April 2024 arXiv papers — page 113

Ali Younis, Erik Sudderth

Particle filters flexibly represent multiple posterior modes nonparametrically, via a collection of weighted samples, but have classically been applied to tracking problems with known dynamics and observation likelihoods. Such generative models may be inaccurate or unavailable for high-dimensional observations like images. We instead leverage training data t

A. G. Moskowitz, T. Gaona, J. Peterson

As AI-generated image (AIGI) methods become more powerful and accessible, it has become a critical task to determine if an image is real or AI-generated. Because AIGI lack the signatures of photographs and have their own unique patterns, new models are needed to determine if an image is AI-generated. In this paper, we investigate the ability of the Contrasti

Lechen Zhang

This paper addresses the challenge of co-designing morphology and control in soft robots via a novel neural network evolution approach. We propose an innovative method to implicitly dual-encode soft robots, thus facilitating the simultaneous design of morphology and control. Additionally, we introduce the large language model to serve as the control center d

Fernanda C. Rodrigues-Machado, Erika Janitz, Simon Bernard, Hamed Bekerat

High-finesse microcavities offer a platform for compact, high-precision sensing by employing high-reflectivity, low-loss mirrors to create effective optical path lengths that are orders of magnitude larger than the device geometry. Here, we investigate the radiation hardness of Fabry-Perot microcavities formed from dielectric mirrors deposited on the tips of

Juan D. Pinto, Luc Paquette

With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of various interrelated research communities -- have turned their attention to leveraging these powerful algorithms within the domain of education. Use cases range from advanced knowl

Fergal Stapleton, Edgar Galván

Evolutionary Algorithms (EAs) play a crucial role in the architectural configuration and training of Artificial Deep Neural Networks (DNNs), a process known as neuroevolution. However, neuroevolution is hindered by its inherent computational expense, requiring multiple generations, a large population, and numerous epochs. The most computationally intensive a

Maurits Reitsma, Julian Keller, Kenneth Blomqvist, Roland Siegwart

We propose an interpretable framework for reading analog gauges that is deployable on real world robotic systems. Our framework splits the reading task into distinct steps, such that we can detect potential failures at each step. Our system needs no prior knowledge of the type of gauge or the range of the scale and is able to extract the units used. We show

STAR Collaboration, M. I. Abdulhamid, B. E. Aboona, J. Adam

With the STAR experiment at the BNL Relativisic Heavy Ion Collider, we characterize $\sqrt{s_\mathrm{NN}}$ = 200 GeV p+Au collisions by event activity (EA) measured within the pseudorapidity range $eta$ $in$ [-5, -3.4] in the Au-going direction and report correlations between this EA and hard- and soft- scale particle production at midrapidity ($\eta$ $\in$

Beatrice Bednarz, Maria-Andromachi Syskaki, Rohit Pachat, Leon Prädel

Electric field control of magnetic properties offers a broad and promising toolbox for enabling ultra-low power electronics. A key challenge with high technological relevance is to master the interplay between the magnetic anisotropy of a ferromagnet and the exchange coupling to an adjacent antiferromagnet. Here, we demonstrate that magneto-ionic gating can

Rishi Rao, Li Zhu

Correlated systems represent a class of materials that are difficult to describe through traditional electronic structure methods. The computational demand to simulate the structural dynamics of such systems, with correlation effects considered, is substantial. Here, we investigate the structural dynamics of $f$- and $d$-electron correlated systems by integr

Renan Assimos, Balázs Márk Békési, Giuseppe Gentile

By introducing a more flexible notion of convexity, we obtain a new Omori-Yau maximum principle for harmonic maps. In the spirit of the Calabi-Yau conjectures, this principle is more suitable for studying the unboundedness of certain totally geodesic projections of minimal submanifolds of higher codimension. We further explore this maximum principle by apply

Yi-Xian Chen, Douglas N. C. Lin

Gravitational instability in the outskirts of Active Galactic Nuclei (AGN) disks lead to disk fragmentation and formation of super-massive (several 10^2Msun) stars with potentially long lifetimes. Alternatively, stars can be captured ex-situ and grow from gas accretion in the AGN disk. However, the number density distribution throughout the disk is limited b

