February 2019 arXiv papers — page 8

Silvia Penati, Davide Polvara

We study quantum integrability of affine Toda theories with a line of defect. In particular, we focus on the problem of constructing quantum higher-spin conserved currents in models defined by two A_r^{(1)} Toda theories separated by a non-trivial type-I defect. For a suitable choice of the defect potential these theories are known to be classically integrab

Mark L. Lewis

We consider groups where the centers of the irreducible characters form a chain. We obtain two alternate characterizations of these groups, and we obtain some information regarding the structure of these groups. Using our results, we are able to classify those groups where the kernels of the irreducible characters form a chain. We show that a result of Nenci

Grzegorz Szamel

We present a theory for the single-particle dynamics in binary mixtures with particle size swaps. The general structure of the theory follows that of the theory for the collective dynamics in binary mixtures with particle size swaps, which we developed previously [G. Szamel, Phys. Rev. E 98, 050601(R) (2018)]. Particle size swaps open up an additional relaxa

Dina Traykova, Emilio Bellini, Pedro G. Ferreira

We study the phenomenology of the beyond Horndeski class of scalar-tensor theories of gravity, which on cosmological scales can be characterised in terms of one extra function of time, $\alpha_{\rm H}$, as well as the usual four Horndeski set of free functions. We show that $\alpha_{\rm H}$ can be directly related to the the damping of the matter power spect

Shawn Walker

Suppose $((\cdots((x^{2}-c_{1})^{2}-c_{2})^{2}\cdots)^{2}-c_{k-1})^{2}-c_{k}$ splits into linear factors over $\mathbb{Z}$ and $c_{k}\neq0$. We show that for each $j$ and each prime $p$, if $p\leq2^{j-1}$ then $p$ divides $c_{j}$. Consequently, $$\ln c_{j}>\frac{1}{4}\cdot2^{j}\,\,\mathrm{for}\,j\geq5$$ If we also have $p\equiv3\,(\mathrm{mod\,4)}$ then $p^{

Ricardo Heras

In a recent study on monopole production [Eur. Phys. J. C (2018) 78: 966], Baines et al added the potential of a magnetic dipole to the Wu-Yang potentials for the Dirac monopole and claimed that this modified Wu-Yang configuration does not affect the Dirac quantisation condition. In this comment, we argue that their claim is incorrect by showing that their m

Kenneth Ascher, Dori Bejleri

We construct various modular compactifications of the space of elliptic K3 surfaces using tools from the minimal model program, and explicitly describe the surfaces parametrized by their boundaries. The coarse spaces of our constructed compactifications admit morphisms to the Satake-Baily-Borel compactification. Finally, we show that one of our spaces is smo

Huizhen Yu

We consider average-cost Markov decision processes (MDPs) with Borel state spaces, countable, discrete action spaces, and strictly unbounded one-stage costs. For the minimum pair approach, we introduce a new majorization condition on the state transition stochastic kernel, in place of the commonly required continuity conditions on the MDP model. We combine t

Maria Papageorgiou, Jason Pye

Can a relativistic quantum field theory be consistently described as a theory of localizable particles? There are many known issues with such a description, indicating an answer in the negative. In this paper, we examine these obstructions by tracing how they (partially) subside in passing to an approximation of ordinary quantum mechanics in the non-relativi

Vitaly Feldman, Jan Vondrak

Algorithmic stability is a classical approach to understanding and analysis of the generalization error of learning algorithms. A notable weakness of most stability-based generalization bounds is that they hold only in expectation. Generalization with high probability has been established in a landmark paper of Bousquet and Elisseeff (2002) albeit at the exp

Matthew J. O'Rourke, Nicholas R. Hutzler

Laser cooling is a powerful method to control molecules for applications in precision measurement, as well as quantum information, many-body physics, and fundamental chemistry. However, many optically-active metal centers in valence states which are promising for these applications, especially precision measurement, are difficult to laser cool. In order to e

R. Gohil, D. R. Ballantyne, G. Li

Nuclear starburst discs (NSDs) are very compact star-forming regions in the centers of galaxies that have been studied as a possible origin for the absorbing gas around a central active galactic nucleus. NSDs may be most relevant at $z\sim 1$ when obscured accretion onto supermassive black holes (SMBHs) is common. This paper describes the characteristics of

