November 2024 arXiv papers — page 67

Ibrahim Kok, Orhan Demirci, Suat Ozdemir

Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles

Jingyun Ning, Madhur Behl

Autonomous racing is gaining attention for its potential to advance autonomous vehicle technologies. Accurate race car dynamics modeling is essential for capturing and predicting future states like position, orientation, and velocity. However, accurately modeling complex subsystems such as tires and suspension poses significant challenges. In this paper, we

Shantanu Jaiswal, Debaditya Roy, Basura Fernando, Cheston Tan

Complex visual reasoning and question answering (VQA) is a challenging task that requires compositional multi-step processing and higher-level reasoning capabilities beyond the immediate recognition and localization of objects and events. Here, we introduce a fully neural Iterative and Parallel Reasoning Mechanism (IPRM) that combines two distinct forms of c

Ola Shorinwa, Jiankai Sun, Mac Schwager

We present FAST-Splat for fast, ambiguity-free semantic Gaussian Splatting, which seeks to address the main limitations of existing semantic Gaussian Splatting methods, namely: slow training and rendering speeds; high memory usage; and ambiguous semantic object localization. We take a bottom-up approach in deriving FAST-Splat, dismantling the limitations of

Takashi Uneyama

We propose a highly coarse-grained simulation model for crystalline polymer solids with crystalline lamellar structures. The mechanical properties of a crystalline polymer solid are mainly determined by the crystalline lamellar structures. This means that coarse-grained models rather than fine-scale molecular models are suitable to study mechanical propertie

Luca Colombo, Luca Spagnuolo, Kapil Saha, Gabriel Giribaldi

This letter reports on Solidly-Mounted Bidimensional Mode Resonators (S2MRs) exploiting a highly-optimized Sezawa mode in 30% Scandium-doped Aluminum Nitride (ScAlN) on Silicon Carbide (SiC) and operating near 16 GHz. Experimental results demonstrate mechanical quality factors (Qm) as high as 380, Bode quality factors (QBode) approaching 500, electromechanic

ChangSheng Shi, GuoBao Zhang, ShuangNan Zhang, XiangDong Li

Context: The connection between quasi-periodic oscillations (QPOs) and magnetic fields has been investigated across various celestial bodies. Magnetohydrodynamics (MHD) waves have been employed to explain the simultaneous upper and lower kilohertz (kHz) QPOs. Nevertheless, the intricate and undefined formation pathways of twin kHz QPOs present a compelling a

U. León-Domínguez, E. D. Flores-Flores, A. J. García-Jasso, M. K. Gómez-Cuellar

Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to 50%. However, challenges persist, especially with the advent of robots and the need to humanize machines for

Luke Hagar, Nathaniel T. Stevens

To design Bayesian studies, criteria for the operating characteristics of posterior analyses - such as power and the type I error rate - are often assessed by estimating sampling distributions of posterior probabilities via simulation. In this paper, we propose an economical method to determine optimal sample sizes and decision criteria for such studies. Usi

Peiyao Wang, Gengping Jiang, Yuan Yan, Longbing Qu

Water molecules are traditionally regarded as passive dielectric media in electrochemical systems. In this work, we challenge this conventional perspective using molecular dynamics simulations and theoretical analysis. We show that interfacial water is polarized differently from bulk water and effectively screens the electrostatic potential between ions and

Yoel Zimmermann, Adib Bazgir, Zartashia Afzal, Fariha Agbere

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular

Pierre Baudoz, Celia Desgrange, Raphaël Galicher, Iva Laginja

The spectroscopic study of mature giant planets and low mass planets (Neptune-like, Earth-like) requires instruments capable of achieving very high contrasts ($10^{-10}-10^{-11}$) at short angular separations. To achieve such high performance on a real instrument, many limitations must be overcome: complex component defects (coronagraph, deformable mirror),

Hadi Afshari, Varun Mapara, Shashi Sourabh, Megh N. Khanal

One of the main approaches to inhibit carrier cooling in semiconductor systems enabling the study of hot carrier solar cell protocols is the use of concentrated illumination to obtain high power densities and create a phonon bottleneck. This, however, typically also increases the lattice temperature of the solar cells significantly. Accordingly, the solar ce

