December 2024 arXiv papers — page 162

Shun-ichiro Koh

A microscopic model of the Brout-Englert-Higgs (BEH) mechanism is proposed. Massless fermions and antifermions do not belong to the Fock space with definite particle-number distribution, but belong to a non-Fock space with indefinite one. From this vast space, their ground-state is selected by a kinematical condition. Due to the interaction between them, the

Shrabani Das, Ahmad Erfanian, Rajat Kanti Nath

Let $G$ be a group and $L(G)$ be the set of all subgroups of $G$. We introduce a bipartite graph $\mathcal{B}(G)$ on $G$ whose vertex set is the union of two sets $G \times G$ and $L(G)$, and two vertices $(a, b) \in G \times G$ and $H \in L(G)$ are adjacent if $H$ is generated by $a$ and $b$. We establish connections between $\mathcal{B}(G)$ and the generat

Michael Iannelli, Sneha Kuchipudi, Vera Dvorak

Retrieval Augmented Generation (RAG) enables Large Language Models (LLMs) to generalize to new information by decoupling reasoning capabilities from static knowledge bases. Traditional RAG enhancements have explored vertical scaling-assigning subtasks to specialized modules-and horizontal scaling-replicating tasks across multiple agents-to improve performanc

Omer Ben-Neria, Eyal Kaplan

We construct a model of the form $L[A,U]$ that exhibits the simplest structural behavior of $\sigma$-complete ultrafilters in a model of set theory with a single measurable cardinal $\kappa$ , yet satisfies $2^\kappa = \kappa^{++}$. This result establishes a limitation on the extent to which structural properties of ultrafilters can determine the cardinal ar

Hiroki Matui

In this paper, we provide a comprehensive classification of Stein's groups, which generalize the well-known Higman-Thompson groups. Stein's groups are defined as groups of piecewise linear bijections of an interval with finitely many breakpoints and slopes belonging to specified additive and multiplicative subgroups of the real numbers. Our main result estab

Yucheng Liu, Gordon Slade

We consider spread-out models of lattice trees and lattice animals on $\mathbb Z^d$, for $d$ above the upper critical dimension $d_{\mathrm c}=8$. We define a correlation length and prove that it diverges as $(p_c-p)^{-1/4}$ at the critical point $p_c$. Using this, we prove that the near-critical two-point function is bounded above by $C|x|^{-(d-2)}\exp[-c(p

Muhammad Umair Danish

With the recent advancements in the field of information industry, critical data in the form of digital images is best understood by the human brain. Therefore, digital images play a significant part and backbone role in many areas such as image processing, vision computing, robotics, and bio-medical. Such use of digital images is practically implementable i

Pingwei Liu, Dan Liu, Shixin Song, Kang Li

Most functional materials possess one single outstanding property and are limited to be used for a particular purpose. Instead of integrating materials with different functions into one module, designing materials with controllable multi-functions is more promising for the electronic industry. In this study, we investigate an unexplored alpha-phase of two-di

Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, Frank Permenter

The denoising process of diffusion models can be interpreted as an approximate projection of noisy samples onto the data manifold. Moreover, the noise level in these samples approximates their distance to the underlying manifold. Building on this insight, we propose a novel method to enhance sample generation by aligning the estimated noise level with the tr

Muhammad Umar Farooq, Awais Khan, Ijaz Ul Haq, Khalid Mahmood Malik

Trust in social media is a growing concern due to its ability to influence significant societal changes. However, this space is increasingly compromised by various types of deepfake multimedia, which undermine the authenticity of shared content. Although substantial efforts have been made to address the challenge of deepfake content, existing detection techn

Lan Wu, Craig Jin, Monisha Mushtary Uttsha, Teresa Vidal-Calleja

Robotic perception is becoming a key technology for navigation aids, especially helping individuals with visual impairments through spatial sonification. This paper introduces a mapping representation that accurately captures scene geometry for sonification, turning physical spaces into auditory experiences. Using depth sensors, we encode an incrementally bu

Roni A. Edwin, Allen Lin

The Gauss circle problem asks for an approximation to the number of lattice points of $\mathbb{Z}^2$ contained in $B_r$, the disk of radius $r$ centered at the origin. Upper, lower, and average bounds have been established for this number-theoretic problem and have been generalized to any lattice in any dimension. We extend this problem to a more general cla

