arXiv papers from January 2020

Adam Block, Youssef Mroueh, Alexander Rakhlin

We study convergence of a generative modeling method that first estimates the score function of the distribution using Denoising Auto-Encoders (DAE) or Denoising Score Matching (DSM) and then employs Langevin diffusion for sampling. We show that both DAE and DSM provide estimates of the score of the Gaussian smoothed population density, allowing us to apply

J. H. Kinsman, D. J. Asher

Using orbital integrations of particles ejected from Comet Halley's passages between 1404 BC and 240 BC, the authors investigate possible outbursts of the Orionids (twin shower of the Eta Aquariids) that may have been observed in the western hemisphere. In an earlier orbital integration study the authors determined there was a high probability linking probab

Csilla Bujtás

It is conjectured that the game domination number is at most $3n/5$ for every $n$-vertex graph which does not contain isolated vertices. It was proved in the recent years that the conjecture holds for several graph classes, including the class of forests and that of graphs with minimum degree at least two. Here we prove that the slightly bigger upper bound $

Jun Fang, Ali Shafiee, Hamzah Abdel-Aziz, David Thorsley

Quantization plays an important role in the energy-efficient deployment of deep neural networks on resource-limited devices. Post-training quantization is highly desirable since it does not require retraining or access to the full training dataset. The well-established uniform scheme for post-training quantization achieves satisfactory results by converting

Vishal Kamat, Samuel Norris

We develop new robust discrete choice tools to learn about the average willingness to pay for a price subsidy and its effects on demand given exogenous, discrete variation in prices. Our starting point is a nonparametric, nonseparable model of choice. We exploit the insight that our welfare parameters in this model can be expressed as functions of demand for

Davide Bacciu, Alessio Micheli, Marco Podda

Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predic

Dmitry V. Karlovets, Valeriy G. Serbo

Effects of the quantum interference in collisions of particles have a twofold nature: they arise because of the auto-correlation of a complex scattering amplitude and due to spatial coherence of the incoming wave packets. Both these effects are neglected in a conventional scattering theory dealing with the delocalized plane waves, although they sometimes mus

Madhav Kumar, Dean Eckles, Sinan Aral

Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of monopolistic firms and assumed product relationships, e.g., complementarity in usage. We develop a new machine-learning-driven me

Antoine Lesage-Landry, Duncan S. Callaway

We extend the regret analysis of the online distributed weighted dual averaging (DWDA) algorithm [1] to the dynamic setting and provide the tightest dynamic regret bound known to date with respect to the time horizon for a distributed online convex optimization (OCO) algorithm. Our bound is linear in the cumulative difference between consecutive optima and d

Daniel J. Yates, Alexander G. Abanov, Aditi Mitra

Almost strong edge-mode operators arising at the boundaries of certain interacting 1D symmetry protected topological phases with \(Z_2\) symmetry have infinite temperature lifetimes that are non-perturbatively long in the integrability breaking terms, making them promising as bits for quantum information processing. We extract the lifetime of these edge-mode

Xinyue Hu, Haoji Hu, Saurabh Verma, Zhi-Li Zhang

Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system dynamics and uncertainties, making traditional numerical approaches infeasible. To address these concerns

YoungJu Choie, Winfried Kohnen, Yichao Zhang

A generalized Riemann hypothesis states that all zeros of the completed Hecke $L$-function $L^*(f,s)$ of a normalized Hecke eigenform $f$ on the full modular group should lie on the vertical line $Re(s)=\frac{k}{2}.$ It was shown by Kohnen that there exists a Hecke eigenform $f$ of weight $k$ such that $L^*(f,s) \neq 0$ for sufficiently large $k$ and any poi

Susana J. Landau

Theories that attempt to unify the four fundamental interactions and alternative theories of gravity predict time and/or spatial variation of the fundamental constants of nature. Different versions of these theories predict different behaviours for these variations. In consequence, experimental and observational bounds are an important tool to check the vali

Lia Papadopoulos, Christopher W. Lynn, Demian Battaglia, Danielle S. Bassett

At the macroscale, the brain operates as a network of interconnected neuronal populations, which display rhythmic dynamics that support interareal communication. Understanding how stimulation of a particular brain area impacts such concerted activity is important for gaining basic insights into brain function and for developing neuromodulation as a therapeut

