October 2022 arXiv papers — page 62

Marisa M. Lee, Efstathios G. Charalampidis, Siyuan Xing, Christopher Chong

This work focuses on the study of time-periodic solutions, including breathers, in a nonlinear lattice consisting of elements whose contacts alternate between strain-hardening and strain-softening. The existence, stability, and bifurcation structure of such solutions, as well as the system dynamics in the presence of damping and driving are studied systemati

Yixiao Song, Kalpesh Krishna, Rajesh Bhatt, Mohit Iyyer

To understand what kinds of linguistic knowledge are encoded by pretrained Chinese language models (LMs), we introduce the benchmark of Sino LINGuistics (SLING), which consists of 38K minimal sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena. Each pair demonstrates the acceptability contrast of a specific syntactic or semantic

Masashi Miura, Go Tsuchiya, Takumu Harada, Keita Sakuma

The addition of artificial pinning centers has led to an impressive increase in critical current density ($J_{\rm c}$) in a superconductor, enabling record-breaking all-superconducting magnets and other applications. $J_{\rm c}$ has reached $\sim 0.2$-$0.3$ $J_{\rm d}$, where $J_{\rm d}$ is the depairing current density, and the numerical factor depends on t

Shashi Pandey, Alok Shukla, Anurag Tripathi

A comparative study of the electrical and optical properties has been done on 3d-doped TiO$_2$. Ti$_{1-x}$M$_x$O$_2$ (M= Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) powder and its corresponding pellets, with doping concentration $x= 0.05$. The samples were prepared using the solid-state route. Optical and electrical measurements have been performed for all prepared s

Xin Wu, Ying Wang, Wei Sun, Fu-Yao Liu

Many Hamiltonian problems in the Solar System are separable or separate into two analytically solvable parts, and thus give a great chance to the development and application of explicit symplectic integrators based on operator splitting and composing. However, such constructions cannot in general be available for curved spacetimes in general relativity and m

Yongjie Sun, Tingting Zhang, Kun Li, Pengpeng Wang

Calibration free wavelength modulation spectroscopy (WMS) is an efficient technique for trace gas monitoring. It is widely used due to its resistance to light intensity fluctuations, strong suppression of low frequency noise, fast response time and excellent environmental adaptability. The calibration free WMS often employs the Levenberg Marquardt algorithm

Jessie M. Henderson, Marianna Podzorova, M. Cerezo, John K. Golden

Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coa

Nicolas Monet, Dongyoon Wee

This technical report introduces our solution, MEEV, proposed to the EgoBody Challenge at ECCV 2022. Captured from head-mounted devices, the dataset consists of human body shape and motion of interacting people. The EgoBody dataset has challenges such as occluded body or blurry image. In order to overcome the challenges, MEEV is designed to exploit multiscal

Deepan Muthirayan, Ruijie Du, Yanning Shen, Pramod P. Khargonekar

We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynamical systems. We show that our algorithm can achieve a sub-linear regret with respect to the class of Disturbance Action Control (DAC) policies, which are a widely studied class of

Daren Zhou

The renormalization of iterated one-pion exchange (OPE) has been studied in Chiral Effective Field Theory ($\chi$EFT) for the antinucleon-nucleon ($\overline{N}\!N$) scattering in some partial waves (Phys. Rev. C 105, 054005 (2022)). We go further for the other higher partial waves but with total angular momenta $J\leq 3$ in this paper. Contact interactions

Frank Hu, Francis He, David J. Yaron

Quantum chemistry provides chemists with invaluable information, but the high computational cost limits the size and type of systems that can be studied. Machine learning (ML) has emerged as a means to dramatically lower cost while maintaining high accuracy. However, ML models often sacrifice interpretability by using components, such as the artificial neura

Stephen Sheng, Keerthi Vasan G. C, Chi Po Choi, James Sharpnack

Strong gravitational lenses allow us to peer into the farthest reaches of space by bending the light from a background object around a massive object in the foreground. Unfortunately, these lenses are extremely rare, and manually finding them in astronomy surveys is difficult and time-consuming. We are thus tasked with finding them in an automated fashion wi