Ulfat Jahan Farha, Zarin Subah, Md Helal Uddin, Harunur Rashid

Changing temperature, precipitation regimes, and sea level rise, often associated with climate change, cause salinity intrusion into groundwater and surface water, affecting aquatic ecosystems. This study investigates the impacts of salinity on the physiological traits of freshwater hydrophytes, including Water Hyacinth (Eichhornia crassipes), Buffalo Spinac

André Vallières, Megan E. Russell, Xinyuan You, David A. Garcia-Wetten

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal qualit

Benjie Wang, Denis Deratani Mauá, Guy Van den Broeck, YooJung Choi

Circuits based on sum-product structure have become a ubiquitous representation to compactly encode knowledge, from Boolean functions to probability distributions. By imposing constraints on the structure of such circuits, certain inference queries become tractable, such as model counting and most probable configuration. Recent works have explored analyzing

Zongfeng Li, Yisheng Lei, Trevor Kling, Mahdi Hosseini

Efficient storage of telecom-band quantum optical information represents a crucial milestone for establishing distributed quantum optical networks. Erbium ions in crystalline hosts provide a promising platform for telecom quantum memories; however, their practical applications have been hindered by demanding operational conditions, such as ultra-high magneti

Zixian Ma, Jianguo Zhang, Zhiwei Liu, Jieyu Zhang

While open-source vision-language models perform well on simple question-answering, they still struggle with complex questions that require both perceptual and reasoning capabilities. We propose LATTE, a family of vision-language models that have LeArned to Think wiTh vision spEcialists. By offloading perception to state-of-the-art vision models, our approac

Charles Romero, Massimo Gaspari, Gerrit Schellenberger, Bradford A. Benson

The hot plasma in galaxy clusters, the intracluster medium (ICM), is expected to be shaped by subsonic turbulent motions, which are key for heating, cooling, and transport mechanisms. The turbulent motions contribute to the non-thermal pressure which, if not accounted for, consequently imparts a hydrostatic mass bias. Accessing information about turbulent mo

Julian Amette Estrada, Marc E. Brachet, Pablo D. Mininni

We investigate a shift in the critical temperature of rotating Bose-Einstein condensates mediated by the melting of the vortex lattice. Numerical simulations reveal that this temperature exhibits contrasting behavior depending on the system configuration: a negative shift occurs for fixed trap potentials due to the expansion of the condensate, while a positi

Saipraneeth Devunuri, Lewis Lehe

This paper introduces a framework that leverages Large Language Models (LLMs) to answer natural language queries about General Transit Feed Specification (GTFS) data. The framework is implemented in a chatbot called TransitGPT with open-source code. TransitGPT works by guiding LLMs to generate Python code that extracts and manipulates GTFS data relevant to a

Carlos E. Budde, Pedro R. D'Argenio, Arnd Hartmanns

We introduce a formal model of transportation in an open-pit mine for the purpose of optimising the mine's operations. The model is a network of Markov automata (MA); the optimisation goal corresponds to maximising a time-bounded expected reward property. Today's model checking algorithms exacerbate the state space explosion problem by applying a discretisat

Dongeon Lee, Sunwoong Yang, Jae-Won Oh, Su-Gil Cho

Environmental pollution and fossil fuel depletion have prompted the need for renewable energy-based power generation. However, its stability is often challenged by low energy density and non-stationary conditions. Wave energy converters (WECs), in particular, need reliable real-time wave height prediction to address these issues caused by irregular wave patt

Vladimir Grigorev

A method combining denoising diffusion probabilistic models (DDPMs) with the string method is presented to generate minimum free energy paths between metastable states in molecular systems. It has been demonstrated in recent work that DDPMs at low noise levels can approximate the gradient of the potential of mean force, allowing efficient sampling of high-di

Emmanuel A. Olowe, Danial Chitnis

The complexity of laboratory environments requires solutions that simplify instrument interaction and enhance measurement automation. Traditional tools often require configuration, software, and programming skills, creating barriers to productivity. Previous approaches, including dedicated software suites and custom scripts, frequently fall short in providin

Joseph Bahder, Hasan Rahman, Matthew D. Sievert, Ivan Vitev

Hard probe tomography of the quark-gluon plasma (QGP) in heavy ion collisions has long been a preeminent goal of the high-energy nuclear physics program. In service of this goal, the isotropic modification of jets and high-energy hadrons has been studied in great detail at the leading-power (eikonal) level, with effects originating from sub-eikonal $\mathcal

