March 2025 arXiv papers — page 79

Yushen Huang, Ertai Luo, Reza Babenezhad, Yifan Sun

Large data applications rely on storing data in massive, sparse graphs with millions to trillions of nodes. Graph-based methods, such as node prediction, aim for computational efficiency regardless of graph size. Techniques like localized approximate personalized page rank (APPR) solve sparse linear systems with complexity independent of graph size, but is i

Xu Du, Jingzhe Wang

TThe paper proposes the Consensus Augmented Lagrange Alternating Direction Inexact Newton (Consensus ALADIN) algorithm, a novel approach for solving distributed consensus optimization problems (DC). Consensus ALADIN allows each agent to independently solve its own nonlinear programming problem while coordinating with other agents by solving a consensus quadr

Alex McManus, Stephen Becker, Nicholas Dwork

The primal-dual hybrid gradient method (PDHG) is useful for optimization problems that commonly appear in image reconstruction. A downside of PDHG is that there are typically three user-set parameters and performance of the algorithm is sensitive to their values. Toward a parameter-free algorithm, we combine two existing line searches. The first, by Malitsky

Ozgur Akgun, Mun See Chang, Ian P. Gent, Christopher Jefferson

Indistinguishable objects often occur when modelling problems in constraint programming, as well as in other related paradigms. They occur when objects can be viewed as being drawn from a set of unlabelled objects, and the only operation allowed on them is equality testing. For example, the golfers in the social golfer problem are indistinguishable. If we do

Ioachim Dusa, Denis Kochan, Maximilian Fürst, Cosimo Gorini

The geometry of a physical system is intimately related to its spectral properties, a concept colloquially referred to as "hearing the shape of a drum". Three-dimensional topological insulator nanowires in a strong magnetic field $B$ generally host Dirac-type quantum Hall (QH) surface states. The surface itself is shaped by spatial variations of the

Thalis H. B. da Silva, Tiago F. T. Cerqueira, Hai-Chen Wang, Miguel A. L. Marques

The kagome lattice has emerged as a fertile ground for exotic quantum phenomena, including superconductivity, charge density waves, and topologically nontrivial states. While AV3Sb5 (A = K, Rb, Cs) compounds have been extensively studied in this context, the broader AB3C5 family remains largely unexplored. In this work, we employ machine-learning-accelerated

Eleonora Maggiorelli, Filippo Riva, Edoardo Giovanni Tolotti

The mechanical process of progressively debonding an adhesive membrane from a substrate is described as a quasistatic variational evolution of sets and herein investigated. Existence of energetic solutions, based on global minimisers of a suitable functional together with an energy balance, is obtained within the natural class of open sets, improving and sim

Razan Ghzouli, Rebekka Wohlrab, Jennifer Horkoff

Context and motivation: In recent years, behavior trees have gained growing interest within the robotics community as a specification and control switching mechanism for the different tasks that form a robotics mission. Problem: Given the rising complexity and prevalence of robotic systems, it is increasingly challenging and important for practitioners to de

Shripad M. Garge, Deep H. Makadiya

Let $R$ be a commutative ring with unity. Consider the twisted Chevalley group $G_{π, σ} (Φ, R)$ of type $ϕ$ over $R$ and its elementary subgroup $E'_{π, σ} (Φ, R)$. This paper investigates the normalizers of $E'_{π, σ}(Φ, R)$ and $G_{π, σ}(Φ, R)$ in the larger group $G_{π, σ}(Φ, S)$, where $S$ is an extension ring of $R$. We establish that under cer

Zheng Li, Quanyu Tang

Let $\{u_n\}_{n=1}^{\infty}$ be the Sylvester&#39;s sequence (sequence A000058 in the OEIS), and let $ a_1 < a_2 < \cdots $ be any other positive integer sequence satisfying $ \sum_{i=1}^\infty \frac{1}{a_i} = 1 $. In this paper, we solve a conjecture of Erdős and Graham, which asks whether $$ \liminf_{n\to\infty} a_n^{\frac{1}{2^n}} < \lim_{n\to\infty} u_n^

Ingrid Kristine Jacobsen, Inga Berre, Jan Martin Nordbotten, Ivar Stefansson

Numerical modeling of elastic wave propagation in the subsurface requires applicability to heterogeneous, anisotropic and discontinuous media, as well as support of free surface boundary conditions. Here we study the cell-centered finite volume method Multi-Point Stress Approximation with weak symmetry (MPSA-W) for solving the elastic wave equation. Finite v