Vito Buffa

In 2013, Masson and Siljander determined a method to prove that the $p$-minimal upper gradient $g_{f_\varepsilon}$ for the time mollification $f_\varepsilon$, $\varepsilon>0$, of a parabolic Newton-Sobolev function $f\in L^p_\mathrm{loc}(0,\tau;N^{1,p}_\mathrm{loc}(\Omega))$, with $\tau>0$ and $\Omega$ open domain in a doubling metric measure space $(\mathbb

Ao Luo, Fan Yang, Xin Li, Dong Nie

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations withi

Scott A. Carter, Daniel Avrahami, Nami Tokunaga

While it is often critical for indoor-location- and proximity-aware applications to know whether a user is in a space or not (e.g., a specific room or office), a key challenge is that the difference between standing on one side or another of a doorway or wall is well within the error range of most RF-based approaches. In this work, we address this challenge

Neal Marquez, Jon Wakefield

There is an increasing focus on reducing inequalities in health outcomes in developing countries. Subnational variation is of particular interest, with geographic data used to understand the spatial risk of detrimental outcomes and to identify who is at greatest risk. While some health surveys provide observations with associated geographic coordinates, many

Dennis B. Bowen, Mark Avara, Vassilios Mewes, Yosef Zlochower

We present an extension of the PatchworkMHD code [1], itself an MHD-capable extension of the Patchwork code [2], for which several algorithms presented here were co-developed. Its purpose is to create a multipatch scheme compatible with numerical simulations of arbitrary equations of motion at any discretization order in space and time. In the Patchwork fram

Li Xiao, Xiang-Gen Xia, Yu-Ping Wang

The robust Chinese remainder theorem (CRT) has been recently proposed for robustly reconstructing a large nonnegative integer from erroneous remainders. It has found many applications in signal processing, including phase unwrapping and frequency estimation under sub-Nyquist sampling. Motivated by the applications in multidimensional (MD) signal processing,

Rita Gil, Francisca F. Fernandes, Noam Shemesh

Functional Magnetic Resonance Imaging (fMRI) is predominantly harnessed for spatially mapping activation foci along distributed pathways. However, resolving dynamic information on activation sequence remains elusive. Here, we show an ultra-fast fMRI (ufMRI) approach - a facilitating non-invasive methodology for mapping Blood-Oxygenation-Level-Dependent (BOLD

Marco Avellaneda, Brian Healy, Andrew Papanicolaou, George Papanicolaou

Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of U.S. equities there is a PCA-based model with a principal eigenportfolio whose return time series lies close to that of an overarching market factor. The authors show that this market factor is the index re

Seokki Lee, Bertram Ludaescher, Boris Glavic

Why and why-not provenance have been studied extensively in recent years. However, why-not provenance, and to a lesser degree why provenance, can be very large resulting in severe scalability and usability challenges. In this paper, we introduce a novel approximate summarization technique for provenance which overcomes these challenges. Our approach uses pat

Dan Edidin, Ryan Richey

We show that a cone theorem for ${\mathbbA}^1-homotopy invariant contravariant functors implies the vanishing of the positive degree part of the operational Chow cohomology rings of a large class of affine varieties. We also discuss how this vanishing relates to a number of questions about representing Chow cohomology classes of GIT quotients in terms of equ

Zhong-Li Liu, Ya-Dong Wei, Xiao-Dong Xu, Wei-Qi Li

Elastic constants and mechanical properties play a pivotal role across multiple disciplines and engineering applications. We introduced the optimized high-efficient strain-matrix set (OHESS) that determines the second-order elastic constants of materials using the stress-strain method. Herein, we systematically investigate the computational efficiency of OHE

Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

We study the problem of regret minimization in partially observable linear quadratic control systems when the model dynamics are unknown a priori. We propose ExpCommit, an explore-then-commit algorithm that learns the model Markov parameters and then follows the principle of optimism in the face of uncertainty to design a controller. We propose a novel way t

Michael Joyce

We survey the recent study of involution Schubert polynomials and a modest generalization that we call degenerate involution Schubert polynomials. We cite several conditions when (degenerate) involution Schubert polynomials have simple factorization formulae. Such polynomials can be computed by traversing through chains in certain weak order posets, and we p

