October 2022 arXiv papers — page 70

Xinliang Liu, Bo Xu, Shuhao Cao, Lei Zhang

Neural operators have emerged as a powerful tool for learning the mapping between infinite-dimensional parameter and solution spaces of partial differential equations (PDEs). In this work, we focus on multiscale PDEs that have important applications such as reservoir modeling and turbulence prediction. We demonstrate that for such PDEs, the spectral bias tow

Marcos T. O. Pimenta, Gelson C. G. Santos, João R. Santos Júnior

In this work, we study a quasilinear elliptic problem involving the 1-laplacian operator, with a discontinuous, superlinear and subcritical nonlinearity involving the Heaviside function $H(\cdot - \beta)$. Our approach is based on an analysis of the associated p-laplacian problem, followed by a thorough analysis of the asymptotic behaviour or such solutions

Sihong He, Lynn Pepin, Guang Wang, Desheng Zhang

As electric vehicle (EV) technologies become mature, EV has been rapidly adopted in modern transportation systems, and is expected to provide future autonomous mobility-on-demand (AMoD) service with economic and societal benefits. However, EVs require frequent recharges due to their limited and unpredictable cruising ranges, and they have to be managed effic

Ruinan Jin, Xiaoxiao Li

Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research and augment medical datasets. Training generative adversarial neural networks (GANs) usually require large amounts of training data. Federated learning (FL) provides a way of training a central model using distributed

Matthew Kolopanis, Jonathan Pober, Daniel C. Jacobs, Samantha McGraw

We present an analysis of Epoch of Reionization data from Phase II of the Murchison Widefield Array using the \texttt{simpleDS} delay spectrum pipeline. Prior work analyzed the same observations using the FHD/$\varepsilon$ppsilon imaging pipeline, and so the present analysis represents the first time that both principal types of 21 cm cosmology power spectru

Caroline Laber-Smith, A. A. Ahmetaj, Eve Armstrong, A. Baha Balantekin

We continue examining statistical data assimilation (SDA), an inference methodology, to infer solutions to neutrino flavor evolution, for the first time using real - rather than simulated - data. The model represents neutrinos streaming from the Sun's center and undergoing a Mikheyev-Smirnov-Wolfenstein (MSW) resonance in flavor space, due to the radially-va

Sebastian Diebold

Developed right at the beginning of the space age in the 1940s, the proportional counter was the first detector used in X-ray astronomy and stayed its workhorse for almost four decades. Although the principle of such a detector seems to be rather simple, over time it underwent considerable performance improvements and the lifetime under orbital conditions wa

Aliza U. Siddiqui, Mark M. Wilde

Bidirectional quantum teleportation is a fundamental protocol for exchanging quantum information between two parties. Specifically, the two individuals make use of a shared resource state as well as local operations and classical communication (LOCC) to swap quantum states. In this work, we concisely highlight the contributions of our companion paper [Siddiq

Valeri Frumkin, Shreyas Gokhale

We present an experimental study of quasiperiodic transitions between a highly ordered square-lattice pattern and a disordered, defect-riddled state, in a circular Faraday system. We show that the transition is driven initially by a long-wave amplitude modulation instability, which excites the oscillatory transition phase instability, leading to the formatio

Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger

Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy. To mitigate this vulnerability, prior work proposed both principled defenses based on differential privacy, as well as heuristic defenses based on gradient compression as countermeasures. These defe

Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng

Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training. However, even as so much of the ASR training process has become automated and more "end-to-end", the data augmentation policy (what augmentation functions to use, and how to apply them) remains hand-crafted. We present Graph-Augment, a techn

Anna Abbatiello, Miroslav Bulíček, Petr Kaplický

We consider a non-Newtonian incompressible heat conducting fluid with prescribed nonuniform temperature on the boundary and with the no-slip boundary conditions for the velocity. We assume no external body forces. For the power-law like models with the power law index bigger than $11/5$ in three dimensions, we identify a class of solutions fulfilling the ent

Alex J. Meyer, Daniel J. Scheeres, Harrison F. Agrusa, Guillaume Noiset

Synchronous binary asteroids can experience libration about their tidally-locked equilibrium, which will result in energy dissipation. This is an important topic to the Asteroid Impact and Deflection Assessment, where excitation caused by the DART kinetic impact in the Didymos binary asteroid system may be reduced through dissipation before Hera arrives to s

