January 2019 arXiv papers — page 31

Dil K. Limbu, Raymond Atta-Fynn, Parthapratim Biswas

We present an information-based total-energy optimization method to produce nearly defect-free structural models of amorphous silicon. Using geometrical, structural and topological information from disordered tetrahedral networks, we have shown that it is possible to generate structural configurations of amorphous silicon, which are superior than the models

Hassen Dhrif, Luis G. Sanchez Giraldo, Miroslav Kubat, Stefan Wuchty

Evolutionary computation (EC) algorithms, such as discrete and multi-objective versions of particle swarm optimization (PSO), have been applied to solve the Feature selection (FS) problem, tackling the combinatorial explosion of search spaces that are peppered with local minima. Furthermore, high-dimensional FS problems such as finding a small set of biomark

Nisha Panwar, Shantanu Sharma, Guoxi Wang, Sharad Mehrotra

Advances in sensing, networking, and actuation technologies have resulted in the IoT wave that is expected to revolutionize all aspects of modern society. This paper focuses on the new challenges of privacy that arise in IoT in the context of smart homes. Specifically, the paper focuses on preventing the user's privacy via inferences through channel and in-h

Rukhsan Ul Haq, Sachin Satish Bharadwaj, Towseef Ali Wani

Schrieffer-Wolff transformation is a very important transformation in Quantum Many Body physics. Yet, there isn't an explicit method in the literature to calculate the generator of this unitary transformation directly from the hamiltonian. In this paper we present an explicit method to compute the generator of the Schrieffer-Wolff transformation in general a

Ahmed Taha, Yi-Ting Chen, Teruhisa Misu, Abhinav Shrivastava

We employ triplet loss as a feature embedding regularizer to boost classification performance. Standard architectures, like ResNet and Inception, are extended to support both losses with minimal hyper-parameter tuning. This promotes generality while fine-tuning pretrained networks. Triplet loss is a powerful surrogate for recently proposed embedding regulari

Artem Borin, Dmitry A. Abanin

An artificial neural network (ANN) with the restricted Boltzmann machine (RBM) architecture was recently proposed as a versatile variational quantum many-body wave function. In this work we provide physical insights into the performance of this ansatz. We uncover the connection between the structure of RBM and perturbation series, which explains the excellen

Frederico J. Sousa, B. Amorim, Eduardo V. Castro

The phase diagram of graphene decorated with magnetic adatoms distributed either on a single sublattice, or evenly over the two sublattices, is computed for adatom concentrations as low as $\sim1\%$. Within the framework of the $s$-$d$ interaction, we take into account disorder effects due to the random positioning of the adatoms and/or to the thermal fluctu

Kean Fallon, Colin Giles, Hunter Rehm, Simon Wagner

Consider the set $\{1,2,\dots,n\} = [n]$ and an equation $eq$. The rainbow number of $[n]$ for $eq$, denoted $\operatorname{rb}([n],eq)$, is the smallest number of colors such that for every exact $\operatorname{rb}([n], eq)$-coloring of $[n]$, there exists a solution to $eq$ with every member of the solution set assigned a distinct color. This paper focuses

Tom Zahavy, Shie Mannor

We study the neural-linear bandit model for solving sequential decision-making problems with high dimensional side information. Neural-linear bandits leverage the representation power of deep neural networks and combine it with efficient exploration mechanisms, designed for linear contextual bandits, on top of the last hidden layer. Since the representation

A. Sanna, L. Moscadelli, C. Goddi, M. Beltran

Centimeter continuum observations of protostellar jets have revealed the presence of knots of shocked gas where the flux density decreases with frequency. This spectrum is characteristic of nonthermal synchrotron radiation and implies the presence of both magnetic fields and relativistic electrons in protostellar jets. Here, we report on one of the few detec

Kamil Danek, David Heyrovsky

Since the first observation of triple-lens gravitational microlensing in 2006, analyses of six more events have been published by the end of 2018. In three events the lens was a star with two planets; four involved a binary star with a planet. Other possible triple lenses, such as triple stars or stars with a planet with a moon, are yet to be detected. The a

