December 2025 arXiv papers — page 102

Lizzy Teryoshin, Mario Hidalgo-Soria, Elena F. Koslover

Intracellular processes often rely on the timely encounter of mobile reaction partners, including intermittently motor-driven organelles. The underlying cytoskeletal network presents a complex landscape that both directs particle movement and introduces quenched disorder through filament organization. We investigate the mean first encounter times for pairs o

Yuki Ueda

We show that the distribution of the spectral maximum of monotonically independent self-adjoint operators coincides with the classical max-convolution of their distributions. In free probability, it was proven that for any probability measures $\sigma,\mu$ on $\mathbb{R}$ there is a unique probability measure $\mathbb{A}_\sigma(\mu)$ satisfying $\sigma\boxpl

Adriano Macarone-Palmieri, Alberto Ferrara, Rosario Lo Franco

Multipartite entanglement is a crucial resource for quantum technologies; however, its scalable generation in noisy quantum devices remains a significant challenge. Here, we propose a low-depth quantum neural network architecture with linear scaling, employing a novel approach to introducing activation functions for entanglement engineering. As a testbed to

Miaohua Zhang, Mohammad Ali Armin, Xuesong Li, Sisi Liang

Marine obstacle detection demands robust segmentation under challenging conditions, such as sun glitter, fog, and rapidly changing wave patterns. These factors degrade image quality, while the scarcity and structural repetition of marine datasets limit the diversity of available training data. Although mask-conditioned diffusion models can synthesize layout-

Dominic Arcona

We use representation theory of $S_n$ to analyze the mixing of cycle type statistics $a_j(σ) = \{\text{# of $j$-cycles of $σ$}\}$ for any fixed $j$ in permutations $σ_t$ resulting from the $t$-step random transposition walk on $S_n$. We also derive analogous results for the star transposition walk. Our approach uses the method of moments; a key ingredient is

Sam K. Miller

We show the existence of a semisimple replete subcategory of Khovanov's Heisenberg category that retains the isomorphism data of objects for the full category. This leads to a noncommutative tensor-triangular geometric example of a monoidal triangulated category whose Balmer spectrum satisfies the tensor product property but which contains one-sided thick te

Emma Dinowitz, Lucy Koch-Hyde, Siobhan O'Connor, Eamonn Olive

A word in a free group is called ``potentially positive'' if it is automorphic to an element which is written with only positive exponents. We will develop automata to analyze properties of potentially positive words. We will use these to give new bounds on the asymptotic growth of potentially positive elements in free groups of 2 to 7 generators. We prove t

Junhang Zhang, U-Wai Lok, Jingke Zhang, Chengwu Huang

The purpose of this study is to enable in-vivo three-dimensional (3-D) ultrasound localization microscopy (ULM) of posterior ocular microvasculature using a 256-channel system and a 1024-element matrix array, and to overcome limitations of restricted transmit angles, sound speed mismatch caused by the crystalline lens and surrounding tissues, and the low sig

Lillian Makhoul, Emily Speakman

Solving difficult mixed-integer nonlinear programs via spatial branch-and-bound requires effective convex outer-approximations of nonconvex sets. In this framework, complex problem formulations are often decomposed into simpler library functions, whose relaxations are then composed to build relaxations of the overall problem. The trilinear monomial serves as

Tatiana Pauletti, Paula Homem de Mello, Thereza Paiva, Vivian V. França

Gender imbalance in Physics remains a persistent global challenge, and Brazil is no exception. While women account for only 24% of Physics faculty in the country, their representation in Quantum Physics is even smaller. In this work, we provide the first comprehensive overview of women working in Theoretical Quantum Physics in Brazil, here referred to as the

Shunyu Yin, Bernardo P. Ferreira, Gawel Kus, Miguel A. Bessa

Artificial neural networks accurately learn nonlinear, path-dependent material behavior. However, training them typically requires large, diverse datasets, often created via synthetic unit cell simulations. This hinders practical adoption because physical experiments on standardized specimens with simple geometries fail to generate sufficiently diverse stres

Baptiste Bergeot

In this paper one first shows that the slow flow of a mechanical system with one unstable mode coupled to a Nonlinear Energy Sink (NES) can be reduced, in the neighborhood of a fold point of its critical manifold, to a normal form of the dynamic saddle-node bifurcation. This allows us to then obtain a scaling law for the slow flow dynamics and to improve the

Mohammad Abu-Shaira, Alejandro Rodriguez, Greg Speegle, Victor Sheng

Online learning updates models incrementally with new data, avoiding large storage requirements and costly model recalculations. In this paper, we introduce "OLR-WA; OnLine Regression with Weighted Average", a novel and versatile multivariate online linear regression model. We also investigate scenarios involving drift, where the underlying patterns