Richard Chakra

In the past two decades, autonomous driving has been catalyzed into reality by the growing capabilities of machine learning. This paradigm shift possesses significant potential to transform the future of mobility and reshape our society as a whole. With the recent advances in perception, planning, and control capabilities, autonomous driving technologies are

John Paul Helveston

This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according to a chosen distribution. The package is faster than other similar packages such as mlogit, gmnl, mi

D. R. Mizuno, Kathleen E. Kraemer, T. A. Kuchar, G. C. Sloan

We present mosaic images of the Small Magellanic Cloud (SMC) observed with the Spitzer IRAC 3.6 $\mu$m and 4.5 $\mu$m bands over two epochs, 2017 August 25 to 2017 September 13, and 2017 November 24 to 2018 February 12. The survey region comprises $\sim$30 square degrees covering the SMC and the Bridge to the Large Magellanic Cloud. The region is covered by

Yahya Alnashri

This paper applies the gradient discretisation method (GDM) for fourth order elliptic variational inequalities. The GDM provides a new formulation of error estimates and a complete convergence analysis of several numerical methods. We show that the convergence is unconditional. Classical assumptions on data are only sufficient to establish the convergence re

Shivesh Pathak, Antonio Russo, Stefan Seritan, Andrew Baczewski

We study the question of when investing additional quantum resources in preparing a ground state will improve the aggregate runtime associated with estimating its energy. We analyze Lin and Tong's near-optimal state preparation algorithm and show that it can reduce a proxy for the runtime, the $T$-gate count, of ground state energy estimation near quadratica

Padinhare Cholakkal Harikesh, Chi-Yuan Yang, Han-Yan Wu, Silan Zhang

Bio-integrated neuromorphic systems promise for new protocols to record and regulate the signaling of biological systems. Making such artificial neural circuits successful requires minimal circuit complexity and ion-based operating mechanisms similar to that of biology. However, simple leaky integrate-and-fire model neurons, commonly realized in either silic

Tristan Heider, Gustav Bihlmayer, Jakub Schusser, Friedrich Reinert

We demonstrate that an important quantum material WTe$_2$ exhibits a new type of geometry-induced spin-filtering effect in photoemission, stemming from low symmetry that is responsible for its exotic transport properties. Through the laser-driven spin-polarized angle-resolved photoemission Fermi surface mapping, we showcase highly asymmetric spin textures of

Andrei P. Igoshev, Rainer Hollerbach

We numerically model evolution of magnetic fields inside a neutron star under the influence of ambipolar diffusion in the weak-coupling mode in the one-fluid MHD approximation. Our simulations are three-dimensional and performed in spherical coordinates. Our model covers the neutron star core and includes crust where the magnetic field decay is due to Ohmic

Francesco Ferrante, Ricardo G. Sanfelice, Sophie Tarbouriech

The problem of designing a stabilizing feedback controller in the presence of saturating actuators and multi-rate (asynchronous) aperiodic state measurements is studied. Specifically, we consider a scenario in which measurements of the plant states are collected at the controller end in a sporadic and asynchronous fashion. A hybrid controller is used to perf

Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi

When fine-tuning large neural networks, it is common to use multiple nodes and to communicate gradients at each optimization step. By contrast, we investigate completely local fine-tuning, which we refer to as lo-fi. During lo-fi, each node is fine-tuned independently without any communication. Then, the weights are averaged across nodes at the conclusion of

Patrick Hosein, Jaimie Greasley

In powder diffraction data analysis, phase identification is the process of determining the crystalline phases in a sample using its characteristic Bragg peaks. For multiphasic spectra, we must also determine the relative weight fraction of each phase in the sample. Machine Learning algorithms (e.g., Artificial Neural Networks) have been applied to perform s

Koichiro Furutani, Andrea Perali, Luca Salasnich

We theoretically investigate the superfluid-normal-state Berezinskii-Kosterlitz-Thouless transition in a binary mixture of bosonic atoms with Rabi coupling under balanced densities. We find the nonmonotonic behavior of the transition temperature with respect to the intercomponent coupling and amplification of the transition temperature for finite values of R

Thomas Lew, Sumeet Singh, Mario Prats, Jeffrey Bingham

We propose a framework to enable multipurpose assistive mobile robots to autonomously wipe tables to clean spills and crumbs. This problem is challenging, as it requires planning wiping actions while reasoning over uncertain latent dynamics of crumbs and spills captured via high-dimensional visual observations. Simultaneously, we must guarantee constraints s