Jia-Qi Zhou, Qi-Ping Su, Chui-Ping Yang

Implementation of discrete-time quantum walk (DTQW) with superconducting qubits is difficult since on-chip superconducting qubits cannot hop between lattice sites. We propose an efficient protocol for the implementation of DTQW in circuit quantum electrodynamics (QED), in which only $N+1$ qutrits and $N$ assistant cavities are needed for an $N$-step DTQW. Th

Shi Zong, Alan Ritter, Graham Mueller, Evan Wright

Breaking cybersecurity events are shared across a range of websites, including security blogs (FireEye, Kaspersky, etc.), in addition to social media platforms such as Facebook and Twitter. In this paper, we investigate methods to analyze the severity of cybersecurity threats based on the language that is used to describe them online. A corpus of 6,000 tweet

Tyler Takeshita, Nicholas C. Rubin, Zhang Jiang, Eunseok Lee

Proposals for near-term experiments in quantum chemistry on quantum computers leverage the ability to target a subset of degrees of freedom containing the essential quantum behavior, sometimes called the active space. This approximation allows one to treat more difficult problems using fewer qubits and lower gate depths than would otherwise be possible. Howe

Saroch Leedumrongwatthanakun, Luca Innocenti, Hugo Defienne, Thomas Juffmann

Reconfigurable quantum circuits are fundamental building blocks for the implementation of scalable quantum technologies. Their implementation has been pursued in linear optics through the engineering of sophisticated interferometers. While such optical networks have been successful in demonstrating the control of small-scale quantum circuits, scaling up to l

Tal Friedman, Guy Van den Broeck

Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a model with strong assumptions that allow for efficiently answering many interesting queries. Recent work on open-world probabilistic databases strengthens the semantics of these prob

Andrea Simonetto, Julien Monteil, Claudio Gambella

In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet vehicles and customer trip requests within a federated optimization architecture. The resulting algorithm is up to four time

Riccardo Moriconi, Marc P. Deisenroth, K. S. Sesh Kumar

Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, BO is practically limited to optimizing 10--20 parameters. To scale BO to high dimensions, we usually make structural assumptions on the decomposition

Pier Luigi Silvestrelli, Marco Nava, Francesco Ancilotto, Luciano Reatto

We have derived the adsorption potential of $^4$He atoms on fluorographene (GF), on graphane and on hexagonal boron nitride (hBN) by a recently developed ab initio method that incorporates the van der Waals interaction. The $^4$He monolayer on GF and on hBN is studied by state-of-the-art quantum simulations at T=0 K. With our adsorption potentials we find th

Muhammad Zaid Hameed, Andras Gyorgy, Deniz Gunduz

We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This is achieved by perturbing channel input symbols at the enc

Ian D. Kivlichan, Craig Gidney, Dominic W. Berry, Nathan Wiebe

Recent work has deployed linear combinations of unitaries techniques to reduce the cost of fault-tolerant quantum simulations of correlated electron models. Here, we show that one can sometimes improve upon those results with optimized implementations of Trotter-Suzuki-based product formulas. We show that low-order Trotter methods perform surprisingly well w

Bernd C. Kellner, Jonathan Sondow

We give a new characterization of the set $\mathcal{C}$ of Carmichael numbers in the context of $p$-adic theory, independently of the classical results of Korselt and Carmichael. The characterization originates from a surprising link to the denominators of the Bernoulli polynomials via the sum-of-base-$p$-digits function. More precisely, we show that such a

Saif Alabachi, Gita Sukthankar, Rahul Sukthankar

Object detection models based on convolutional neural networks (CNNs) demonstrate impressive performance when trained on large-scale labeled datasets. While a generic object detector trained on such a dataset performs adequately in applications where the input data is similar to user photographs, the detector performs poorly on small objects, particularly on

E. Chiaraluce, G. Bruni, F. Panessa, M. Giroletti

A strong effort has been devoted to understand the physical origin of radio emission from low-luminosity AGN (LLAGN), but a comprehensive picture is still missing. We used high-resolution ($\le$1 arcsec), multi-frequency (1.5, 5.5, 9 and 14 GHz) NSF's Karl G. Jansky Very Large Array (VLA) observations to characterise the state of the nuclear region of ten Se

