October 2025 arXiv papers — page 199

Muhammad Abdullah Soomro, Fatima Muhammad Anwar

The Precision Time Protocol (PTP), standardized as IEEE 1588, provides sub-microsecond synchronization across distributed systems and underpins critical infrastructure in telecommunications, finance, power systems, and industrial automation. While prior work has extensively analyzed PTP's vulnerability to network-based attacks, prompting the development of c

Aleksandar Petrov, Pierre Fernandez, Tomáš Souček, Hady Elsahar

Despite rapid progress in deep learning-based image watermarking, the capacity of current robust methods remains limited to the scale of only a few hundred bits. Such plateauing progress raises the question: How far are we from the fundamental limits of image watermarking? To this end, we present an analysis that establishes upper bounds on the message-carry

Mert Kayaalp, Caner Turkmen, Oleksandr Shchur, Pedro Mercado

Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve competitive performance on large-scale benchmarks using much f

Mário J. de Oliveira

We express the probabilistic character associated to the wave function by treating it as a stochastic variable. This is accomplished by means of a stochastic equation for the wave function whose noise changes the phase of the wave function but not its absolute value, so that the norm of the wave function is strictly conserved along a stochastic trajectory. W

Saleh Ahmed Khan, Sudipto Saha, Ahmed Ibreljic, Stephen Margiotta

This study demonstrates a substantial enhancement of breakdown voltage in $\beta$-Ga$_2$O$_3$ Schottky diodes through an approach that combines fast neutron irradiation with controlled post-irradiation electro-thermal annealing. Devices irradiated with 1 MeV neutrons at a high fluence of 1E15 n/cm^2 exhibited substantial degradation, including a drastic redu

Devon Stork, Erika DeBenedictis

Terraforming Mars is an age old science fiction concept now worth revisiting through the lens of modern science and technology. This document serves as a summary of contemporary ideas about Mars terraforming, prepared for attendees of the 2025 Green Mars Workshop. It presents one illustrative story of how Mars might be transformed into a habitable world. The

Abdur Rehman Anwar Qureshi, Adrian Rebmann, Timotheus Kampik, Matthias Weidlich

Business process management is increasingly practiced using data-driven approaches. Still, classical imperative process models, which are typically formalized using Petri nets, are not straightforwardly applicable to the relational databases that contain much of the available structured process execution data. This creates a gap between the traditional world

Yuqi Zhang, Yuxin Yang, Feixiong Chen, Cheng-Chang Lu

Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework that combines quantum computation with deep learning, formulating structure prediction as a problem of energy

Houari Benammar Ammar

Let \( f: X \to Y \) be an algebraic fiber space, where \( X \) and \( Y \) are smooth projective varieties of dimensions \( n \) and \( m \), respectively. In \cite{Caopaun}, Cao and P\u{a}un proved \( C_{n,m} \) when \( Y \) has maximal Albanese dimension. In this paper, we prove \( C_{n,m} \) in the case where the Albanese dimension $\alpha(Y)$ of \( Y \)

R. Alexander Knipper, Indrani Dey, Souvika Sarkar, Hari Narayanan

Virtual Labs offer valuable opportunities for hands-on, inquiry-based science learning, yet teachers often struggle to adapt them to fit their instructional goals. Third-party materials may not align with classroom needs, and developing custom resources can be time-consuming and difficult to scale. Recent advances in Large Language Models (LLMs) offer a prom

Aochong Oliver Li, Tanya Goyal

Reasoning LLMs are trained to verbalize their reasoning process, yielding strong gains on complex tasks. This transparency also opens a promising direction: multiple reasoners can directly collaborate on each other's thinking within a shared trajectory, yielding better inference efficiency and exploration. A key prerequisite, however, is the ability to asses

Alberto Ronca, Antoine Cornet, Jie Shen, Thierry Deschamps

The existence of multiple amorphous states, or polyamorphism, remains one of the most debated phenomena in disordered matter, particularly regarding its microscopic origin and impact on glassy dynamics. Profiting of the enhanced data quality provided by brilliant synchrotrons, we combined high pressure X-ray photon correlation spectroscopy and X-ray diffract