Tim Mitchell

The main two algorithms for computing the numerical radius are the level-set method of Mengi and Overton and the cutting-plane method of Uhlig. Via new analyses, we explain why the cutting-plane approach is sometimes much faster or much slower than the level-set one and then propose a new hybrid algorithm that remains efficient in all cases. For matrices who

Duzhe Wang, Haoda Fu, Po-Ling Loh

We present nonparametric algorithms for estimating optimal individualized treatment rules. The proposed algorithms are based on the XGBoost algorithm, which is known as one of the most powerful algorithms in the machine learning literature. Our main idea is to model the conditional mean of clinical outcome or the decision rule via additive regression trees,

Guilherme Mazanti, Islam Boussaada, Silviu-Iulian Niculescu, Yacine Chitour

It has been observed in several recent works that, for some classes of linear time-delay systems, spectral values of maximal multiplicity are dominant, a property known as multiplicity-induced-dominancy (MID). This paper starts the investigation of whether MID holds for delay differential-algebraic systems by considering a single-delay system composed of two

A. T. Costa, D. L. R. Santos, N. M. R. Peres, J. Fernández-Rossier

Magnons dominate the magnetic response of the recently discovered insulating ferromagnetic two dimensional crystals such as CrI$_3$. Because of the arrangement of the Cr spins in a honeycomb lattice, magnons in CrI$_3$ bear a strong resemblance with electronic quasiparticles in graphene. Neutron scattering experiments carried out in bulk CrI$_3$ show the exi

M. Gaczkowski, P. Górka, D. J. Pons

We obtain a compact Sobolev embedding for $H$-invariant functions in compact metric-measure spaces, where $H$ is a subgroup of the measure preserving bijections. In Riemannian manifolds, $H$ is a subgroup of the volume preserving diffeomorphisms: a compact embedding for the critical exponents follows. The results can be viewed as an extension of Sobolev embe

Alfonso Ballon-Bayona, Luis A. H. Mamani

We investigate nonlinear extensions of the holographic soft wall model proposed by Karch, Katz, Son and Stephanov \cite{Karch:2006pv} with a positive quadratic dilaton. We consider a Higgs potential for the tachyonic field that brings a more natural realisation of chiral symmetry breaking in the infrared regime. Utilising the AdS/CFT dictionary and holograph

Victor DeCaria, Michael Schneier

This report presents a series of implicit-explicit (IMEX) variable timestep algorithms for the incompressible Navier-Stokes equations (NSE). With the advent of new computer architectures there has been growing demand for low memory solvers of this type. The addition of time adaptivity improves the accuracy and greatly enhances the efficiency of the algorithm

Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive $p$-persistent slotted Aloh

Steve Tsham Mpinda Ataky, Jonathan de Matos, Alceu de S. Britto, Luiz E. S. Oliveira

Data imbalance is a major problem that affects several machine learning (ML) algorithms. Such a problem is troublesome because most of the ML algorithms attempt to optimize a loss function that does not take into account the data imbalance. Accordingly, the ML algorithm simply generates a trivial model that is biased toward predicting the most frequent class

Cole Franks, Ankur Moitra

Estimating the shape of an elliptical distribution is a fundamental problem in statistics. One estimator for the shape matrix, Tyler's M-estimator, has been shown to have many appealing asymptotic properties. It performs well in numerical experiments and can be quickly computed in practice by a simple iterative procedure. Despite the many years the estimator

Cristhian Montoya, Jhoana P. Romero-Leiton

In this work, two mathematical models for malaria under resistance are presented. More precisely, the first model shows the interaction between humans and mosquitoes inside a patch under infection of malaria when the human population is resistant to antimalarial drug and mosquitoes population is resistant to insecticides. For the second model, human-mosquito

Ryan Smith, Daniel Palin, Philokypros P. Ioulianou, Vassilios G. Vassilakis

Many IoT devices, especially those deployed at the network edge have limited power resources. A number of attacks aim to exhaust these resources and drain the batteries of such edge nodes. In this work, we study the effects of a variety of battery draining attacks against edge nodes. Through simulation, we clarify the extent to which such attacks are able to