D. Blas, F. Del Porro, M. Herrero-Valea, J. Radkovski

We formulate the quantum version of non-projectable Hořava gravity as a Lagrangian theory with a path integral in the configuration space with an ultra-local in time, but non-local in space, field-dependent measure. Using auxiliary fields, we cast the measure into a local form satisfying several bosonic and fermionic symmetries. We perform an explicit one-lo

Arunima Bhattacharya, Francisco Campanario, Sauro Carlotti, Jamie Chang

Gluon fusion, $gg\to HH$, is the dominant Higgs-pair production process at the Large Hadron Collider (LHC) and provides the first direct access to the trilinear Higgs self-interaction. The process is loop-induced, with the main contribution emerging from top-quark loops within the Standard Model. In the past, the QCD corrections have been calculated and foun

Roman Nekrasov, Stefano Fossati, Indika Kumara, Damian Andrew Tamburri

Large Language Models (LLMs) currently exhibit low success rates in generating correct and intent-aligned Infrastructure as Code (IaC). This research investigated methods to improve LLM-based IaC generation, specifically for Terraform, by systematically injecting structured configuration knowledge. To facilitate this, an existing IaC-Eval benchmark was signi

Sergey Prosandeev, Charles Paillard, Laurent Bellaiche

Density functional theory calculations are conducted to understand and reveal the origin of the complex shear strain morphology and of the polar Moiré topological pattern recently observed in twisted BaTiO$_3$ bilayers. Our first-principles calculations, along with an original analysis of them allowing the decomposition of forces into the acoustic and optica

Diego López-Alcalá, Alberto M. Ruiz, Andrei Shumilin, José J. Baldoví

Altermagnetism represents a novel class of collinear antiferromagnetism exhibiting non-relativistic spin splitting without net magnetization, driven by lattice symmetry rather than spin-orbit coupling (SOC). Here, we introduce a coordination-driven chemical strategy to realize altermagnetic (AM) spin splitting in two-dimensional (2D) planar tetracoordinated

Youngkyu Lee, Francesc Levrero Florencio, Jay Pathak, George Em Karniadakis

The convergence behavior of classical iterative solvers for parametric partial differential equations (PDEs) is often highly sensitive to the domain and specific discretization of PDEs. Previously, we introduced hybrid solvers by combining the classical solvers with neural operators for a specific geometry 1, but they tend to under-perform in geometries not

Riccardo Grazi, Henrik Johannesson, Dario Ferraro, Niccolò Traverso Ziani

Reliable charging protocols are crucial for advancing quantum batteries toward practical use. We investigate a transverse-field Ising chain as a quantum battery, focusing on the combined role of qubit interactions in the battery model and finite charging time. This interplay yields smoother and more controllable charging compared to sudden protocols or non-i

Jingeon An-Lacroix, Kiichi Tashiro

It is known that there is a strong relation between the parabolic Allen--Cahn equation and the mean curvature flow, in the sense that the parabolic Allen--Cahn equation can be considered as a "diffused" mean curvature flow. In this work, we derive a forced mean curvature flow \[ v=-H-\partial_ν\log |\nabla u|+f(u)/|\nabla u|, \] satisfied by level su

Massimo Giovannini

The large-scale limits on the relic signals of gravitational radiation complement the bounds coming from the interferometric detectors (in the audio band) and from the pulsar timing arrays (in the nHz range). Within this inclusive perspective the spectral energy density of the gravitons is sharply suppressed in the aHz region even though the high frequency s

Takeshi Matsumura, Takumi Hasegawa, Ryuma Nakajima, Kenshin Kurauchi

Soft phonon modes in tetragonal CeCoSi, which undergoes a structural transition at $T_0=12$ K followed by antiferromagnetic order at $T_{\text{N}}=9.5$ K, have been investigated using high-resolution inelastic x-ray scattering. Pronounced softening was detected in the transverse acoustic modes corresponding to the $(yz+zx)$-type monoclinic distortion, consis

Elisabeth Giacobino, Maxime J. Jacquet

These lecture notes develop polariton fluids of light as programmable simulators of quantum fields on tailored curved spacetimes, with emphasis on acoustic horizons and the Hawking effect. After introducing exciton-polariton physics in semiconductor microcavities, we detail the theoretical tools to study the mean field and the quantum hydrodynamics of this d

Christian Saugbjerg Lange, Ella Elisabeth Lassen, Rasmus Vesterager Gothelf, Lars Bojer Madsen

