorized, largely arbitrary. Yet, systematic relationships between phonetics and semantics have been observed in many specific cases. To what degree could those systematic relationships manifest themselves in large scale, quantitative investigations--both in previously identified and unidentified phenomena? This work undertakes a di

Paul Mansanarez

In the pathbreaking article \cite{LED16}, an integral representation of the derivatives of entropy along the heat flow of a probability measure was established under suitable moment conditions. These integral representations have found significant applications in diverse domains - notably in information theory (e.g., entropy power inequalities, monotonicity

Junsong Peng, Xiuqi Wu, Huiyu Kang, Anran Zhou

We review recent advances in the study of nonlinear dynamics in mode-locked fibre lasers operating in the breathing (pulsating) soliton regime. Leveraging advanced diagnostics and control strategies -- including genetic algorithms -- we uncover a rich spectrum of dynamical behaviours, including frequency-locked breathers, fractal Farey hierarchies, Arnold to

Argyrios Loules, Nektarios Vlahakis

Ultra-relativistic plasma outflows are intrinsically connected with gamma-ray bursts. Over the years, a large number of analytical and numerical works has been devoted to understanding the intricacies of their complex dynamics, with most of these past studies performed in the ideal MHD regime. We propose a self-similar formalism, based on the expansion of th

Qiushi Wu, Yue Xiao, Dhilung Kirat, Kevin Eykholt

Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project maintainers, who work with the reporter to fix it and eventually close the issue. However, across the program, there are often similar code segments, which may also contain the bug, but were missed during discovery.

Rajesh Mangannavar, Prasad Tadepalli

We introduce an uncertainty-aware graph representation framework for learning to guide planning in Partially Observable Markov Decision Processes (POMDPs). Unlike existing approaches that require domain or problem size specific neural architectures, GammaZero leverages a unified graph-based belief representation that enables generalization across problem siz

Chenyang Li

A fully discrete Crank--Nicolson Leap--Frog (CNLF) scheme is proposed and analyzed for the unsteady bioconvection flow problem with concentration-dependent viscosity. Spatial discretization is handled via the Galerkin finite element method (FEM), while temporal discretization employs the CNLF method for the linear terms and a semi-implicit approach for the n

Iryna Dubovets'ka, Mykhailo Moklyachuk

The problem of optimal linear estimation of linear functionals depending on the unknown values of a periodically correlated stochastic process from observations of the process with additive noise is considered. Formulas for calculating the mean square error and the spectral characteristic of the optimal linear estimate of the functionals are proposed in the

Xiaoqian Shen, Wenxuan Zhang, Jun Chen, Mohamed Elhoseiny

Understanding and reasoning over long videos pose significant challenges for large video language models (LVLMs) due to the difficulty in processing intensive video tokens beyond context window and retaining long-term sequential information. Retrieval-Augmented Generation (RAG) has demonstrated effectiveness in processing long context for Large Language Mode

Benedikt Schroer, Damiano Caprioli, Pasquale Blasi

Phenomenological studies of cosmic-ray self-confinement often hinge on the linear theory for the growth rate of the streaming instability and for the damping rate of the generated magnetic modes. Largely different expressions exist, especially for the rate of nonlinear Landau damping, which is often assumed to be the most important damping mechanism in warm

César Guerra-Solano, Zhuochun Li, Xiang Lorraine Li

Large language models (LLMs) can exhibit biases in reasoning capabilities due to linguistic modality, performing better on tasks in one language versus another, even with similar content. Most previous works evaluate this through reasoning tasks where reliance on strategies or knowledge can ensure success, such as in commonsense or math tasks. However, abstr

Denis Ovichinnikov, Hemant Kavadia, Satya Keerti Chand Kudupudi, Ilya Rempel

Cornami Mx2 accelerates of Fully Homomorphic Encryption (FHE) applications, enabled by breakthrough work [1], which are otherwise compute limited. Our processor architecture is based on the systolic array of cores with in-memory compute capability and a network on chip (NoC) processor architecture called the "FracTLcore compute fabric processor" (Mx2). Here,

Tabia Tanzin Prama, Christopher M. Danforth, Peter Sheridan Dodds

News media serves a crucial role in disseminating information and shaping public perception, especially during periods of political unrest. Using over 50,0000 YouTube comments on news coverage from July 16 to August 6, 2024, we investigate the emotional dynamics and evolving discourse of public perception during the July 2024 Student-People's Uprising in Ban

