October 2025 arXiv papers — page 127

Sagar Mandal

10 is the smallest positive integer which is whether solitary or friendly is still an open question in mathematics. In this paper, we provide upper bounds for each of the prime divisors of a friend of 10. This paper is precisely a generalization of a recent paper [4] in which necessary upper bounds for the 2nd, 3rd, and 4th smallest prime divisors of a frien

Aditi Sen, Partha Lahiri

In the age of big data, nonprobability surveys are becoming increasingly abundant. Data integration techniques involving both probability and nonprobability surveys are being extensively used for providing improved estimates for finite population estimation. While much of the existing research has focused on mitigating selection bias in nonprobability survey

Daniel Funck

In this paper, we study the moduli space of unipotent Weil-Deligne representations valued in a split reductive group $G$ and characterise which irreducible components are smooth. We apply the smoothness results proved to show that a certain space of ordinary automorphic forms is a locally generically free module over the corresponding global deformation ring

Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou

Optimization of deep neural networks (DNNs) has been a driving force in the advancement of modern machine learning and artificial intelligence. With DNNs characterized by a prolonged sequence of nonlinear propagation, determining their optimal parameters given an objective naturally fits within the framework of Optimal Control Programming. Such an interpreta

Steve Fan, Paul Pollack

For each positive integer $n$, we denote by $\omega^*(n)$ the number of shifted-prime divisors $p-1$ of $n$, i.e., \[\omega^*(n):=\sum_{p-1\mid n}1.\] First introduced by Prachar in 1955, this function has interesting applications in primality testing and bears a strong connection with counting Carmichael numbers. Prachar showed that for a certain constant $

Yanqing Xu, Jingjing Cui, Yongxu Zhu, Zhiguo Ding

Pinching-antenna systems have emerged as a novel and transformative flexible-antenna architecture for next-generation wireless networks. They offer unprecedented flexibility and spatial reconfigurability by enabling dynamic positioning and activation of radiating elements along a signal-guiding medium (e.g., dielectric waveguides), which is not possible with

Soumik Pal, Tim Mesikepp

This is a free textbook suitable for a one-semester course on Markov chains, covering basics of finite-state chains, many classical models, asymptotic behavior and mixing times, Monte Carlo methods, and martingales and harmonic functions. It is designed to fill a gap in the literature by being suitable for undergraduates; much of the theory is thus built fro

Michaël Lévesque, Paul Charbonneau

Gyrochronology, a method for dating aged field stars ($\gtrsim$ a few Gyr) based on their rotation rate, has recently been shown to fail for many stars older than the sun. The explanation most often put forth is that a shutdown or mode change in the stellar dynamo leads to a sharp decrease in angular momentum loss in magnetized coronal winds. In this paper,

Mohammadsajad Alipour, Mohammad Mohammadi Amiri

Model merging aims to combine multiple fine-tuned models into a single set of weights that performs well across all source tasks. While prior work has shown that merging can approximate the performance of individual fine-tuned models for each task, it largely overlooks scenarios where models are compressed into low-rank representations, either through low-ra

Juhyeong Kim, Yejin Kim, Youngbin Lee, Hyunwoo Byun

We present FinAI Data Assistant, a practical approach for natural-language querying over financial databases that combines large language models (LLMs) with the OpenAI Function Calling API. Rather than synthesizing complete SQL via text-to-SQL, our system routes user requests to a small library of vetted, parameterized queries, trading generative flexibility

Matthew D. Merris, Tim Andersen

In the evolving domains of Machine Learning and Data Analytics, existing dataset characterization methods such as statistical, structural, and model-based analyses often fail to deliver the deep understanding and insights essential for innovation and explainability. This work surveys the current state-of-the-art conventional data analytic techniques and exam

John M. McBride

The origins of consonance in human music has long been contested, and today there are three primary hypotheses: aversion to roughness, preference for harmonicity, and learned preferences from cultural exposure. While the evidence is currently insufficient to disentangle the contributions of these hypotheses, I propose several reasons why roughness is an espe

Jacob LaMountain, Amogh Raju, Dan Wasserman, Viktor A. Podolskiy

The ability to control the spatial distribution of light, particularly in deep sub-wavelength areas, is important for a range of materials science, microscopy, and communications applications. Separately, materials science and communications rely on the ability to temporally shape the evolution of electromagnetic pulses. In this work we investigate theoretic