Quantum-optical descriptions of strong-field processes have attracted significant attention in recent years. Typically, the theoretical modeling has been conducted in the Schrödinger picture, where results are only obtainable under certain approximations, while, in contrast, the Heisenberg picture has remained relatively unexplored. In this work, we develop

Pratyay Ghosh, Frédéric Mila

We investigate the spin-$1/2$ Heisenberg antiferromagnet on the ruby lattice with uniform first- and second-neighbor interactions, which forms a two-dimensional network of corner-sharing tetrahedra. Using infinite projected entangled pair states (iPEPS), we study the ground state of the system to find that it assumes a gapped threefold-degenerate simplex cry

Yong Fang, Na Li, Hangguan Shan, Eryun Liu

Multivariate Time Series (MTS) forecasting plays a vital role in various real-world applications, such as traffic management and predictive maintenance. Existing approaches typically model MTS data in either Euclidean or Riemannian space, limiting their ability to capture the diverse geometric structures and complex spatio-temporal dependencies inherent in r

Vikash Jangir, Sourojit K. Mazumder, Sudip K. Mazumder

We present an investigation into the role of anode grid pitch and excitation spectrum on the performance of high-power optoelectronic switches utilizing Fe-doped $β$-Ga$_2$O$3$. By systematically varying the anode grid pitch ($20-80\ μ\text{m}$) and the excitation spectrum ($235-500\ \text{nm}$), we identify a crucial sub-bandgap regime, centered at $272\ \t

Muhammad Fitrah Alfian Rangga Sakti

We construct the exact stellar configurations that contain an ordinary perfect-fluid matter that interacts minimally with a condensate of gravitons with distinct pressure conditions on the surface. We propose vanishing transverse pressure on the surface for, namely graviton condensate type 1 and vanishing radial pressure on the surface for type 2. The condit

Joseph A. Farmer, Aidan Murray, Johannes Krotz, Ryan G. McClarren

We present Generative Monte Carlo (GMC), a novel paradigm for particle transport simulation that integrates generative artificial intelligence directly into the stochastic solution of the linear Boltzmann equation. By reformulating the cell-transmission problem as a conditional generation task, we train neural networks using conditional flow matching to samp

Shengyu He, Jiaxi Yu, Antoine Rocher, Daniel Forero-Sánchez

Spectroscopic redshift errors, including redshift uncertainty and catastrophic failures, can bias cosmological measurements from galaxy redshift surveys at sub-percent level. In this work, we investigate their impact on the full-shape analysis using contaminated mock catalogs. We find that redshift uncertainty introduces a scale-dependent damping effect on t

Mizuki Funato, Yohei Sawada

Despite the necessity for accurate flood prediction, many regions lack sufficient river discharge observations. Although numerous models for daily river discharge prediction exist, achieving high accuracy, interpretability, and efficiency under data-scarce conditions remains a major challenge. We address this with a novel method, HYdrological Prediction with

Zhengyao Ding, Ziyu Li, Yujian Hu, Youyao Xu

Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating accessible and accurate diagnostic tools. While cardiac magnetic resonance imaging (CMR) provides gold-standard insights into cardiac structure and function, its clinical utility is limited by high cost and complexity. In contrast, electrocardiography (ECG) is inexpensive

Biao Wang

Recently, Donoso, Le, Moreira and Sun studied the asymptotic behavior of the averages of completely multiplicative functions over the Gaussian integers. They derived Wirsing's theorem for Gaussian integers, answered a question of Frantzikinakis and Host for sum of two squares, and obtained a variant of a theorem of Bergelson and Richter on ergodic averag

Quincy Huhn, Jean Ragusa, Youngsoo Choi

Deterministic solutions to the Sn transport equation can be computationally expensive to calculate. Reduced Order Models (ROMs) provide an efficient means of approximating the Full Order Model (FOM) solution. We propose a novel approach for constructing ROMs of the Sn radiation transport equation, Offline Maximizing Minimally Invasive (OMMI) Proper Orthogona

Benjamin Christoffersen, Keith Humphreys, Alessandro Gasparini, Birzhan Akynkozhayev

Joint models are well suited to modelling linked data from laboratories and health registers. However, there are few examples of joint models that allow for (a) multiple markers, (b) multiple survival outcomes (including terminal events, competing events, and recurrent events), (c) delayed entry and (d) scalability. We propose a full likelihood approach for

Behrooz Mamandipoor, Chun-Nan Hsu, Martin Krause, Ulrich H. Schmidt

Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mortality risk among critically ill patients after their initial 24 hour intensive care unit (ICU) admission. We

