March 2025 arXiv papers — page 164

Burcu Gürbüz, Aytül Gökçe, Segun I. Oke, Michael O. Adeniyi

As offered by the World Health Organisation (WHO), close to half of the population in the world's resides in dengue-risk zones. Dengue viruses are transmitted to individuals by Aedes mosquito species infected bite (Ae. Albopictus of Ae. aegypti). These mosquitoes can transmit other viruses, including Zika and Chikungunya. In this research, a mathematical mod

Chenyu Zhang, Yihao Luo, Lei Zhu, Martyn G Boutelle

Accurate reconstruction of multi-chamber cardiac anatomy from medical images is a cornerstone for patient-specific modeling, physiological simulation, and interventional planning. However, current reconstruction pipelines fundamentally rely on surface-wise geometric supervision and model each chamber in isolation, resulting in anatomically implausible inter-

Yoshiaki Uchida, Go Watanabe

This study explores the mechanisms behind the helical structures in cholesteric liquid crystalline (CLC) phases using molecular dynamics simulations. By adding chiral agents to the nematic liquid crystalline (NLC) compound, 5CB, the research examines how the shape and motion chirality of the agents influence the overall twisting behavior. The results show th

Boris Hanin, Tianze Jiang

We study the singular values (and Lyapunov exponents) for products of $N$ independent $n\times n$ random matrices with i.i.d. entries. Such matrix products have been extensively analyzed using free probability, which applies when $n\to \infty$ at fixed $N$, and the multiplicative ergodic theorem, which holds when $N\to \infty$ while $n$ remains fixed. The re

Zekun Li, Malcolm Grossman, Eric, Qasemi

Geospatial question answering (QA) is a fundamental task in navigation and point of interest (POI) searches. While existing geospatial QA datasets exist, they are limited in both scale and diversity, often relying solely on textual descriptions of geo-entities without considering their geometries. A major challenge in scaling geospatial QA datasets for reaso

Antonio Vitale, Emanuela Guglielmi, Rocco Oliveto, Simone Scalabrino

Unreadable code could be a breeding ground for errors. Thus, previous work defined approaches based on machine learning to automatically assess code readability that can warn developers when some code artifacts (e.g., classes) become unreadable. Given datasets of code snippets manually evaluated by several developers in terms of their perceived readability,

Francesco Bozzola, Lorenzo Brasco

We discuss some properties of the capacitary inradius for an open set. This is an extension of the classical concept of inradius (i.e. the radius of a largest inscribed ball), which takes into account capacitary effects. Its introduction dates back to the pioneering works of Vladimir Maz'ya. We present some variants of this object and their mutual relations,

R. Aloisio

The Pierre Auger Observatory, the world's largest cosmic ray detector, plays a pivotal role in exploring the frontiers of physics beyond the standard model of particle physics. By the observation of ultra-high energy cosmic rays, Auger provides critical insights into two major scenarios: super heavy dark matter and Lorentz invariance violation. Super heavy d

Scott Willenbrock

It has been known since the 1950's that an unstable particle is associated with a complex pole in the propagator. This had to be rediscovered twice: in the early 1970's in the context of hadronic resonances, and in the early 1990's in the context of the $Z$ boson. The physical mass of the particle is the real part of the pole in the complex energy plane. In

Saeed S. I. Almishal, Matthew Furst, Yueze Tan, Jacob T. Sivak

High-entropy oxide (HEO) thermodynamics transcend temperature-centric approaches, spanning a multidimensional landscape where oxygen chemical potential plays a decisive role. Here, we experimentally demonstrate how controlling the oxygen chemical potential coerces multivalent cations into divalent states in rock salt HEOs. We construct a preferred valence ph

B. K. Alpert, M. Balata, D. T. Becker, D. A. Bennett

The determination of the absolute neutrino mass scale remains a fundamental open question in particle physics, with profound implications for both the Standard Model and cosmology. Direct kinematic measurements, independent of model-dependent assumptions, provide the most robust approach to address this challenge. In this Letter, we present the most stringen

Ilijas Farah

In this note I present a readable version of the proof of my 2001 result, giving a sufficient and necessary condition for a function on a combinatorial cube to essentially (locally) depend on at most one variable (see the end of the paper for the motivation), as well as some limiting results.