Mohammad Madadi, Pu Zhang

The representation theory of tensor functions is a powerful mathematical tool for constitutive modeling of anisotropic materials. A major limitation of the traditional theory is that many point groups require fourth- or sixth-order structural tensors, which significantly impedes practical engineering applications. Recent advances have introduced a reformulat

Riccardo Zattra, Giacomo Baggio, Umberto Casti, Augusto Ferrante

Transformers, powered by the attention mechanism, are the backbone of most foundation models, yet they suffer from quadratic complexity and difficulties in dealing with long-range dependencies in the input sequence. Recent work has shown that state space models (SSMs) provide a promising alternative. In this paper, we introduce the COFFEE (COntext From FEEdb

Cy S. David, Daniel Lecoanet, Pascale Garaud

Magnetism is thought to play an important role in the evolution and dynamics of stars, though little is known about magnetic fields deep within stellar interiors. A promising avenue for probing these fields uses asteroseismic observations of global oscillations that result from the coupling of acoustic waves in the convective zone to internal gravity waves (

Thanh Son Phung, Douglas Thain

The rise of Generative AI introduces a new class of HPC workloads that integrates lightweight LLMs with traditional high-throughput applications to accelerate scientific discovery. The current design of HPC clusters is inadequate to support this new class however, either incurring long wait times on static batch queues or repeatedly paying expensive LLM star

Maksym Luz, Mykhailo Moklyachuk

This paper deals with the problem of optimal mean-square filtering of the linear functionals $A{\xi}=\int_{0}^{\infty}a(t)\xi(-t)dt$ and $A_T{\xi}=\int_{0}^Ta(t)\xi(-t)dt$ which depend on the unknown values of random process $\xi(t)$ with stationary $n$th increments from observations of process $\xi(t)+\eta(t)$ at points $t\leq0$, where $\eta(t)$ is a statio

M. Tanious, R. Le Gal, A. Faure, S. Maret

(abridged) Streamers are newly identified channels that transport mass from large, molecular-cloud scales down to small, protoplanetary-disk scales. To better understand their impact on planet formation, it is essential to study their physical and chemical properties. In this framework, we aim to characterize the longest streamer identified in carbon chain e

Rohan Shenoy

The purpose of this note is to prove the celebrated Discrete Renewal Theorem in a common special case. We use only very elementary methods from real analysis, rather than markov chain theory, complex analysis, or generating functions. Provided is an introduction to a 1d discrete renewal process via a board game example, our proof the discrete renewal theorem

Panagiotis Gianniotis

We prove that a three dimensional compact Ricci flow that encounters a Type I singularity has uniformly bounded diameter up to the singular time, thus giving an affirmative answer - for Type I singularities - to a conjecture of Perelman. To achieve this, we introduce a concept of a neck-region for a Ricci flow, analogous to the neck-regions introduced by Jia

Abdulrahman Alhaidari, Balaji Palanisamy, Prashant Krishnamurthy

Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that e

Adam Morris, Timothy Pelham, Edmund R. Hunt

This paper introduces a method for designing spatially intelligent robot swarm behaviors to localize concealed radio emitters. We use differential evolution to generate geometric patrol routes that localize unknown signals independently of emitter parameters, a key challenge in electromagnetic surveillance. Patrol shape and antenna type are shown to influenc

Opeoluwa Owoyele

This paper presents a data-driven approach, referred to as Quantized Skeletal Learning (QSL), for generating skeletal mechanisms. The approach has two key components: (1) a weight vector that can be used to eliminate relatively unimportant species and reactions, and (2) an end-to-end differentiable program whose loss-function gradients, with respect to the w

Andrei Enea

This paper presents a web-based JavaScript editor designed to help children aged 8-10 transition from block-based to text-based programming. The system introduces a simplified domain-specific language (DSL) focused on visual art, combining authentic JavaScript syntax with immediate, creative visual feedback. A four-week pilot study (N = 15) demonstrated sign

Shehenaz Hossain, Haithem Afli

Correct answers do not necessarily reflect cultural understanding. We introduce CRaFT, an explanation-based multilingual evaluation framework designed to assess how large language models (LLMs) reason across cultural contexts. Rather than scoring outputs solely based on accuracy, CRaFT evaluates model explanations using four interpretable metrics: Cultural F