Team Olmo, :, Allyson Ettinger, Amanda Bertsch

We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function calling, coding, instruction following, general chat, and knowledge recall. This release includes the entire model flow, i.e., the full lifecycle of the family of models, includin

A. Miraval Zanon, G. Illiano, F. Ambrosino, D. de Martino

Millisecond pulsar (MSP) binaries are unique laboratories for studying matter and radiation under extreme conditions that are unattainable on Earth. Recent detections of optical millisecond pulsations from three systems in distinct evolutionary stages have opened an entirely new observational window to investigate particle acceleration, pulsar-disk interplay

S. R. Mane

The usual theoretical model for synchrotron radiation in circular accelerators (synchrotrons and storage rings) is to treat a single electron moving in a horizontal circle in a uniform vertical magnetic field, but the true situation in real storage rings is more complicated and exhibits much richer physics. The magnetic fields are inhomogeneous, and there is

Patrick LaChance, Aklant Kumar Bhowmick, Rupert A. C. Croft, Tiziana Di Matteo

We leverage the overmassive black holes ($\rm M_{BH}/M_{\ast} \approx0.1$) present in a realization of the BRAHMA cosmological hydrodynamic simulation suite to investigate their role in the emission of the unique ``little red dot'' (LRD) objects identified by the James Webb Space Telescope (JWST). We find that these black holes can produce LRD-like observabl

Sindhuja Madabushi, Dawood Wasif, Jin-Hee Cho

Federated Learning (FL) has emerged as a leading privacy-preserving machine learning paradigm, enabling participants to share model updates instead of raw data. However, FL continues to face key challenges, including weak client incentives, privacy risks, and resource constraints. Assessing client reliability is essential for fair incentive allocation and en

Masamune Oguri, Yen-Ting Lin, Nobuhiro Okabe, Naomi Ota

We construct samples of optically-selected clusters and photometric luminous red galaxies (LRGs) from the Hyper Suprime-Cam Subaru Strategic Program final year dataset covering $\sim 1200$~deg$^2$. The cluster catalogs extend out to the redshift of $1.38$ and contain more than 10000 clusters with richness larger than $15$, where the richness is defined to be

Dawid Malarz, Filip Manjak, Maciej Zięba, Przemysław Spurek

The rapid progress of text-to-image diffusion models raises significant concerns regarding the unauthorized reproduction of trademarked content. While prior work targets general concepts (e.g., styles, celebrities), it fails to address specific brand identifiers. Brand recognition is multi-dimensional, extending beyond explicit logos to encompass distinctive

Zeming Du, Qitan Shao, Hongfei Liu, Yong Zhang

While accurate traffic forecasting is vital for Intelligent Transportation Systems (ITS), effectively communicating predicted conditions via natural language for human-centric decision support remains a challenge and is often handled separately. To address this, we propose CrossTrafficLLM, a novel GenAI-driven framework that simultaneously predicts future sp

Alexander D. McWeeney

We show that the space of polynomially bounded ancient solutions to the biharmonic heat equation on a complete manifold with polynomial volume growth is bounded by the dimensions of spaces of polynomially bounded biharmonic functions. This generalizes the work of Colding and Minicozzi in [6] for ancient caloric functions.

Alban Gauthier, Valentin Deschaintre, Alexandre Lanvin, Fredo Durand

Digital content creation is experiencing a profound change with the advent of deep generative models. For texturing, conditional image generators now allow the synthesis of realistic RGB images of a 3D scene that align with the geometry of that scene. For appearance modeling, SVBRDF prediction networks recover material parameters from RGB images. Combining t

Akhil Sharma, Shaikh Yaser Arafat, Jai Kumar Sharma, Ken Huang

The increasing operational reliance on complex Multi-Agent Systems (MAS) across safety-critical domains necessitates rigorous adversarial robustness assessment. Modern MAS are inherently heterogeneous, integrating conventional Multi-Agent Reinforcement Learning (MARL) with emerging Large Language Model (LLM) agent architectures utilizing Retrieval-Augmented

Santiago Torres, Roberto Raddi, Alberto Rebassa-Mansergas, Leandro G. Althaus

The ESA Gaia mission has revolutionized our understanding of the white dwarf population, delivering an unprecedented census of these nearby remnants and revealing previously unseen structures in the Hertzsprung-Russell (HR) diagram. However, while Gaia has expanded the scope of white dwarf astrophysics, it has also exposed new questions related to atmospheri

