May 2024 arXiv papers — page 109

Jonathan P Williams, Yang Liu

Building on the recent development of the model-free generalized fiducial (MFGF) paradigm (Williams, 2023) for predictive inference with finite-sample frequentist validity guarantees, in this paper, we develop an MFGF-based approach to decision theory. Beyond the utility of the new tools we contribute to the field of decision theory, our work establishes a f

Robin Bloomfield, Kate Netkachova, John Rushby

A traditional assurance case employs a positive argument in which reasoning steps, grounded on evidence and assumptions, sustain a top claim that has external significance. Human judgement is required to check the evidence, the assumptions, and the narrative justifications for the reasoning steps; if all are assessed good, then the top claim can be accepted.

Federico Castagna, Isabel Sassoon, Simon Parsons

Recent years witnessed significant performance advancements in deep-learning-driven natural language models, with a strong focus on the development and release of Large Language Models (LLMs). These improvements resulted in better quality AI-generated output but rely on resource-expensive training and upgrading of models. Although different studies have prop

Kristie Denlinger, Stephen Wechsler, Kyle Mahowald

English allows for both compounds (e.g., London-made) and phrasal paraphrases (e.g., made in London). While these constructions have roughly the same truth-conditional meaning, we hypothesize that the compound allows less freedom to express the nature of the semantic relationship between the participle and the pre-participle nominal. We thus predict that the

Ezgi Eren, Jiabing Li

It is well-recognized that Air Cargo revenue management is quite different from its passenger airline counterpart. Inherent demand volatility due to short booking horizon and lumpy shipments, multi-dimensionality and uncertainty of capacity as well as the flexibility in routing are a few of the challenges to be handled for Air Cargo revenue management. In th

Muhammed Patel, Xinwei Chen, Linlin Xu, Yuhao Chen

Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data. In response, our weakly supervised learning method provides a compelling alternative by utilizing lower-resolution regional lab

Francisco Calvillo, Luc Devroye, Gábor Lugosi

Recommendation systems are pivotal in aiding users amid vast online content. Broutin, Devroye, Lugosi, and Oliveira proposed Subtractive Random Forests (\textsc{surf}), a model that emphasizes temporal user preferences. Expanding on \textsc{surf}, we introduce a model for a multi-choice recommendation system, enabling users to select from two independent sug

Noel D. Richardson, Andrea R. Daly, Peredur M. Williams, Grant M. Hill

Several long-period binaries with a carbon-rich Wolf-Rayet star and an O star produce dust in their wind collisions. In eccentric binaries, this is seen most strongly near periastron passage. The exact conditions leading to dust creation require orbital properties to be determined, which is difficult owing to their long periods. Recently, the binary system W

Benjamin Williams, Erin M. Schliep, Bailey Fosdick, Ryan Elmore

In this paper, we propose two novel basketball metrics: ``expected points'' for team-based comparisons and ``expected points above average (EPAA)'' as a player-evaluation tool. Established within the Bayesian hierarchical model framework, teams and players are clustered based on their shooting propensities and abilities using posterior predictive distributio

Junhao Song, Yingfang Yuan, Kaiwen Chang, Bing Xu

To advance the circular economy (CE), it is crucial to gain insights into the evolution of public attention, cognitive pathways of the masses concerning circular products, and to identify primary concerns. To achieve this, we collected data from diverse platforms, including Twitter, Reddit, and The Guardian, and utilised three topic models to analyse the dat

S. X. Li, W. L. Mu, J. B. You, X. Q. Shao

In contrast to the classical optimization process required by the quantum approximate optimization algorithm, FALQON, a feedback-based algorithm for quantum optimization [A. B. Magann {\it et al.,} {\color{blue}Phys. Rev. Lett. {\bf129}, 250502 (2022)}], enables one to obtain approximate solutions to combinatorial optimization problems without any classical

Dan Bohus, Sean Andrist, Nick Saw, Ann Paradiso

We introduce an open-source system called SIGMA (short for "Situated Interactive Guidance, Monitoring, and Assistance") as a platform for conducting research on task-assistive agents in mixed-reality scenarios. The system leverages the sensing and rendering affordances of a head-mounted mixed-reality device in conjunction with large language and vision model