Konrad Koenigsmann, Sankha Subhra Bakshi, Peter Schauss, Gia-Wei Chern

The interplay of topology with nonequilibrium driving and dissipation in open quantum systems has recently attracted significant interest in condensed matter physics. In this work, we investigate a driven, dissipative Haldane model using large-scale numerical simulations of Lindblad dynamics. We show that the system evolves into a time-periodic quasi-steady

Jan Kwiatkowski, Jarosław A. Chudziak

Quantitative trading strategies rely on accurately ranking stocks to identify profitable investments. Effective portfolio management requires models that can reliably order future stock returns. Transformer models are promising for understanding financial time series, but how different training loss functions affect their ability to rank stocks well is not y

Sania Asif, Zhixiang Wu

In this paper, we present a unified framework for studying cohomology theories of various operators in the context of pseudoalgebras. The central tool in our approach is the notion of a quasi-twilled Lie pseudoalgebra. We introduce two types of deformation maps. Type I unifies modified $r$ matrices, crossed homomorphisms, derivations, and homomorphisms; and

Tian Liu, Alex Cann, Ian Colbert, Mehdi Saeedi

While the rapid advancements in the reinforcement learning (RL) research community have been remarkable, the adoption in commercial video games remains slow. In this paper, we outline common challenges the Game AI community faces when using RL-driven NPCs in practice, and highlight the intersection of RL with traditional behavior trees (BTs) as a crucial jun

Maha Mosaad A Alghamdi, Nikolai Leonenko, Andriy Olenko

This paper studies high-order partial differential equations with random initial conditions that have both long-memory and cyclic behavior. The cases of random initial conditions with the spectral singularities, both at zero (representing classical long-range dependence) and at non-zero frequencies (representing cyclic long-range dependence), are investigate

Gabriele S. Ilha, C. M. Harrison, V. Mainieri, Ann Njeri

AGN feedback is a well known mechanism in the evolution of galaxies. One open question is the driving mechanism of galaxy-scale outflows. At low redshift, radio jets often interact with the ISM, generating turbulence and driving ionized outflows. Despite this evidence at low redshift, relatively few studies have investigated the radio-ionized gas connection

Saeed Moradi, Koosha Esmaeilzadeh Khorasani, Sara Rouhani

Interoperability is essential for transforming blockchains from isolated networks into collaborative ecosystems, unlocking their full potential. While significant progress has been made in public blockchain interoperability, bridging permissioned and permissionless blockchains poses unique challenges due to differences in access control, architectures, and s

N. Erzincan, N. Aksaker, A. Akyuz, Q. Parker

In this study, we investigate the physical and chemical properties of planetary nebulae (PNe) from the Milky Way Galaxy using the largest number of sources to date, with 1,449 True PNe from the HASH database. Among the Galactic components thin disk, thick disk, halo, and bulge-most PNe are concentrated in the Galactic disk, with a median angular size of 12 a

Pranay Kocheta, Nayan Sanjay Bhatia, Katia Obraczka

Non-intrusive monitoring of vital signs has become increasingly important in a variety of healthcare settings. In this paper, we present PulseFi, a novel low-cost non-intrusive system that uses Wi-Fi sensing and artificial intelligence to accurately and continuously monitor heart rate and breathing rate, as well as detect apnea events. PulseFi operates using

Gabriel Raulet, Dmitriy Morozov, Aydin Buluc, Katherine Yelick

Computing fixed-radius near-neighbor graphs is an important first step for many data analysis algorithms. Near-neighbor graphs connect points that are close under some metric, endowing point clouds with a combinatorial structure. As computing power and data acquisition methods advance, diverse sources of large scientific datasets would greatly benefit from s

Arman Maesumi, Tanish Makadia, Thibault Groueix, Vladimir G. Kim

Many network architectures exist for learning on meshes, yet their constructions entail delicate trade-offs between difficulty learning high-frequency features, insufficient receptive field, sensitivity to discretization, and inefficient computational overhead. Drawing from classic local-global approaches in mesh processing, we introduce PoissonNet, a novel

Mohammed Baragilly, Hend Gabr

Determining the appropriate number of clusters in unsupervised learning is a central problem in statistics and data science. Traditional validity indices such as Calinski-Harabasz, Silhouette, and Davies-Bouldin-depend on centroid-based distances and therefore degrade in high-dimensional or contaminated data. This paper proposes a new robust, nonparametric c

Baoming Shi, Lei Zhang, Qiang Du

Saddle points provide a hierarchical view of the energy landscape, revealing transition pathways and interconnected basins of attraction, and offering insight into the global structure, metastability, and possible collective mechanisms of the underlying system. In this work, we propose a stochastic saddle-search algorithm to circumvent exact derivative and H

