November 2025 arXiv papers — page 175

Rui Yang, Ziyu Zhu, Yanwei Li, Jingjia Huang

Capturing spatial relationships from visual inputs is a cornerstone of human-like general intelligence. Several previous studies have tried to enhance the spatial awareness of Vision-Language Models (VLMs) by adding extra expert encoders, which brings extra overhead and usually harms general capabilities. To enhance the spatial ability in general architectur

Haotian Chen, Chenyang Huang, Alexander Rodríguez, Aashutosh Mistry

Despite the long history of electrochemistry, there is a lack of quantitative algorithms that rigorously correlate experiment with theory. Electrochemical modeling has had advanced across empirical, analytical, numerical, and data-driven paradigms. Data-driven machine learning and physics based electrochemical modeling, however, have not been explicitly link

Junwen Pan, Qizhe Zhang, Rui Zhang, Ming Lu

Temporal search aims to identify a minimal set of relevant frames from tens of thousands based on a given query, serving as a foundation for accurate long-form video understanding. Existing works attempt to progressively narrow the search space. However, these approaches typically rely on a hand-crafted search process, lacking end-to-end optimization for lea

Andy B. Day, Neelarnab Raha

We study the algebraic hyperbolicity of certain subvarieties of homogeneous varieties, building on the techniques introduced by Coskun-Riedl, Yeong and Mioranci. This generalizes earlier known results for hypersurfaces to higher codimensions. In particular, we observe that if $X=X_1\cap\cdots\cap X_k$ is a very general complete intersection of degree $d_j$ h

Lily Koffman, Sunan Gao, Xinkai Zhou, Andrew Leroux

Large health surveys increasingly collect high-dimensional functional data from wearable devices, and function on scalar regression (FoSR) is often used to quantify the relationship between these functional outcomes and scalar covariates such as age and sex. However, existing methods for FoSR fail to account for complex survey design. We introduce inferentia

Kjell S. Heinrich, Douglas S. Seth, Mats Ehrnstrom, Simen Å. Ellingsen

New data-driven methods have advanced the discovery of governing equations from observations, enabling parsimonious models for complex systems. Here, we 'rediscover' a shallow-water equation closely related to Korteweg--de Vries (KdV) using only video recordings of solitons in a simple flume. Two fundamentally different approaches -- weak-form sparse identif

Yuexin Wu, Shiqi Wang, Vasile Rus

Disease diagnosis is a central pillar of modern healthcare, enabling early detection and timely intervention for acute conditions while guiding lifestyle adjustments and medication regimens to prevent or slow chronic disease. Self-reports preserve clinically salient signals that templated electronic health record (EHR) documentation often attenuates or omits

Abigail Lin

Predicting the effect of amino acid mutations on enzyme thermodynamic stability (DDG) is fundamental to protein engineering and drug design. While recent deep learning approaches have shown promise, they often process sequence and structure information independently, failing to capture the intricate coupling between local structural geometry and global seque

Kang Yang, Yuanlin Yang, Yuning Chen, Sikai Yang

Precision agriculture demands continuous and accurate monitoring of soil moisture (M) and key macronutrients, including nitrogen (N), phosphorus (P), and potassium (K), to optimize yields and conserve resources. Wireless soil sensing has been explored to measure these four components; however, current solutions require recalibration (i.e., retraining the dat

Òscar L. Crosas, Emanuele Mereghetti

We compute $\mathcal O(\alpha^2 Z)$ radiative corrections to superallowed $\beta$ decays with a heavy-particle effective field theory that systematically describes the interactions of low-energy ultrasoft photons with nuclei. We calculate two-loop virtual and one-loop real-virtual amplitudes by reducing the Feynman integrals to a set of master integrals, whi

Maojiang Su, Jerry Yao-Chieh Hu, Sophia Pi, Han Liu

We derive a deterministic, non-asymptotic upper bound on the Kullback-Leibler (KL) divergence of the flow-matching distribution approximation. In particular, if the $L_2$ flow-matching loss is bounded by $\epsilon^2 > 0$, then the KL divergence between the true data distribution and the estimated distribution is bounded by $A_1 \epsilon + A_2 \epsilon^2$. He

Vsevolod I. Yashin, Vladimir V. Yatsulevich, Aleksey K. Fedorov, Evgeniy O. Kiktenko

Simulation of stabilizer circuits is a well-studied problem in quantum information processing, with a number of highly optimized algorithms available. Yet, we argue that further improvements can arise from the theoretical structure of stabilizer operations themselves. We focus on the subclass of stabilizer circuits composed of Calderbank-Shor-Steane (CSS)-pr