Claire Halloran, Jesus Lizana, Malcolm McCulloch

Decarbonizing the building stock in cold countries by replacing fossil fuel boilers with heat pumps is expected to drastically increase electricity demand. While heating flexibility could reduce the impact of additional demand from heat pumps on the power system, characterizing the national spatial distribution of heating flexibility capacity to incorporate

David Ardia, Keven Bluteau

We propose an approach to construct text-based time-series indices in an optimal way--typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices focus

Daniel Aguilar, Breyner Chacón

Maintaining low, non-negative and stable inflation levels is a necessary condition for the stability of the economy as a whole, because the monetary authorities of most industrialized countries, including the Central Bank of Costa Rica since 2005, they have oriented their monetary policy precisely to that task. Still Thus, both in Costa Rica and internationa

Chinedu Ekuma

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs like Google gemini-pro a

Oliver Kramer

Large Language Models (LLMs) exhibit world knowledge and inference capabilities, making them powerful tools for various applications. This paper proposes a feedback loop mechanism that leverages these capabilities to tune Evolution Strategies (ES) parameters effectively. The mechanism involves a structured process of providing programming instructions, execu

Wenkai Zhang, Zhiying Wang

DNA, with remarkable properties of high density, durability, and replicability, is one of the most appealing storage media. Emerging DNA storage technologies use composite DNA letters, where information is represented by probability vectors, leading to higher information density and lower synthesizing costs than regular DNA letters. However, it faces the pro

Anjana Wijekoon, David Corsar, Nirmalie Wiratunga, Kyle Martin

The evolution of Explainable Artificial Intelligence (XAI) has emphasised the significance of meeting diverse user needs. The approaches to identifying and addressing these needs must also advance, recognising that explanation experiences are subjective, user-centred processes that interact with users towards a better understanding of AI decision-making. Thi

Carlos Alberto Betancur-Silvera, Aurea Espinosa-Ceron, Boris A. Malomed, Jorge Fujioka

The propagation of light beams in photovoltaic pyroelectric photorefractive crystals is modelled by a specific generalization of the nonlinear Schr\"odinger equation (GNLSE). We use the variational approximation (VA) to predict the propagation of solitary-wave inputs in the crystal, finding that the VA equations involve the dilogarithm special function. The

Omar Abdelaziz, Mohamed Sami Shehata

Locating an object in a sequence of frames, given its appearance in the first frame of the sequence, is a hard problem that involves many stages. Usually, state-of-the-art methods focus on bringing novel ideas in the visual encoding or relational modelling phases. However, in this work, we show that bounding box regression from learned joint search and templ

Matthew Raffel, Victor Agostinelli, Lizhong Chen

Large language models (LLMs) have achieved state-of-the-art performance in various language processing tasks, motivating their adoption in simultaneous translation. Current fine-tuning methods to adapt LLMs for simultaneous translation focus on prompting optimization strategies using either data augmentation or prompt structure modifications. However, these

Andrew J Fox, Michael D. Graham

The dynamics of flexible filaments entrained in flow, important for understanding many biological and industrial processes, are computationally expensive to model with full-physics simulations. This work describes a data-driven technique to create high-fidelity low-dimensional models of flexible fiber dynamics using machine learning; the technique is applied

Hanzhi Yang

The exploration of under-ice environments presents unique challenges due to limited access for scientific research. This report investigates the potential of deploying a fully actuated Remotely Operated Vehicle (ROV) for shallow area exploration beneath ice sheets. Leveraging advancements in marine robotics technology, ROVs offer a promising solution for ext

Hang Zhou, Ke Ma, Shixiao Liang, Xiaopeng Li

Automated Vehicles (AVs) promise significant advances in transportation. Critical to these improvements is understanding AVs' longitudinal behavior, relying heavily on real-world trajectory data. Existing open-source trajectory datasets of AV, however, often fall short in refinement, reliability, and completeness, hindering effective performance metrics anal

Jinge Wu, Hang Dong, Zexi Li, Haowei Wang

Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-ba

Juan G. Calvo, Mario I. Simoy, Juan P. Aparicio, José E. Chacón

We develop a stochastic two-patch epidemic model with nonlinear recidivism to investigate infectious disease dynamics in heterogeneous populations. Extending a deterministic framework, we introduce stochasticity to account for random transmission, recovery, and inter-patch movement fluctuations. We showcase the interplay between local dynamics and migration