Vijay Singh, Uthpala Herath, Benny Wah, Xingyu Liao

Dynamical Mean Field Theory (DMFT) is a successful method to compute the electronic structure of strongly correlated materials, especially when it is combined with density functional theory (DFT). Here, we present an open-source computational package (and a library) combining DMFT with various DFT codes interfaced through the Wannier90 package. The correlate

Z. Shang, A. Hashemi, Y. Berencén, H. -P. Komsa

Silicon carbide is a very promising platform for quantum applications because of extraordinary spin and optical properties of point defects in this technologically-friendly material. These properties are strongly influenced by crystal vibrations, but the exact relationship between them and the behavior of spin qubits is not fully investigated. We uncover the

Alexandra Koulouri, Ville Rimpilainen

The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propo

Vítor Albiero, Krishnapriya K. S., Kushal Vangara, Kai Zhang

We present a comprehensive analysis of how and why face recognition accuracy differs between men and women. We show that accuracy is lower for women due to the combination of (1) the impostor distribution for women having a skew toward higher similarity scores, and (2) the genuine distribution for women having a skew toward lower similarity scores. We show t

Warren R. Brown, Mukremin Kilic, Alekzander Kosakowski, Jeff J. Andrews

We present the final sample of 98 detached double white dwarf (WD) binaries found in the Extremely Low Mass (ELM) Survey, a spectroscopic survey targeting <0.3 Msun He-core WDs completed in the Sloan Digital Sky Survey footprint. Over the course of the survey we observed ancillary low mass WD candidates like GD278, which we show is a P=0.19 d double WD binar

Asuman Güven Aksoy, Mehmet Kiliç, Sahin Koçak

We investigate isometric embeddings of finite metric trees into $(\mathbb{R}^n,d_{1})$ and $( \mathbb{R}^n, d_{\infty})$. We prove that a finite metric tree can be isometrically embedded into $(\mathbb{R}^n,d_{1})$ if and only if the number of its leaves is at most $2n$. We show that a finite star tree with at most $2^n$ leaves can be isometrically embedded

Kaili Jiang, Martín A. Mosquera, Yan Oueis, Adam Wasserman

The accuracy of charge-transfer excitation energies, solvatochromic shifts and other environmental effects calculated via various density embedding techniques depend critically on the approximations employed for the non-additive non-interacting kinetic energy functional, $T_{\scriptscriptstyle\rm s}^{\scriptscriptstyle\rm nad}[n]$. Approximating this functio

Guillaume Sagnol, Daniel Schmidt genannt Waldschmidt

We consider the stochastic extensible bin packing problem (SEBP) in which $n$ items of stochastic size are packed into $m$ bins of unit capacity. In contrast to the classical bin packing problem, the number of bins is fixed and they can be extended at extra cost. This problem plays an important role in stochastic environments such as in surgery scheduling: P

Santiago Gonzalez, Risto Miikkulainen

Metalearning of deep neural network (DNN) architectures and hyperparameters has become an increasingly important area of research. Loss functions are a type of metaknowledge that is crucial to effective training of DNNs, however, their potential role in metalearning has not yet been fully explored. Whereas early work focused on genetic programming (GP) on tr

Cecilia Romaro, Antonio Carlos Roque, Jose Roberto Castilho Piqueira

There is a strong nexus between the network size and the computational resources available, which may impede a neuroscience study. In the meantime, rescaling the network while maintaining its behavior is not a trivial mission. Additionally, modeling patterns of connections under topographic organization presents an extra challenge: to solve the network bound

Xing Chu, Na Huang, Zhiyong Sun

This paper presents a class of event-triggering rules for dynamical control systems with guaranteed positive minimum inter-event time (MIET). We first propose an event-based function design with guaranteed control performance under a clock-like variable for general nonlinear systems, and later specify them to general linear systems. Compared to the existing

Noah Golowich, Sarath Pattathil, Constantinos Daskalakis, Asuman Ozdaglar

In this paper we study the smooth convex-concave saddle point problem. Specifically, we analyze the last iterate convergence properties of the Extragradient (EG) algorithm. It is well known that the ergodic (averaged) iterates of EG converge at a rate of $O(1/T)$ (Nemirovski, 2004). In this paper, we show that the last iterate of EG converges at a rate of $O