Guojie Li, Tianyi Liu, Anwar P. P. Abdul Majeed, Muhammad Ateeq

Medical image segmentation demands models that achieve high accuracy while maintaining computational efficiency and clinical interpretability. While recent Kolmogorov-Arnold Networks (KANs) offer powerful adaptive non-linearities, their full-channel spline transformations incur a quadratic parameter growth of $\mathcal{O}(C^{2}(G+k))$ with respect to the cha

B. Davids, N. E. Esker, J. Jaeyoung, Y. K. Kim

The mean transmission efficiency of the EMMA recoil mass spectrometer at TRIUMF has been measured with 6 different angular apertures at 17 kinetic energy/charge deviations with respect to the central, reference trajectory. Measurements performed using a 148Gd alpha source installed at the target position of the spectrometer are compared to ion-optical calcul

Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy

Transformer-based language models of code have achieved state-of-the-art performance across a wide range of software analytics tasks, but their practical deployment remains limited due to high computational costs, slow inference speeds, and significant environmental impact. To address these challenges, recent research has increasingly explored knowledge dist

Christopher Bogart, Aparna Warrier, Arav Agarwal, Ross Higashi

As artificial intelligence (AI) systems become ubiquitous in professional contexts, there is an urgent need to equip workers, often with backgrounds outside of STEM, with the skills to use these tools effectively as well as responsibly, that is, to be AI literate. However, prevailing definitions and therefore assessments of AI literacy often emphasize founda

Xian-Hong Huang, Hui-Kai Su, Chi-Chia Sun, Jun-Wei Hsieh

This paper introduces a cutting-edge approach to cross-modal interaction for tiny object detection by combining semantic-guided natural language processing with advanced visual recognition backbones. The proposed method integrates the BERT language model with the CNN-based Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN-Net), incorporating innov

Agnieszka Janiuk, Joseph Saji, Gerardo Urrutia

Several gamma ray bursts have recently been associated with a kilonova emission. We study the mechanisms which could account for this effect, by means of radioactive decay of elements synthesized in accretion disk wind. We model the r-process nucleosynthesis in the accretion disk wind system, asscociated with the prompt GRB phase. We compute the time-depende

Aliakbar Daemi, Tye Lidman

We prove that for three-manifolds satisfying a certain algebraic condition on their fundamental group, null-homotopic knots are determined by their complements. This answers a Kirby Problem posed by Boileau for this special case of 3-manifolds. The argument uses techniques in instanton Floer homology and SU(2)-representation varieties.

Dily Duan Yi Ong, Will Handley

Bayesian inference is central to modern cosmology. While parameter estimation is achievable with unnormalised posteriors traditionally obtained via MCMC methods, comprehensive model comparison and tension quantification require Bayesian evidences and normalised posteriors, which remain computationally prohibitive for many researchers. To address this, we pre

Eliane Younes, Elie Hachem, Marc Bernacki

Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size d

Sindhu Muthukrishnan, Phanindra Dewan, Tanishq Tejaswi, Michelle B Sebastian

Glassy dynamics in active biological cells remain a subject of debate, as cellular activity rarely slows enough for true glassy features to emerge. In this study, we address this paradox of glassy dynamics in epithelial cells by integrating experimental observations with an active vertex model. We demonstrate that while crowding is essential, it is not suffi

CMS Collaboration

A tau lepton identification algorithm, DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons ($\tau_\mathrm{h}$) from quark or gluon jets and electrons and muons that are misreconstructed as $\tau_\mathrm{h}$ candidates. The latest version of this algor

Sanghyeon Chang, Srikar Arani, Nishant Sai Nuthalapati, Youngjoon Suh

Flow boiling is an efficient heat transfer mechanism capable of dissipating high heat loads with minimal temperature variation, making it an ideal thermal management method. However, sudden shifts between flow regimes can disrupt thermal performance and system reliability, highlighting the need for accurate and low-latency real-time monitoring. Conventional

Alexander W. Bloedel, Weijie Zhong

This paper introduces a framework for modeling the cost of information acquisition based on the principle of cost-minimization. We study the reduced-form \emph{indirect cost} of information generated by the sequential minimization of a primitive \emph{direct cost} function. Indirect cost functions: (i) are characterized by a novel recursive property, \emph{s