Rosa M. Mérida, Gaia Gaspar, Yoshihisa Asada, Marcin Sawicki

The Little Red Dot (LRD) paradigm comprises three main unknowns that are intrinsically connected: (1) What is the nature of these sources? (2) How do they form? (3) How do they evolve? Larger spectroscopic samples and high-resolution data are needed to delve deeper into the mechanisms ruling these sources. Understanding their formation and evolution requires

Erik Karlsson Öhman, Daqing Wang, R. Matthias Geilhufe, Christian Schäfer

Interfacing light with quantum systems is an integral part of quantum technology, with the most essential building block being single-photon emitters. Although various platforms exist, each with its individual strengths, molecular emitters boast a unique advantage -- namely the flexibility to tailor their design to fit the requirements of a specific task. Ho

Ollie Burke, Martina Muratore, Graham Woan

We investigate the impact of missing input data on the construction of second-generation Time Delay Interferometry (TDI) variables, which enable data analysis for the Laser Interferometer Space Antenna (LISA). TDI relies on the introduction of precise time delays into the raw interferometric data streams before they are combined to suppress otherwise dominan

Andrea Loi, Roberto Mossa, Fabio ZUddas

We study the Bergman metric and introduce the Bergman dual on Cartan-Hartogs (CH) domains. For a bounded domain D in C^n with Bergman kernel K_D, we define the Bergman dual of (D, g_D) as (D*, g_D*), where D* is the maximal domain on which the modified kernel K_D*(z, zbar) = K_D(z, -zbar) is positive, and g_D* is the Kahler metric obtained from K_D*. For a C

Alan Williams, Jorge Cortés, Alexander Scheinker

We generalize the Safe Extremum Seeking algorithm to address the minimization of an unknown objective function subject to multiple unknown inequality and equality constraints, relying on recent results of gradient flow systems. These constraints may represent safety or other critical conditions. The proposed ES algorithm functions as a general nonlinear prog

David Keeney, Wenlong Shi, Rohit Thomas, Yang Jiao

Hyperuniform materials, characterized by anomalously suppressed long-wavelength density fluctuations, exhibit unique optical and photonic properties distinct from both crystalline and random media. While most prior studies have focused on single-species systems, we investigate the broader class of \textit{multihyperuniform} systems inspired by biological pho

Ali Hussaini Umar, Franky Kevin Nando Tezoh, Jean Barbier, Santiago Acevedo

In supervised classification tasks, models are trained to predict a label for each data point. In real-world datasets, these labels are often noisy due to annotation errors. While the impact of label noise on the performance of deep learning models has been widely studied, its effects on the networks' hidden representations remain poorly understood. We addre

N. van Dongen, A. J. F. van Hoof, S. Calero, J. M. Vicent-Luna

Direct air capture of carbon dioxide (CO$_2$) is one of the most promising strategies to mitigate rising atmospheric CO$_2$ levels. Among various techniques, adsorption using porous materials is a viable method for extracting CO$_2$ from air, even under humid conditions. However, identifying optimal adsorbent materials remains a significant challenge. Moreov

A. Lamura

Phase separation in the presence of external forces has attracted considerable attention since the initial works for solid mixtures. Despite this, only very few studies are available which address the segregation process of liquid-vapor systems under gravity. We present here an extensive study which takes into account both hydrodynamic and gravitational effe

Miguel Berasategui

We prove that if $\mathcal{X}$ is a quasi-greedy Markushevich basis of a Banach space $\mathbb{X}$, its dual basis $\mathcal{X}^*$ spans a norming subspace of $\mathbb{X}^*$. We also prove this result for weaker forms of quasi-greediness, and study the cases of other greedy-like properties from the literature.

Ali Baheri

Non-Euclidean foundation models increasingly place representations in curved spaces such as hyperbolic geometry. We show that this geometry creates a boundary-driven asymmetry that backdoor triggers can exploit. Near the boundary, small input changes appear subtle to standard input-space detectors but produce disproportionately large shifts in the model's re

Aymen Alsaadi, Jonathan Ash, Mikhail Titov, Matteo Turilli

Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-lea

Tristan M. Lawrie, Oliver M. Brown

Recent work by Lawrie et al. [PRR 7, 023209 (2025)] introduced a non-diffracting resonant angular filter on a network of thin channels (modelled via quantum graph theory) that exhibits unit transmission of acoustic waves at a discrete, symmetry-paired set of incidence angles determined solely by the graph topology, while transmission at all other angles is s