R. I. de Oliveira Junior, M. O. Tahim, G. Alencar, R. R. Landim

In this paper we study the localization of a model with kinetic gauge mixing on a thin membrane. The model we discuss is a theory for millicharged particles, proposed by Holdom in 1985, and that now is object of study in the LHC. We propose a geometrical coupling between the gauge fields, the Ricci scalar and the Ricci tensor. We show that it is possible to

Yossi Mosbacher, Micha Weiss, Hagar Landsman, Nadav Priel

We identify a number of crystalline structures with promising characteristics to serve as a detection medium for a novel Dark Matter (DM) detector with a low threshold energy. A detector of this kind can be specifically useful in application requiring the detection of nuclear recoils, such as in direct detection of low mass DM, coherent neutrino scattering a

Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki, Le An Ha

We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and m

Ramiro D. Camino, Christian A. Hammerschmidt, Radu State

Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models. Recent studies introduced solutions to the problem of imputing missing values based on deep generative models. Previous experiments with Generative Adversarial Networks and Variational Autoencoders showed interesting results

David Cushing, Supanat Kamtue, Norbert Peyerimhoff, Leyna Watson May

We give a classification of all connected quartic graphs which are (infinity) curvature sharp in all vertices with respect to Bakry-\'Emery curvature. The result is based on a computer classification by F. Gurr and L. Watson May and a combinatorial case by case investigation.

Danny Panknin, Stefan Chmiela, Klaus-Robert Müller, Shinichi Nakajima

Inhomogeneities in real-world data, e.g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference. Accounting for them can greatly improve predictive power when physical resources or computation time is limited. In this paper, we draw on recent

Elke C. Aschenauer, Ignacio Borsa, Rodolfo Sassot, Charlotte Van Hulse

We present a quantitative assessment of the impact a future Electron-Ion Collider would have in the determination of parton distribution functions in the proton and parton-to-hadron fragmentation functions through semi-inclusive deep-inelastic electron-proton scattering data. Specifically, we estimate the kinematic configurations for which the forthcoming da

Siddharth Mayya, Gennaro Notomista, Dylan Shell, Seth Hutchinson

In robot swarms operating under highly restrictive sensing and communication constraints, individuals may need to use direct physical proximity to facilitate information exchange. However, in certain task-related scenarios, this requirement might conflict with the need for robots to spread out in the environment, e.g., for distributed sensing or surveillance

Jan Bok, Nikola Jedličková, Jana Maxová

The \emph{Wiener index} is a widely studied topological index of graphs. One of the main problems in the area is to determine which graphs of given properties attain the extremal values of Wiener index. In this paper we resolve an open problem posed by Du in 2012. To this end we determine the unicyclic bipartite graphs with given size of parts having the max

Kurniawan D. Irianto

A router is a device, in the computer networks, that is used to forward a data packet. Because of its function, the router becomes an important role in measuring the performance of computer networks. It needs to process the incoming data packets with high speed and minimum delay. Therefore, the router must have supported with a good security system as well.

Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh

Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also vulnerable to adversarial examples and

Baihan Lin

Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (RN) as an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable

Michael Burke, Svetlin Penkov, Subramanian Ramamoorthy

Hybrid systems are a compact and natural mechanism with which to address problems in robotics. This work introduces an approach to learning hybrid systems from demonstrations, with an emphasis on extracting models that are explicitly verifiable and easily interpreted by robot operators. We fit a sequence of controllers using sequential importance sampling un

Kurniawan D. Irianto, Demetres D. Kouvatsos

The growth of wireless devices affects the availability of limited frequencies or spectrum bands as it has been known that spectrum bands are a natural resource that cannot be added. Meanwhile, the licensed frequencies are idle most of the time. Cognitive radio is one of the solutions to solve those problems. Cognitive radio is a promising technology that al

Prashanth L. A., Sanjay P. Bhat

This paper presents a unified approach based on Wasserstein distance to derive concentration bounds for empirical estimates for two broad classes of risk measures defined in the paper. The classes of risk measures introduced include as special cases well known risk measures from the finance literature such as conditional value at risk (CVaR), optimized certa