Anthony Ransford, M. S. Allman, Jake Arkinstall, J. P. Campora

We report on Quantinuum Helios, a 98-qubit trapped-ion quantum processor based on the quantum charge-coupled device (QCCD) architecture. Helios features $^{137}$Ba$^{+}$ hyperfine qubits, all-to-all connectivity enabled by a rotatable ion storage ring connecting two quantum operation regions by a junction, speed improvements from parallelized operations, and

Jakub Paplham, Vojtech Franc

We introduce a novel method for Photo Dating which estimates the year a photograph was taken by leveraging information from the faces of people present in the image. To facilitate this research, we publicly release CSFD-1.6M, a new dataset containing over 1.6 million annotated faces, primarily from movie stills, with identity and birth year annotations. Uniq

Manan Vyas, M. Mijaíl Martínez-Ramos, Parisa Majari, Thomas H. Seligman

Starting from the Pearson Correlation Matrix of stock returns and from the desire to obtain a reduced number of parameters relevant for the dynamics of a financial market, we propose to take the idea of a sectorial matrix, which would have a large number of parameters, to the reduced picture of a real symmetric $2 \times 2$ matrix, extreme case, that still c

Xiaodong Wang, Jing Huang, Kevin J Liang

Recent studies have demonstrated the effectiveness of clustering-based approaches for self-supervised and unsupervised learning. However, the application of clustering is often heuristic, and the optimal methodology remains unclear. In this work, we establish connections between these unsupervised clustering methods and classical mixture models from statisti

Olivier Dietrich, Merlin Alfredsson, Emilia Arens, Nando Metzger

Natural disasters demand rapid damage assessment to guide humanitarian response. Here, we investigate whether medium-resolution Earth observation images from the Copernicus program can support building damage assessment, complementing very-high resolution imagery with often limited availability. We introduce xBD-S12, a dataset of 10,315 pre- and post-disaste

Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar, Yiwen Song

Pre-trained Time Series Foundational Models (TSFMs) represent a significant advance, capable of forecasting diverse time series with complex characteristics, including varied seasonalities, trends, and long-range dependencies. Despite their primary goal of universal time series forecasting, their efficacy is far from uniform; divergent training protocols and

Jingxuan Xu, Ken Deng, Weihao Li, Songwei Yu

Evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer workflows. Existing benchmarks often focus on algorithmic problems or Python-centric bug fixing, leaving critical dimensions of software engineering underexplored. To address these gaps

Yukuan Tao, Madalin Guta, Gerardo Adesso

Quantifying the energetic cost of implementing quantum operations is essential for assessing the efficiency and scalability of quantum sensing and information-processing technologies. Here, we introduce a framework for analysing the interplay between complexity and energy cost of quantum processes. In particular, we apply our framework to a sequential quantu

Naveen Raj Manoharan, Hassan Iqbal, Krishna Kumar

Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and granular flows) while generalizing to unseen geometries due to their inherent inductive biases. However, existing models are typically trained for a single material type and fail to generalize across distinct con

Karan Fernandes, Feng-Li Lin, Chris D. White

The description of low-energy (``soft") gravitons using universal theorems continues to attract attention. In this paper, we consider the emission of two soft gravitons, using a previously developed formalism that describes (next-to) soft graviton emission in terms of generalised Wilson lines (GWLs). Based on Schwinger's proper time methods, the GWL allows f

Jake Kettinger

In this paper, we investigate groupoids coming from configurations of lines in three-dimensional space. Given a point and two skew lines in $\mathbb{P}^{3}_{K}$ over a field $K$, there exists a unique line containing the given point and meeting the two given lines. We use this construction to define a projection function from one line to another by using a s

Lauren Kasper, Alexander Austregesilo, Fernando Barbosa, Cody Dickover

Transition Radiation Detectors (TRDs) are useful for electron identification and hadron suppression in high energy nuclear and particle physics experiments. Conventional wire-chamber TRDs face operational limitations due to space charge effects, motivating the replacement of the amplification stage with MicroPattern Gaseous Detectors (MPGDs). This work explo

Justin Williams, Kishor Datta Gupta, Roy George, Mrinmoy Sarkar

The deployment of artificial intelligence models at the edge is increasingly critical for autonomous robots operating in GPS-denied environments where local, resource-efficient reasoning is essential. This work demonstrates the feasibility of deploying small Vision-Language Models (VLMs) on mobile robots to achieve real-time scene understanding and reasoning

Wenqian Chen, Amanda Howard, Panos Stinis

Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remains challenging, often leading to limited accuracy and efficiency. In this work, we introduce a hybrid adaptive sampling and weighting method to enhance the performance of physics-i

Andreas Kouloumpris, Georgios L. Stavrinides, Maria K. Michael, Theocharis Theocharides