Johannes Schneider, Rene Abraham

The rapid integration of Artificial Intelligence (AI) into organizational technology frameworks has transformed how organizations engage with AI-driven models, influencing both operational performance and strategic innovation. With the advent of foundation models, the importance of structured platforms for AI model exchange has become paramount for organizat

Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao

This paper presents a nonlinear integrated guidance and control (IGC) approach for flexible leader-follower formation flight of fixed-wing unmanned aerial vehicles (UAVs) while accounting for high-fidelity aerodynamics and thrust dynamics. Unlike conventional leader-follower schemes that fix the follower's position relative to the leader, the follower is ste

Wayne Ge

In this paper, we introduce super-minimally $k$-connected graphs, those $k$-connected graphs in which no proper subgraph is $k$-connected. For $k$ greater than or equal to three, this class lies strictly between the classes of minimally $k$-connected graphs and uniformly $k$-connected graphs. In particular, we determine the minimum number of degree-$3$ verti

Elle

Reward models (RMs) are central to the alignment of language models (LMs). An RM often serves as a proxy for human preferences to guide downstream LM behavior. However, our understanding of RM behavior is limited. Our work (i) formalizes a framework for measuring the alignment of opinions captured by RMs, (ii) investigates the extent to which RMs demonstrate

Margaret E Carrington, Bryce T. Friesen, Doug Pickering, Shane Sangster

The earliest phase of an ultrarelativistic heavy ion collision can be described as a highly populated system of gluons called glasma. The system's dynamics is governed by the classical Yang-Mills equation. Solutions can be found at early times using a proper time expansion. Since the expansion parameter is the time, this method is necessarily limited to the

Paolo Zanardi, Emanuel Dallas, Faidon Andreadakis

We introduce the novel concept of mereological quantum phase transition (m-QPTs). Our framework is based on a variational family of operator algebras defining generalized tensor product structures (g-TPS), a parameter-dependent Hamiltonian, and a quantum scrambling functional. By minimizing the scrambling functional, one selects a g-TPS, enabling a pullback

Fan Zhou, Chang Tian, Tim Van de Cruys

Generating stylistic text with specific attributes is a key problem in controllable text generation. Recently, diffusion models have emerged as a powerful paradigm for both visual and textual generation. Existing approaches can be broadly categorized into classifier-free guidance (CFG) and classifier guidance (CG) methods. While CFG effectively preserves sem

Borhan Ahmadi, Aravinth Balaji Ravichandran, Paweł Mazurek, Shabir Barzanjeh

Quantum hardware increasingly relies on energy reserves that can later be converted into useful work; yet, most battery-like proposals demand coherent drives or engineered non-equilibrium resources, limiting practicality in noisy settings. We develop an autonomous charging paradigm in which an ensemble of identical two-level units, collectively coupled to a

Pierre Lison, Mark Anderson

While de-identification models can help conceal the identity of the individuals mentioned in a document, they fail to address linkage risks, defined as the potential to map the de-identified text back to its source. One straightforward way to perform such linkages is to extract phrases from the de-identified document and check their presence in the original

Zeling Xiong, Christopher Heins, Thibaut Devolder, Fabian Kammerbauer

Forecasting complex, chaotic signals is a central challenge across science and technology, with implications ranging from secure communications to climate modeling. Here we demonstrate that magnons - the collective spin excitations in magnetically ordered materials - can serve as an efficient physical reservoir for predicting such dynamics. Using a magnetic

Mathew Thomas Arun, Nabeel Thahir

Einsteins gravity with a cosmological constant $\Lambda$ in four dimensions can be reformulated as a $\lambda \phi^4$ theory characterized solely by the dimensionless coupling $\lambda \propto G_N \Lambda$ ($G_N$ being Newton's constant). The quantum triviality of this theory drives $\lambda \to 0$, and a deviation from this behavior could be generated by ma

Jinho Cha, Eunchan D. Cha, Emily Yoo, Hyoshin Song

Background: Chronic diseases impose a sustained burden on healthcare systems through progressive deterioration and long-term costs. Although adherence-enhancing interventions are widely promoted, their return on investment (ROI) remains uncertain, particularly under heterogeneous patient behavior and socioeconomic variation. Methods: We developed a simulatio

