April 2023 arXiv papers — page 142

Khouloud Abdelli, Carsten Tropschug, Helmut Griesser, Stephan Pachnicke

Passive optical networks (PONs) have become a promising broadband access network solution. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize networks faults. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical netwo

Sagar P. Shah, Michael N. Olaya, Evgenia Plaka, Joseph McDonald

Efficient structural repair of wind turbine blades is essential to limiting global warming and reducing the Levelized Cost of Energy (LCOE). Repairs carried out up-tower are sensitive to environmental conditions whose effect on the material properties during processing needs to be accounted for to accurately predict the repair outcome. This study investigate

Rohit Sharma, Abhinav Gupta, Arnav Gupta, Bo Li

The estimation and monitoring of SpO2 are crucial for assessing lung function and treating chronic pulmonary diseases. The COVID-19 pandemic has highlighted the importance of early detection of changes in SpO2, particularly in asymptomatic patients with clinical deterioration. However, conventional SpO2 measurement methods rely on contact-based sensing, pres

Renato Ferreira Pinto, Nathaniel Harms

Distribution testing is a fundamental statistical task with many applications, but we are interested in a variety of problems where systematic mislabelings of the sample prevent us from applying the existing theory. To apply distribution testing to these problems, we introduce distribution testing under the parity trace, where the algorithm receives an order

Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley

How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for ea

Caleb Dilsavor, James Marshall Reber

Given a transitive Anosov diffeomorphism or flow on a closed connected Riemannian manifold $M$, the Livshits theorem states that a H\"{o}lder function $\varphi : M \to \mathbb{R}$ is a coboundary if all of its periods vanish. We explain how a finer statistical understanding of the distribution of these periods can be used to obtain a stronger version of the

Valerio La Gatta, Luca Luceri, Francesco Fabbri, Emilio Ferrara

The proliferation of harmful content shared online poses a threat to online information integrity and the integrity of discussion across platforms. Despite various moderation interventions adopted by social media platforms, researchers and policymakers are calling for holistic solutions. This study explores how a target platform could leverage content that h

Tiago Cruz

Important correspondences in representation theory can be regarded as restrictions of the Morita--Tachikawa correspondence. Moreover, this correspondence motivates the study of many classes of algebras like Morita algebras and gendo-symmetric algebras. Explicitly, the Morita--Tachikawa correspondence describes that endomorphism algebras of generators-cogener

WLNC Liyanage, Nan Tang, Lizabeth Quigley, Julie A. Borchers

Magnetic skyrmions are topologically protected chiral spin textures which present opportunities for next-generation magnetic data storage and logic information technologies. The topology of these structures originates in the geometric configuration of the magnetic spins - more generally described as the structure. While the skyrmion structure is most often d

Joan Morris, Gregory Puleo

The slow coloring game was introduced by Mahoney, Puleo, and West and it is played by two players, Lister and Painter, on a graph \(G\). In round \(i\), Lister marks a nonempty subset \(M\) of \(V(G)\). By doing this he scores \(|M|\) points. Painter responds by deleting a maximal independent subset of \(M\). This process continues until all vertices are del

Krzysztof Byrski, Przemysław Spurek, Jacek Tabor

Gaussian Mixture Models (GMM) do not adapt well to curved and strongly nonlinear data. However, we can use Gaussians in the curvilinear coordinate systems to solve this problem. Moreover, such a solution allows for the adaptation of clusters to the complicated shapes defined by the family of functions. But still, it is challenging to model clusters as closed

Li Li, Jean-Pierre S. El Rami, Adrian Taylor, James Hailing Rao

This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high-fidelity, best achieved through real network emulation, with the need for running large numbers of training e

Thomas Kunz, Christian Fisher, James La Novara-Gsell, Christopher Nguyen

Hardening cyber physical assets is both crucial and labor-intensive. Recently, Machine Learning (ML) in general and Reinforcement Learning RL) more specifically has shown great promise to automate tasks that otherwise would require significant human insight/intelligence. The development of autonomous RL agents requires a suitable training environment that al