Gwenevere Frank, Gopabandhu Hota, Keli Wang, Abhinav Uppal

In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster-than real-time. This system, which is currently under construction at the UC San Diego Sup

Eric Ziebell, Ratmir Miftachov, Bernhard Stankewitz, Laura Hucker

Iterative learning procedures are ubiquitous in machine learning and modern statistics. Regularision is typically required to prevent inflating the expected loss of a procedure in later iterations via the propagation of noise inherent in the data. Significant emphasis has been placed on achieving this regularisation implicitly by stopping procedures early. T

L. Sabin, Q. Zhang, L. F. Miranda, M. A. Gómez-Muñoz

We examined dust polarisation within the planetary nebula (PN) K 3-35 using the Atacama Large Millimeter/Submillimeter Array (ALMA). This investigation aimed to identify and trace the magnetic field within the PN, as it potentially plays a crucial role in shaping this bipolar nebula. Our findings include a marginal detection of the polarised region and low f

Farshad Rostami Ghadi, Masoud Kaveh, Francisco Hernando-Gallego, Diego Martin

This letter studies the impact of fluid antenna system (FAS) technology on the performance of unmanned aerial vehicle (UAV)-assisted multiuser communication networks. Specifically, we consider a scenario where a fixed-position antenna (FPA) base station (BS) serves K FAS-equipped users with the assistance of a UAV acting as an aerial relay. The BS employs ra

Kazuaki Ishibashi, Junta Igarashi, Alberto Anadón, Michel Hehn

We study laser induced ultrafast magnetization reversal in a ferromagnetic spin valve by comparing the effects of direct laser excitation and ultrashort hot electron pulses. A wedged Cu layer is deposited atop the spin valve to tune energy transmission to the magnetic stack for both optical and hot-electron excitation. We demonstrate single shot magnetizatio

Haocong Luo, İsmail Emir Yüksel, Ataberk Olgun, A. Giray Yağlıkçı

Modern DRAM is vulnerable to read disturbance (e.g., RowHammer and RowPress) that significantly undermines the robust operation of the system. Repeatedly opening and closing a DRAM row (RowHammer) or keeping a DRAM row open for a long period of time (RowPress) induces bitflips in nearby unaccessed DRAM rows. Prior works on DRAM read disturbance either 1) per

Ignacio S. Gomez, Matheus Gabriel Alves Santos, Daniela de Almeida dos Santos, Ronaldo Thibes

In this work, we show a connection between superstatistics and position-dependent mass (PDM) systems in the context of the canonical ensemble. The key point is to set the fluctuation distribution of the inverse temperature in terms od the system PDM. For PDMs associated to Tsallis and Kaniadakis nonextensive statistics, the pressure and entropy of the ideal

Chiara Schiavo, Elena Camuffo, Leonardo Badia, Simone Milani

3D Gaussian Splatting (3DGS) has significantly improved the efficiency and realism of three-dimensional scene visualization in several applications, ranging from robotics to eXtended Reality (XR). This work presents SAGE (Semantic-Driven Adaptive Gaussian Splatting in Extended Reality), a novel framework designed to enhance the user experience by dynamically

Guillermo Bernárdez, Miquel Ferriol-Galmés, Carlos Güemes-Palau, Mathilde Papillon

Computer networks are the foundation of modern digital infrastructure, facilitating global communication and data exchange. As demand for reliable high-bandwidth connectivity grows, advanced network modeling techniques become increasingly essential to optimize performance and predict network behavior. Traditional modeling methods, such as packet-level simula

Shiva Upadhye, Jiaxuan Li, Richard Futrell

Speech errors are a natural part of communication, yet they rarely lead to complete communicative failure because both speakers and comprehenders can detect and correct errors. Although prior research has examined error monitoring and correction in production and comprehension separately, integrated investigation of both systems has been impeded by the scarc

Han Lin Shang, Cristian F. Jiménez-Varón

Existing mortality forecasting methods focus on age-specific mortality rates, which lie in an unconstrained space and overlook the distributional nature of life-table death counts. Few studies have developed and compared forecasting methods that model the shape and dynamics of the age distribution of deaths, especially at the subnational level, where data qu

Alberto Hernández-Espinosa, Luan Ozelim, Felipe S. Abrahão, Hector Zenil

We introduce an increasing-complexity, open-ended, and human-agnostic metric to evaluate foundational and frontier AI models in the context of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) claims. Unlike other tests that rely on human-centric questions and expected answers, or on pattern-matching methods, the test here introdu