William Barham, Brian K. Tran, Ben S. Southworth, Florian Schäfer

The recently proposed information geometric regularization (IGR) was the first inviscid regularization of the multi-dimensional compressible Euler equations, which enabled the simulation of realistic compressible fluid models at an unprecedented scale. However, the thermodynamic effects of this regularization have not yet been understood in a principled mann

Gia Dvali, Lucy Komisel

Vacuum bubbles, formed in first order phase transitions, have important implications for cosmology. In particular, they source gravitational waves. Usually, it is assumed that, once bubbles are materialized, their state, further evolution and mergers are well-described classically. This paper will show that this intuition breaks down for bubbles which posses

Agung Septiadi, Minzhao Lyu, Hassan Habibi Gharakheili, Vijay Sivaraman

Online government services are increasingly regarded as critical national infrastructure. Because these services directly influence public trust, any disruption can have significant societal and political consequences. Yet their supporting infrastructures remain vulnerable to outages from natural disasters, geopolitical tensions, and targeted attacks. Centra

Vivian Lin, Kuk Jin Jang, Wenwen Si, Insup Lee

Diffusion models have shown promise in forecasting future data from multivariate time series. However, few existing methods account for recurring structures, or patterns, that appear within the data. We present Pattern-Guided Diffusion Models (PGDM), which leverage inherent patterns within temporal data for forecasting future time steps. PGDM first extracts

Amir Hossein Ghasemi

We present a relational framework in which temporal structure is not fundamental but emerges from correlations within a globally stationary quantum state. Each subsystem includes an internal clock, and conditional states evolve effectively with respect to these internal readings. The construction naturally extends to relativistic motion, gravitational redshi

Anirban Roy, Anthony Pullen, Patrick C. Breysse, Rachel S. Somerville

The epoch of reionization (EoR), marking the Universe's transition from a neutral to ionized state, represents a pivotal phase for understanding the formation of the first stars and galaxies. Intensity mapping of atomic and molecular lines, such as $[\mathrm{CII}]$ and CO J-ladder transitions, across a broad redshift range is a powerful tool for investigatin

Mauricio Cataldo, Daniel Cuevas

Traditionally, the embedding procedure for spherically symmetric spacetimes has been restricted to the equatorial plane $\theta = \pi/2$. This conventional approach, however, encounters a fundamental limitation: not every spherically symmetric geometry admits an isometric embedding of its equatorial slice into three-dimensional Euclidean space. When such emb

Abdelhamid Salem, Kai-Kit Wong, Hyundong Shin, Yangyang Zhang

In this letter, we investigate the fundamental limits of localization in fluid antenna systems (FAS) utilizing a Fisher-information-theoretic framework. We develop a unified model to quantify the localization information extractable from time-of-arrival (ToA) and angle-of-arrival (AoA) measurements, explicitly capturing the synthetic aperture effects induced

Ibon Gracia, Morteza Lahijanian

Controlling stochastic systems with unknown dynamics and under complex specifications is specially challenging in safety-critical settings, where performance guarantees are essential. We propose a data-driven policy synthesis framework that yields formal performance guarantees for such systems using conditional mean embeddings (CMEs) and uncertain Markov dec

Alessandro Casa, Thomas Brendan Murphy, Michael Fop

Recently, growing consumer awareness of food quality and sustainability has led to a rising demand for effective food authentication methods. Vibrational spectroscopy techniques have emerged as a promising tool for collecting large volumes of data to detect food adulteration. However, spectroscopic data pose significant challenges from a statistical viewpoin

Zhao-Qian Yao, Zhen-Ni Xu, Yu-Yang Xiao, Craig D. Roberts

Poincar\'e-covariant Bethe-Salpeter wave functions are used to calculate light-front wave functions (LFWFs) of the pion, $\pi$, and an analogue state, $\pi_{s\bar s}$. The current masses of the degenerate valence constituents in the $\pi_{s\bar s}$ are around $25$-times larger than those of the pion's valence constituents. Both valence spin-antialigned ($\ma

Marvin Rothmeier, Elisabeth R. Adams, Karsten Schindler, Andre Beck

TrES-5b is one of only three ultra-hot Jupiters (UHJs) with suggestions of a possibly decreasing orbital period that have persisted through multiple independent analyses (G. Maciejewski et al. 2021; S. R. Hagey et al. 2022; E. S. Ivshina & J. N. Winn 2022; W. Wang et al. 2024; L. C. Yeh et al. 2024). While WASP-12 b's decreasing period is well-explained by t

Matthew P. Leighton, Christopher W. Lynn

Non-Markovian dynamics are ubiquitous across physics, biology, and engineering. Yet our understanding of non-Markovian processes significantly lags that of simpler Markovian processes, due largely to a lack of tractable models. In this article, we present a minimal model of non-Markovian dynamics in which the current state copies past states with arbitrary h