Yun-Han Li, Ryan Gabrys, Jin Sima, Ilan Shomorony

Motivated by testing for pathogenic diseases we consider a new nonadaptive group testing problem for which: (1) positives occur within a burst, capturing the fact that infected test subjects often come in clusters, and (2) that the test outcomes arise from semiquantitative measurements that provide coarse information about the number of positives in any test

Ye-Jin Kim, Levi D. Palmer, Wonseok Lee, Nicholas J. Heller

Electron energy-loss spectroscopy (EELS) can measure similar information to X-ray, UV-Vis, and IR spectroscopies but with atomic resolution and increased scattering cross sections. Recent advances in electron monochromators have expanded EELS capabilities from chemical identification to the realms of synchrotron-level core-loss measurements and to low-loss,

Mengqi Lin, Gongjun Xu

Cognitive Diagnosis Models (CDMs) provide a powerful statistical and psychometric tool for researchers and practitioners to learn fine-grained diagnostic information about respondents' latent attributes. There has been a growing interest in the use of CDMs for polytomous response data, as more and more items with multiple response options become widely used.

M. Lisovenko, Z. Pan, P. S. Barry, T. Cecil

Silicon-based dielectric is crucial for many superconducting devices, including high-frequency transmission lines, filters, and resonators. Defects and contaminants in the amorphous dielectric and at the interfaces between the dielectric and metal layers can cause microwave losses and degrade device performance. Optimization of the dielectric fabrication, de

Alaina N. Talboy, Elizabeth Fuller

Assessments of algorithmic bias in large language models (LLMs) are generally catered to uncovering systemic discrimination based on protected characteristics such as sex and ethnicity. However, there are over 180 documented cognitive biases that pervade human reasoning and decision making that are routinely ignored when discussing the ethical complexities o

Karen Avetisyan, Arthur Malajyan, Tsolak Ghukasyan, Arutyun Avetisyan

We present a simple cross-lingual plagiarism detection method applicable to a large number of languages. The presented approach leverages open multilingual thesauri for candidate retrieval task and pre-trained multilingual BERT-based language models for detailed analysis. The method does not rely on machine translation and word sense disambiguation when in u

Aniket Pramanik, Mathews Jacob

Model-based deep learning methods that combine imaging physics with learned regularization priors have been emerging as powerful tools for parallel MRI acceleration. The main focus of this paper is to determine the utility of the monotone operator learning (MOL) framework in the parallel MRI setting. The MOL algorithm alternates between a gradient descent st

Xuguang Ai, Ramakanth Kavuluru

End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE typically involves identifying entities (or named entity recognition (NER)) and associated relations, while most RE tasks simply assume that the entities are provided upfront and end up

Chuang Niu, Qing Lyu, Christopher D. Carothers, Parisa Kaviani

Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology. Fully mining such big data requires multitasking; otherwise, occult but important aspects may be overlooked, adversely affecting clinical management and population healthcare. Despite remarkable successes of A

Sergey S. Ketkov

This study addresses a class of linear mixed-integer programming (MILP) problems that involve uncertainty in the objective function parameters. The parameters are assumed to form a random vector, whose probability distribution can only be observed through a finite training data set. Unlike most of the related studies in the literature, we also consider uncer

Agnieszka Janiuk, Narjes Shahamat Dehsorkh, Dominika Krol

Long Gamma Ray Bursts (GRBs) originate from the collapse of massive, rotating stars. We aim to model the process of stellar collapse in the scenario of a self-gravitating collapsing star. We account for the changes in Kerr metric induced by the growth of the black hole, accretion of angular momentum, as well as the self-gravity effect due to a large mass of

Neha Singh, Tomasz Bulik, Krzysztof Belczynski, Marek Cieslar

The Einstein Telescope (ET) is a proposed third-generation, wide-band gravitational wave (GW) detector. Given its improved detection sensitivity in comparison to the second-generation detectors, it will be capable of exploring the Universe with GWs up to very high redshifts. In this paper, we present a population-independent method to infer the functional fo