Esther Y. H. Lin, Yimin Ding, Jogendra Kundu, Yatong An

Eye tracking (ET) is a key enabler for Augmented and Virtual Reality (AR/VR). Prototyping new ET hardware requires assessing the impact of hardware choices on eye tracking performance. This task is compounded by the high cost of obtaining data from sufficiently many variations of real hardware, especially for machine learning, which requires large training d

Xiao Jiang, Grace J. Gang, J. Webster Stayman

This work introduces CTorch, a PyTorch-compatible, GPU-accelerated, and auto-differentiable projector toolbox designed to handle various CT geometries with configurable projector algorithms. CTorch provides flexible scanner geometry definition, supporting 2D fan-beam, 3D circular cone-beam, and 3D non-circular cone-beam geometries. Each geometry allows view-

Honghao Tan, Haibo Wang, Diany Pressato, Yisen Xu

Generative AI systems powered by Large Language Models (LLMs) usually use content moderation to prevent harmful content spread. To evaluate the robustness of content moderation, several metamorphic testing techniques have been proposed to test content moderation software. However, these techniques mainly focus on general users (e.g., text and image generatio

Geonsun Lee, Yue Yang, Jennifer Healey, Dinesh Manocha

Maintaining engagement in immersive meetings is challenging, particularly when users must catch up on missed content after disruptions. While transcription interfaces can help, table-fixed panels have the potential to distract users from the group, diminishing social presence, while avatar-fixed captions fail to provide past context. We present EngageSync, a

Alexandre Assouline, Taige Wang, Heun Mo Yoo, Ruihua Fan

We use high-resolution chemical potential measurements to extract the entropy of monolayer and bilayer graphene in the quantum Hall regime via the Maxwell relation $\left.\frac{d\mu}{dT}\right|_N = -\left.\frac{dS}{dN}\right|_T$. Measuring the entropy from $T=300$K down to $T=200$mK, we identify the sequential emergence of quantum Hall ferromagnetism, fracti

Mario González-Sánchez, Srishti Singh, Hema Srinivasan

Given two coprime numbers $p<q$, KW semigroups contain $p,q$ and are contained in $\langle p,q,r \rangle$ where $2r= p,q, p+q$ whichever is even. These semigroups were first introduced by Kunz and Waldi. Kunz and Waldi proved that all $KW$ semigroups of embedding dimension $n\geq 4$ have Cohen-Macaulay type $n-1$ and first Betti number ${n \choose 2}$. In th

Simone Francescato

After successfully completing Phase I upgrades during LHC Long Shutdown 2, the ATLAS detector is back in operation with several upgrades implemented. The most important and challenging upgrade is in the Muon Spectrometer, where the two inner forward muon stations have been replaced with the New Small Wheels (NSW) system. One of the two detector technologies

Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue

Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically generated pap

Chengkai Huang, Junda Wu, Yu Xia, Zixu Yu

Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems that extend beyond the capabilities of standalone models. By empowering LLMs to perceive external environments, integrate multimodal information, and interact with various tools, these agentic systems exhibit greater autonomy and adaptability across complex t

Rakesh Pandey, Aina Palau, Javier Serna, Rolf Kuiper

Using optical time series with Telescopi Joan Or\'o (TJO), Gaia, TESS, and NEOWISE archival data, we performed a variability study on the candidate bloated massive young stellar object (MYSO) IRAS 19520+2759. This is the first time that a bloated star candidate has been tested for the theoretically predicted periodic variability. The source is found to be va

Raymond Tat

In designing an experiment to measure a neutron electric dipole moment (nEDM), it is often necessary to determine the behavior of an ensemble of spins under time-dependent and randomly fluctuating magnetic fields. This is particularly relevant for the proposed nEDM@SNS experiment, which features ultra-cold neutrons (UCNs) and helium-3 atoms occupying the sam

Eun Jeong Oh, Seungjun Ahn, Tristan Tham, Min Qian

Accurate survival predicting models are essential for improving targeted cancer therapies and clinical care among cancer patients. In this article, we investigate and develop a method to improve predictions of survival in cancer by leveraging two-phase data with expert knowledge and prognostic index. Our work is motivated by two-phase data in nasopharyngeal