Minghui Song, Guohua Zou, Alan T. K. Wan

Model averaging has gained significant attention in recent years due to its ability of fusing information from different models. The critical challenge in frequentist model averaging is the choice of weight vector. The bootstrap method, known for its favorable properties, presents a new solution. In this paper, we propose a bootstrap model averaging approach

Luca Stefanescu, Louis Edwards-Pratt, Jeremy O'Connor, Ezra Tsegaye

Robust quantum control can achieve noise-resilience of quantum systems and quantum technological devices. While the need for noise-resilience grows with the number of fluctuating quantities, and thus typically with the number of qubits, most numerically exact optimal control techniques are limited to systems of few interacting qubits. This paper exploits qua

Frederick Dauphin, Andreea Petric, Étienne Artigau, Andrew W. Stephens

Subtracting the changing sky contribution from the near-infrared (NIR) spectra of faint astronomical objects is challenging and crucial to a wide range of science cases such as estimating the velocity dispersions of dwarf galaxies, studying the gas dynamics in faint galaxies, measuring accurate redshifts, and any spectroscopic studies of faint targets. Since

Travis Garrett, Anna Janicek, J. Todd Fayard, Jennifer Elle

Ultrashort pulsed lasers (USPL) can produce thin columns of plasma in air via femtosecond filamentation, and these plasmas have been found to generate broadband TeraHertz (THz) and Radio Frequency (RF) radiation. A recent theory argues that the currents driven at the boundary of the plasma excite a Surface Plasmon Polariton (SPP) surface wave (in particular

Premchand V Chandra, Anuja Vijayan, Pradeep Kumar P

Cryogenic fluids have extensive applications as fuel for launch vehicles in space applications and research. The physics of cryogenic flows are highly complex due to the sensitive nature of phase transformation from liquid to bubbly liquid and vapor, eventually resulting in cavitating flows at the ambient temperature owing to the very low boiling point of cr

Huyen Trang Hoang, Quy Thuong Lê, Hoang Long Nguyen

Using toric modifications and some compatibility we compute the local $p$-adic zeta function of a plane curve singularity. Thanks to the compatibility, we can work over the analytic change of variables formula for $p$-adic integrals, hence avoid adapting to the algebraic setting and Denef's formula.

Subhojyoti Mukherjee, Anusha Lalitha, Sailik Sengupta, Aniket Deshmukh

Multi-objective alignment from human feedback (MOAHF) in large language models (LLMs) is a challenging problem as human preferences are complex, multifaceted, and often conflicting. Recent works on MOAHF considered a-priori multi-objective optimization (MOO), where human preferences are known at training or inference time. In contrast, when human preferences

Nils Margenberg, Markus Bause

This paper presents a mathematical foundation for physical models in nonlinear optics through the lens of evolutionary equations. It focuses on two key concepts: well-posedness and exponential stability of Maxwell equations, with models that include materials with complex dielectric properties, dispersion, and discontinuities. We use a Hilbert space framewor

Chuanzhou Zhu, Peter J. Ehlers, Hendra I. Nurdin, Daniel Soh

Quantum Reservoir Computing (QRC) leverages quantum systems to perform complex computational tasks with exceptional efficiency and reduced energy consumption. We introduce a minimalistic QRC framework utilizing as few as five atoms in a single-mode optical cavity, combined with continuous quantum measurement. The system is conveniently scalable, as newly add

Thibault Le Sellier De Chezelles, Maxime Gasse, Alexandre Drouin, Massimo Caccia

The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs). Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. In

Abdulrahman Kerim, Leandro Soriano Marcolino, Erickson R. Nascimento, Richard Jiang

Supervised machine learning methods require large-scale training datasets to perform well in practice. Synthetic data has been showing great progress recently and has been used as a complement to real data. However, there is yet a great urge to assess the usability of synthetically generated data. To this end, we propose a novel UCB-based training procedure

Shane P. Kelly, Yaroslav Tserkovnyak

We consider the stray magnetic field noise outside a two-dimensional superconductor. Our considerations are motivated by recent experiments, which observed an enhancement in the magnetic field noise below the superconducting critical temperature based on the relaxation of diamond nitrogen-vacancy centers. Such enhancement is not captured by the standard two-

Marc Pulupa, Stuart D. Bale, Immanuel Christopher Jebaraj, Orlando Romeo

The Parker Solar Probe (PSP) spacecraft observed a large coronal mass ejection (CME) on 5 September 2022, shortly before closest approach during the 13th PSP solar encounter. For several days following the CME, PSP detected a storm of Type III radio bursts. Stokes parameter analysis of the radio emission indicates that the Type III storm was highly circularl