Alexandr A. Kalinin, Anne E. Carpenter, Shantanu Singh, Matthew J. O'Meara

Quantitative analysis of multidimensional biological images is useful for understanding complex cellular phenotypes and accelerating advances in biomedical research. As modern microscopy generates ever-larger 2D and 3D datasets, existing computational approaches are increasingly limited by their scalability, efficiency, and integration with modern scientific

Yin Tang, Yanyuan Ma, Jiwei Zhao

A randomized controlled trial (RCT) is widely regarded as the gold standard for assessing the causal effect of a treatment or intervention, assuming perfect implementation. In practice, however, randomization can be compromised for various reasons, such as one-sided noncompliance. In this paper, we first systematically study the likelihood-based identifiabil

Sungmin Cho, Sungbum Park, Insoo Oh

In this work, we introduce MUSE (Model-based Uncertainty-aware Similarity Estimation), a training-free framework designed for model-based zero-shot 2D object detection and segmentation. MUSE leverages 2D multi-view templates rendered from 3D unseen objects and 2D object proposals extracted from input query images. In the embedding stage, it integrates class

Teale W. Masrani, Geoffrey Messier, Amy Voida, Gina Dimitropoulos

Frontline staff of emergency shelters face challenges such as vicarious trauma, compassion fatigue, and burnout. The technology they use is often not designed for their unique needs, and can feel burdensome on top of their already cognitively and emotionally taxing work. While existing literature focuses on data-driven technologies that automate or streamlin

Torkel E Loman, Ruth E Baker

Mathematical modelling has traditionally relied on detailed system knowledge to construct mechanistic models. However, the advent of large-scale data collection and advances in machine learning have led to an increasing use of data-driven approaches. Recently, hybrid models have emerged that combine both paradigms: well-understood system components are model

Islam Akef Ebeid, Haoteng Tang, Pengfei Gu

Introduction Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing drug development. Existing in-silico methods use direct sequence embeddings from Protein Language Models (PLMs). Others use Graph Neural Networks (GNNs) for 3D protein structures. This study explores less computationally inten

Qinmiao Chen, Guangzhou Geng, Hong Liang, Wai Chun Wong

Metasurfaces composed of subwavelength nanostructures enable simultaneous control of polarization and wavefront, greatly enhancing holographic information capacity. Building on this capability, we extend holography into the quantum domain by experimentally realizing Bell-state holograms-distinct holographic images encoded in polarization-entangled Bell state

Faezeh Dehghan Tarzjani, Bhaskar Krishnamachari

The p-persistent CSMA protocol is central to random-access MAC analysis, but predicting saturation throughput in heterogeneous multi-hop wireless networks remains a hard problem. Simplified models that assume a single, shared interference domain can underestimate throughput by 48-62% in sparse topologies. Exact Markov-chain analyses are accurate but scale ex

David Roqui, Adèle Cormier, nistor Grozavu, Ann Bourges

Cultural heritage sites face accelerating degradation due to climate change, yet tradi- tional monitoring relies on unimodal analysis (visual inspection or environmental sen- sors alone) that fails to capture the complex interplay between environmental stres- sors and material deterioration. We propose a lightweight multimodal architecture that fuses sensor

B. Hadzhiyska, Y. Gong, Y. Hsu, P. A. Gallardo

We present a measurement of the pairwise kinematic Sunyaev-Zel'dovich (kSZ) signal using the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Sample (BGS) Data Release 1 (DR1) galaxy sample overlapping with the Atacama Cosmology Telescope (ACT) CMB temperature map. Our analysis makes use of $1.6$ million galaxies with stellar masses $\log M_\star/M_

Oluwashina K. Adegoke, Javier A. Garcia, Guglielmo Mastroserio, Elias Kammoun

IGR J17091-3624 is the only black hole X-ray binary candidate, aside from the well-studied black hole system GRS 1915+105, observed to exhibit a wide range of structured variability patterns in its light curves. In 2025, the source underwent a ``failed'' outburst: it brightened in the hard state but did not transition to the soft state before returning to qu

Edoardo Allegrini, Ananth Shreekumar, Z. Berkay Celik

Agentic AI systems, which leverage multiple autonomous agents and large language models (LLMs), are increasingly used to address complex, multi-step tasks. The safety, security, and functionality of these systems are critical, especially in high-stakes applications. However, the current ecosystem of inter-agent communication is fragmented, with protocols suc