Francesco Amicucci, Paola Leaci, Pia Astone, Sabrina D'Antonio

Continuous gravitational-wave signals (CWs), which are typically emitted by rapidly rotating neutron stars with non-axisymmetric deformations, represent particularly intriguing targets for the Advanced LIGO-Virgo-KAGRA detectors. These detectors operate within sensitivity bands that encompass more than half of the known pulsars in our galaxy existing in bina

Sidney Wong, Andrew Li

This paper presents the systems and results for the Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL) shared task at the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech-2025). We took a `bag-of-sounds' approach by training our hate speech detection system on the speech (audio) data

H. Yilmaz, P. Sosa-Lizama, M. Knauft, K. Küster

With the discovery of superconductivity under pressure in the Ruddlesden-Popper (RP) bilayer La$_3$Ni$^{2.5+}_2$O$_7$ and trilayer La$_4$Ni$^{2.66+}_3$O$_{10}$, a new field of nickelate superconductors opened up. In this respect, Sr-Ni-O RP-type phases represent alternative systems that exist with partial cation substitution. We demonstrate that by Y-doping

James Burgess, Xiaohan Wang, Yuhui Zhang, Anita Rau

How do two individuals differ when performing the same action? In this work, we introduce Video Action Differencing (VidDiff), the novel task of identifying subtle differences between videos of the same action, which has many applications, such as coaching and skill learning. To enable development on this new task, we first create VidDiffBench, a benchmark d

Pablo Martin Maier, Serguei Patchkovskii, Mikhail Ivanov, Olga Smirnova

The measurement of tunneling times in strong-field ionization has been the topic of much controversy in recent years, with the attoclock and Larmor clock being two of the main contenders for correctly reproducing these times. By expressing the attoclock as the weak value of temporal delay, we extend its meaning beyond the traditional setup. This allows us to

Sakirat Wolly, Xiaozhe Wang

An accurate distribution network model is crucial for monitoring, state estimation and energy management. However, existing data-driven methods often struggle with scalability or impose a heavy computational burden on large distribution networks. In this paper, leveraging natural load dynamics, we propose a two-stage line estimation method for multiphase unb

Aviad Barzilai, Yotam Gigi, Amr Helmy, Vered Silverman

Foundation models have had a significant impact across various AI applications, enabling use cases that were previously impossible. Contrastive Visual Language Models (VLMs), in particular, have outperformed other techniques in many tasks. However, their prevalence in remote sensing (RS) is still limited, due to the scarcity of diverse remote-sensing visual-

Zaineh Abughazzah, Emna Baccour, Ahmed Refaey, Amr Mohamed

Open Radio Access Networks (O-RAN) are transforming telecommunications by shifting from centralized to distributed architectures, promoting flexibility, interoperability, and innovation through open interfaces and multi-vendor environments. However, O-RAN's reliance on cloud-based architecture and enhanced observability introduces significant security and re

Qiang Zhu, Yuxuan Jiang, Shuyuan Zhu, Fan Zhang

Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal va

Ruiji Liu, Abigail Breitfeld, Srinivasan Vijayarangan, George Kantor

This paper presents a pipeline that combines high-resolution orthomosaic maps generated from UAS imagery with GPS-based global navigation to guide a skid-steered ground robot. We evaluated three path planning strategies: A* Graph search, Deep Q-learning (DQN) model, and Heuristic search, benchmarking them on planning time and success rate in realistic simula

Cevahir Yildirim, Alba M. Franco-Pereira, Rosa E. Lillo

Recent developments in big data analysis, machine learning, Industry 4.0, and IoT applications have enabled the monitoring and processing of multi-sensor data collected from systems, allowing for the prediction of the "Remaining Useful Life" (RUL) of system components. Particularly in the aviation industry, Prognostic Health Management (PHM) has become one o

Depanshu Sani, Saket Anand

Traditional classifiers treat all labels as mutually independent, thereby considering all negative classes to be equally incorrect. This approach fails severely in many real-world scenarios, where a known semantic hierarchy defines a partial order of preferences over negative classes. While hierarchy-aware feature representations have shown promise in mitiga

Jongwon Park, Heesoo Jung, Hogun Park

Recent Self-Supervised Learning (SSL) methods encapsulating relational information via masking in Graph Neural Networks (GNNs) have shown promising performance. However, most existing approaches rely on random masking strategies in either feature or graph space, which may fail to capture task-relevant information fully. We posit that this limitation stems fr

Guillaume Quétant, Pavlo Molchanov, Slava Voloshynovskiy

We present a semi-supervised fine-tuning framework for foundation models that utilises mutual information decomposition to address the challenges of training for a limited amount of labelled data. Our approach derives two distinct lower bounds: i) for the downstream task space, such as classification, optimised using conditional and marginal cross-entropy al

