December 2025 arXiv papers — page 106

Timothy A. Brumfiel, Revanth Konda, Drew Elliott, Jaydev P. Desai

To address the issues that arise due to the manual navigation of guidewires in endovascular interventions, research in medical robotics has taken a strong interest in developing robotically steerable guidewires, which offer the possibility of enhanced maneuverability and navigation, as the tip of the guidewire can be actively steered. The COaxially Aligned S

Miguel Jiménez-Redondo, Chiara Schleif, Julianna Palotás, János Sarka

Rotationally resolved spectra of the HNC$^+$ and HCN$^+$ molecular ions have been recorded in the spectral range between 6200 and 6800 \rcm\ using a cryogenic ion trap instrument. The rovibrational transitions were probed using two different action spectroscopy schemes, namely laser-induced reaction (LIR) and leak-out spectroscopy (LOS). Various vibrational

Xiao-Bin Qiang, Han-Yi Xu, Ren-Jie Tong, Shuai Li

Linear magnetoresistance is a hallmark of 3D Weyl metals in the quantum limit. Recently, a pronounced linear magnetoresistance has also been observed in 2D graphene [Xin et al., Nature 616, 270 (2023)]. However, a comprehensive theoretical understanding remains elusive. By employing the self-consistent Born approximation, we derive the analytical expressions

Laura Partanen, Antti Sipila, Md Sanaul Haque, Jari Porras

The global climate is experiencing a rapid and unprecedented warming trend. The ICT sector is a notable contributor to global greenhouse gas emissions, with its environmental impact continuing to expand. Addressing this issue is vital for achieving the objectives of the Paris Agreement, particularly the goal of limiting global temperature rise to 1.5{\deg}C.

Alexa A. Sochaniwsky, Paul D. McNicholas

A family of parsimonious ultrametric mixture models with the Manly transformation is developed for clustering high-dimensional and asymmetric data. Advances in Gaussian mixture modeling sufficiently handle high-dimensional data but struggle with the common presence of skewness. While these advances reduce the number of free parameters, they often provide lim

Jian Yang, Shawn Guo, Lin Jing, Wei Zhang

Code large language models (Code LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming languages (PLs) have varying impacts during pre-training that significantly affect base model performance, leading to inaccurate performance prediction. Besides, existing works fo

Sareh Eslamzadeh, Saheb Soroushfar

In this paper, we investigate the thermodynamic behavior of a horizonless compact object within the framework of Rastall-Rainbow (RR) gravity. Working with local shell thermodynamics for gravastar and an exterior fiducial temperature, we show that the RR modification bends temperature to produce two extrema and a stable mass remnant at zero temperature. We s

C. Ventura, M. Tailo, P. Ventura, F. D'Antona

Context: The study of the Globular Cluster 47 Tuc offers the opportunity to shed new light on the debated issue on the presence of multiple populations in Globular Clusters, as recent results from HST photometry and high-resolution spectroscopy outlined star-to-star differences in the surface chemical composition. Aims: The goal of the present investigation

Vikram Nadig

We establish homological stability for automorphisms of symmetric bilinear forms over a class of principal ideal domains that includes all fields, the integers, the Gaussian integers, and the Eisenstein integers. In conjunction with Grothendieck-Witt theoretic calculations, this determines a large part of the stable cohomology of the odd orthogonal groups $O

Andry N. Rabenantoandro

In this note, we introduce a new topological index of a graph G that we term peripheral hyper-Wiener index, denoted PWW(G). It is a natural extension of the peripheral Wiener index PW(G) initiated in [NB17] and is to the peripheral Wiener index what the hyper-Wiener index is to the Wiener index. We investigate its basic properties. We compute the peripheral

Maike C. de Jongh, Cristian Spitoni, Emilio N. M. Cirillo

Optimal growth of structures governed by spatially stochastic dynamics arises in many scientific settings, for example in processes such as solution-based crystallization and the formation of microbial biofilms on patterned substrates or microfluidic networks. In this work, we investigate lattice growth using a two-dimensional, zero-temperature stochastic mo

Mohammed N. Khamees, Kai Sun

The primary goal of Optimal Power Flow (OPF) is to optimize the operation of a power system while meeting the demand and adhering to operational constraints. This paper presents a new approach for AC OPF. First, the approach constructs a Voronoi diagram by distributing multiple sample points representing potential solutions throughout the search space. Then,

Ruiyan Wang, Teng Hu, Kaihui Huang, Zihan Su

Pose-guided video generation refers to controlling the motion of subjects in generated video through a sequence of poses. It enables precise control over subject motion and has important applications in animation. However, current pose-guided video generation methods are limited to accepting only human poses as input, thus generalizing poorly to pose of othe