Rae A. Corrigan Grove, Robert Stanton, Michael E. Wall, Anders M. N. Niklasson

Shadow molecular dynamics provide an efficient and stable atomistic simulation framework for flexible charge models with long-range electrostatic interactions. While previous implementations have been limited to atomic monopole charge distributions, we extend this approach to flexible multipole models. We derive detailed expressions for the shadow energy fun

Amer Sinha, Thomas Mesnard, Ryan McKenna, Daogao Liu

We introduce VaultGemma 1B, a 1 billion parameter model within the Gemma family, fully trained with differential privacy. Pretrained on the identical data mixture used for the Gemma 2 series, VaultGemma 1B represents a significant step forward in privacy-preserving large language models. We openly release this model to the community

Keidai Iiyama, Grace Gao

The establishment of a sustainable human presence on the Moon demands robust positioning, navigation, and timing (PNT) services capable of supporting both surface and orbital operations. This paper presents a comprehensive trade-off analysis of lunar frozen-orbit constellations for the Lunar Augmented Navigation Service (LANS), focusing on how the number of

Sayed Hamid Hosseini Dolatabadi, Yuchen Dong, Tanveer Hossain Bhuiyan, Bo Zeng

Natural disasters threaten the resilience of power systems, causing widespread power outages that disrupt critical loads (e.g., hospitals) and endanger public safety. Compared to the conventional restoration methods that often have long response times, leveraging government-controlled electric school buses (ESBs) with large battery capacity and deployment re

C. Beaugé, E. Gianuzzi, N. Trógolo, A. M. Leiva

The recent discovery of narrow rings around minor bodies has raised many questions regarding their origin and current dynamics. Sharp ring boundaries seem indicative of shepherding moonlets, but none have been found. All rings lie close to spin-orbit resonances (SORs) with the central body, particularly the 1/3, even though it is not clear how these may be r

Mahsa Bastankhah, Grace Liu, Dilip Arumugam, Thomas L. Griffiths

In this work, we take a first step toward elucidating the mechanisms behind emergent exploration in unsupervised reinforcement learning. We study Single-Goal Contrastive Reinforcement Learning (SGCRL), a self-supervised algorithm capable of solving challenging long-horizon goal-reaching tasks without external rewards or curricula. We combine theoretical anal

Darko Sasanski, Dimitar Peshevski, Riste Stojanov, Dimitar Trajanov

Computational gastronomy increasingly relies on diverse, high-quality recipe datasets to capture regional culinary traditions. Although there are large-scale collections for major languages, Macedonian recipes remain under-represented in digital research. In this work, we present the first systematic effort to construct a Macedonian recipe dataset through we

Pavan Chawhan, Matthew D. Duez, Francois Foucart, Patrick Chi-Kit Cheong

Two-dimensional axisymmetric simulations of binary neutron star (BNS) merger remnant are a cheap alternative to 3D simulations. To maintain realism for secular timescales, simulations must avoid accumulated errors from drifts in conserved quantities and artificial heating, and they must model turbulent transport in a way that remains plausible throughout the

Nikos Pagonas, Yeounoh Chung, Kostis Kaffes, Arvind Krishnamurthy

We introduce Cortex, a prototype workflow-aware serving platform designed for agentic workloads. The core principle of Cortex is stage isolation: it provisions dedicated resource pools for each distinct stage of an agentic workflow. This simple yet powerful strategy mitigates inter-stage interference in compute and memory, leading to better KV cache utilizat

Waqar Muhammad Ashraf, Talha Ansar, Abdulelah S. Alshehri, Peipei Chen

We introduce a neural network-driven robust optimisation framework that integrates data-driven domain as a constraint into the nonlinear programming technique, addressing the overlooked issue of domain-inconsistent solutions arising from the interaction of parametrised neural network models with optimisation solvers. Applied to a 1180 MW capacity combined cy

Christian Krattenthaler, Brandt Kronholm, Paul Marsh

We establish an alternative, ``perpendicular" collection of generating functions for the coefficients of Gaussian polynomials, $\begin{bmatrix}N+m\\m\end{bmatrix}_q$. We provide a general characterization of these perpendicular generating functions. For small values of $m$, unimodality of the coefficients of Gaussian polynomials is easily proved from these g

José A. Carrillo, Young-Pil Choi, Dowan Koo, Oliver Tse

We investigate the large-time behavior of the pressureless Euler system with nonlocal velocity alignment and interaction forces, with the aim of characterizing the asymptotic convergence of classical solutions under general interaction potentials $W$ and communication weights. We establish quantitative convergence in three settings. In one dimension with $(\