Thami Nkosi

This paper presents a deterministic algorithmic approach of exploring the solution space of the Subset Sum Problem. The algorithm presented is input-robust and structurally adaptive. Exploration is guided and narrows into areas in the solution space where solutions are possible, referred to as in-bound solution space, skipping all areas where solutions are i

Richie Li, Sicheng Chen

Transformer-based large language models (LLMs) rely heavily on intensive matrix multiplications for attention and feed-forward layers, with the Q, K, and V linear projections in the Multi-Head Self-Attention (MHA) module constituting a decisive performance bottleneck. In this work, we introduce a highly optimized tiled matrix multiplication accelerator on a

Subhankar Bhadra, Marianna Pensky, Srijan Sengupta

Massive network datasets are becoming increasingly common in scientific applications. Existing community detection methods encounter significant computational challenges for such massive networks due to two reasons. First, the full network needs to be stored and analyzed on a single server, leading to high memory costs. Second, existing methods typically use

Te Han, Paul Robertson, Caleb I. Cañas, Gudmundur Stefansson

We introduce NEIDSpecMatch, a tool developed to extract stellar parameters from spectra obtained with the NEID spectrograph. NEIDSpecMatch is based on SpecMatch-Emp and HPFSpecMatch, which estimate stellar parameters by comparing the observed spectrum to well-characterized library spectra. This approach has proven effective for M dwarfs. Utilizing a library

Emiel van Miltenburg, Chenghua Lin

This article provides a brief overview of the field of Natural Language Generation. The term Natural Language Generation (NLG), in its broadest definition, refers to the study of systems that verbalize some form of information through natural language. That information could be stored in a large database or knowledge graph (in data-to-text applications), but

Hugo Guadalupe Reyna-Castañeda, María de los Ángeles Sandoval Romero

In this paper, we prove the existence and uniqueness of the conditional expectation of an event $A$ given a $\sigma$-algebra $\mathcal{G}$ as a linear problem in the Lebesgue spaces $L^{p}$ associated with a probability space through the Riesz Representation Theorems. For the $L^{2}$ case, we state the Dirichlet's principle. Then, we extend this principle fo

Philipp Becker, Abhinav Mehrotra, Ruchika Chavhan, Malcolm Chadwick

Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. However, the quadratic scaling properties of the attention in DiTs hinder image generation with higher resolution or on devices with limited resources. This work introduces an efficient diffusion transformer (EDi

Ashley Stanton-Yonge, Thomas M. Mitchell, Philip G. Meredith, Neil Hughes

We have developed a new True Triaxial Apparatus (TTA) for rock deformation consisting of six servo-controlled loading rams that transmit maximum stresses of 220 MPa in the two horizontal axes and 400 MPa in the vertical axis to 50 mm side cubic rock samples. The sample and loading platens are introduced in a steel vessel where rock specimens can be subjected

Zhaoxin Li, Zhang Xi-Jia, Batuhan Altundas, Letian Chen

Semantic interpretability in Reinforcement Learning (RL) enables transparency and verifiability of decision-making. Achieving semantic interpretability in reinforcement learning requires (1) a feature space composed of human-understandable concepts and (2) a policy that is interpretable and verifiable. However, constructing such a feature space has tradition

Elias Hess-Childs, Renaud Raquépas, Keefer Rowan

We show that bounded divergence-free vector fields $u : [0,\infty) \times \mathbb{R}^d \to\mathbb{R}^d$ decrease the ''concentration'', quantified by the modulus of absolute continuity with respect to the Lebesgue measure, of solutions to the associated advection-diffusion equation when compared to solutions to the heat equation. In particular, for symmetric

Kasia Jankiewicz

We show that the fundamental group of a geometrically clean graph of finite rank free groups does not need to be virtually compact special, answering a question of Wise. This implies that the class of the virtually VH-clean graphs of finite rank free groups is a proper subclass of the class of virtually geometrically clean graphs of finite rank free groups.