Volker Runde, Nico Spronk, Matthew Wiersma

Runde and Spronk showed in 2004 that there are non-amenable groups $G$, including $\mathbb F_2$, {whose Fourier-Stieltjes algebra, $B(G)$,} is operator Connes-amenable. This result was surprising since the measure algebra $M(G)$ is Connes-amenable if and only if $G$ is amenable, which might lead one to guess that $B(G)$ should be operator Connes-amenable if

Galina Weinstein

Einstein's distinction between principle theories and constructive theories is methodological rather than metaphysical. Principle theories such as thermodynamics and relativity articulate empirically distilled constraints that delimit admissible microphysical models, while constructive theories remain provisional and revisable. This paper reconstructs Einste

Brian R. La Cour

The presence of negative values in the Wigner quasiprobability distribution is deemed one of the hallmarks of nonclassical phenomena in quantum systems. Here we demonstrate a classical model of squeezed light that, when combined with post-selection on amplitude threshold-crossing detection events, is capable of reproducing observed behavior of single-photon

Luna Bloin-Wibe, Erich Fischer, Leonard Göke, Reto Knutti

Renewable energy sources play a major role in future net-zero energy systems. However, achieving energy system resilience remains challenging, since renewables depend on weather fluctuations, and future energy systems are subject to major design uncertainty. Existing literature mostly treats these types of uncertainty separately. Therefore, the assessment of

Chethana Prasad Kabgere, Shylaja S S

Federated Learning (FL) enables decentralized model training without sharing raw data, but model weight distortion remains a major challenge in resource constrained IoT networks. In multi tier Federated IoT (Fed-IoT) systems, unstable connectivity and adversarial interference can silently alter transmitted parameters, degrading convergence. We propose DP-EMA

Yuto Nakamura, Shintaro Sato, Naofumi Ohnishi

Reduced-order models (ROMs) that capture changes in fluid systems due to variations in parameters, such as the Reynolds number or the shape of a stationary body placed in the flow, are attracting increasing attention in engineering applications. In this study, we identify linear operators that characterize the behavior of fluid systems across a wide paramete

Yici Liu, Qi Wei Oung, Hoi Leong Lee

Electroencephalographic (EEG) signals have long been applied in the field of affective brain-computer interfaces (aBCIs). Cross-subject EEG-based emotion recognition has demonstrated significant potential in practical applications due to its suitability across diverse people. However, most studies on cross-subject EEG-based emotion recognition neglect the pr

Sandra Albrechtsen, Max Pitz, Roman Schaut

We show that if a subset $\Psi$ of the ends of a graph $G$ can be displayed by a tree-decomposition of finite adhesion, then it can also be displayed by a linked such tree-decomposition. This tree-decomposition captures all combinatorial information of the ends in $\Psi$: their degrees, their sets of dominating vertices, and their combined degrees.

David Lindner, Charlie Griffin, Tomek Korbak, Roland S. Zimmermann

Automated control monitors could play an important role in overseeing highly capable AI agents that we do not fully trust. Prior work has explored control monitoring in simplified settings, but scaling monitoring to real-world deployments introduces additional dynamics: parallel agent instances, non-negligible oversight latency, incremental attacks between a

Khakim Egamberganov, Yao Yao

We consider the axisymmetric Euler equations in $\mathbb{R}^3$ without swirl, and establish several upper and lower bounds for the growth of solutions. On the one hand, we obtain an upper bound $t^2$ for the radial moment $\int_{\mathbb{R}^3} r\omega^\theta dx$, which is the conjectured optimal rate by Childress (Phys. D 237(14-17):1921-1925, 2008). On the o

Richard Tanburn, Danny Hendron, Philip Maini, Silviana Amethyst

When faced with a mathematical model, often the first step is to reduce the complexity of the model by turning variables and parameters into dimensionless quantities. This process is often performed by hand, relying on a skill practiced over many years, and attempted for small models. Nondimensionalization is often considered an art, as there is no formal me

Arpit Jadon, Joshua Niemeijer, Yuki M. Asano

Generative foundation models contain broad visual knowledge and can produce diverse image variations, making them particularly promising for advancing domain generalization tasks. They can be used for training data augmentation, but synthesizing comprehensive target-domain variations remains slow, expensive, and incomplete. We propose an alternative: using d

Harm Derksen

We consider the action of a permutation group $G$ of order $k$ on the tropical polynomial semiring in $n$ variables. We prove that the sub-semiring of invariant polynomials is finitely generated if and only if $G$ is generated by $2$-cycles. There do exist finitely many separating invariants of degree at most $\max\{n,{n\choose 2}\}$. Separating tropical inv

