December 2024 arXiv papers — page 94

Efraín Rojas, G. Cruz

We explore the correspondence between the parallel surfaces framework, and the minimal surfaces framework, to uncover and apply new aspects of the geometrical and mechanical content behind the so-called Lovelock-type brane gravity (LBG). We show how this type of brane gravity emerges naturally from a Dirac-Nambu-Goto (DNG) action functional built up from the

Manas Sajjan, Vinit Singh, Sabre Kais

Neural-network quantum states (NQS) offer a versatile and expressive alternative to traditional variational ans\"atze for simulating physical systems. Energy-based frameworks, like Hopfield networks and Restricted Boltzmann Machines, leverage statistical physics to map quantum states onto an energy landscape, functioning as memory descriptors. Here, we show

Léa Cassé, Bernhard Pfahringer, Albert Bifet, Frédéric Magniette

Near-term quantum machine learning must balance expressivity, optimization, and hardware constraints. We study quantum re-uploading units (QRUs) as compact circuits and compare them, at matched parameter count, to a standard mono-encoded variational quantum circuit (VQC) baseline. On a three-feature calorimetry classification task, we train a single-qubit QR

Golo A. Wimmer, Ben S. Southworth, Koki Sagiyama, Xian-Zhu Tang

We present a novel spatial discretization for the anisotropic heat conduction equation, aimed at improved accuracy at the high levels of anisotropy seen in a magnetized plasma, for example, for magnetic confinement fusion. The new discretization is based on a mixed formulation, introducing a form of the directional derivative along the magnetic field as an a

Behrooz Zarebavani, Danny M. Kaufman, David I. W. Levin, Maryam Mehri Dehnavi

Cholesky linear solvers are a critical bottleneck in challenging applications within computer graphics and scientific computing. These applications include but are not limited to elastodynamic barrier methods such as Incremental Potential Contact (IPC), and geometric operations such as remeshing and morphology. In these contexts, the sparsity patterns of the

Yinxuan Wang, Sudip Vhaduri

The goal of this project is to classify four different insect sounds: cicada, beetle, termite, and cricket. One application of this project is for pest control to monitor and protect our ecosystem. Our project leverages data augmentation, including pitch shifting and speed changing, to improve model generalization. This project will test the performance of D

Dirk Kuiper, Gabriella Contardo, Daniela Huppenkothen, Jason W. T. Hessels

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, with diverse time-frequency patterns and emission properties that require explanation. With one possible exception, FRBs are detected only in the radio, so analyzing their dynamic spectra is therefore crucial to disentangling the physical processes governing their gen

Jake J. Xia

This study investigates the emergence of power-law and other concentrated distributions through a feedback loop model in crowd interactions. Agents act by their response functions to observations and external forces, while observations change by the aggregated actions of all agents, weighted by their respective influence, i.e. power or wealth. Agents wealth

Jamiree Harrison, Enoch Yeung

The estimation of static parameters in dynamical systems and control theory has been extensively studied, with significant progress made in estimating varying parameters in specific system types. Suppose, in the general case, we have data from a system with parameters that depend on an independent variable such as time or space. Further, suppose the system's

Riku Murai, Eric Dexheimer, Andrew J. Davison

We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre. We introduce efficient methods for pointmap m

Hao Li, Shamit Lal, Zhiheng Li, Yusheng Xie

We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training scaled DiTs ranging from 0.3B upto 8B parameters on datasets up to 600M images. We find that U-ViT, a pure self-attention based DiT model provides a simpler design and scales more

Dionysios Varelas, Elena Bonan, Lewis Anderson, Anders Englesson

Machine learning (ML) systems have become vital in the mobile gaming industry. Companies like King have been using them in production to optimize various parts of the gaming experience. One important area is in-app purchases: purchases made in the game by players in order to enhance and customize their gameplay experience. In this work we describe how we dev

Alaa Eddine Anis Sahraoui

Adaptive user interfaces adapt their contents, presentation, or behavior mostly in a sudden, fluctuating, and abrupt way, which may cause negative effects on the end users, such as cognitive disruption. Instead, adaptivity should be regular, constant, and progressive. To assess these requirements, we present Taoist, a hidden Markov model-based approach and s

