November 2018 arXiv papers — page 96

Paula Escorcielo, Daniel Perrucci

We prove that, under some additional assumption, Putinar's Positivstellensatz holds on cylinders of type $S \times {\mathbb R}$ with $S = \{x \in {\mathbb R}^n | g_1(x) \ge 0, ..., g_s(x) \ge 0\}$ such that the quadratic module generated by $g_1, ..., g_s$ in ${\mathbb R}[X_1, ..., X_n]$ is archimedean, and we provide a degree bound for the representatio

Shih-Han Hung, Kesha Hietala, Shaopeng Zhu, Mingsheng Ying

Quantum computation is a topic of significant recent interest, with practical advances coming from both research and industry. A major challenge in quantum programming is dealing with errors (quantum noise) during execution. Because quantum resources (e.g., qubits) are scarce, classical error correction techniques applied at the level of the architecture are

Giulia Cusin, Irina Dvorkin, Cyril Pitrou, Jean-Philippe Uzan

We investigate the discrepancy pointed out by Jenkins et al. in Ref. [1] between the predictions of anisotropies of the astrophysical gravitational wave (GW) background, derived using different methods in Cusin et al. [2] and in Jenkins et al. [3]. We show that this discrepancy is not due to our treatment of galaxy clustering, contrary to the claim made in R

Laura Doval, Vasiliki Skreta

We develop a tool akin to the revelation principle for dynamic mechanism-selection games in which the designer can only commit to short-term mechanisms. We identify a canonical class of mechanisms rich enough to replicate the outcomes of any equilibrium in a mechanism-selection game between an uninformed designer and a privately informed agent. A cornerstone

Eric A. Vance, Heather S. Smith

Statistics and data science are especially collaborative disciplines that typically require practitioners to interact with many different people or groups. Consequently, interdisciplinary collaboration skills are part of the personal and professional skills essential for success as an applied statistician or data scientist. These skills are learnable and tea

Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski

Annotating the right data for training deep neural networks is an important challenge. Active learning using uncertainty estimates from Bayesian Neural Networks (BNNs) could provide an effective solution to this. Despite being theoretically principled, BNNs require approximations to be applied to large-scale problems, where both performance and uncertainty e

Sergey I. Tertychniy

The explicit formulas for the maps interconnecting the sets of solutions of the special double confluent Heun equation and the equation of the RSJ model of overdamped Josephson junction in case of shifted sinusoidal bias are given. The approach these are based upon leans on the extensive application of eigenfunctions of certain linear operator acting on func

Quoc Hoan Tran, Van Tuan Vo, Yoshihiko Hasegawa

The structure of real-world networks is usually difficult to characterize owing to the variation of topological scales, the nondyadic complex interactions, and the fluctuations in the network. We aim to address these problems by introducing a general framework using a method based on topological data analysis. By considering the diffusion process at a single

Karam Chand, Subhayan Mandal

Recently a data set containing linear and circular polarisation information of a collection of six hundred pulsars has been released. The operative radio wavelength for the same was 21 cm. Pulsars radio emission process is modelled either with synchroton / superconducting self-Compton route or with curvature radiation route. These theories fall short of acco

Luca Bortolussi, Guido Sanguinetti

The success of modern Artificial Intelligence (AI) technologies depends critically on the ability to learn non-linear functional dependencies from large, high dimensional data sets. Despite recent high-profile successes, empirical evidence indicates that the high predictive performance is often paired with low robustness, making AI systems potentially vulner

Yacine Chitour, Zhenyu Liao, Romain Couillet

In this paper, we propose a geometric framework to analyze the convergence properties of gradient descent trajectories in the context of linear neural networks. We translate a well-known empirical observation of linear neural nets into a conjecture that we call the \emph{overfitting conjecture} which states that, for almost all training data and initial cond

Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio

The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this "weight transport problem" (Grossberg, 1987), two more biologically plausible algorithms, proposed by Liao et al. (2016) and Li

David A. Robb, Jonatan Scharff Willners, Nicolas Valeyrie, Francisco J. Chiyah Garcia

Autonomous underwater vehicles (AUVs) are being tasked with increasingly complex missions. The acoustic communications required for AUVs are, by the nature of the medium, low bandwidth while adverse environmental conditions underwater often mean they are also intermittent. This has motivated development of highly autonomous systems, which can operate indepen