Angie Boggust, Donghao Ren, Yannick Assogba, Dominik Moritz

Automated interpretability aims to translate large language model (LLM) features into human understandable descriptions. However, natural language feature descriptions can be vague, inconsistent, and require manual relabeling. In response, we introduce semantic regexes, structured language descriptions of LLM features. By combining primitives that capture li

Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos Kanatsoulis

Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and tasks. The core challenge is the diversity of relational data, with varying heterogeneous schemas, graph structures and functional dependencies. In this paper, we present the Relationa

Jonah Kudler-Flam, Edward Witten

We examine the behavior of sequences of states in the large $N$ limit of AdS/CFT duality in cases in which the bulk duals involve baby universes or black holes. Such sequences generally fail to converge as pure states. Under suitable conditions, such as diverging coarse-grained entropy, they can converge to mixed states for the large $N$ algebra, as in the c

Adeline Wihardja, Juan Carlos Nieto Fuentes, Daniel Rittel, Kaushik Bhattacharya

Liquid crystal elastomers are rubbery solids that couple liquid crystalline order and deformation. This coupling leads to properties that are attractive for a number of applications in soft robotics and energy absorption. This paper is motivated by the latter application, and provides a systematic experimental study of a particular class of liquid crystal el

G. Pérez Cuéllar, M. Sabido

In previous works, entropic gravity and ungravity have been considered as possible solutions to the dark energy and dark matter problems. To test the viability of these models, modifications to planetary orbits are calculated for ungravity and different models of entropic gravity. Using the gravitational sector of unparticles, an equation for the contributio

Julián Amette Estrada, Alexandros Alexakis, Marc E. Brachet, Pablo D. Mininni

Vortex lattices -- highly ordered arrays of vortices -- are known to arise in quantum systems such as type II superconductors and Bose-Einstein condensates. More recently, similar arrangements have been reported in classical rotating fluids. However, the mechanisms governing their formation, stability, and eventual breakdown remain poorly understood. We expl

Lucía Alonso Mozo, Olivier Hénot, Phillipo Lappicy

Periodic orbits are important objects of discrete dynamical systems, but finding them is not always easy. We present a self-contained introductory account, aimed at non-experts, to prove their existence and study their stability using the aid of the computer. The method consists in three main steps. First, we reformulate the problem of identifying a $p$-peri

Frantisek Hakl, Vit Fojtik

We provide an upper bound on the number of neurons required in a shallow neural network to approximate a continuous function on a compact set with a given accuracy. This method, inspired by a specific proof of the Stone-Weierstrass theorem, is constructive and more general than previous bounds of this character, as it applies to any continuous function on an

Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan, Zien Sheikh Ali

Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries require cultural and visual information, everyday knowledge, particularly in low-resource and underrepresented languages. We introduce OASIS, a large-scale culturally grounded multimodal QA dataset covering images, text,

Kshitish Ghate, Andy Liu, Devansh Jain, Taylor Sorensen

As large language models (LLMs) are deployed globally, creating pluralistic systems that can accommodate the diverse preferences and values of users worldwide becomes essential. We introduce EVALUESTEER, a benchmark to measure LLMs' and reward models' (RMs) steerability towards users' value and stylistic preference profiles grounded in psychology and human-L

Tongtong Li, Anne Gelb, Yoonsang Lee

Accurate data assimilation (DA) for systems with piecewise-smooth or discontinuous state variables remains a significant challenge, as conventional covariance-based ensemble Kalman filter approaches often fail to effectively balance observations and model information near sharp features. In this paper we develop a structurally informed DA framework using ens

Carson Hunsberger, David Schwab, Roshan Eapen, Puneet Singla

The normal forms provide useful approximations for many trajectories of interest within the circular restricted three-body problem. This paper aims to thoroughly compare two of these forms: the Birkhoff normal form and the resonant normal form, highlighting the strengths of each for the representation of center manifold trajectories. A method of station-keep

Luca Wolf, Tobias Buck, Bjoern Malte Schaefer

Neural ODEs are a widely used, powerful machine learning technique in particular for physics. However, not every solution is physical in that it is an Euler-Lagrange equation. We present Helmholtz metrics to quantify this resemblance for a given ODE and demonstrate their capabilities on several fundamental systems with noise. We combine them with a second or

Pablo Santos-Sanz, Altair R. Gomes-Júnior, Bruno E. Morgado, Yucel Kilic

Ring systems have been discovered around several small bodies in the outer Solar System through stellar occultations. While such measurements provide key information about ring geometry and dynamical interactions, little is known about their origins, lifetimes, evolutionary pathways, or compositions. Here we report near-infrared observations with the James W