Nidhi Pashine, Dong Wang, Robert Baines, Medha Goyal

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depen

Sander Beckers

In (Beckers, 2025) I introduced nondeterministic causal models as a generalization of Pearl's standard deterministic causal models. I here take advantage of the increased expressivity offered by these models to offer a novel definition of actual causation (that also applies to deterministic models). Instead of motivating the definition by way of (often subje

Akkamahadevi Hanni, Jonathan Montaño, Yu Zhang

When users work with AI agents, they form conscious or subconscious expectations of them. Meeting user expectations is crucial for such agents to engage in successful interactions and teaming. However, users may form expectations of an agent that differ from the agent's planned behaviors. These differences lead to the consideration of two separate decision m

Kim William Torre, Raoul D. Schram, Joost de Graaf

Stokesian Dynamics (SD) is a powerful computational framework for simulating the motion of particles in a viscous Newtonian fluid under Stokes-flow conditions. Traditional SD implementations can be computationally expensive as they rely on the inversion of large mobility matrices to determine hydrodynamic interactions. Recently, however, the simulation of th

Irmak Balçik, Stephanie Chan, Yuan Liu, Bianca Viray

We develop techniques for determining the fibers of a morphism of curves $\phi: C \to D$ over a nonarchimedean local field $K$. These results have applications to studying closed point on curves over global fields since closed points on $C$ of large degree or of very small degree are known to all arise as fibers of morphisms.

Lucas Finazzi, Leandro Gagliardi, Alexis Luszczak, Felipe Soriano

In this work, the SiPiC (SiPM Pinhole Camera) Payload is detailed. This Payload will potentially be integrated in two distinct satellite missions, which will operate in Low Earth Orbit (LEO) and in a High Altitude Orbit (HAO), respectively. The SiPiC Payload has two main objectives: to test Silicon Photomultipliers (SiPMs) as visible light sensors for stimul

Jiayi Hu, Fengyang Wang, Xinlang Zhu

This paper explores the Fano variety of lines in hypersurfaces, particularly focusing on those with mild singularities. Our first result explores the irreducibility of the variety $\Sigma$ of lines passing through a singular point $y$ on a hypersurface $Y \subset \mathbb{P}^n$. Our second result studies the Fano variety of lines of cubic hypersurfaces with m

Nabil L. Youssef, S. G. Elgendi, A. A. Kotb, Ebtsam H. Taha

This paper is a continuation of our investigation of the anisotropic conformal change of a conic pseudo-Finsler surface $(M,F)$, namely, the change $\overline{F}(x,y)=e^{\phi(x,y)}F(x,y)$ \cite{first paper}. We obtain the relationship between some important geometric objects of $F $ and their corresponding objects of $\overline{F}$, such as Berwald, Landsber

Bernhard Maass, Wouter Ryssens, Michael Bender, Daniel P. Burdette

We present the first measurements with a new collinear laser spectroscopy setup at the Argonne Tandem Linac Accelerator System utilizing its unique capability to deliver neutron-rich refractory metal isotopes produced by the spontaneous fission of 252Cf. We measured isotope shifts from optical spectra for nine radioactive ruthenium isotopes 106-114Ru, reachi

Niels J. Gommesen

The notion of citizen science is often referred to as the means of engaging public members in scientific research activities that can advance the reach and impact of technoscience. Despite this, few studies have addressed how human-machine collaborations in a citizen science context enable and constrain scientific citizenship and citizens' epistemic agencies

Austin Rodriguez, Justin S. Smith, Jose L. Mendoza-Cortes

Integrating machine learning into reactive chemistry, materials discovery, and drug design is revolutionizing the development of novel molecules and materials. Machine Learning Interatomic Potentials (MLIPs) accurately predict energies and forces at quantum chemistry levels, surpassing traditional methods. Incorporating force fitting into MLIP training signi

Felipe Soriano, Lucas Finazzi, Gabriel Sanca, Federico Golmar

A voltage amplifier, based on the BFU500XRR NPN transistor in a common-emitter configuration, was developed for the readout of the fast output of an ONSEMI MicroFC-10035 Silicon Photomultiplier (SiPM). This amplifier was tested and characterized under dark and illuminated SiPM conditions. For the design presented in this work, a Gain of $(20.0 \pm 0.7)$ dB a