Shiqian Ding, Yewei Wu, Ian A. Finneran, Justin J. Burau

Complex molecular structure demands customized solutions to laser cooling by extending its general set of principles and practices. Yttrium monoxide (YO) has unique intramolecular interactions. The Fermi-contact interaction dominates over the spin-rotation coupling, resulting in two manifolds of closely spaced states, with one of them possessing a negligible

Anirban N. Chowdhury, Guang Hao Low, Nathan Wiebe

Preparation of Gibbs distributions is an important task for quantum computation. It is a necessary first step in some types of quantum simulations and further is essential for quantum algorithms such as quantum Boltzmann training. Despite this, most methods for preparing thermal states are impractical to implement on near-term quantum computers because of th

Saaketh Desai, Samuel Temple Reeve, James F. Belak

The two main thrusts of computational science are more accurate predictions and faster calculations; to this end, the zeitgeist in molecular dynamics (MD) simulations is pursuing machine learned and data driven interatomic models, e.g. neural network potentials, and novel hardware architectures, e.g. GPUs. Current implementations of neural network potentials

Hicham Zoubeir, Samir Kabbaj

In this paper we define Gevrey polyanalytic classes of order N on the unit disk D and we obtain for these classes a characteristic expansion into N-analytic polynomials on suitable neighborhoods of D. As an application of our main theorem, we perform for the Gevrey polyanalytic classes of order N on the unit disk D, an analogue to E. M. Dyn'kin's theorem. We

João E. Batista, Sara Silva

One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain t

Philip Boalch

We study moduli spaces of meromorphic connections (with arbitrary order poles) over Riemann surfaces together with the corresponding spaces of monodromy data (involving Stokes matrices). Natural symplectic structures are found and described both explicitly and from an infinite dimensional viewpoint (generalising the Atiyah-Bott approach). This enables us to

Rita M. C. de Almeida, Guilherme S. Y. Giardini, Mendeli Vainstein, James A. Glazier

Active Matter models commonly consider particles with overdamped dynamics subject to a force (speed) with constant modulus and random direction. Some models include also random noise in particle displacement (Wiener process) resulting in a diffusive motion at short time scales. On the other hand, Ornstein-Uhlenbeck processes consider Langevin dynamics for th

Claude Carlet, Kwang Ho Kim, Sihem Mesnager

Using recent results on solving the equation $X^{2^k+1}+X+a=0$ over a finite field $\mathbb{F}_{2^n}$, we address an open question raised by the first author in WAIFI 2014 concerning the APN-ness of the Kasami functions $x\mapsto x^{2^{2k}-2^k+1}$ with $gcd(k,n)=1$, $x\in\mathbb{F}_{2^n}$.

Christos Valelis, Fotios K. Anagnostopoulos, Spyros Basilakos, Emmanuel N. Saridakis

The existence or not of pathologies in the context of Lagrangian theory is studied with the aid of Machine Learning algorithms. Using an example in the framework of classical mechanics, we make a proof of concept, that the construction of new physical theories using machine learning is possible. Specifically, we utilize a fully-connected, feed-forward neural

Lawrence W. Cheuk, Loïc Anderegg, Yicheng Bao, Sean Burchesky

We measure inelastic collisions between ultracold CaF molecules by combining two optical tweezers, each containing a single molecule. We observe collisions between $^2\Sigma$ CaF molecules in the absolute ground state $|X,v=0, N=0,F=0\rangle$, and in excited hyperfine and rotational states. In the absolute ground state, we find a two-body loss rate of $7(4)

Timothy M. Herr, Therese A. Nelson, Luke V. Rasmussen, Yinan Zheng

OBJECTIVE: To design and evaluate new pharmacogenomic (PGx) clinical decision support (CDS) alerts, built to adhere to PGx CDS design principles developed through socio-technical approaches. MATERIALS AND METHODS: Based on previously identified design principles, we created 11 new PGx CDS alert designs and developed an interactive web application containing

B. McKernan, K. E. S. Ford, R. O'Shaughnessy

Advanced LIGO \& Advanced Virgo are detecting a large number of binary stellar origin black hole (BH) mergers. A promising channel for accelerated BH merger lies in active galactic nucleus (AGN) disks of gas around super-masssive black holes. Here we investigate the relative number of compact object mergers in AGN disk models, including BH, neutron stars (NS

Martin Šechný

In case of inquiry-based education, the priority is the pupil's activity, developing his/her practical and research skills. We can use the knowledge and skills obtained by the pupil from other subjects. Applied informatics into physics seems to be a suitable application of digital literacy. Open IT tools are the ones of the available IT tools that bring seve

Timothy M. Herr, Therese A. Nelson, Justin B. Starren

OBJECTIVE: To better understand clinician needs and preferences for the display of pharmacogenomic (PGx) information in clinical decision support (CDS) tools. MATERIALS AND METHODS: We developed a semi-structured interview to collect feedback and preferences in six key areas of PGx CDS design, from clinicians who had prior experience with live PGx CDS tools.