Emanuel Rodríguez Cirone

Let l be a commutative ring with unit. Garkusha constructed a functor from the category of l-algebras into a triangulated category D, that is a universal excisive and homotopy invariant homology theory. Later on, he provided different descriptions of D, as an application of his motivic homotopy theory of algebras. Using these, it can be shown that D is trian

Fionn Murtagh

It is demonstrated how linear computational time and storage efficient approaches can be adopted when analyzing very large data sets. More importantly, interpretation is aided and furthermore, basic processing is easily supported. Such basic processing can be the use of supplementary, i.e. contextual, elements, or particular associations. Furthermore pixella

Karoliina Lehtinen, Udi Boker

The complexity of parity games is a long standing open problem that saw a major breakthrough in 2017 when two quasi-polynomial algorithms were published. This article presents a third, independent approach to solving parity games in quasi-polynomial time, based on the notion of register game, a parameterised variant of a parity game. The analysis of register

Jie Li, Lei Gu, Ruqian Wu

By using the first-principles calculations and model analyses, we found that the combination of defected tungsten disulfide monolayer and sparse manganese adsorption may give a KK` valley spin splitting up to 210 meV. This system also has a tunable magnetic anisotropy energy, a clean band gap, and an appropriate band alignment, with the Fermi level sitting r

Guannan Zhang, Jiaxin Zhang, Jacob Hinkle

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variety of design tasks in real-world engineering applications, where practitioners would replace their computationally intensive physical models (e.g., high-resolution fluid simulators)

James Damon

We consider geodesic flows between hypersurfaces in $\R^n$. However, rather than consider using geodesics in $\R^n$, which are straight lines, we consider an induced flow using geodesics between the tangent spaces of the hypersurfaces viewed as affine hyperplanes. For naturality, we want the geodesic flow to be invariant under rigid transformations and homot

Xiao Li, Allan H. MacDonald, Hua Chen

Most canted antiferromagnets are also anomalous Hall antiferromagnets (AHE AFMs), i.e. they have an anomalous Hall response and other responses with the same symmetry requirements. We suggest that AHE AFMs are promising materials as hosts for high-temperature quantum anomalous Hall effects. By considering models of two-dimensional (001) perovskite layers wit

Stanislav Peshterliev, Alexander Hsieh, Imre Kiss

Voice-assistants text classification and named-entity recognition (NER) models are trained on millions of example utterances. Because of the large datasets, long training time is one of the bottlenecks for releasing improved models. In this work, we develop F10-SGD, a fast optimizer for text classification and NER elastic-net linear models. On internal datas

Georg Böcherer, Diego Lentner, Alessandro Cirino, Fabian Steiner

Linear layered probabilistic shaping (LLPS) is proposed, an architecture for linear codes to efficiently encode to shaped code words. In the previously proposed probabilistic amplitude shaping (PAS) architecture, a distribution matcher (DM) maps information bits to shaped bits, which are then systematically encoded by appending uniformly distributed parity b

Luca Rossetto, Mahnaz Amiri Parian, Ralph Gasser, Ivan Giangreco

This paper presents an after-the-fact summary of the participation of the vitrivr system to the 2019 Video Browser Showdown. Analogously to last year's report, the focus of this paper lies on additions made since the original publication and the system's performance during the competition.

Rishav Chourasia, Adish Singla

Ensemble methods have been widely applied in Reinforcement Learning (RL) in order to enhance stability, increase convergence speed, and improve exploration. These methods typically work by employing an aggregation mechanism over actions of different RL algorithms. We show that a variety of these methods can be unified by drawing parallels from committee voti

Kurniawan D. Irianto, Juan A. Cabrera, Giang T. Nguyen, Hani Salah

Well-known error detection and correction solutions in wireless communications are slow or incur high transmission overhead. Recently, notable solutions like PRAC and DAPRAC, implementing partial packet recovery with network coding, could address these problems. However, they perform slowly when there are many errors. We propose S-PRAC, a fast scheme for par

Sandip Banerjee, Sujoy Bhore

Given a graph $G=(V,E)$, the dominating set problem asks for a minimum subset of vertices $D\subseteq V$ such that every vertex $u\in V\setminus D$ is adjacent to at least one vertex $v\in D$. That is, the set $D$ satisfies the condition that $|N[v]\cap D|\geq 1$ for each $v\in V$, where $N[v]$ is the closed neighborhood of $v$. In this paper, we study two v

Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar

We study the problem of meta-learning through the lens of online convex optimization, developing a meta-algorithm bridging the gap between popular gradient-based meta-learning and classical regularization-based multi-task transfer methods. Our method is the first to simultaneously satisfy good sample efficiency guarantees in the convex setting, with generali

Hervé Le Ferrand

In this article, we are interested in the life and scientific work of the Belgian mathematician Paul Mansion. The year 2019 marks the centenary of his passing. We bring some new insights into Paul Mansion's work thanks to his scientific correspondence at the Royal Library in Brussels.

Igor Uljarevic

We prove a generalization of the Monge-Cayley-Salmon theorem on osculation and ruled submanifolds using elementary geometric measure theory.

Sandra Fortini, Sonia Petrone

Bayesian methods are often optimal, yet increasing pressure for fast computations, especially with streaming data, brings renewed interest in faster, possibly sub-optimal, solutions. The extent to which these algorithms approximate Bayesian solutions is a question of interest, but often unanswered. We propose a methodology to address this question in predict

J. P. Boroński, J. Kupka, P. Oprocha

We show that every (invertible, or noninvertible) minimal Cantor system embeds in $\mathbb{R}$ with vanishing derivative everywhere. We also study relations between local shrinking and periodic points.

Shweta Bhardwaj, Mukundhan Srinivasan, Mitesh M. Khapra

Recently,there has been a lot of interest in building compact models for video classification which have a small memory footprint (<1 GB). While these models are compact, they typically operate by repeated application of a small weight matrix to all the frames in a video. E.g. recurrent neural network based methods compute a hidden state for every frame of t

Patrick Stähli, Maju Kuriakose, Martin Frenz, Michael Jaeger

Computed ultrasound tomography in echo mode (CUTE) allows determining the spatial distribution of speed-of-sound (SoS) inside tissue using handheld pulse-echo ultrasound (US). This technique is based on measuring the changing phase of beamformed echoes obtained under varying transmit (Tx) and/or receive (Rx) steering angles. The SoS is reconstructed by inver

Miha E. Habič, Radek Honzík

We investigate the extent to which ultrapowers by normal measures on $\kappa$ can be correct about powersets $\mathcal{P}(\lambda)$ for $\lambda>\kappa$. We consider two versions of this questions, the capturing property $\mathrm{CP}(\kappa,\lambda)$ and the local capturing property $\mathrm{LCP}(\kappa,\lambda)$. $\mathrm{CP}(\kappa,\lambda)$ holds if there

Walaa I. Eshraim

We expand the study of the pseudoscalar glueball and its first excited state by constructing an interaction Lagrangian which produces the two- and three-body decays of the pseudoscalar glueball, $J^{PC}=0^{-+}$, into the (pseudo)scalar and the excited (pseudo)scalar mesons as well as by constructing other two different chiral Lagrangians which describe the t

Pawel Korus, Nasir Memon

Forensic analysis of digital photographs relies on intrinsic statistical traces introduced at the time of their acquisition or subsequent editing. Such traces are often removed by post-processing (e.g., down-sampling and re-compression applied upon distribution in the Web) which inhibits reliable provenance analysis. Increasing adoption of computational meth

Xiangqian Meng, Erkan Nane

We consider non-linear time-fractional stochastic heat type equation $$\frac{\partial^\beta u}{\partial t^\beta}+\nu(-\Delta)^{\alpha/2} u=I^{1-\beta}_t \bigg[\int_{\mathbb{R}^d}\sigma(u(t,x),h) \stackrel{\cdot}{\tilde N }(t,x,h)\bigg]$$ and $$\frac{\partial^\beta u}{\partial t^\beta}+\nu(-\Delta)^{\alpha/2} u=I^{1-\beta}_t \bigg[\int_{\mathbb{R}^d}\sigma(u(

Kanhaiya L. Pandey, Tanvi Karwal, Subinoy Das

The Hubble tension between the $\Lambda$CDM-model-dependent prediction of the current expansion rate $H_0$ using Planck data and direct, model-independent measurements in the local universe from the SH0ES collaboration disagree at $>3.5\sigma$. Moreover, there exists a milder $\sim 2\sigma$ tension between similar predictions for the amplitude $S_8$ of matte