Vjosa Blakaj, Matthias C. Caro, Anouar Kouraich, Daniel Malz

Gibbs states play a central role in quantum statistical mechanics as the standard description of thermal equilibrium. Traditionally, their use is justified either by a heuristic, a posteriori reasoning, or by derivations based on notions of typicality or passivity. In this work, we show that Gibbs states are completely characterized by assuming dynamical sta

Julien Marché, Gregor Masbaum

We study the signature $\sigma_g(\frac q p)$ of $\mathrm{SU}_2$-TQFT vector spaces associated to surfaces of genus $g$, as a function of the defining root of unity $\zeta=e^{i\pi q/p}$. We prove that $\frac{1}{p^2}\sigma_2(\frac{q}{p})$ converges to $\Lambda(\theta)=\frac{16}{\pi^3}\sum\limits_{n\ge 1, \textrm{ odd}}\frac{1}{n^3\sin(n\pi\theta)}$ when $\frac

Xiao Han

We investigate the notion of Gaussian domination for the spin $O(N)$ model on general finite graphs. We begin by proving a general inequality for spin correlations under the assumption of Gaussian domination, which directly implies long-range order at low temperatures for graphs with bounded Green's function. Usually, Gaussian domination is proved via reflec

Linqing Peng, Tian Qiu, Nadine Bradbury, Xuezhi Bian

$\Lambda$-doubling of diatomic molecules is a subtle microscopic phenomenon that has long attracted the attention of experimental groups, insofar as rotation of molecular $\textit{nuclei}$ induces small energetic changes in the (degenerate) $\textit{electronic}$ state. A direct description of such a phenomenon clearly requires going beyond the Born-Oppenheim

Sylvia Ploeckinger

We report the presence of a systematic excess in the molecular hydrogen fraction ($f_{\mathrm{H2}} = 2 \, n_{\mathrm{H2}}/n_{\mathrm{H}}$) in studies that use a reduced chemistry network to calculate $f_{\mathrm{H2}}$ of gas with a non-zero metal mass fraction. This is common practice in simulations of galaxy formation in which following the non-equilibrium

Murat Yaslioglu

Common Method Variance (CMV) is a recurring problem that reduces survey accuracy. Popular fixes such as the Harman single-factor test, correlated uniquenesses, common latent factor models, and marker variable approaches have well known flaws. These approaches either poorly identify issues, rely too heavily on researchers' choices, omit real information, or r

Alexander Guterman, Andrey Yurkov

This paper is the second in the series of papers devoted to the explicit description of linear maps preserving the Cullis' determinant of rectangular matrices with entries belonging to an arbitrary ground field which is large enough. In this part we solve the linear preserver problem for the Cullis' determinant defined on the spaces of matrices of size $n\ti

Yi Peng, Hina Saeeda, Hans-Martin Heyn, Jennifer Horkoff

With the rise of AI-enabled cyber-physical systems, data annotation has become a critical yet often overlooked process in the development of these intelligent information systems. Existing work in requirements engineering (RE) has explored how requirements for AI systems and their data can be represented. However, related interviews with industry professiona

Daniyal Ganiuly, Nurzhau Bolatbek, Assel Smaiyl

Cyber-physical systems (CPS) such as unmanned aerial vehicles are vulnerable to slow degradation that develops without causing immediate or obvious failures. Small sensor biases or timing irregularities can accumulate over time, gradually reducing stability while standard monitoring mechanisms continue to report normal operation. Detecting this early phase o

Khawla Elhadri, Jörg Schlötterer, Christin Seifert

In data-driven applications relying on tabular data, where interpretability is key, machine learning models such as decision trees and linear regression are applied. Although neural networks can provide higher predictive performance, they are not used because of their blackbox nature. In this work, we present XNNTab, a neural architecture that combines the e

Johan J. Bolhuis, Andrea Moro, Stephen Crain, Sandiway Fong

Large Language Models are useless for linguistics, as they are probabilistic models that require a vast amount of data to analyse externalized strings of words. In contrast, human language is underpinned by a mind-internal computational system that recursively generates hierarchical thought structures. The language system grows with minimal external input an

Thalyssa Baiocco-Rodrigues, Antoine Olivier, Reda Belbahri, Thomas Duboudin

As the therapeutic target for Inflammatory Bowel Disease (IBD) shifts toward histologic remission, the accurate assessment of microscopic inflammation has become increasingly central for evaluating disease activity and response to treatment. In this work, we introduce IMILIA (Interpretable Multiple Instance Learning for Inflammation Analysis), an end-to-end