Michael Igbinoba

Tropical cyclone reconnaissance data, such as that of Hurricane Lane, often exhibits discrepancies between flight-level wind measurements and SFMR data, leading to uncertainty in determining peak intensity. In this study, I analyze Hurricane Lane's recon data alongside similar cases like Hurricane Dorian (2019), Hurricane Felix (2007), and Hurricane Matthew

Robert P. Crease, Vladimir Shiltsev

This article, the third in a series about the Russian scientist Mikhail Lomonosov (1711-1765), covers the first decade of his research at the St. Petersburg Academy of Sciences, from his return from an educational his trip abroad in 1741, to the mid-1750s. Lomonosov's major focus was on the establishment of the first Russian laboratory used to introduce mode

Xiaomeng Zhu, Talha Bilal, Pär Mårtensson, Lars Hanson

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six indus

F. Pinto-Gómez, F. De Soto, J. Rodríguez-Quintero

Several continuum and lattice investigations of the QCD three-gluon vertex have recently exposed its key properties, some intimately connected with the low-momentum behavior of the two-point gluon Green's function and specially relevant for the emergence of a mass scale in this latter, via the Schwinger mechanism. In the present study, we report on a lattice

Vesna Iršič Chenoweth

The connected domination game is a variation of the domination game where the played vertices must form a connected subgraph at all stages of the game. In this paper we prove that deciding whether the game connected domination number is smaller than a given integer is PSPACE-complete using log-space reductions for both Dominator- and Staller-start connected

Andrew Snowden

In recent work with Harman, we introduced a notion of measure on a class of finite relational structures. In this note, we consider measures on the class of permutations, i.e., finite sets with two total orders. Using a method of Nekrasov, we show that there are exactly 37 measures. Two of these measures lead to a new pre-Tannakian tensor category.

Gloria Koenigsberger, Diana Estrella-Trujillo

Periastron brightening events, also known as the heartbeat phenomenon, are a clear manifestation of interaction effects in binary systems. We explore the role of tidal shear energy dissipation in stars undergoing periastron brightening events by performing a computation from first principles that uses a quasi-hydrodynamic Lagrangian scheme to simultaneously

Y. Ussenov, M. N. Shneider, S. Yatom, Y. Raitses

It is shown experimentally that the photodetachment yield of surplus electrons created by plasma-induced charging of non-conductive surfaces of dielectric materials depends on the initial surface charge density and do not correlate with the tabulated affinity values of these materials. This unexpected result obtained using laser-stimulated photodetachment fo

Zhongqiang Hu, Qiuyuan Wang, Chung-Tao Chou, Justin T. Hou

Nonlinear phenomena represent one of the central topics in the study of wave-matter interactions and constitute the key blocks for various applications in optical communication, computing, sensing, and imaging. In this work, we show that by employing the interactions between microwave photons and electron spins of nitrogen-vacancy (NV) centers, one can reali

O. Koshkarov, L. Chacon

In this paper, we extend the operator-split asymptotic-preserving, semi-Lagrangian algorithm for time dependent anisotropic heat transport equation proposed in [Chac\'on et al., JCP, 272, 719-746, 2014] to use a fully implicit time integration with backward differentiation formulas. The proposed implicit method can deal with arbitrary heat-transport anisotro

Tilas Kabengele, Yash M. Lokare, J. B. Marston, Brenda M. Rubenstein

The Chemical Master Equation (CME) provides a highly accurate, yet extremely resource-intensive representation of a stochastic chemical reaction network and its kinetics due to the exponential scaling of its possible states with the number of reacting species. In this work, we demonstrate how quantum algorithms and hardware can be employed to model stochasti

Steven Dale Cutkosky, Stephen Landsittel

In this paper we prove a volume = multiplicity type theorem for epsilon multiplicity. We show that epsilon multiplicity is a limit of Amao multiplicities.