Linu Malakkal, Shuxiang Zhou, Himani Mishra, Jia Hong Ke

A recent experimental study on a spent uranium dioxide (UO2) fuel sample from Belgium Reactor 3 (BR3) identified a unique pair structure formed by the noble metal phase (NMP) and fission gas (xenon [Xe]) precipitate. However, the fundamental mechanism behind this structure remains unclear. The present study aims to provide an understanding of the interaction

Lucas Squillante

In this Ph.D. Thesis, a systematic review is performed on the derivation and generalization of the Gr\"uneisen parameter followed by its unprecedented applications to several distinct scenarios, such as magnetic model systems, zero-field quantum phase transitions, the maximization of caloric effects close to any critical-end point based on entropy arguments,

Benjamin D. M. Jones, Lana Mineh, Ashley Montanaro

We numerically benchmark 30 optimisers on 372 instances of the variational quantum eigensolver for solving the Fermi-Hubbard system with the Hamiltonian variational ansatz. We rank the optimisers with respect to metrics such as final energy achieved and function calls needed to get within a certain tolerance level, and find that the best performing optimiser

Naoki Masuda, Kazuyuki Aihara

Cooperation is commonly found in ecological and social systems even when it apparently seems that individuals can benefit from selfish behavior. We investigate how cooperation emerges with the spatial prisoner's dilemma played in a class of networks ranging from regular lattices to random networks. We find that, among these networks, small-world topology is

Zi Wang, Fei Wu, Feng Yu, Yurui Zhou

Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy an

James Allen, Daniel Belkin, Bryan K. Clark

A fundamental question is understanding the rate at which random quantum circuits converge to the Haar measure. One quantity which is important in establishing this rate is the spectral gap of a random quantum ensemble. In this work we establish a new bound on the spectral gap of the t-th moment of a one-dimensional brickwork architecture on N qudits. This b

Tetiana Bas

This study investigates gender bias in large language models (LLMs) by comparing their gender perception to that of human respondents, U.S. Bureau of Labor Statistics data, and a 50% no-bias benchmark. We created a new evaluation set using occupational data and role-specific sentences. Unlike common benchmarks included in LLM training data, our set is newly

Daigo Oue, Boris Shapiro, Mário G. Silveirinha

In this work, we develop an analytical framework to understand quantum friction across distinct stability regimes, providing approximate expressions for frictional forces both in the deep stable regime and near the critical threshold of instability. Our primary finding is analytical proof that, near the instability threshold, the quantum friction force diver

Arnaud Delorme, Dung Truong, Luca Pion-Tonachini, Scott Makeig

ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance

Yanina Gonzalez, Victoria Torres

In this paper, we present a comprehensive account of all Laguerre-type differential operators $D$ that are symmetric with respect to a $2\times 2$ irreducible weight $W$ on the interval $(0, \infty)$. These operators are associated with monic orthogonal polynomials ${P_n}$, which satisfy the equation $DP_n = P_n\Delta_n$ for a certain lower triangular eigenv

Alonso Delfín, Carla Farsi, Judith Packer

For $p \in [1, \infty)$, we generalize the concept of classical spectral triples by extending the framework from Hilbert spaces to $L^p$-spaces, and from C*-algebras to $L^p$-operator algebras. In addition, we define an $L^p$-spectral triple to be metric when the state space of the algebra has a $p$-quantum compact metric space structure. Specifically, we co

Kondo Gnanvo, Florian Hauenstein, Sara Liyanaarachchi, Nilanga Liyanage

One of the future plans at Jefferson Lab is running electron scattering experiments with large acceptance detectors at luminosities $> 10^{37}cm^{-2}s^{-1}$. These experiments allow the measurements of the Double Deeply Virtual Compton Scattering (DDVCS) reaction, an important physics process in the formalism of Generalized Parton Distributions, which has ne

Andrea Pinto, Akshay Rangamani, Tomaso Poggio

While previous optimization results have suggested that deep neural networks tend to favour low-rank weight matrices, the implications of this inductive bias on generalization bounds remain underexplored. In this paper, we apply Maurer's chain rule for Gaussian complexity to analyze how low-rank layers in deep networks can prevent the accumulation of rank an