Toby Pereira

This paper describes COWPEA (Candidates Optimally Weighted in Proportional Election using Approval voting), a method of proportional representation that uses approval voting, also known as random priority, though underdeveloped in the literature. COWPEA optimally elects an unlimited number of candidates with potentially different weights to a body, rather th

Yang Liu, Luiz Gustavo Hafemann

Training data is a critical requirement for machine learning tasks, and labeled training data can be expensive to acquire, often requiring manual or semi-automated data collection pipelines. For tracking applications, the data collection involves drawing bounding boxes around the classes of interest on each frame, and associate detections of the same "instan

John M. Li, Amal Ahmed, Steven Holtzen

We present Lilac, a separation logic for reasoning about probabilistic programs where separating conjunction captures probabilistic independence. Inspired by an analogy with mutable state where sampling corresponds to dynamic allocation, we show how probability spaces over a fixed, ambient sample space appear to be the natural analogue of heap fragments, and

János Kollár

We show that a function is real analytic at the origin iff it is arc-analytic, has a subanalytic graph, and its restriction to every monomial curve is analytic. This complements recent results of Kucharz and Kurdyka.

Avraham Aizenbud, Uri Bader

We study equivariant morphisms from zero dimensional schemes to varieties and show that, under suitable assumptions, all such morphisms factor via a canonical one. We relate the above to Algebraic Representations of Ergodic Actions.

Aravind Sasidharan Pillai

Fake job postings have become prevalent in the online job market, posing significant challenges to job seekers and employers. Despite the growing need to address this problem, there is limited research that leverages deep learning techniques for the detection of fraudulent job advertisements. This study aims to fill the gap by employing a Bidirectional Long

M. S. Bandstra, N. Abgrall, R. J. Cooper, D. Hellfeld

Static gamma-ray detector systems that are deployed outdoors for radiological monitoring purposes experience time- and spatially-varying natural backgrounds and encounters with man-made nuisance sources. In order to be sensitive to illicit sources, such systems must be able to distinguish those sources from benign variations due to, e.g., weather and human a

Theo Jules, Gal Brener, Tal Kachman, Noam Levi

The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative perspective and for fundamental reasons. A core challenge is to understand the geometry and topography of the landscape that guides the optimization. In this work, we employ standard

Ramzi Abou Chahine, Steven Vasquez, Pooyan Fazli, Hasti Seifi

Social touch provides a rich non-verbal communication channel between humans and robots. Prior work has identified a set of touch gestures for human-robot interaction and described them with natural language labels (e.g., stroking, patting). Yet, no data exists on the semantic relationships between the touch gestures in users' minds. To endow robots with tou

Kristin Courtney, Wilhelm Winter

We write arbitrary separable nuclear C*-algebras as limits of inductive systems of finite-dimensional C*-algebras with completely positive connecting maps. The characteristic feature of such CPC*-systems is that the maps become more and more orthogonality preserving. This condition makes it possible to equip the limit, a priori only an operator space, with a

Andrew Halterman, Philip A. Schrodt, Andreas Beger, Benjamin E. Bagozzi

Event data, or structured records of ``who did what to whom'' that are automatically extracted from text, is an important source of data for scholars of international politics. The high cost of developing new event datasets, especially using automated systems that rely on hand-built dictionaries, means that most researchers draw on large, pre-existing datase

Nicholas Gahman, Vinayak Elangovan

Document similarity is an important part of Natural Language Processing and is most commonly used for plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity algorithm could have a major positive impact on the field of Natural Language Processing. This report sets out to examine the numerous document similari

Maria Oprea, Mark Walth, Robert Stephany, Gabriella Torres Nothaft

The intersection of machine learning and dynamical systems has generated considerable interest recently. Neural Ordinary Differential Equations (NODEs) represent a rich overlap between these fields. In this paper, we develop a continuous time neural network approach based on Delay Differential Equations (DDEs). Our model uses the adjoint sensitivity method t

Duc-Tien Dang-Nguyen, Sohail Ahmed Khan, Cise Midoglu, Michael Riegler

Cheapfake is a recently coined term that encompasses non-AI ("cheap") manipulations of multimedia content. Cheapfakes are known to be more prevalent than deepfakes. Cheapfake media can be created using editing software for image/video manipulations, or even without using any software, by simply altering the context of an image/video by sharing the media alon