Jake Kettinger

In this paper, we categorize all isomorphism classes of quasi-elliptic surfaces over a field $k$ of characteristic 2 or 3. For every quasi-elliptic surface $X$, we classify all possible sequences of blow-downs from $X$ to the projective plane $\mathbb{P}^2_k$. We then use these categorizations to identify all unexpected plane cubic curves in characteristic 2

Michael Lönne

We investigate locally closed subspaces of projectivized strata of abelian differentials which classify trigonal curves with canonical divisor a multiple of a trigonal divisor. We describe their orbifold structure using linear systems on Segre-Hirzebruch surfaces and obtain results for their orbifold fundamental groups. Most notable among these orbifolds is

Ololade Babatunde, Tomisin Ayodabo, Raqibul Raqibul

This study addresses challenges in traditional assignment submission methods used in higher education by introducing and evaluating a customized Git-based submission system. Employing iterative software development and user-centered design methodologies, the system was integrated within a real-world university environment. Empirical evaluation, including usa

Haomiao Chen, Keith W Jamison, Mert R. Sabuncu, Amy Kuceyeski

Neural encoding models aim to predict fMRI-measured brain responses to natural images. fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain. However, conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent outputs. This removes spatial context, di

Nathan X. Kodama

Far from equilibrium, neural systems self-organize across multiple scales. Exploiting multiscale self-organization in neuroscience and artificial intelligence requires a computational framework for modeling the effective non-equilibrium dynamics of stochastic neural trajectories. Non-equilibrium thermodynamics and representational geometry offer theoretical

Erfan Abbasgholinejad, Sean R. Muleady, Jacob Bringewatt, Anthony J. Brady

Estimating extensive combinations of local parameters in distributed quantum systems is a central problem in quantum sensing, with applications ranging from magnetometry to timekeeping. While optimal strategies are known for sensing non-interacting Hamiltonians in quantum sensor networks, fundamental limits in the presence of uncontrolled interactions remain

Luca Brunelli, Michele Cicoli, Francisco Gil Pedro

We exploit the techniques of dynamical systems to study the cosmological evolution of cosmic fundamental strings and effective strings arising from branes wrapped on internal cycles. We also include the whole potential of the volume modulus characterised by an early time run-away towards a late time minimum. We analyse the overshoot problem with and without

Jeffrey Meier

We exhibit an infinite family of indecomposable Klein bottles in the 4-sphere with order-4 meridians.

Grayson Byrd, Corban Rivera, Bethany Kemp, Meghan Booker

Replicating human-level intelligence in the execution of embodied tasks remains challenging due to the unconstrained nature of real-world environments. Novel use of large language models (LLMs) for task planning seeks to address the previously intractable state/action space of complex planning tasks, but hallucinations limit their reliability, and thus, viab

Leandro Da Rold, Franco A. Gigena, Jaime S. Guzmán Guerrero

We build a model of lepton flavor in a slice of AdS$_5$. We add to the 5D SM fields a set of neutrino fields, as well as a horizontal U(1) symmetry and a flavon field, all propagating in the bulk. The electroweak and U(1) symmetries are spontaneously broken by a potential localized on the infrared boundary. We show that in a flavor anarchic scenario, by suit

Kürşat Tekbıyık, Güneş Karabulut Kurt, Antoine Lesage-Landry

Unmanned aerial vehicle (UAV) communications demand accurate yet interpretable air-to-ground (A2G) channel models that can adapt to nonstationary propagation environments. While deterministic models offer interpretability and deep learning (DL) models provide accuracy, both approaches suffer from either rigidity or a lack of explainability. To bridge this ga

Ingroj Shrestha, Padmini Srinivasan

Although prior work on bias mitigation has focused on promoting social equality and demographic parity, less attention has been given to aligning LLM's outputs to desired distributions. For example, we might want to align a model with real-world distributions to support factual grounding. Thus, we define bias as deviation from a desired distribution, which m

Eric Jahns, Davi Moreno, Michel A. Kinsy

Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to Artificial Neural Networks (ANNs) by utilizing sparse and asynchronous processing through discrete spike-based computation. However, the performance of deep SNNs remains limited by their reliance on simple neuron models, such as the Leaky Integrate-and-Fire (LIF) model, which ca