Olena S. Iadlovska, Kamal Thapa, Mojtaba Rajabi, Mateusz Mrukiewicz

An oblique helicoidal state of a cholesteric liquid crystal (ChOH) is capable of continuous change of the pitch P in response to an applied electric field. Such a structure reflects 50% of the unpolarized light incident along the ChOH axis in the electrically tunable band determined by P/2. Here we demonstrate that at an oblique incidence of light, ChOH refl

Junchi Wang, Lei Ke

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user int

Aparajita Dasgupta, Vishvesh Kumar, Shyam Swarup Mondal, Michael Ruzhansky

Let $\mathbb G$ be a graded Lie group with homogeneous dimension $Q$. In this paper, we study the Cauchy problem for a semilinear hypoelliptic damped wave equation involving a positive Rockland operator $\mathcal{R}$ of homogeneous degree $\nu\geq 2$ on $\mathbb G$ with power type nonlinearity $|u|^p$ and initial data taken from negative order homogeneous So

Miqaela K. Weller, David H. Weinberg, James W. Johnson

We examine the galactic chemical evolution (GCE) of $^4$He in one-zone and multi-zone models, with particular attention to theoretical predictions and empirical constraints on IMF-averaged yields. Published models of massive star winds and core collapse supernovae span a factor of 2 -- 3 in the IMF-averaged $^4$He yield, $y\mathrm{_{He}^{CC}}$. Published mod

Alexandre de Camargo, Ricky T. Q. Chen, Rodrigo A. Vargas-Hernández

Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constraint. This study illustrates how leveraging contemporary generative models, notably normalizing flows (NFs), c

Donghyun Lee, Je-Yong Lee, Genghan Zhang, Mo Tiwari

Large Language Models (LLMs) have dramatically advanced AI applications, yet their deployment remains challenging due to their immense inference costs. Recent studies ameliorate the computational costs of LLMs by increasing their activation sparsity but suffer from significant performance degradation on downstream tasks. In this work, we introduce a new fram

Cemil Selcuk

This note pursues two primary objectives. First, we analyze the outcomes of an all-pay auction within a store where buyers with and without financial constraints arrive at varying rates, and where buyer types are private information. Second, we investigate the selection of an auction format (comprising first-price, second-price, and all-pay formats) in a com

Joshua Feinglass, Jayaraman J. Thiagarajan, Rushil Anirudh, T. S. Jayram

Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image. This is an oversimplification of the process of novel category recognition, where different regions of the image may have properties from different seen classes and thus have differen

Siyang Liu, Trish Maturi, Bowen Yi, Siqi Shen

We explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories. Through a diverse set of prompts tailored to ensure response robustness, we find a general inclination of LLM values towards younger demographics, especially when compared to the US population. A

Diana Powell, Xi Zhang

We present a new two-dimensional, bin-scheme microphysical model of cloud formation in the atmospheres of hot Jupiters that includes the effects of longitudinal gas and cloud transport. We predict cloud particle size distributions as a function of planetary longitude and atmospheric height for a grid of hot Jupiters with equilibrium temperatures ranging from

Louis Bremaud, Olivier Giraud, Denis Ullmo

The design of coherent and efficient policies to address infectious diseases and their consequences requires to model not only epidemics dynamics, but also individual behaviors, as the latter has a strong influence on the former. In our work, we provide a theoretical model for this problem, taking into account the social structure of a population. This model

Michail Anthropelos, Scott Robertson

We consider a market of risky financial assets whose participants are an informed trader, a representative uninformed trader, and noisy liquidity providers. We prove the existence of a market-clearing equilibrium when the insider internalizes her power to impact prices, but the uninformed trader takes prices as given. Compared to the associated competitive e

Iuliia Kotseruba, John K. Tsotsos

Accurate prediction of drivers' gaze is an important component of vision-based driver monitoring and assistive systems. Of particular interest are safety-critical episodes, such as performing maneuvers or crossing intersections. In such scenarios, drivers' gaze distribution changes significantly and becomes difficult to predict, especially if the task and co

Nicolai Dorka, Janusz Marecki, Ammar Anwar

Addressing the challenge of a digital assistant capable of executing a wide array of user tasks, our research focuses on the realm of instruction-based mobile device control. We leverage recent advancements in large language models (LLMs) and present a visual language model (VLM) that can fulfill diverse tasks on mobile devices. Our model functions by intera

Daniel Kelshaw, Luca Magri

Computing distances on Riemannian manifolds is a challenging problem with numerous applications, from physics, through statistics, to machine learning. In this paper, we introduce the metric-constrained Eikonal solver to obtain continuous, differentiable representations of distance functions on manifolds. The differentiable nature of these representations al