Dotan Goberman, Anjan Roy, Rami Pugatch

To double the cellular population of ribosomes, a fraction of the active ribosomes is allocated to synthesize ribosomal proteins. Subsequently, these ribosomal proteins enter the ribosome self-assembly process, synthesizing new ribosomes and forming the well-known ribosome autocatalytic subcycle. Neglecting ribosome lifetime and the duration of the self-asse

Kateryna A. Kvasova, Evan N. Kirby

We present a new analytical galactic chemical evolution (GCE) model with gas inflow, internally caused outflow, and extra gas loss after a period of time. The latter mimics the ram pressure stripping of a dwarf satellite galaxy near the pericenter of its orbit around a host galaxy. The new model is called Inflow with Ram Pressure Stripping (IRPS). We fit the

Riley M. T. Connors, Joey Neilsen, Aarran W. Shaw, James F. Steiner

V4641 Sgr is a low-mass black hole X-ray binary system with somewhat puzzling spectral characteristics during its soft state. Recent high-resolution spectroscopic studies of V4641 Sgr have revealed strong ionized emission line features in both the optical and X-ray bands, including P-Cygni signatures, and an unusually low soft state luminosity, indicating th

Tao Yan, Claudio J. Tessone

The Uniswap is a Decentralized Exchange (DEX) protocol that facilitates automatic token exchange without the need for traditional order books. Every pair of tokens forms a liquidity pool on Uniswap, and each token can be paired with any other token to create liquidity pools. This characteristic motivates us to employ a complex network approach to analyze the

Samir Abdaljalil, Hasan Kurban, Erchin Serpedin

Large Language Models (LLMs) are increasingly used in various contexts, yet remain prone to generating non-factual content, commonly referred to as "hallucinations". The literature categorizes hallucinations into several types, including entity-level, relation-level, and sentence-level hallucinations. However, existing hallucination datasets often fail to ca

Dhruv Gautam, Spandan Garg, Jinu Jang, Neel Sundaresan

Recent advances in language model (LM) agents and function calling have enabled autonomous, feedback-driven systems to solve problems across various digital domains. To better understand the unique limitations of LM agents, we introduce RefactorBench, a benchmark consisting of 100 large handcrafted multi-file refactoring tasks in popular open-source reposito

C. Reichhardt, C. J. O. Reichhardt

We examine the dynamics of a two-dimensional stripe, bubble, and crystal forming system interacting with a periodic one-dimensional substrate under an applied drive that is rotated with respect to the substrate periodicity direction $x$. We find that the stripes remain strongly directionally locked to the $x$ direction for an extended range of drives before

Arpan Dutta, Rumi Ghosh

Let $(K,v)$ be a valued field. Take an extension of $v$ to a fixed algebraic closure $L$ of $K$. In this paper we show that an element $a\in L$ admits a complete distinguished chain over $K$ if and only if the extension $(K(a)|K,v)$ is defectless and unibranched. This characterization generalizes the known result in the henselian case. In particular, our res

Johannes Schönberger, Viktor Larsson, Marc Pollefeys

For several decades, RANSAC has been one of the most commonly used robust estimation algorithms for many problems in computer vision and related fields. The main contribution of this paper lies in addressing a long-standing error baked into virtually any system building upon the RANSAC algorithm. Since its inception in 1981 by Fischler and Bolles, many varia

Andrei Chubarau, Yinan Wang, James J. Clark

We introduce Neural Radiance and Gaze Fields (NeRGs), a novel approach for representing visual attention in complex environments. Much like how Neural Radiance Fields (NeRFs) perform novel view synthesis, NeRGs reconstruct gaze patterns from arbitrary viewpoints, implicitly mapping visual attention to 3D surfaces. We achieve this by augmenting a standard NeR

Matthias Schöffel, Marinus Wiedner, Esteban Garces Arias, Paula Ruppert

Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing, yet their effectiveness in handling historical languages remains largely unexplored. This study examines the performance of open-source LLMs in part-of-speech (POS) tagging for Old Occitan, a historical language characterized by non-standardized orthography

Fan Yin, Zifeng Wang, I-Hung Hsu, Jun Yan

Large language models (LLMs) have exhibited the ability to effectively utilize external tools to address user queries. However, their performance may be limited in complex, multi-turn interactions involving users and multiple tools. To address this, we propose Magnet, a principled framework for synthesizing high-quality training trajectories to enhance the f

Prarthana Bhattacharyya, Joshua Mitton, Ryan Page, Owen Morgan

We present an advance in wearable technology: a mobile-optimized, real-time, ultra-low-power event camera system that enables natural hand gesture control for smart glasses, dramatically improving user experience. While hand gesture recognition in computer vision has advanced significantly, critical challenges remain in creating systems that are intuitive, a