The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the devices at the edge of the network, their limited computational, communication, and energy capacities, as well as their differ

Liz Blum, Lily Brustkern, Rosetta Hawkins, Neil R. Nicholson

We define the Sign Game as a two-player game played on a simple undirected mathematical graph $G$. The players alternate turns, assigning vertices of $G$ either $1$ or $-1$, and edges take on the value of the product of their endvertices. The game ends when all vertices are assigned values, and the score of the game is the sum of all edge values. One player'

Sayed Sadiqul Islam, Tony J. Puthenpurakal

Let $R=K[x_1,\ldots,x_m]$ where $K$ is an uncountable algebraically closed field of characteristic $0$. For a prime ideal $P$ of $R$, let $\mu_j(P,M)$ be the $j$-th Bass number of an $R$-module $M$ with respect to the prime $P$. For $1\leq g\leq m-1$, we construct a set $\mathcal{S}_g(t)$ such that $\mathcal{S}_g(t)\subseteq \mathcal{S}_g(t+1)$ for all $t\ge

Tuan Anh Tran, Duy M. H. Nguyen, Hoai-Chau Tran, Michael Barz

Recent advances in 3D point cloud transformers have led to state-of-the-art results in tasks such as semantic segmentation and reconstruction. However, these models typically rely on dense token representations, incurring high computational and memory costs during training and inference. In this work, we present the finding that tokens are remarkably redunda

Alexander M. Imre, Lutz Hammer, Ulrike Diebold, Michele Riva

Quantitative low-energy electron diffraction [LEED $I(V)$ or LEED $I(E)$, the evaluation of diffraction intensities $I$ as a function of the electron energy] is a versatile technique for the study of surface structures. The technique is based on optimizing the agreement between experimental and calculated intensities. Today, the most commonly used measure of

Natalia Gherghel, Hari K. Kunduri

We investigate the stability of homogeneous minimal submanifolds in two families of closed Einstein manifolds, the Page space $\mathbb{CP}^2 \# \overline{\mathbb{CP}^2}$ and the Sasaki-Einstein spaces $Y^{p,q}$, which are equipped with cohomogeneity-one Einstein metrics admitting the isometric action of $SU(2) \times U(1)$ and $U(1) \times U(1) \times SU(2)$

Zehua Cao, Richard J. Hill, Ryan Plestid, Peter Vander Griend

Superallowed ($0^+\rightarrow0^+$) beta decays currently provide the most precise extraction of quark mixing in the Standard Model. Their interpretation as a measurement of $|V_{ud}|$ relies on a reliable first-principles computation of QED radiative corrections expressed as a series in $Z\alpha$ and $\alpha$. In this work, we provide the first model-indepen

Yanchao Wang, Xu You, Mehdi Baghdadi

This paper proposes a fault-tolerant control strategy based on a tilt-rotor quadcopter prototype, utilizing nonlinear model predictive control to maintain both attitude and position stability in the event of rotor failure. The control strategy employs an extended state observer to predict model deviations following a fault and adjusts the original model in t

Nosratollah Jafari, Abdullah Guvendi

This study investigates a modified two-body Dirac equation in (2+1)-dimensional spacetime, inspired by Amelino-Camelia's doubly special relativity (DSR). We begin by deriving a covariant two-body Dirac equation that, in the absence of DSR modifications, reduces to a Bessel-type wave equation. Incorporating corrections from the chosen DSR model modifies this

Kasra Fallah, Leonardo F. Toso, James Anderson

We study adversarially robust multitask adaptive linear quadratic control; a setting where multiple systems collaboratively learn control policies under model uncertainty and adversarial corruption. We propose a clustered multitask approach that integrates clustering and system identification with resilient aggregation to mitigate corrupted model updates. Ou

Cameron N. Coleal, Randy A. Bartels, Jesse W. Wilson

Transient absorption microscopy measures excited-state kinetics based on the imaginary part of the pump-induced perturbation to the complex refractive index, i.e. $\Im \{\Delta\mathcal N\}$, with applications in both materials and biomedical sciences. Its complement, transient phase microscopy, enabled by stable inline birefringent interferometry, measures t

Frauke Andersen, William Rudman, Ruochen Zhang, Carsten Eickhoff

Circuit discovery is a key step in many mechanistic interpretability pipelines. Current methods, such as Path Patching, are computationally expensive and have limited in-depth circuit analysis for smaller models. In this study, we propose Accelerated Path Patching (APP), a hybrid approach leveraging our novel contrastive attention head pruning method to dras