Eloy Ayón-Beato, Daniel Flores-Alfonso, Mokhtar Hassaine

We start this paper by concisely rederiving ModMax, which is nothing but the unique nonlinear extension of Maxwell's equations preserving conformal and duality invariance. The merit of this new derivation is its transparency and simplicity since it is based on an approach where the elusive duality invariance is manifest. In the second part, we couple the Mod

Yolanda Cabrera Casado, Maria Inez Cardoso Gonçalves, Daniel Gonçalves, Dolores Martín Barquero

The leitmotiv of this paper is linking algebraic properties of an evolution algebra with combinatorial properties of the (possibly several) graphs that one can associate to the algebra. We link nondegeneracy, zero annihilator, absorption property, von Neumann regularity, and primeness with suitable properties in the associated graph. In the presence of semip

Alessandro Cheli, Niklas Heim

We introduce the third major version of Metatheory.jl, a Julia library for general-purpose metaprogramming and symbolic computation. Metatheory.jl provides a flexible and performant implementation of e-graphs and Equality Saturation (EqSat) that addresses the two-language problem in high-level compiler optimizations, symbolics and metaprogramming. We present

Yifei Lin, Hanqiu Deng, Xingyu Li

Nowadays large computers extensively output logs to record the runtime status and it has become crucial to identify any suspicious or malicious activities from the information provided by the realtime logs. Thus, fast log anomaly detection is a necessary task to be implemented for automating the infeasible manual detection. Most of the existing unsupervised

Iuliia Kotseruba, John K. Tsotsos

Driving is a visuomotor task, i.e., there is a connection between what drivers see and what they do. While some models of drivers' gaze account for top-down effects of drivers' actions, the majority learn only bottom-up correlations between human gaze and driving footage. The crux of the problem is lack of public data with annotations that could be used to t

Noel Jeffrey Pinton, Alexandre Bousse, Catherine Cheze-Le-Rest, Dimitris Visvikis

This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models i

Valentina Ghidini, Michael Multerer, Jacopo Quizi, Rohan Sen

This work introduces the definition of observation-specific explanations to assign a score to each data point proportional to its importance in the definition of the prediction process. Such explanations involve the identification of the most influential observations for the black-box model of interest. The proposed method involves estimating these explanati

GRAVITY Collaboration, R. Abuter, A. Amorim, M. Benisty

The detection of low-mass planets orbiting the nearest stars is a central stake of exoplanetary science, as they can be directly characterized much more easily than their distant counterparts. Here, we present the results of our long-term astrometric observations of the nearest binary M-dwarf Gliese 65 AB (GJ65), located at a distance of only 2.67 pc. We mon

Yu-Feng Song, Youjin Deng, Yuan-Yao He

The Hubbard model at temperatures above the N\'{e}el transition, despite being a paramagnet, can exhibit rich physics due to the interplay of Fermi surface, on-site interaction $U$ and thermal fluctuations. Nevertheless, the understanding of the crossover physics remains only at a qualitative level, because of the intrinsically smooth behavior. Employing an

Rohan Bali, Ashley N. Tittelbaugh, Shelbi L. Jenkins, Anuj Agrawal

We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single broadband quasi-deterministic time-frequency heralded Einstein-Podolsky-Rosen (EPR) pair source, and develop a routing and spectrum allocation scheme for distributing entangled photon pairs o

Xiaohang Tang, Sam Wong, Kevin Pu, Xi Chen

Programming instructors often conduct collaborative learning activities, like Peer Instruction, to foster a deeper understanding in students and enhance their engagement with learning. These activities, however, may not always yield productive outcomes due to the diversity of student mental models and their ineffective collaboration. In this work, we introdu

Anton Husakou, Zukhriddin Ruziev, Kamoliddin Koraboev, Felipe Morales

The choice of the most suitable analytic photoionization model in solids is a challenging task with no default solution. Here we show how the best formalism can be determined based on the waveform of the pulse reflected by a sample due to photoionized almost-free electrons in the conduction band. For a typical case of diamond, we compare three simple models

Ian Thorvaldson, Dean Poulos, Christian M. Moehle, Saiful H. Misha

Spin qubits in silicon are strong contenders for realizing a practical quantum computer. This technology has made remarkable progress with the demonstration of single and two-qubit gates above the fault-tolerant threshold and entanglement of up to three qubits. However, maintaining high fidelity operations while executing multi-qubit algorithms has remained