Peifeng Fan, Hong Qin, Jianyuan Xiao, Nong Xiang

A general field theory for classical particle-field systems is developed. Compared with the standard classical field theory, the distinguish feature of a classical particle-field system is that the particles and fields reside on different manifolds. The fields are defined on the 4D space-time, whereas each particle's trajectory is defined on the 1D time-axis

Xinyu Li, Venkata Chebiyyam, Katrin Kirchhoff

Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral fe

H. P. Ojeda Collado, Gonzalo Usaj, José Lorenzana, C. A. Balseiro

The BCS model of an isolated superconductor initially prepared in a nonequilibrium state, predicts the existence of interesting dynamical phenomena in the time-dependent order parameter as decaying oscillations, persistent oscillations and overdamped dynamics. To make contact with real systems remains an open challenge as one needs to introduce dissipation d

Steve Huntsman

From basic considerations of the Lie group that preserves a target probability measure, we derive the Barker, Metropolis, and ensemble Markov chain Monte Carlo (MCMC) algorithms, as well as variants of waste-recycling Metropolis-Hastings and an altogether new MCMC algorithm. We illustrate these constructions with explicit numerical computations, and we empir

Joan R. Najita

As the next decade approaches, it is once again time for the US astronomical community to assess its investment priorities for the coming decade on the ground and in space. This report, created to aid NOAO in its planning for the 2020 Decadal Survey on Astronomy and Astrophysics, reviews the outcome of the previous Decadal Survey (Astro2010); describes the t

E. Orlando

Cosmic rays interacting with gas and photon fields in the Galaxy produce interstellar gamma-ray emission (IGE), which accounts for almost 50% of the photons detected at gamma-ray energies. Models of this IGE have to be very accurate for interpreting the high-quality observations by present gamma-ray telescopes, such as Fermi Large Area Telescope (LAT). Stand

Theodor Bjorkmo, M. C. David Marsh

In negatively curved field spaces, inflation can be realised even in steep potentials. Hyperinflation invokes the `centrifugal force' of a field orbiting the hyperbolic plane to sustain inflation. We generalise hyperinflation by showing that it can be realised in models with any number of fields ($N_f\geq2$), and in broad classes of potentials that, in parti

Yijie Zeng, Luyang Wang, Dao-Xin Yao

Hourglass-like band structures protected by nonsymmorphic space group symmetries can appear along high-symmetry lines or in high-symmetry surfaces in the Brillouin zone. In this work, from symmetry analysis, we demonstrate that $n$-hourglass-like band structures, a generalization of hourglass-like band structures, which host a number of Weyl points, are enfo

Go Ogiya, Frank C. van den Bosch, Oliver Hahn, Sheridan B. Green

The abundance and demographics of dark matter substructure is important for many areas in astrophysics and cosmological $N$-body simulations have been the primary tool used to investigate them. However, it has recently become clear that the simulations are subject to numerical artefacts, which hampers a proper treatment of the tidal evolution of subhaloes. U

Nimisha Kumari, Bethan L. James, Mike J. Irwin, Alessandra Aloisi

We use integral field spectroscopic (IFS) observations from Gemini Multi-Object Spectrograph-North (GMOS-N) to analyse the ionised gas in the principal star-forming region in the blue compact dwarf galaxy SBS 1415+437. The IFS data enable us to map the weak auroral line [O III] $\lambda$4363 at a spatial scale of $\sim$6.5 pc across a region of $\sim$143 $\t

Benjamin Dive, Nikolaos Koukoulekidis, Stefanos Mousafeiris, Florian Mintert

Coherent superpositions are one of the hallmarks of quantum mechanics and are vital for any quantum mechanical device to outperform the classically achievable. Generically, superpositions are verified in interference experiments, but despite their longstanding central role we know very little about how to extract the number of coherently superposed amplitude

Sota Arakawa, Misako Tatsuuma, Naoya Sakatani, Taishi Nakamoto

Understanding the heat transfer mechanism within dust aggregates is of great importance for many subjects in planetary science. We calculated the coordination number and the thermal conductivity through the solid network of compressed dust aggregates. We found a simple relationship between the coordination number and the filling factor and revealed that the