Andreas Kouloumpris, Georgios L. Stavrinides, Maria K. Michael, Theocharis Theocharides

With the advent of the Internet of Things (IoT), novel critical applications have emerged that leverage the edge/hub/cloud paradigm, which diverges from the conventional edge computing perspective. A growing number of such applications require a streamlined architecture for their effective execution, often comprising a single edge device with sensing capabil

John Joseph M. Carrasco, Sai Sasank Chava, Alex Edison, Aslan Seifi

We present a novel robust framework for systematically constructing $D$-dimensional four-point higher-derivative contact amplitudes. Our modular block ("LEGO"-like) approach builds amplitudes directly from manifestly gauge-invariant kinematic blocks, color-weight factors, and scalar Mandelstam polynomials. Symmetries (Bose/Fermi) are imposed algebraically, a

Junmin An, Nathan Kaplan, Jon-Lark Kim, Jinquan Luo

There has been recent interest in the study of shortest self-orthogonal embeddings of binary linear codes, since many such codes are optimal self-orthogonal codes. Several authors have studied the length of a shortest self-orthogonal embedding of a given binary code $\mathcal C$, or equivalently, the minimum number of columns that must be added to a generato

Aayush Saxena

We apply variational autoencoders to automatically discover galaxy populations using publicly available high-redshift \textit{JWST} spectra without prior classification knowledge. Our unsupervised method identifies distinct astrophysical classes of unique and exciting galaxy types, demonstrating automated discovery capabilities for large spectroscopic survey

Nguyen Tran Gia Bao, Ton Nu Quynh Trang, Phan Bach Thang, Nam Thoai

Point defects, often formed during the growth of Janus MoSSe, act as built-in scatterers and affect carrier transport in electronic devices based on Janus MoSSe. In this study, we employ first-principles calculations to investigate the impact of common defects, such as sulfur vacancies, selenium vacancies, and chalcogen substitutions, on electron transport,

Keren Li, Zidong Lin, Zheng An, Guanru Feng

Scaling up quantum devices is a central challenge for realizing practical quantum computation. Modular quantum architectures promise scalability, yet experiments to date have relied on either $\sim\!10^{3}$-qubit monolithic chips or fragile interconnects with high loss. Here, we introduce a classical linkage scheme that merges multiple independent quantum pr

Adrián González Casanova, Noemi Kurt, Imanol Nuñez Morales, José Luis Pérez

In this paper, we study consistent and partially exchangeable sequences of Markov chains on a finite state space. We provide a characterisation of the admissible transition rates via a decomposition into individual and coordinated motion of particles. As a consequence, we find a characterisation of multitype Lambda-coalescents with multiple switches. Moreove

Han Huang, Pakawut Jiradilok, Elchanan Mossel

Random geometric graphs are random graph models defined on metric measure spaces. A random geometric graph is generated by first sampling points from a metric space and then connecting each pair of sampled points independently with a probability that depends on their distance. In recent work of Huang, Jiradilok, and Mossel~\cite{HJM24}, the authors study the

Bingzhi Zhang, Quntao Zhuang

Random circuit sampling (RCS) is a leading approach to demonstrate quantum advantage, with its believed classical hardness rooted in anticoncentration of output distributions and average-case hardness of probability estimation. Here we show that this association is not fundamental. We introduce holographic random circuit sampling (HRCS), a spatiotemporal pro

Dogucan Yaman, Seymanur Akti, Fevziye Irem Eyiokur, Alexander Waibel

We propose a text-to-talking-face synthesis framework leveraging latent speech representations from HierSpeech++. A Text-to-Vec module generates Wav2Vec2 embeddings from text, which jointly condition speech and face generation. To handle distribution shifts between clean and TTS-predicted features, we adopt a two-stage training: pretraining on Wav2Vec2 embed

Nasrin Sadeghzadeh, Najmeh Sajjadi Moghadam

This paper gives new insights into the class of Generalized Douglas Weyl ($GDW$)-metrics. This projective invariant class of Finsler metrics, contains some well-known Finsler metrics such as Douglas, Weyl and $R$-quadratic metrics. Here, some new sub-classes of $GDW$-metrics are constructed and considered as the explicit Finsler metrics. Many illustrative an

Aparna Maya Warrier, Arav Agarwal, Jaromir Savelka, Christopher Bogart

As artificial intelligence (AI) increasingly shapes decision-making across domains, there is a growing need to support AI literacy among learners beyond computer science. However, many current approaches rely on programming-heavy tools or abstract lecture-based content, limiting accessibility for non-STEM audiences. This paper presents findings from a study