Marco Brito, Carlos Herdeiro, Nicolas Sanchis-Gual, Etevaldo dos Santos Costa Filho

We study the dynamical stability of Proca-Higgs stars, in spherical symmetry. These are solutions of the Einstein-Proca-Higgs model, which features a Higgs-like field coupled to a Proca field, both of which minimally coupled to the gravitational field. The corresponding stars can be regarded as Proca stars with self-interactions, while avoiding the hyperboli

Kainat Yasmeen Shobha Sundar Ram

Narrowband radar micro-Doppler signatures are heavily used to identify and classify human activities. When the radar is operated in through-wall environments, the complex electromagnetic propagation phenomenology introduces considerable distortions in the micro-Doppler signatures through attenuation and multipath. The problem is particularly severe in inhomo

Yanan Sun, Edward Valachovic

Air quality is a critical component of environmental health. Monitoring and analysis of particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) plays a pivotal role in understanding air quality changes. This study focuses on the application of a new bandpass bootstrap approach, termed the Variable Bandpass Periodic Block Bootstrap (VBPBB), f

Ben Jaderberg, Antonio A. Gentile, Atiyo Ghosh, Vincent E. Elfving

In this work we explore how quantum scientific machine learning can be used to tackle the challenge of weather modelling. Using parameterised quantum circuits as machine learning models, we consider two paradigms: supervised learning from weather data and physics-informed solving of the underlying equations of atmospheric dynamics. In the first case, we demo

Morgan Saidel, Shreyas Vissapragada, Jessica Spake, Heather A. Knutson

The lack of close-in Neptune-mass exoplanets evident in transit surveys has largely been attributed either to photoevaporative mass loss or high-eccentricity migration. To distinguish between these two possibilities, we investigate the origins of TOI-1259 A b, a Saturn-mass (0.4 M$_J$, 1.0 R$_J$) exoplanet lying along the upper edge of the Neptune desert. TO

Thomas W. Baumgarte, Stuart L. Shapiro

Primordial black holes (PBHs), if they exist, may collide with and be captured by neutron stars. We adopt a relativistic point-mass approximation to study this capture, the subsequent confinement of the PBH of mass $m$ inside the neutron star of mass $M_* \gg m$, and the PBH's growth by accretion of stellar material. Building on earlier treatments we systema

Najmul Haque, Munshi G. Mustafa

In this review, we present the key aspects of modern thermal perturbation theory based on the hard thermal loop (HTL) approximation, including its theoretical foundations and applications within quantum electrodynamics (QED) and quantum chromodynamics (QCD) plasmas. To maintain conciseness, we focus on scenarios in thermal equilibrium, examining a variety of

Stephen J. Parke, Renata Zukanovich Funchal

We revisit a method for determining the neutrino mass ordering by using precision measurements of the atmospheric $\Delta m^2$'s in both electron neutrino and muon neutrino disappearance channels, proposed by the authors in 2005 (hep-ph/0503283). The mass ordering is a very important outstanding question for our understanding of the elusive neutrino and dete

Luís Machado, José Natário, Jorge Drumond Silva

We study the motion of an elastic rigid rod which is radially free-falling towards a Schwarzschild black hole. This is accomplished by reducing the corresponding free-boundary PDE problem to a sequence of ODEs, which we integrate numerically. Starting with a rod at rest, we show that it is possible to choose its initial compression profile so that its midpoi

Gianfranco Bertone, A. Renske A. C. Wierda, Daniele Gaggero, Bradley J. Kavanagh

Dark matter overdensities around black holes can be searched for by looking at the characteristic imprint they leave on the gravitational waveform of binary black hole mergers. Current theoretical predictions of the density profile of dark matter overdensities are based on highly idealised formation scenarios, in which black holes are assumed to grow adiabat

Albert Sneppen, Darach Watson, Rasmus Damgaard, Kasper E. Heintz

The spectral features in the optical/near-infrared counterparts of neutron star mergers (kilonovae, KNe), evolve dramatically on hour timescales. To examine the spectral evolution we compile a temporal series complete at all observed epochs from 0.5 to 9.4 days of the best optical/near-infrared (NIR) spectra of the gravitational-wave detected kilonova AT2017