Yi Shi, Eran Ginossar, Michael Stern, Marzena Szymanska

Superconducting circuits are one of the leading candidates for storing and manipulating quantum information. Among them, qubits embedded with intrinsic noise protection have seen rapid advancements in recent years. This noise protection is typically realized by isolating the computational states from local sources of noise. Here, we propose an interacting sp

Muhammad Faheemur Rahman, Wayne Burleson

Memristive crossbar arrays (MCA) are emerging as efficient building blocks for in-memory computing and neuromorphic hardware due to their high density and parallel analog matrix-vector multiplication capabilities. However, the physical properties of their nonvolatile memory elements introduce new attack surfaces, particularly under fault injection scenarios.

Ali Eslami, Mohammad Pirani

Connected and Autonomous Vehicles (CAVs) are transforming modern transportation by enabling cooperative applications such as vehicle platooning, where multiple vehicles travel in close formation to improve efficiency and safety. However, the heavy reliance on inter-vehicle communication makes platoons highly susceptible to attacks, where even subtle manipula

Oscar Garcia-Montero, Sören Schlichting, Jie Zhu

It is well understood that subnuclear fluctuations in the initial state of heavy-ion collisions have an important impact on the creation of long-range correlations in the transverse plane. This is also true for the creation of particle correlations along the beam direction, which can be measured in particle detectors, e.g. through longitudinal decorrelation

Zhiyuan Wu, Yijiong Lin, Yongqiang Zhao, Xuyang Zhang

Robotic pushing is a fundamental manipulation task that requires tactile feedback to capture subtle contact forces and dynamics between the end-effector and the object. However, real tactile sensors often face hardware limitations such as high costs and fragility, and deployment challenges involving calibration and variations between different sensors, while

Nicolai T A Haydn, Gin Park

In this paper we consider $\phi$-mixing measures and show that the limiting return times distribution is compound Poisson distribution as the target sets shrink to a zero measure set. The approach we use generalises a method given by Galves and Schmitt in 1997 for the first entry time to higher orders.

Philipp Bauerfeind, Amir Salarpour, David Fernandez, Pedram MohajerAnsari

Scenario simulation is central to testing autonomous driving systems. Scenic, a domain-specific language (DSL) for CARLA, enables precise and reproducible scenarios, but NL-to-Scenic generation with large language models (LLMs) suffers from scarce data, limited reproducibility, and inconsistent metrics. We introduce NL2Scenic, an open dataset and framework w

Yazid Janati, Alain Durmus, Jimmy Olsson, Eric Moulines

Diffusion models enable the synthesis of highly accurate samples from complex distributions and have become foundational in generative modeling. Recently, they have demonstrated significant potential for solving Bayesian inverse problems by serving as priors. This review offers a comprehensive overview of current methods that leverage \emph{pre-trained} diff

Huiliang Zhang, Di Wu, Arnaud Zinflou, Benoit Boulet

Building energy management is essential for achieving carbon reduction goals, improving occupant comfort, and reducing energy costs. Coordinated building energy management faces critical challenges in exploiting spatial-temporal dependencies while ensuring operational safety across multi-building systems. Current multi-building energy systems face three key

Rene Davila, Everardo Barcenas, Rocio Aldeco-Perez

Formal verification entails testing software to ensure it operates as specified. Smart contracts are self-executing contracts with the terms of the agreement directly written into lines of code. They run on blockchain platforms and automatically enforce and execute the terms of an agreement when meeting predefined conditions. However, Smart Contracts, as sof

Taekyun Lee, Tommaso Balercia, Heasung Kim, Hyeji Kim

This paper introduces a novel framework for high-accuracy outdoor user equipment (UE) positioning that applies a conditional generative diffusion model directly to high-dimensional massive MIMO channel state information (CSI). Traditional fingerprinting methods struggle to scale to large, dynamic outdoor environments and require dense, impractical data surve

Wael Rashwan, Hossam M. Zawbaa, Sourav Dutta, Haytham Assem

Detecting out-of-scope (OOS) user utterances remains a key challenge in task-oriented dialogue systems and, more broadly, in open-set intent recognition. Existing approaches often depend on strong distributional assumptions or auxiliary calibration modules. We present DROID (Dual Representation for Out-of-Scope Intent Detection), a compact end-to-end framewo