Marek Lassak

We prove a theorem on the relationships between the lengths of sides of a spherical quadrilateral with three right angles. They are analogous to the relationships in the Lambert quadrilateral in the hyperbolic plane. We apply this theorem in the proof of our second theorem that if $C$ is a two-dimensional spherical convex body of diameter $\delta \in (\frac{

Hui Zhong, Keyi Ju, Jiachen Shen, Xinyue Zhang

Existing quantum computers can only operate with hundreds of qubits in the Noisy Intermediate-Scale Quantum (NISQ) state, while quantum distributed computing (QDC) is regarded as a reliable way to address this limitation, allowing quantum computers to achieve their full computational potential. However, similar to classical distributed computing, QDC also fa

Giang Nguyen, Ivan Brugere, Shubham Sharma, Sanjay Kariyappa

Interpretability in Table Question Answering (Table QA) is critical, especially in high-stakes domains like finance and healthcare. While recent Table QA approaches based on Large Language Models (LLMs) achieve high accuracy, they often produce ambiguous explanations of how answers are derived. We propose Plan-of-SQLs (POS), a new Table QA method that makes

Mariam Almutairi, Melike Yildiz Aktas, Nawar Wali, Shutonu Mitra

The rapid spread of misinformation on social media, especially during crises, challenges public decision-making. To address this, we propose HierTKG, a framework combining Temporal Graph Networks (TGN) and hierarchical pooling (DiffPool) to model rumor dynamics across temporal and structural scales. HierTKG captures key propagation phases, enabling improved

Jiajia Guo, Selim Esedoglu

We present new level set methods for multiphase, anisotropic (weighted) motion by mean curvature of networks, focusing on wetting-dewetting problems where one out of three phases is stationary -- a good testbed for checking whether complicated junction conditions are correctly enforced. The new schemes are vectorial median filters: The level set values at th

Christopher J. Fluke, Hugo K. Walsh, Lewis de Zoete Grundy, Brian Brady

Data visualisation is an essential ingredient of scientific analysis, discovery, and communication. Along with a human (to do the looking) and the data (something to look at), an image display device is a key component of any data visualisation workflow. For the purpose of this work, standard displays include combinations of laptop displays, peripheral monit

Tianyi Chen, Charalampos E. Tsourakakis

Community detection is a central task in graph analytics. Given the substantial growth in graph size, scalability in community detection continues to be an unresolved challenge. Recently, alongside established methods like Louvain and Infomap, motif-based community detection has emerged. Techniques like Tectonic are notable for their advanced ability to iden

Boli Zhou, Ziqi Zhou, Paul M. Goldbart

Candidates for random network media include, e.g., systems consisting of long, flexible macromolecules cross-linked (i.e., permanently bonded) together at random to form the network. Owing to the random architecture, the characteristics of the thermal motion of the elements of these media vary randomly from point to point, provided the medium has been cross-

Thomas Drury, Jonathan W. Bartlett, David Wright, Oliver N. Keene

The creation of the ICH E9 (R1) estimands framework has led to more precise specification of the treatment effects of interest in the design and statistical analysis of clinical trials. However, it is unclear how the new framework relates to causal inference, as both approaches appear to define what is being estimated and have a quantity labelled an estimand

Yisheng Lei, Zongfeng Li, Mahdi Hosseini

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Here, we report a storage efficiency greater than 28% in a Tm$^{3+}$: YAG crystal in elevated temperatures and without compromising the memory bandwidth. Using various pumping and optimization techniques, we demonstrate multi-frequency window

Ronnie de Souza Santos, Italo Santos, Robson Santos, Cleyton Magalhaes

Software engineering (SE) faces significant diversity challenges in both academia and industry, with underrepresented students encountering hostile environments, limited representation, and systemic biases that hinder their academic and professional success. Despite significant research on the exclusion experienced by students from underrepresented groups in

Jorik Melsen, Thomas Flöss, P. Daniel Meerburg

This paper explores a novel application of spherical convolutional neural networks (CNNs) to detect primordial non-Gaussianity in the cosmic microwave background (CMB), a key probe of inflationary dynamics. While effective, traditional estimators encounter computational challenges, especially when considering summary statistics beyond the bispectrum. We prop

Peter DeBello, Daniel Tubbenhauer

Using that the dicyclic group is the type D subgroup of SU(2), we extend the Temperley-Lieb diagrammatics to give a diagrammatic presentation of the complex representation theory of the dicyclic group.