Kostas Filippas, Carlos Nunez, Jeroen van Gorsel

In the framework of six-dimensional conformal field theories with ${\cal N}=(1,0)$ supersymmetry we develop the map between the holographic description, the field theoretical description and the associated Hanany-Witten set-ups. General expressions that calculate various observables are presented. The study of string solitons singles out a special background

Thomas Schuster, Felix Flicker, Ming Li, Svetlana Kotochigova

The Hopf insulator is a weak topological insulator characterized by an insulating bulk with conducting edge states protected by an integer-valued linking number invariant. The state exists in three-dimensional two-band models. We demonstrate that the Hopf insulator can be naturally realized in lattices of dipolar-interacting spins, where spin exchange plays

William DeRocco, Peter W. Graham, Daniel Kasen, Gustavo Marques-Tavares

A dark photon is a well-motivated new particle which, as a component of an associated dark sector, could explain dark matter. One strong limit on dark photons arises from excessive cooling of supernovae. We point out that even at couplings where too few dark photons are produced in supernovae to violate the cooling bound, they can be observed directly throug

Matvey Borodin, Hannah Han, Kaylee Ji, Tanya Khovanova

We discuss two different systems of number representations that both can be called 'base 3/2'. We explain how they are connected. Unlike classical fractional extension, these two systems provide a finite representation for integers. We also discuss a connection between these systems and 3-free sequences.

Robert L. Schuhmann, Catherine Heymans, Joe Zuntz

We demonstrate that a joint analysis of LSST-like ground-based imaging with Euclid-like space-based imaging leads to increased precision and accuracy in galaxy shape measurements. At galaxy magnitudes of $i \sim 24.5$, a combined survey analysis increases the effective galaxy number density for cosmic shear studies by $\sim 50$ percent in comparison to an an

Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups corresponding to each graph are known a pr

Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li

Recent works have cast some light on the mystery of why deep nets fit any data and generalize despite being very overparametrized. This paper analyzes training and generalization for a simple 2-layer ReLU net with random initialization, and provides the following improvements over recent works: (i) Using a tighter characterization of training speed than rece

Zhishen Huang, Stephen Becker

We consider the problem of finding local minimizers in non-convex and non-smooth optimization. Under the assumption of strict saddle points, positive results have been derived for first-order methods. We present the first known results for the non-smooth case, which requires different analysis and a different algorithm.

Burak Çakmak, Manfred Opper

We propose an iterative algorithm for solving the Thouless-Anderson-Palmer (TAP) equations of Ising models with arbitrary rotation invariant (random) coupling matrices. In the thermodynamic limit, we prove by means of the dynamical functional method that the proposed algorithm converges when the so-called de Almeida Thouless (AT) criterion is fulfilled. More

B. W. Mulligan, K. Zhang, J. C. Wheeler

We explore the possible nature of high-velocity features in Type~Ia supernovae by presenting synthetic spectra generated from hydrodynamic models of interaction between the supernova and a compact circumstellar shell. We use TARDIS to compute the spectra and compare model spectra to data from SN~2011fe at 2, 5, and 9 days after the explosion. We apply abunda

Holger Gies, René Sondenheimer, Alessandro Ugolotti, Luca Zambelli

Recent studies have provided evidence for the existence of new asymptotically free trajectories in non-Abelian particle models without asymptotic symmetry in the high-energy limit. We extend these results to a general ${\rm SU}(N_{\rm L})\times {\rm SU}(N_{\rm c})$ Higgs-Yukawa model that includes the non-Abelian sector of the standard model, finding further

Surabhi Sachdev, Sarah Caudill, Heather Fong, Rico K. L. Lo

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period i

Ross Gruetzemacher, David Paradice, Kang Bok Lee

Transformative AI technologies have the potential to reshape critical aspects of society in the near future. However, in order to properly prepare policy initiatives for the arrival of such technologies accurate forecasts and timelines are necessary. A survey was administered to attendees of three AI conferences during the summer of 2018 (ICML, IJCAI and the

Alberto Chiarini, Maximilian Nitzschner

We investigate percolation of the vacant set of random interlacements on $\mathbb{Z}^d$, $d\geq 3$, in the strongly percolative regime. We consider the event that the interlacement set at level $u$ disconnects the discrete blow-up of a compact set $A\subseteq \mathbb{R}^d$ from the boundary of an enclosing box. We derive asymptotic large deviation upper boun

