November 2025 arXiv papers — page 50

Kehua Chen, Tianlu Mao, Xinzhu Ma, Hao Jiang

Recently, 3D Gaussian Splatting and its derivatives have achieved significant breakthroughs in large-scale scene reconstruction. However, how to efficiently and stably achieve high-quality geometric fidelity remains a core challenge. To address this issue, we introduce MetroGS, a novel Gaussian Splatting framework for efficient and robust reconstruction in c

Yu Cui, Yifei Liu, Hang Fu, Sicheng Pan

Research on the safety evaluation of large language models (LLMs) has become extensive, driven by jailbreak studies that elicit unsafe responses. Such response involves information already available to humans, such as the answer to "how to make a bomb". When LLMs are jailbroken, the practical threat they pose to humans is negligible. However, it remains uncl

Adrian Koenigstein, Martin J. Steil, Stefan Floerchinger

In order to find reliable and efficient numerical approximation schemes, we suggest to identify the Functional Renormalization Group flow equations of one-particle irreducible two-point functions as Hamilton-Jacobi(-Bellman)-type partial differential equations. Based on this reformulation and reinterpretation we adopt a numerical scheme for the solution of f

Sungyu Jeong, Minsu Kim, Byungsub Kim

We propose a technique to assist in converting a reference layout of an analog circuit into the procedural layout generator by efficiently reusing available generators for sub-cell creation. The proposed convolutional neural network (CNN) model automatically detects sub-cells that can be generated by available generator scripts in the library, and suggests u

Gaurav Kumar, Akrati Saxena, Chandrakala Meena

Real-world complex systems are often better modeled as hypergraphs, where edges represent group interactions involving multiple entities. Understanding and quantifying homophily (similarity-driven association) in such networks is essential for analyzing community formation and information flow. We propose a hyperedge-centric framework to quantify homophily i

Bingchen Li, Xin Li, Jiaqi Xu, Jiaming Guo

Image restoration (IR) models are typically trained to recover high-quality images using L1 or LPIPS loss. To handle diverse unknown degradations, zero-shot IR methods have also been introduced. However, existing pre-trained and zero-shot IR approaches often fail to align with human preferences, resulting in restored images that may not be favored. This high

Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu

Advertising (Ad) is a cornerstone of the digital economy, yet the moderation of video advertisements remains a significant challenge due to their complexity and the need for precise violation localization. While recent advancements, such as the RAVEN model, have improved coarse-grained violation detection, critical gaps persist in fine-grained understanding,

Ehsan Karimi, Nhut Le, Maryam Rahnemoonfar

Timely and accurate assessment of damages following natural disasters is essential for effective emergency response and recovery. Recent AI-based frameworks have been developed to analyze large volumes of aerial imagery collected by Unmanned Aerial Vehicles, providing actionable insights rapidly. However, creating and annotating data for training these model

Yanxiao Liu

One-shot information theory addresses scenarios in source coding and channel coding where the signal blocklength is assumed to be 1. In this case, each source and channel can be used only once, and the sources and channels are arbitrary and not required to be memoryless or ergodic. We study the achievability part of one-shot information theory, i.e., we cons

Sergey Bolotin, Dmitry Treschev

We consider the problem of spectral stability of traveling wave solutions $u=\gamma(x-Wt)$ for a system of viscous conservation laws $\partial_t u + \partial_x F(u) = \partial^2_x u$. Such solutions correspond to heteroclinic trajectories $\gamma$ of a system of ODE. In general conditions of stability can be obtained only numerically. We propose a model clas

Fabian Schramm, Nicolas Perrin-Gilbert, Justin Carpentier

We propose a refinement of temporal-difference learning that enforces first-order Bellman consistency: the learned value function is trained to match not only the Bellman targets in value but also their derivatives with respect to states and actions. By differentiating the Bellman backup through differentiable dynamics, we obtain analytically consistent grad

Blas Fernández, Jae-Ho Lee, Jongyook Park

Let $\Gamma$ denote a distance-regular graph with vertex set $X$ and diameter $D \geq 3$. Fix a vertex $x \in X$. Let the field $\mathbb{F}$ be either $\mathbb{R}$ or $\mathbb{C}$. Let $\operatorname{Mat}_X(\mathbb{F})$ denote the $\mathbb{F}$-algebra of matrices whose rows and columns are indexed by $X$ and all entries in $\mathbb{F}$. The Terwilliger algeb