Louis Rosenberg, Hans Schumann, Christopher Dishop, Gregg Willcox

Conversational Swarm Intelligence (CSI) is an AI-facilitated method for enabling real-time conversational deliberations and prioritizations among networked human groups of potentially unlimited size. Based on the biological principle of Swarm Intelligence and modelled on the decision-making dynamics of fish schools, CSI has been shown in prior studies to amp

Manuel Pichardo Marcano, Liliana E. Rivera Sandoval, Thomas J. Maccarone, Rene D. Rohrmann

Using archival Hubble Space Telescope observations, we report the discovery of four variable low-mass white dwarfs ($0.18 \, M_\odot \leq M \leq 0.5 \,M_\odot$) in the globular cluster NGC 6397. One source exhibits a periodic optical modulation of $5.21 \pm 0.02$ hours, which we interpret as potentially due to the rotation of a magnetic helium core WD (He WD

Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy

Despite the impressive advances achieved using deep learning for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in tasks such as identifying neurological disorders. For functional Magnetic Resonance Imaging (fMRI), while data may be abundantly available from healthy controls

Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg

Model personalization allows a set of individuals, each facing a different learning task, to train models that are more accurate for each person than those they could develop individually. The goals of personalization are captured in a variety of formal frameworks, such as multitask learning and metalearning. Combining data for model personalization poses ri

Eric Yocam, Anthony Rizi, Mahesh Kamepalli, Varghese Vaidyan

As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and depl

Matthieu Fradelizi, Elie Nakhle

In this paper, we establish different sharp forms of Mahler's conjecture for $s$-concave even functions in dimensions $n$, for $n=1$ and $2$, for $s>-1/n$, thus generalizing our previous results in \cite{FN} on log-concave even functions in dimension 2, which corresponds to the case $s=0$. The functional volume product of an even $s$-concave function $g$ is

Teng Li, Hulya Seferoglu

Distributed inference techniques can be broadly classified into data-distributed and model-distributed schemes. In data-distributed inference (DDI), each worker carries the entire Machine Learning (ML) model but processes only a subset of the data. However, feeding the data to workers results in high communication costs, especially when the data is large. An

Yihong Jin, Ze Yang, Xinhe Xu

As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes

T. D. Tran, D. Babjak, A. Kovalenko, K. Singh

The efficient detection of light from trapped ions in free space is paramount for most of their applications. We propose a scheme to enhance the photon collection from linear ion strings. It employs the constructive interference of light scattered from ions along the axial direction in linear Paul traps. The coherent enhancement of photon collection is numer

D. A. A. Wickremasinghe, K. Yonehara

The Neutrinos at the Main Injector (NuMI) beamline at Fermilab generates an intense muon neutrino beam for the NOvA (NuMI Off-axis $\nu_e$ Appearance) long-baseline neutrino experiment. Over the years, the NuMI beamline has been pivotal in advancing neutrino physics, providing invaluable data and insights. This proceeding paper discusses updates and the less

Nezhueyotl Izquierdo, Ruixue Li, Peter R. Christenson, Sang-Hyun Oh

Dielectrophoretic (DEP) sensing is an extremely important sensing modality that enables the rapid capture and detection of polarizable particles of nano-scale size. This makes it a versatile tool for applications in medical diagnostics, environmental monitoring, and materials science. Because DEP relies upon the creation of sharp electrode edges, its sensiti

Chrisil Ouseph, Andrew S. Toms

Let $A$ be a unital, simple and Z-stable C$^*$-algebra. We show that the set of positive elements in $A$ (resp. $A \otimes K$) belonging to a fixed non-compact Cuntz class is contractible as a topological subspace of $A$ (resp. $A \otimes K$). In light of earlier work by Zhang, Jiang and Hua in the compact case, we deduce a complete calculation of the homoto