José G. Fernández-Trincado, Ronald Mennickent, Mauricio Cabezas, Olga Zamora

We report the serendipitous discovery of a nitrogen-rich, mildly metal-poor ([Fe/H]=-1.08) giant star in a single-lined spectroscopic binary system found in the SDSS-IV Apache Point Observatory Galactic Evolution Experiment (APOGEE-2) survey, Data Release 14 (DR14). Previous work has assumed that the two percent of halo giants with unusual elemental abundanc

Natalia Korolkova, Gerd Leuchs

In this review we discuss intriguing properties of apparently classical optical fields, that go beyond purely classical context and allow us to speak about quantum characteristics of such fields and about their applications in quantum technologies. We briefly define the genuinely quantum concepts of entanglement and steering. We then move to the boarder line

C. Güttler, T. Mannel, A. Rotundi, S. Merouane

Before Rosetta, the space missions Giotto and Stardust shaped our view on cometary dust, supported by plentiful data from Earth based observations and interplanetary dust particles collected in the Earth's atmosphere. The Rosetta mission at comet 67P/Churyumov-Gerasimenko was equipped with a multitude of instruments designed to study cometary dust. While an

Michael Kapralov, Ameya Velingker, Amir Zandieh

The Discrete Fourier Transform (DFT) is a fundamental computational primitive, and the fastest known algorithm for computing the DFT is the FFT (Fast Fourier Transform) algorithm. One remarkable feature of FFT is the fact that its runtime depends only on the size $N$ of the input vector, but not on the dimensionality of the input domain: FFT runs in time $O(

Amichai Lampert

We investigate the structure of polynomials of degree four in many variables over a fixed prime field $\mathbb{F}=\mathbb{F}_{p}$. In 2007, Green and Tao proved that if a polynomial $f:\mathbb{F}^{n}\rightarrow\mathbb{F}$ is poorly distributed, then it is a function of a few polynomials of smaller degree. In 2009, Haramaty and Shpilka found an effective boun

S. Amaducci, L. Cosentino, M. Barbagallo, N. Colonna

The 235U(n,f) cross section was measured in a wide energy range at n_TOF relative to 6Li(n,t) and 10B(n,alpha), with high resolution and in a wide energy range, with a setup based on a stack of six samples and six silicon detectors placed in the neutron beam. This allowed us to make a direct comparison of the reaction yields under the same experimental condi

Fangxin Shang, Hao Zhang

Training recurrent neural networks (RNNs) with backpropagation through time (BPTT) has known drawbacks such as being difficult to capture longterm dependencies in sequences. Successful alternatives to BPTT have not yet been discovered. Recently, BP with synthetic gradients by a decoupled neural interface module has been proposed to replace BPTT for training

Marco Martins Afonso, Philippe Meliga, Eric Serre

With the aim of providing a first step in the quest for a reduction of the aerodynamic drag on the rear-end of a car, we study the phenomena of separation and reattachment of an incompressible flow focusing on a specific aerodynamic geometry, namely a backward-slanted step at 25 degrees of inclination. The ensuing recirculation bubble provides the basis for

W. J. Pearson, L. Wang, J. W. Trayford, C. E. Petrillo

Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. With one of the main difficulties of merger studies being the lack of a truth

Michael Jurkutat, Marija Avramovska, Grant V. M. Williams, Daniel Dernbach

Nuclear relaxation is an important thermodynamic probe of electronic excitations, in particular in conducting and superconducting systems. Here, an empirical phenomenology based on all available literature data for planar Cu in hole-doped cuprates is developed. It is found that most of the seemingly different relaxation rates among the systems are due to a t

Nicolas Curien, Cyril Marzouk

The infinite discrete stable Boltzmann maps are "heavy-tailed" generalisations of the well-known Uniform Infinite Planar Quadrangulation. Very efficient tools to study these objects are Markovian step-by-step explorations of the lattice called peeling processes. Such a process depends on an algorithm which selects at each step the next edge where the explora