Yunfan Li, Mouxing Yang, Dezhong Peng, Taihao Li

This paper proposes to perform online clustering by conducting twin contrastive learning (TCL) at the instance and cluster level. Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respe

Minsu Kim, Youngjoon Yu, Sungjune Park, Yong Man Ro

These days, although deep neural networks (DNNs) have achieved a noticeable progress in a wide range of research area, it lacks the adaptability to be employed in the real-world applications because of the environment discrepancy problem. Such a problem originates from the difference between training and testing environments, and it is widely known that it c

Aida Behmard, Jason Sevilla, Jim Fuller

Planet engulfment can be inferred from enhancement of refractory elements in the photosphere of the engulfing star following accretion of rocky planetary material. Such refractory enrichments are subject to stellar interior mixing processes, namely thermohaline mixing induced by an inverse mean-molecular-weight gradient between the convective envelope and ra

Jiyang Guan, Jian Liang, Ran He

An off-the-shelf model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner. Model fingerprinting aims to verify whether a suspect model is stolen from the victim model, which gains more and more attention nowadays. Previous methods always leverage the transferable adversarial examples as th

Limin Ma, Zhonghua Qiao

This paper proposes a decoupled numerical scheme of the time-dependent Ginzburg--Landau equations under the temporal gauge. For the magnetic potential and the order parameter, the discrete scheme adopts the second type Ned${\rm \acute{e}}$lec element and the linear element for spatial discretization, respectively; and a linearized backward Euler method and t

Shogo Kusano, Masayuki Uchida

We consider structural equation modeling (SEM) with latent variables for diffusion processes based on high-frequency data. We derive the quasi-likelihood estimators for parameters in the SEM. The goodness-of-fit test based on the quasi-likelihood ratio is proposed. Furthermore, the asymptotic properties of our proposed estimators are examined.

César Jesús-Valls

A major challenge of particle physics is determining the neutrino mass ordering (MO). Due to matter effects, the flavor content of the neutrino flux from a Core-Collapse Supernova (CCSN) depends on the true neutrino MO resulting in markedly different energy and angle distributions for the measured lepton in water Cherenkov neutrino detectors. In this article

Shuyin Xia, Xiaoyu Lian, Guoyin Wang, Xinbo Gao

Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing. Consequently, these methods are neither efficient nor robust to label noise. Therefore, we propose a frame-work called granular-ball fuzzy set by introducing granular-ball computing into fuzzy set. The computational framework

Enting Ying, Tianyang Xiong, Shihui Guo, Ming Qiu

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devic

Ruben Recabarren, Bogdan Carbunar, Nestor Hernandez, Ashfaq Ali Shafin

Studies of online influence operations, coordinated efforts to disseminate and amplify disinformation, focus on forensic analysis of social networks or of publicly available datasets of trolls and bot accounts. However, little is known about the experiences and challenges of human participants in influence operations. We conducted semi-structured interviews

Kyungsu Lee, Jaeseung Yang, Haeyun Lee, Jae Youn Hwang

The simulation of human neurons and neurotransmission mechanisms has been realized in deep neural networks based on the theoretical implementations of activation functions. However, recent studies have reported that the threshold potential of neurons exhibits different values according to the locations and types of individual neurons, and that the activation

C. Reuter, J. S. Spilker, J. D. Vieira, D. P. Marrone

We present the average rest-frame spectrum of the final catalog of dusty star-forming galaxies (DSFGs) selected from the South Pole Telescope SZ survey (SPT-SZ) and measured with Band 3 of the Atacama Large Millimeter/submillimeter Array (ALMA). This work builds on the previous average rest-frame spectrum, given in Spilker et al. (2014) for the first 22 sour

Abdul Rafae Khan, Hrishikesh Kanade, Girish Amar Budhrani, Preet Jhanglani

This paper describes the Stevens Institute of Technology's submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask $1$ Hindi/English to Hinglish and subtask $2$ Hinglish to English translation. Our findings lie in the improvements made through the use of large pre-trained multilingual NMT m