Nitya Sathyavageeswaran, Anand D. Sarwate, Narayan B. Mandayam, Roy D. Yates

We study the trade-off between Age of Information (AoI) and maximal leakage (MaxL) in discrete-time status updating systems. A source generates time-stamped update packets that are processed by a server that delivers them to a monitor. An adversary, who eavesdrops on the server-monitor link, wishes to infer the timing of the underlying source update sequence

Dezhi Ran, Zhi Gong, Yuzhe Guo, Mengzhou Wu

While Large Language Model (LLM) agents show great potential for automated UI navigation such as automated UI testing and AI assistants, their efficiency has been largely overlooked. Our motivating study reveals that inefficient UI representation creates a critical performance bottleneck. However, UI representation optimization, formulated as the task of aut

Alexander Guterman, Andrey Yurkov

This paper is the first in the series of papers devoted to the explicit description of linear maps preserving the Cullis' determinant of rectangular matrices with entries belonging to an arbitrary ground field which is large enough. The Cullis' determinant is defined for every matrix of size $n\times k$, where $n \ge k \ge 1$ and is equal to the ordinary det

Friederike Butt, Lars Esser, Markus Müller

Practical large-scale quantum computation requires both efficient error correction and robust implementation of logical operations. Three-dimensional (3D) color codes are a promising candidate for fault-tolerant quantum computation due to their transversal non-Clifford gates, but efficient decoding remains challenging. In this work, we extend previous decode

Thomas Bsaibes, Charles Condos, Jack Manley, Jon Pratt

Torsion pendulums provide an opportunity to trap large masses in a potential weak enough to explore two-body gravitation. Cooled to, and then released from a ground state, weak quantum effects, including those from gravity, might reveal themselves in the evolving decoherence of a torsion pendulum, if its baseline dissipation were sufficiently dilute for quan

Youssef Megahed, Inok Lee, Robin Ducharme, Kevin Dick

Prenatal ultrasound is the cornerstone for detecting congenital anomalies of the kidneys and urinary tract, but diagnosis is limited by operator dependence and suboptimal imaging conditions. We sought to assess the performance of a self-supervised ultrasound foundation model for automated fetal renal anomaly classification using a curated dataset of 969 two-

Álvaro Arboleya, Gabriele Casagrande, Adolfo Guarino, Matteo Morittu

We complete the study initiated in \cite{Arboleya:2024vnp} and investigate three-dimensional (3D) flux vacua of type II orientifold reductions on twisted tori that include a single type of spacetime-filling O$p$-plane with $\,p=2,\ldots,9$. Restricting to $\textrm{SO}(3)$-invariant setups -- also known as RSTU-models -- and setting axions to zero, we exhaust

Kanat Abdukhalikov, Gyanendra K. Verma

We construct two new families of linear codes by modifying the generator matrices of generalized Reed-Solomon (GRS) codes. For these codes, we explicitly derive parity-check matrices and establish necessary and sufficient conditions ensuring the MDS property. Additionally, we explore subfamilies within these constructions that are non-GRS MDS codes. We also

Anika Islam, Tasfia Tahsin, Zaarin Anjum, Md. Bakhtiar Hasan

Accurate and timely identification of plant leaf diseases is essential for resilient and sustainable agriculture, yet most deep learning approaches rely on large annotated datasets and computationally intensive models that are unsuitable for data-scarce and resource-constrained environments. To address these challenges we present a few-shot learning approach

Noa Cohen, Nurit Spingarn-Eliezer, Inbar Huberman-Spiegelglas, Tomer Michaeli

Text-to-Image (TTI) models generate images based on text prompts, which often leave certain aspects of the desired image ambiguous. When faced with these ambiguities, TTI models have been shown to exhibit biases in their interpretations. These biases can have societal impacts, e.g., when showing only a certain race for a stated occupation. They can also affe

E. Zubieta, C. M. Espinoza, D. Antonopoulou, W. C. G. Ho

Pulsar glitches are unresolved increments of the rotation rate that sometimes trigger an enhancement of the spin-down rate. On occasions, the augmented spin-down decays gradually in an exponential manner, particularly after the largest glitch events. The young pulsar PSR J0537-6910 exhibits the highest known glitching rate, with 60 events detected in nearly

Joe Forth, Robert Malinowski, Giorgio Volpe

Droplets, sub-millilitre liquid volumes with at least one interface, have traditionally served as compartments for storing, transporting, and delivering materials. Beyond familiar applications in food, coatings, and consumer goods, they find cutting-edge use in energy storage, sensing, and tissue engineering. The next frontier is their integration into anima