Flavio Mercati

This paper is the second part of a series that develops the mathematical framework necessary for studying field theories on ``T-Minkowski'' noncommutative spacetimes. These spacetimes constitute a class of noncommutative geometries, introduced in Part I, that are each invariant under distinct quantum group deformations of the Poincar\'e group. All these nonc

Maissa Salama, Charlotte Guthery, Vincent Chambouleyron, Rebecca Jensen-Clem

We present the first on-sky segmented primary mirror closed-loop piston control using a Zernike wavefront sensor (ZWFS) installed on the Keck II telescope. Segment co-phasing errors are a primary contributor to contrast limits on Keck and will be necessary to correct for the next generation of space missions and ground-based extremely large telescopes (ELTs)

Christen Millerdurai, Hiroyasu Akada, Jian Wang, Diogo Luvizon

Monocular egocentric 3D human motion capture is a challenging and actively researched problem. Existing methods use synchronously operating visual sensors (e.g. RGB cameras) and often fail under low lighting and fast motions, which can be restricting in many applications involving head-mounted devices. In response to the existing limitations, this paper 1) i

Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen

In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. Notably, the established COCO benchmark has propelled the development of modern detection and segmentation systems. However, the COCO segmentation benchmark has seen comparatively slow improvement over the la

Nicholas Jones, Eytan Modiano

We analyze the problem of scheduling in wireless networks to meet end-to-end service guarantees. Using network slicing to decouple the queueing dynamics between flows, we show that the network's ability to meet hard throughput and deadline requirements is largely influenced by the scheduling policy. We characterize the feasible throughput/deadline region for

Mohamed El Banani, Amit Raj, Kevis-Kokitsi Maninis, Abhishek Kar

Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task, their intermediate representations are useful for other visual tasks such as detection and segmentation. Given that such models can classify, delineate, and localize obje

Ipsita Mandal, Hermann Freire

In this review, we survey the current progress in computing transport properties in semimetals which harbour non-Fermi liquid phases. We first discuss the widely-used Kubo formalism, which can be applied to the effective theory describing the stable non-Fermi liquid phase obtained via a renormalization group procedure and, hence, is applicable for temperatur

Sunny Sanyal, Ravid Shwartz-Ziv, Alexandros G. Dimakis, Sujay Sanghavi

Large Language Models (LLMs) are known for their performance, but we uncover a significant structural inefficiency: a phenomenon we term attention collapse. In many pre-trained decoder-style LLMs, the attention matrices in deeper layers degenerate, collapsing to near rank-one structures. These underutilized layers, which we call lazy layers, are redundant an

Eoin Ó Colgáin, Maria Giovanna Dainotti, Salvatore Capozziello, Saeed Pourojaghi

We demonstrate that a $\sim 2 \sigma$ discrepancy with the Planck-$\Lambda$CDM cosmology in DESI Luminous Red Galaxy (LRG) data in the redshift range $0.4 < z < 0.6$ with effective redshift $z_{\textrm{eff}} = 0.51$ translates into an unexpectedly large $\Omega_m$ value, $\Omega_m = 0.67^{+0.18}_{-0.17}$. We independently confirm that this anomaly drives the

Alberto Zingaro, Irmantas Burba, David Oks, Mauro Fontana

Systematic in vivo validations of computational models of the aortic valve remain scarce, despite successful validation against in vitro data. Utilizing a combination of computed tomography and 4D flow magnetic resonance imaging data, we developed patient-specific fluid-structure interaction models of the aortic valve immersed in the aorta for five patients

Yanting Wang, Wei Zou, Jinyuan Jia

Few-shot classification with foundation models (e.g., CLIP, DINOv2, PaLM-2) enables users to build an accurate classifier with a few labeled training samples (called support samples) for a classification task. However, an attacker could perform data poisoning attacks by manipulating some support samples such that the classifier makes the attacker-desired, ar

Leif Azzopardi, Mateusz Dubiel, Martin Halvey, Jeffery Dalton

The conversational search task aims to enable a user to resolve information needs via natural language dialogue with an agent. In this paper, we aim to develop a conceptual framework of the actions and intents of users and agents explaining how these actions enable the user to explore the search space and resolve their information need. We outline the differ

Jean Cerqueira Berni, Hugo Luiz Mariano

In this paper, we introduce the concept of a "von Neumann regular $\mathcal{C}^{\infty}$-ring", which is a model for a specific equational theory. We delve into the characteristics of these rings and demonstrate that each Boolean space can be effectively represented as the image of a von Neumann regular $\mathcal{C}^{\infty}$-ring through a specific functor.