Chongbin Zheng, Jaime Agudo-Canalejo, Jonathon Howard, Evelyn Tang

Microtubules capture chromosomes during mitosis by stochastically switching between growth and shrinkage at catastrophe events. They display strikingly rich biochemistry and dynamics, regulated by a stabilizing cap with distinct conformational states. Microtubule lengths at catastrophe are observed to follow a peaked distribution, while their growth "stutter

Rohan Shenoy, Peter Kempthorne

In the context of time-subordinated Brownian motion models, Fourier theory and methodology are proposed to modelling the stochastic distribution of time increments. Gaussian Variance-Mean mixtures and time-subordinated models are reviewed with a key example being the Variance-Gamma process. A non-parametric characteristic function decomposition of subordinat

Madeeha Nazish, Faisal Akram

In this work, we calculate the production cross sections of the 1S and 1P states of the $B_{c}$ meson for proton-nucleus and nucleus-nucleus colliding beam experiments at LHC and RHIC. We provide estimates for the Au nucleus at RHIC energies and for the Pb and Xe nuclei at LHC energies. Previous estimates of these cross sections have ignored quark anti-quark

Carter Blair, Kate Larson

Current frameworks for consensus statement generation with large language models lack the inherent structure needed to provide provable fairness guarantees when aggregating diverse free-form opinions. We model the task as a multi-objective, token-level Markov Decision Process (MDP), where each objective corresponds to an agent's preference. Token-level rewar

Yasemin Kara, Stef Nomden, Ekin Özman

In this paper, we investigate solutions to the Diophantine equation $ A a^p + B b^p = C c^3 $ over number fields using the modular method. Assuming certain standard modularity conjectures, we first establish an asymptotic result for general number fields satisfying an appropriate $S$-unit condition. In particular, we verify that this condition holds for seve

Giacomo Cacciapaglia, Francesco Sannino, Jessica Turner

The presence of a topological susceptibility in the electroweak sector of the Standard Model motivates the existence of a good quality weak axion $a_W$, associated with the spontaneous breaking of $B\!+\!L$. Its anomalous couplings and tiny mass, generated from electroweak instantons, render $a_W$ photophobic. We find that the strongest bound on the associat

Xiaoxi Huang, Qi Song, Gautam Gurung, Daniel A. Pharis

We report measurements of the current-induced spin torque produced by the delafossite antiferromagnet PdCrO2 and acting on an adjacent ferromagnetic permalloy layer. The spin torque increases strongly as the temperature is reduced through the Neel temperature, when the PdCrO2 transitions from a paramagnetic phase to a noncollinear antiferromagnetic state. Th

Rohan Walia, Mitchell Black, Andrew Schoer, Kevin Leahy

Safety-critical control is imperative for deploying autonomous systems in the real world. Control Barrier Functions (CBFs) offer strong safety guarantees when accurate system and sensor models are available. However, widely used additive, fixed-noise models are not representative of complex sensor modalities with state-dependent error characteristics. Althou

Cesar A. Amaral, Vinícius L. Oliveira, Juan P. L. C. Salazar, Eduardo I. Duzzioni

Computational Fluid Dynamics (CFD) is central to science and engineering, but faces severe scalability challenges, especially in high-dimensional, multiscale, and turbulent regimes. Traditional numerical methods often become prohibitively expensive under these conditions. Quantum computing and quantum-inspired methods have been investigated as promising alte

Juan Castillo, Emanuele Cozzo

Public discourse emerges from the interplay between individuals' willingness to voice their opinions and the structural features of the social networks in which they are embedded. In this work we investigate how choice homophily and triadic closure shape the emergence of the spiral of silence, the phenomenon whereby minority views are progressively silenced

Zixian Yang, Sushil Mahavir Varma, Lei Ying

We study a two-sided market, wherein, price-sensitive heterogeneous customers and servers arrive and join their respective queues. A compatible customer-server pair can then be matched by the platform, at which point, they leave the system. Our objective is to design pricing and matching algorithms that maximize the platform's profit, while maintaining reaso

Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese, Pietro Michiardi

Transfer entropy measures directed information flow in time series, and it has become a fundamental quantity in applications spanning neuroscience, finance, and complex systems analysis. However, existing estimation methods suffer from the curse of dimensionality, require restrictive distributional assumptions, or need exponentially large datasets for reliab

Youheng Zheng

We propose and simulate a protocol to evolve a quantum particle forward in time such that its trajectory closely matches that of the particle's Newtonian counterpart. Using short bursts of Schr\"odinger time-evolution interleaved with positive operator-valued measurements (POVMs) in the coherent basis, we demonstrate quantum-classical convergence for duratio