Guo-Niu Han

The cyclotomic Eulerian polynomials and the cyclotomic Mahonian polynomials have each been the subject of extensive studies in Combinatorics, with particular attention to their signed versions. In contrast, the joint study of cyclotomic Euler-Mahonian polynomials has received far less consideration. To the best of our knowledge, the only prior result in this

Ning Ma, Jianjun Zhao, Foutse Khomh, Shaukat Ali

Unlike classical software, where logging and runtime tracing can effectively reveal internal execution status, quantum circuits possess unique properties, such as the no-cloning theorem and measurement-induced collapse, that prevent direct observation or duplication of their states. These characteristics make it especially challenging to monitor the executio

Qingyu Shi, Size Wu, Jinbin Bai, Kaidong Yu

Visual tokenizers play a crucial role in diffusion models. The dimensionality of latent space governs both reconstruction fidelity and the semantic expressiveness of the latent feature. However, a fundamental trade-off is inherent between dimensionality and generation quality, constraining existing methods to low-dimensional latent spaces. Although recent wo

Andrea Santoro, Marco Nurisso, Giovanni Petri

Traditional graph signal processing (GSP) methods applied to brain networks focus on signals defined on the nodes. Thus, they are unable to capture potentially important dynamics occurring on the edges. In this work, we adopt an edge-centric GSP approach to analyze edge signals constructed from 100 unrelated subjects of the Human Connectome Project. Specific

Junyu Liu, Siwen Yang, Dexiu Ma, Qian Niu

Human papillomavirus (HPV) vaccine hesitancy poses significant public health challenges, particularly in Japan where proactive vaccination recommendations were suspended from 2013 to 2021. The resulting information gap is exacerbated by misinformation on social media, and traditional ways cannot simultaneously address individual queries while monitoring popu

Konstantinos Kalimeris, Leonidas Mindrinos

A broad class of inverse problems deals with determining certain parameters, from measurement data, in models which are associated to certain partial differential equations. In this work we focus on the heat equation on a finite interval and we determine the dimensionless diffusion parameter from a single measurement. Our results extend to estimating additio

Michiel Min, Jo Barstow, Laura C. Mayorga, Hannah Wakeford

Cool gas giant exoplanets, particularly those with properties similar to those of Jupiter and Saturn, remain poorly characterized due to current observational limitations. This white paper outlines the transformative science case for the Habitable Worlds Observatory (HWO) to directly image and spectroscopically characterize a broad range of gaseous exoplanet

Haoxuan Qu, Qiuchi Xiang, Yujun Cai, Yirui Wu

Unexploitable example generation aims to transform personal images into their unexploitable (unlearnable) versions before they are uploaded online, thereby preventing unauthorized exploitation of online personal images. Recently, this task has garnered significant research attention due to its critical relevance to personal data privacy. Yet, despite recent

Ahmed Abul Hasanaath, Hamzah Luqman

Continuous sign language recognition (CSLR) requires precise spatio-temporal modeling to accurately recognize sequences of gestures in videos. Existing frameworks often rely on CNN-based spatial backbones combined with temporal convolution or recurrent modules. These techniques fail in capturing fine-grained hand and facial cues and modeling long-range tempo

Fan Bu, Rongfeng Li, Zijin Li, Ya Li

Large-scale optical music recognition (OMR) research has focused mainly on Western staff notation, leaving Chinese Jianpu (numbered notation) and its rich lyric resources underexplored. We present a modular expert-system pipeline that converts printed Jianpu scores with lyrics into machine-readable MusicXML and MIDI, without requiring massive annotated train

Zenghui Zhou, Pak-Lok Poon, Zheng Zheng, Xiao-Yi Zhang

Metamorphic testing (MT) alleviates the oracle problem by checking metamorphic relations (MRs) across multiple test executions. The fault detection effectiveness of MT is influenced not only by the choice and quality of MRs, but also by how source test cases and metamorphic groups (MGs) are selected. While substantial research has focused on designing, gener

Kenza Memlouk

We consider multiple zeta values, which are periods of mixed Tate motives over $\mathbb Z$. For a given multiple zeta value $\zeta$, there exists a unique minimal motive $M(\zeta)$ such that $\zeta$ is a period of $M(\zeta)$. In general, the motive $M(\zeta)$ is difficult to compute. In this article, we compute the minimal motive $M(a,b)$ associated to a giv