Hazem Fahmy, Sabita Mahrajan

This paper proposes an extensive overview of safety applications and approaches as it relates to automated driving from the prospectives of sensor configurations, vehicle dynamics modelling, tyre modeling, and estimation approaches. First, different Advanced-Driver Assistance Systems (ADAS) are introduced along with the main sensing components and technologi

Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu

Feature fusion plays a crucial role in unconstrained face recognition where inputs (probes) comprise of a set of $N$ low quality images whose individual qualities vary. Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set. However, attention mechanisms cannot scale to large $

D. K. Sinclair, J. B. Kogut

We simulate Lattice QED in a constant external magnetic field using the RHMC algorithm. We seek evidence for chiral symmetry breaking predicted by truncated Schwinger-Dyson methods. Since the predicted values of the dynamical electron mass and chiral condensate at the physical fine structure constant are too small to be measured, we simulate at a larger valu

Sarah Scherotzke, Nicolo Sibilla

In this article we study the equivariant elliptic cohomology of complex toric varieties. We prove a partial reconstruction theorem showing that equivariant elliptic cohomology encodes considerable non-trivial information on the equivariant 1-skeleton of a toric variety X (although it stops short of being a complete invariant of its GKM graphs). Elliptic coho

Zhenrui Yue, Huimin Zeng, Bernhard Kratzwald, Stefan Feuerriegel

Question answering (QA) has recently shown impressive results for answering questions from customized domains. Yet, a common challenge is to adapt QA models to an unseen target domain. In this paper, we propose a novel self-supervised framework called QADA for QA domain adaptation. QADA introduces a novel data augmentation pipeline used to augment training Q

Alicia Parrish, Harsh Trivedi, Nikita Nangia, Vishakh Padmakumar

The use of language-model-based question-answering systems to aid humans in completing difficult tasks is limited, in part, by the unreliability of the text these systems generate. Using hard multiple-choice reading comprehension questions as a testbed, we assess whether presenting humans with arguments for two competing answer options, where one is correct

Hiromitsu Sawaoka, Alexander Frenett, Abdullah Nasir, Tasuku Ono

We demonstrate and characterize Zeeman-Sisyphus (ZS) deceleration of a beam of ytterbium monohydroxide (YbOH). Our method uses a combination of large magnetic fields ($\sim$ 2.5 T) and optical spin-flip transitions to decelerate molecules while scattering only $\sim$ 10 photons per molecule. We study the challenges associated with the presence of internal mo

Masato Hagiwara, Maddie Cusimano, Jen-Yu Liu

Modeling real-world sound is a fundamental problem in the creative use of machine learning and many other fields, including human speech processing and bioacoustics. Transformer-based generative models and some prior work (e.g., DDSP) are known to produce realistic sound, although they have limited control and are hard to interpret. As an alternative, we aim

Andrea Nava, Gabriele Campagnano, Pasquale Sodano, Domenico Giuliano

We use the Lindblad equation method to investigate the onset of a mobility edge and the topological phase transition in the disordered SSH chain connected to two external baths in the large bias limit. From the scaling properties of the nonequilibrium stationary current flowing across the system, we recover the localization/delocalization in the disordered c

Alexei Novikov, Stephen White

Dictionary learning, the problem of recovering a sparsely used matrix $\mathbf{D} \in \mathbb{R}^{M \times K}$ and $N$ $s$-sparse vectors $\mathbf{x}_i \in \mathbb{R}^{K}$ from samples of the form $\mathbf{y}_i = \mathbf{D}\mathbf{x}_i$, is of increasing importance to applications in signal processing and data science. When the dictionary is known, recovery

Solene Bechelli

The past years have seen a considerable increase in cancer cases. However, a cancer diagnosis is often complex and depends on the types of images provided for analysis. It requires highly skilled practitioners but is often time-consuming and error-prone. If Machine Learning and deep learning algorithms have been widely used, a comprehensive review of the tec

Srikar Kasi, John Kaewell, Shahab Hamidi-Rad, Kyle Jamieson

The use of quantum computation for wireless network applications is emerging as a promising paradigm to bridge the performance gap between in-practice and optimal wireless algorithms. While today's quantum technology offers limited number of qubits and low fidelity gates, application-based quantum solutions help us to understand and improve the performance o