Chenyin Gao, Peter B. Gilbert, Larry Han

Learning the Individual Treatment Effect (ITE) is essential for personalized decision-making, yet causal inference has traditionally focused on aggregated treatment effects. While integrating conformal prediction with causal inference can provide valid uncertainty quantification for ITEs, the resulting prediction intervals are often excessively wide, limitin

Rabimba Karanjai, Yang Lu, Dana Alsagheer, Keshav Kasichainula

Logs are critical resources that record events, activities, or messages produced by software applications, operating systems, servers, and network devices. However, consolidating the heterogeneous logs and cross-referencing them is challenging and complicated. Manually analyzing the log data is time-consuming and prone to errors. LogBabylon is a centralized

David Massart, Marc Zeicher

PubMed's current search interface makes it tedious to systematically search for medical and research literature on drugs that could potentially be used to treat a given pathology, including patients with genetically altered tumors. This is because physicians must search separately for each drug-pathology combination (or drug-gene combination). To streamline

Yutong Xie, Yiyao Liu, Zhuang Ma, Lin Shi

The deployment of large language models (LLMs) in diverse applications requires a thorough understanding of their decision-making strategies and behavioral patterns. As a supplement to a recent study on the behavioral Turing test, this paper presents a comprehensive analysis of five leading LLM-based chatbot families as they navigate a series of behavioral e

Itay Beit-Halachmi, Ido Kaminer

Fundamental mathematical constants appear in nearly every field of science, from physics to biology. Formulas that connect different constants often bring great insight by hinting at connections between previously disparate fields. Discoveries of such relations, however, have remained scarce events, relying on sporadic strokes of creativity by human mathemat

Yannik Mahlau, Frederik Schubert, Konrad Bethmann, Reinhard Caspary

We introduce an efficient open-source python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Leveraging a flexible reverse-mode automatic differentiation implementation, our software enables gradient-based optimization over large simulation volumes. Gradient computation is imp

Jinhe Bi, Yujun Wang, Haokun Chen, Xun Xiao

Multimodal Large Language Models (MLLMs) have significantly advanced visual tasks by integrating visual representations into large language models (LLMs). The textual modality, inherited from LLMs, equips MLLMs with abilities like instruction following and in-context learning. In contrast, the visual modality enhances performance in downstream tasks by lever

Samy Ateia, Udo Kruschwitz

We present BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. The system uses large language models (LLMs) for query expansion, snippet extraction, and answer generation while maintaining transparency through citation links to the source documents and displaying generated queries for further edi

Sergei Chmutov, Qingying Deng, Joanna A. Ellis-Monaghan, Sergei Lando

The classical Thistlethwaite theorem for links can be phrased as asserting that the Kauffman bracket of a link can be obtained from an evaluation of the Bollob\'as-Riordan polynomial of a ribbon graph associated to one of the link's Kauffman states. In this paper, we extend this result to knotoids, which are a generalization of knots that naturally arises in

Bassel El Mabsout

Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learning-based approaches, and embedded syste

R. Moseley, G. Bruce Berriman, Christopher R. Gelino, John C. Good

We describe the development of the Keck Observatory Archive (KOA) Data Discovery Service, a web-based dashboard that returns metadata for wide-area queries of the entire archive in seconds. Currently in beta, this dashboard will support exploration, visualization, and data access across multiple instruments. This effort is underpinned by an open-source, VO-c

Michael James Martin, Aaron Andersen, Charles Tripp, David Sickinger

NREL's computational sciences center hosts the largest high-performance computing (HPC) capabilities dedicated to energy research while functioning as a living laboratory for energy-efficient computing. NREL's HPC capabilities support the research needs of the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In ten years of ope

Felipe José D. Sousa, Sarah P. Cerqueira, João Queiroz, Angelo Loula

The computational generation of poems is a complex task, which involves several sound, prosodic and rhythmic resources. In this work we present PROPOE 2, with the extension of structural and rhythmic possibilities compared to the original system, generating poems from metered sentences extracted from the prose of Brazilian literature, with multiple rhythmic

Georgii Oblapenko

In the present work, a novel particle merging scheme is proposed for PIC-DSMC simulations, based on the solution of a Non-negative Least Squares problem. The merging algorithm conserves arbitrary moments of the velocity distribution function, and a collision rate-conserving version of the algorithm is presented as well. Numerical simulations show excellent p