Saeed Maleki, John Crassidis, Yang Cheng, Matthias Schmid

This study presents a theoretical structure for the monocular pose estimation problem using the total least squares. The unit-vector line-of-sight observations of the features are extracted from the monocular camera images. First, the optimization framework is formulated for the pose estimation problem with observation vectors extracted from unit vectors fro

Coleman DeLude, Rakshith Sharma, Santhosh Karnik, Christopher Hood

In this paper we consider the problem of localizing a set of broadband sources from a finite window of measurements. In the case of narrowband sources this can be reduced to the problem of spectral line estimation, where our goal is simply to estimate the active frequencies from a weighted mixture of pure sinusoids. There exists a plethora of modern and clas

Zhenggang Tang, Balakumar Sundaralingam, Jonathan Tremblay, Bowen Wen

We present a system for collision-free control of a robot manipulator that uses only RGB views of the world. Perceptual input of a tabletop scene is provided by multiple images of an RGB camera (without depth) that is either handheld or mounted on the robot end effector. A NeRF-like process is used to reconstruct the 3D geometry of the scene, from which the

Xiao Wang, Wen-ge Wang

The so-called eigenstate thermalization hypothesis (ETH), which has been tested in various manybody models by numerical simulations, supplies a way of understanding eventual thermalization and is believed to be important for understanding processes of thermalization. Two functions play important roles in the application of ETH, one for averaged diagonal elem

Man Ho Chan

A recent study shows that gravitational scattering of dark matter, in the form of massive objects with mass $m \sim 10^3-10^4M_{\odot}$, could provide a possible solution to alleviate the small-scale structure problems of cold dark matter. The scattering cross section is velocity-dependent so that this scenario can explain why self-interaction of dark matter

Pavithiran G, Sharan Padmanabhan, Nuvvuru Divya, Aswathy V

Doctors typically write in incomprehensible handwriting, making it difficult for both the general public and some pharmacists to understand the medications they have prescribed. It is not ideal for them to write the prescription quietly and methodically because they will be dealing with dozens of patients every day and will be swamped with work.As a result,

Joshua R Tempelman, Alexander F Vakakis, Kathryn H Matlack

The characteristics of topologically protected wave propagation is typically predicted via the band structure of the primitive unit cell, using Berry curvature to predict localized interface or boundary states (as well as their degree of localization), and the dispersion relation to predict propagating group velocity. However, practical systems are finite in

Ashish Rajendra Sai, Harald Vranken

There is a growing interest in understanding the energy and environmental footprint of digital currencies, specifically in cryptocurrencies such as Bitcoin and Ethereum. These cryptocurrencies are operated by a geographically distributed network of computing nodes, making it hard to accurately estimate their energy consumption. Existing studies, both in acad

Miguel Martin, Vasantha Ramani, Clayton Miller

This paper studies heat fluxes from contributors and mitigators of urban heat islands using thermal images and weather data. Thermal images were collected from an observatory operating on the rooftop of a building between November 2021 and April 2022. Over the same period, an automatic weather station network was used to measure weather conditions at several

Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett

Local optimization presents a promising approach to expensive, high-dimensional black-box optimization by sidestepping the need to globally explore the search space. For objective functions whose gradient cannot be evaluated directly, Bayesian optimization offers one solution -- we construct a probabilistic model of the objective, design a policy to learn ab

F. S. Passos, A. R. C. Buarque

The dynamics of nonlinear flip-flop quantum walk with amplitude-dependent phase shifts with pertubing potential barrier is investigated. Through the adjustment between uniform local perturbations and a Kerrlike nonlinearity of the medium we find a rich set of dynamic profiles. We will show the existence of different Hadamard quantum walking regimes, includin

Samuele Sandrini, Marco Faroni, Nicola Pedrocchi

A good estimation of the actions' cost is key in task planning for human-robot collaboration. The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot. This paper proposes an approach to learning actions' costs and coupling between actions executed concurrently by humans

Julie Desjardins, Rosa Winter

The blow-up of the anticanonical base point on a del Pezzo surface $S$ of degree 1 gives rise to a rational elliptic surface $\mathscr{E}$ with only irreducible fibers. The sections of minimal height of $\mathscr{E}$ are in correspondence with the $240$ exceptional curves on $S$. A natural question arises when studying the configuration of these curves: if a