Leif Azzopardi, Vishwa Vinay

This paper introduces the concept of accessibility from the field of transportation planning and adopts it within the context of Information Retrieval (IR). An analogy is drawn between the fields, which motivates the development of document accessibility measures for IR systems. Considering the accessibility of documents within a collection given an IR Syste

Mingmeng Geng, Roberto Trotta

Based on one million arXiv papers submitted from May 2018 to January 2024, we assess the textual density of ChatGPT's writing style in their abstracts through a statistical analysis of word frequency changes. Our model is calibrated and validated on a mixture of real abstracts and ChatGPT-modified abstracts (simulated data) after a careful noise analysis. Th

Alexander N. Craddock, Anne Lazenby, Gabriel Bello Portmann, Rourke Sekelsky

Distributing high-fidelity, high-rate entanglement over telecommunication infrastructure is one of the main paths towards large-scale quantum networks, enabling applications such as quantum encryption and network protection, blind quantum computing, distributed quantum computing, and distributed quantum sensing. However, the fragile nature of entangled photo

Jieshuang Wang, Frank M. Rieger, Yosuke Mizuno

High-resolution radio observations of nearby active galactic nuclei have revealed extended, limb-brightened structures in their inner jets. This ties in with other multi-wavelength observations from radio to X-ray and gamma-ray, indicating that a structured jet model is required. While electrons need to be kept energized to account for the observed features,

Matteo Tucat, Anirbit Mukherjee, Procheta Sen, Mingfei Sun

We present and analyze a novel regularized form of the gradient clipping algorithm, proving that it converges to global minima of the loss surface of deep neural networks under the squared loss, provided that the layers are of sufficient width. The algorithm presented here, dubbed $\delta-$GClip, introduces a modification to gradient clipping that leads to a

Chase Stokes, Chelsea Sanker, Bridget Cogley, Vidya Setlur

Interpreting uncertain data can be difficult, particularly if the data presentation is complex. We investigate the efficacy of different modalities for representing data and how to combine the strengths of each modality to facilitate the communication of data uncertainty. We implemented two multimodal prototypes to explore the design space of integrating spe

Rebecca von Kuk, Johannes K. L. Michel, Zhiquan Sun

The transverse momentum-dependent fragmentation functions (TMD FFs) of heavy (bottom and charm) quarks, which we recently introduced, are universal building blocks that enter predictions for a large number of observables involving final-state heavy quarks or hadrons. They enable the extension of fixed-order subtraction schemes to quasi-collinear limits, and

Abhijeet, Mohamed Naveed Gul Mohamed, Aayushman Sharma, Suman Chakravorty

This paper investigates the central role played by the Hamiltonian in continuous-time nonlinear optimal control problems. We show that the strict convexity of the Hamiltonian in the control variable is a sufficient condition for the existence of a unique optimal trajectory, and the nonlinearity/non-convexity of the dynamics and the cost are immaterial. The a

Shihao Wei, Christopher Boudreaux, Zhongfeng Su, Zhan Wu

Drawing on the attention based view, this study explores the joint effects of natural disaster intensity at the country level with personal attributes in terms of gender, human capital, and fear of failure on the likelihood to enter social entrepreneurship. Using data on 107,386 observations across 30 countries, we find that natural disaster intensity has a

Roberto Souto Maior de Barros Filho, Paulo Borba

This work's main goal is to understand if Information Flow Control (IFC), a security technique used for discovering leaks in software, could be used to indicate the presence of dynamic semantic conflicts between developers contributions in merge scenarios. However, as defining if a dynamic semantic conflict exists involves understanding the expected behaviou

Theodore Kareta, John W. Noonan, Kathryn Volk, Ryder H. Strauss

The discovery of the transient Jupiter co-orbital comet P/2019 LD2 (ATLAS) drew significant interest. Not only will LD2 transition between being a Centaur and a Jupiter Family Comet (JFC) in 2063, the first time this process can be observed as it happens, it is also very active for its large heliocentric distance. We present observations and orbital integrat