Valentina Peralta Clarke, Hans de Ferrante, Francisco Perez Galarce, Joris van de Klundert

Kidney Exchange Programs (KEPs) promote access to living donor trans- plantation for patients suffering from end stage renal disease. The HLA compatibility between transplant recipients and donors plays an important role when solving the matching problems occuring in KEPs. Compatibility defines the feasible solution space and often occurs in a weighted form

Saman Hosseini, Lee W. Cohnstaedt, Matin Marjani, Caterina Scoglio

Many West Nile virus (WNV) forecasting frameworks incorporate entomological or avian surveillance data, which may be unavailable in some regions. We introduce a novel data-parsimonious probabilistic model to predict both the timing of outbreak onset and the seasonal severity of WNV spillover. Our approach combines a temperature-driven compartmental model of

Victor Buchstaber, Mikhail Kornev

In this work, we establish connections between the theory of algebraic $n$-valued monoids and groups and the theories of discriminants and projective duality. We show that the composition of projective duality followed by the M\"obius transformation $z\mapsto 1/z$ defines a shift operation $\mathbb{M}_n(\mathbb{C}P^1)\mapsto \mathbb{M}_{n-1}(\mathbb{C}P^1)$

Jie Hao, Xiaochuan Gong, Jie Xu, Zhengdao Wang

Geometry-aware optimization algorithms, such as Muon, have achieved remarkable success in training deep neural networks (DNNs). These methods leverage the underlying geometry of DNNs by selecting appropriate norms for different layers and updating parameters via norm-constrained linear minimization oracles (LMOs). However, even within a group of layers assoc

Jinwei Hu, Yi Dong, Shuang Ao, Zhuoyun Li

LLM-powered Multi-Agent Systems (LLM-MAS) unlock new potentials in distributed reasoning, collaboration, and task generalization but also introduce additional risks due to unguaranteed agreement, cascading uncertainty, and adversarial vulnerabilities. We argue that ensuring responsible behavior in such systems requires a paradigm shift: from local, superfici

Tanmay Khale

Suppose $K$ is an imaginary quadratic field, and let $N_K$ denote the field norm in the ring of integers $O_K$. Let $B(x_0,r) = \{x \in O_K: |N_K(x-x_0)| < r\}$. Let $G_K(X) = \max \{r > 0: \text{there exists } x_0 \in O_K \text{ such that } |N_K(x_0)| \leq X \text{ and } B(x_0,r) \text{ contains no primes} \}$. We show that $ G_{K}(X) \gg_K (\log X) \frac{\

Wei Zou, Yupei Liu, Yanting Wang, Ying Chen

LLM-integrated applications are vulnerable to prompt injection attacks, where an attacker contaminates the input to inject malicious instructions, causing the LLM to follow the attacker's intent instead of the original user's. Existing prompt injection detection methods often have sub-optimal performance and/or high computational overhead. In this work, we p

Mandar Khasnis, Geetanjali Phatak, Vinayak Sholapurkar

The Cauchy dual subnormality problem (for short, CDSP) asks whether the Cauchy dual of a $2$-isometry is subnormal. In this article, we prove that if $\mu$ is a sum of unit point mass measures at two non-antipodal points on the unit circle, then the Cauchy dual $M_z'$ of the multiplication operator $M_z$ on the Dirichlet-type space $D(\mu)$ is not subnormal.

Maksym Luz, Mikhail Moklyachuk

The problem of optimal estimation of linear functionals $A {\xi}=\int_{0}^{\infty} a(t)\xi(t)dt$ and $A_T{\xi}=\int_{0}^{T} a(t)\xi(t)dt$ depending on the unknown values of random process $\xi(t)$, $t\in R$, with stationary $n$th increments from observations of ttis process for $t<0$ is considered. Formulas for calculating mean square error and spectral char

Paul Mansanarez, Guillaume Poly, Yvik Swan

Consider $F$ an element of the $p$-th Wiener chaos $\WW_p$, and denote by $\prob_F$ its law. For a positive integer $m$, let $\boldsymbol{\gamma}_{F,m}$ be the Radon measure with density $x \mapsto \frac{e^{-x^2/2}}{\sqrt{2\pi}} \left(1 + \sum_{k=3}^{4m-1} \frac{\E[H_k(F)]}{k!}\, H_k(x)\right)$, where $H_k$ is the $k$-th Hermite polynomial. The main goal of