Yu Xuan, Xiangyu Zhang, Shuyue Stella Li, Zihan Shen

The detection of abnormal fetal heartbeats during pregnancy is important for monitoring the health conditions of the fetus. While adult ECG has made several advances in modern medicine, noninvasive fetal electrocardiography (FECG) remains a great challenge. In this paper, we introduce a new method based on affine combinations of adaptive filters to extract F

Huolin L. Xin, Mike Hu

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer, and redox centers for electronic, electrocatalytic, and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are

Ehsan Askari, Guillaume Crevecoeur

The value of unknown parameters of multibody systems is crucial for prediction, monitoring, and control, sometimes estimated using a biased physics-based model leading to incorrect outcomes. Discovering motion equations of multibody systems from time-series data is challenging as they consist of complex rational functions, constants as function arguments, an

Marco Faroni, Manuel Beschi, Nicola Pedrocchi

Human awareness in robot motion planning is crucial for seamless interaction with humans. Many existing techniques slow down, stop, or change the robot's trajectory locally to avoid collisions with humans. Although using the information on the human's state in the path planning phase could reduce future interference with the human's movements and make safety

Martin Strauss, Matteo Torcoli, Bernd Edler

Deep generative models for Speech Enhancement (SE) received increasing attention in recent years. The most prominent example are Generative Adversarial Networks (GANs), while normalizing flows (NF) received less attention despite their potential. Building on previous work, architectural modifications are proposed, along with an investigation of different con

Lingfeng Sun, Haichao Zhang, Wei Xu, Masayoshi Tomizuka

The purpose of multi-task reinforcement learning (MTRL) is to train a single policy that can be applied to a set of different tasks. Sharing parameters allows us to take advantage of the similarities among tasks. However, the gaps between contents and difficulties of different tasks bring us challenges on both which tasks should share the parameters and what

Shubham Kedia, Sambhu Harimanas Karumanchi

In this work, we have implemented a SLAM-assisted navigation module for a real autonomous vehicle with unknown dynamics. The navigation objective is to reach a desired goal configuration along a collision-free trajectory while adhering to the dynamics of the system. Specifically, we use LiDAR-based Hector SLAM for building the map of the environment, detecti

Hajime Sotani, Shinsuke Ota

Low-mass neutron stars are directly associated with the nuclear saturation parameters because their central density is definitely low. We have already found a suitable combination of nuclear saturation parameters for expressing the neutron star mass and gravitational redshift, i.e., $\eta\equiv (K_0L^2)^{1/3}$ with the incompressibility for symmetric nuclear

Be'eri Greenfeld

We prove that there exist finitely generated, stably finite algebras which are non linear sofic. This was left open by Arzhantseva and P\u{a}unescu in 2017.

Afsana Rahman, Ruhul Amin

Social media and messaging platforms have become a support system for those in fear of COVID-19 while, at the same time, becoming the root cause of spreading hate, inaccurate representations, and false realities. As technology has morphed into a commodity for daily tasks and actions, this article may be useful for people of all ages and backgrounds who are i

Eric L. Sandquist, Andrew J. Buckner, Matthew D. Shetrone, Samuel C. Barden

We present analyses of improved photometric and spectroscopic observations for two detached eclipsing binaries at the turnoff of the open cluster NGC 752: the 1.01 day binary DS And and the 15.53 d BD $+$37 410. For DS And, we find $M_1 = 1.692\pm0.004\pm0.010 M_\odot$, $R_1 = 2.185\pm0.004\pm0.008 R_\odot$, $M_2 = 1.184\pm0.001\pm0.003 M_\odot$, and $R_2 =

Yiding Feng, Rad Niazadeh, Amin Saberi

Matching and pricing are two critical levers in two-sided marketplaces to connect demand and supply. The platform can produce more efficient matching and pricing decisions by batching the demand requests. We initiate the study of the two-stage stochastic matching problem, with or without pricing, to enable the platform to make improved decisions in a batch w