Wanqin Nie, Kai Tu, Minglu Ye, Shuqin Sun

In this paper, we propose a double iteratively reweighted algorithm to solve nonconvex and nonsmooth optimization problems, where both the objectives and constraint functions are formulated by concave compositions to promote group-sparse structures. At each iteration, we combine convex surrogate with first-order information to construct linearly constrained

Valentin Gillet

The aim of this article is to study the dynamics of random products of weighted shifts on a separable Fr\'echet sequence space. That is, given a measure-preserving dynamical system $(\Omega, \mathcal{F}, \mu, \tau)$, a Fr\'echet sequence space $X$ with a basis $(e_n)_{n \geq 0}$, and a strongly measurable map $T : \Omega \to \mathcal{B}(X)$ taking values in

Jun Wen Law, Bryan K. Mignone, Dharik S. Mallapragada

Low-carbon liquid fuels play a key role in energy system decarbonization scenarios. This study uses a multi-sector capacity expansion model of the contiguous United States to examine fuels production in deeply decarbonized energy systems. Our analysis evaluates how the shares of biofuels, synthetic fuels, and fossil liquid fuels change under varying assumpti

G. Ammirati, F. Martelli, F. Toschi, S. Turchini

In this study, we investigate the impact of formamidinium bromide (FABr) over-stoichiometry in the precursor solution on the optoelectronic properties and morphology of the resulting films of formamidinium lead bromide (FAPbBr_3). Optical characterization, including steady-state absorption, photoluminescence (PL), and femtosecond transient absorption spectro

Weitao Liu

This paper introduces a novel Kalman filter framework designed to achieve robust state estimation under both process and measurement noise. Inspired by the Weighted Observation Likelihood Filter (WoLF), which provides robustness against measurement outliers, we applied generalized Bayesian approach to build a framework considering both process and measuremen

Jianfeng Xu, Zeyan Li

The rapid scaling of artificial intelligence models has revealed a fundamental tension between model capacity (storage) and inference efficiency (computation). While classical information theory focuses on transmission and storage limits, it lacks a unified physical framework to quantify the thermodynamic costs of generating information from compressed laws

Xihe Qiu, Gengchen Ma, Haoyu Wang, Chen Zhan

Sleep stage classification based on electroencephalography (EEG) is fundamental for assessing sleep quality and diagnosing sleep-related disorders. However, most traditional machine learning methods rely heavily on prior knowledge and handcrafted features, while existing deep learning models still struggle to jointly capture fine-grained time-frequency patte

Fabio Moretti, Flavio Bombacigno

We formulate a generalized $k$-essence model in the presence of a Palatini $f(\mathcal{R})$ gravitational sector. In the corresponding biscalar-tensor theory, we discuss the distinguished dynamical properties of the two scalar fields, elucidating how the Palatini scalaron can be still algebraically solved in terms of matter, the $k$-essence field and its kin

Ruiyang Xu

The processing of high-dimensional streaming data commonly utilizes online streaming feature selection (OSFS) techniques. However, practical implementations often face challenges with data incompleteness due to equipment failures and technical constraints. Online Sparse Streaming Feature Selection (OS2FS) tackles this issue through latent factor analysis-bas

Prateek Garg, Bhavya Kohli, Sunita Sarawagi

Masked Diffusion Models (MDMs) have emerged as one of the most promising paradigms for generative modeling over discrete domains. It is known that MDMs effectively train to decode tokens in a random order, and that this ordering has significant performance implications in practice. This observation raises a fundamental question: can we design a training fram

Jacob Martin, Carlo Giovanni Camarda

Small-area mortality estimation is inherently difficult, as random fluctuations from low death counts can obscure real geographic differences. We introduce a flexible model that borrows strength across age, space, and time to estimate mortality schedules and trends in very small populations. The approach ensures smooth patterns across these dimensions while

Georgios Maragkopoulos, Lazaros Chavatzoglou, Aikaterini Mandilara, Dimitris Syvridis

In finance, predictive models must balance accuracy and interpretability, particularly in credit risk assessment, where model decisions carry material consequences. We present a quantum neural network (QNN) based on a single qudit, in which both data features and trainable parameters are co-encoded within a unified unitary evolution generated by the full Lie

Moazzam Umer Gondal, Hamad Ul Qudous, Daniya Siddiqui, Asma Ahmad Farhan

This paper introduces the retrieval-augmented framework for automatic fashion caption and hashtag generation, combining multi-garment detection, attribute reasoning, and Large Language Model (LLM) prompting. The system aims to produce visually grounded, descriptive, and stylistically interesting text for fashion imagery, overcoming the limitations of end-to-