Alessio Russo, Yichen Song, Aldo Pacchiano

We study the sample complexity of pure exploration in an online learning problem with a feedback graph. This graph dictates the feedback available to the learner, covering scenarios between full-information, pure bandit feedback, and settings with no feedback on the chosen action. While variants of this problem have been investigated for regret minimization,

Marius F. R. Juston, Alex Gisi, William R. Norris, Dustin Nottage

This paper presents the Adaptive Personalized Control System (APECS) architecture, a novel framework for human-in-the-loop control. An architecture is developed which defines appropriate constraints for the system objectives. A method for enacting Lipschitz and sector bounds on the resulting controller is derived to ensure desirable control properties. An an

Maurizio Ferrari Dacrema, Michael Benigni, Nicola Ferro

Graph-based techniques relying on neural networks and embeddings have gained attention as a way to develop Recommender Systems (RS) with several papers on the topic presented at SIGIR 2022 and 2023. Given the importance of ensuring that published research is methodologically sound and reproducible, in this paper we analyze 10 graph-based RS papers, most of w

Jin-Xin Hu, Justin C. W. Song

Magneto-electric coupling enables the manipulation of magnetization by electric fields and vice versa. While typically found in heavy element materials with large spin-orbit coupling, recent experiments on rhombohedral-stacked pentalayer graphene (RPG) have demonstrated a {\it longitudinal magneto-electric coupling} (LMC) without spin-orbit coupling. Here we

Anh-Kiet Duong

This paper presents our solution for the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls Workshop at WACV 2025. The competition focuses on recognizing Activities of Daily Living (ADLs) performed by the elderly, covering six action categories with a diverse dataset. Our approach builds upon a state-of-the-art action recognit

Kailash C Sahu, Jay Anderson, Stefano Casertano, Howard Bond

The long-duration Galactic-bulge microlensing event OGLE-2011-BLG-0462 produced relativistic astrometric deflections of the source star, which we measured using HST observations taken at 8 epochs over ~6 years. Analysis of the microlensing light curve and astrometry led our group (followed by other independent groups) to conclude that the lens is an isolated

Weina Jin, Nicholas Vincent, Ghassan Hamarneh

"why" we develop AI. Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation. In this position paper, we explore the "why" question of AI. We denote answers to the "why" question the imaginations of AI, which depict our general visions, frames, and mindsets for the prospects of AI. We identify

Joey Wilson, Marcelino Almeida, Sachit Mahajan, Martin Labrie

In this paper, we present a novel algorithm for quantifying uncertainty and information gained within 3D Gaussian Splatting (3D-GS) through P-Optimality. While 3D-GS has proven to be a useful world model with high-quality rasterizations, it does not natively quantify uncertainty or information, posing a challenge for real-world applications such as 3D-GS SLA

Christopher Zach

Lifted neural networks (i.e. neural architectures explicitly optimizing over respective network potentials to determine the neural activities) can be combined with a type of adversarial training to gain robustness for internal as well as input layers, in addition to improved generalization performance. In this work we first investigate how adversarial robust

Kefan Song, Runnan Jiang, Rohan Chandra, Shangtong Zhang

This paper addresses a critical societal consideration in the application of Reinforcement Learning (RL): ensuring equitable outcomes across different demographic groups in multi-task settings. While previous work has explored fairness in single-task RL, many real-world applications are multi-task in nature and require policies to maintain fairness across al

Victor M. S. da C. Dias, Danilo de P. Kuritza, Igor S. S. de Oliveira, Jose E. Padilha

In this study, we conduct a first-principles analysis to explore the structural and electronic properties of curved biphenylene/graphene lateral junctions (BPN/G). We start our investigation focusing on the energetic stability of BPN/G by varying the width of the graphene region, BPN/Gn. The electronic structure of BPN/Gn reveals (i) the formation of metalli

Sami Ortakaya

In this study, we investigate the optical absorption within the conduction-subbands of a cylindrical GaN/AlN quantum wire. We analyze the optical absorption rate and the real part of the dielectric function for both quantum wire (QWR) and quantum dot (QD) structures in the presence of donor-impurity states. The results cover that the density of states associ

Monica Pragliola, Luca Calatroni, Alessandro Lanza

We consider a bilevel optimisation strategy based on normalised residual whiteness loss for estimating the weighted total variation parameter maps for denoising images corrupted by additive white Gaussian noise. Compared to supervised and semi-supervised approaches relying on prior knowledge of (approximate) reference data and/or information on the noise mag