Nattapat Boonprakong, Benjamin Tag, Tilman Dingler

Given the prevalence of online misinformation and our scarce cognitive capacity, Internet users have been shown to frequently fall victim to such information. As some studies have investigated psychological factors that make people susceptible to believe or share misinformation, some ongoing research further put these findings into practice by objectively id

Yasuhiro Hatsugai, Yoshihito Kuno

A topological pump of the $SU(Q)$ quantum chain is proposed associated with a current due to a local $[U(1)]^{\otimes Q}$ gauge invariance of colored fermions. The $SU(Q)$ invariant dimer phases are characterized by the $Z_Q$ Berry phases as a topological order parameter with a $d$-dimensional twist space ($d=Q-1$) as a synthetic Brillouin zone. By inclusion

Gus Lehrer, Yang Zhang

Let $W$ be a finite Coxeter group. We give an algebraic presentation of what we refer to as ``the non-crossing algebra'', which is associated to the hyperplane complement of $W$ and to the cohomology of its Milnor fibre. This is used to produce simpler and more general chain (and cochain) complexes which compute the integral homology and cohomology groups of

Ioana Craiciu, Boris Korzh, Andrew D. Beyer, Andrew Mueller

Superconducting nanowire single photon detectors are a key technology for quantum information and science due to their high efficiency, low timing jitter, and low dark counts. In this work, we present a detector for single 1550 nm photons with up to 78% detection efficiency, timing jitter below 50 ps FWHM, 158 counts/s dark count rate - as well as a world-le

Shao-Heng Ko, Erin Taylor, Pankaj K. Agarwal, Kamesh Munagala

In this paper, we propose to use the concept of local fairness for auditing and ranking redistricting plans. Given a redistricting plan, a deviating group is a population-balanced contiguous region in which a majority of individuals are of the same interest and in the minority of their respective districts; such a set of individuals have a justified complain

Nafis Sadeq, Canwen Xu, Julian McAuley

Masked language modeling is widely used for pretraining large language models for natural language understanding (NLU). However, random masking is suboptimal, allocating an equal masking rate for all tokens. In this paper, we propose InforMask, a new unsupervised masking strategy for training masked language models. InforMask exploits Pointwise Mutual Inform

Chia-Yu Li, Ngoc Thang Vu

We propose a novel method that combines CycleGAN and inter-domain losses for semi-supervised end-to-end automatic speech recognition. Inter-domain loss targets the extraction of an intermediate shared representation of speech and text inputs using a shared network. CycleGAN uses cycle-consistent loss and the identity mapping loss to preserve relevant charact

Ethan Payne, Eric Thrane

Hierarchical Bayesian inference is an essential tool for studying the population properties of compact binaries with gravitational waves. The basic premise is to infer the unknown prior distribution of binary black hole and/or neutron star parameters such component masses, spin vectors, and redshift. These distributions shed light on the fate of massive star

Fan Wu, Sanyam Lakhanpal, Qian Li, Kookjin Lee

Recent policy initiatives have acknowledged the importance of disaggregating data pertaining to diverse Asian ethnic communities to gain a more comprehensive understanding of their current status and to improve their overall well-being. However, research on anti-Asian racism has thus far fallen short of properly incorporating data disaggregation practices. O

Mohamed Elsayed, A. Rupam Mahmood

Second-order optimization uses curvature information about the objective function, which can help in faster convergence. However, such methods typically require expensive computation of the Hessian matrix, preventing their usage in a scalable way. The absence of efficient ways of computation drove the most widely used methods to focus on first-order approxim

G. G. Piva, C. Anteneodo

We investigate a nonlocal generalization of the Fisher-KPP equation, which incorporates logistic growth and diffusion, for a single species population in a viable patch (refuge). In this framework, diffusion plays an homogenizing role, while nonlocal interactions can destabilize the spatially uniform state, leading to the emergence of spontaneous patterns. N

Wei Zhang, Jiaxi Cao, Xiang Wang, Enqi Tian

In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality. Thus head-mounted gaze tracking technology has received attention as an essential part of human-computer interaction. However, unavoidable slippage of head-mounted devices (HMD) often results higher gaze tracking errors an