Omar Abdelaziz, Mohamed Shehata, Mohamed Mohamed

Single object tracking is a vital task of many applications in critical fields. However, it is still considered one of the most challenging vision tasks. In recent years, computer vision, especially object tracking, witnessed the introduction or adoption of many novel techniques, setting new fronts for performance. In this survey, we visit some of the cuttin

Mareike Dressler, Simon Foucart, Mioara Joldes, Etienne de Klerk

This article is concerned with an extension of univariate Chebyshev polynomials of the first kind to the multivariate setting, where one chases best approximants to specific monomials by polynomials of lower degree relative to the uniform norm. Exploiting the Moment-SOS hierarchy, we devise a versatile semidefinite-programming-based procedure to compute such

Rohit Chhiber, Francesco Pecora, Arcadi V Usmanov, William H Matthaeus

The transition from subAlfv\'enic to superAlfv\'enic flow in the solar atmosphere is examined by means of Parker Solar Probe (PSP) measurements during solar encounters 8 to 14. Around 220 subAlfv\'enic periods with a duration $\ge$ 10 minutes are identified. The distribution of their durations, heliocentric distances, and Alfv\'en Mach number are analyzed an

Alejo Lopez-Avila, Jinhua Du, Abbas Shimary, Ze Li

The rapid growth of streaming media and e-commerce has driven advancements in recommendation systems, particularly Sequential Recommendation Systems (SRS). These systems employ users' interaction histories to predict future preferences. While recent research has focused on architectural innovations like transformer blocks and feature extraction, positional e

Sifat Chowdhury, Yu Zhang

Large-scale power outages caused by extreme weather events are one of the major factors weakening grid resilience. In order to prevent the critical infrastructure from cascading failure, power lines are often proactively de-energized under the threat of a progressing wildfire. In this context, the potential of microgrid (MG) functioning in islanded mode can

Matthew N. H. Chow, Vikas Buchemmavari, Sivaprasad Omanakuttan, Bethany J. Little

Leakage out of the computational subspace is a major limitation of current state-of-the-art neutral-atom quantum computers and a significant challenge for scalable systems. In a quantum processor with cesium atoms, we demonstrate proof-of-principle circuit-based conversion of leakage errors to erasure errors via Leakage Detection Units (LDUs), which non-dest

Lewis D Griffin, Nicholas Riggs

Matrix Games are a type of unconstrained wargame used by planners to explore scenarios. Players propose actions, and give arguments and counterarguments for their success. An umpire, assisted by dice rolls modified according to the offered arguments, adjudicates the outcome of each action. A recent online play of the Matrix Game QuAI Sera Sera had six player

Ignacio Filippon, Christine Tanner, Jeannette A. von Jackowski, Georg Schulz

The effectiveness of a series of optically transparent aligners for orthodontic treatments depends on the anchoring of each tooth. In contrast with roots, the crowns' positions and orientations are measurable with intraoral scans, thus avoiding any X-ray dose. Exemplified by two patients, we demonstrate that three-dimensional crown movements could be determi

Huseyin Tuna Erdinc, Rafael Orozco, Felix J. Herrmann

In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality datasets, which are often inaccessible in subsurface applications. Our method leverages incomplete well and seismic observations to produce high-fidelity velocity samples without requi

Yoshinao Katsu, Jiawen Zhang, Michael E. Baker

Due to alternative splicing in an ancestral DNA-binding domain (DBD) of the mineralocorticoid receptor (MR), humans contain two almost identical MR transcripts with either 984 amino acids (MR-984) or 988 amino acids (MR-988), in which their DBDs differ by only four amino acids, Lys,Cys,Ser,Trp (KCSW). Human MRs also contain mutations at two sites, codons 180

Shaz Furniturewala, Surgan Jandial, Abhinav Java, Pragyan Banerjee

Existing debiasing techniques are typically training-based or require access to the model's internals and output distributions, so they are inaccessible to end-users looking to adapt LLM outputs for their particular needs. In this study, we examine whether structured prompting techniques can offer opportunities for fair text generation. We evaluate a compreh

Daniel Jang, Davide Gusmini, Peng Mun Siew, Andrea D'Ambrosio

This paper introduces a novel Monte Carlo (MC) method to simulate the evolution of the low-earth orbit environment, enhancing the MIT Orbital Capacity Analysis Tool (MOCAT). In recent decades, numerous space environment models have been developed by government agencies and research groups to understand and predict the dynamics of space debris. Our MC approac