Luca Aceto, Daniele Gorla, Stian Lybech

Many type systems have been presented in the literature for variants of the pi-calculus, but none of them are able to handle composite subjects such as those found in the language epi, which features polyadic synchronisation. The purpose of this paper is to address the question of how to type composite subjects in a general fashion. We assess the validity of

Wenjie Li, Jiawei Li, Pengcheng Zeng, Christian Schroeder de Witt

Addressing data integrity challenges, such as unlearning the effects of data poisoning after model training, is necessary for the reliable deployment of machine learning models. State-of-the-art influence functions, such as EK-FAC and TRAK, often fail to accurately attribute abnormal model behavior to the specific poisoned training data responsible for the d

Saba Ahmadi, Siddharth Bhandari, Avrim Blum

We investigate the concept of algorithmic replicability introduced by Impagliazzo et al. 2022, Ghazi et al. 2021, Ahn et al. 2024 in an online setting. In our model, the input sequence received by the online learner is generated from time-varying distributions chosen by an adversary (obliviously). Our objective is to design low-regret online algorithms that,

Johannes Braathen, Martin Gabelmann, Tania Robens, Panagiotis Stylianou

In this work, we explore the discovery potential of the Inert Doublet Model (IDM) via the vector boson fusion (VBF) channel at a muon collider with centre-of-mass energy of 10 TeV. The Inert Doublet Model is a two-Higgs-doublet model variant with an unbroken discrete $\mathbb{Z}_2$ symmetry, featuring new stable scalar particles that can serve as dark matter

Vignesh Manoharan, Vijaya Ramachandran

We present results for the distance sensitivity oracle (DSO) problem, where one needs to preprocess a given directed weighted graph $G=(V,E)$ in order to answer queries about the shortest path distance in $G$ from vertex $s$ to vertex $t$ avoiding edge $e$, for any $s,t \in V, e \in E$. DSO enables optimal re-routing under a link failure, and can serve as a

Björn Engquist, Kui Ren, Yunan Yang

We propose and analyze a class of adaptive sampling algorithms for multimodal distributions on a bounded domain, which share a structural resemblance to the classic overdamped Langevin dynamics. We first demonstrate that this class of linear dynamics with adaptive diffusion coefficients and vector fields can be interpreted and analyzed as weighted Wasserstei

S. Fasahat, N. Fiuza-Maneiro, B. Schäfer, K. Xu

So far, the striking sign reversal in the near-ambient slope of the gap temperature dependence of colloidal CsPbCl3 perovskite nanocrystals (NCs) compared to its Br counterpart, remains unresolved. Pure bromide NCs exhibit a linear gap increase with increasing temperature, to which thermal expansion and electron-phonon interaction equally contribute. In cont

Brian B. Luczak

In this paper, we will provide a result on the relativistic Euler equations for an ideal gas equation of state and a physical vacuum boundary. More specifically, we will prove a priori estimates for the linearized system in weighted Sobolev spaces. Our focus will be on choosing the correct thermodynamic variables, developing a weighted book-keeping scheme, a

Annabelle Canestraight, Zhen Huang, Vojtech Vlcek

We explore the principles of many-body Hamiltonian complexity reduction via downfolding on an effective low-dimensional representation. We present a unique measure of fidelity between the effective (reduced-rank) description and the full many-body treatment for arbitrary (i.e., ground and excited) states. When the entire problem is mapped on a system of inte

Yang Wang, Hassan A. Karimi

With the increasing impacts of climate change, there is a growing demand for accessible tools that can provide reliable future climate information to support planning, finance, and other decision-making applications. Large language models (LLMs), such as GPT-4, present a promising approach to bridging the gap between complex climate data and the general publ

Silvia Barbina, Enrique Casanovas

Free Steiner triple systems (STS) are infinite structures that are naturally characterised by a universal property. We consider the class of free STSs from a model theoretic viewpoint. We show that free STSs on any number of generators are elementarily equivalent. We axiomatise their theory and show that it is stable.