Tyler Waterman, Peter Germ, Marc Calaf, Eric Pardyjak

Surface heterogeneity, particularly complex patterns of surface heating, significantly influences mesoscale atmospheric flows, yet observational constraints and modeling limitations have hindered comprehensive understanding and model parameterization. This study introduces a framework combining satellite remote sensing and Doppler LiDAR to observationally ev

Joy Chen, Ricardo Garcia-Mayoral

We investigate the energy cascade in wall-bounded turbulence by analysing the interscale transfer between streamwise and spanwise length scales in periodic channels. This transfer originates from the nonlinear interactions in the advective term of the Navier-Stokes equations, which satisfy the classical triadic compatibility relations. Each triadic interacti

Kelvin Wandia, Chenoa Tremblay, Michael A. Garrett, Alex Andersson

Stellar and planetary magnetic fields play a crucial role in the habitability of a planet and the integrity of its atmosphere. The recently claimed detection of biosignatures, methane, carbon dioxide and dimethyl sulfide/disulfide, in the atmosphere of K2-18 b, a sub-Neptune orbiting an M dwarf star present an intriguing question regarding the stellar magnet

Luca Girardi, Gabriel Maquignaz, Stefano Mintchev

Natural flyers use soft wings to seamlessly enable a wide range of flight behaviours, including agile manoeuvres, squeezing through narrow passageways, and withstanding collisions. In contrast, conventional quadrotor designs rely on rigid frames that support agile flight but inherently limit collision resilience and squeezability, thereby constraining flight

Jiacheng Tang

We give a categorical explanation for many properties of profinite coproducts of profinite groups, which were previously proven on a case-by-case basis. All of these properties take the form "certain functors preserve profinite coproducts". We give various examples to show how our framework can be applied. We also point out connections to internal ca

Aman Khalid, Sarah Brough, Garreth Martin, Lucas C. Kimmig

Tidal features provide signatures of recent galaxy mergers, offering insights into the role of mergers in galaxy evolution. The Vera C. Rubin Observatory's upcoming Legacy Survey of Space and Time (LSST) will allow for an unprecedented study of tidal features around millions of galaxies. We use mock images of galaxies at $z\sim0$ ($z\sim0.2$ for \textsc{NewH

Maynard Koch, Raphael Hiesgen, Marcin Nawrocki, Thomas C. Schmidt

Identifying active IPv6 addresses is challenging. Various methods emerged to master the measurement challenge in this huge address space, including hitlists, new probing techniques, and AI-generated target lists. In this paper, we apply active Subnet-Router anycast (SRA) probing, a commonly unused method to explore the IPv6 address space. We compare our resu

Andreas Gross, Arne Kuhrs, Martin Ulirsch, Dmitry Zakharov

We propose an elementary tropical analogue of a reductive group that combines the datum of a Weyl group and the tropicalization of a fixed maximal torus. For the classical groups, as well as $G_2$, these tropical reductive groups admit descriptions as tropical matrix groups that resemble their classical counterparts. Employing this perspective, we introduce

Aupendu Kar, Krishnendu Ghosh, Prabir Kumar Biswas

Continual learning is an emerging topic in the field of deep learning, where a model is expected to learn continuously for new upcoming tasks without forgetting previous experiences. This field has witnessed numerous advancements, but few works have been attempted in the direction of image restoration. Handling large image sizes and the divergent nature of v

A. Pasetto, O. Vilchis, A. Bonafede, E. F. Jiménez-Andrade

We present high-frequency, full-polarisation Jansky Very Large Array (VLA) radio data at X-band of the radio relic: MACS J0717.5+3745. Radio relics trace shock waves in the intracluster medium (ICM) produced during mergers. Understanding the physical characteristics of relics is important for determining their nature, whether for example they are thermal ICM

Luca Santosuosso, Simon Camal, Arthur Lett, Guillaume Bontron

This study develops a scalable co-optimization strategy for the joint bidding of cascaded hydropower, wind, and solar energy units, treated as a unified entity in the day-ahead market. Although hydropower flexibility can manage the stochasticity of renewable energy, the underlying bidding problem is complex due to intricate coupling constraints and nonlinear

Stephon Alexander, Heliudson Bernardo, Jacob Kuntzleman, Max Pezzelle

We propose a resolution to the longstanding problem of perturbative normalizability in canonical quantum gravity of the Lorentzian Chern-Simons-Kodama (CSK) state with a positive cosmological constant in four dimensions. While the CSK state is an exact solution to the Hamiltonian constraint in the self-dual formulation and semiclassically describes de Sitter