Yuhan Liu, Roland Tóth, Maarten Schoukens

Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar st

Guo Meng, Philipp Lauber, Zhixin Lu, Andreas Bergmann

The spatio-temporal evolution of the energetic particles in the transport time scale in tokamak plasmas is a key issue of the plasmas confinement, especially in burning plasmas. In order to include sources and sinks and collisional slowing down processes, a new solver, ATEP-3D was implemented to simulate the evolution of the EP distribution in the three-dime

V. Wolf, B. Stecklum, A. Caratti o Garatti, P. A. Boley

Accretion bursts from low-mass young stellar objects (YSOs) are known for many decades. In recent years, the first accretion bursts of massive YSOs (MYSOs) have been observed. These phases of intense protostellar growth are of particular importance for studying massive star formation. Bursts of MYSOs are accompanied by flares of Class II methanol masers (her

Pietro Farina, Subrata Biswas, Eren Yıldız, Khakim Akhunov

Batteryless systems frequently face power failures, requiring extra runtime buffers to maintain inference progress and leaving only a memory space for storing ultra-tiny deep neural networks (DNNs). Besides, making these models responsive to stochastic energy harvesting dynamics during inference requires a balance between inference accuracy, latency, and ene

Nazanin Mohammadi Sepahvand, Vincent Dumoulin, Eleni Triantafillou, Gintare Karolina Dziugaite

As deep learning models are becoming larger and data-hungrier, there are growing ethical, legal and technical concerns over use of data: in practice, agreements on data use may change over time, rendering previously-used training data impermissible for training purposes. These issues have driven increased attention to machine unlearning: removing "the influe

A. R. Nouri-Zonoz, F. Hassani, M. Kunz

We build an emulator based on the polynomial chaos expansion (PCE) technique to efficiently model the non-linear effects associated with the clustering of the $k$-essence dark energy in the effective field theory (EFT) framework. These effects can be described through a modification of Poisson's equation, denoted by the function $\mu(k,z)$, which in general

Mohamed Ilyes Lakhal, Richard Bowden

This paper addresses the problem of diversity-aware sign language production, where we want to give an image (or sequence) of a signer and produce another image with the same pose but different attributes (\textit{e.g.} gender, skin color). To this end, we extend the variational inference paradigm to include information about the pose and the conditioning of

Ruizhi Cheng, Nan Wu, Matteo Varvello, Eugene Chai

Due to the widespread adoption of "work-from-home" policies, videoconferencing applications (e.g., Zoom) have become indispensable for remote communication. However, they often lack immersiveness, leading to the so-called "Zoom fatigue" and degrading communication efficiency. The recent debut of Apple Vision Pro, a mobile headset that supports "spatial perso

Stephan van Staden

This paper defines pointwise clustering metrics, a collection of metrics for characterizing the similarity of two clusterings. These metrics have several interesting properties which make them attractive for practical applications. They can take into account the relative importance of the various items that are clustered. The metric definitions are based on

Silva Mezinska, Alexander Dorn, Thomas Pfeifer, Klaus Bartschat

We demonstrate a left-right asymmetry control of the photo\-electron angular distribution in multi\-photon ionization of Li atoms by a bichromatic laser field. By delaying the fundamental (780 nm) and its second harmonic relative to each other in steps of 130 atto\-seconds, we can vary the relative phase between the two laser fields with sub-wavelength accur

Emilse Cabrera, Arman Esmaili, Alexander A. Quiroga

The neutrino sector of the standard model of particles can contain more than one sterile neutrino states. Generally, existence of more sterile states leads to better, or at least equally good, fit to the short baseline anomalous data due to the larger number of parameters and interferences which create features in the oscillation pattern. However, for experi

Mark Allen, Dennis Kriventsov, Henrik Shahgholian

We investigate general semilinear (obstacle-like) problems of the form $\Delta u = f(u)$, where $f(u)$ has a singularity/jump at $\{u=0\}$ giving rise to a free boundary. Unlike many works on such equations where $f$ is approximately homogeneous near $u = 0$, we work under assumptions allowing for highly oscillatory behavior. We establish the $C^\infty$ regu