Krzysztof Malarz, Maciej Wołoszyn

In this paper, the social impact theory introduced by Latan\'e is reconsidered. A fully differentiated society is considered; that is, initially every actor has their own opinion. The equivalent of Muller's ratchet guards that -- even for the non-deterministic case (with a positive social temperature) -- any opinion once removed from the opinion space does n

Saikrishna Manojkumar, Christine L. Page, Leonid Pogorelyuk, Susan F. Redmond

Maintaining wavefront stability while directly imaging exoplanets over long exposure times is an ongoing problem in the field of high-contrast imaging. Robust and efficient high-order wavefront sensing and control systems are required for maintaining wavefront stability to counteract mechanical and thermal instabilities. Dark zone maintenance (DZM) has been

Darren Fürst, Andreas Aßmuth

Cloud services have become an essential infrastructure for enterprises and individuals. Access to these cloud services is typically governed by Identity and Access Management systems, where user authentication often relies on passwords. While best practices dictate the implementation of multi-factor authentication, it's a reality that many such users remain

Massa Baali, Xiang Li, Hao Chen, Syed Abdul Hannan

Speaker verification is a typical zero-shot learning task, where inference of unseen classes is performed by comparing embeddings of test instances to known examples. The models performing inference must hence naturally generate embeddings that cluster same-class instances compactly, while maintaining separation across classes. In order to learn to do so, th

Ichitaro Yamazaki, Andrew J. Higgins, Erik G. Boman, Daniel B. Szyld

We integrate random sketching techniques into block orthogonalization schemes needed for s-step GMRES. The resulting block orthogonalization schemes generate the basis vectors whose overall orthogonality error is bounded by machine precision as long as each of the corresponding block vectors are numerically full rank. We implement these randomized block orth

Caio Henrique Silva de Souza, Mark Spivakovsky

In recent decades, the defect of finite extensions of valued fields has emerged as the main obstacle in several fundamental problems in algebraic geometry such as the local uniformization problem. Hence, it is important to identify defectless fields and study properties related to defect. In this paper we study the relations between the following properties

Gosuke Kojima, Kohei Honda, Satoshi Nakano, Manabu Yamada

This paper presents a novel approach to motion planning for two-wheeled drones that can drive on the ground and fly in the air. Conventional methods for two-wheeled drone motion planning typically rely on gradient-based optimization and assume that obstacle shapes can be approximated by a differentiable form. To overcome this limitation, we propose a motion

Jingru Wang, Wen Ding, Xiaotong Zhu

In the modern financial sector, the exponential growth of data has made efficient and accurate financial data analysis increasingly crucial. Traditional methods, such as statistical analysis and rule-based systems, often struggle to process and derive meaningful insights from complex financial information effectively. These conventional approaches face inher

John F. Suárez-Pérez, Catalina Gómez, Mauricio Neira, Marcela Hernández Hoyos

We present the Deep-learning Transient Astronomical Object (Deep-TAO), a dataset of 1,249,079 annotated images from the Catalina Real-time Transient Survey, including 3,807 transient and 12,500 non-transient sequences. Deep-TAO has been curated to provide a clean, open-access, and user-friendly resource for benchmarking deep learning models. Deep-TAO covers

Shi Feng

We establish upper and lower bounds for the expected Wasserstein distance between the random empirical measure and the uniform measure on the Boolean cube. Our analysis leverages techniques from Fourier analysis, following the framework introduced in \cite{bobkov2021simple}, as well as methods from large deviations theory.

Xing Pan, Keping Qiu, Qizhou Zhang

Over the past decades, observational evidence of circumstellar disks around massive protostars has been steadily accumulating. However, there have also been cases of non-detections in high-mass star-forming regions, leaving the role and prevalence of disks around massive protostars still uncertain. We used high-resolution (0.2") NOrthern Extended Millimeter

Samah A. M. Ghanem

Flowshop machine scheduling has been of main interest in several applications where the timing of its processes plays a fundamental role in the utilization of system resources. Addressing the optimal sequencing of the jobs when equivalent failures across machines exist is a decision of particular relevance to the general scheduling problem. Such failures all

Faisal Abbas, Mohammad Salehian, Peter Hou, Jonathan Moores

Pharmaceutical tablet formulation and process development, traditionally a complex and multi-dimensional decision-making process, necessitates extensive experimentation and resources, often resulting in suboptimal solutions. This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Drivi

Mihaela-Larisa Clement, Mónika Farsang, Felix Resch, Mihai-Teodor Stanusoiu

Autonomous agents that rely purely on perception to make real-time control decisions require efficient and robust architectures. In this work, we demonstrate that augmenting RGB input with depth information significantly enhances our agents' ability to predict steering commands compared to using RGB alone. We benchmark lightweight recurrent controllers that

Yanyan Li, Youxu Fang, Zunjie Zhu, Kunyi Li

Simultaneously localizing camera poses and constructing Gaussian radiance fields in dynamic scenes establish a crucial bridge between 2D images and the 4D real world. Instead of removing dynamic objects as distractors and reconstructing only static environments, this paper proposes an efficient architecture that incrementally tracks camera poses and establis