Rahul Maurya, Jaydeb Sarkar, Aryaman Sensarma

We present complete classifications of automorphisms of two closed subalgebras of the bounded analytic functions on the open unit disc $\mathbb{D}$, namely, the subalgebra of functions vanishing at the origin, and the subalgebra of functions whose first derivative vanishes at the origin. The later subalgebra is known as the Neil algebra. We also characterize

Kaya Güven Akbaş, Fatih Erman, O. Teoman Turgut

We study the spectral properties of a Schr\"{o}dinger operator $H_0$ modified by $\delta$ interactions and show explicitly how the poles of the new Green's function are rearranged relative to the poles of original Green's function of $H_0$. We prove that the new bound state energies are interlaced between the old ones, and the ground state energy is always l

Adrian Radu, Carlos A. Duque

This article presents an approach to the two-dimensional Schr\"odinger equation based on automatic learning methods with neural networks. It is intended to determine the ground state of a particle confined in any two-dimensional potential, starting from the knowledge of the solutions to a large number of arbitrary sample problems. A network architecture with

Isaac Harris

In this paper, we consider a regularization strategy for the factorization method when there is noise added to the data operator. The factorization method is a qualitative method used in shape reconstruction problems. These methods are advantageous to use due to the fact that they are computationally simple and require little a priori knowledge of the object

Brandon Alberts

We define a group with local data over a number field $K$ as a group $G$ together with homomorphisms from decomposition groups ${\rm Gal}(\overline{K}_p/K_p)\to G$. Such groups resemble Galois groups, just without global information. Motivated by the use of random groups in the study of class group statistics, we use the tools given by Sawin-Wood to construc

Sina Ahmadi, Milind Agarwal, Antonios Anastasopoulos

The Perso-Arabic scripts are a family of scripts that are widely adopted and used by various linguistic communities around the globe. Identifying various languages using such scripts is crucial to language technologies and challenging in low-resource setups. As such, this paper sheds light on the challenges of detecting languages using Perso-Arabic scripts,

Soheyla Feyzbakhsh, Zhiyu Liu, Shizhuo Zhang

Let $Y_d$ be a del Pezzo threefold of Picard rank one and degree $d\geq 2$. In this paper, we apply two different viewpoints to study $Y_d$ via a particular admissible subcategory of its bounded derived category, called the Kuznetsov component: (i) Brill-Noether reconstruction. We show that $Y_d$ can be uniquely recovered as a Brill-Noether locus of Bridgela

Petra Awad, James H. H. Chan, Martin Millon, Frederic Courbin

The microlensing signal in the light curves of gravitationally lensed quasars can shed light on the dark matter (DM) composition in their lensing galaxies. Here, we investigate a sample of six lensed quasars from the most recent and best COSMOGRAIL observations: HE~1104$-$1805, HE~0435$-$1223, RX~J1131$-$1231, WFI~2033$-$4723, PG~1115$+$080, and J1206$+$4332

Sina Ahmadi, Zahra Azin, Sara Belelli, Antonios Anastasopoulos

One of the major challenges that under-represented and endangered language communities face in language technology is the lack or paucity of language data. This is also the case of the Southern varieties of the Kurdish and Laki languages for which very limited resources are available with insubstantial progress in tools. To tackle this, we provide a few appr

Rubens Augusto Amaro Junior, Alfredo Gay Neto, Liang-Yee Cheng

A 3D fluid-structure interaction solver based on an improved weakly-compressible moving particle simulation (WC-MPS) method and a geometrically nonlinear shell structural model is developed and applied to hydro-elastic free-surface flows. The fluid-structure coupling is performed by a polygon wall boundary model that can handle particles and finite elements

Daniella Bar-Lev, Adir Kobovich, Orian Leitersdorf, Eitan Yaakobi

Constrained coding is a fundamental field in coding theory that tackles efficient communication through constrained channels. While channels with fixed constraints have a general optimal solution, there is increasing demand for parametric constraints that are dependent on the message length. Several works have tackled such parametric constraints through iter