Roman Bögli, Leandro Lerena, Christos Tsigkanos, Timo Kehrer

TLA+ is a formal specification language used for designing, modeling, documenting, and verifying systems through model checking. Despite significant interest from the research community, knowledge about usage of the TLA+ ecosystem in practice remains scarce. Industry reports suggest that software engineers could benefit from insights, innovations, and soluti

Clayton Conner, Ali Sarikhani, Theo Volz, Mathew Pollard

Intercalated van der Waals (vdW) magnetic materials host unique magnetic properties due to the interplay of competing interlayer and intralayer exchange couplings, which depend on the intercalant concentration within the van der Waals gaps. Magnetic vdW compound chromium telluride, Cr$_{1+x}$Te$_2$, has demonstrated rich magnetic phases at various Cr concent

Negar Babashah, Hasti Karimi, Masoud Seddighin, Golnoosh Shahkarami

We study the problem of minimizing metric distortion in multi-winner elections, where a committee of size $k$ is selected from a set of candidates based on voters' ordinal preferences. We assume that voters and candidates are embedded on a line metric, and social cost is determined by the underlying metric distances. The distortion of a voting rule is the wo

Aman Sharma, Nicolas Schunck, Kyle Wendt

Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technology. To this day, however, all the theoretical models that can scale at the level of the chart of isotopes remain semi phenomenological. Because they are fitted locally, their predi

Shuhui Cai, Huafeng Qin, Huapei Wang, Chenglong Deng

The evolution of the lunar magnetic field can reveal the Moon's interior structure, thermal history, and surface environment. The mid-to-late stage evolution of the lunar magnetic field is poorly constrained, and thus the existence of a long-lived lunar dynamo remains controversial. The Chang'e-5 mission returned the heretofore youngest mare basalts from Oce

Matthew Route

The recent discovery of planetesimals orbiting white dwarfs has renewed interest in the final chapters of the evolution of planetary systems. Although observational and theoretical studies have examined the dynamical evolution of these systems, studies of their magnetic star-planet interactions, as powered by unipolar induction, have thus far only been asses

S M Mojahidul Ahsan, Anurag Dhungel, Mrittika Chowdhury, Md Sakib Hasan

In this chapter, we aim to explore an in-depth exploration of the specialized hardware accelerators designed to enhance Artificial Intelligence (AI) applications, focusing on their necessity, development, and impact on the field of AI. It covers the transition from traditional computing systems to advanced AI-specific hardware, addressing the growing demands

Alex Glynn

Since generative artificial intelligence (AI) tools such as OpenAI's ChatGPT became widely available, researchers have used them in the writing process. The consensus of the academic publishing community is that such usage must be declared in the published article. Academ-AI documents examples of suspected undeclared AI usage in the academic literature, disc

Rahm Ranjan, David Ahmedt-Aristizabal, Mohammad Ali Armin, Juno Kim

Gait analysis using computer vision is an emerging field in AI, offering clinicians an objective, multi-feature approach to analyse complex movements. Despite its promise, current applications using RGB video data alone are limited in measuring clinically relevant spatial and temporal kinematics and establishing normative parameters essential for identifying

Ziang Yin, Meng Zhang, Amir Begovic, Rena Huang

Electronic-photonic integrated circuits (EPICs) offer transformative potential for next-generation high-performance AI but require interdisciplinary advances across devices, circuits, architecture, and design automation. The complexity of hybrid systems makes it challenging even for domain experts to understand distinct behaviors and interactions across desi

Brian C. Seymour, Yanbei Chen

Measurement of gravitational waves can provide precision tests of the nature of black holes and compact objects. In this work, we test Giddings' nonviolent nonlocality proposal, which posits that quantum information is transferred via a nonlocal interaction that generates metric perturbations around black holes. In contrast to firewalls, these quantum fluctu

Andrei D. Polyanin, Nikolay A. Kudryashov

For the first time, the general nonlinear Schr\"odinger equation is investigated, in which the chromatic dispersion and potential are specified by two arbitrary functions. The equation in question is a natural generalization of a wide class of related nonlinear partial differential equations that are often used in various areas of theoretical physics, includ

Gong Zhang, Ignatius William Primaatmaja, Yue Chen, Si Qi Ng

The power of quantum random number generation is more than just the ability to create truly random numbers$\unicode{x2013}$it can also enable self-testing, which allows the user to verify the implementation integrity of certain critical quantum components with minimal assumptions. In this work, we develop and implement a self-testing quantum random number ge