Andrea Giammanco, Marwa Al Moussawi, Matthieu Boone, Tim De Kock

In cultural heritage conservation, it is increasingly common to rely on non-destructive imaging methods based on the absorption or scattering of photons ($X$ or $\gamma$ rays) or neutrons. However, physical and practical issues limit these techniques: their penetration depth may be insufficient for large and dense objects, they require transporting the objec

Mason L. Rhodes, Michael Kreshchuk, Shivesh Pathak

We report a first-of-its-kind analysis on post-Trotter simulation of U(1), SU(2) and SU(3) lattice gauge theories including fermions in arbitrary spatial dimension. We provide explicit circuit constructions as well as T-gate counts and logical qubit counts for Hamiltonian simulation. We find up to 25 orders of magnitude reduction in space-time volume over Tr

Leonardo Rodrigues Cadorim, Lucas Veneziani de Toledo, Edson Sardella

In this work we study the role of the heat diffusion equation in simulating the resistive state of superconducting films. By analyzing the current-voltage and current-resistance characteristic curves for temperatures close to $T_c$ and various heat removal scenarios, we demonstrate that heat diffusion notably influences the behavior of the resistive state, s

Shuotao Diao, Suvrajeet Sen

Stochastic programming models can lead to very large-scale optimization problems for which it may be impossible to enumerate all possible scenarios. In such cases, one adopts a sampling-based solution methodology in which case the reliability of the resulting decisions may be suspect. For such instances, it is advisable to adopt methodologies that promote va

Vladimir A. Sabelnikov, Andrei N. Lipatnikov

Inter-scale kinetic energy transfer in turbulent flows is accompanied by very intense and intermittent spatial-temporal fluctuations. Such intermittency is expected to be particularly prominent in premixed flames, where heat release, density variations, dilatation, and chemical reactions are localized to spatial scales that are substantially smaller than sca

Luc Devroye, Gábor Lugosi, Piotr Zwiernik

In many statistical applications, the dimension is too large to handle for standard high-dimensional machine learning procedures. This is particularly true for graphical models, where the interpretation of a large graph is difficult and learning its structure is often computationally impossible either because the underlying graph is not sufficiently sparse o

Zachary W. Anderson, Marin Spaić, Nikolaos Biniskos, Liam Thompson

Understanding the extent and role of inhomogeneity is a pivotal challenge in the physics of cuprate superconductors. While it is known that structural and electronic inhomogeneity is prevalent in the cuprates, it has proven difficult to disentangle compound-specific features from universally relevant effects. Here we combine advanced neutron and x-ray diffus

D. Aristoff, M. Johnson, G. Simpson, R. J. Webber

In the study of stochastic systems, the committor function describes the probability that a system starting from an initial configuration $x$ will reach a set $B$ before a set $A$. This paper introduces an efficient and interpretable algorithm for approximating the committor, called the "fast committor machine" (FCM). The FCM uses simulated trajectory data t

Danilo T. Alves

The creation of particles by the excitation of the quantum vacuum in a cavity with a moving mirror was predicted in 1969. Here, we investigate that, in addition to real particles, the excitation of the quantum vacuum in a dynamical cavity can also result in the creation of a certain amount of positive vacuum energy around these particles. We show that while

Daniel-Rolf Harbeck, Brook Taylor, Annie Kirby, Mark Bowman

Las Cumbres Observatory (LCOGT) operates a global network of robotic 0.4, 1.0, and 2.0-meter telescopes to facilitate scientific research and education in time-domain astronomy. LCOGT's flagship educational program, Global Sky Partners (GSP), awards up to 1500 hours per year of telescope time to individuals and organizations that run their own, fully support

Mihai D. Staic

In this paper we introduce the $r$-equilibrium problem and discuss connections to the map $det^{S^r}$. The case $r=2$ is an application of Newton's third law of motion, while $r=3$ deals with equilibrium of torque-like forces.