Liam Gladden, Victor Ivo, Pavel Kovtun, Andrei O. Starinets

Equilibrium states of ${\cal N}=4$ supersymmetric Yang-Mills theory can be characterized by the temperature and three chemical potentials, corresponding to the ${\rm U}(1)^3$ subgroup of the $R$-symmetry group. We investigate the phase diagram of the theory at strong coupling, in the grand canonical ensemble in flat space, using its holographic description v

S. V. Tikhonov

In this paper, we construct the leash-metric that transforms the set of (partially) mixing actions of a Hausdorff locally compact group with a countable neighborhood base into a complete separable metric space.

Mohamed Ayoub Ben Ayad, Jelena Mitrovic, Michael Granitzer

We introduce Krony-PT, a compression technique for GPT-2 based on Kronecker products. We specifically target the feed-forward weights of each transformer block, and systematically compress the feed-forward layer matrices to various degrees. We introduce a modified Van Loan decomposition to initialize new Kronecker factors, and also propose a new pruning-base

Georgios M. Gkolemis, Adwin Richie Lee, Amine Roudani

This report presents a systematic market-neutral, multi-factor investment strategy for New York Stock Exchange equities with the objective of delivering steady returns while minimizing correlation with the market. A robust feature set is integrated combining momentum-based indicators, fundamental factors, and analyst recommendations. Using various statistica

Madiyar Alimov, Temirlan Meiramkhanov

This study investigates the domain generalization capabilities of three state-of-the-art object detection models - YOLOv8s, RT-DETR, and YOLO-NAS - within the unique driving environment of Kazakhstan. Utilizing the newly constructed ROAD-Almaty dataset, which encompasses diverse weather, lighting, and traffic conditions, we evaluated the models' performance

Tianning Tang, Yuntian Chen, Rui Cao, Wouter Mostert

Many supervised machine learning methods have revolutionised the empirical modelling of complex systems. These empirical models, however, are usually "black boxes" and provide only limited physical explanations about the underlying systems. Instead, so-called "knowledge discovery" methods can be used to explore the governing equations that describe observed

Saaketh Desai, Sadhvikas Addamane, Jeffrey Y. Tsao, Igal Brener

Advances in robotic control and sensing have propelled the rise of automated scientific laboratories capable of high-throughput experiments. However, automated scientific laboratories are currently limited by human intuition in their ability to efficiently design and interpret experiments in high-dimensional spaces, throttling scientific discovery. We presen

Sebouh J. Paul, Ryan Milton, Sebastián Morán, Barak Schmookler

Key measurements at the future Electron-Ion Collider (EIC), including first-of-their-kind studies of kaon structure, require the detection of $\Lambda^0$ at forward angles. We present a feasibility study of $\Lambda^0 \to n\pi^0$ measurements using a high-granularity Zero Degree Calorimeter to be located about 35 m from the interaction point. We introduce a

Daniela Bubboloni, Nicolas Pinzauti

We define a class of finite groups based on the properties of the closed twins of their power graphs and study the structure of those groups. As a byproduct, we obtain results about finite groups admitting a partition by cyclic subgroups.

Hung-Yu Yang, Joseph J. Cuozzo, Anand Johnson Bokka, Gang Qiu

The research on supercurrent diodes has surged rapidly due to their potential applications in electronic circuits at cryogenic temperatures. To unlock this functionality, it is essential to find supercurrent diodes that can work consistently at zero magnetic field and under ubiquitous stray fields generated in electronic circuits. However, a supercurrent dio

Carla Henríquez-Baez, Marcela Lagos, Evelyn Rodríguez, Aldo Vera

In this paper, we construct an exact solution of the Einstein $SU(N)$-Skyrme model in $D=4$ space-time dimensions describing a charged and rotating black hole with toroidal horizon. Rotation is added by applying an improper coordinate transformation to the known static toroidal black hole with Skyrme hair, while the electric charge is supplemented by conside