Patryk Niżeniec, Marcin Iwanowski

This paper introduces a novel pipeline for generating large-scale, highly realistic, and automatically labeled datasets for computer vision tasks in robotic environments. Our approach addresses the critical challenges of the domain gap between synthetic and real-world imagery and the time-consuming bottleneck of manual annotation. We leverage 3D Gaussian Spl

Vítor M. Hanriot, Luiz C. B. Torres, Antônio P. Braga

While large margin classifiers are originally an outcome of an optimization framework, support vectors (SVs) can be obtained from geometric approaches. This article presents advances in the use of Gabriel graphs (GGs) in binary and multiclass classification problems. For Chipclass, a hyperparameter-less and optimization-less GG-based binary classifier, we di

K. Decker French, Brenna Mockler, Nicholas Earl, Tanner Murphey

Tidal Disruption Events (TDEs) provide an opportunity to study supermassive black holes that are otherwise quiescent. The Vera C. Rubin Legacy Survey of Space and Time will be capable of discovering thousands of TDEs each year, allowing for a dramatic increase in the number of discovered TDEs. The optical light curves from TDEs can be used to model the physi

Andrei Igoshev, Nicolás A. Moraga, Andreas Reisenegger, Calum S. Skene

Magnetic field evolution in neutron star cores is not fully understood. We describe the field evolution both for one barotropic fluid as well as two collisionally coupled barotropic fluids with different density profiles using the anelastic approximation and the Navier-Stokes equations to simulate the evolution in three dimensions. In the one-fluid case, a s

Sarah A. Kovac, Amir Caspi, Daniel B. Seaton, Paul Bryans

The Citizen CATE 2024 next-generation experiment placed 43 identical telescope and camera setups along the path of totality during the total solar eclipse (TSE) on 8 April 2024 to capture a 60-minute movie of the inner and middle solar corona in polarized visible light. The 2024 TSE path covered a large geographic swath of North America and we recruited and

Frederik Johannes Zuiderveen Borgesius

This PhD thesis discusses how European law could improve privacy protection in the area of behavioural targeting. Behavioural targeting, also referred to as online profiling, involves monitoring people's online behaviour, and using the collected information to show people individually targeted advertisements. To protect privacy in the area of behavioural tar

Stefan Kulk, Frederik Zuiderveen Borgesius

When reviewing a job application letter, going on a first date, or considering doing business with someone, the first thing many people do is entering the person's name in a search engine. A search engine can point searchers to information that would otherwise have remained obscure. If somebody searched for the name of Spanish lawyer Mario Costeja Gonz\'alez

Benjamin Horowitz, Zarija Lukić, Kentaro Nagamine, Yuri Oku

We present the extension of the differentiable hydrodynamics code, diffhydro, enabling scalable PDE-constrained inference and integrated hybrid physics-ML models for a wide range of astrophysical applications. New physics additions include radiative heating/cooling, OU-driven turbulence, and self-gravity via multigrid Poisson. We demonstrate good agreement w

Mahum Pervez, Ariq Haqq, Nathan A. McMahon, Christian Arenz

We investigate Riemannian gradient flows for preparing ground states of a desired Hamiltonian on a quantum device. We show that the number of steps of the corresponding Riemannian gradient descent (RGD) algorithm that prepares a ground state to a given precision depends on the structure of the Hamiltonian. Specifically, we develop an upper bound for the numb

Artem Timoshenko, Caio Waisman

We propose a framework that aligns Conditional Average Treatment Effect (CATE) estimation with profit maximization. Our method recognizes that, for customers with extreme treatment effects, additional estimation accuracy is unlikely to change the recommended actions. In contrast, accuracy is critical near the decision boundary, where treatment effects are cl

Malte Silbernagel, Albert Alonso, Jens Petersen, Bulat Ibragimov

Accurately predicting topologically correct masks remains a difficult task for general segmentation models, which often produce fragmented or disconnected outputs. Fixing these artifacts typically requires hand-crafted refinement rules or architectures specialized to a particular task. Here, we show that Neural Cellular Automata (NCA) can be directly re-purp

Chaohua Yang, Dugang Liu, Shiwei Li, Yuwen Fu

Multi-scenario multi-task recommendation (MSMTR) systems must address recommendation demands across diverse scenarios while simultaneously optimizing multiple objectives, such as click-through rate and conversion rate. Existing MSMTR models typically consist of four information units: scenario-shared, scenario-specific, task-shared, and task-specific network

Tamás Szklenár, Attila Bódi, Róbert Szabó

In this project we use data obtained by Zwicky Transient Facility to develop and test a neural-network-based, multiband classification algorithm to classify periodic variable stars (i.e. pulsating variable stars and eclipsing binaries). The aim is to utilize the algorithm on LSST data once they become available. Phase-folded light curve images and period inf