N. K. Raut, I. H. Senevirathne, T. Ganey, P. Dhakal

Plasma processing of superconducting radio frequency (SRF) cavities has shown an improvement in accelerating gradient by reducing the radiation due to field emission and multipacting. Plasma processing is a common technique where the free oxygen produced by the plasma breaks down and removes hydrocarbons from surfaces. This increases the work function and re

A. R. L. Kavner, I. Jovanovic

Ionization produced by low-energy nuclear recoils is among the primary direct signatures of dark matter interactions. Despite the urgency of dark matter detection and the recent measurements of coherent elastic neutrino-nucleus scattering, detector response to nuclear recoils is not well characterized in the keVnr and sub-keVnr regime across a variety of mat

Akshay Chandran, Fabien Evrard, Berend van Wachem

In the present paper, we model the velocity disturbance generated by a regularized forcing near a planar wall, which, along with the temporal nature of the forcing, provides an estimate of the unsteady velocity disturbance of the particle near a planar wall. We use the analytical solution for a singular in-time transient Stokeslet near a planar wall (Felderh

Jiří Fadrný, Michal Neset, Martin Bielak, Miroslav Ježek

Conditional addition of photons represents a crucial tool for optical quantum state engineering and it forms a fundamental building block of advanced quantum photonic devices. Here we report on experimental implementation of the conditional addition of several photons. We demonstrate the addition of one, two, and three photons to input coherent states with v

Adam Sky, Michael Neunteufel, Jack S. Hale, Andreas Zilian

We introduce a unified method for constructing the basis functions of a wide variety of partially continuous tensor-valued finite elements on simplices using polytopal templates. These finite element spaces are essential for achieving well-posed discretisations of mixed formulations of partial differential equations that involve tensor-valued functions, such

Gabriel Soares Rocha, Lorenzo Gavassino, Mayank Singh, Jean-François Paquet

We introduce a minimal model of ultracentral heavy-ion collisions to study the relation between the speed of sound of the produced plasma and the final particles' energy and multiplicity. We discuss how the particles' multiplicity $N_{\textrm{tot}}$ and average energy $E_{\textrm{tot}}/N_{\textrm{tot}}$ is related to the speed of sound $c_s$ by $c_s^2=d \ln

M. Brady, J. Bean, A. Seifahrt, D. Kasper

M dwarf stars provide us with an ideal opportunity to study nearby small planets. The HUMDRUM (HUnting for M Dwarf Rocky planets Using MAROON-X) survey uses the MAROON-X spectrograph, which is ideally suited to studying these stars, to measure precise masses of a volume-limited ($<\,30$ pc) sample of transiting M dwarf planets. TOI-1450 is a nearby (22.5 pc)

Lexing Ying

In online learning, the data is provided in a sequential order, and the goal of the learner is to make online decisions to minimize overall regrets. This note is concerned with continuous-time models and algorithms for several online learning problems: online linear optimization, adversarial bandit, and adversarial linear bandit. For each problem, we extend

Kholoud AlDosari, AIbtisam Osman, Omar Elharrouss, Somaya AlMaadeed

The Unmanned Aerial Vehicles (UAVs) market has been significantly growing and Considering the availability of drones at low-cost prices the possibility of misusing them, for illegal purposes such as drug trafficking, spying, and terrorist attacks posing high risks to national security, is rising. Therefore, detecting and tracking unauthorized drones to preve

L. Bufaiçal, E. M. Bittar

Here, we present a review of the phenomenology of the spontaneous exchange bias effect, a phenomenon in which some materials exhibit unidirectional magnetic anisotropy even without the assistance of an external magnetic field applied during its cooling process. We review and discuss the most critical advances in this field of research that flourished more th

Grace Fiacco, Neil J. Cornish, Hang Yu

Double white dwarfs are important gravitational wave sources for LISA, as they are some of the most numerous compact systems in our universe. Here we consider finite-sized effects due to tidal interactions, as they are expected to have a measurable impact on these systems. Previous studies suggested that tidal effects would allow the individual masses to be

Kevin G. Hare, Chatchai Noytaptim

In this article, we study some potential theoretical and topological aspects of the generalized Mandelbrot set introduced by Baker and DeMarco. For $\alpha$ real, we study the set of all totally real algebraic parameters $c$ such that $\alpha$ is preperiodic under the iteration of the one-parameter family $f_c(x) = x^2 + c$. We show that when $|\alpha| < 2$

A. J. Schwartz

We report precision measurements of lifetimes of charmed mesons and baryons performed by the Belle II experiment. Specifically, we measure $D_s^+$, $D^+$, $D^0$, $\Lambda_c^+$, and $\Omega_c^0$ lifetimes. Our results for $\tau(D_s^+)$, $\tau(D^+)$, $\tau(D^0)$, and $\tau(\Lambda_c^+)$ are the world's most precise; our result for $\tau(\Omega_c^0)$ confirms t