M. J. Jacquet, T. Boulier, F. Claude, A. Maitre

Analogue gravity enables the study of fields on curved spacetimes in the laboratory. There are numerous experimental platforms in which amplification at the event horizon or the ergoregion has been observed. Here, we demonstrate how optically generating a defect in a polariton microcavity enables the creation of one- and two-dimensional, transsonic fluid flo

O. Gurtug, M. Mangut, M. Halilsoy

Gravitational lensing caused by the gravitational field of massive objects has been studied and acknowledged for a long period of time. In this paper, however, we propose a different mechanism where the bending of light stems from the non-linear interaction of gravitational, electromagnetic and axion waves that creates the high curvature zone in the space-ti

Abhishek Kumar, Ben Poole

While the impact of variational inference (VI) on posterior inference in a fixed generative model is well-characterized, its role in regularizing a learned generative model when used in variational autoencoders (VAEs) is poorly understood. We study the regularizing effects of variational distributions on learning in generative models from two perspectives. F

James Usevitch, Dimitra Panagou

Many algorithms have been proposed in prior literature to guarantee resilient multi-agent consensus in the presence of adversarial attacks or faults. The majority of prior work present excellent results that focus on discrete-time or discretized continuous-time systems. Fewer authors have explored applying similar resilient techniques to continuous-time syst

C. E. Starrett, N. R. Shaffer, D. Saumon, R. Perriot

A new model for the electrical conductivity of dense plasmas with a mixture of ion species, containing no adjustable parameters, is presented. The model takes the temperature, mass density and relative abundances of the species as input. It takes into account partial ionization, ionic structure, and core-valence orthogonality, and uses quantum mechanical cal

Nikolas Schonsheck

The aim of this short paper is to establish a spectral algebra analog of the Bousfield-Kan "fibration lemma" under appropriate conditions. We work in the context of algebraic structures that can be described as algebras over an operad $\mathcal{O}$ in symmetric spectra. Our main result is that completion with respect to topological Quillen homology (or TQ-co

Parker Riley, Daniel Gildea

Recent embedding-based methods in unsupervised bilingual lexicon induction have shown good results, but generally have not leveraged orthographic (spelling) information, which can be helpful for pairs of related languages. This work augments a state-of-the-art method with orthographic features, and extends prior work in this space by proposing methods that c

Gongjun Choi, Motoo Suzuki, Tsutomu T. Yanagida

We present a dark sector model addressing both the Hubble tension and the core-cusp problem. The model is based on a hidden Abelian gauge symmetry group with some chiral fermions required by the anomaly cancellation conditions, producing a candidate for the decaying fermion dark matter as a solution to the Hubble tension. Moreover, the sub-keV mass regime an

Vincent E. Elfving, Marta Millaruelo, José A. Gámez, Christian Gogolin

Accurate quantum chemistry simulations remain challenging on classical computers for problems of industrially relevant sizes and there is reason for hope that quantum computing may help push the boundaries of what is technically feasible. While variational quantum eigensolver (VQE) algorithms may already turn noisy intermediate scale quantum (NISQ) devices i

Simon Catterall, Nouman Butt, David Schaich

We investigate the phase structure of a four dimensional SO(4) invariant lattice Higgs-Yukawa model comprising four reduced staggered fermions interacting with a real scalar field. The fermions belong to the fundamental representation of the symmetry group while the three scalar field components transform in the self-dual representation of SO(4). We explore

Kevin McCloskey, Eric A. Sigel, Steven Kearnes, Ling Xue

DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up to billions of unique small molecules. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collec