Henry W. Abrahamson, Ermin Wei

In distributed problems where consensus between agents is required but average consensus is not desired, it can be necessary for each agent to know not only the data of each other agent in the network, but also the origin of each piece of data before consensus can be reached. However, transmitting large tables of data with IDs can cause the size of an agent'

Marco Morucci, Cynthia Rudin, Alexander Volfovsky

We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching, a longstanding tool in observational causal inference. Interpretability is paramount in many high-stakes application of causal inference. Current tools for nonparametric estimation of both average and individ

Andrew Patterson, Samuel Neumann, Martha White, Adam White

Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times significant computational resources. While compute resources available per dollar have continued to grow rapidly, so have the scale of typical experiments in reinforcement learning. It is now common to benchmark agents with millions

Jason A. Meziere, Yu Luo, Yi Zia, LK Beland

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density fu

Kyungduk Kim, Stefan Bittner, Yuhao Jin, Yongquan Zeng

We experimentally investigate spatio-temporal lasing dynamics in semiconductor microcavities with various geometries, featuring integrable or chaotic ray dynamics. The classical ray dynamics directly impacts the lasing dynamics, which is primarily determined by the local directionality of long-lived ray trajectories. The directionality of optical propagation

J. Bec, K. Gustavsson, B. Mehlig

When very small particles are suspended in a fluid in motion, they tend to follow the flow. How such tracer particles are mixed, transported, and dispersed by turbulent flow has been successfully described by statistical models. Heavy particles, with mass densities larger than that of the carrying fluid, can detach from the flow. This results in preferential

Harry Li, Gabriel Appleby, Camelia Daniela Brumar, Remco Chang

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. O

S. Gödtel, C. Lämmerzahl

In this work, we study low-lying collective excitations of a Bose-Einstein condensate with Newtonian and Yukawa-like two-particle interaction and derive boundaries for both Yukawa parameters. Using a variational approach, we explicitly show for spherical condensate that the corresponding frequencies depend on the gravitational interaction strength. The acqui

Giuseppe Maria Coclite, Maria Colombo, Gianluca Crippa, Nicola De Nitti

We consider a class of nonlocal conservation laws with exponential kernel and prove that quantities involving the nonlocal term $W:=\mathbb{1}_{(-\infty,0]}(\cdot)\exp(\cdot) \ast \rho$ satisfy an Ole\u{\i}nik-type entropy condition. More precisely, under different sets of assumptions on the velocity function $V$, we prove that $W$ satisfies a one-sided Lips

Siddharth Somasundaram, Akshat Dave, Connor Henley, Ashok Veeraraghavan

The goal of non-line-of-sight (NLOS) imaging is to image objects occluded from the camera's field of view using multiply scattered light. Recent works have demonstrated the feasibility of two-bounce (2B) NLOS imaging by scanning a laser and measuring cast shadows of occluded objects in scenes with two relay surfaces. In this work, we study the role of time-o

Antonio Alex-Amor, Carlos Molero, Mário G. Silveirinha

This paper presents an analytical framework for the study of scattering and diffraction phenomena in spacetime-modulated metallic gratings. Using a Lorentz transformation, it is shown that a particular class of spacetime-modulated gratings behave effectively as moving media. We take advantage of this property to derive a closed analytical solution for the wa

Alan Lew, Eran Nevo, Yuval Peled, Orit E. Raz

Jord\'an and Tanigawa recently introduced the $d$-dimensional algebraic connectivity $a_d(G)$ of a graph $G$. This is a quantitative measure of the $d$-dimensional rigidity of $G$ which generalizes the well-studied notion of spectral expansion of graphs. We present a new lower bound for $a_d(G)$ defined in terms of the spectral expansion of certain subgraphs

Jun Jet Tai, Jim Wong, Mauro Innocente, Nadjim Horri

Unmanned aerial vehicles (UAVs) have numerous applications, but their efficient and optimal flight can be a challenge. Reinforcement Learning (RL) has emerged as a promising approach to address this challenge, yet there is no standardized library for testing and benchmarking RL algorithms on UAVs. In this paper, we introduce PyFlyt, a platform built on the B