Qi Chen, Fabio Ramos, Alán Aspuru-Guzik, Florian Shkurti

Bayesian Optimization (BO) is a key methodology for accelerating molecular discovery by estimating the mapping from molecules to their properties while seeking the optimal candidate. Typically, BO iteratively updates a probabilistic surrogate model of this mapping and optimizes acquisition functions derived from the model to guide molecule selection. However

Matthew P. Leighton, Christopher W. Lynn

Non-Markovian stochastic processes are ubiquitous in biology. Nevertheless, we lack a general framework for quantifying historical dependencies. In this Letter, we propose an information-theoretic approach to decompose history dependence in systems with non-Markovian dynamics, quantifying the information encoded in dependencies of each order. In minimal mode

Ming-Yang Zhuang, Jinyi Shangguan, Yuan Bian, Yue Shen

Dust and cold gas are not uncommon in nearby early-type galaxies (ETGs), and represent an important aspect of their evolution. However, their origin has been debated for decades. Potential sources include internal processes (e.g., mass loss from evolved stars), external mechanisms (e.g., minor mergers or cooling flows), or a combination of both. Gas-rich min

Narasinga Rao Miniskar, Mohammad Alaul Haque Monil, Elaine Wong, Vicente Leyton-Ortega

Extreme heterogeneity in emerging HPC systems are starting to include quantum accelerators, motivating runtimes that can coordinate between classical and quantum workloads. We present a proof-of-concept hybrid execution framework integrating the IRIS asynchronous task-based runtime with the XACC quantum programming framework via the Quantum Intermediate Repr

Samuel Rothfarb, Megan C. Davis, Ivana Matanovic, Baikun Li

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We introduce Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), an active learning framework where large language models autonomously design, exec

Jacob Ender, Chris Kapulkin

We present a new algorithm for computing the first discrete homology group of a graph. By testing the algorithm on different data sets of random graphs, we find that it significantly outperforms other known algorithms.

Jakub Ślęzak

Codifference is a commonly used measure of dependence for stable vectors and processes for which covariance is infinite. However, we argue that it can also be used for other heavy-tail distributions and it provides useful information for other non-Gaussian distributions as well, no matter the tails. Motivated by this, we analyse codifference using as little

Sophia Tang

Spherical equivariant graph neural networks (EGNNs) provide a principled framework for learning on three-dimensional molecular and biomolecular systems, where predictions must respect the rotational symmetries inherent in physics. These models extend traditional message-passing GNNs and Transformers by representing node and edge features as spherical tensors

Giulio Aielli, Oleg Brandt, Jon Burr, Oliver Kortner

The ANUBIS experiment aims to search for long-lived particles at the Large Hadron Collider (LHC) at CERN. To assess the feasibility of the project, a prototype detector, proANUBIS, was designed, constructed, and prepared for installation in the UX1 ATLAS experimental cavern at the LHC. The primary physics goals of proANUBIS are to determine the technical lim

C. Reichhardt, C. J. O. Reichhardt

We consider a binary system of particles with repulsive interactions that move in opposite or perpendicular directions to each other under an applied external drive. For opposite driving, at higher drives a phase-separated laned state forms that has strong hysteresis in the velocity-force curve and the fraction of topological defects as the drive is cycled u

Mike Peel, Dave Clements, Tony Mroczkowski, Allen Foster

The exploration of the time-variable astronomical sky at submm wavelengths is rapidly becoming more feasible with large sky surveys by Cosmic Microwave Background telescopes with tens of thousands of detectors. Observations with the Atacama Cosmology Telescope and South Pole Telescope have already detected some transients, and Simons Observatory and CCAT are

Haoyuan Cai, Sulaiman A. Alghunaim, Ali H. Sayed

In Part I of this work [1], we developed an accelerated algorithmic framework, DAMA (Decentralized Accelerated Minimax Approach), for nonconvex Polyak-Lojasiewicz (PL) minimax optimization over decentralized multi-agent networks. To further enhance convergence in online and offline scenarios, Part I of this work [1] also proposed a novel accelerated gradient

Anselme Ndikumana, Kim Khoa Nguyen, Adel Larabi, Mohamed Cheriet

Deploying fiber optics as a last-mile solution in rural areas is not economically viable due to low population density. Nevertheless, providing high-speed internet access in these regions is essential to promote digital inclusion. 5G Fixed Wireless Access (5G FWA) has emerged as a promising alternative; however, its one-hop topology limits coverage. To overc