Julia Dyck, Odile Sauzet

We develop the Bayesian Power generalized Weibull shape parameter (BPgWSP) test as statistical method for signal detection of possible drug-adverse event associations using electronic health records for pharmacovigilance. The Bayesian approach allows the incorporation of prior knowledge about the likely time of occurrence along time-to-event data. The test i

Ali K. AlShami, Ananya Kalita, Ryan Rabinowitz, Khang Lam

As the Computer Vision community rapidly develops and advances algorithms for autonomous driving systems, the goal of safer and more efficient autonomous transportation is becoming increasingly achievable. However, it is 2024, and we still do not have fully self-driving cars. One of the remaining core challenges lies in addressing the novelty problem, where

The VEXAG Exploration Strategy Study Analysis Workgroup

The 2023-2032 Planetary Science and Astrobiology Decadal Survey Origins, Worlds, and Life recommended that "NASA develop scientific exploration strategies, as it has for Mars, in areas of broad scientific importance, e.g., Venus... that have an increasing number of U.S. missions and international collaboration opportunities" (OWL, p.22-10). In NASA's initial

Paul Barry

We define the triple Riordan group, whose elements consist of $4$-tuples of power series $(g, f_1, f_2, f_3)$ with $g\in \mathbf{R}[[x^3]]$, and $f_1, f_2, f_3 \in x\mathbf{R}[[x^3]]$, for an appropriate ring $\mathbf{R}$. The construction of this group generalizes that of the double Riordan group, and lays the pattern for further generalizations.

Qihang Fang, Chengcheng Tang, Bugra Tekin, Yanchao Yang

Recent advancements in models linking natural language with human motions have shown significant promise in motion generation and editing based on instructional text. Motivated by applications in sports coaching and motor skill learning, we investigate the inverse problem: generating corrective instructional text, leveraging motion editing and generation mod

KeYuan Ma, I. Plokhikh, J. N. Graham, C. Mielke

The interplay between superconductivity and charge order is a central focus in condensed matter research, with kagome lattice systems offering unique insights. The kagome superconductor LaRu$_{3}$Si$_{2}$ ($T_{\rm c}$ ${\simeq}$ 6.5 K) exhibits a hierarchy of charge order transitions: primary ($T_{\rm co,I}$ ${\simeq}$ 400 K), secondary ($T_{\rm co,II}$ ${\s

Jude Pereira, Tanmay Vachaspati

We consider SU(2) gauge theory with a scalar field in the fundamental representation. The model is known to contain electric field solutions sourced by the scalar field that are distinct from embedded Maxwell electric fields. We examine the perturbative stability of the solution and identify a region of parameter space where the solution is stable. In the re

Lisa Jeffrey, Matthew Koban, Steven Rayan

We introduce the notion of a Nakajima bundle representation. Given a labelled quiver and a variety or manifold $X$, such a representation involves an assignment of a complex vector bundle on $X$ to each node of the doubled quiver; to the edges, we assign sections of, and connections on, associated twisted bundles. We for the most part restrict attention in o

Ken Wharton, Roderick Sutherland, Titus Amza, Raylor Liu

Although entangled state vectors cannot be described in terms of classically realistic variables, localized in space and time, any given entanglement experiment can be built from basic quantum circuit components with well-defined locations. By analyzing the (local) weak values for any given run of a quantum circuit, we present evidence for a localized accoun

Julia Bernatska

In this paper the fields of multiply periodic, or Kleinian $\wp$-functions are exposed. Such a field arises on the Jacobian variety of an algebraic curve, and provides natural algebraic models of the Jacobian and Kummer varieties, possesses the addition law, and accommodates dynamical equations with solutions. All this will be explained in detail for plane a

Anass El Aouni, Quentin Gaudel, Charly Regnier, Simon Van Gennip

Accurate ocean forecasting is crucial in different areas ranging from science to decision making. Recent advancements in data-driven models have shown significant promise, particularly in weather forecasting community, but yet no data-driven approaches have matched the accuracy and the scalability of traditional global ocean forecasting systems that rely on

Krishnasai Addala, Kabir Dev Paul Baghel, Dhruv Jain, Navya Gupta

This study explores the effectiveness of using knowledge graphs generated by large language models to decompose high school-level physics questions into sub-questions. We introduce a pipeline aimed at enhancing model response quality for Question Answering tasks. By employing LLMs to construct knowledge graphs that capture the internal logic of the questions