Damien Beaulieu, Andreea Petric, Carmelle Robert, Katherine Alatalo

We present star formation rates based on cold and ionized gas measurements of Mrk 266 (NGC 5256), a system composed of two colliding gas-rich galaxies, each hosting an active galactic nucleus. Using $^{12}$CO (1-0) observations with the Combined Array for Research in Millimeter-Wave Astronomy (CARMA), we find a total H$_2$ mass in the central region of $1.1\

Benjamin Brown, Kai Zhang, Xiao-Li Meng

Two linearly uncorrelated binary variables must be also independent because non-linear dependence cannot manifest with only two possible states. This inherent linearity is the atom of dependency constituting any complex form of relationship. Inspired by this observation, we develop a framework called binary expansion linear effect (BELIEF) for understanding

Daniel Sturm, Sajjad Moazeni

Optical computing has been recently proposed as a new compute paradigm to meet the demands of future AI/ML workloads in datacenters and supercomputers. However, proposed implementations so far suffer from lack of scalability, large footprints and high power consumption, and incomplete system-level architectures to become integrated within existing datacenter

James Greenberg, Brendan M. Heffernan, Tomohiro Tetsumoto, Antoine Rolland

Controlling the coherence between light and matter has enabled the radiation of electromagnetic waves with spectral purity and stability that defines the Syst\`eme International (SI) second. While transitions between hyperfine levels in atoms are accessible in the microwave and optical domains, faithfully transferring such stability to other frequency ranges

José Ribeiro, Níkolas Carneiro, Ronnie Alves

Strategies based on Explainable Artificial Intelligence (XAI) have promoted better human interpretability of the results of black box models. This opens up the possibility of questioning whether explanations created by XAI methods meet human expectations. The XAI methods being currently used (Ciu, Dalex, Eli5, Lofo, Shap, and Skater) provide various forms of

Robin K. S. Hankin

In this short article I introduce the spray package, which provides some functionality for handling sparse arrays. The package uses the C++ Standard Template Library's map class to store and retrieve elements. One natural application for sparse arrays is multivariate polynomials and I give two examples of the package in use, one drawn from the fields of rand

Igor Chagas Santos

In this paper, we generalize the idea of equiaffine structure to the case of frontals and we define the Blaschke vector field of a frontal. We also investigate some necessary and sufficient conditions that a frontal needs to satisfy to have a Blaschke vector field and provide some examples. Finally, taking the theory developed here into account we present a

Thomas G. Mertens, Gustavo J. Turiaci

We review recent developments in Jackiw-Teitelboim (JT) gravity. This is a simple solvable model of quantum gravity in two dimensions (that arises e.g. from the s-wave sector of higher dimensional gravity systems with spherical symmetry). Due to its solvability, it has proven to be a fruitful toy model to analyze important questions such as the relation betw

Josh Kline

In the setting of a metric space equipped with a doubling measure supporting a $(1,1)$-Poincar\'e inequality, we study the problem of minimizing the BV-energy in a bounded domain $\Omega$ of functions bounded between two obstacle functions inside $\Omega$, and whose trace lies between two prescribed functions on the boundary of $\Omega.$ If the class of cand

S. Chatterjee, P. P. Bhaduri, S. Chattopadhyay

The yield of $J/\psi$ mesons, produced in proton-nucleus ($p+A$) and nucleus-nucleus ($A+A$) collisions are estimated within a Glauber model ansatz for the upcoming low energy heavy-ion collision experiments at SPS and FAIR. A data driven parametrization is employed to incorporate the effects of Cold Nuclear Matter (CNM) on the $J/\psi$ production cross-sect

Valerie Hsieh, Dorri Halbertal, Nathan R. Finney, Ziyan Zhu

Twisted van der Waals multilayers are widely regarded as a rich platform to access novel electronic phases, thanks to the multiple degrees of freedom such as layer thickness and twist angle that allow control of their electronic and chemical properties. Here, we propose that the stacking domains that form naturally due to the relative twist between successiv

Yuhao Chen, Hayden Gunraj, E. Zhixuan Zeng, Robbie Meyer

Recently, there has been tremendous interest in industry 4.0 infrastructure to address labor shortages in global supply chains. Deploying artificial intelligence-enabled robotic bin picking systems in real world has become particularly important for reducing stress and physical demands of workers while increasing speed and efficiency of warehouses. To this e