Xiaochi Qian, Zixuan Xie, Xinyu Liu, Shangtong Zhang

This paper establishes the first almost sure convergence rate and the first maximal concentration bound with exponential tails for general contractive stochastic approximation algorithms with Markovian noise. As a corollary, we also obtain convergence rates in $L^p$. Key to our successes is a novel discretization of the mean ODE of stochastic approximation a

Elias Raffoul, Xingpeng Li

To achieve net-zero carbon emissions, electrification in the transportation sector plays an important role. Significant increase of electric vehicles (EV) has been observed nationally and globally. While the transition to EVs presents substantial environmental benefits, it would lead to several challenges to the power grid due to EV charging activities. Grow

Pierre Baudoz, Elsa Huby, Olivier Dupuis, Faouzi Boussaha

MICADO, the European Extremely Large Telescope first light imager will feature a dedicated high contrast imaging mode specifically designed for observing and characterizing exoplanets and circumstellar disks. Its improved sensitivity and angular resolution, compared to existing instruments will significantly increase our knowledge on these planetary systems.

Tomasz Krawczyk

In the work [$\mathcal{O}(m\cdot n)$ algorithms for the recognition and isomorphism problems on circular-arc graphs, SIAM J. Comput. 24(3), 411--439, (1995)], Wen-Lian Hsu claims three results concerning the class of circular-arc graphs: - the design of so-called \emph{decomposition trees} that represent the structure of all normalized intersection models of

Emilio Ferrucci, Timothy Herschell, Christian Litterer, Terry Lyons

Utilising classical results on the structure of Hopf algebras, we develop a novel approach for the construction of cubature formulae on Wiener space based on unshuffle expansions. We demonstrate the effectiveness of this approach by constructing the first explicit degree-7 cubature formula on $d$-dimensional Wiener space with drift, in the sense of Lyons and

Daniel Rogalski

We study the spectrum of closed subcategories in a quasi-scheme, i.e. a Grothendieck category $X$. The closed subcategories are the direct analogs of closed subschemes in the commutative case, in the sense that when $X$ is the category of quasi-coherent sheaves on a quasi-projective scheme $S$, then the closed subschemes of $S$ correspond bijectively to the

Qicheng Tang, Xueda Wen

Critical systems host nontrivial entanglement structure that is generally sensitive to additional couplings. In the present work, we study the effect of weak measurements on the entanglement Hamiltonian of massless free fermions which are prepared in their critical ground state. While the power-law decaying correlation and logarithmic growing entanglement en

Rana Alotaibi, Yuanyuan Tian, Stefan Grafberger, Jesús Camacho-Rodríguez

Customer demand, regulatory pressure, and engineering efficiency are the driving forces behind the industry-wide trend of moving from siloed engines and services that are optimized in isolation to highly integrated solutions. This is confirmed by the wide adoption of open formats, shared component libraries, and the meteoric success of integrated data lake e

Ryan W. Penny, Zachery Kutschke, A. John Hart

Qualification of high-performance metal components produced by laser powder bed fusion (LPBF) must identify process-induced porous defects that reduce ductility and nucleate fatigue cracking. Detecting such defects via optical monitoring of LPBF provides a path towards in-process quality control without downstream testing such as by computed tomography. Howe

Marie-Charlotte Brandenburg, Roland Púček

This article introduces the theory of Veronese polytopes, a broad generalisation of cyclic polytopes. These arise as convex hulls of points on curves with one or more connected components, obtained as the image of the rational normal curve in affine charts. We describe their facial structure by extending Gale's evenness condition, and provide a further combi

Bryce Cyr, Jens Chluba, Pranav Bharadwaj Gangrekalve Manoj

Axions and axion-like particles (ALPs) remain highly motivated extensions to the standard model due to their ability to address open questions such as the relic abundance of dark matter and the strong CP problem. Axions are also capable of undergoing a resonant mixing with photons when the masses of the two fields are roughly equal, producing a wide array of

Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu, Siyang Yuan

Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into large-scale recommendation models. However, simply increasing the model size in recommendation systems, even with large amounts of data, does not always result in the expected perf

Jiangang Hao, Michael Fauss

The COVID-19 pandemic has accelerated the implementation and acceptance of remotely proctored high-stake assessments. While the flexible administration of the tests brings forth many values, it raises test security-related concerns. Meanwhile, artificial intelligence (AI) has witnessed tremendous advances in the last five years. Many AI tools (such as the ve