Aleksa Cvetanović, Predrag Tadić

In this paper, we focus on generating a synthetic question answering (QA) dataset using an adapted Translate-Align-Retrieve method. Using this method, we created the largest Serbian QA dataset of more than 87K samples, which we name SQuAD-sr. To acknowledge the script duality in Serbian, we generated both Cyrillic and Latin versions of the dataset. We invest

Karl Mayer, Ciarán Ryan-Anderson, Natalie Brown, Elijah Durso-Sabina

We implement logically encoded three-qubit circuits for the quantum Fourier transform (QFT), using the [[7,1,3]] Steane code, and benchmark the circuits on the Quantinuum H2-1 trapped-ion quantum computer. The circuits require multiple logical two-qubit gates, which are implemented transversally, as well as logical non-Clifford single-qubit rotations, which

Christoph Schürmann, Norbert Langer

The majority of massive stars reside in binary systems, which are expected to experience mass transfer during their evolution. However, so far the conditions under which mass transfer leads to a common envelope, and thus possibly to a merging of both stars, are not well understood. Main uncertainties arise from the possible swelling of the mass gainer, and f

Federico Battisti, Marian Ivanov, Xianguo Lu

This study introduces a Kalman Filter tailored for homogeneous gas Time Projection Chambers (TPCs), adapted from the algorithm utilized by the ALICE experiment. In order to describe semi-circular paths in the plane perpendicular to the magnetic field, we introduce a novel mirror rotation technique into the Kalman Filter algorithm, enabling effective tracking

Huan Zhang, Justin Finkel, Dorian S. Abbot, Edwin P. Gerber

Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and d

Christoph Schürmann, Norbert Langer, Joana A. Kramer, Pablo Marchant

Massive binary evolution models are needed to predict massive star populations in star forming galaxies, the supernova diversity, and the number and properties of gravitational wave sources. Such models are often computed using so called rapid binary evolution codes, which approximate the evolution of the binary components based on detailed single star model

Xin Tie, Muheon Shin, Changhee Lee, Scott B. Perlman

$\textbf{Purpose}$: Automatic quantification of longitudinal changes in PET scans for lymphoma patients has proven challenging, as residual disease in interim-therapy scans is often subtle and difficult to detect. Our goal was to develop a longitudinally-aware segmentation network (LAS-Net) that can quantify serial PET/CT images for pediatric Hodgkin lymphom

Ziang Liu, Ayush Bhandari, Bruno Clerckx

The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC). Towards this end, in this paper, we consider unlimited sensing framework (USF) enabled full-duplex system. We show that by injecting folding non-linearities in the sensing pipeline, one can not only suppress sel

Nikolai Kuznetsov, Alberto Nardi, Johann Riemensberger, Alisa Davydova

Optical amplification, crucial for modern communication and data center interconnects, primarily relies on erbium-doped fiber amplifiers (EDFAs) to enhance signals without distortion. While EDFAs were historically decisive for the introduction of dense wavelength-division multiplexing, they only cover a portion of the low-loss spectrum of optical fibers. Pio

Aniss Aiman Medbouhi, Giovanni Luca Marchetti, Vladislav Polianskii, Alexander Kravberg

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) -- a similarity score for comparing datasets in a hyperbolic space

Liangzhi Wang, Chen Chen, Jie Zhang, Carlo Fischione

This paper considers a downlink cell-free multiple-input multiple-output (MIMO) network in which multiple multi-antenna access points (APs) serve multiple users via coherent joint transmission. In order to reduce the energy consumption by radio frequency components, each AP selects a subset of antennas for downlink data transmission after estimating the chan

Mark V. Lawson

In this paper, we describe \'etale Boolean right restriction monoids in terms of Boolean inverse monoids.

Chelsea A. Williams, Antonio A. Gentile, Vincent E. Elfving, Daniel Berger

We propose quantum methods for solving differential equations that are based on a gradual improvement of the solution via an iterative process, and are targeted at applications in fluid dynamics. First, we implement the Jacobi iteration on a quantum register that utilizes a linear combination of unitaries (LCU) approach to store the trajectory information. S