Mona Wang, Jonathan Mayer

We study how legislation that restricts speech can induce online self-censorship and alter online discourse, using the recent Hong Kong national security law as a case study. We collect a dataset of 7 million historical Tweets from Hong Kong users, supplemented with historical snapshots of Tweet streams collected by other researchers. We compare online activ

Kirti Joshi

Let $p$ be a prime number. Let $X/E$ be a geometrically connected, smooth, quasi-projective variety over a finite extension $E/\mathbb{Q}_p$. In this paper I demonstrate the existence of isomorphs of the tempered (and hence also \'etale) fundamental group of $X/E$ which are labeled by distinct arithmetic holomorphic structures, just as isomorphs of the funda

Christian Weilbach, William Harvey, Frank Wood

We introduce a framework for automatically defining and learning deep generative models with problem-specific structure. We tackle problem domains that are more traditionally solved by algorithms such as sorting, constraint satisfaction for Sudoku, and matrix factorization. Concretely, we train diffusion models with an architecture tailored to the problem sp

Arturo Jaramillo, James Melbourne

In this paper we develop tools for studying limit theorems by means of convexity. We establish bounds for the discrepancy in total variation between probability measures $\mu$ and $\nu$ such that $\nu$ is log-concave with respect to $\mu$. We discuss a variety of applications, which include geometric and binomial approximations to sums of random variables, a

Naoki Kouda, Kyohei Eguchi, Ryuji Okazaki, Masafumi Tamura

Confined electrons in low dimensions host desirable material functions for downscaled electronics as well as advanced energy technologies. Thermoelectricity is a most fascinating example, since the dimensionality modifies the electron density of states dramatically, leading to enhanced thermopower as experimentally examined in artificial two-dimensional (2D)

Nimish Shah, Wannes Meert, Marian Verhelst

A growing number of applications like probabilistic machine learning, sparse linear algebra, robotic navigation, etc., exhibit irregular data flow computation that can be modeled with directed acyclic graphs (DAGs). The irregularity arises from the seemingly random connections of nodes, which makes the DAG structure unsuitable for vectorization on CPU or GPU

Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves

A key part of learning to program is learning to understand programming error messages. They can be hard to interpret and identifying the cause of errors can be time-consuming. One factor in this challenge is that the messages are typically intended for an audience that already knows how to program, or even for programming environments that then use the info

Chong-Chong He, Massimo Ricotti

We simulate the formation and collapse of prestellar cores at few-AU resolution in a set of radiation-magneto-hydrodynamic simulations of giant molecular clouds (GMCs) using the grid-based code RAMSES-RT. We adopt, for the first time to our best knowledge, realistic initial/boundary conditions by zooming-in onto individual massive prestellar cores within the

Thuy-Trang Vu, Shahram Khadivi, Xuanli He, Dinh Phung

Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT). This paper investigates whether the domain information can be transferred across languages on the composition of multi-domain and multilingual NMT, particularly for the incomplete data condition where in-domain bitext is missing for some language p

Aditya Tripathi, Chibuzor Fabian Ugwu, Viktar S. Asadchy, Ihar Faniayeu

Optical nonreciprocity is manifested as a difference in the transmission of light for the opposite directions of excitation. Nonreciprocal optics is traditionally realized with relatively bulky components such as optical isolators based on the Faraday rotation, hindering the miniaturization and integration of optical systems. Here we demonstrate free-space n

R. Pablo Arribillaga, Agustin G. Bonifacio

In a voting problem with a finite set of alternatives to choose from, we study the manipulation of tops-only rules. Since all non-dictatorial (onto) voting rules are manipulable when there are more than two alternatives and all preferences are allowed, we look for rules in which manipulations are not obvious. First, we show that a rule does not have obvious

Zejian Liu, Meng Li

Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal mo

Lili Zheng, Genevera I. Allen

In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Exi

Houssem Meghnoudj, Bogdan Robu, Mazen Alamir

In this study we focus on the diagnosis of Parkinson's Disease (PD) based on electroencephalogram (EEG) signals. We propose a new approach inspired by the functioning of the brain that uses the dynamics, frequency and temporal content of EEGs to extract new demarcating features of the disease. The method was evaluated on a publicly available dataset containi

Krešimi Veselić

We give estimates for the changes of the eigenvalues of the Klein Gordon operator under the change of the potential. In some relevant situations we improve the existing estimates. We test our results on some exactly solvable models (Coulomb potential, Klein-Gordon oscillator).