Felix Huber, Victor Magron, Jurij Volčič

State, trace, and moment polynomials are polynomial expressions in several operator or random variables and positive functionals on their products (states, traces or expectations). While these concepts, and in particular their positivity and optimization, arose from problems in quantum information theory, yet they naturally fit under the umbrella of multivar

Arnau Perez, Xavier Vizcaino

Retrieval Augmented Generation (RAG) systems struggle with processing multimodal documents of varying structural complexity. This paper introduces a novel multi-strategy parsing approach using LLM-powered OCR to extract content from diverse document types, including presentations and high text density files both scanned or not. The methodology employs a node

Dor Lev-Ari, Ido Tishby, Ofer Biham, Eytan Katzav

We present analytical results for the distribution of first return (FR) times of non-backtracking random walks (NBWs) on undirected configuration model networks consisting of $N$ nodes with degree distribution $P(k)$. We focus on the case in which the network consists of a single connected component. Starting from a random initial node $i$ at time $t=0$, an

Xin Sun, Daniel Ståhl, Kristian Sandahl

The role of regression testing in software testing is crucial as it ensures that any new modifications do not disrupt the existing functionality and behaviour of the software system. The desired outcome is for regression tests to yield identical results without any modifications made to the system being tested. In practice, however, the presence of Flaky Tes

F. Schaden, T. Riebner, I. Morawetz, L. Toscani De Col

The 10-minute radiative lifetime of the first excited $^{229}$Th$^{4+}$ nuclear state in ionic crystals provides narrow spectroscopic linewidths, enabling the realization of a solid-state nuclear clock. Due to the 4+ noble gas configuration, electronic readout or state initialization schemes known from atomic clocks are inaccessible. This elongates the inter

Ozlem Turgut, Ibrahim Kok, Suat Ozdemir

Today, crop diversification in agriculture is a critical issue to meet the increasing demand for food and improve food safety and quality. This issue is considered to be the most important challenge for the next generation of agriculture due to the diminishing natural resources, the limited arable land, and unpredictable climatic conditions caused by climate

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We measure the branching fraction of the decay $B^0 \to J/\psi \omega$ using data collected with the Belle II detector at the SuperKEKB collider. The data contain $(387 \pm 6) \times 10^6$ $B\overline{B}$ meson pairs produced in energy-asymmetric $e^+e^-$ collisions at the $\Upsilon (4S)$ resonance. The measured branching fraction $\mathcal{B}(B^0 \to J/\psi

Emil Albrychiewicz, Andrés Franco Valiente, Vi Hong

We investigate canonically quantized open string solutions associated to the analytically continued action for the recently proposed tropical limit of topological A-type models, tropological sigma models, with various boundary conditions. These solutions naturally give rise to a non-relativistic counterpart of branes, which we name tropical branes. We provid

Rodolfo Capdevilla, Tao Han

In high-energy collisions far above the electroweak scale, the effects of electroweak symmetry breaking are expected to become parametrically small $\delta \sim M_W/E$. This defines the extent to which the electroweak gauge symmetry is restored: $(i)$ the physics of the transverse gauge bosons and fermions is described by a massless theory in the unbroken ph

Yichen Yan, Quang Vuong, Rebecca K Metcalfe, Tianyu Guan

Transportability analysis is a causal inference framework used to evaluate the external validity of randomized clinical trials (RCTs) or observational studies. Most existing transportability analysis methods require individual patient-level data (IPD) for both the source and the target population, narrowing its applicability when only target aggregate-level

B. L. Español, M. Noseda, P. J. Cobelli, P. D. Mininni

We combine experiments in a von K\'arm\'an flow with numerical simulations of Taylor-Green and homogeneous and isotropic turbulence to study the effect of the local flow geometry on particle pair dispersion. To characterize particle dispersion we use the pair dispersion angle, defined as the angle between the relative position and relative velocity of partic

Summer Eldridge, Benjamin Schweinhart

All isometrically invariant Markov (strictly local) fields on binary assignments are induced by energy functions that can be represented as linear combinations of area, perimeter, and Euler characteristic. This class of model includes the Ising model, both ferro- and antiferro-magnetic, with and without a field, as well as the "triplet" Ising model We determ