Kamal N. Soltanov

This article studies the uniqueness of the weak solution of the incompressible Navier-Stokes Equations in the 3-dimensional case. Here, the investigation is provided using two different approaches. The first (the main) result is obtained for given functions possessing a certain smoothness using the new approach. The second result is without the complementary

Vasily Ilin, Jingwei Hu, Zhenfu Wang

We propose a particle method for numerically solving the Landau equation, inspired by the score-based transport modeling (SBTM) method for the Fokker-Planck equation. This method can preserve some important physical properties of the Landau equation, such as the conservation of mass, momentum, and energy, and decay of estimated entropy. We prove that matchin

Anish Bhattacharya, Nishanth Rao, Dhruv Parikh, Pratik Kunapuli

We demonstrate the capabilities of an attention-based end-to-end approach for high-speed vision-based quadrotor obstacle avoidance in dense, cluttered environments, with comparison to various state-of-the-art learning architectures. Quadrotor unmanned aerial vehicles (UAVs) have tremendous maneuverability when flown fast; however, as flight speed increases,

Jan Martin Nordbotten, Eirik Keilegavlen

In this paper, we construct a simple and robust two-point finite volume discretization applicable to isotropic linearized elasticity, valid in also in the incompressible Stokes' limit. The discretization is based only on co-located, cell-centered variables, and has a minimal discretization stencil, using only the two neighboring cells to a face to calculate

Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of

Rodolfo Cunha Carnier

In the present paper we prove the compactness theorem with respect to partial structures and quasi-truth, using the technique of ultraproducts. Partial structures and quasi-truth are two notions developed within the partial structures approach, which is a philosophical conception that emerged in the context of contemporary philosophy of science. Nevertheless

Kevin Buzzard

We discuss how the concept of equality is used by mathematicians (including Grothendieck), and what effect this has when trying to formalise mathematics. We challenge various reasonable-sounding slogans about equality.

Adamu Issifu, Débora P. Menezes, Zeinab Rezaei, Tobias Frederico

This work investigates the evolution of proto-neutron stars (PNSs) from birth as neutrino-rich objects to maturity as cold-catalyzed neutrino-poor objects with nucleonic and non-nucleonic degrees of freedom. The focus is on the star's core where the nucleons, hyperons, and the $\Delta$-isobars are expected to dissolve into a ``soup" of deconfined quarks, at

Hoda Abdolalizade, Ekrem Aydiner

In this Letter, using von Neumann entropy we examine the entanglement entropy for the neutrino oscillations in the presence of the subsequent phase shift. We numerically show that the entanglement entropy for the subsequent periods of the two-flavor neutrino oscillations increases asymmetrically with time depending on the space-time deformation. We also expl

Mina Ghashami, Soumya Smruti Mishra

The SemEval 2024 BRAINTEASER task challenges language models to perform lateral thinking -- a form of creative, non-linear reasoning that remains underexplored in NLP. The task comprises two subtasks, Sentence Puzzle and Word Puzzle, requiring models to defy conventional commonsense associations. We present a system that fine-tunes DeBERTaV3 using HuggingFac

Musanna Galib, Okan K. Orhan, Jian Liu, Mauricio Ponga

Atomic and molecular layer deposition (ALD/MLD) coatings are promising solutions for preventing dendrite formation in aqueous and non-aqueous Li/Na/Zn metal batteries. Notably, alumina and alucone coatings have emerged as highly effective against dendrite formation in Zn anodes. Despite their demonstrated efficacy, a comprehensive understanding of their chem

Masatoshi Kitagawa

In this paper, we deal with the $\mathcal{U}(\mathfrak{g})$-action on a $\mathfrak{g}$-module on which a larger algebra $\mathcal{A}$ acts irreducibly. Under a mild condition, we will show that the support of the $\mathcal{Z}(\mathfrak{g})$-action is a union of affine subspaces in the dual of a Cartan subalgebra modulo the Weyl group action. As a consequence

O. A. Malafeyev, V. Vekovtsev

This article explores the interaction of two agents during a geopolitical operation. Collaborative work is considered, rather than being done alone. However, each agent has the goal of maximizing personal net profit. We will have 3 different situations depending on the order of the players moves, in each of which we will present the game in both expanded and