Sai Prasanna, Sri Ananda Seelan

This paper describes our submission for the SemEval-2019 Suggestion Mining task. A simple Convolutional Neural Network (CNN) classifier with contextual word representations from a pre-trained language model was used for sentence classification. The model is trained using tri-training, a semi-supervised bootstrapping mechanism for labelling unseen data. Tri-t

Jaeseop Ahn, Jimyeong Kim, Ihyeok Seo

In this paper we study spatial analyticity of solutions to the defocusing nonlinear Schr\"odinger equations $iu_t + \Delta u = |u|^{p-1}u$, given initial data which is analytic with fixed radius. It is shown that the uniform radius of spatial analyticity of solutions at later time $t$ cannot decay faster than $1/|t|$ as $|t|\rightarrow\infty$. This extends t

Yu Li, Zhiping Yin, Zhonghao Liu, Weiyi Wang

We use inelastic neutron scattering to study energy and wave vector dependence of spin fluctuations in SrCo$_2$As$_2$, derived from SrFe$_{2-x}$Co$_x$As$_2$ iron pnictide superconductors. Our data reveals the coexistence of antiferromagnetic (AF) and ferromagnetic (FM) spin fluctuations at wave vectors $\textbf{Q}_{\rm AF}$=(1,0) and $\textbf{Q}_{\rm FM}$=(0

Emiel Lorist, Mark Veraar

In this paper we introduce Calder\'on-Zygmund theory for singular stochastic integrals with operator-valued kernel. In particular, we prove $L^p$-extrapolation results under a H\"ormander condition on the kernel. Sparse domination and sharp weighted bounds are obtained under a Dini condition on the kernel, leading to a stochastic version of the solution to t

Craig Innes, Alex Lascarides

Methods for learning and planning in sequential decision problems often assume the learner is aware of all possible states and actions in advance. This assumption is sometimes untenable. In this paper, we give a method to learn factored markov decision problems from both domain exploration and expert assistance, which guarantees convergence to near-optimal b

Vered Shwartz, Ido Dagan

Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that as expected

S. Armatas, C. Bouratzis, A. Hillaris, C. E. Alissandrakis

Aims. We examine high time resolution dynamic spectra for fine structures in type II solar radio bursts Methods. We used data obtained with the (SAO) receiver of the Artemis-JLS (ARTEMIS-IV) solar radio spectrograph in the 450-270 MHz range at 10 ms cadence and identified more than 600 short, narrowband features. Their characteristics, such as instantaneous

Sourav Karar, Shounak Datta, Sibasish Ghosh, A. S. Majumdar

We explore a thermodynamical effect of anharmonicity in quantum mechanical oscillators. We show that small quartic perturbations to the oscillator potential lead to an enhancement of performance of quantum refrigerators for both the Otto and Stirling cycles. A similar nonlinearity driven enhancement of performance is also observed for an analogous spin-qubit

Remi Bonnefoi, Lilian Besson, Julio Manco-Vasquez, Christophe Moy

In this paper, we propose and evaluate different learning strategies based on Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices to improve their access to the network and their autonomy, while taking into account the impact of encountered radio collisions. For that end, several heuristics employing Upper-Confident Bound (UCB) alg

Florian Besau, Thomas Hack, Peter Pivovarov, Franz E. Schuster

The spherical centroid body of a centrally-symmetric convex body in the Euclidean unit sphere is introduced. Two alternative definitions - one geometric, the other probabilistic in nature - are given and shown to lead to the same objects. The geometric approach is then used to establish a number of basic properties of spherical centroid bodies, while the pro

Leah Comment, Brent A. Coull, Corwin Zigler, Linda Valeri

Bayesian causal inference offers a principled approach to policy evaluation of proposed interventions on mediators or time-varying exposures. We outline a general approach to the estimation of causal quantities for settings with time-varying confounding, such as exposure-induced mediator-outcome confounders. We further extend this approach to propose two Bay

Markus J. Aschwanden

Coronal loops in active regions are often characterized by quasi-circular and helically twisted (sigmoidal) geometries, which are consistent with dipolar potential field models in the former case, and with nonlinear force-free field models with vertical currents in the latter case. Alternatively, Parker-type nanoflare models of the solar corona hypothesize t