Josef Koumar, Timotej Smoleň, Kamil Jeřábek, Tomáš Čejka

Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. This study evaluates state-of-the-art deep learning models on CESNET-TimeSeries24, a recently published, comprehensive real-world network traffic dataset from the ISP network CESNET3 spanning multivari

Xuan Shen, Weize Ma, Jing Liu, Changdi Yang

Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer vision, supporting numerous real-world applications. However, deploying accurate depth estimation models on resource-limited edge devices, especially Application-Specific Integrated Circuits (ASICs), is challenging due to the high computational and memory demands. Recent advancements

Anni Zhou, Raheem Beyah, Rishikesan Kamaleswaran

In critical care settings, timely and accurate predictions can significantly impact patient outcomes, especially for conditions like sepsis, where early intervention is crucial. We aim to model patient-specific reward functions in a contextual multi-armed bandit setting. The goal is to leverage patient-specific clinical features to optimize decision-making u

Jinlong Li, Cristiano Saltori, Fabio Poiesi, Nicu Sebe

The lack of a large-scale 3D-text corpus has led recent works to distill open-vocabulary knowledge from vision-language models (VLMs). However, these methods typically rely on a single VLM to align the feature spaces of 3D models within a common language space, which limits the potential of 3D models to leverage the diverse spatial and semantic capabilities

Zitong Yu, Nu Ri Choi, Zezhang Yang, Nancy A. Obuchowski

A recently proposed scatter-window and deep learning-based attenuation compensation (AC) method for myocardial perfusion imaging (MPI) by single-photon emission computed tomography (SPECT), namely CTLESS, demonstrated promising performance on the clinical task of myocardial perfusion defect detection with retrospective data acquired on SPECT scanners from a

Adrian D. Scheppe, Michael V. Pak

One of the greatest challenges when designing new technologies that make use of non-trivial quantum materials is the difficulty associated with predicting material-specific properties, such as critical temperature, gap parameter, etc. There is naturally a great amount of interest in these types of condensed matter systems because of their application to quan

Adrian D. Scheppe, Michael V. Pak

The current state of Quantum computing (QC) is extremely optimistic, and we are at a point where researchers have produced highly sophisticated quantum algorithms to address far reaching problems. However, it is equally apparent that the noisy quantum environment is a larger threat than many may realize. The noisy intermediate scale quantum (NISQ) era can be

Antoni Ciepłucha, Marcin Utnicki, Maciej Wołoszyn, Krzysztof Malarz

In this paper, the 60-year-old concept of long-range interaction in percolation problems introduced by Dalton, Domb, and Sykes, is reconsidered. With Monte Carlo simulation -- based on Newman-Ziff algorithm and finite-size scaling hypothesis -- we estimate 64 percolation thresholds for random site percolation problem on a square lattice with neighborhoods th

Abdou Oussama Benabida

We show that the Calabi-Yau metrics with isolated conical singularities of Hein-Sun admit polyhomogeneous expansions near their singularities. Moreover, we show that, under certain generic assumptions, natural families of smooth Calabi-Yau metrics on crepant resolutions and on polarized smoothings of conical Calabi-Yau manifolds degenerating to the initial c

Minheng Xiao, Xian Yu

In many practical reinforcement learning tasks, feedback is only provided at the end of a long horizon, leading to sparse and delayed rewards. Existing reward redistribution methods typically assume that per-step rewards are independent, thus overlooking interdependencies among state-action pairs. In this paper, we propose a Gaussian process based Likelihood

Thomas A. Witten, Anna Movsheva

The principles behind the sharp, singular structures in a crumpled sheet are well understood. Here we discuss more general ways of exploiting such sharp structures to control the shape of a sheet by deforming or forcing it elsewhere. Often, the induced shape leads to further sharp structures -- ``sub-singularities". Though weaker and softer than the primary

Bum Geun Park, Taeho Lee, Donghwan Lee

This paper introduces Q-learning with gradient target tracking, a novel reinforcement learning framework that provides a learned continuous target update mechanism as an alternative to the conventional hard update paradigm. In the standard deep Q-network (DQN), the target network is a copy of the online network's weights, held fixed for a number of iteration