Allen Tu, Kartik Narayan, Joshua Gleason, Jennifer Xu

Face recognition in unconstrained environments such as surveillance, video, and web imagery must contend with extreme variation in pose, blur, illumination, and occlusion, where conventional visual quality metrics fail to predict whether inputs are truly recognizable to the deployed encoder. Existing FIQA methods typically rely on visual heuristics, curated

Devang Dhanuka

Large Language Models (LLMs) are increasingly being integrated into software development processes, with the potential to transform team workflows and productivity. This paper investigates how LLMs affect team collaboration throughout the Software Development Life Cycle (SDLC). We reframe and update a prior study with recent developments as of 2025, incorpor

Mattia Samory, Diana Pamfile, Andrew To, Shruti Phadke

Online communities rely on a mix of platform policies and community-authored rules to define acceptable behavior and maintain order. However, these rules vary widely across communities, evolve over time, and are enforced inconsistently, posing challenges for transparency, governance, and automation. In this paper, we model the relationship between rules and

Moein E. Samadi, Andreas Schuppert

Foundation models have rapidly advanced AI, raising the question of whether their decisions will ultimately surpass human strategies in real-world domains. The exponential, and possibly super-exponential, pace of AI development makes such analysis elusive. Nevertheless, many application areas that matter for daily life and society show only modest gains so f

Chris Quigg

Global celebration greeted the 2012 discovery at CERN's Large Hadron Collider of a particle that matches the textbook description of the Higgs boson. That achievement validated a remarkable chain of theoretical reasoning that combined the prescriptive notion of electroweak gauge symmetry with a simple, but \emph{ad hoc}, embodiment of spontaneous symmetry br

Nicholas Choustikov, Harley Katz, Alex J. Cameron, Aayush Saxena

Now detected out to redshifts of $z\sim 14.5$, the rest-frame ultraviolet and optical spectra of galaxies encode numerous physical properties of the interstellar medium (ISM). Accurately extracting these properties from spectra remains a key challenge that numerical simulations are uniquely suited to address. We present a study of the observed ISM of galaxie

Jon Nelson, Joel Rajakumar, Michael J. Gullans

It has been known for almost 30 years that quantum circuits with interspersed depolarizing noise converge to the uniform distribution at $\omega(\log n)$ depth, where $n$ is the number of qubits, making them classically simulable. We show that under the realistic constraint of geometric locality, this bound is loose: these circuits become classically simulab

P. Deligne, G. Lusztig

Consider a Chevalley group over a finite field F_q such that the longest element in the Weyl group is central. In this paper we study the effect of changing q to -q in the polynomials which give the character values of unipotent representations of our group at semisimple elements.

P. R. Cabral-Passos, P. S. Azevedo, V. H. Moraes, B. L. Ramalho

This work draws on the conjecture that fingerprints of stochastic event sequences can be retrieved from electroencephalographic data (EEG) recorded during a behavioral task. To test this, we used the Goalkeeper Game (game.numec.prp.usp.br). Acting as a goalkeeper, the participant predicted each kick in a probabilistic sequence while EEG activity was recorded

Fikret Mert Gultekin, Oscar Lilja, Ranim Khojah, Rebekka Wohlrab

In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In many software teams, cybersecurity experts are either entirely absent or represented by only a small number of specialists. As a result, the workload for these experts becomes high, and software engineers would need to condu

Changchang Sun, Vickie Chen, Yan Yan

Recently, deep supervised cross-modal hashing methods have achieve compelling success by learning semantic information in a self-supervised way. However, they still suffer from the key limitation that the multi-label semantic extraction process fail to explicitly interact with raw multimodal data, making the learned representation-level semantic information

Ludovico Lami

Given a sequence of random variables $X^n=X_1,\ldots, X_n$, discriminating between two hypotheses on the underlying probability distribution is a key task in statistics and information theory. Of interest here is the Stein exponent, i.e. the largest rate of decay (in $n$) of the type II error probability for a vanishingly small type I error probability. When

Juan Vicente Gutiérrez-Santacreu

This paper is devoted to the design and analysis of a numerical algorithm for approximating solutions of a degenerate cross-diffusion system, which models particular instances of taxis-type migration processes under local sensing mechanisms. The degeneracy leads to solutions that are very weak due to the low regularity themselves. Specifically, the solutions