Ahmed Arafa, Jing Yang, Sennur Ulukus, H. Vincent Poor

A real-time status updating system is considered, in which an energy harvesting sensor is acquiring measurements regarding some physical phenomenon and sending them to a destination through an erasure channel. The setting is online, in which energy arrives in units according to a Poisson process with unit rate, with arrival times being revealed causally over

Álvaro Fernández-Galiana, Lee McCuller, Jeff Kissel, Lisa Barsotti

We present a tabletop six-axis vibration isolation system, compatible with Ultra-High Vacuum (UHV), which is actively damped and provides 25 dB of isolation at 10 Hz and 65 dB at 100 Hz. While this isolation platform has been primarily designed to support optics in the Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors, it is suitable for a

Carolyn Kim, Osbert Bastani

Machine learning has shown much promise in helping improve the quality of medical, legal, and financial decision-making. In these applications, machine learning models must satisfy two important criteria: (i) they must be causal, since the goal is typically to predict individual treatment effects, and (ii) they must be interpretable, so that human decision m

Jérôme Durand-Lose, Hendrik Jan Hoogeboom, Nataša Jonoska

We consider non cooperative binding in so called `temperature 1', in deterministic (here called {\it confluent}) tile self-assembly systems (1-TAS) and prove the standing conjecture that such systems do not have universal computational power. We call a TAS whose maximal assemblies contain at least one ultimately periodic assembly path {\it para-periodic}. We

Denilso Camargo, Dante Minniti

This work reports the discovery of three new globular clusters (GCs) towards the Galactic bulge - Camargo 1107, 1108, and 1109. The discovery was made using the WISE, 2MASS, VVV, and Gaia-DR2 photometry. The new findings are old (12.0-13.5 Gyr) and metal-poor GCs ([Fe/H] < -1.5 dex) located in the bulge area close to the Milky Way (MW) mid-plane. Although th

Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing

We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. In this work, we decompose the prediction error for adversarial examples (robust err

Simon S. Du, Wei Hu

We prove that for an $L$-layer fully-connected linear neural network, if the width of every hidden layer is $\tilde\Omega (L \cdot r \cdot d_{\mathrm{out}} \cdot \kappa^3 )$, where $r$ and $\kappa$ are the rank and the condition number of the input data, and $d_{\mathrm{out}}$ is the output dimension, then gradient descent with Gaussian random initialization

Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang

Statistical inference based on lossy or incomplete samples is often needed in research areas such as signal/image processing, medical image storage, remote sensing, signal transmission. In this paper, we propose a nonparametric testing procedure based on samples quantized to $B$ bits through a computationally efficient algorithm. Under mild technical conditi

Boris Karanov, Domaniç Lavery, Polina Bayvel, Laurent Schmalen

We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find that this sliding window BRNN (SBRNN), ba

Samina Masood, Holly Mein

We show that the magnetic moment of leptons is significantly modified in thermal background as compared to the corresponding vacuum value. We compare the magnetic moment of all different leptons near nucleosynthesis. It is shown that the significance of thermal corrections depends on the temperature of the universe and the respective lepton mass. In the earl

Min Wen, Osbert Bastani, Ufuk Topcu

It has recently been shown that if feedback effects of decisions are ignored, then imposing fairness constraints such as demographic parity or equality of opportunity can actually exacerbate unfairness. We propose to address this challenge by modeling feedback effects as Markov decision processes (MDPs). First, we propose analogs of fairness properties for t

Matthew O'Kelly, Varundev Sukhil, Houssam Abbas, Jack Harkins

In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could change the automotive industry. Today, the question of safety stands between reality and solved. Given the right platform the CPS community is poised to offer unique insights. However, testing the limits of saf

Michał Oszmaniec, Tanmoy Biswas

For any resource theory it is essential to identify tasks for which resource objects offer advantage over free objects. We show that this identification can always be accomplished for resource theories of quantum measurements in which free objects form a convex subset of measurements on a given Hilbert space. To this aim we prove that every resource measurem