Elvis Kimara, Mozhgan Hadadi, Jackson Godbersen, Aditya Balu

The development of artificial intelligence (AI) and machine learning (ML) based tools for 3D phenotyping, especially for maize, has been limited due to the lack of large and diverse 3D datasets. 2D image datasets fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address thi

Benedikt Lienkamp, Mike Hewitt, Axel Parmentier, Maximilian Schiffer

With an increasing need for more flexible mobility services, we consider an operational problem arising in the planning of Demand Adaptive Systems (DAS). Motivated by the decision of whether to accept or reject passenger requests in real time in a DAS, we introduce the operational route planning problem of DASs. To this end, we propose an algorithmic framewo

Louis-Bernard St-Cyr, David Sénéchal

The Emery model (aka the three-band Hubbard model) offers a simplified description of the copper-oxide planes that form the building blocks of high-temperature superconductors. By contrast with the even simpler one-band Hubbard model, it differentiates between copper and oxygen orbitals and thus between oxygen occupation ($n_p$) and copper occupation ($n_d$)

Samuel Creedon, Volodymyr Mazorchuk

For a permutation $w$ in the symmetric group $\mathfrak{S}_{n}$, let $L(w)$ denote the simple highest weight module in the principal block of the BGG category $\mathcal{O}$ for the Lie algebra $\mathfrak{sl}_{n}(\mathbb{C})$. We first prove that $L(w)$ is Kostant negative whenever $w$ consecutively contains certain patterns. We then provide a complete answer

Jiaqing Zhang, Miguel Contreras, Jessica Sena, Andrea Davidson

Patient mobility monitoring in intensive care is critical for ensuring timely interventions and improving clinical outcomes. While accelerometry-based sensor data are widely adopted in training artificial intelligence models to estimate patient mobility, existing approaches face two key limitations highlighted in clinical practice: (1) modeling the long-term

José Joaquín Carvajal, Davood Damircheli, Thomas Führer, Francisco Fuica

We propose and analyze a general framework for space-time finite element methods that is based on least-squares finite element methods for solving a first-order reformulation of the thick parabolic obstacle problem. Discretizations based on simplicial and prismatic meshes are studied and we show a priori error estimates for both. Convergence rates are derive

Fenglu Hong, Ravi Raju, Jonathan Lingjie Li, Bo Li

Speculative decoding is an effective method for accelerating inference of large language models (LLMs) by employing a small draft model to predict the output of a target model. However, when adapting speculative decoding to domain-specific target models, the acceptance rate of the generic draft model drops significantly due to domain shift. In this work, we

Kefan Song, Jin Yao, Runnan Jiang, Rohan Chandra

As Large Language Models (LLMs) become increasingly powerful and accessible to human users, ensuring fairness across diverse demographic groups, i.e., group fairness, is a critical ethical concern. However, current fairness and bias research in LLMs is limited in two aspects. First, compared to traditional group fairness in machine learning classification, i

Alben Rome Bagabaldo, Jürgen Hackl

National Highway Traffic Safety Administration reported 7,345 pedestrian fatalities in the United States in 2022, making pedestrian safety a pressing issue in urban mobility. This study presents a novel probabilistic simulation framework integrating dynamic pedestrian crossing models and Monte Carlo simulations to evaluate safety under varying traffic condit

Michael Borinsky, Andrea Favorito

We find empirically that the value of Feynman integrals follows a $\log$-$\Gamma$ distribution at large loop order. This result opens up a new avenue towards the large-order behavior in perturbative quantum field theory. Our study of the primitive contribution to the scalar $\phi^4$ beta function in four dimensions up to 17 loops provides accompanying eviden

Lorenzo Dello Schiavo, Giacomo Enrico Sodini

Let $(M,g)$ be a Riemannian manifold with Riemannian distance $\mathsf{d}_g$, and $\mathcal{M}(M)$ be the space of all non-negative Borel measures on $M$, endowed with the Hellinger-Kantorovich distance $\mathsf{H\! K}_{\mathsf{d}_g}$ induced by $\mathsf{d}_g$. Firstly, we prove that $\left(\mathcal{M}(M),\mathsf{H\! K}_{\mathsf{d}_g}\right)$ is a universall