William Fiore, Maura A. McLaughlin, Gabriella Agazie, Akash Anumarlapudi

Pulse profile stability is a central assumption of standard pulsar timing methods. Thus, it is important for pulsar timing array experiments such as the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) to account for any pulse profile variability present in their data sets. We show that in the NANOGrav 15-yr data set, the integrated pu

Katelyn Lee, Runsheng Wang, Ava Chen, Lauren Winterbottom

Wearable robotics have the capacity to assist stroke survivors in assisting and rehabilitating hand function. Many devices that use surface electromyographic (sEMG) for control rely on extrinsic muscle signals, since sEMG sensors are relatively easy to place on the forearm without interfering with hand activity. In this work, we target the intrinsic muscles

Helena García Escudero, Kevork N. Abazajian

We examine the performance of the six-parameter $\Lambda$CDM model and its extensions in light of recent cosmological observations, with particular focus on neutrino properties inferred from cosmology. Using a broad suite of nine combinations of datasets, with three separate analyses of the Planck Cosmic Microwave Background (CMB) data, and three separate su

Arend Hintze, Christoph Adami

The tragedy of the commons illustrates a fundamental social dilemma where individual rational actions lead to collectively undesired outcomes, threatening the sustainability of shared resources. Strategies to escape this dilemma, however, are in short supply. In this study, we explore how artificial intelligence (AI) agents can be leveraged to enhance cooper

Natalie Brownlowe, Christopher R. Cornwell, Ethan Montes, Gabriel Quijano

The choice of architecture of a neural network influences which functions will be realizable by that neural network and, as a result, studying the expressiveness of a chosen architecture has received much attention. In ReLU neural networks, the presence of stably unactivated neurons can reduce the network's expressiveness. In this work, we investigate the pr

Robert Salazar, Camilo Bayona-Roa

In this paper, we explore classical electromagnetic radiation using a dual four-dimensional potential $\Theta^\mu$ approach. Our focus is on the Planar Dipole Blade Antenna (PDBA), a system consisting of two flat conductive regions on the $xy$-plane, separated by a gap $\mathcal{G}$, with alternating potentials applied to the conductors. This method emphasiz

Raphael Shu, Nilaksh Das, Michelle Yuan, Monica Sunkara

AI agents powered by large language models (LLMs) have shown strong capabilities in problem solving. Through combining many intelligent agents, multi-agent collaboration has emerged as a promising approach to tackle complex, multi-faceted problems that exceed the capabilities of single AI agents. However, designing the collaboration protocols and evaluating

Saqeeb Adnan, Zilong Hua, Puspa Upreti, Hao Ma

Vibrational and magnetic properties of single-crystal uranium-thorium dioxide (UxTh1-xO2) with a full range of 0<x<1 is investigated. Thorium dioxide is a diamagnet whose thermal properties are governed by lattice vibration. The addition of paramagnetic uranium ion leads to the emergence of magnetic effects that alter the thermophysical properties noticeably

Savini Kashmira, Jayanaka L. Dantanarayana, Joshua Brodsky, Ashish Mahendra

Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in commercial use cases. RAG primarily relies on the query-chunk text-to-text similarity in the embedding space for retrieval and can fail to capture deeper semantic relationships acro

Han Huang

In this paper we review aspects of anti de Sitter/conformal field theory (AdS/CFT) duality and the notion of holographic renormalization group (RG) flow. We start by discussing supersymmetry and construct the N = 4 super Yang-Mills theory in d = 4 by Kaluza-Klein dimensional reduction method. Then, we study the large-N limit and how it leads to the AdS/CFT d

Kavindu Ravishan, Dániel Szabó, Niels van Berkel, Aku Visuri

Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challe

Sydney Balkovitz, Alyssa Croco, Jake Garda, Maggie Hatch

We adapt the social force model of crowd dynamics to capture the evacuation during a zombie outbreak from an academic building. Individuals navigate the building, opening doors, and evacuate to the nearest exit. Zombies chase the uninfected individuals, and once caught there is a probability of a susceptible individual being infected or killed, or for the zo

Hiroki Kaifu, Sandra M. Troian

The newest and most powerful electronic chips for applications like artificial intelligence generate so much heat that liquid based cooling has become indispensable to prevent breakdown from thermal runaway effects. While cooling schemes like microfluidic networks or liquid immersion are proving effective for now, further progress requires tackling an age ol