Michele Perna, Santiago Arribas, Xihan Ji, Cosimo Marconcini

Studying the distribution and properties of ionised gas in outflows driven by AGN is crucial for understanding the feedback mechanisms at play in extragalactic environments. In this study, we explore the connection between ionised outflows traced by rest-frame UV absorption and optical emission lines in GS133, a Compton thick AGN at z = 3.47. We combine obse

Rui Cao, Yuming Jiang, Michael Schlichtkrull, Andreas Vlachos

Multimodal Large Language Models (MLLMs) can enhance trustworthiness by aligning with human preferences. As human preference labeling is laborious, recent works employ evaluation models for assessing MLLMs' responses, using the model-based assessments to automate preference dataset construction. This approach, however, faces challenges with MLLMs' lengthy an

Caleb Chou, Andee Kaplan

Sport climbing is an athletic discipline comprised of three sub-disciplines -- lead climbing, bouldering, and speed climbing. These three sub-disciplines have distinct goals, resulting in specialization of athletes into one of the three events. The year 2020 marked the first inclusion of sport climbing in the Olympic Games. While this decision was met with e

Rup Kumar Chowdhury, Md Samiul Islam, Marie Barthelemy, Nicolas Beyer

The discovery of in-plane anisotropic excitons in two-dimensional layered semiconductors enables state-of-the-art nanophotonic applications. A fundamental yet unknown parameter of these quasiparticles is the coherence time (T_2 ), which governs the quantum dephasing timescale, over which the coherent superposition of excitons can be maintained and manipulate

Florentin Putz, Steffen Haesler, Matthias Hollick

Trustworthy digital communication requires the secure exchange of contact information, but current approaches lack usability and scalability for larger groups of users. We evaluate the usability of two secure contact exchange systems: the current state of the art, SafeSlinger, and our newly designed protocol, PairSonic, which extends trust from physical enco

Florentin Putz, Steffen Haesler, Thomas Völkl, Maximilian Gehring

Securely exchanging contact information is essential for establishing trustworthy communication channels that facilitate effective online collaboration. However, current methods are neither user-friendly nor scalable for large groups of users. In response, we introduce PairSonic, a novel group pairing protocol that extends trust from physical encounters to o

Pietro Freni

I prove the statement in the title using results from arXiv:2404.07646(2). This shows that Question~1.1 in [1] has negative answer for certain expansions of a valued field.

Sahil S. Patel, Desmond Zeya Chen, David Castle, Clement Ma

Basket trials can efficiently evaluate a single treatment across multiple diseases with a common shared target. Prior methods for randomized basket trials required baskets to have the same sample and effect sizes. To that end, we developed a general randomized basket trial with an interim analysis (RaBIt) that allows for unequal sample sizes and effect sizes

Haojia Sun, Yaqi Wang, Shuting Zhang

We designed a Retrieval-Augmented Generation (RAG) system to provide large language models with relevant documents for answering domain-specific questions about Pittsburgh and Carnegie Mellon University (CMU). We extracted over 1,800 subpages using a greedy scraping strategy and employed a hybrid annotation process, combining manual and Mistral-generated que

Sanjana Agrawal, Saúl A. Blanco

We study the problem of collaborative best-arm identification in stochastic linear bandits under a fixed-budget scenario. In our learning model, we first consider multiple agents connected through a star network, interacting with a linear bandit instance in parallel. We then extend our analysis to arbitrary network topologies. The objective of the agents is

Mohnish Harwani, Juan C. Verduzco, Brian H. Lee, Alejandro Strachan

Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, a major challenge remains the acquisition of the initial data and the development of workflows to generate new data at each iteration. In this study, we demonstrate a significant speedup in an optimization task by reusi

Markus Dablander

Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. It is one of the key research areas in molecular machine learning and computational drug discovery. Recently, message-passing graph neural networks (GNNs) have emerged as a novel method to learn differentiable features directly from molecular graphs. While

Nerijus Bertalis, Paul Granse, Ferhat Gül, Florian Hauss

Assigning a set of labels to a given text is a classification problem with many real-world applications, such as recommender systems. Two separate research streams address this issue. Hierarchical Text Classification (HTC) focuses on datasets with label pools of hundreds of entries, accompanied by a semantic label hierarchy. In contrast, eXtreme Multi-Label