Steve Fan, Paul Pollack

We study how large and small elasticity can be for orders belonging to a fixed quadratic field, in terms of the corresponding conductors. For example, we show that if $K$ is an imaginary quadratic field, then the order of conductor $f$ in $K$ has elasticity exceeding $(\log{f})^{c_1 \log\log\log{f}}$ for all $f$ that are sufficiently large. On the other hand

Nelson Lojo, Rafael González, Rohan Philip, José Antonio Parejo

Requirements Elicitation (RE) is a crucial software engineering skill that involves interviewing a client and then devising a software design based on the interview results. Teaching this inherently experiential skill effectively has high cost, such as acquiring an industry partner to interview, or training course staff or other students to play the role of

Pramit Saha, Divyanshu Mishra, Netzahualcoyotl Hernandez-Cruz, Olga Patey

Congenital Heart Disease (CHD) is one of the leading causes of fetal mortality, yet the scarcity of labeled CHD data and strict privacy regulations surrounding fetal ultrasound (US) imaging present significant challenges for the development of deep learning-based models for CHD detection. Centralised collection of large real-world datasets for rare condition

Iman Beheshti

Understanding the risk and protective factors associated with Parkinsons disease (PD) is crucial for improving outcomes for patients, individuals at risk, healthcare providers, and healthcare systems. Studying these factors not only enhances our knowledge of the disease but also aids in developing effective prevention, management, and treatment strategies. T

Yongle Zhang, Phuong-Anh Nguyen-Le, Kriti Singh, Ge Gao

News reading helps individuals stay informed about events and developments in society. Local residents and new immigrants often approach the same news differently, prompting the question of how technology, such as LLM-powered chatbots, can best enhance a reader-oriented news experience. The current paper presents an empirical study involving 144 participants

Shuaifeng Li, Xiaoming Mao

Recently, a new frontier in computing has emerged with physical neural networks(PNNs) harnessing intrinsic physical processes for learning. Here, we explore topological mechanical neural networks(TMNNs) inspired by the quantum spin Hall effect(QSHE) in topological metamaterials, for machine learning classification tasks. TMNNs utilize pseudospin states and t

Matthew Bousquet, Jiawei Zhan, Chunxin Luo, Alex B. Martinson

Using a combination of first principles molecular dynamics simulations (FPMD) and electronic structure calculations, we characterize the atomistic structure and vibrational properties of a photocatalytic surface of In$_2$O$_3$, a promising photoelectrode for the production of hydrogen peroxide. We then investigate the surface in contact with water and show t

Friedrich Hübner, Leonardo Biagetti, Jacopo De Nardis, Benjamin Doyon

We derive exact equations governing the large-scale dynamics of hard rods, including diffusive effects that go beyond ballistic transport. Diffusive corrections are the first-order terms in the hydrodynamic gradient expansion and we obtain them through an explicit microscopic calculation of the dynamics of hard rods. We show that they differ significantly fr

Thanh Nguyen Cung, Son Duong Hong

We denote $\mathcal{P}$ = $\{P(x)|$ $P(n) \mid n!$ for infinitely many $n\}$. This article identifies some polynomials that belong to $\mathcal{P}$. Additionally, we also denote $P^+(m)$ as the largest prime factor of $m$. Then, a consequence of this work shows that there are infinitely many $n \in \mathbb{N}$ so that $P^+(f(n)) < n^{\frac{3}{4}+\varepsilon}

Martin Suda

Clause selection is arguably the most important choice point in saturation-based theorem proving. Framing it as a reinforcement learning (RL) task is a way to challenge the human-designed heuristics of state-of-the-art provers and to instead automatically evolve -- just from prover experiences -- their potentially optimal replacement. In this work, we presen

Daniel Eyles, Adam Stokes, Ahsan Nazir

We show that the Cauchy-Schwarz inequality provides a simple yet general bound that limits the accuracy of light-matter theories which retain only finite numbers of material energy levels. A corollary is that unitary rotations within a truncated space cannot transform between gauges, because the contrary assumption yields incorrect predictions. In particular

Liam Peet-Pare

This paper provides an explanation of NTRU, a post quantum encryption scheme, while also providing a gentle introduction to cryptography. NTRU is a very efficient lattice based cryptosystem that appears to be safe against attacks by quantum computers. NTRU's efficiency suggests that it is a strong candidate as an alternative to RSA, ElGamal, and ECC for the

Jiahao Cao, Hou-Cheng Yang, Guanyu Hu

Basketball analytics has significantly advanced our understanding of the game, with shot selection emerging as a critical factor in both individual and team performance. With the advent of player tracking technologies, a wealth of granular data on shot attempts has become available, enabling a deeper analysis of shooting behavior. However, modeling shot sele