Ainhoa Zubiaur, Roberto Raddi, Santiago Torres

Thanks to the recent space-borne mission Gaia, there is a nearly complete sample of white dwarfs up to about 100 parsecs from the Sun, which may have very diverse origins. We aim to compute the Galactic orbits for white dwarfs observed in our Solar neighborhood, in order to analyze the most probable regions of the Galaxy where they could have formed, the dis

Sam Christian, Andrew Vanderburg, Juliette Becker, Adam L. Kraus

Studying the relative orientations of the orbits of exoplanets and wide-orbiting binary companions (semimajor axis greater than 100 AU) can shed light on how planets form and evolve in binary systems. Previous observations by multiple groups discovered a possible alignment between the orbits of visual binaries and the exoplanets that reside in them. In this

Sayan Bandyapadhyay, Eden Chlamtáč, Zachary Friggstad, Mahya Jamshidian

In this work, we study pairwise fair clustering with $\ell \ge 2$ groups, where for every cluster $C$ and every group $i \in [\ell]$, the number of points in $C$ from group $i$ must be at most $t$ times the number of points in $C$ from any other group $j \in [\ell]$, for a given integer $t$. To the best of our knowledge, only bi-criteria approximation and ex

Narjes Nourzad, Bhaskar Krishnamachari

In dynamic and resource-constrained environments, such as multi-hop wireless mesh networks, traditional routing protocols often falter by relying on predetermined paths that prove ineffective in unpredictable link conditions. Shortest Anypath routing offers a solution by adapting routing decisions based on real-time link conditions. However, the effectivenes

Yichuan Shi, Olivera Kotevska, Viktor Reshniak, Abhishek Singh

Federated Learning (FL) has emerged as a leading paradigm for decentralized, privacy preserving machine learning training. However, recent research on gradient inversion attacks (GIAs) have shown that gradient updates in FL can leak information on private training samples. While existing surveys on GIAs have focused on the honest-but-curious server threat mo

George K. Fordjour, Alfred J. Kalyanapu

Ashland City, Tennessee, located within the Lower Cumberland Sycamore watershed, is highly susceptible to flooding due to increased upstream water levels. This study aimed to develop a robust flood prediction model for the city, utilizing water level data at 30-minute intervals from ten USGS gauge stations within the watershed. A Gated Recurrent Unit (GRU) n

Supritha Bhowmick, Diptimoy Ghosh, Farman Ullah

In this paper we compute 1-loop corrections to the bispectrum in the decoupling limit of the Effective Field Theory of Inflation (EFToI). We regulate the divergences by employing dimensional regularization and work in $d=3+\delta$ dimensions. We find that the final results feature analytic structures of the form $\log{\left(k_i/k_T\right)}$ and $\log{\left(H

Emily Kesler

Classical cybersecurity is often perceived as a rigid science discipline filled with computer scientists and mathematicians. However, due to the rapid pace of technology development and integration, new criminal enterprises, new defense tactics, and the understanding of the human element, cybersecurity is quickly beginning to encompass more than just compute

Jianglin Lan

This paper presents a model predictive control (MPC) for dynamic systems whose nonlinearity and uncertainty are modelled by deep neural networks (NNs), under input and state constraints. Since the NN output contains a high-order complex nonlinearity of the system state and control input, the MPC problem is nonlinear and challenging to solve for real-time con

Linda Mhalla, Valérie Chavez-Demoulin, Philippe Naveau

Causal asymmetry is based on the principle that an event is a cause only if its absence would not have been a cause. From there, uncovering causal effects becomes a matter of comparing a well-defined score in both directions. Motivated by studying causal effects at extreme levels of a multivariate random vector, we propose to construct a model-agnostic causa

Yilun Chen, Shuai Yang, Haifeng Huang, Tai Wang

Prior studies on 3D scene understanding have primarily developed specialized models for specific tasks or required task-specific fine-tuning. In this study, we propose Grounded 3D-LLM, which explores the potential of 3D large multi-modal models (3D LMMs) to consolidate various 3D vision tasks within a unified generative framework. The model uses scene refere

Sarod Yatawatta

Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operational aspects of astronomy are guided and executed by expert astronomers. Reinforcement learning is a mechanism where we (as humans and astronomers) can teach agents of artificial i