Leah F. South, Toni Karvonen, Chris Nemeth, Mark Girolami

The numerical approximation of posterior expected quantities of interest is considered. A novel control variate technique is proposed for post-processing of Markov chain Monte Carlo output, based both on Stein's method and an approach to numerical integration due to Sard. The resulting estimators are proven to be polynomially exact in the Gaussian context, w

Maria Piarulli, Ingo Tews

To obtain an understanding of the structure and reactions of nuclear systems from first principles has been a long-standing goal of nuclear physics. In this respect, few- and many-body systems provide a unique laboratory for studying nuclear interactions. During the past decades, the development of accurate representations of the nuclear force has undergone

Bjorn Engquist, Yunan Yang

Full-waveform inversion (FWI) is today a standard process for the inverse problem of seismic imaging. PDE-constrained optimization is used to determine unknown parameters in a wave equation that represent geophysical properties. The objective function measures the misfit between the observed data and the calculated synthetic data, and it has traditionally be

Giovanny A. Fuentes Salvo

The interval numbers is the set of compact intervals of $\mathbb{R}$ with addition and multiplication operation, which are very useful for solving calculations where there are intervals of error or uncertainty, however, it lacks an algebraic structure with an inverse element, both additive and multiplicative This fundamental disadvantage results in overestim

M. Tahan, T. Hu

The main objectives in driving multiple LED strings include achieving uniform current control and high performance PWM dimming for all strings. This work proposes a new multiple string LED driver to achieve not only current balance, but also flexible and wide range PWM dimming ratio for each string. A compact single-inductor multiple-output topology is adopt

Z. E. Brubaker, Y. Xiao, P. Chow, C. Kenney-Benson

We have performed pressure dependent X-ray diffraction and resonant X-ray emission spectroscopy experiments on USb$_2$ to further characterize the AFM-FM transition occurring near 8 GPa. We have found the magnetic transition coincides with a tetragonal to orthorhombic transition resulting in a 17% volume collapse as well as a transient $\textit{f}$-occupatio

Marcos Eduardo Valle, Rodolfo Anibal Lobo

Recurrent correlation neural networks (RCNNs), introduced by Chiueh and Goodman as an improved version of the bipolar correlation-based Hopfield neural network, can be used to implement high-capacity associative memories. In this paper, we extend the bipolar RCNNs for processing hypercomplex-valued data. Precisely, we present the mathematical background for

Ralf Witte, Abhishek Sarkar, Leonardo Velasco, Robert Kruk

High entropy oxides (HEO) are a recently introduced class of oxide materials, which are characterized by a large number of elements (i.e. five or more) sharing one lattice site which crystallize in a single phase structure. One complex example of the rather young HEO family are the rare-earth transition metal perovskite high entropy oxides. In this comprehen

Tankut Can, Kamesh Krishnamurthy, David J. Schwab

Recurrent neural networks (RNNs) are powerful dynamical models for data with complex temporal structure. However, training RNNs has traditionally proved challenging due to exploding or vanishing of gradients. RNN models such as LSTMs and GRUs (and their variants) significantly mitigate these issues associated with training by introducing various types of gat

Huijie Qiao

In this paper, we study the convergence for solutions to a sequence of (possibly degenerate) stochastic differential equations with jumps, when the coefficients converge in some appropriate sense. Our main tools are the superposition principles. And then we analyze some special cases and give some concrete and verifiable conditions.

Raúl A. Briceño, Maxwell T. Hansen, Andrew W. Jackura

Using the general formalism presented in Refs. [1,2], we study the finite-volume effects for the $\mathbf{2}+\mathcal{J}\to\mathbf{2}$ matrix element of an external current coupled to a two-particle state of identical scalars with perturbative interactions. Working in a finite cubic volume with periodicity $L$, we derive a $1/L$ expansion of the matrix eleme

Jun-Jie Huang, Pier Luigi Dragotti

In this paper, we introduce a Deep Convolutional Analysis Dictionary Model (DeepCAM) by learning convolutional dictionaries instead of unstructured dictionaries as in the case of deep analysis dictionary model introduced in the companion paper. Convolutional dictionaries are more suitable for processing high-dimensional signals like for example images and ha

Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu, Michael Chertkov

In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of PDEs is enforcing physical constraints and boundary conditions. In this work, we propose a general framework to directly embed the notion of an incompressible fluid into Convolutional Neural Network