Yue Zhang, Hongliang Fei, Dingcheng Li, Tan Yu

Prompt learning is a new learning paradigm which reformulates downstream tasks as similar pretraining tasks on pretrained models by leveraging textual prompts. Recent works have demonstrated that prompt learning is particularly useful for few-shot learning, where there is limited training data. Depending on the granularity of prompts, those methods can be ro

T. M. Rocha Filho, M. L. Lucio, Fulvio A. Scorza, M. A. Moret

We discuss the relationships between the outcome of the COVID-19 pandemic in Brazil at the municipal level and different health, social, demographic, and economic indices. We obtain significant correlations between the data gathered for each municipalitiy and the proportion of cases and deaths by COVID-19 and the results by municipality of the 2018 Brazilian

Michael Miller Yoder, Lynnette Hui Xian Ng, David West Brown, Kathleen M. Carley

This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differenc

Nataša Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel

Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences. However, these problems often require sampling new designs that satisfy multiple properties of interest in addition to learning the data distribution. This multi-objective optimization becomes more challenging when properties are

Changjian Shui, Gezheng Xu, Qi Chen, Jiaqi Li

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of group sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all s

Joshua Cesare Placidi, Yishu Miao, Zixu Wang, Lucia Specia

Scene Text Recognition (STR) models have achieved high performance in recent years on benchmark datasets where text images are presented with minimal noise. Traditional STR recognition pipelines take a cropped image as sole input and attempt to identify the characters present. This infrastructure can fail in instances where the input image is noisy or the te

S. A. Moiseev, M. M. Minnegaliev, E. S. Moiseev, K. I. Gerasimov

We derive the area theorem for light pulses interacting with inhomogeneously broadened ensemble of two-level atoms in a single-mode optical waveguide and present its analytical solution for Gaussian-type modes, which demonstrates the significant difference from the formation of $2\pi$ pulses by plane waves. We generalize this theorem to the description of ph

Inbar Fried, Jason A. Akulian, Ron Alterovitz

The prospect of using autonomous robots to enhance the capabilities of physicians and enable novel procedures has led to considerable efforts in developing medical robots and incorporating autonomous capabilities. Motion planning is a core component for any such system working in an environment that demands near perfect levels of safety, reliability, and pre

S. Birrer, M. Millon, D. Sluse, A. J. Shajib

Multiply lensed sources experience a relative time delay in the arrival of photons. This effect can be used to measure absolute distances and the Hubble constant ($H_0$) and is known as time-delay cosmography. The methodology is independent of the local distance ladder and early-universe physics and provides a precise and competitive measurement of $H_0$. Wi

Jingjing Li, Nicole Montgomery, Reza Mousavi

With the proliferation of social activism online, brands face heightened pressure from consumers to publicly address these issues. Yet, the optimal brand response strategy (i.e., whether and how to respond) in these contexts remains unclear. This research investigates consumers' reactions to brand response strategies (e.g., engage vs. not) during social acti

Valerian-Alin Fodor, Nicolae Popovici

We consider scalar equilibrium problems governed by a bifunction in a finite-dimensional framework. By using classical arguments in Convex Analysis, we show that under suitable generalized convexity assumptions imposed on the bifunction, the solutions of the equilibrium problem can be characterized by means of extreme or exposed points of the feasible domain

Dexter Cahoy, Joseph Sedransk

To improve the precision of inferences and reduce costs there is considerable interest in combining data from several sources such as sample surveys and administrative data. Appropriate methodology is required to ensure satisfactory inferences since the target populations and methods for acquiring data may be quite different. To provide improved inferences w

Mustapha Kaci, Sonia Radjef

In this paper, we present a novel method for solving multiobjective linear programming problems (MOLPP) that overcomes the need to calculate the optimal value of each objective function. This method is a follow-up to our previous work on sensitivity analysis, where we developed a new geometric approach. The first step of our approach is to divide the space o

Zeeshan Khan, C. V. Jawahar, Makarand Tapaswi

Dense video understanding requires answering several questions such as who is doing what to whom, with what, how, why, and where. Recently, Video Situation Recognition (VidSitu) is framed as a task for structured prediction of multiple events, their relationships, and actions and various verb-role pairs attached to descriptive entities. This task poses sever