Alfred Curry, Subhanjoy Mohanty, James E. Owen

Many known rocky exoplanets are so highly irradiated that their dayside surfaces are molten, and `silicate atmospheres', composed of rock-forming elements, are generated above these lava pools. The compositions of these `lava planet' atmospheres are of great interest because they must be linked to the composition of the underlying rocky interiors. It may be

Ryan Mok, Faraaz Akhtar, Louis Clare, Christine Li

Grading assessments is time-consuming and prone to human bias. Students may experience delays in receiving feedback that may not be tailored to their expectations or needs. Harnessing AI in education can be effective for grading undergraduate physics problems, enhancing the efficiency of undergraduate-level physics learning and teaching, and helping students

Encarnacion Algaba, Eric Remila, Philippe Solal

This paper introduces the class of cooperative games with generalized coalition configuration. This new class of games corresponds to cooperative games with coalition configuration and restricted cooperation. A coalition configuration is a collection of coalitions covering the agent set. The restriction of cooperation between agents is represented by a set s

Nitesh Bharadwaj Gundavarapu, Luke Friedman, Raghav Goyal, Chaitra Hegde

Video understanding has witnessed significant progress with recent video foundation models demonstrating strong performance owing to self-supervised pre-training objectives; Masked Autoencoders (MAE) being the design of choice. Nevertheless, the majority of prior works that leverage MAE pre-training have focused on relatively short video representations (16

Cynthia Dwork, Pranay Tankala, Linjun Zhang

We initiate the study of differentially private learning in the proportional dimensionality regime, in which the number of data samples $n$ and problem dimension $d$ approach infinity at rates proportional to one another, meaning that $d/n\to\delta$ as $n\to\infty$ for an arbitrary, given constant $\delta\in(0,\infty)$. This setting is significantly more cha

Luca Pajola, Saskia Laura Schröer, Pier Paolo Tricomi, Mauro Conti

Billions of individuals engage with Online Social Networks (OSN) daily. The owners of OSN try to meet the demands of their end-users while complying with business necessities. Such necessities may, however, lead to the adoption of restrictive data access policies that hinder research activities from "external" scientists -- who may, in turn, resort to other

Marcio Maciel Bastos, Luiz Max Carvalho, Eduardo Correa Araujo, Flávio Codeço Coelho

In face of climate change and increasing urbanization, the predictive mosquito-borne diseases (MBD) transmission models require constant updates. Thus, is urgent to comprehend the driving forces of this non stationary behavior, observed through spatial and incidence expansion. We observed that temperature is a critical driver in predictive models for MBD tra

Samuel Jelbart, Christian Kuehn, Alejandro Martínez Sánchez

We study the exchange of stability in scalar reaction-diffusion equations which feature a slow passage through transcritical and pitchfork type singularities in the reaction term, using a novel adaptation of the geometric blow-up method. Our results are consistent with known results on bounded spatial domains which were obtained by Butuzov, Nefedov & Schneid

Miguel Berasategui, Pablo M. Berná, Andrea García

We characterize the approximation spaces of a broad class of bases - which includes almost greedy bases - in terms of weighted Lorentz spaces. For those bases, we also find necessary and sufficient conditions under which the approximation spaces and greedy classes are the same.

Kenneth Shaw, Yulong Li, Jiahui Yang, Mohan Kumar Srirama

To train generalist robot policies, machine learning methods often require a substantial amount of expert human teleoperation data. An ideal robot for humans collecting data is one that closely mimics them: bimanual arms and dexterous hands. However, creating such a bimanual teleoperation system with over 50 DoF is a significant challenge. To address this, w

Xin Dong, Yonggan Fu, Shizhe Diao, Wonmin Byeon

We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, while SSM heads enable efficient context summarization. Additionally, we introduce learnable meta tokens that

J. Pflamm-Altenburg

Numerical QUMOND-simulations of star clusters orbiting in a Galactic disk potential show that the leading tidal arm of open star clusters contains tendentially more members than the trailing arm. However, these type of simulations are performed by solving the field-equations of QUMOND and already become non-practical for star cluster masses at around 5000 Ms