Zhiyu Yin, Harry Arnold, James F Drake, Marc Swisdak

The factors that control the maximum energy attained by protons and electrons during magnetic reconnection are investigated analytically and using large-scale simulations with the \textit{kglobal} model. Previous work revealed that a strong ambient guide field strongly impacts particle energy gain during reconnection, suppressing energy gain from Fermi refle

Mohammad Reza Fasihi, Brian L. Mark

Unlicensed spectrum supports diverse traffic with stringent Quality-of-Service (QoS) requirements. In NR-U/Wi-Fi coexistence,the values of MAC parameters critically influence delay, collision behavior, and airtime fairness and efficiency. In this paper, we investigate the impact of (i) cost scaling and violation modeling, (ii) choice of MAC parameters, and (

Anran Qi, Changjian Li, Adrien Bousseau, Niloy J. Mitra

Disocclusion occurs when object movement reveals previously hidden content. Existing image-to-video methods provide different forms of motion and disocclusion control, yet the design space they span remains poorly understood. In this work, we systematically explore an algorithmic design space spanning motion specification (text-based versus tracking-based) a

Sanghita Chandra, Robert Cameron, Damien Przybylski, Sami K. Solanki

Numerical simulations of the solar chromosphere have progressed towards reproducing spicules, which are transient features observed at the solar limb using spectral lines such as H$\alpha$, Ca II H&K, or Mg II h&k. Two types of spicules, referred to as types I and II, have been identified in observations and studied in previous numerical works. The statistic

Adarsh Kumar, Carlton M. Baugh, Suttikoon Koonkor, Giorgio Manzoni

Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbolic regression trained on a simulation to derive simple, explicit mathematical expressions that estimate galaxy stellar masses from basic observables: photometry and redshift. Using

Jefferson Tang, Pavel A. Volkov

We show that twisted interfaces between superconductors can serve as a phase-sensitive platform for the detection and characterization of pair density waves (PDW). In the presence of an in-plane magnetic field, the critical Josephson current of a twisted PDW interface is maximal at a finite field value, determined by the twist angle and the PDW period -- an

Arkadeb Pal, P. H. Lee, J. Khatua, C. W. Wang

Low-dimensional magnetic systems based on honeycomb lattices provide a promising platform for exploring exotic quantum phenomena that emerge from the intricate interplay of competing spin, orbital, lattice, and dipolar degrees of freedom. Here, we present a comprehensive study of the layered honeycomb lattice antiferromagnet BiCrTeO$_6$ using magnetization,

Feiyang Lin, Theodore Lysek

We characterize components of the locally free locus $\operatorname{Quot}^{n,d}_{\mathbb{P}^1}(\mathcal{O}(\vec{e}))^{\circ}$ of the Quot scheme associated to any vector bundle on $\mathbb{P}^1$. Specifically, we show that the components are in bijection with certain combinatorial objects which we call strongly stable pairs. Using our explicit understanding

Przemyslaw Chojecki

We study the special role of mathematics and coding inside the moduli space of psychometric batteries for AI agents. Building on the AAI framework and GVU dynamics from previous works, we define the Mathematics Fiber and show that, when paired with formal proof kernels (e.g. Lean, Coq), GVU flows on this fiber admit spectrally stable self-improvement regimes

Juan C. Gonçalves-Dosantos, Ricardo Martínez, Juan D. Moreno-Ternero, Joaquín Sánchez-Soriano

We explore the resolution of claims problems with history. At a given period of time, a group of agents holds claims over an insufficient endowment, as they did in previous periods. The solution to the present-period problem might be influenced by the solutions at previous-periods problems (history). We introduce a natural historical operator, which extends

Lukas Beringer, Mathias Steinhuber, Klaus Richter, Steven Tomsovic

Using the key properties of chaos, i.e. ergodicity and exponential instability, as a resource to control classical dynamics has a long and considerable history. However, in the context of controlling "chaotic" quantum unitary dynamics, the situation is far more tenuous. The classical concepts of exponential sensitivity to trajectory initial conditions and er

Karen Wolf, Pierre Fernique, Hans-Peter Piepho

A common assumption in the spatial analysis of agricultural field trials is stationarity. In practice, however, this assumption is often violated due to unaccounted field effects. For instance, in plant breeding field trials, this can lead to inaccurate estimates of plant performance. Based on such inaccurate estimates, breeders may be impeded in selecting t