Zaid Abdullah, Steven Kisseleff, Eva Lagunas, Vu Nguyen Ha

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial

Holden Lee, Zeyu Shen

In the field of sampling algorithms, MCMC (Markov Chain Monte Carlo) methods are widely used when direct sampling is not possible. However, multimodality of target distributions often leads to slow convergence and mixing. One common solution is parallel tempering. Though highly effective in practice, theoretical guarantees on its performance are limited. In

Kyle Franson, Brendan P. Bowler

Dynamical masses of giant planets and brown dwarfs are critical tools for empirically validating substellar evolutionary models and their underlying assumptions. We present a measurement of the dynamical mass and an updated orbit of PZ Tel B, a young brown dwarf companion orbiting a late-G member of the $\beta$ Pic moving group. PZ Tel A exhibits an astromet

Matthew Grasinger

Polymer network models construct the constitutive relationships of a broader polymer network from the behavior of a single polymer chain (e.g. viscoelastic response to applied forces, applied electromagnetic fields, etc.). Network models have been used for multiscale phenomena in a variety of contexts such as rubber elasticity, soft multifunctional materials

Mohit Kumar, Bernhard A. Moser, Lukas Fischer

Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach to mitigate the accuracy-loss issue in classification. In this approach, a representation of the affine hull of given data points is learned in Reproducing Kernel Hilbert Spaces (RK

Gourav Prateek Sharma, Dhruvin Patel, Joachim Sachs, Marilet De Andrade

Over the last decade, society and industries are undergoing rapid digitization that is expected to lead to the evolution of the cyber-physical continuum. End-to-end deterministic communications infrastructure is the essential glue that will bridge the digital and physical worlds of the continuum. We describe the state of the art and open challenges with resp

Claudio Mendoza, José E. Méndez-Delgado, Manuel Bautista, Jorge García-Rojas

We use the PyNeb 1.1.16 Python package to evaluate the atomic datasets available for the spectral modeling of [Fe II] and [Fe III], which list level energies, A-values, and effective collision strengths. Most datasets are reconstructed from the sources, and new ones are incorporated to be compared with observed and measured benchmarks. For [Fe III], we arriv

Kyle Singh, Jenna Van Dyke

We investigate structure that describes physical data in gravitational systems that is, to one degree or another, independent of the metric and affine structure. We dub such structure surplus structure and seek to incorporate it into our ontological commitments. An emphasis is placed on those structures which are required to constrain our models motivated by

Jacob Piland, Christopher Sweet, Priscila Saboia, Charles Vardeman

Energy-based models (EBM) have become increasingly popular within computer vision. EBMs bring a probabilistic approach to training deep neural networks (DNN) and have been shown to enhance performance in areas such as calibration, out-of-distribution detection, and adversarial resistance. However, these advantages come at the cost of estimating input data pr

Faik Ozan Ozhan, Arda Gulersoy, Ugur Aygun, Afsun Sahin

We developed a prototype device for dynamic gaze and accommodation measurements based on 4 Purkinje reflections (PR) suitable for use in AR and ophthalmology applications. PR1&2 and PR3&4 are used for accurate gaze and accommodation measurements, respectively. Our eye model was developed in ZEMAX and matches the experiments well. Our model predicts the accom

Lifu Tu, Jin Qu, Semih Yavuz, Shafiq Joty

Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rather limited. This is partly due to the high cost of obtaining non-English conversational data, which results in limited coverage. In this work, we introduce XSGD for cross-lingual

Yifan Chen, Houman Owhadi, Florian Schäfer

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They

Emma R. Toner, Mark Rucker, Zhiyuan Wang, Maria A. Larrazabal

Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety disorder. In this study, we present results using passively collected data from a within-subject experiment that assessed physiological response across different social contexts (i.