CMS Collaboration

A first search is presented for vector-like leptons (VLLs) decaying into a light long-lived pseudoscalar boson and a standard model $\tau$ lepton. The pseudoscalar boson is assumed to have a mass below the $\tau^+\tau^-$ threshold, so that it decays exclusively into two photons. It is identified using the CMS muon system. The analysis is carried out using a

Julius Lange, Leonid Komissarov, Nicole Wyttenbach, Andrea Anelli

The design and development of effective drug formulations is a critical process in pharmaceutical research, particularly for small molecule active pharmaceutical ingredients. This paper introduces a novel agentic preformulation pathway assistant (Appa), leveraging large language models coupled to experimental databases and a suite of machine learning models

Markus Linckelmann, Jialin Wang

We give some criteria for the Lie algebra $\mathrm{HH}^1(B)$ to be solvable, where $B$ is a $p$-block of a finite group algebra, in terms of the action of an inertial quotient of $B$ on a defect group of $B$.

Anna P. Kwossek, David J. Prömel, Josef Teichmann

We identify various classes of neural networks that are able to approximate continuous functions locally uniformly subject to fixed global linear growth constraints. For such neural networks the associated neural stochastic differential equations can approximate general stochastic differential equations, both of It\^o diffusion type, arbitrarily well. Moreov

Aya Abouelela, Michael Turaev, Roman Kramer, Moritz Janning

We study the temporal, driven-dissipative dynamics of open photon Bose-Einstein condensates (BEC) in a dye-filled microcavity, taking the condensate amplitude and the noncondensed fluctuations into account on the same footing by means of a cumulant expansion within the Lindblad formalism. The fluctuations fundamentally alter the dynamics in that the BEC alwa

J. Zeng, T. Aramaki, K. Aoyama, S. Arai

The Gamma-Ray and AntiMatter Survey (GRAMS) is a next-generation balloon/satellite mission utilizing a Liquid Argon Time Projection Chamber (LArTPC) detector to measure both MeV gamma rays and antinuclei produced by dark matter annihilation or decay. The GRAMS can identify antihelium-3 events based on the measurements of X-rays and charged pions from the dec

Zhan Cheng, Bolin Shen, Tianming Sha, Yuan Gao

Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs), where adversaries reconstruct surrogate models by querying the victim model. Existing defense mechanisms, such as watermarking and fingerprinting, suffer from poor real-t

Joshua Hatherley

Artificial intelligence (AI) is expected to revolutionize the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in a variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI's progress in medicine, however, has led to

Paul B. May, Andrew O. Finley

The USDA Forest Inventory and Analysis (FIA) program conducts a national forest inventory for the United States through a network of permanent field plots. FIA produces estimates of area averages and totals for plot-measured forest variables through design-based inference, assuming a fixed population and a probability sample of field plot locations. The fixe

R. Landman, S. Y. Haffert, J. D. Long, J. R. Males

Pyramid wavefront sensors (PWFSs) are the preferred choice for current and future extreme adaptive optics (XAO) systems. Almost all instruments use the PWFS in its modulated form to mitigate its limited linearity range. However, this modulation comes at the cost of a reduction in sensitivity, a blindness to petal-piston modes, and a limit to the sensor's abi

Tianze Luo, Xingchen Miao, Wenbo Duan

Flow matching offers a robust and stable approach to training diffusion models. However, directly applying flow matching to neural vocoders can result in subpar audio quality. In this work, we present WaveFM, a reparameterized flow matching model for mel-spectrogram conditioned speech synthesis, designed to enhance both sample quality and generation speed fo

Alastair Haig, Minmin Wang

We propose a random bipartite graph with weights assigned to both parts of the vertex sets. Edges are formed independently with probabilities that depend on these weights. This bipartite graph naturally gives rise to a random intersection graph which has nontrivial clustering properties and inhomogeneous vertex degrees. We focus on the situation where the we

Edzer Pebesma, Martin Fleischmann, Josiah Parry, Jakub Nowosad

Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package d

Federico Ambrosino, Davide Gaiotto

We develop a general formalism to describe the Renormalization Group Flow of Schur indices and fusion algebras of BPS line defects in four-dimensional ${\cal N}=2$ Supersymmetric Quantum Field Theories. The formalism includes and extends known results about the Seiberg-Witten description of these structures. Another application of the formalism is to describ

Piotr Błaszczyk, Anna Petiurenko

In this paper, we reconstruct Euclid's theory of similar triangles, as developed in Book VI of the \textit{Elements}, along with its 20th-century counterparts, formulated within the systems of Hilbert, Birkhoff, Borsuk and Szmielew, Millman and Parker, as well as Hartshorne. In the final sections, we present recent developments concerning non-Archimedean fie