Ian R. Brunton, Connor O'Mahoney, Brian D. Fields, Adrian L. Melott

The spectacular outbursts of energy associated with supernovae (SNe) have long motivated research into their potentially hazardous effects on Earth and analogous environments. Much of this research has focused primarily on the atmospheric damage associated with the prompt arrival of ionizing photons within days or months of the initial outburst, and the high

Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun

In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation. To overcome the "curse of multilinguality", these models often opt for scaling up the number of parameters, which makes their use in resource-constra

Xiaojun Xu, Linyi Li, Bo Li

Recent studies show that training deep neural networks (DNNs) with Lipschitz constraints are able to enhance adversarial robustness and other model properties such as stability. In this paper, we propose a layer-wise orthogonal training method (LOT) to effectively train 1-Lipschitz convolution layers via parametrizing an orthogonal matrix with an unconstrain

Anne-Lise Viotti, Chen Li, Gunnar Arisholm, Lutz Winkelmann

Few-cycle pulses present an essential tool to track ultrafast dynamics in matter and drive strong field effects. To address photon-hungry applications, high average power lasers are used which, however, cannot directly provide sub-100 fs pulse durations. Post-compression of laser pulses by spectral broadening and dispersion compensation is the most efficient

Giannis Daras, Negin Raoof, Zoi Gkalitsiou, Alexandros G. Dimakis

We find a surprising connection between multitask learning and robustness to neuron failures. Our experiments show that bilingual language models retain higher performance under various neuron perturbations, such as random deletions, magnitude pruning and weight noise compared to equivalent monolingual ones. We provide a theoretical justification for this ro

Budhaditya Deb, Guoqing Zheng, Ahmed Hassan Awadallah

Recent work has shown that language models (LMs) trained with multi-task \textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with improved performance compared to prompt tuning. MTIL illustrates that LMs can extract and use information about the task from instructions beyond the surface patterns of the inputs and

Kevin Dick, Daniel G. Kyrollos, James R. Green

Across many domains, real-world problems can be represented as a network. Nodes represent domain-specific elements and edges capture the relationship between elements. Leveraging high-performance computing and optimized link prediction algorithms, it is increasingly possible to evaluate every possible combination of nodal pairs enabling the generation of a c

Kejian Qu, Zachary W. Riedel, Irián Sánchez-Ramírez, Simon Bettler

The discovery of new low-dimensional transition metal chalcogenides is contributing to the already prosperous family of these materials. In this study, needle-shaped single crystals of a new quasi-one-dimensional material (Nb$_4$Se$_{15}$I$_2$)I$_2$ were grown by chemical vapor transport, and the structure was solved by single crystal X-ray diffraction (XRD)

Philip Bretz, Lou Kondic, Miro Kramar

We consider a sheared granular system experiencing intermittent dynamics of stick-slip type via discrete element simulations. The considered setup consists of a two-dimensional system of soft frictional particles sandwiched between solid walls, one of which is exposed to a shearing force. The slip events are detected using stochastic state space models appli

Yan Zhou, Joshua O. Island, Matt Grau

We present a new platform facilitating quantum logic control of polar molecular ions in a segmented ring ion trap, paving the way for precision measurements. This approach focuses on achieving near-unity state preparation and detection, as well as long spin coherence. A distinctive aspect lies in separating state preparation and detection conducted in a stat

Johnny Tian-Zheng Wei, Tom Kocmi, Christian Federmann

In MT evaluation, pairwise comparisons are conducted to identify the better system. In conducting the comparison, the experimenter must allocate a budget to collect Direct Assessment (DA) judgments. We provide a cost effective way to spend the budget, but show that typical budget sizes often do not allow for solid comparison. Taking the perspective that the