Colin W. Macrie, Liliana Rivera Sandoval, Yuri Cavecchi, Tin Long Sunny Wong

We studied the spectral energy distribution (SED) of 22 known AM~CVns with orbital periods ($P_{orb}$) larger than 35~min using multiwavelength public photometric data to estimate the effective temperature of the accreting white dwarf. We find an infrared (IR) excess in all systems when compared to a single blackbody, both when the disk should be extended an

Francisco J. Beron-Vera, Erwin Luesink

This paper investigates the geometric structure of a quasigeostrophic approximation to a recently introduced reduced-gravity thermal rotating shallow-water model that accounts for stratification. Specifically, it considers a low-frequency approximation of a model for flow above the ocean thermocline, governed by primitive equations with buoyancy variations i

M. Shifman

In connection with recent discoveries of heavy-quark containing exotic states publications discussing $Qq$ diquarks ($Q,q$ stand for a heavy and light quarks, respectively) proliferated in the literature. After a brief summary of the diquark concept I review various general reasons why the $Qq$ diquark (with sufficinetly heavy $Q$) does not exist. Then I arg

Abdulkadir Canatar, SueYeon Chung

A key problem in deep learning and computational neuroscience is relating the geometrical properties of neural representations to task performance. Here, we consider this problem for continuous decoding tasks where neural variability may affect task precision. Using methods from statistical mechanics, we study the average-case learning curves for $\varepsilo

M. A. Ganaie, Vrushank Ahire, Anouck Girard

This paper introduces the Granular Ball K-Class Twin Support Vector Classifier (GB-TWKSVC), a novel multi-class classification framework that combines Twin Support Vector Machines (TWSVM) with granular ball computing. The proposed method addresses key challenges in multi-class classification by utilizing granular ball representation for improved noise robust

Youfang Lin, Jinji Fu, Haomin Wen, Jiyuan Wang

In Location-Based Services (LBS), such as food delivery, a fundamental task is segmenting Areas of Interest (AOIs), aiming at partitioning the urban geographical spaces into non-overlapping regions. Traditional AOI segmentation algorithms primarily rely on road networks to partition urban areas. While promising in modeling the geo-semantics, road network-bas

Johanna Devaney, Daniel McKemie, Alex Morgan

pyAMPACT (Python-based Automatic Music Performance Analysis and Comparison Toolkit) links symbolic and audio music representations to facilitate score-informed estimation of performance data in audio as well as general linking of symbolic and audio music representations with a variety of annotations. pyAMPACT can read a range of symbolic formats and can outp

Bohan Li, Jiazhe Guo, Hongsi Liu, Yingshuang Zou

Generating high-fidelity, controllable, and annotated training data is critical for autonomous driving. Existing methods typically generate a single data form directly from a coarse scene layout, which not only fails to output rich data forms required for diverse downstream tasks but also struggles to model the direct layout-to-data distribution. In this pap

Amir DN Cohen, Shauli Ravfogel, Shaltiel Shmidman, Yoav Goldberg

In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples. While much of the recent progress in NLP has focused on scaling data size, we argue that diversity in relation types is more crucial for FSRC performance. In this work, we demonstrate that training on a diverse set of relations significantl

Lucas Jaffe, Avideh Zakhor

In person search, we detect and rank matches to a query person image within a set of gallery scenes. Most person search models make use of a feature extraction backbone, followed by separate heads for detection and re-identification. While pre-training methods for vision backbones are well-established, pre-training additional modules for the person search ta

I. Georgantopoulos, E. Pouliasis, A. Ruiz, A. Akylas

The Compton-thick Active Galactic Nuclei (AGN) arguably constitute the most elusive class of sources as they are absorbed by large column densities above logN_H(cm^-2)=24. These extreme absorptions hamper the detection of the central source even in hard X-ray energies. In this work, we use both SWIFT and NuSTAR observations in order to derive the most accura

Pieter van Staden, Peter Forsyth, Yuying Li

Leveraged Exchange Traded Funds (LETFs), while extremely controversial in the literature, remain stubbornly popular with both institutional and retail investors in practice. While the criticisms of LETFs are certainly valid, we argue that their potential has been underestimated in the literature due to the use of very simple investment strategies involving L