P. Rivière-Marichalar, R. le Gal, A. Fuente, D. Semenov

Protoplanetary disks inherit their chemical composition from their natal molecular cloud, but the extent to which this material is preserved versus reset through chemical reprocessing remains an open question. Understanding this balance is a major topic in astrochemistry. Comparing the chemical composition of the envelope and the protoplanetary disk is key t

Huisoo Lee, Jisu Han, Hyunsouk Cho, Wonjun Hwang

Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to source data. Recent advances in Foundation Models (FMs) have introduced new opportunities for leveraging external semantic knowledge to guide SFDA. However, relying on a single FM is often insufficient, as it tends to bias adaptation

Qian Zhang, Zhuo Sun, Yao Zhang, Zhiwen Yu

Inter-agent communication serves as an effective mechanism for enhancing performance in collaborative multi-agent reinforcement learning(MARL) systems. However, the inherent communication latency in practical systems induces both action decision delays and outdated information sharing, impeding MARL performance gains, particularly in time-critical applicatio

Yu. Sachkov

We study a family of Riemannian problems on the Heisenberg group that tends to the sub-Riemannian problem on this group.

Dongha Lee, Jinhee Park, Minjun Kim, Junseok Kwon

Low-Rank Adaptation (LoRA) is highly sensitive to initialization, yet existing schemes construct the initial subspace from statistics at the pretrained point, capturing pre-adaptation geometry rather than how the adapter must move during learning. We examine the early adaptation trajectory and uncover a temporal asymmetry: task-induced activation boundaries-

Emma Y. Lenander, Silas B. Schack, Kim Lefmann, Henrik M. Rønnow

We present an analytic convoluted-gap function, eq. 11 in the manuscript, for fitting dispersion gaps measured on triple-axis spectrometers (TAS). At the gap, the instrumental resolution skews the signal, producing a high-energy tail that complicates fitting. Our function assumes an instrumental $Q$-resolution with two equal wide and one narrow direction (ty

Lisa Piccinin, Valentina Breschi, Chiara Ravazzi, Fabrizio Dabbene

In this paper, we propose a new framework for the design of incentives aimed at promoting innovation diffusion in social influence networks. In particular, our framework relies on an extension of the Friedkin and Johnsen opinion dynamics model characterizing the effects of (i) short-memory incentives, which have an immediate yet transient impact, and (ii) lo

Arthur F. Ramos, Anjolina G. de Oliveira, Ruy J. G. B. de Queiroz, Tiago M. L. de Veras

Computational paths treat propositional equality as explicit paths built from labelled deduction steps and rewrite rules. This view originates in work by de Queiroz and collaborators [1] and yields a weak groupoid structure for equality, together with a computational account of homotopy inspired by homotopy type theory. In this paper we present a complete me

R. R. Chichili, J. A. Sulskis, R. Sharankova, B. Vamanan

The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift correction, employing a prototype-based classification approach. Our model utilizes a 34-dimensional feature set (RF setting

Rosita Kokotanekova, Aurélie Guilbert-Lepoutre, Matthew M. Knight, Jean-Baptiste Vincent

Centaurs populate relatively short-lived and rapidly evolving orbits in the giant-planet region and are believed to be one of the solar system's most complex and diverse populations. Most Centaurs are linked to origins in the dynamically excited component of the trans-Neptunian region, and are often considered an intermediate phase in the evolution of Jupite

Yu. Sachkov

We consider two families of Lorentzian problems on the Heisenberg group and their asymptotic behaviour as the parameter of a family tends to a limit.

Julia Bierent, David Jordan, Matthias Vancraeynest, Monica Vazirani

We compute the dimensions of $\text{GL}_N$-skein modules of genus-one mapping tori $T^2\times_\gamma S^1$, for an arbitrary diffeomorphism of $T^2$, and for generic quantum parameter. These are most cleanly expressed via a generating function over all $N$, which we dub the skein partition function, and for which we compute an explicit Euler product expansion

Zhifeng Xie, Keyi Zhang, Yiye Yan, Yuling Guo

Film set design plays a pivotal role in cinematic storytelling and shaping the visual atmosphere. However, the traditional process depends on expert-driven manual modeling, which is labor-intensive and time-consuming. To address this issue, we introduce FilmSceneDesigner, an automated scene generation system that emulates professional film set design workflo

Zihao Feng

In this Letter, through the comparison between experiment and numerical simulation, we reveal the dynamic mechanism underlying the abnormal polarity effect in SF6 short-gap DC breakdown, as well as a novel criterion for predicting negative breakdown voltage. Using the traditional single-streamer breakdown criterion, the simulated positive breakdown voltage a