The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta

We present evidence that the time delay between the multiple rings of elves is not caused by the ground reflection of the electromagnetic pulse produced by intracloud lightning. To investigate temporal differences of multi-elves, we analyzed data from four storms occurring at various times and distances from the Pierre Auger Observatory in Malarg\"ue, Argent

Srijan Bharati Das, Michael Terres

Velocity distribution functions (VDF) are an essential observable for studying kinetic and wave-particle processes in solar wind plasmas. To experimentally distinguish modes of heating, acceleration, and turbulence in the solar wind, precise representations of particle phase space VDFs are needed. In the first paper of this series, we developed the Slepian B

Suprokash Hazra

In this article, we introduce special domains and discuss the geometry of these domains, which includes showing that every pseudoconvex truncated tube domain is a special domain. Next, we prove a theorem for the envelope of special domains in $\C^n ~(n\geq 2)$. Our theorem on special domains is a generalization of a recent result by Jarnicki-Pflug on the env

Giacomo Brunello, Stefano De Angelis, David A. Kosower

Leveraging the computational framework presented in reference [JHEP 07, 062 (2024)], we evaluate the analytic scattering waveform in General Relativity to second order, $G^3 M^3 /r b^2$ and to all orders in velocity. This new representation of the next-to-leading order waveform is well-suited for numerical evaluation. Integrating the [modulus square of the]

Zsolt Páles, Paweł Pasteczka

The purpose of this paper is to establish several necessary and sufficient conditions to ensure the validity of a general functional inequality in terms of generalized quasi-arithmetic means. In particular cases, we consider H\"older-, Minkowski-, and Jensen-type inequalities. Generalized quasi-arithmetic means are defined by taking strictly monotone generat

Justin D. Weisz, Michael Muller, Kush R. Varshney

What better way to understand the impact of AI on software engineering than to ask AI itself? We constructed Story Arena, a multi-agent "writer's room" in which multiple AI agents, independently imbued with a position statement on the future of software engineering, converse with each other to develop a shared vision. They then use this shared vision to coll

DAMPE Collaboration, Francesca Alemanno, Qi An, Philipp Azzarello

In most particle acceleration or propagation theories, the characteristic features of the cosmic ray spectra due to acceleration limits or propagation phase changes are charge dependent. Alternatively, the interaction scenario would expect mass dependent spectral features in general. The observational verification of which relation takes effect in nature is

Constanza Fierro, Fabien Roger

Providing high-quality feedback to Large Language Models (LLMs) on a diverse training distribution can be difficult and expensive, and providing feedback only on a narrow distribution can result in unintended generalizations. To better leverage narrow training data, we propose contrastive weight steering, a simple post-training method that edits the model pa

Yahui Fu, Zi Haur Pang, Tatsuya Kawahara

User satisfaction in dialogue systems is inherently subjective. When the same response strategy is applied across users, minority users may assign different satisfaction ratings than majority users due to variations in individual intents and preferences. However, existing alignment methods typically train one-size-fits-all models that aim for broad consensus

Tiago Dinis, Miguel Correia, Roger Tavares

As cyber threats continue to grow in complexity, traditional security mechanisms struggle to keep up. Large language models (LLMs) offer significant potential in cybersecurity due to their advanced capabilities in text processing and generation. This paper explores the use of LLMs with retrieval-augmented generation (RAG) to obtain threat intelligence by com

Lefeng Zhou, Anne Graf, Georg Raithel

We study the theory of, and propose an experimental design for, a Sagnac tractor atom interferometer based on a photonic integrated circuit (PIC). The atoms are trapped in counter-rotating azimuthal optical lattices, formed by interfering evanescent fields of laser modes injected into circular PIC waveguides. We develop quantum models for the radial and azim

Laura Alejandra Encinar Gonzalez, John Folkesson, Rudolph Triebel, Riccardo Giubilato

Robust loop closure detection is a critical component of Simultaneous Localization and Mapping (SLAM) algorithms in GNSS-denied environments, such as in the context of planetary exploration. In these settings, visual place recognition often fails due to aliasing and weak textures, while LiDAR-based methods suffer from sparsity and ambiguity. This paper prese

Zicong Fan, Edoardo Remelli, David Dimond, Fadime Sener

The ability to grasp objects, signal with gestures, and share emotion through touch all stem from the unique capabilities of human hands. Yet creating high-quality personalized hand avatars from images remains challenging due to complex geometry, appearance, and articulation, particularly under unconstrained lighting and limited views. Progress has also been