Dragos Secrieru, Garyk Brixi, Yoshua Bengio, Taiji Suzuki

Multi-hybrid architectures are poised to take over language modeling due to better quality and performance. We introduce a hierarchical decomposition framework for linear recurrences that allows us to develop algorithms aligned with GPU memory hierarchies, yielding Sliding Window Recurrences. We focus specifically on truncating recurrences to hardware-aligne

Haoyuan Cai, Sulaiman A. Alghunaim, Ali H. Sayed

In this work and its accompanying Part II [1], we develop an accelerated algorithmic framework, DAMA (Decentralized Accelerated Minimax Approach), for nonconvex Polyak-Lojasiewicz minimax optimization over decentralized multi-agent networks. Our approach integrates online and offline stochastic minimax algorithms with various decentralized learning strategie

Silvina Gatica

We present molecular dynamics simulations of the adsorption of mixed CO2-water vapors on a graphene flakes substrate, a model inspired by the microporous structure of activated carbons. Adsorption strength is quantified through a reduced energy measure that avoids ambiguities associated with defining adsorption regions. We find that CO2 adsorbs more strongly

Dennys Reis, Renato Mafra Moysés, Lino Misoguti, Antônio Martins Figueiredo Neto

Lyotropic chromonic liquid crystals are formed by the self-assembly of aromatic compounds in concentrated solutions. Despite numerous applications of chromonic systems in optical and photonic devices, they all make use of the anisotropic linear optical properties of the nematic or columnar liquid crystalline phases. This paper extends the investigations of c

Taylan Demir, Atakan Koçyiğit

Astronomical light curves are noisy and irregular, so compression must reduce size without erasing weak transients. We propose a fractional wavelet compression method where wavelet coefficients are regularized via an Atangana Baleanu Caputo derivative with a nonsingular Mittag Leffler kernel. The induced long memory smoothing suppresses noise while preservin

Victor Livernoche, Andreea Musulan, Zachary Yang, Jean-François Godbout

Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes actually appear and circulate across social platforms during major events in democratic countries. In this study, we present one of the first in-depth analyses of how these realistic synthetic media shape the political landscape online, focusin

Bhargav Chickmagalur Nanjundappa, Spandan Maaheshwari

Large Language Models (LLMs) have become integral to software engineering workflows, yet their effectiveness degrades significantly in multi-turn conversations. Recent studies demonstrate an average 39% performance drop when instructions are delivered across multiple turns, with models making premature assumptions and failing to course correct (Laban et al.,

Patrick Egenlauf, Iva Březinová, Sabine Andergassen, Miriam Klopotek

Out-of-equilibrium quantum many-body systems exhibit rapid correlation buildup that underlies many emerging phenomena. Exact wave-function methods to describe this scale exponentially with particle number; simpler mean-field approaches neglect essential two-particle correlations. The time-dependent two-particle reduced density matrix (TD2RDM) formalism offer

Julian Jeggle, Raphael Wittkowski

In this book chapter, we review how systems of simple motile agents can be used as a pathway to intelligent systems. It is a well known result from nature that large groups of entities following simple rules, such as swarms of animals, can give rise to much more complex collective behavior in a display of emergence. This begs the question whether we can emul

Francisca Kemper, Rosie Chen, Axel Weiss, Caroline Bot

The Large and Small Magellanic Clouds (LMC, SMC) are nearby dwarf galaxies whose proximity uniquely enables molecular cloud-scale resolution observations across the entire Magellanic system, a capability unmatched in any other external galaxy. Their low metallicities resemble conditions near the peak of cosmic star formation, allowing resolved studies of int

Matheus Corrêa Domingos, Valdivino Alexandre de Santiago Júnior, Juliana Aparecida Anochi, Elcio Hideiti Shiguemori

Forecasting meteorological variables is challenging due to the complexity of their processes, requiring advanced models for accuracy. Accurate precipitation forecasts are vital for society. Reliable predictions help communities mitigate climatic impacts. Based on the current relevance of artificial intelligence (AI), classical machine learning (ML) and deep

Frank M. Abel, Paige Burke, Daniel Wines, Brian Donovan

Automation and high-throughput characterization and synthesis for material development are becoming increasingly common; these approaches require machine learning (ML) tools to assess material properties, ideally based on a single measurement. Here, ML models are developed to predict magnetization from X-ray diffraction (XRD) for iron oxide nanoparticles. Ou