Pascal Goldschmid, Aamir Ahmad

Multi-rotor UAVs face limited flight time due to battery constraints. Autonomous docking on blimps with onboard battery recharging and data offloading offers a promising solution for extended UAV missions. However, the vulnerability of blimps to wind gusts causes trajectory deviations, requiring precise, obstacle-aware docking strategies. To this end, this w

Weike Deng, Wentao Liu, Kui Xiao, Jiliang Jing

We investigate quasinormal modes (QNMs) of scalar, electromagnetic, and axial gravitational perturbations in slowly rotating Kalb-Ramond (KR) black holes, where an antisymmetric tensor field induces spontaneous Lorentz symmetry breaking. Working consistently to first order in the dimensionless spin parameter, we derive the corresponding master equations and

Shuyu Cao, Minxin Chen, Yucheng Song, Zhaozhong Chen

Small object detection in Unmanned Aerial Vehicle (UAV) imagery is a persistent challenge, hindered by low resolution and background clutter. While fusing RGB and infrared (IR) data offers a promising solution, existing methods often struggle with the trade-off between effective cross-modal interaction and computational efficiency. In this letter, we introdu

Runxin Zhang, Yulin Shao, Yuanwei Liu

We propose a directional pinching-antenna system (DiPASS), a comprehensive framework that transitions PASS modeling from idealized abstraction to physical consistency. DiPASS introduces the first channel model that accurately captures the directional, pencil-like radiation of pinching antennas, incorporates a practical waveguide attenuation of 1.3 dB/m, and

Michael Harris, Ming-Lun Hsieh, Shunsuke Yamana

We construct the five-variable $p$-adic $L$-function attached to Hida families on $\mathrm U(2,1)\times\mathrm U(1,1)$, interpolating the square-root of Rankin-Selberg $L$-values in the \emph{shifted piano} range. Our construction relies on a new theta operator and its $p$-adic variation which plays a role analogous to the classical Ramanujan-Serre theta ope

Mohammad Abboush, Ahmad Hatahet, Andreas Rausch

A well-known testing method for the safety evaluation and real-time validation of automotive software systems (ASSs) is Fault Injection (FI). In accordance with the ISO 26262 standard, the faults are introduced artificially for the purpose of analyzing the safety properties and verifying the safety mechanisms during the development phase. However, the curren

Zijian Wang, Yanxiang Ma, Chang Xu

Chain-of-Thought (CoT) reasoning is a critical capability for large language models (LLMs), enabling them to tackle com- plex multi-step tasks. While base LLMs, pre-trained on general text corpora, often struggle with reasoning due to a lack of specialized training, recent studies reveal their latent reason- ing potential tied to hidden states. However, exis

Rong Feng, Suman Saha

Obfuscation poses a persistent challenge for software engineering tasks such as program comprehension, maintenance, testing, and vulnerability detection. While compiler optimizations and third-party code often introduce transformations that obscure program intent, existing analysis tools and large language models (LLMs) struggle to recover the original seman

Andrzej Okolow, Jakub Szymankiewicz

We express the vacuum Einstein constraints in terms of differential forms - the forms include one-forms constituting an orthonormal coframe of the spatial metric. We show that if the metric is real-analytic, then the constraints can be always expressed locally as a system of first order PDE's - this system is obtained by a special choice of the coframe, whic

Jin-Yang Shen, Xin-Yi Liu, Jin-Rui Wu, Yue-Liang Wu

We construct a holographic QCD model based on the Einstein--dilaton--flavor framework with 2+1 flavors and investigate its phase structure using machine-learning techniques. At zero chemical potential, the model reproduces the equation of state and chiral transition in quantitative agreement with lattice QCD results. By varying the light and strange quark ma

Mikhail Zubarev

Variational quantum algorithms face a fundamental trainability crisis: barren plateaus render optimization exponentially difficult as system size grows. While recent Lie algebraic theory precisely characterizes when and why these plateaus occur, no practical optimization method exists with finite-sample guarantees for navigating them. We present the sequenti

Beilin Chu, Weike You, Mengtao Li, Tingting Zheng

The rapid progress of GANs and Diffusion Models poses new challenges for detecting AI-generated images. Although CLIP-based detectors exhibit promising generalization, they often rely on semantic cues rather than generator artifacts, leading to brittle performance under distribution shifts. In this work, we revisit the nature of semantic bias and uncover tha