Muhammad Saud Ul Hassan, Derek Vasquez, Hamza Asif, Christian Hubicki

In this paper, we present an energy-conservation based control architecture for stable dynamic motion in quadruped robots. We model the robot as a Spring-loaded Inverted Pendulum (SLIP), a model well-suited to represent the bouncing motion characteristic of running gaits observed in various biological quadrupeds and bio-inspired robotic systems. The model pe

Hamza Virk, Sandro Amaglobeli, Zuhayr Syed

Inverse Game Theory (IGT) methods based on the entropy-regularized Quantal Response Equilibrium (QRE) offer a tractable approach for competitive settings, but critically assume the agents' rationality parameter (temperature $\tau$) is known a priori. When $\tau$ is unknown, a fundamental scale ambiguity emerges that couples $\tau$ with the reward parameters

Alexandr Kostochka, Dadong Peng, Liang Zhang

Given a graph $H$, the Tur\'{a}n number ${\rm ex}(n,H)$ of $H$ is the maximum number of edges of an $n$-vertex simple graph containing no $H$ as a subgraph. Let $kK_p$ denote the disjoint union of $k$ copies of the complete graph $K_p$. In this paper, utilizing the idea of the proof of the Hajnal-Szemer\'{e}di Theorem and discharging, we determine the value

Dean Wampler, Dave Nielson, Alireza Seddighi

This article provides a comprehensive systematic literature review of academic studies, industrial applications, and real-world deployments from 2018 to 2025, providing a practical guide and detailed overview of modern Retrieval-Augmented Generation (RAG) architectures. RAG offers a modular approach for integrating external knowledge without increasing the c

Ma Xiaofan, Yan Lirong, Zhao Weijia, Zeng Weiping

Ethnic clothing is a vital carrier of cultural identity, yet its digital preservation often results in static displays that fail to convey deep cultural meaning or foster user engagement. Existing practices lack a systematic design framework for translating the hierarchical cultural connotations of these garments into dynamic, personalized, and identity-prom

Shubhr Singh, Kiran Bhat, Xavier Riley, Benjamin Resnick

The proliferation of distorted, compressed, and manipulated music on modern media platforms like TikTok motivates the development of more robust audio fingerprinting techniques to identify the sources of musical recordings. In this paper, we develop and evaluate new neural audio fingerprinting techniques with the aim of improving their robustness. We make tw

Vojtech Kostal, Brennon L. Shanks, Pavel Jungwirth, Hector Martinez-Seara

Molecular dynamics is a valuable tool to probe biological processes at the atomistic level - a resolution often elusive to experiments. However, the credibility of molecular models is limited by the accuracy of the underlying force field, which is often parametrized relying on ad hoc assumptions. To address this gap, we present a Bayesian framework for learn

Samarth Chopra, Alex McMoil, Ben Carnovale, Evan Sokolson

While Vision-Language-Action (VLA) models map visual inputs and language instructions directly to robot actions, they often rely on costly hardware and struggle in novel or cluttered scenes. We introduce EverydayVLA, a 6-DOF manipulator that can be assembled for under $300, capable of modest payloads and workspace. A single unified model jointly outputs disc

Yiting He, Zhishuai Liu, Weixin Wang, Pan Xu

Off-dynamics reinforcement learning (RL), where training and deployment transition dynamics are different, can be formulated as learning in a robust Markov decision process (RMDP) where uncertainties in transition dynamics are imposed. Existing literature mostly assumes access to generative models allowing arbitrary state-action queries or pre-collected data

Csaba Vincze

As an application of Brouwer's fixed-point theorem we prove that a continuously differentiable convex function with gradient of constant norm is an affine mapping. It is a first-order characterization of affine mappings among continuously differentiable convex functions, because neither the second-order condition of convexity nor related operators are used.

Alexander Htet Kyaw, Haotian Ma, Sasa Zivkovic, Jenny Sabin

We present an AI-assisted Augmented Reality assembly workflow that uses deep learning-based object recognition to identify different assembly components and display step-by-step instructions. For each assembly step, the system displays a bounding box around the corresponding components in the physical space, and where the component should be placed. By conne

Zehui Feng, Weichuan Wang, Xiaohan Chen, Ting Han

Visual Quality Assessment (QA) seeks to predict human perceptual judgments of visual fidelity. While recent multimodal large language models (MLLMs) show promise in reasoning about image and video quality, existing approaches mainly rely on supervised fine-tuning or rank-only objectives, resulting in shallow reasoning, poor score calibration, and limited cro