Yuqian Jiang, Nick Walker, Minkyu Kim, Nicolas Brissonneau

When developing general purpose robots, the overarching software architecture can greatly affect the ease of accomplishing various tasks. Initial efforts to create unified robot systems in the 1990s led to hybrid architectures, emphasizing a hierarchy in which deliberative plans direct the use of reactive skills. However, since that time there has been signi

Mizanur Rahman, Mashrur Chowdhury, Jerome McClendon

The expected low market penetration of connected vehicles (CVs) in the near future could be a constraint in estimating traffic flow parameters, such as average travel speed of a roadway segment and average space headway between vehicles from the CV broadcasted data. This estimated traffic flow parameters from low penetration of connected vehicles become nois

Tanumoy Mandal, Subhadip Mitra, Swapnil Raz

Motivated by the persistent anomalies in the semileptonic $B$-meson decays, we investigate the competency of LHC data to constrain the $R_{D^{(*)}}$-favoured parameter space in a charge $-1/3$ scalar leptoquark ($\mathcal S_1$) model. We consider some scenarios with one large free coupling to accommodate the $R_{D^{(*)}}$ anomalies. As a result, some of them

Diego Correa, Pablo Pisani, Alan Rios Fukelman, Konstantin Zarembo

By considering a Gaussian truncation of ${\cal N}=4$ super Yang-Mills, we derive a set of Dyson equations that account for the ladder diagram contribution to connected correlators of circular Wilson loops. We consider different numbers of loops, with different relative orientations. We show that the Dyson equations admit a spectral representation in terms of

Bjoern S. Schmekel

The Brown-York quasi-local energy of a charged rotating black hole described by the Kerr-Newman metric and enclosed by a fixed-radius surface is computed. No further assumptions on the angular momentum or the radial coordinate in Boyer-Lindquist coordinates were made. The result can be expressed in terms of incomplete elliptic integrals and is used to analyz

J. Ph. Guillet, E. Pilon, Y. Shimizu, M. S. Zidi

This article is the first of a series of three presenting an alternative method to compute the one-loop scalar integrals. This novel method enjoys a couple of interesting features as compared with the method closely following 't Hooft and Veltman adopted previously. It directly proceeds in terms of the quantities driving algebraic reduction methods. It a

Giorgio Galanti, Fabrizio Tavecchio, Marco Roncadelli, Carmelo Evoli

Prompted by the increasing interest of axion-like particles (ALPs) for very-high-energy (VHE) astrophysics, we have considered a full scenario for the propagation of a VHE photon/ALP beam emitted by a BL Lac and reaching us in the light of the most up-to-date astrophysical information and for energies up to above $100 \, \rm TeV$. During its trip, the beam -

Sesuai Y. Madanha

Let $ G $ be a finite non-solvable group with a primitive irreducible character $ χ$ that vanishes on one conjugacy class. We show that $ G $ has a homomorphic image that is either almost simple or a Frobenius group. We also classify such groups $ G $ with a composition factor isomorphic to a sporadic group, an alternating group $ \rm{A}_{n} $, $ n\geq 5 $ o

Nguyen Bich Van

In this work we describe all simple modules over Leavitt path algebras as induced modules from irreducible representations of the isotropy groups.

Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility. Yet, we do not know why swarm-based algorithms work well and neither we can com

Teresa Yeo, Parameswaran Kamalaruban, Adish Singla, Arpit Merchant

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a t

Wayne Treible, Scott Sorensen, Andrew D. Gilliam, Chandra Kambhamettu

Digital Surface Model generation from satellite imagery is a difficult task that has been largely overlooked by the deep learning community. Stereo reconstruction techniques developed for terrestrial systems including self driving cars do not translate well to satellite imagery where image pairs vary considerably. In this work we present neural network tailo

Jan M. Pawlowski, Julian M. Urban

Short autocorrelation times are essential for a reliable error assessment in Monte Carlo simulations of lattice systems. In many interesting scenarios, the decay of autocorrelations in the Markov chain is prohibitively slow. Generative samplers can provide statistically independent field configurations, thereby potentially ameliorating these issues. In this

Mubariz Zaffar, Shoaib Ehsan, Michael Milford, Klaus Mcdonald Maier

This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition. This framework draws inspiration from human-memorability, utilizes the traditional image entropy concept and computes the static content in an image; thereby presenting a tri-folded criterion to assess the 'memorability' of an image for visual