L. Sansoni, E. Stefanutti, C. Benedetti, I. Gianani

Quantum walks represent an excellent testbed for investigating the interplay between unitary coherent and incoherent dissipative processes. Thanks to photonic quantum interferometers of considerable size, experimental studies could be performed, devoted to investigating the consequences of different sorts of realistic noise in these systems. In this work we

Krishang Sharma

Accurate Remaining Useful Life (RUL) prediction coupled with uncertainty quantification remains a critical challenge in aerospace prognostics. This research introduces a novel uncertainty-aware deep learning framework that learns aleatoric uncertainty directly through probabilistic modeling, an approach unexplored in existing CMAPSS-based literature. Our hie

R. Bermudez Schettino, A. Dasmeh, L. Brinkmann

As large language models (LLMs) become increasingly prevalent, understanding human-LLM interactions is emerging as a central priority in psychological research. Online experiments offer an efficient means to study human-LLM interactions, yet integrating LLMs into established survey platforms remains technically demanding, particularly when aiming for ecologi

Yaping Chai, Haoran Xie, Joe S. Qin

Aspect category sentiment analysis (ACSA) has achieved remarkable progress with large language models (LLMs), yet existing approaches primarily emphasize sentiment polarity while overlooking the underlying emotional dimensions that shape sentiment expressions. This limitation hinders the model's ability to capture fine-grained affective signals toward specif

Xiaohong Chen, Wayne Yuan Gao, Likang Wen

We propose a new formulation of the maximum score estimator that uses compositions of rectified linear unit (ReLU) functions, instead of indicator functions as in Manski (1975,1985), to encode the sign alignment restrictions. Since the ReLU function is Lipschitz, our new ReLU-based maximum score criterion function is substantially easier to optimize using st

Phong Le, Mees Lindeman, Raquel G. Alhama

The structure of naming systems in natural languages hinges on a trade-off between high informativeness and low complexity. Focusing on the domain of kinship naming, we analyze such trade-off while addressing simplifying assumptions of prior work, namely: (i) universal communicative need across languages, and (ii) optimal listeners. To that aim, we collect d

Juan-José Guzmán-Landa, Jesús Vázquez-Osorio, Juan-Manuel Torres-Moreno, Ligia Quintana Torres

In this paper, we describe a symbolic model for the automatic orthographic unification of Nawatl text documents. Our model is based on algorithms that we have previously used to analyze sentences in Nawatl, and on the corpus called $\pi$-yalli, consisting of texts in several Nawatl orthographies. Our automatic unification algorithm implements linguistic rule

Minchong Chen, Xiaoyun Yuan, Junzhe Wan, Jianing Zhang

The miniaturization of thermal sensors for mobile platforms inherently limits their spatial resolution and textural fidelity, leading to blurry and less informative images. Existing thermal super-resolution (SR) methods can be grouped into single-image and RGB-guided approaches: the former struggles to recover fine structures from limited information, while

Young-Pil Choi, Seungchan Lee, Sihyun Song

We introduce a modified Consensus-Based Optimization model that admits a fully unified and rigorous analysis of its finite-particle dynamics, the associated McKean--Vlasov equation, and their optimization behavior under a single set of structural framework. The key ingredient is a regularized Gibbs weight that stabilizes the consensus point and avoids degene

Siqi Ding, Zitong Zhang, Guoyang Shi, Xingyu Li

As artificial intelligence emerges as a transformative enabler for fusion energy commercialization, fast and accurate solvers become increasingly critical. In magnetic confinement nuclear fusion, rapid and accurate solution of the Grad-Shafranov equation (GSE) is essential for real-time plasma control and analysis. Traditional numerical solvers achieve high

Shaolong Guo, Yuntao Wang, Zhou Su, Yanghe Pan

Rapid advances in large language models and agentic AI are driving the emergence of the Internet of Agents (IoA), a paradigm where billions of autonomous software and embodied agents interact, coordinate, and collaborate to accomplish complex tasks. A key prerequisite for such large-scale collaboration is agent capability discovery, where agents identify, ad

Josep M. Trigo-Rodríguez, Maria Gritsevich, Jürgen Blum

3I/ATLAS is only the second confirmed cometary object known to enter the Solar System from interstellar space. Cosmogonic considerations suggest that this body may possess relatively high tensile strength and a substantial metal fraction. We present photometric observations along its inbound trajectory toward perihelion, together with a spectroscopic compari

Hai Ci, Ziheng Peng, Pei Yang, Yingxin Xuan

Diffusion-based editing enables realistic modification of local image regions, making AI-generated content harder to detect. Existing AIGC detection benchmarks focus on classifying entire images, overlooking the localization of diffusion-based edits. We introduce DiffSeg30k, a publicly available dataset of 30k diffusion-edited images with pixel-level annotat