Dale E. Gary, Bin Chen, James F. Drake, Gregory D. Fleishman

The Frequency Agile Solar Radiotelescope (FASR) has been strongly endorsed as a top community priority by both Astronomy & Astrophysics Decadal Surveys and Solar & Space Physics Decadal Surveys in the past two decades. Although it was developed to a high state of readiness in previous years (it went through a CATE analysis and was declared ``doable now"), th

David Ruiz, Pieralberto Sicbaldi, Jing Wu

In this paper, we prove the existence of nontrivial contractible domains $\Omega\subset\mathbb{S}^{d}$, $d\geq2$, such that the overdetermined elliptic problem \begin{equation*} \begin{cases} -\varepsilon\Delta_{g} u +u-u^{p}=0 &\mbox{in $\Omega$, } u>0 &\mbox{in $\Omega$, } u=0 &\mbox{on $\partial\Omega$, } \partial_{\nu} u=\mbox{constant} &\mbox{on $\parti

Manuel Alberto M. Ferreira

We present formulas to compute the busy cycle renewal function for the $M|G|\infty$ queue and exemplify for some service time distributions. The busy cycle renewal function value in t is the number of busy periods that begin in the interval from 0 till $t$. This number is of crucial importance in practical applications of $M|G|\infty$ queue.

Marcelo V. Flamarion, Tao Gao, Roberto Ribeiro-Jr, Alex Doak

Waves with constant vorticity and electrohydrodynamics flows are two topics in fluid dynamics that have attracted much attention from scientists for both the mathematical challenge and their industrial applications. The coupling of electric fields and vorticity is of significant research interest. In this paper, we study the flow structure of steady periodic

Shuting Tao, Peng Peng, Qi Li, Hongwei Wang

Class imbalance has a detrimental effect on the predictive performance of most supervised learning algorithms as the imbalanced distribution can lead to a bias preferring the majority class. To solve this problem, we propose a Supervised Contrastive Learning (SCL) method with Tree-structured Parzen Estimator (TPE) technique for imbalanced tabular datasets. C

Mikaël Pichot, Erik Séguin

We introduce an Ulam-type stability condition for positive definite maps defined on a countable group and prove that this condition characterizes amenability.

Nestor Mirabal

The origin of ultra-high energy cosmic rays (UHECRs) is one of the most mystifying issues in astroparticle physics. It has been suggested that gamma-ray bursts (GRBs) are excellent acceleration sites for cosmic rays. The propagation of UHECRs from the GRB host galaxy to the Earth should generate delayed secondary photons and neutrinos. Here we present a dedi

Ruolin Ye, Wenqiang Xu, Haoyuan Fu, Rajat Kumar Jenamani

We present RCareWorld, a human-centric simulation world for physical and social robotic caregiving designed with inputs from stakeholders, including care recipients, caregivers, occupational therapists, and roboticists. RCareWorld has realistic human models of care recipients with mobility limitations and caregivers, home environments with multiple levels of

Laura Hanu, James Thewlis, Yuki M. Asano, Christian Rupprecht

Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and consist of mostly clean samples where all modalities are well-correlated with the content. Thus, current video-text retrieval literature largely focuses on video titles or audio transc

Ting Cheng, Manfred Lindner, Manibrata Sen

The Standard Model (SM) of particle physics, being a local, unitary and Lorentz-invariant quantum field theory, remains symmetric under the combined action of Charge, Parity, and Time Reversal (CPT) symmetry. This automatically implies that fundamental properties of particles and antiparticles should be equal in magnitude. These fundamental tenets of the CPT

Kiana Burton, Meredith A. MacGregor, Rachel A. Osten

We report the detection of three large millimeter flaring events from the nearby Sun-like, $\epsilon$ Eridani, found in archival ALMA 12m and ACA observations at 1.33 mm taken from 2015 January 17-18 and 2016 October 24-November 23, respectively. This is the first time that flares have been detected from a Sun-like star at millimeter wavelengths. The largest

Darcey Riley, David Chiang

Language models suffer from various degenerate behaviors. These differ between tasks: machine translation (MT) exhibits length bias, while tasks like story generation exhibit excessive repetition. Recent work has attributed the difference to task constrainedness, but evidence for this claim has always involved many confounding variables. To study this questi