Ludovico Lami

Given two families of quantum states $A$ and $B$, called the null and the alternative hypotheses, quantum hypothesis testing is the task of determining whether an unknown quantum state belongs to $A$ or $B$. Mistaking $A$ for $B$ is a type I error, and vice versa for the type II error. In quantum Shannon theory, a fundamental role is played by the Stein expo

Leiyao Cui, Zihang Zhao, Sirui Xie, Wenhuan Zhang

Autonomous manipulation of articulated objects remains a fundamental challenge for robots in human environments. Vision-based methods can infer hidden kinematics but can yield imprecise estimates on unfamiliar objects. Tactile approaches achieve robust control through contact feedback but require accurate initialization. This suggests a natural synergy: visi

Mengjie Yang, Ching Hua Lee

We establish a novel mechanism for topological transitions in non-Hermitian systems that are controlled by the system size. Based on a new paradigm known as exceptional-bound (EB) band engineering, its mechanism hinges on the unique critical scaling behavior near an exceptional point, totally unrelated to the well-known non-Hermitian skin effect. Through a s

J. Tuziemski, J. Pawłowski, P. Tarasiuk, Ł. Pawela

A robust definition of quantum runtime is essential for assessing the performance of quantum algorithms and claims of quantum advantage. While for most classical hardware the total runtime is well approximated by computation plus a weakly varying constant, on current quantum hardware a clean experimental separation between "pure computation" and "overhead" i

Yongzhi Li, Saining Zhang, Yibing Chen, Boying Li

Personalized image generation aims to faithfully preserve a reference subject's identity while adapting to diverse text prompts. Existing optimization-based methods ensure high fidelity but are computationally expensive, while learning-based approaches offer efficiency at the cost of entangled representations influenced by nuisance factors. We introduce Spot

Paulo Magalhães Junior, Renan Maneli Mezabarba, Rodrigo Santos Monteiro

A closure operator on a set $X$ is a function $\operatorname{cl}: \wp(X) \to \wp(X)$ satisfying, for all $A, B \subseteq X$, the following properties: extensivity, $A \subseteq \operatorname{cl}(A)$; monotonicity, which states that if $A \subseteq B$ then $\operatorname{cl}(A) \subseteq \operatorname{cl}(B)$; and preservation of unions, $\operatorname{cl}(A

Mohammed Alsubaie, Wenxi Liu, Linxia Gu, Ovidiu C. Andronesi

Magnetic Resonance Imaging (MRI) is a critical tool in modern medical diagnostics, yet its prolonged acquisition time remains a critical limitation, especially in time-sensitive clinical scenarios. While undersampling strategies can accelerate image acquisition, they often result in image artifacts and degraded quality. Recent diffusion models have shown pro

Yutaro Shoji, Katarina Trailović

We introduce a robust numerical method for determining intersection numbers of Lefschetz thimbles in multivariable settings. Our approach employs the multiple shooting method to solve the upward flow equations from the saddle points to the original integration cycle, which also enables us to determine the signs of the intersection numbers. The method demonst

Thomas Iadecola

In quantum many-body systems with kinetically constrained dynamics, the Hilbert space can split into exponentially many disconnected subsectors, a phenomenon known as Hilbert-space fragmentation. We study the interplay of such fragmentation with symmetries, focusing on charge conserving systems with charge conjugation and translation symmetries as a concrete

E. Ceccarelli, D. Massari, M. Palla, A. Mucciarelli

We present the results of the first systematic and dedicated high-resolution chemical analysis of the Thamnos substructure, a candidate relic of the process of hierarchical merger of the Milky Way. The analysis was perfomed in comparison with the Gaia-Sausage-Enceladus (GSE) remnant, within the fully self-consistent and homogeneous framework established by t

C. C. Pedersen, M. R. M. Knudsen, K. Valeckas, L. Izzo

We report on phase-resolved optical spectroscopy and photometry in the R and B bands of the white dwarf candidate ZTF 185139.81+171430.3. The source has been reported to be variable with a large amplitude of close to 1 magnitude, in the R band, and a short period of 12.37 min. We confirm this period and interpret it as the spin period of the white dwarf. The