Awni Altabaa, Siyu Chen, John Lafferty, Zhuoran Yang

Systematic, compositional generalization beyond the training distribution remains a core challenge in machine learning -- and a critical bottleneck for the emergent reasoning abilities of modern language models. This work investigates out-of-distribution (OOD) generalization in Transformer networks using a GSM8K-style modular arithmetic on computational grap

Haotian Feng

Reaction-diffusion systems represent one of the most fundamental formulations used to describe a wide range of physical, chemical, and biological processes. With the increasing adoption of neural networks, recent research has focused on solving differential equations using machine learning techniques. However, the theoretical foundation explaining why neural

Rohan Shenoy, Peter Kempthorne

This paper explores the concept of random-time subordination in modelling stock-price dynamics, and We first present results on the Laplace distribution as a Gaussian variance-mixture, in particular a more efficient volatility estimation procedure through the absolute moments. We generalise the Laplace model to characterise the powerful variance gamma model

Md Sakhawat Hossain Himel, Rohan Dharmarathna, Netra Prasad Dhakal, Kelum Perera

Low power consumption is critical for smart windows for temperature control and privacy. The recently discovered ferroelectric nematic liquid crystals exhibit strong coupling of the ferroelectric polarization with electric fields, making them promising candidates for energy-efficient electrochromic devices. Here we investigate the electrochromic properties o

Bane Vasic, Valentin Savin, Michele Pacenti, Shantom Borah

Quantum error correction (QEC) is a cornerstone of quantum computing, enabling reliable information processing in the presence of noise. Sparse stabilizer codes -- referred to generally as quantum low-density parity-check (QLDPC) codes -- have risen to the forefront of QEC research in recent years. This can be attributed to several key factors. First, classi

Jungmin Lee, Seonghyuk Hong, Juyong Lee, Jaeyoon Lee

We introduce InsideOut, an extension of 3D Gaussian splatting (3DGS) that bridges the gap between high-fidelity RGB surface details and subsurface X-ray structures. The fusion of RGB and X-ray imaging is invaluable in fields such as medical diagnostics, cultural heritage restoration, and manufacturing. We collect new paired RGB and X-ray data, perform hierar

Ruth Britto, Holmfridur S. Hannesdottir

We develop a geometric framework in Feynman-parameter space to determine constraints on the sequential discontinuities of Feynman integrals. Our method is based on tracking the deformation of the integration contour as external kinematics are analytically continued. This procedure imposes powerful constraints on the analytic structure of Feynman integrals, p

Matthew Finlayson, Xiang Ren, Swabha Swayamdipta

The ubiquity of closed-weight language models with public-facing APIs has generated interest in forensic methods, both for extracting hidden model details (e.g., parameters) and for identifying models by their outputs. One successful approach to these goals has been to exploit the geometric constraints imposed by the language model architecture and parameter

Bogdan S. Damski

We discuss the most general form of the Lorentz transformation in 1+1 dimensional spacetime, focusing mainly on its superluminal branch. For this purpose, we introduce the 2-velocity of a reference frame and the clockwork postulate. Basic special relativity effects are discussed in the proposed framework. Different forms of the superluminal Lorentz transform

L. Ya. Glozman

We review the chiral spin symmetry, which is a symmetry of the color charge and of the confining electric part of QCD. Observation of this symmetry in the vacuum upon truncation of the near-zero modes of the Dirac operator implies that the hadron mass in the light quark sector is not due to the quark condensate of the vacuum and that confinement and chiral s

David R. Anderson, Jose I. Vines, Katharine Hesse, Louise Dyregaard Nielsen

We report the discovery of NGTS-11 c, a transiting warm Neptune ($P \approx 12.8$ d; $M_{p} = 1.2^{+0.3}_{-0.2} M_{\mathrm{Nep}}$; $R_{p} = 1.24 \pm 0.03 R_{\mathrm{Nep}}$), in an orbit interior to the previously reported transiting warm Saturn NGTS-11 b ($P \approx 35.5$ d). We also find evidence of a third outer companion orbiting the K-dwarf NGTS-11. We f

Marco Barbero-Mota, Eric V. Strobl, John M. Still, William W. Stead

We provide an accessible description of a peer-reviewed generalizable causal machine learning pipeline to (i) discover latent causal sources of large-scale electronic health records observations, and (ii) quantify the source causal effects on clinical outcomes. We illustrate how imperfect multimodal clinical data can be processed, decomposed into probabilist