Miranda Carou Laiño, Veronika Chobanova, Miriam Lucio Martínez

The identification of anomalous events, not explained by the Standard Model of particle physics, and the possible discovery of exotic physical phenomena pose significant theoretical, experimental and computational challenges. The task will intensify at next-generation colliders, such as the High-Luminosity Large Hadron Collider (HL-LHC). Consequently, consid

Mingyang Jiang, Yueyuan Li, Jiaru Zhang, Songan Zhang

The growing demand for parking has increased the need for automated parking planning methods that can operate reliably in confined spaces. In restricted and complex environments, high-precision maneuvers are required to achieve a high success rate in planning, yet existing approaches often rely on explicit action modeling, which faces challenges when accurat

Xun Wu, Shaohan Huang, Wenhui Wang, Ting Song

In this paper, we present BitNet Distillation (BitDistill), a lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ternary weights {-1, 0, 1}) for specific downstream tasks, achieving strong task-specific performance with minimal computational cost. Specifically, BitDistill incorporates three key

Huifang Lyu, James Alvey, Noemi Anau Montel, Mauro Pieroni

Simulation-based inference (SBI) is emerging as a new statistical paradigm for addressing complex scientific inference problems. By leveraging the representational power of deep neural networks, SBI can extract the most informative simulation features for the parameters of interest. Sequential SBI methods extend this approach by iteratively steering the simu

Lukas Gienapp, Christopher Schröder, Stefan Schweter, Christopher Akiki

Large language model development relies on large-scale training corpora, yet most contain data of unclear licensing status, limiting the development of truly open models. This problem is exacerbated for non-English languages, where openly licensed text remains critically scarce. We introduce the German Commons, the largest collection of openly licensed Germa

Kelvin Szolnoky, Anders Blilie, Nita Mulliqi, Toyonori Tsuzuki

Background: Cribriform morphology in prostate cancer is a histological feature that indicates poor prognosis and contraindicates active surveillance. However, it remains underreported and subject to significant interobserver variability amongst pathologists. We aimed to develop and validate an AI-based system to improve cribriform pattern detection. Methods:

Jia Yun Chua, Argyrios Zolotas, Miguel Arana-Catania

Remote sensing has become a vital tool across sectors such as urban planning, environmental monitoring, and disaster response. While the volume of data generated has increased significantly, traditional vision models are often constrained by the requirement for extensive domain-specific labelled data and their limited ability to understand the context within

Quoc Hung Le, Thanh Le-Cong, Bach Le, Bowen Xu

As Large Language Models (LLMs) become increasingly integrated into software development workflows, they also become prime targets for adversarial attacks. Among these, backdoor attacks are a significant threat, allowing attackers to manipulate model outputs through hidden triggers embedded in training data. Detecting such backdoors remains a challenge, and

Masoud Mirzaei, Alexey Shkarin, Burak Gurlek, Johannes Zirkelbach

High-resolution spectroscopy allows one to probe weak interactions and to detect subtle phenomena. While such measurements are routinely performed on atoms and molecules in the gas phase, spectroscopy of adsorbed species on surfaces is faced with challenges. As a result, previous studies of surface-adsorbed molecules have fallen short of the ultimate resolut

Saksham Mahajan, Ravi Kumar, Aferdita Xhameni, Gautham Venu

We study $V_{\mathrm{B}}^-$ centres generated by helium focused ion beam (FIB) irradiation in thin ($\sim$70 nm) hBN nanoflakes, in order to investigate the effect of implantation conditions on the key parameters that influence the magnetic field sensitivity of $V_{\mathrm{B}}^-$ quantum sensors. Using a combination of photoluminescence, optically detected m

G. Pagnini, P. Di Matteo, M. Haywood, P. Bianchini

Globular clusters (GCs) and their associated stellar streams are key tracers of the hierarchical assembly history of the Milky Way. $\omega$ Centauri, the most massive and chemically complex GC in the Galaxy, is widely believed to be the remnant nucleus of an accreted dwarf galaxy. Identifying its associated debris and that of chemically similar clusters can

L. Jin, A. Ravlić, P. Giuliani, K. Godbey

Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field vacuum; however, its high computational cost limits model calibration and unc