Vaibhav Kalvakota

In this Essay, we will look at the relation between the No Transmission principle and the Strong cosmic censorship (SCC), which we will highlight in the background of quantum gravity. We show that taking quantum gravity into account, one can provide a complete picture of the instability of the inner horizon and the principle that two independent CFTs, under

Michael Gomez, Pedro M. Reis, Basile Audoly

We study elastic ribbons subject to large, tensile pre-stress confined to a central region within the cross-section. These ribbons can buckle spontaneously to form helical shapes, featuring regions of alternating chirality (phases) that are separated by so-called perversions (phase boundaries). This instability cannot be described by classical rod theory, wh

Jen-Wei Wang, Lingfeng Sun, Xinghao Zhu, Qiyang Qian

Pick-and-place is an important manipulation task in domestic or manufacturing applications. There exist many works focusing on grasp detection with high picking success rate but lacking consideration of downstream manipulation tasks (e.g., placing). Although some research works proposed methods to incorporate task conditions into grasp selection, most of the

Xianpeng Liu, Ce Zheng, Kelvin Cheng, Nan Xue

The main challenge of monocular 3D object detection is the accurate localization of 3D center. Motivated by a new and strong observation that this challenge can be remedied by a 3D-space local-grid search scheme in an ideal case, we propose a stage-wise approach, which combines the information flow from 2D-to-3D (3D bounding box proposal generation with a si

Jaxen Godfrey, Bruce Edelman, Ben Farr

Observations of gravitational waves (GWs) from merging compact binaries have become a regular occurrence. The continued advancement of the LIGO-Virgo-KAGRA (LVK) Collaboration detectors have now produced a catalog of over 90 such mergers, from which we can begin to uncover the formation history of merging compact binaries. In this work, we search for subpopu

Stephen G. Naculich

Color-factor symmetry is a property of tree-level gauge-theory amplitudes containing at least one gluon. BCJ relations among color-ordered amplitudes follow directly from this symmetry. Color-factor symmetry is also a feature of biadjoint scalar theory amplitudes as well as of their equations of motion. In this paper, we present a new proof of color-factor s

Chongyang Shi, Abhishek N. Kulkarni, Hazhar Rahmani, Jie Fu

This paper studies a language-based opacity enforcement in a two-player, zero-sum game on a graph. In this game, player 1 (P1) wins if it can achieve a secret temporal goal described by the language of a finite automaton, no matter what strategy the opponent player 2 (P2) selects. In addition, P1 aims to win while making its goal opaque to a passive observer

Giacomo Pedretti, John Moon, Pedro Bruel, Sergey Serebryakov

Structured, or tabular, data is the most common format in data science. While deep learning models have proven formidable in learning from unstructured data such as images or speech, they are less accurate than simpler approaches when learning from tabular data. In contrast, modern tree-based Machine Learning (ML) models shine in extracting relevant informat

Martin Avanzini, Georg Moser, Michael Schaper

In this work, we study the fully automated inference of expected result values of probabilistic programs in the presence of natural programming constructs such as procedures, local variables and recursion. While crucial, capturing these constructs becomes highly non-trivial. The key contribution is the definition of a term representation, denoted as infer[.]

Steffen Lewitzka, Vinícius Pinto

We present a logical system that combines the well-known classical epistemic concepts of belief and knowledge with a concept of evidence such that the intuitive principle \textit{`evidence yields belief and knowledge'} is satisfied. Our approach relies on previous works of the first author \cite{lewjlc2, lewigpl, lewapal} who introduced a modal system contai

Alireza Salemi, Amirhossein Abaskohi, Sara Tavakoli, Yadollah Yaghoobzadeh

Multilingual pre-training significantly improves many multilingual NLP tasks, including machine translation. Most existing methods are based on some variants of masked language modeling and text-denoising objectives on monolingual data. Multilingual pre-training on monolingual data ignores the availability of parallel data in many language pairs. Also, some

Noga Alon, Fan Wei

We prove that for each $d \geq 3$ the set of all limit points of the second largest eigenvalue of growing sequences of $d$-regular graphs is $[2\sqrt{d-1},d]$. A similar argument shows that the set of all limit points of the smallest eigenvalue of growing sequences of $d$-regular graphs with growing (odd) girth is $[-d, -2 \sqrt{d-1}]$. The more general ques