Halley Young, Osbert Bastani, Mayur Naik

Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art gen

Hugo A. Akitaya, Cordelia Avery, Joseph Bergeron, Erik D. Demaine

We study the problem of deciding whether a crease pattern can be folded by simple folds (folding along one line at a time) under the infinite all-layers model introduced by [Akitaya et al., 2017], in which each simple fold is defined by an infinite line and must fold all layers of paper that intersect this line. This model is motivated by folding in manufact

Samuel Abreu, Lance J. Dixon, Enrico Herrmann, Ben Page

We compute the symbol of the two-loop five-point amplitude in $\mathcal N=8$ supergravity. We write an ansatz for the amplitude whose rational prefactors are based on not only 4-dimensional leading singularities, but also $d$-dimensional ones, as the former are insufficient. Our novel $d$-dimensional unitarity-based approach to the systematic construction of

Osbert Bastani

Reinforcement learning is a promising approach to learning robotics controllers. It has recently been shown that algorithms based on finite-difference estimates of the policy gradient are competitive with algorithms based on the policy gradient theorem. We propose a theoretical framework for understanding this phenomenon. Our key insight is that many dynamic

Yuan Tian, Yingcai Zheng

Tidal force plays an important role in the evolution of the planet-moon system. The tidal force of a moon can excite seismic waves in the planet it is orbiting. A tidal-seismic resonance is expected when a tidal force frequency matches a free-oscillation frequency of the planet. Here we show that when the moon is close to the planet, the tidal-seismic resona

Matthew Willetts, Stephen J Roberts, Christopher C Holmes

In semi-supervised learning for classification, it is assumed that every ground truth class of data is present in the small labelled dataset. Many real-world sparsely-labelled datasets are plausibly not of this type. It could easily be the case that some classes of data are found only in the unlabelled dataset -- perhaps the labelling process was biased -- s

Arianna Di Cintio, Chris B. Brook, Andrea V. Macciò, Aaron A. Dutton

The existence of galaxies with a surface brightness $\mu$ lower than the night sky has been known since three decades. Yet, their formation mechanism and emergence within a $\rm\Lambda CDM$ universe has remained largely undetermined. For the first time, we investigated the origin of Low Surface Brightness (LSB) galaxies with M$_{\star}$$\sim$10$^{9.5-10}$M$_

Hiroaki Matsunaga

We elucidate some exact relations between light-cone and covariant string field theories on the basis of the homological perturbation lemma for $A_{\infty }$. The covariant string field splits into the light-cone string field and trivial excitations of BRST quartets: The latter generates the gauge symmetry and covariance. We first show that the reduction of

Santiago Mazuelas, Aritz Perez

Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training data. This paper presents a unifying framework for supervised classification with general ensembles of training data, and proposes the learning methodology

Martin Speirs

Let k be a perfect field of characteristic p>0, let A_d be the coordinate ring of the coordinate axes in affine d-space over k, and let I_d be the ideal defining the origin. We evaluate the relative K-groups K_q(A_d,I_d) in terms of p-typical Witt vectors of k. When d=2 the result is due to Hesselholt, and for K_2 it is due to Dennis and Krusemeyer. We also

Mario Jurić, R. Lynne Jones, J. Bryce Kalmbach, Peter Whidden

A foundational goal of the Large Synoptic Survey Telescope (LSST) is to map the Solar System small body populations that provide key windows into understanding of its formation and evolution. This is especially true of the populations of the Outer Solar System -- objects at the orbit of Neptune $r > 30$AU and beyond. In this whitepaper, we propose a minimal

Tobias Holder, Raquel Queiroz, Thomas Scaffidi, Navot Silberstein

An increasing number of low carrier density materials exhibit a surprisingly large transport mean free path due to inefficient momentum relaxation. Consequently, charge transport in these systems is markedly non-ohmic but rather ballistic or hydrodynamic, features which can be explored by driving current through narrow channels. Using a kinetic equation appr

Sandro Caranzano, Mariateresa Crosta

The essay presents the results of a joint study of astronomy and archeology that has allowed to define the foundation date of the city of Turin as a Roman colony, called Iulia Augusta Taurinorum. This multidisciplinary research represents a new reading of the historical-archaeological sources and the use of astronomy according to the Etruscan-Latin gromatica

Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner

Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to balanced graph par

Fernando A. Garcia, Oleh Ivashko, Daniel E. McNally, Lakshmi Das

It is currently understood that high temperature superconductivity (SC) in the transition metal $(M)$ substituted iron arsenides Ba(Fe$_{1-x}$$M$$_{x}$)$_{2}$As$_{2}$ is promoted by magnetic excitations with wave vectors $(\pi,0)$ or $(0,\pi)$. It is known that while a small amount of Co substitution leads to SC, the same does not occur for Mn for any value

Paul B Rimmer, Oliver Shorttle

There are two dominant and contrasting classes of origin of life scenarios: those predicting that life emerged in submarine hydrothermal systems, where chemical disequilibrium can provide an energy source for nascent life; and those predicting that life emerged within subaerial environments, where UV catalysis of reactions may occur to form the building bloc

B. Marcote, Z. Paragi

Fast Radio Bursts (FRBs) are transient sources that emit a single radio pulse with a duration of only a few milliseconds. Since the discovery of the first FRB in 2007, tens of similar events have been detected. However, their physical origin remains unclear, and a number of scenarios even larger than the number of known FRBs has been proposed during these ye

Yongjin Park, Abhishek Sarkar, Khoi Nguyen, Manolis Kellis

Summary statistics of genome-wide association studies (GWAS) teach causal relationship between millions of genetic markers and tens and thousands of phenotypes. However, underlying biological mechanisms are yet to be elucidated. We can achieve necessary interpretation of GWAS in a causal mediation framework, looking to establish a sparse set of mediators bet

Motaz Alfarraj, Yazeed Alaudah, Zhiling Long, Ghassan AlRegib

We explore the use of multiresolution analysis techniques as texture attributes for seismic image characterization, especially in representing subsurface structures in large migrated seismic data. Namely, we explore the Gaussian pyramid, the discrete wavelet transform, Gabor filters, and the curvelet transform. These techniques are examined in a seismic stru

Andrew Warren

We study fluctuations of ergodic averages generated by actions of amenable groups. In the setting of an abstract ergodic theorem for locally compact second countable amenable groups acting on uniformly convex Banach spaces, we deduce a highly uniform bound on the number of fluctuations of the ergodic average for a class of F{\o}lner sequences satisfying an a

Guillaume Sartoretti, William Paivine, Yunfei Shi, Yue Wu

State-of-the-art distributed algorithms for reinforcement learning rely on multiple independent agents, which simultaneously learn in parallel environments while asynchronously updating a common, shared policy. Moreover, decentralized control architectures (e.g., CPGs) can coordinate spatially distributed portions of an articulated robot to achieve system-le

Junlei Wang, Chao Lei, Allan H Macdonald, Christian Binek

Chromia is a magnetoelectric insulator whose bulk magnetoelectric susceptibility contains a non-zero pseudoscalar component, like that present in magnetized topological insulators. We reveal the dynamic axion field of chromia by measuring the temperature dependence of its non-linear magnetoelectric response using a lock-in technique with an AC electric field

Dimitrios Pilavakis, Efstathios Paparoditis, Theofanis Sapatinas

We consider strictly stationary stochastic processes of Hilbert space-valued random variables and focus on fully functional tests for the equality of the lag-zero autocovariance operators of several independent functional time series. A moving block bootstrap-based testing procedure is proposed which generates pseudo random elements that satisfy the null hyp

Adria Ruiz, Oriol Martinez, Xavier Binefa, Jakob Verbeek

Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically requires to explicitly label all the factors of interest in training images. To alleviate the annotation cost, we introduce a learning settin

Adriana M. Pires, Axel D. Schwope, Frank Haberl, Vyacheslav E. Zavlin

Previous XMM-Newton observations of the thermally emitting isolated neutron star RX J1605.3+3249 provided a candidate for a shallow periodic signal and evidence of a fast spin down, which suggested a high dipolar magnetic field and an evolution from a magnetar. We obtained a large programme with XMM-Newton to confirm its candidate timing solution, understand

David Goz, Sara Bertocco, Luca Tornatore, Giuliano Taffoni

This work arises on the environment of the ExaNeSt project aiming at design and development of an exascale ready supercomputer with low energy consumption profile but able to support the most demanding scientific and technical applications. The ExaNeSt compute unit consists of densely-packed low-power 64-bit ARM processors, embedded within Xilinx FPGA SoCs.