N. N. Shakhvorostova, A. M. Sobolev, D. A. Ladeyshchikov, S. Y. Parfenov

This paper presents high-resolution ALMA observations of the massive young stellar object G25.65+1.05, which is known to host water maser super flares. To investigate the nature of compact continuum sources that have been previously identified in this region, we analyzed 1.3 mm dust continuum and molecular line emission. The central millimeter peak MM1 coinc

Changjun Zhou, Jintao Zheng, Leyou Yang, Pengfei Wang

Federated Unlearning (FUL) focuses on client data and computing power to offer a privacy-preserving solution. However, high computational demands, complex incentive mechanisms, and disparities in client-side computing power often lead to long times and higher costs. To address these challenges, many existing methods rely on server-side knowledge distillation

Chuan Mao, Haoqi Yuan, Ziye Huang, Chaoyi Xu

Reinforcement learning (RL) has achieved great success in dexterous grasping, significantly improving grasp performance and generalization from simulation to the real world. However, fine-grained functional grasping, which is essential for downstream manipulation tasks, remains underexplored and faces several challenges: the complexity of specifying goals an

Georgios Doultsinos, Antonis Delakouras, David Petrosyan

We analytically derive the lower error bound for the preparation of any maximally entangled state of two atoms involving Rydberg-state interactions. This fundamental bound represents the minimum achievable error $E \geq ( 1 + π/2 ) Γ/B$ due to spontaneous decay $Γ$ of the Rydberg states and their finite interaction strength $B$, assuming that all other techn

Huw Llewelyn

Clinicians and scientists have traditionally focussed on whether their findings will be replicated and are very familiar with the concept. The probability that a replication study yields an effect with the same sign, or the same statistical significance as an original study depends on the sum of the variances of the effect estimates. On this basis, when P eq

Robert Tang

We consider epimorphisms and several variant notions -- split, effective, regular, strong, and extremal -- and determine which of these coincide in the metric coarse and coarsely Lipschitz categories. In particular, we characterise extremal epis in the coarsely Lipschitz category via a relative maximality condition on the codomain metric; this can be viewed

Ajaharul Islam

We study bottomonium dynamics in a momentum-space anisotropic quark-gluon plasma (QGP) using the quantum trajectories (QTraj) framework. The real part of the heavy-quark potential is obtained from a minimal extension of the Karsch-Mehr-Satz (KMS) potential, while the angle-averaged imaginary part is derived to leading order in the anisotropy parameter $ξ$ an

Carla Monteiro, Valentina Corbetta, Regina Beets-Tan, Luís F. Teixeira

Automatic polyp segmentation is crucial for improving the clinical identification of colorectal cancer (CRC). While Deep Learning (DL) techniques have been extensively researched for this problem, current methods frequently struggle with generalization, particularly in data-constrained or challenging settings. Moreover, many existing polyp segmentation metho

Haimiao Chen

For a prime knot $K$, we give sufficient conditions for the existence of a component $\mathcal{C}$ of the irreducible ${\rm SL}(2,\mathbb{C})$-character variety of $K$ with $\dim\mathcal{C}>1$, and give a lower bound for $\dim\mathcal{C}$. Specifically, we improve a result of Paoluzzi and Porti on Montesinos knots, and positively answer a question posed by C

Francesca Da Ros, Luca Di Gaspero, Kevin Roitero

Recent advances in Large Language Models (LLMs) open new perspectives for automation in optimization, yet little is known about whether their internal representations capture problem structure or algorithmic behavior. We investigate whether representations learned by frozen, open-weight LLMs for combinatorial optimization instances can support downstream dec

Sara Peña-Gutiérrez, Giorgio Gosti, Hongsheng Chen, Giancarlo Ruocco

Emergent learning transforms a disordered optical medium into a photonic device capable of storage, recognition, and classification of arbitrary memory patterns. First, we show that the intensity at the output of a multiply scattering system can be described by a dyadic matrix, the optical-synaptic matrix, exhibiting the same form as a Hebbian synaptic matri

Lindsay Bassman Oftelie, Michele Campisi

The emerging field of quantum thermodynamics is beginning to reveal the intriguing role that information can play in quantum thermal engines. Information enters as a resource when considering feedback-controlled thermal machines. While both a general theory of quantum feedback control as well as specific examples of quantum feedback-controlled engines have b

Yang-Hui He, Alexander Kasprzyk, Q Le, Dmitrii Riabchenko

Linear error-correcting codes form the mathematical backbone of modern digital communication and storage systems, but identifying champion linear codes (linear codes achieving or exceeding the best known minimum Hamming distance) remains challenging. By training a transformer to predict the minimum Hamming distance of a class of linear codes and pairing it w