A. F. Marino, G. S. Da Costa, A. R. Casey, M. Asplund

We present results from the analysis of high-resolution spectra obtained with the Keck HIRES spectrograph for a sample of 17 candidate extremely metal-poor (EMP) stars originally selected from commissioning data obtained with the SkyMapper telescope. Fourteen of the stars have not been observed previously at high dispersion. Three have [Fe/H]<=-3.0 while the

Sabreen Hammouda, Manuel Angst

We report the controlled growth of single crystals of intercalated layered Lu$ _{1+n} $Fe$ _{2+n} $O$ _{4+3n-\delta} $ ($ n $=1,2) with different oxygen stoichiometries ${\delta}$. For the first time crystals sufficiently stoichiometric to exhibit superstructure reflections in X-ray diffraction attributable to charge ordering were obtained. The estimated cor

Frederic Charve

In this article we prove highly improved and flexible Strichartz-type estimates allowing us to generalize the asymptotics we obtained for a stratified and rotating incompressible Navier-Stokes system: for large (and less regular) initial data, we obtain global well-posedness, asymptotics (as the Rossby number $\epsilon$ goes to zero) and convergence rates as

Jun Yin, Salvatore Calabrese, Edoardo Daly, Amilcare Porporato

It is well known that evaporative cooling of Earth's surface water reduces the amount of radiation that goes into sensible heat, namely the portion of radiation that produces higher temperatures. However, a rigorous use of long-term hydrologic measurements and the related theories of hydrologic partitioning have not yet been fully exploited to quantify these

Barbara Kaltenbacher, Vanja Nikolić

We study the Jordan--Moore--Gibson--Thompson (JMGT) equation, a third order in time wave equation that models nonlinear sound propagation, in the practically relevant setting of Neumann and absorbing boundary conditions. In the analysis, we pay special attention to dependencies on the coefficient $\tau$ of the third order time derivative that plays the physi

Solenne Gaucher, Olga Klopp

Estimating the matrix of connections probabilities is one of the key questions when studying sparse networks. In this work, we consider networks generated under the sparse graphon model and the in-homogeneous random graph model with missing observations. Using the Stochastic Block Model as a parametric proxy, we bound the risk of the maximum likelihood estim

Peter Zeidman, Amirhossein Jafarian, Mohamed L. Seghier, Vladimir Litvak

This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). This involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connec

Lorenzo Traldi

Joyce showed that for a classical knot $K$, the involutory medial quandle $\text{IMQ}(K)$ is isomorphic to the core quandle of the homology group $H_1(X_2)$, where $X_2$ is the cyclic double cover of $\mathbb S ^3$, branched over $K$. It follows that $|\text{IMQ}(K)| = | \det K |$. In the present paper, the extension of Joyce's result to classical links is d

Paul Breutmann

Moduli spaces of global $\mathbb G$-shtukas play a crucial role in the Langlands program for function fields. We analyze their functoriality properties following a change of the curve and a change of the group scheme $\mathbb G$ under various aspects. In particular, we prove two finiteness results which are of interest in the study of stratifications of thes

Pavel Kornilovitch

A three-fermion problem in a three-dimensional lattice with anisotropic hopping is solved by discretizing the Schroedinger equation in momentum space. Interparticle interaction comprises on-site Hubbard repulsion and in-plane nearest-neighbor attraction. By comparing the energy of three-fermion bound clusters (trions) with the energy of one pair plus one fre

Olivier Garet

The aim of this note is to provide a Master Theorem for some discrete divide and conquer recurrences: $$X_{n}=a_n+\sum_{j=1}^m b_j X_{\lfloor{\frac{n}{m_j}}\rfloor},$$ where the $m_i$'s are integers with $m_i\ge 2$. The main novelty of this work is there is no assumption of regularity or monotonicity for $(a_n)$. Then, this result can be applied to various s

Jérémie Vidal, David Cébron, Asif Ud-Doula, Evelyne Alecian

Surface magnetic fields have been detected in 5 to 10% of isolated massive stars, hosting outer radiative envelopes. They are often thought to have a fossil origin, resulting from the stellar formation phase. Yet, magnetic massive stars are scarcer in (close) short-period binaries, as reported by the BinaMIcS (Binarity and Magnetic Interaction in various cla