Emma Rosenfeld, Craig Gidney, Gabrielle Roberts, Alexis Morvan

Fault-tolerant quantum computing requires a universal gate set, but the necessary non-Clifford gates represent a significant resource cost for most quantum error correction architectures. Magic state cultivation offers an efficient alternative to resource-intensive distillation protocols; however, testing the proposal's assumptions represents a challenging d

Alessio Buscemi, Tom Deckenbrunnen, Fahria Kabir, Kateryna Mishchenko

The implementation of the AI Act requires practical mechanisms to verify compliance with legal obligations, yet concrete and operational mappings from high-level requirements to verifiable assessment activities remain limited, contributing to uneven readiness across Member States. This paper presents a structured mapping that translates high-level AI Act req

Hossein Sharify, Behnam Raoufi, Mahdy Ramezani, Khosrow Hajsadeghi

We present a compact, quantization-ready acoustic scene classification (ASC) framework that couples an efficient student network with a learned teacher ensemble and knowledge distillation. The student backbone uses stacked depthwise-separable "expand-depthwise-project" blocks with global response normalization to stabilize training and improve robustness to

Amirkia Rafiei Oskooei, Eren Caglar, Ibrahim Sahin, Ayse Kayabay

The real-time deployment of cascaded generative AI pipelines for applications like video translation is constrained by significant system-level challenges. These include the cumulative latency of sequential model inference and the quadratic ($\mathcal{O}(N^2)$) computational complexity that renders multi-user video conferencing applications unscalable. This

Sibo Tian, Minghui Zheng, Xiao Liang

Stochastic human motion prediction is critical for safe and effective human-robot collaboration (HRC) in industrial remanufacturing, as it captures human motion uncertainties and multi-modal behaviors that deterministic methods cannot handle. While earlier works emphasize highly diverse predictions, they often generate unrealistic human motions. More recent

Anning Tian, Byunghyun Ko, Kaichen Qu, Mengyuan Liu

Real-time deployment of prostate MRI segmentation on clinical workstations is often bottlenecked by computational load and memory footprint. Deep learning-based prostate gland segmentation approaches remain challenging due to anatomical variability. To bridge this efficiency gap while still maintaining reliable segmentation accuracy, we propose KLO-Net, a dy

B. Fitos, G. Takács

We consider a prime example of simulating interacting relativistic QFT with cold atoms: the realisation of the sine-Gordon model by tunnel-coupled quasi-1D Bose gases. While experiments have shown that it can realise the sine-Gordon model in equilibrium, studies of non-equilibrium dynamics have revealed a phase-locking behaviour that stands in contrast to pr

Dragomir Davidovic

Time--convolutionless (TCL) master equations can break down at long times: time-local perturbative generators develop secular growth in correlation-dominated regimes. We mitigate this by a regulated, partially resummed reconstruction of the dynamical map around a Davies reference semigroup, expressed through a non--Markovian density-matrix correlator C(t) th

Leonardo Lopes

This dissertation presents a systematic theoretical investigation into realizing a condensed matter analogue of the Chiral Magnetic Effect (CME) in a quasi-planar, 2+1D system. The research establishes a conceptual bridge between the anomalous transport phenomena of high-energy physics and the emergent electronic properties of engineered honeycomb lattices.

Charlie Becker, David John Gagne, Julie Demuth, John S. Schreck

Accurately forecasting winter precipitation type and its transitions is critical for high-impact decision making. However, existing methods struggle in thermodynamically ambiguous regimes, and most do not quantify forecast uncertainty from a single model run. We developed an evidential neural network that predicts calibrated probabilities for four winter pre

Rachit Bansal, Aston Zhang, Rishabh Tiwari, Lovish Madaan

Progress on training and architecture strategies has enabled LLMs with millions of tokens in context length. However, empirical evidence suggests that such long-context LLMs can consume far more text than they can reliably use. On the other hand, it has been shown that inference-time compute can be used to scale performance of LLMs, often by generating think

Taylan Demir, Atakan Koçyiğit

We propose a one-dimensional viscoelastic seismic-wave model driven by the Atangana-BaleanuCaputo fractional derivative with a non-singular Mittag-Leffler kernel. A finite-difference discretization in space and an Adams-Bashforth-Moulton predictor-corrector scheme in time are used to compute solutions for several fractional orders. Simulations indicate that

Joaquín González-Nuevo, Laura Bonavera, Juan Alberto Cano, David Crespo

Strong gravitational lensing is a powerful probe of cosmology, dark matter (DM), and high-redshift galaxy evolution, but current samples of strongly lensed galaxies (SLGs) remain far too small to exploit its full potential. $\textit{Herschel}$'s submillimeter (submm) surveys demonstrated that submm selection provides the most efficient and least biased route