Fang Zeng, Zhiliang Lyu, Quanzheng Li, Xiang Li

This study introduces "RadCouncil," a multi-agent Large Language Model (LLM) framework designed to enhance the generation of impressions in radiology reports from the finding section. RadCouncil comprises three specialized agents: 1) a "Retrieval" Agent that identifies and retrieves similar reports from a vector database, 2) a "Radiologist" Agent that genera

Aman Patel, Arpita Singhal, Austin Wang, Anusri Pampari

Recent advances in self-supervised models for natural language, vision, and protein sequences have inspired the development of large genomic DNA language models (DNALMs). These models aim to learn generalizable representations of diverse DNA elements, potentially enabling various genomic prediction, interpretation and design tasks. Despite their potential, e

Daniel Lester, Joris Heyman, Yves Meheust, Tanguy Le Borgne

Recent studies have revealed the central role of chaotic stretching and folding at the pore scale in controlling mixing within porous media, whether the solid phase is discrete (as in granular and packed media) or continuous (as in vascular networks and open porous structures). Despite its widespread occurrence, a unified theory of chaotic mixing across thes

O. A. Dobush, M. A. Shpot

Inspired by previous studies in statistical physics [see, in particular, Kozitsky at al., A phase transition in a Curie-Weiss system with binary interactions, Condens. Matter Phys. 23, 23502 (2020)] we introduce a discrete Gauss-Poisson probability distribution function \begin{equation}\label{GPD}\tag{A1} p_{GP}(n ;z,r)=\left[R(r;z)\right]^{-1}\frac{\mbox{e}

Ailton Oliveira, Daniel Suzuki, Sávio Bastos, Ilan Correa

This work investigates the use of machine learning applied to the beam tracking problem in 5G networks and beyond. The goal is to decrease the overhead associated to MIMO millimeter wave beamforming. In comparison to beam selection (also called initial beam acquisition), ML-based beam tracking is less investigated in the literature due to factors such as the

Avinash Anand, Kritarth Prasad, Chhavi Kirtani, Ashwin R Nair

Large Language Models (LLMs) have demonstrated strong capabilities in text-based tasks but struggle with the complex reasoning required for physics problems, particularly in advanced arithmetic and conceptual understanding. While some research has explored ways to enhance LLMs in physics education using techniques such as prompt engineering and Retrieval Aug

Priya Sundaresan, Hengyuan Hu, Quan Vuong, Jeannette Bohg

While imitation learning (IL) offers a promising framework for teaching robots various behaviors, learning complex tasks remains challenging. Existing IL policies struggle to generalize effectively across visual and spatial variations even for simple tasks. In this work, we introduce SPHINX: Salient Point-based Hybrid ImitatioN and eXecution, a flexible IL p

Dávid Pataki, András Pályi

Spin qubits in quantum dots provide a promising platform for realizing large-scale quantum processors since they have a small characteristic size of a few tens of nanometers. One difficulty of controlling e.g., a few thousand qubits on a single chip is the large number of control lines. The crossbar control architecture has been proposed to reduce the number

Benjamin Amend, Christopher L. Fryer, Matthew R. Mumpower, Oleg Korobkin

We investigate the detectability of gamma-ray emission from long-lived radioactive isotopes in r-process-enriched remnants, focusing on how assumptions about their spatial distribution introduce uncertainty into detection prospects. Using a suite of physically motivated models for the Galactic distribution of kilonova and supernova remnants, we simulate synt

T. Zingales, L. Malavolta, L. Borsato, D. Turrini

We present the discovery of two mini Neptunes near a 2:1 orbital resonance configuration orbiting the K0 star TOI-1803. We describe their orbital architecture in detail and suggest some possible formation and evolution scenarios. Using CHEOPS, TESS, and HARPS-N datasets we can estimate the radius and the mass of both planets. We used a multidimensional Gauss

J. Galen Wang, Umesh Dhumal, Monica E. A. Zakhari, Roseanna N. Zia

Monodisperse, purely repulsive, hard spheres (MPRHS) are an important model system for mechanistically exploring phase behavior in atomic systems and colloids. Since the 1940s, phase transitions in these systems have been obtained via simulation, theory, and experiments. But there is a gap in this literature: despite decades of reports of phase transition fr

Zhenkai Qin, Baozhong Wei, Caifeng Gao, Jianyuan Ni

Time series forecasting is a critical task in domains such as energy, finance, and meteorology, where accurate long-term predictions are essential. While Transformer-based models have shown promise in capturing temporal dependencies, their application to extended sequences is limited by computational inefficiencies and limited generalization. In this study,