Zeping Liu, Ni Lao, Zhangyu Wang, Junfeng Jiao

Vision Transformer (ViT) has been widely used in computer vision tasks with excellent results by providing representations for a whole image or image patches. However, ViT lacks detailed localized image representations at arbitrary positions when applied to geospatial tasks that involve multiple geospatial data modalities, such as overhead remote sensing (RS

Brahim Lemkalli, Ozgur T. Tugut, Qingxiang Ji, Richard Craster

In this work, we examine the application of phononic metamaterials for elastic impact noise insulation in tiled flooring, through the development of an innovative ceramic metatile that incorporates phononic crystals with optimized joint configurations. First, we optimize the geometrical and material parameters of the proposed metatile, which is composed of s

Daniel Spegel-Lexne, João Manoel Barbosa Pereira, Alvaro Alarcón, Joakim Argillander

Dynamic storage of qubits is crucial for quantum communication networks. Here we present an adjustable buffer capable of storing photonic polarization quantum states in a fiber loop controllable by a poled fiber modulator.

Sixu Li, Ben Keller, Yingyan Celine Lin, Brucek Khailany

3D intelligence leverages rich 3D features and stands as a promising frontier in AI, with 3D rendering fundamental to many downstream applications. 3D Gaussian Splatting (3DGS), an emerging high-quality 3D rendering method, requires significant computation, making real-time execution on existing GPU-equipped edge devices infeasible. Previous efforts to accel

Andre G. C. Pacheco, Athus Cavalini, Giovanni Comarela

Large Language Models (LLMs) have emerged as powerful tools for generating human-like text, transforming human-machine interactions. However, their widespread adoption has raised concerns about their potential to influence public opinion and shape political narratives. In this work, we investigate the geopolitical biases in US and Chinese LLMs, focusing on h

Aly M. Kassem, Bernhard Schölkopf, Zhijing Jin

Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance by dynamically assigning queries to the most appropriate model based on query complexity. Despite recent advances showing that preference-data-based routers can outperform traditional methods, current evaluation benchmarks remain limited. T

Afrah Farea, Saiful Khan, Mustafa Serdar Celebi

Physics-informed neural networks (PINNs) have emerged as promising methods for solving partial differential equations (PDEs) by embedding physical laws within neural architectures. However, these classical approaches often require a large number of parameters to achieve reasonable accuracy, particularly for complex PDEs. In this paper, we present a quantum-c

Jiewei Feng, Ken R. Duffy, Muriel Médard

In addition to a proposed codeword, error correction decoders that provide blockwise soft output (SO) return an estimate of the likelihood that the decoding is correct. Following Forney, such estimates are traditionally only possible for list decoders where the soft output is the likelihood that a decoding is correct given it is assumed to be in the list. Re

Nicolas C. Cardenas, Diego Viali dos Santos, Daniel Magalhaaes Lima, Hernan Oliver Daza Gutierrez

Examining the dissemination dynamics of foot-and-mouth disease (FMD) is critical for revising national response plans. We developed a stochastic SEIR metapopulation model to simulate FMD outbreaks in Bolivia and explore how the national response plan impacts the dissemination among all susceptible species. We explored variations in the control strategies, ma

Devojyoti Kansabanik, Angelos Vourlidas, Soham Dey, Surajit Mondal

Calibration of instrumental polarization is critical for measuring polarized radio emissions from astrophysical sources to extract the magnetic field information in astrophysical, heliospheric, and terrestrial plasmas. At meter wavelengths, calibration of radio polarimetric observations is particularly challenging because of the scarcity of bright polarized

Molly Kennedy, Ayyoob Imani, Timo Spinde, Akiko Aizawa

Large Language Models (LLMs) are widely used for text generation, making it crucial to address potential bias. This study investigates ideological framing bias in LLM-generated articles, focusing on the subtle and subjective nature of such bias in journalistic contexts. We evaluate eight widely used LLMs on two datasets-POLIGEN and ECONOLEX-covering politica

Baturalp Yalcin, Jihun Kim, Javad Lavaei

This paper investigates a subgradient-based algorithm to solve the system identification problem for linear time-invariant systems with non-smooth objectives. This is essential for robust system identification in safety-critical applications. While existing work provides theoretical exact recovery guarantees using optimization solvers, the design of fast lea