Sivakumara K. Tadikonda, Douglas C. Freesland, Robin R. Minor, Daniel B. Seaton

We investigate the coronal imaging capabilities of the Solar UltraViolet Imager (SUVI) on the Geostationary Operational Environmental Satellite-R series spacecraft. Nominally Sun-pointed, SUVI provides solar images in six Extreme UltraViolet (EUV) wavelengths. On-orbit data indicated that SUVI had sufficient dynamic range and sensitivity to image the corona

Robert E. Gaunt

In this paper, we obtain inequalities for some integrals involving the modified Lommel function of the first kind $t_{\mu,\nu}(x)$. In most cases, these inequalities are tight in certain limits. We also deduce a tight double inequality, involving the modified Lommel function $t_{\mu,\nu}(x)$, for a generalized hypergeometric function. The inequalities obtain

Julian Adamek, Christian T. Byrnes, Mateja Gosenca, Shaun Hotchkiss

We recently showed that postulated ultracompact minihalos with a steep density profile do not form in realistic simulations with enhanced initial perturbations. In this paper we assume that a small fraction of the dark matter consists of primordial black holes (PBHs) and simulate the formation of structures around them. We find that in this scenario halos wi

Jan de Boer, Rik van Breukelen, Sagar F. Lokhande, Kyriakos Papadodimas

We investigate the possibility that the geometry dual to a typical AdS black hole microstate corresponds to the extended AdS-Schwarzschild geometry, including a region spacelike to the exterior. We argue that this region can be described by the mirror operators, a set of state-dependent operators in the dual CFT. We probe the geometry of a typical state by c

Uwe Guenther, Frank Stefani

The $\mathcal{PT}-$symmetric quantum mechanical $V=ix^3$ model over the real line, $x\in\mathbb{R}$, is infrared (IR) truncated and considered as Sturm-Liouville problem over a finite interval $x\in\left[-L,L\right]\subset\mathbb{R}$. Via WKB and Stokes graph analysis, the location of the complex spectral branches of the $V=ix^3$ model and those of more gene

Daniel Britzger, Carlo Ewerz, Sasha Glazov, Otto Nachtmann

The tensor-pomeron model is applied to low-x deep-inelastic lepton-nucleon scattering and photoproduction. We consider c.m. energies in the range 6 - 318 GeV and Q^2 < 50 GeV^2. In addition to the soft tensor pomeron, which has proven quite successful for the description of soft hadronic high-energy reactions, we include a hard tensor pomeron. We also includ

Vien V. Mai, Mikael Johansson

This paper introduces an efficient second-order method for solving the elastic net problem. Its key innovation is a computationally efficient technique for injecting curvature information in the optimization process which admits a strong theoretical performance guarantee. In particular, we show improved run time over popular first-order methods and quantify

Jayam Patel, Yicong Xu, Carlo Pinciroli

We present an augmented reality human-swarm interface that combines two modalities of interaction: environment-oriented and robot-oriented. The environment-oriented modality allows the user to modify the environment (either virtual or physical) to indicate a goal to attain for the robot swarm. The robot-oriented modality makes it possible to select individua

Rossana Mastrandrea, Fabrizio Piras, Andrea Gabrielli, Nerisa Banaj

Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robu

Ming Cheng, Yi Qin, Akil Narayan, Xinghui Zhong

We develop a numerical framework to implement the cumulative density function (CDF) method for obtaining the probability distribution of the system state described by a kinematic wave model. The approach relies on Monte Carlo Simulations (MCS) of the fine-grained CDF equation of system state, as derived by the CDF method. This fine-grained CDF equation is so

Mickael Albertus

The raking-ratio method is a statistical and computational method which adjusts the empirical measure to match the true probability of sets of a finite partition. We study the asymptotic behavior of the raking-ratio empirical process indexed by a class of functions when the auxiliary information is given by estimates. We suppose that these estimates result f

Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang

Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities may release taxi data for multiple years w

Paul L. Schechter, Raymond A. Wynne

Witt (1996) has shown that for an elliptical potential, the four images of a quadruply lensed quasar lie on a rectangular hyperbola that passes through the unlensed quasar position and the center of the potential as well. Wynne and Schechter (2018) have shown that, for the singular isothermal elliptical potential (SIEP), the four images also lie on an `ampli

Ssohrab Borhanian, K. G. Arun, Harald P. Pfeiffer, B. S. Sathyaprakash

Gravitational waves from the coalescence of two black holes carry the signature of the strong field dynamics of binary black holes. In this work we have used numerical relativity simulations and post-Newtonian theory to investigate this dynamics. Post-Newtonian theory is a low-velocity expansion that assumes the companion bodies to be point-particles, while

Peter Zalom, Joeri de Bruijckere, Rocco Gaudenzi, Herre S. J. van der Zant

We investigate theoretically and experimentally the singlet-triplet Kondo effect induced by a magnetic field in a molecular junction. Temperature dependent conductance, $G(T)$, is calculated by the numerical renormalization group, showing a strong imprint of the relevant low energy scales, such as the Kondo temperature, exchange and singlet-triplet splitting

Kunal Garg, Dimitra Panagou

In this work, we study finite-time stability of switched and hybrid systems in the presence of unstable modes. We present sufficient conditions in terms of multiple Lyapunov functions for the origin of the system to be finite time stable. More specifically, we show that even if the value of the Lyapunov function increases in between two switches, i.e., if th

Hari Kumar Yadalam, Upendra Harbola

We study heat current and the full statistics of heat fluctuations in a capacitively-coupled double quantum dot system. This work is motivated by recent theoretical studies and experimental works on heat currents in quantum dot circuits. As expected intuitively, within the (static) mean-field approximation, the system at steady-state decouples into two singl

Baohua Sun, Lin Yang, Catherine Chi, Wenhan Zhang

The Super Characters method addresses sentiment analysis problems by first converting the input text into images and then applying 2D-CNN models to classify the sentiment. It achieves state of the art performance on many benchmark datasets. However, it is not as straightforward to apply in Latin languages as in Asian languages. Because the 2D-CNN model is de

Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil

In this paper we consider solving saddle point problems using two variants of Gradient Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent (OGDA) methods. We show that both of these algorithms admit a unified analysis as approximations of the classical proximal point method for solving saddle point problems. This viewpoint e

Vanja Nikolić, Barbara Wohlmuth

We study the spatial discretization of Westervelt's quasilinear strongly damped wave equation by piecewise linear finite elements. Our approach employs the Banach fixed-point theorem combined with a priori analysis of a linear wave model with variable coefficients. Degeneracy of the semi-discrete Westervelt equation is avoided by relying on the inverse estim

Jonte Hance, Will McCutcheon, Patrick Yard, John Rarity

We formalize Salih et al's Counterfactual Communication Protocol (arXiv2018), which allows it not only to be used in with other modes than polarization, but also for interesting extensions (e.g. sending superpositions from Bob to Alice).

Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In this work, we propose learning both the energy function and an amortized approximate sampling mechanism using a neural generator network, which provides an efficient approximation of the log-likelihood gradient. The resul

Bálint J. Tóth, Gergely Palla, Enys Mones, Gergő Havadi

Hierarchical networks are prevalent in nature and society, corresponding to groups of actors - animals, humans or even robots - organised according to a pyramidal structure with decision makers at the top and followers at the bottom. While this phenomenon is seemingly universal, the underlying governing principles are poorly understood. Here we study the eme

Miklós Bóna

We prove that for any fixed $n$, and for most permutation patterns $q$, the number $\textup{Av}_{n,\ell}(q)$ of $q$-avoiding permutations of length $n$ that consist of $\ell$ skew blocks is a monotone decreasing function of $\ell$. We then show that this implies that for most patterns $q$, the generating function $\sum_{n\geq 0} \textup{Av}_n(q)z^n$ of the s