Jiaozi Wang, Sourav Nandy, Markus Kraft, Tomaž Prosen

Diffusive transport is a ubiquitous phenomenon, yet the microscopic origin of diffusion in interacting physical systems remains a challenging question, irrespective of whether quantum effects are dominant or not. In this work, we study infinite temperature spin diffusion in a classical integrable, space-time discrete version of anisotropic Landau-Lifshitz ma

Mohan Ramesh, Mark Azer, Fabian B. Flohr

Current autonomous driving (AD) simulations are critically limited by their inadequate representation of realistic and diverse human behavior, which is essential for ensuring safety and reliability. Existing benchmarks often simplify pedestrian interactions, failing to capture complex, dynamic intentions and varied responses critical for robust system deploy

Wenlong Wang, Wen Huang, Zai Yang

Spectral compressed sensing involves reconstructing a spectral-sparse signal from a subset of uniformly spaced samples, with applications in radar imaging and wireless channel estimation. By fully exploiting the signal structures, this problem is formulated as a rank-constrained semidefinite program subject to Hankel-Toeplitz structural constraints in our pr

Robert Bredereck, Eva Deltl, Leon Kellerhals, Jannik Peters

We study core stability in non-centroid clustering under the max-loss objective, where each agent's loss is the maximum distance to other members of their cluster. We prove that for all $k\geq 3$ there exist metric instances with $n\ge 9$ agents, with $n$ divisible by $k$, for which no clustering lies in the $\alpha$-core for any $\alpha<2^{\frac{1}{5}}\sim

Luiz A. C. A. Schiavo, Natasha L. S. Jeffrey, Gert J. J. Botha, James A. McLaughlin

Solar flares are explosive releases of magnetic energy stored in the solar corona, driven by magnetic reconnection. These events accelerate electrons, generating hard X-ray emissions and often display Quasi Periodic Pulsations (QPPs) across the energy spectra. However, the energy transfer process remains poorly constrained, with competing theories proposing

Jichao Chen, YangYang Qu, Ruibo Tang, Dirk Slock

WiFi-based human pose estimation (HPE) has attracted increasing attention due to its resilience to occlusion and privacy-preserving compared to camera-based methods. However, existing WiFi-based HPE approaches often employ regression networks that directly map WiFi channel state information (CSI) to 3D joint coordinates, ignoring the inherent topological rel

A. N. Kuzmichev, P. M. Vetoshko, E. I. Pavluk, A. A. Holin

This work advances the frontier of low-damping magnetic materials, directly addressing the demand for ultra-low-loss components in quantum computing and cryogenic electronics. Here we demonstrate a new approach to get single crystal micrometer-thick yttrium iron garnet (YIG) films with low damping through isolating and mitigating interfacial paramagnetic con

Dora Krekovic, Mario Kusek, Ivana Podnar Zarko, Danh Le-Phuoc

The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion, increased latency and high energy consumption. This is particularly problematic in resource-constrained and remote environments where bandwidth is limited, and battery-dependent de

Xiaoxiang Jiao, Wenduo Zou

Given a closed Riemannian manifold $(M^{n+1},g)$,$3\leq n+1\leq7$.In this paper,we will prove that for any $c>0$,suppose the number of closed $c-CMC$ hypersurfaces is finite,then there exists a metric $h$ on $M$ such that the $c-CMC$ hypersurfaces in $(M,g)$ are also $c-CMC$ hypersurfaces in $(M,h)$ and the number of $c-CMC$ hypersurfaces in $(M,h)$ is stric

Alexander Ganz, Marco Spinelli

We revisit the framework of Newer General Relativity, defined by all independent quadratic invariants of the non-metricity tensor, including the unique quadratic parity-violating term. We analyze linear perturbations around a flat FLRW background and find that the theory generically exhibits ghost instabilities and/ or propagates more degrees of freedom than

Chih-Duo Hong, Hongjian Jiang, Anthony W. Lin, Oliver Markgraf

Automata extraction is a method for synthesising interpretable surrogates for black-box neural models that can be analysed symbolically. Existing techniques assume a finite input alphabet, and thus are not directly applicable to data sequences drawn from continuous domains. We address this challenge with deterministic register automata (DRAs), which extend f