Mary Lai Salvana, Jian Cao, Mikyoung Jun

Variables contained within the global oceans can detect and reveal the effects of the warming climate as the oceans absorb huge amounts of solar energy. Hence, information regarding the joint spatial distribution of ocean variables is critical for climate monitoring. In this paper, we investigate the spatial correlation structure between ocean temperature an

Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu

Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We us

Keigo Kamei, Koretaka Yuge

For classical discrete system under constant composition, typically reffered to as substitutional alloys, correspondence between interatomic many-body interactions and structure in thermodynamic equilibrium exhibit profound, complicated nonlinearity (canonical nonlinearity). Our recent studies clarify that the nonlinearity can be reasonablly described both b

Cheng Zhang, Jie Wang

Transformer-based QG models can generate question-answer pairs (QAPs) with high qualities, but may also generate silly questions for certain texts. We present a new method called tag-set sequence learning to tackle this problem, where a tag-set sequence is a sequence of tag sets to capture the syntactic and semantic information of the underlying sentence, an

Carlos Borges, Manas Rachh, Leslie Greengard

The acoustic inverse obstacle scattering problem consists of determining the shape of a domain from measurements of the scattered far field due to some set of incident fields (probes). For a penetrable object with known sound speed, this can be accomplished by treating the boundary alone as an unknown curve. Alternatively, one can treat the entire object as

Sarmed A Rahman, Randy Lewis, Emanuele Mendicelli, Sarah Powell

The Hamiltonian approach can be used successfully to study the real-time evolution of a non-Abelian lattice gauge theory on the available noisy quantum computers. In this work, results from the real-time evolution of SU(2) pure gauge theory on IBM hardware are presented. The long real-time evolution spanning dozens of Trotter steps with hundreds of CNOT gate

Olivier Guichard, Eugen Rogozinnikov, Anna Wienhard

Using Lusztig's total positivity in split real Lie groups V. Fock and A. Goncharov have introduced spaces of positive (framed) representations. For general semisimple Lie groups a generalization of Lusztig's total positivity was recently introduced by O. Guichard and A. Wienhard. They also introduced the associated space of positive representations. Here we

Runlong Zhou, Ruosong Wang, Simon S. Du

We study regret minimization for reinforcement learning (RL) in Latent Markov Decision Processes (LMDPs) with context in hindsight. We design a novel model-based algorithmic framework which can be instantiated with both a model-optimistic and a value-optimistic solver. We prove an $\tilde{O}(\sqrt{\mathsf{Var}^\star M \Gamma S A K})$ regret bound where $\til

Vivian Liu, Jo Vermeulen, George Fitzmaurice, Justin Matejka

Text-to-image AI are capable of generating novel images for inspiration, but their applications for 3D design workflows and how designers can build 3D models using AI-provided inspiration have not yet been explored. To investigate this, we integrated DALL-E, GPT-3, and CLIP within a CAD software in 3DALL-E, a plugin that generates 2D image inspiration for 3D

Kathrin Kromer, Chunhai Lyu, Menno Door, Pavel Filianin

The absolute atomic mass of $^{208}$Pb has been determined with a fractional uncertainty of $7\times 10^{-11}$ by measuring the cyclotron-frequency ratio $R$ of $^{208}$Pb$^{41+}$ to $^{132}$Xe$^{26+}$ with the high-precision Penning-trap mass spectrometer Pentatrap and computing the binding energies $E_{\text{Pb}}$ and $E_{\text{Xe}}$ of the missing 41 and

Taha Tekdoğan, Serkan Göktaş, Ayse Yilmazer-Metin

As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and performance characterization studies of GNNs is increasing. So far, we have seen many studies that investigate and present the performance and computational efficiency of GNNs. However, the work done so far has been carried out using a few high-level GNN frameworks

Samuel T. Hart, Maher A. Dayeh, Radoslav Bučík, Mihir I. Desai

Using ion measurements from Ultra-Low-Energy Isotope Spectrometer (ULEIS) observations onboard Advanced Composition Explorer (ACE) and Solar Isotope Spectrometer (SIS) observations onboard the Solar Terrestrial Observatory (STEREO)-A and STEREO-B spacecraft, we have identified 854 3He-rich time periods between 1997 September and 2021 March. We include all ev