Abigail Moran, Chiara M. F. Mingarelli, Megan Bedell, Deborah Good

Pulsar distances are notoriously difficult to measure, and play an important role in many fundamental physics experiments, such as pulsar timing arrays (PTAs). Here we perform a cross-match between International PTA pulsars (IPTA) and Gaia's DR2 and DR3. We then combine the IPTA pulsar's parallax with its binary companion's parallax, found in Gaia, to improv

Tingtao Zhou, Hongping Deng, Yi-Xian Chen, Douglas N. C. Lin

We study the long-term radial transport of micron to mm-size grain in protostellar disks (PSDs) based on diffusion and viscosity coefficients measured from 3D global stratified-disk simulations with a Lagrangian hydrodynamic method. While gas-drag tend to transport dust species radially inwards, stochastic diffusion can spread a considerable fraction of dust

Oswin So, Paul Drews, Thomas Balch, Velin Dimitrov

Game theoretic methods have become popular for planning and prediction in situations involving rich multi-agent interactions. However, these methods often assume the existence of a single local Nash equilibria and are hence unable to handle uncertainty in the intentions of different agents. While maximum entropy (MaxEnt) dynamic games try to address this iss

Paul Marconnet, Dimitrios Tsimpis

We study 4d Friedmann-Lema\^{i}tre-Robertson-Walker cosmologies obtained from time-dependent compactifications of Type IIA 10d supergravity on various classes of 6d manifolds (Calabi-Yau, Einstein, Einstein-K\"{a}hler). The cosmologies we present are universal in that they do not depend on the detailed features of the compactification manifold, but only on t

Eliot Finch, Giorgia Bartolucci, Daniel Chucherko, Ben G. Patterson

Double white dwarfs (DWDs) will be the most numerous gravitational-wave (GW) sources for the Laser Interferometer Space Antenna (LISA). Most of the Galactic DWDs will be unresolved and will superpose to form a confusion noise foreground, the dominant LISA noise source around $\sim 0.5\mathrm{-}3\,\mathrm{mHz}$. A small fraction of these sources will stand ou

Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh

In November 2019 we began operating FLEET (Finding Luminous and Exotic Extragalactic Transients), a machine learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Using FLEET, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between November 2019 and January 2022. Bas

Sebastian Gomez, V. Ashley Villar, Edo Berger, Suvi Gezari

We present an expansion of FLEET, a machine learning algorithm optimized to select transients that are most likely to be tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on the light curves and host galaxy information of 4,779 spectroscopically classified transients. For transients with a probability of being a TDE, \ptde$>

Anna Cecilie Åsland, Johannes Bakkelund, Even Thingstad, Håkon I. Røst

Vicinal surfaces of bismuth are unique test-beds for investigating one-dimensional (1D) spin-polarised surface states that may one day be used in spintronic devices. In this work, two such states have been observed for the (112) surface when measured using angle-resolved photoemission spectroscopy (ARPES) and spin-resolved ARPES, and when calculated using a

Grace M. Sommers, David A. Huse, Michael J. Gullans

Random quantum circuits continue to inspire a wide range of applications in quantum information science and many-body quantum physics, while remaining analytically tractable through probabilistic methods. Motivated by an interest in deterministic circuits with similar applications, we construct classes of \textit{nonrandom} unitary Clifford circuits by impos

Benjamin T. Jones, Michael Hu, Vladimir G. Kim, Adriana Schulz

The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in CAD's native format; the parametric boundary representation (B-Rep). Several data sets of mechanical parts in B-Rep format have recently been released for machine learning research

Andrea De Luca, Martino Marelli, Sandro Mereghetti, Ruben Salvaterra

Time-domain analysis of an archival XMM-Newton observation unveiled a very unusual variability pattern in the soft X-ray emission of PSR J1311-3430, a black widow millisecond pulsar in a tight binary (P_B=93.8 min) with a very low-mass (M~0.01 Msun) He companion star, known to show flaring emission in the optical and in the X-rays. A series of six pulses wit

D. del Ser, O. Fors, M. del Alcázar, V. Dyachenko

Searching for Earth-sized planets in data from Kepler's extended mission (K2) is a niche that still remains to be fully exploited. The TFAW survey is an ongoing project that aims to re-analyze all light curves in K2 C1-C8 and C12-C18 campaigns with a wavelet-based detrending and denoising method, and the period search algorithm TLS to search for new transit