A. Whitbeck, J. Berlioz, K. Danison-Fieldhouse, K. Hazelwood

We present progress on the development of a machine learning (ML) regulation system for third-order resonant extraction of the beam delivered to the Mu2e experiment at Fermilab. We consider classical and ML-based controllers optimized on semi-analytic simulations and provide performance comparisons for several models. Additionally, we discuss the efficiency

Hao-Nan Liu, Zhi-Wei Liu, Luciano Abreu, Li-Sheng Geng

The femtoscopic $ D_s^+D_s^-$ correlations are investigated to predict the signature of the not-yet-established $X(3960)$ state reported by the LHCb Collaboration, in three scenarios: resonant, virtual, or bound. In the last two scenarios, it might also be identified as the state $X(3930)$. The formalism employed to generate this structure dynamically is bas

Eni Musta, Joris Mooij

Understanding how the causal effect of a treatment evolves over time, including the potential for waning, is important for informed decisions on treatment discontinuation or repetition. For example, waning vaccine protection influences booster dose recommendations, while cost-effectiveness analyses require accounting for long-term efficacy of treatments. How

F. Acerbi, M. Andreotti, A. Balboni, E. Bertolini

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino experiment based in the USA and composed of a Near Detector (ND) complex at Fermi National Laboratory (FNAL), and a Far Detector (FD) complex located at the Sanford Underground Research Facility (SURF) $\sim$1300\,km distant. DUNE will study neutrino oscillations looking for unresolv

David Bricher, Andreas Mueller

Over the last years collaborative robots have gained great success in manufacturing applications where human and robot work together in close proximity. However, current ISO/TS-15066-compliant implementations often limit the efficiency of collaborative tasks due to conservative speed restrictions. For this reason, this paper introduces a deep-learning-based

Charlie Pilgrim, Andrew M Bate, Anna Sigalou, Mélisande Aellen

Collective vigilance describes how animals in groups benefit from the predator detection efforts of others. Empirical observations typically find either a many-eyes strategy with all (or many) group members maintaining a low level of individual vigilance, or a sentinel strategy with one (or a few) individuals maintaining a high level of individual vigilance

Sergio Revuelta, Hai I. Wang, Mischa Bonn, Enrique Canovas

We report the observation of a phonon bottleneck effect impacting the thermal depopulation of photoexcited shallow defects in high-resistivity silicon. Using time-resolved terahertz (THz) spectroscopy, near-band-gap excitation produces a pronounced temporal delay in photoconductivity, indicating that a fraction of photogenerated charge carriers is temporaril

Dominic Breit, Anatole Gaudin

We develop a sharp maximal regularity theory for the resolvent and evolution Stokes equations with no-slip boundary conditions, focusing on bounded domains of low regularity. Our framework covers the full scales of Besov and Sobolev spaces, $B^s_{p,q}$ and $H^{s,p}$, including endpoint cases such as $L^\infty$. Our approach also allows extending the classica

Shenghan Zhao, Yuzhen Lin, Ximeng Yang, Qiaochu Lu

The advent of financial technology has witnessed a surge in the utilization of deep learning models to anticipate consumer conduct, a trend that has demonstrated considerable potential in enhancing lending strategies and bolstering market efficiency. We study multi-horizon demand forecasting on e-commerce transactions using the UCI Online Retail II dataset.

Yuxuan Ma, Valentino Santucci, Carsten Witt

A suitable choice of the representation of candidate solutions is crucial for the efficiency of evolutionary algorithms and related metaheuristics. We focus on problems in permutation spaces, which are at the core of numerous practical applications of such algorithms, e.g. in scheduling and transportation. Inversion vectors (also called Lehmer codes) are an

Marina N. Popova, Mosab Diab, Boris Z. Malkin

A novel, to the best of our knowledge, ultralow-temperature luminescence thermometry strategy is proposed, based on a measurement of relative intensities of hyperfine components in the spectra of Ho$^{3+}$ ions doped into a crystal. A $^{7}$LiYF$_4$:Ho$^{3+}$ crystal is chosen as an example. First, we show that temperatures in the range 10-35 K can be measur

Ziyun Li, Ben Dai, Huancheng Hu, Henrik Boström

Flow-based generative models synthesize data by integrating a learned velocity field from a reference distribution to the target data distribution. Prior work has focused on endpoint metrics (e.g., fidelity, likelihood, perceptual quality) while overlooking a deeper question: what do the sampling trajectories reveal? Motivated by classical mechanics, we intr

Xianqiang Li, Zhuowei Liu

For a locally compact sofic group continuously acting on a compact metric space, we first study the relative sofic entropy and prove an additive inequality relating sofic entropy and relative sofic entropy. Moreover, it is shown that the relative variational principle remains valid in this paper. Secondly, the topological pressure for locally compact sofic g