Kazi Sadman Kabir, Abbas Semnani

This paper introduces a novel atmospheric pressure and frequency-tunable microwave plasma jet utilizing evanescent-mode cavity resonator technology. The design uses a substrate-integrated waveguide approach, where two PCB substrates are assembled to form the resonant microwave plasma jet structure. This configuration provides excellent matching performance a

Gordon Owusu Boateng, Hani Sami, Ahmed Alagha, Hanae Elmekki

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled cap

Phúc H. Le Khac, Graham Healy, Alan F. Smeaton

This paper addresses key challenges in object-centric representation learning of video. While existing approaches struggle with complex scenes, we propose a novel weakly-supervised framework that emphasises geometric understanding and leverages pre-trained vision models to enhance object discovery. Our method introduces an efficient slot decoder specifically

Guangyao Dou

Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose risks by learning and generating copyrighted material, leading to significant legal and ethical concerns. In a potential real-world scenario, model owners may need to continuously address copyright infringement in order to address requests for content removal tha

Michael Bendersky, Donald Metzler, Marc Najork, Xuanhui Wang

This article describes the history of information retrieval on personal document collections.

Ella Kartika Pek, Zilong Hua, Amey Khanolkar, J. Matthew Mann

The degradation of thermal conductivity in advanced nuclear fuels due to the accumulation of fission products and irradiation-induced defects is inevitable, and must be considered as part of safety and efficiency analyses of nuclear reactors. This study examines the thermal conductivity of a zirconium-doped ThO$_2$ crystal, synthesized via the hydrothermal m

Petr Kravchuk, Jeremy A. Mann

Motivated by the problem of multi-twist operators in general CFTs, we study the leading-twist states of the $N$-body problem in AdS at large spin $J$. We find that for the majority of states the effective quantum-mechanical problem becomes semiclassical with $\hbar=1/J$. The classical system at $J=\infty$ has $N-2$ degrees of freedom, and the classical phase

Ruizhi Pu, Gezheng Xu, Ruiyi Fang, Binkun Bao

Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning. While recent works have already shown that incorporating various classification-based regularizers can produce enhanced outcomes, the role of classification remains elusive in DI

Krzysztof Sienicki

This paper explores the intersection of identity, individuality, and reality through competing frameworks, including classical metaphysics, quantum mechanics, and computational theories. Traditional metaphysical notions of fixed identity are challenged by advancements in cloning, teletransportation, and digital replication, which reveal the fluid and relatio

Yue Jin, Shuangqing Wei, Giovanni Montana

In human society, the conflict between self-interest and collective well-being often obstructs efforts to achieve shared welfare. Related concepts like the Tragedy of the Commons and Social Dilemmas frequently manifest in our daily lives. As artificial agents increasingly serve as autonomous proxies for humans, we propose a novel multi-agent reinforcement le

Andrey Morgulis, Karrar Malal

We consider two models of predator-prey community with prey-taxis, one relies on Patlak-Keller-Segel law (Lee et al, 2009), the other one employs the Cattaneo model of heat transfer following Dolak and Hillen (2003). Thus, the former one uses the prey density gradient for directing the predators flux, and the latter one -- for directing the vector density of

Hamidreza Fereidouni, Abdelhakim Senhaji Hafid, Dimitrios Makrakis, Yaser Baseri

The proliferation of Internet services has led to an increasing need to protect private data. User authentication serves as a crucial mechanism to ensure data security. Although robust authentication forms the cornerstone of remote service security, it can still leave users vulnerable to credential disclosure, device-theft attacks, session hijacking, and ina

Lokesh Kumar Sharma, Suresh Parekh, Anil Kumar Yadav

This study investigates the evolution of Friedmann-Robertson-Walker (FRW) cosmological models within the $f(Q)$ gravity framework, utilizing a specific $f(Q)$ formulation and a novel Hubble parameter $H(z)$ parameterization to probe the universe's accelerating expansion. A central aspect is the application of advanced machine learning techniques for cosmolog

Ioannis Papadimitriou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis

We present RAG Playground, an open-source framework for systematic evaluation of Retrieval-Augmented Generation (RAG) systems. The framework implements and compares three retrieval approaches: naive vector search, reranking, and hybrid vector-keyword search, combined with ReAct agents using different prompting strategies. We introduce a comprehensive evaluat