Hugo Carneiro, Stefan Wermter

Active speaker detection (ASD) in multimodal environments is crucial for various applications, from video conferencing to human-robot interaction. This paper introduces FabuLight-ASD, an advanced ASD model that integrates facial, audio, and body pose information to enhance detection accuracy and robustness. Our model builds upon the existing Light-ASD framew

Mohammadali Shakerdargah, Shan Lu, Chao Gao, Di Niu

The advent of foundation models have revolutionized various fields, enabling unprecedented task accuracy and flexibility in computational linguistics, computer vision and other domains. Attention mechanism has become an essential component of foundation models, due to their superb capability of capturing correlations in a sequence. However, attention results

Hong Jun Jeon, Songbin Liu, Yuantong Li, Jie Lyu

The exploding popularity of online content and its user base poses an evermore challenging matching problem for modern recommendation systems. Unlike other frontiers of machine learning such as natural language, recommendation systems are responsible for collecting their own data. Simply exploiting current knowledge can lead to pernicious feedback loops but

Keerthi Kunnumkai, Antonella Palmese, Amanda M Farah, Mattia Bulla

With GW170817 being the only multimessenger gravitational wave (GW) event with an associated kilonova detected so far, there exists a pressing need for realistic estimation of the GW localization uncertainties and rates, as well as optimization of available telescope time to enable the detection of new kilonovae. We simulate GW events assuming a data-driven

Vedran Čačić, Matea Čelar, Marko Horvat, Zvonko Iljazović

In this work, we study the computability of topological graphs, which are obtained by gluing arcs and rays together at their endpoints. We prove that every semicomputable graph in a computable metric space can be approximated, with arbitrary precision, by its computable subgraph with computable endpoints.

Nitish Shukla, Arun Ross

A facial morph is an image strategically created by combining two face images pertaining to two distinct identities. The goal is to create a face image that can be matched to two different identities by a face matcher. Face demorphing inverts this process and attempts to recover the original images constituting a facial morph. Existing demorphing techniques

Nikolaj Glazunov

Our object of study is extremal functions which are defined by distance functions of convex bodies. These functions take values in the moduli spaces of algebraic and geometric objects associated with these ${\mathbb Z}$-modules (geometric lattices) and with convex bodies. In most cases, convex bodies are $2$-dimensional Minkowski balls whose boundaries are M

Mashaekh Tausif Ehsan, Saifuddin Zafar, Apurba Sarker, Sourav Das Suvro

Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a graph-based representation for modeling medium-entropy alloys (MEAs). Hybrid Monte-Carlo molecular dynamics (MC/MD) sim

Danial Motlagh, Robert A. Lang, Paarth Jain, Jorge A. Campos-Gonzalez-Angulo

Vibronic interactions between nuclear motion and electronic states are critical for the accurate modeling of photochemistry. However, accurate simulations of fully quantum non-adiabatic dynamics are often prohibitively expensive for classical methods beyond small systems. In this work, we present a quantum algorithm based on product formulas for simulating t

Bryanne McDonough, Olivia Curtis, Tereasa Brainerd

We present radial profiles of luminosity-weighted age, $age_L$, and $\Delta \Sigma_{SFR}$ for various populations of high- and low- mass central and satellite galaxies in the TNG100 cosmological simulation. Using these profiles, we investigate the impact of intrinsic and environmental factors on the radial distribution of star formation. For both central gal

Kazi Tawseef Rahman, Mustofa Sakif Shahriar, Mashaekh Tausif Ehsan, Mohammad Nasim Hasan

Copper (Cu) segregation in medium and high-entropy alloys (M/HEAs) has shown significant influence on alloy properties. In this study, we investigate the effect of Cu segregation on evolution of irradiation-induced defects in FeNiCu, a model MEA, using hybrid molecular dynamics (MD) and Monte Carlo (MC) simulations. Thermodynamically driven hybrid MC/MD anne

Sahabub Jahedi, Jayita Lahiri, Amir Subba

We study the estimation of anomalous charged triple gauge couplings (cTGCs) parameterized in a model-independent Standard Model effective field theory (SMEFT) framework via $WW$ production followed by semi-leptonic decay at the $e^+e^-$ colliders. The anomalous $(WWV~(V=\gamma,Z))$ couplings are given in terms of Wilson coefficients of three CP-conserving an