Jan Eube, Heiko Röglin

Connected clustering denotes a family of constrained clustering problems in which we are given a distance metric and an undirected connectivity graph $G$ that can be completely unrelated to the metric. The aim is to partition the $n$ vertices into a given number $k$ of clusters such that every cluster forms a connected subgraph of $G$ and a given clustering

Ruchuan Ou, Learta Januzi, Jonas Schießl, Michael Heinrich Baumann

The consideration of stochastic uncertainty in optimal and predictive control is a well-explored topic. Recently Polynomial Chaos Expansions (PCE) have received considerable attention for problems involving stochastically uncertain system parameters and also for problems with additive stochastic i.i.d. disturbances. While there exist a number of open-source

Wenxuan Mu, Jinzhong Ning, Di Zhao, Yijia Zhang

In-context learning (ICL) with large language models (LLMs) has emerged as a promising paradigm for named entity recognition (NER) in low-resource scenarios. However, existing ICL-based NER methods suffer from three key limitations: (1) reliance on dynamic retrieval of annotated examples, which is problematic when annotated data is scarce; (2) limited genera

Henrique A. Fernandes, Roberto da Silva, Paulo F. Gomes

In this work, we investigate the phase diagrams of the Yaldram and Khan catalytic surface model on square and hexagonal lattices when exchange diffusion is allowed for carbon monoxide (CO) and nitrogen (N) atoms. To reach our goal, we carried out steady-state Monte Carlo (MC) simulations over $4\times 10^5$ points, for both lattices, in order to obtain a fra

Daniel Lomholt Christensen, Sandra Cabeza, Thilo Pirling, Kim Lefmann

Monochromator and analyzer systems that rely on bent single crystals are in use throughout the neutron scattering community. We here introduce a new component to the neutron simulation software package McStas, that simulates these bent single crystals. We then compare the performance of this component to like software in SIMRES, and Ncrystal. These simulatio

Fan Nie, Jiangqun Ni, Jian Zhang, Bin Zhang

The widespread application of AIGC contents has brought not only unprecedented opportunities, but also potential security concerns, e.g., audio-visual deepfakes. Therefore, it is of great importance to develop an effective and generalizable method for multi-modal deepfake detection. Typically, the audio-visual correlation learning could expose subtle cross-m

Ling-Long Gao, Yan Liu, Rui-Long Zhao

We present an analytical study of the interior structure of hairy rotating black holes in three-dimensional Einstein gravity, minimally coupled to a complex scalar field with a super-exponential potential. The interior dynamics of these black holes are characterized by an infinite sequence of Kasner epochs, separated by inversion and transitions, each of whi

Surender Kumar, Markus Fröhlich, Stefan Velja, Marco Kögel

Large chemical tunability and strong light-matter interactions make colloidal transition metal dichalcogenide (TMD) nanostructures particularly suitable for light-emitting applications. However, ultrafast exciton decay and quenched photoluminescence (PL) limit their potential. Combining femtosecond transient absorption spectroscopy with first-principles calc

Matjaž Konvalinka, T. Kyle Petersen

The Eulerian numbers form a triangular array with many interesting properties. The numbers arise from various combinatorial and probabilistic interpretations, and have been studied in a variety of mathematical contexts. In this article we examine two distinct alternating sign formulas for the Eulerian numbers and show how they can be proved using a sign-reve

Emanuele Pardini, Katerina Papagiannouli

Unbalanced optimal transport (UOT) provides a flexible way to match or compare nonnegative finite Radon measures. However, UOT requires a predefined ground transport cost, which may misrepresent the data's underlying geometry. Choosing such a cost is particularly challenging when datasets live in heterogeneous spaces, often motivating practitioners to adopt

Yen-Chi Lee

While the zero-drift first arrival position (FAP) channel exhibits a Cauchy-distributed lateral displacement, nonzero drift in practical systems introduces advective transport that regularizes this singular limit. This letter characterizes the drift-induced transition of FAP distribution from heavy-tailed algebraic regime to exponential regularization. By as

Maxence Corman, Llibert Aresté Saló, Katy Clough

In shift-symmetric Einstein-scalar-Gauss-Bonnet gravity, stationary black holes have a non-vanishing scalar charge. During the inspiral, the phase evolution is modified by several effects, primarily an additional scalar dipole radiation, which enters at -1PN order. This effect accelerates the inspiral when compared to general relativity, when including corre