Daniel Kosakowski, Mark Ivan Ugalino, Robert Fisher, Or Graur

The radiosotope $^{44}$Ti is produced through $\alpha$-rich freezeout and explosive helium burning in type Ia supernovae (SNe Ia). In this paper, we discuss how the detection of $^{44}$Ti, either through late-time light curves of SNe Ia, or directly via gamma rays, can uniquely constrain the origin of SNe Ia. In particular, building upon recent advances in t

Egidijus Kukstas, Michael L. Balogh, Ian G. McCarthy, Yannick M. Bahe

Recent observations have shown that the environmental quenching of galaxies at z ~ 1 is qualitatively different to that in the local Universe. However, the physical origin of these differences has not yet been elucidated. In addition, while low-redshift comparisons between observed environmental trends and the predictions of cosmological hydrodynamical simul

E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu

We perform a search for galaxy-galaxy strong lens systems using a convolutional neural network (CNN) applied to imaging data from the first public data release of the DECam Local Volume Exploration Survey (DELVE), which contains $\sim 520$ million astronomical sources covering $\sim 4,000$ $\mathrm{deg}^2$ of the southern sky to a $5\sigma$ point-source dept

Pasquale Di Bari, Adam Murphy

The right-handed (RH) Higgs-induced neutrino mixing (RHINO) model explains neutrino masses and origin of matter in the universe within a unified picture. The mixing, effectively described by a dimension five operator, is responsible both for the production of dark neutrinos, converting a small fraction of seesaw neutrinos acting as source, and for their deca

Natalie R. Hinkel, Patrick A. Young, Caleb H. Wheeler

Understanding stellar composition is fundamental not only to our comprehension of the galaxy, especially chemical evolution, but it can also shed light on the interior structure and mineralogy of exoplanets, which are formed from the same material as their host stars. Unfortunately, the underlying mathematics describing stellar mass fractions and stellar ele

T. E. O'Brien, G. Anselmetti, F. Gkritsis, V. E. Elfving

An important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Prior to fault-tolerant quantum computing, robust error mitigation strategies are necessary to continue this growth. Here, we study physical simulation within the seniority-zero electron pairing subspace, which affords both

Christopher Fechisin, Kunal Sharma, Przemyslaw Bienias, Steven L. Rolston

Rydberg arrays merge the collective behavior of ordered atomic arrays with the controllability and optical nonlinearities of Rydberg systems, resulting in a powerful platform for realizing photonic many-body physics. As an application of this platform, we propose a protocol for quantum non-demolition (QND) photon counting. Our protocol involves photon storag

Luca Martucci, Nicolo Risso, Timo Weigand

We propose quantum gravitational constraints on effective four-dimensional theories with N=1 supersymmetry. These Swampland constraints arise by demanding consistency of the worldsheet theory of a class of axionic, or EFT, strings whose existence follows from the Completeness Conjecture of quantum gravity. Modulo certain assumptions, we derive positivity bou

Brandon T. Radzom, Anthony J. Taylor, Amy J. Barger, Lennox L. Cowie

The Hawaii Survey Field SSA22 is the fourth deepest Chandra X-ray field. To allow for the fullest exploration of this field, we present new optical spectroscopy from Keck/DEIMOS and Keck/LRIS, which, in combination with the literature, brings the spectroscopic completeness of the 2--8 keV sample to 62%. We also make optical spectral classifications and estim

Hideki Maeda

We obtain the general $n(\ge 4)$-dimensional static solution with an $(n-2)$-dimensional Einstein base manifold for a perfect fluid obeying a linear equation of state $p=-(n-3)\rho/(n+1)$. It is a generalization of Semiz's four-dimensional general solution with spherical symmetry and consists of two different classes. Through the Buchdahl transformation, the

Arjun Bagchi, Daniel Grumiller, M. M. Sheikh-Jabbari

We propose that 3d black holes are an ensemble of tensionless null string states. These microstates typically have non-zero winding. We evaluate their partition function in the limit of large excitation numbers and show that their combinatorics reproduces the Bekenstein-Hawking entropy and its semiclassical logarithmic corrections.

Fabrizio Gentile, Crescenzo Tortora, Giovanni Covone, Léon V. E. Koopmans

The unprecedented number of gravitational lenses expected from new-generation facilities such as the ESA Euclid telescope and the Vera Rubin Observatory makes it crucial to rethink our classical approach to lens-modelling. In this paper, we present LeMoN (Lens Modelling with Neural networks): a new machine-learning algorithm able to analyse hundreds of thous