Chamsi Hssaine, Huseyin Topaloglu, Garrett van Ryzin

We study a variation of the canonical online resource allocation problem in which resources are throughput, rather than budget, constrained. As in the classical setting, the decision-maker must assign sequentially arriving jobs to one of multiple available resources. However, in addition to the assignment costs incurred from these decisions, the decision-mak

Nikolaos Kalntis, Gurtej Kanwar, Marcus Petschlies, Simone Romiti

We report on our preliminary results from the lattice-QCD computation of the hadronic light-by-light (HLbL) contribution to the anomalous magnetic moment of the muon. We use twisted-mass $N_f = 2 + 1 + 1$ gauge ensembles at the physical point generated by the Extended Twisted Mass Collaboration (ETMC).

David Aldous, Svante Janson

In the critical beta-splitting model of a random $n$-leaf rooted tree, clades are recursively split into sub-clades, and a clade of $m$ leaves is split into sub-clades containing $i$ and $m-i$ leaves with probabilities $\propto 1/(i(m-i))$. The height of a uniform random leaf can be represented as the absorption time of a certain {\em harmonic descent} Marko

Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber

Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. However, recent work has questioned their faithfulness, as they may not accurately reflect the model's internal reasoning process regarding its predicted answer. In contrast, highlight explanations--input fragments

Siddarth Kannan, Terry Dekun Song

We compute the $S_n$-equivariant topological Euler characteristic of the Kontsevich moduli space $\overline{\mathcal{M}}_{1, n}(\mathbb{P}^r, d)$. Letting $\overline{\mathcal{M}}_{1, n}^{\mathrm{nrt}}(\mathbb{P}^r, d) \subset \overline{\mathcal{M}}_{1, n}(\P^r, d)$ denote the subspace of maps from curves without rational tails, we solve for the motive of $\o

Jianan Pan, Marlena Bannick, Fei Gao

Cross-sectional incidence estimation based on recency testing has become a widely used tool in HIV research. Recently, this method has gained prominence in HIV prevention trials to estimate the "placebo" incidence that participants might experience without preventive treatment. The application of this approach faces challenges due to non-representative sampl

Amar N. Chatterjee, Teklebirhan Abraha, Fahad Al Basir, Delfim F. M. Torres

In this research, we have derived a mathematical model for within human dynamics of COVID-19 infection using delay differential equations. The new model considers a 'latent period' and 'the time for immune response' as delay parameters, allowing us to study the effects of time delays in human COVID-19 infection. We have determined the equilibrium points and

Peiyao Zhang, Peter Gehlbach, Marin Kobilarov, Iulian Iordachita

A potential Retinal Vein Occlusion (RVO) treatment involves Retinal Vein Cannulation (RVC), which requires the surgeon to insert a microneedle into the affected retinal vein and administer a clot-dissolving drug. This procedure presents significant challenges due to human physiological limitations, such as hand tremors, prolonged tool-holding periods, and co

Arup Majumdar, P. Sam Johnson, Ram N. Mohapatra

In this paper, we introduce the generalized Cauchy dual $w(T) = T(T^{*}T)^{\dagger}$ of a closed operator $T$ with the closed range between Hilbert spaces and present intriguing findings that characterize the Cauchy dual of $T$. Additionally, we establish the result $w(T^{n}) = (w(T))^{n}$, for all $n \in \mathbb{N}$, where $T$ is a quasinormal EP operator.

Tiago Fiorini da Silva

Ion Beam Analysis (IBA) is an established tool for material characterization, providing precise information on elemental composition, depth profiles, and structural information in the region near the surface of materials. However, traditional data processing methods can be slow and computationally intensive, limiting the efficiency and speed of the analysis.

Jacques Grah

We posit that $d_n^2 < 2p_{n+1}$ holds for all $n\geq 1$, where $p_n$ represents the $n$th prime and $d_n$ stands for the $n$th prime gap i.e. $d_n := p_{n+1} - p_n$. Then, the presence of a prime between successive perfect squares, as well as the validity of $\Delta_n := \sqrt{p_{n+1}} - \sqrt{p_n} < 1$ are derived. Next, $\pi(x)$ being the number of primes