Mehmet Samet Duran, Tevfik Aytekin

In recent years, automatic text summarization has witnessed significant advancement, particularly with the development of transformer-based models. However, the challenge of controlling the readability level of generated summaries remains an under-explored area, especially for languages with complex linguistic features like Turkish. This gap has the effect o

Beatriz Hernandez-Molinero, Raul Jimenez, Carlos Peña Garay

Low-energy neutrinos from the cosmic background are captured by objects in the sky that contain material susceptible of single beta decay. Neutrons, which compose most of a neutron star, capture low-energy neutrinos from the cosmic neutrino background and release a high-energy electron in the MeV range. Also, planets contain unstable isotopes that capture th

Leslie W. Looney, Zhe-Yu Daniel Lin, Zhi-Yun Li, John J. Tobin

Circumstellar disk dust polarization in the (sub)millimeter is, for the most part, not from dust grain alignment with magnetic fields but rather indicative of a combination of dust self-scattering with a yet unknown alignment mechanism that is consistent with mechanical alignment. While the observational evidence for scattering has been well established, tha

I. Komis, K. G. Makris, K. Busch, R. El-Ganainy

Wave transport in disordered media is a fundamental problem with direct implications in condensed matter, materials science, optics, atomic physics, and even biology. The majority of studies are focused on Hermitian systems to understand disorder-induced localization. However, recent studies of non-Hermitian disordered media have revealed unique behaviors, w

Junjie Wen, Murtazo Nazarov

We introduce a high-order finite element method for approximating the Vlasov-Poisson equations. This approach employs continuous Lagrange polynomials in space and explicit Runge-Kutta schemes for time discretization. To stabilize the numerical oscillations inherent in the scheme, a new anisotropic nonlinear artificial viscosity method is introduced. Numerica

Foivos Charalampakos, Thomas Tsouparopoulos, Iordanis Koutsopoulos

Explainable AI is a crucial component for edge services, as it ensures reliable decision making based on complex AI models. Surrogate models are a prominent approach of XAI where human-interpretable models, such as a linear regression model, are trained to approximate a complex (black-box) model's predictions. This paper delves into the balance between the p

Robert E. Patterson, Regina Buccello-Stout, Mary E. Frame, Anna M. Maresca

One of the most vital cognitive skills to possess is the ability to make sense of objects, events, and situations in the world. In the current paper, we offer an approach for creating artificially intelligent agents with the capacity for sensemaking in novel environments. Objectives: to present several key ideas: (1) a novel unified conceptual framework for

Bryan Min, Allen Chen, Yining Cao, Haijun Xia

The overview-detail design pattern, characterized by an overview of multiple items and a detailed view of a selected item, is ubiquitously implemented across software interfaces. Designers often try to account for all users, but ultimately these interfaces settle on a single form. For instance, an overview map may display hotel prices but omit other user-des

I. Fornasiero, F. Ubertosi, M. Gitti

We analyze the X-ray and radio properties of the galaxy cluster Abell 2009 (z=0.152) to complete the in-depth study of a subsample of objects from the ROSAT Brightest Cluster Sample with relatively high X-ray flux and H$\alpha$ line luminosity, which is a promising diagnostic of the presence of cool gas in the cluster cores. Our aim is to investigate the fee

Lazaros Tsaloukidis, Francisco Peña-Benítez, Piotr Surówka

Traditionally applied within equilibrium states, the charge-vortex dualities are expanded to address the complex dynamics of superfluids and ideal fluids under non-static conditions. We have constructed explicit mappings of finite temperature fluid dynamics to gauge theories, enabling a dual description where vortices in both superfluids and ideal fluids are

Andrea Di Pinto, Silke Klemm, Adriano Viganò

We present a new exact solution of Einstein-Maxwell field equations which represents a rotating black hole with both electric and magnetic charges immersed in a universe which itself is also rotating and magnetized, i.e. the dyonic Kerr-Newman black hole in a Melvin-swirling universe. We show that the solution is completely regular and free of any type of si

V. Rodríguez Morales, M. Mezcua, H. Domínguez Sánchez, A. Audibert

Active Galactic Nuclei (AGN) feedback is one of the most important mechanisms in galaxy evolution. It is usually found in massive galaxies and regulates star formation. Although dwarf galaxies are assumed to be regulated by supernova feedback, recent studies show evidence for the presence of AGN outflows and feedback in dwarf galaxies. We investigate the pre