Sebastian Egginger, Kristina Kirova, Sonja Bruckner, Stefan Hillmich

The prospect of quantum solutions for complicated optimization problems is contingent on mapping the original problem onto a tractable quantum energy landscape, e.g. an Ising-type Hamiltonian. Subsequently, techniques like adiabatic optimization, quantum annealing, and the Quantum Approximate Optimization Algorithm (QAOA) can be used to find the ground state

Michal P. Heller, Fabio Ori, Jacopo Papalini, Tim Schuhmann

We utilize the recent connection between the high energy limit of the double-scaled SYK model and two-dimensional de Sitter solutions of sine dilaton gravity to identify the length of a family of geodesics spanned between future and past infinities with Krylov spread complexity. This constitutes an explicit top-down microscopic realization of holographic com

María Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco, Giovanni Franco Gabriel Marraffini

Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process is prone to errors and biases, such as the illusion of causality, in which people perceive a causal relationship between two variables despite lacking supporting evidence. This co

Alex Iosevich, Akos Magyar, Alex McDonald, Brian McDonald

Given a set $X$ and a collection ${\mathcal H}$ of functions from $X$ to $\{0,1\}$, the VC-dimension measures the complexity of the hypothesis class $\mathcal{H}$ in the context of PAC learning. In recent years, this has been connected to geometric configuration problems in vector spaces over finite fields. In particular, it is easy to show that the VC-dimen

Sebastian Egginger, Kristina Kirova, Sonja Bruckner, Stefan Hillmich

Encoding combinatorial optimization problems into physically meaningful Hamiltonians with tractable energy landscapes forms the foundation of quantum optimization. Numerous works have studied such efficient encodings for the class of Quadratic Unconstrained Binary Optimization (QUBO) problems. However, many real-world tasks are constrained, and handling equa

Jinkun Chen, Sher Badshah, Xuemin Yu, Sijia Han

What if artificial agents could not just communicate, but also evolve, adapt, and reshape their worlds in ways we cannot fully predict? With llm now powering multi-agent systems and social simulations, we are witnessing new possibilities for modeling open-ended, ever-changing environments. Yet, most current simulations remain constrained within static sandbo

David Marcano, Adrian Dobra

We introduce efficient MCMC algorithms for Bayesian inference for single-factor models with correlated residuals where the residuals' distribution is a Gaussian graphical model. We call this family of models single-factor graphical models. We extend single-factor graphical models to datasets that also involve binary and ordinal categorical variables and to t

Christopher S. Jackson

Many of the most fundamental observables | position, momentum, phase-point, and spin-direction | cannot be measured by an instrument that obeys the orthogonal projection postulate. Continuous-in-time measurements provide the missing theoretical framework to make physical sense of such observables. The elements of the time-dependent instrument define a group

Supriti Sinhamahapatra, Jan Niehues

State-of-the-art (SOTA) Automatic Speech Recognition (ASR) systems primarily rely on acoustic information while disregarding additional multi-modal context. However, visual information are essential in disambiguation and adaptation. While most work focus on speaker images to handle noise conditions, this work also focuses on integrating presentation slides f

Naruya Kondo, Yuto Asano, Yoichi Ochiai

We present Instant Skinned Gaussian Avatars, a real-time and cross-platform 3D avatar system. Many approaches have been proposed to animate Gaussian Splatting, but they often require camera arrays, long preprocessing times, or high-end GPUs. Some methods attempt to convert Gaussian Splatting into mesh-based representations, achieving lightweight performance

Giulia Pruto, Laura Keating, Rahul Kannan, Ewald Puchwein

Metal absorbers represent a powerful probe of galaxy feedback and reionization, as highlighted by both observational and theoretical results showing an increased abundance of low-ionised metal species at higher redshifts. The origin of such absorbers is currently largely unknown because of the low number of galaxy counterparts detected, suggesting that they

Kin Kwan Leung, Mouloud Belbahri, Yi Sui, Alex Labach

Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the complexity of real-world RAG systems, there are many potential causes for erroneous outputs. Understanding the range of errors that can occur in practice is crucial for robust deplo

Sean K. Terry, Etienne Bachelet, Farzaneh Zohrabi, Himanshu Verma

As part of the Galactic Bulge Time Domain Survey (GBTDS), the Nancy Grace Roman Galactic Exoplanet Survey (RGES) will use microlensing to discover cold outer planets and free-floating planets unbound to stars. NASA has established several science requirements for the GBTDS to ensure RGES success. A key advantage of RGES is Roman's high angular resolution, wh