Ke Wang, Zhiqiang Wang, Qijin Chen, K. Levin

While there are many different mechanisms which have been proposed to understand the physics behind light induced ``superconductivity", what seems to be common to the class of materials in which this is observed are strong pairing correlations, which are present in the normal state. Here we argue, that the original ideas of Eliashberg are applicable to such

Daniel R. Lester, Michael G. Trefry, Guy Metcalfe, Marco Dentz

At all scales, porous materials stir interstitial fluids as they are advected, leading to complex distributions of matter and energy. Of particular interest is whether porous media naturally induce chaotic advection at the Darcy scale, as these stirring kinematics profoundly impact basic processes such as solute transport and mixing, colloid transport and de

Benjamin S. Ruben, William L. Tong, Hamza Tahir Chaudhry, Cengiz Pehlevan

Given a fixed budget for total model size, one must choose between training a single large model or combining the predictions of multiple smaller models. We investigate this trade-off for ensembles of random-feature ridge regression models in both the overparameterized and underparameterized regimes. Using deterministic equivalent risk estimates, we prove th

Max van Horssen, Maarten van Pruijssen

We study non-symmetric Jacobi polynomials of type $BC_1$ by means of vector-valued and matrix-valued orthogonal polynomials. The interpretation as matrix-valued orthogonal polynomials allows us to introduce shift operators for the non-symmetric Jacobi polynomials. The shift operators are differential-reflection operators and we present four of these operator

Youhan Sohn, Saeed Pezeshki, Francois Barthelat

Entangled matter provides intriguing perspectives in terms of deformation mechanisms, mechanical properties, assembly and disassembly. However, collective entanglement mechanisms are complex, occur over multiple length scales, and they are not fully understood to this day. In this report, we propose a simple pick-up test to measure the entanglement in staple

Saeed Pezeshki, Youhan Sohn, Vivien Fouquet, Francois Barthelat

Entangled matter displays unusual and attractive properties and mechanisms: tensile strength, capabilities for assembly and disassembly, damage tolerance. While some of the attributes and mechanisms share some traits with traditional granular materials, fewer studies have focused on entanglement and strength and there are large gaps in our understanding of t

Alexander Iksanov

An alternative proof is given for the main result of the article referred to in the title and published in ECP (2024). The proof exploits the theory of regenerative composition structures due to Gnedin and Pitman. The present article is a slight revision of my note written up in May 2024 as a reaction to the preprint arxiv.org version of the paper by Aldous,

Oscar Lares, Hao Zhen, Jidong J. Yang

Reliable and interpretable traffic crash modeling is essential for understanding causality and improving road safety. This study introduces a novel approach to predicting collision types by utilizing a comprehensive dataset fused from multiple sources, including weather data, crash reports, high-resolution traffic information, pavement geometry, and facility

Vitalii Shpakivskyi

The infinite-dimensional family of exact solutions of the Klein--Gordon equation is constructed by the hypercomplex method.

Junpeng Wan

The Branch Target Buffer (BTB) plays a critical role in efficient CPU branch prediction. Understanding the design and implementation of the BTB provides valuable insights for both compiler design and the mitigation of hardware attacks such as Spectre. However, the proprietary nature of dominant CPUs, such as those from Intel, AMD, Apple, and Qualcomm, means

Jiahe Pan, Jonathan Eden, Denny Oetomo, Wafa Johal

Shared control systems aim to combine human and robot abilities to improve task performance. However, achieving optimal performance requires that the robot's level of assistance adjusts the operator's cognitive workload in response to the task difficulty. Understanding and dynamically adjusting this balance is crucial to maximizing efficiency and user satisf

Roame A. Hildebrand, Wance Wang, Connor Goham, Alessandro Restelli

The comb-like spectrum added to laser light by an electro-optic modulator (EOM) finds use in a wide range of applications, including coherent optical communication, atomic spectroscopy, and laser frequency and phase stabilization. In some cases a sideband-free optical frequency shift is preferred, such as in laser offset locking using an optical cavity, sing

Adam H. Fuller, Pradyut Karmakar

Let $\Sigma \rightarrow G$ be a twist over a locally compact Hausdorff \'{e}tale groupoid $G$. Given $f$ in the reduced C$^*$-algebra $C_r^*(\Sigma;G)$ with open support $U \subseteq G$ we ask when $f$ lies in the closure of the compactly supported sections on $U$. Suppose $G$ satisfies the rapid decay property with respect to a length function $L$. We give