Florian U. Bernlochner, Stephan Duell, Zoltan Ligeti, Michele Papucci

Precise measurements of $b\to c\tau\bar\nu$ decays require large resource-intensive Monte Carlo (MC) samples, which incorporate detailed simulations of detector responses and physics backgrounds. Extracted parameters may be highly sensitive to the underlying theoretical models used in the MC generation. Because new physics (NP) can alter decay distributions

Ethan Levien, Trevor GrandPre, Ariel Amir

In exponentially proliferating populations of microbes, the population typically doubles at a rate less than the average doubling time of a single-cell due to variability at the single-cell level. It is known that the distribution of generation times obtained from a single lineage is, in general, insufficient to determine a population's growth rate. Is there

Srivatsan Balakrishnan, Onkar Parrikar

We study half-space/Rindler modular Hamiltonians for excited states created by turning on sources for local operators in the Euclidean path integral in relativistic quantum field theories. We derive a simple, manifestly Lorentzian formula for the modular Hamiltonian to all orders in perturbation theory in the sources. We apply this formula to the case of sha

Shengyuan Huang

For a closed embedding of smooth schemes $X\hookrightarrow S$ with a fixed first order splitting, one can construct HKR isomorphisms between the derived scheme $X\times^R_S X$ and the total space of the shifted normal bundle $\mathbb{N}_{X/S}[-1]$, due to Arinkin-C\u{a}ld\u{a}raru, Arinkin-C\u{a}ld\u{a}raru-Hablicsek, and Grivaux. In this paper, we study fun

Steven A. Kivelson, Andrew C. Yuan, B. J. Ramshaw, Ronny Thomale

A variety of precise experiments have been carried out to establish the character of the superconducting state in Sr2RuO4. Many of these appear to imply contradictory conclusions concerning the symmetries of this state. Here, we propose that these results can be reconciled if we assume that there is a near-degeneracy between a d_{x^2-y^2} (B_{1g} in group th

Simeon Bird, Yu Feng, Christian Pedersen, Andreu Font-Ribera

We revisit techniques for performing cosmological simulations with both baryons and cold dark matter when each fluid has different initial conditions, as is the case at the end of the radiation era. Most simulations do not reproduce the linear prediction for the difference between the cold dark matter and baryon perturbations. We show that this is due to the

Adrian M. Price-Whelan, David W. Hogg, Hans-Walter Rix, Rachael L. Beaton

Many problems in contemporary astrophysics---from understanding the formation of black holes to untangling the chemical evolution of galaxies---rely on knowledge about binary stars. This, in turn, depends on discovery and characterization of binary companions for large numbers of different kinds of stars in different chemical and dynamical environments. Curr

Xiaolong Deng, Alexander L. Burin, Ivan M. Khaymovich

We consider a 2d dipolar system, $d=2$, with the generalized dipole-dipole interaction $\sim r^{-a}$, and the power $a$ controlled experimentally in trapped-ion or Rydberg-atom systems via their interaction with cavity modes. We focus on the dilute dipolar excitation case when the problem can be effectively considered as single-particle with the interaction

Kazuyuki Sugimura, Tomoaki Matsumoto, Takashi Hosokawa, Shingo Hirano

We study the formation of massive Population III binary stars using a newly developed radiation hydrodynamics code with the adaptive mesh refinement and adaptive ray-tracing methods. We follow the evolution of a typical primordial star-forming cloud obtained from a cosmological hydrodynamics simulation. Several protostars form as a result of disk fragmentati

Jun-Jie Huang, Pier Luigi Dragotti

Inspired by the recent success of deep neural networks and the recent efforts to develop multi-layer dictionary models, we propose a Deep Analysis dictionary Model (DeepAM) which is optimized to address a specific regression task known as single image super-resolution. Contrary to other multi-layer dictionary models, our architecture contains L layers of ana

Sebastian Gómez-Gordillo, Stavros Akras, Denise R. Gonçalves, Wolfgang Steffen

Accurate distance estimates of astrophysical objects such as planetary nebulae (PNe), and nova and supernova remnants, among others, allow us to constrain their physical characteristics, such as size, mass, luminosity, and age. An innovative technique based on the expansion parallax method, the so-called distance mapping technique (DMT), provides distance ma