Jonathan R. Gaunt, Adam Owen

FeynCraft is a browser-based game that is designed to teach players the particle interactions of the Standard Model of particle physics, and how to link these interactions together to produce valid Feynman diagrams. It is primarily targeted at undergraduates and lecturers in introductory courses in particle physics, but we anticipate that it should also be u

Emanuel Garbin, Guy Adam, Oded Krams, Zohar Barzelay

We present a novel, zero-shot pipeline for creating hyperrealistic, identity-preserving 3D avatars from a few unstructured phone images. Existing methods face several challenges: single-view approaches suffer from geometric inconsistencies and hallucinations, degrading identity preservation, while models trained on synthetic data fail to capture high-frequen

Loris Del Grosso, David E. Kaplan, Francesco Serra

We show that both abelian and non-abelian gauge theories admit configurations in which the fields behave as if in the presence of static charge densities, or ``shadow charges". These correspond to nontrivial initial conditions for the fields that generate gauge transformations, the Gauss' law operators. In non-abelian theories, such configurations seem to de

Randall Clark, Vacslav Glukhov, Georgy Subbotin, Maxim Nurgaliev

We present a parsimonious and robust machine learning approach for identifying plasma confinement states in fusion power plants (FPPs) where reliable identification of the low-confinement (L-mode) and high-confinement (H-mode) regimes is critical for safe and efficient operation. Unlike research-oriented devices, FPPs must operate with a severely constrained

Haziq Mohammad Khalid, Athikash Jeyaganthan, Timothy Do, Yicheng Fu

Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations characterize everyday interactions with LLMs, this degradation poses a severe challenge to real world usability. We hypothesize that abrupt increases in model uncertainty signal m

Milad Hoseinpour, Vladimir Dvorkin

The optimal power flow (OPF) is a multi-valued, non-convex mapping from loads to dispatch setpoints. The variability of system parameters (e.g., admittances, topology) further contributes to the multiplicity of dispatch setpoints for a given load. Existing deep learning OPF solvers are single-valued and thus fail to capture the variability of system paramete

Tommaso Mencattini, Riccardo Cadei, Francesco Locatello

Randomized Controlled Trials are one of the pillars of science; nevertheless, they rely on hand-crafted hypotheses and expensive analysis. Such constraints prevent causal effect estimation at scale, potentially anchoring on popular yet incomplete hypotheses. We propose to discover the unknown effects of a treatment directly from data. For this, we turn unstr

Hemjyoti Das, Christian Ott

In this work, we present a novel control approach based on partial feedback linearization (PFL) for the stabilization of a suspended aerial platform with an attached load. Such systems are envisioned for various applications in construction sites involving cranes, such as the holding and transportation of heavy objects. Our proposed control approach consider

Puyu Yang, Vincent Traag, Rodrigo Costas, Giovanni Colavizza

Wikipedia is one of the largest online encyclopedias, which relies on scientific publications as authoritative sources. The increasing prevalence of open access (OA) publishing has expanded the public availability of scientific knowledge; however, its impact on the dynamics of knowledge contestation within collaborative environments such as Wikipedia remains

Guanghui Hu, Andreas Rathsfeld, Jiayi Zhang, Ruming Zhang

We propose a new radiation condition for an infinite inhomogeneous two-dimensional medium which is periodic in the vertical direction and remains invariant in the horizontal direction. The classical Rayleigh-expansion radiation condition does not apply to our case, because this would require the medium to be inhomogeneous in a half plane. We utilize the Floq

L. M. Máñez-Espina, B. Amrahi, I. Faniayeu, R. Cichelero

Reciprocity breaking at optical frequencies typically relies on bulky magnets, dynamic modulation, or nonlinearities, all of which hinder chip-scale integration and the handling of unpolarised light. We introduce a fully passive, subwavelength metasurface that achieves polarisation-insensitive one-way transparency by combining self-magnetised ferrite nanodis

Moritz Grillo, Tobias Hofmann

We study the expressivity of sparse maxout networks, where each neuron takes a fixed number of inputs from the previous layer and employs a, possibly multi-argument, maxout activation. This setting captures key characteristics of convolutional or graph neural networks. We establish a duality between functions computable by such networks and a class of virtua

John R. Doyle, Wade Hindes

Given a number field $K$, we completely classify the preperiodic portraits of the maps $x^d+c$ where $c\in K$ is an algebraic integer and $d$ is sufficiently large depending on the degree of $K$. Specifically, we show that there are exactly thirteen such portraits up to the natural action of roots of unity. In particular, we obtain some of the main results o