V. N. Glazkov, Yu. V. Krasnikova, I. K. Rodygina, H. -A. Krug von Nidda

We report results of a multi-frequency (0.8-60 GHz) electron spin resonance study of the spin dynamics in the quasi-2D square lattice antiferromagnet Ba$_2$MnGe$_2$O$_7$ both in antiferromagnetically ordered and paramagnetic phases. We directly observe two zero-field gaps in the excitation spectrum of the ordered phase, the larger one being due to easy-plane

Yan Jin, Mengke Li, Yang Lu, Yiu-ming Cheung

Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heavily biased toward the majority classes. To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed di

Shaull Almagor, Omer Yizhaq

Jumping automata are finite automata that read their input in a non-consecutive manner, disregarding the order of the letters in the word. We introduce and study jumping automata over infinite words. Unlike the setting of finite words, which has been well studied, for infinite words it is not clear how words can be reordered. To this end, we consider three s

David Aasen, Jeongwan Haah, Zhi Li, Roger S. K. Mong

We investigate the evolution of quantum information under Pauli measurement circuits. We focus on the case of one- and two-dimensional systems, which are relevant to the recently introduced Floquet topological codes. We define local reversibility in context of measurement circuits, which allows us to treat finite depth measurement circuits on a similar footi

Mine Dogucu, Sibel Kazak, Joshua Rosenberg

With the rise of the popularity of Bayesian methods and accessible computer software, teaching and learning about Bayesian methods are expanding. However, most educational opportunities are geared toward statistics and data science students and are less available in the broader STEM fields. In addition, there are fewer opportunities at the K-12 level. With t

Christoph Dlapa, Gregor Kälin, Zhengwen Liu, Rafael A. Porto

We describe the formalism to compute gravitational-wave observables for compact binaries via the effective field theory framework in combination with modern tools from collider physics. We put particular emphasis on solving the "multi-loop" integration problem via the methodology of differential equations and expansion by regions. This allows us to "bootstra

Navnath Daundkar, Rekha Santhanam, Soumyadip Thandar

In this article, we investigate the higher topological complexity of oriented Seifert fibered manifolds that are Eilenberg--MacLane spaces $K(G,1)$ with infinite fundamental group $G$. We first refine the cohomological lower bounds for higher topological complexity by introducing the notion of higher topological complexity weights. As an application, we show

Scott Robertson

We study a continuous time economy where throughout time, insiders receive private signals regarding the risky assets' terminal payoff. We prove existence of a partial communication equilibrium where, at each private signal time, the public receives a signal of the same form as the associated insider, but of lower quality. This causes a jump in both the publ

Antoine Goldsborough, Stefanie Zbinden

Random walks cannot, in general, be pushed forward by quasi-isometries. Tame Markov chains were introduced as a `quasi-isometry invariant' are a generalization of random walks. In this paper, we construct several examples of tame Markov chains on the free group exhibiting `exotic' behaviour; one, where the drift is not well defined and one where the drift is

Salvatore Tirone, Raffaele Salvia, Stefano Chessa, Vittorio Giovannetti

We demonstrate an asymmetry between the beneficial effects one can obtain using non-local operations and non-local states to mitigate the detrimental effects of environmental noise in the work extraction from quantum battery models. Specifically, we show that using non-local recovery operations after the noise action can in general increase the amount of wor

Johannes Krah

We construct a non-full exceptional collection of maximal length consisting of line bundles on the blow-up of the projective plane in 10 points in general position. This provides a counterexample to a conjecture of Kuznetsov and to a conjecture of Orlov.

Yuan Gao, James M. Scott

We study the existence and uniqueness of solutions to the vector field Peierls-Nabarro model for curved dislocations in a transversely isotropic medium. Under suitable assumptions for the misfit potential on the slip plane, we reduce the 3D Peierls-Nabarro model to a nonlocal scalar Ginzburg-Landau equation. For a particular range of elastic coefficients, th