George Webber, Andrew J. Reader

Recovering true signals from noisy measurements is a central challenge in inverse problems spanning medical imaging, geophysics, and signal processing. Current methods balance prior signal priors (regularization) with agreement with noisy data (data-fidelity). Conventional data-fidelity loss functions, such as mean-squared error (MSE) or negative log-likelih

Nathan L. Burwig, Damien A. Easson

We establish a new no-go theorem for cosmology: spatially flat ($k=0$) and open ($k=-1$) Friedmann--Robertson--Walker (FRW) non-static spacetimes cannot be simultaneously nonsingular, geodesically complete, and consistent with the averaged null energy condition (ANEC). Equivalently, any dynamic flat or open universe that is complete must violate the ANEC. By

Karun Gadge, Abhinav Prem, Rishabh Jha

Periodically driven quantum systems can host non-equilibrium phenomena without static analogs, including in their entanglement dynamics. Here, we discover $temporal$ $entanglement$ $transitions$ (TET) in a Floquet spin chain, which correspond to a quantum phase transition in the spectrum of the entanglement Hamiltonian and are signaled by dynamical spontaneo

Kyle Boone, Georgios Valogiannis, Marco Gatti, Cora Dvorkin

We present forecasts for constraints on the matter density ($\Omega_m$) and the amplitude of matter density fluctuations at 8h$^{-1}$Mpc ($\sigma_8$) from CMB lensing convergence maps and galaxy weak lensing convergence maps. For CMB lensing convergence auto statistics, we compare the angular power spectra ($C_\ell$'s) to the wavelet scattering transform (WS

Julian Demeio, Sam Streeter, Rosa Winter

We prove that the Hilbert property is satisfied by certain del Pezzo surfaces of degree one and Picard rank 1 over fields finitely generated over $\mathbb{Q}$. We generalize results of the first author on elliptic surfaces and employ constructions used by Desjardins and the third author to prove density of rational points. Our results are the first on the Hi

Martin Hoferichter, Gabriele Levati

While electron and muon dipole moments are well-established precision probes of physics beyond the standard model, it is notoriously challenging to test realistic new-physics (NP) scenarios for the $\tau$ lepton. Constructing suitable asymmetries in $e^+e^-\to\tau^+\tau^-$ has emerged as a promising such avenue, providing access to the electric and magnetic

Carlos Palenzuela, Miguel Bezares, Steven Liebling, Federico Schianchi

We present MHDuet, an open source evolution code for general relativistic magnetohydrodynamics with neutrino transport. The code solves the full set of Einstein equations coupled to a relativistic, magnetized fluid with an M1 neutrino radiation scheme using advanced techniques, including adaptive mesh and large eddy simulation techniques, to achieve high acc

Soichiro Shimamori, Yifan Wang

We study quenched disorder localized on a $p$-dimensional subspacetime in a $d$-dimensional conformal field theory. Motivated by the logarithmic behavior often associated with disorder, we introduce a defect setup in which bulk local operators transform in ordinary conformal representations, while defect local operators assemble into logarithmic multiplets.

Estuti Shukla, Alireza Rashti, Rossella Gamba, David Radice

We present the second release of the $\texttt{GR-Athena++}$ waveform catalog, comprising four new quasi-circular, non-precessing, spinning binary black hole simulations. These simulations are performed at high resolutions and represent a step toward generating high-fidelity gravitational waveforms that can eventually meet the accuracy requirements of upcomin

Alexa Bartlett, Joseph DeRose, Martin White

In this work, we forecast the number of, and requirements on, N-body simulations needed to train hybrid effective field theory (HEFT) emulators for a range of use cases, using a hybrid of HMcode and perturbation theory as a surrogate model. Our accuracy goals, determined with careful consideration of statistical and systematic uncertainties, are $1\%$ accura

Svitlana Kondovych, Asle Sudbø, Flavio S. Nogueira

We study an unconventional phase transition in ferroelectrics where the polarization field is constrained to be divergence-free, allowing only loop-like configurations. This local constraint fundamentally alters the critical behavior, driving the system beyond the Landau-Ginzburg-Wilson paradigm. A renormalization group analysis shows that the polarization a