Manuel Piarulli, Sylvain Marsat, Elise M. Sänger, Alessandra Buonanno

Laser Interferometer Space Antenna (LISA) observations of massive black hole binaries (MBHBs) will provide long duration inspiral signals with high signal-to-noise ratio (SNR) data, ideal for testing general relativity (GR) in the strong-field and relativistic regime regime. We present an extension of the Flexible Theory-Independent (FTI) framework, adapted

Claire Guillaume, Florent Renaud, Nicolas F. Martin, Benoit Famaey

Stellar streams are sensitive to perturbations from, e.g., giant molecular clouds, bars and spiral arms, infalling dwarf galaxies, or globular clusters which can imprint gaps, clumps, spurs, and asymmetries in tails. In addition to these effects, the impact of a galactic major merger on a population of stellar streams remains to be explored. Here, we focus o

Su-un Lee, Soumik Ghosh, Changhun Oh, Kyungjoo Noh

We study the classical simulability of noisy random quantum circuits under general noise models. While various classical algorithms for simulating noisy random circuits have been proposed, many of them rely on the anticoncentration property, which can fail when the circuit depth is small or under realistic noise models. We propose a new approach based on the

Isaac C. F. Wong, Francesco Cireddu, Milan Wils, Tom Colemont

We introduce a Bayesian null-stream method to constrain calibration errors in closed-geometry gravitational-wave (GW) detector networks. Unlike prior methods requiring electromagnetic counterparts or waveform models, this method uses sky-independent null streams to calibrate the detectors with any GW signals, independent of general relativity or waveform ass

Naomi R. Solomons, Damian Markham

Networks of sensors are a promising scheme to deliver the benefits of quantum technologies in coming years, offering enhanced precision and accuracy for distributed metrology through the use of large entangled states. Recent work has additionally explored the privacy of these schemes, meaning that local parameters can be kept secret while a joint function of

Julia M. Comerford, Joseph Simon

Pulsar timing arrays have recently found evidence for nanohertz gravitational waves that are consistent with being produced by a cosmological population of binary supermassive black holes (SMBHs). However, the amplitude of this gravitational wave background is larger than predicted from theoretical and empirical models of SMBH binary populations. We investig

Yifan F. Zhang, Su-un Lee, Liang Jiang, Sarang Gopalakrishnan

While quantum computing can accomplish tasks that are classically intractable, the presence of noise may destroy this advantage in the absence of fault tolerance. In this work, we present a classical algorithm that runs in $n^{\rm{polylog}(n)}$ time for simulating quantum circuits under local depolarizing noise, thereby ruling out their quantum advantage in

Alena Romanova, Wolfgang Dür

We discuss how blind quantum computing generalizes to multi-level quantum systems (qudits), which offers advantages compared to the qubit approach. Here, a quantum computing task is delegated to an untrusted server while simultaneously preventing the server from retrieving information about the computation it performs, the input, and the output, enabling sec

XRISM Collaboration, Marc Audard, Hisamitsu Awaki, Ralf Ballhausen

The dynamics of the intra-cluster medium (ICM), the hot plasma that fills galaxy clusters, are shaped by gravity-driven cluster mergers and feedback from supermassive black holes (SMBH) in the cluster cores. XRISM measurements of ICM velocities in several clusters offer insights into these processes. We compare XRISM measurements for nine galaxy clusters (Vi

Yihui Quek

Classical hardness-of-sampling results are largely established for random quantum circuits, whereas analog simulators natively realize time evolutions under geometrically local Hamiltonians. Does a typical such Hamiltonian already yield classically-intractable dynamics? We answer this question in the affirmative for the ensemble of geometrically-2-local Hami

K. Yakut, B. Kalomeni, S. Rappaport, Veselin Kostov

Using high-precision observations from the space-based \textit{Gaia} and \textit{TESS} missions, complemented by ground-based spectroscopic data and multi-band photometric surveys, we perform a detailed investigation of the Galactic open cluster NGC~2506. We present a new analysis of the intermediate-age open cluster NGC~2506, using joint fits to the radial

Ethan Torres, Xingyang Yu

Given two otherwise decoupled $D$-dimensional CFTs which possess a common (finite) symmetry subcategory, one can consider entangled boundary states of their $(D+1)$-dimensional SymTFTs. This roughly corresponds to performing a gauging of the tensor product of two CFTs, and we call this phenomena ``SymTFT entanglement" (or ``S-entanglement" for short). In the