Elena Giorgi

We prove boundedness and polynomial decay statements for solutions to the spin $\pm2$ generalized Teukolsky system on a Reissner-Nordström background with small charge. The first equation of the system is the generalization of the standard Teukolsky equation in Schwarzschild for the extreme component of the curvature $α$. The second equation, coupled with th

Tomohiro Fujita, Ryo Tazaki, Kenji Toma

We find that the polarimetric observations of protoplanetary disks are useful to search for ultra-light axion dark matter. Axion dark matter predicts the rotation of the linear polarization plane of propagating light, and protoplanetary disks are ideal targets to observe it. We show that a recent observation puts the tightest constraint on the axion-photon c

J. W. McIver, B. Schulte, F. -U. Stein, T. Matsuyama

Many striking non-equilibrium phenomena have been discovered or predicted in optically-driven quantum solids, ranging from light-induced superconductivity to Floquet-engineered topological phases. These effects are expected to lead to dramatic changes in electrical transport, but can only be comprehensively characterized or functionalized with a direct inter

Joong-Ho Won, Hua Zhou, Kenneth Lange

This paper studies the problem of maximizing the sum of traces of matrix quadratic forms on a product of Stiefel manifolds. This orthogonal trace-sum maximization (OTSM) problem generalizes many interesting problems such as generalized canonical correlation analysis (CCA), Procrustes analysis, and cryo-electron microscopy of the Nobel prize fame. For these a

Bertrand Higy, Peter Bell

End-to-end approaches have recently become popular as a means of simplifying the training and deployment of speech recognition systems. However, they often require large amounts of data to perform well on large vocabulary tasks. With the aim of making end-to-end approaches usable by a broader range of researchers, we explore the potential to use end-to-end m

Orazio Scarlatella, Aashish A. Clerk, Marco Schirò

We study the spectral properties of Markovian driven-dissipative quantum systems, focusing on the nonlinear quantum van der Pol oscillator as a paradigmatic example. We discuss a generalized Lehmann representation, in which single-particle Green's functions are expressed in terms of the eigenstates and eigenvalues of the Liouvillian. Applying it to the q

Feryal Behbahani, Kyriacos Shiarlis, Xi Chen, Vitaly Kurin

Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically relies on manually generated demonstrations or specially deployed sensors and has not generally been able to leverage the copious demonstrations available in the wild: those that c

Zuchao Li, Jiaxun Cai, Hai Zhao

Easy-first parsing relies on subtree re-ranking to build the complete parse tree. Whereas the intermediate state of parsing processing is represented by various subtrees, whose internal structural information is the key lead for later parsing action decisions, we explore a better representation for such subtrees. In detail, this work introduces a bottom-up s

Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He

Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information that allows developers and support engineers to monitor their systems and dissect anomalous behaviors and errors. The increasing scale and complexity of modern software systems, however, make the volume of logs explodes. In many c

Chen Cai, Yusu Wang

Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and for representation learning on graphs in general. As the developed methodologies become more sophisticated, it is important to understand whic

Joan Sola Peracaula, Adria Gomez-Valent, Javier de Cruz Perez

Investigations on dark energy (DE) are currently inconclusive about its time evolution. Hints of this possibility do however glow now and then in the horizon. Herein we assess the current status of dynamical dark energy (DDE) in the light of a large body of updated $SNIa+H(z)+BAO+LSS+CMB$ observations, using the full Planck 2015 CMB likelihood. The performan

Alana Cavalcante, Mauricio Corrêa, Simone Marchesi

This paper is devoted to the study of holomorphic distributions of dimension and codimension one on smooth weighted projective complete intersection Fano manifolds threedimensional, with Picard number equal to one. We study the relations between algebro-geometric properties of the singular set of singular holomorphic distributions and their associated sheave

Francesco Russo, Giovanni Staglianò

Recent results of Hassett, Kuznetsov and others pointed out countably many divisors $C_d$ in the open subset of $\mathbb{P}^{55}=\mathbb{P}(H^0(\mathcal{O}_{\mathbb{P}^5}(3)))$ parametrizing all cubic 4-folds and lead to the conjecture that the cubics corresponding to these divisors should be precisely the rational ones. Rationality has been proved by Fano f

G. Papadopoulos

We present a definition of null G-structures on Lorentzian manifolds and investigate their geometric properties. This definition includes the Robinson structure on 4-dimensional black holes as well as the null structures that appear in all supersymmetric solutions of supergravity theories. We also identify the induced geometry on some null hypersurfaces and

Helge Spieker, Arnaud Gotlieb, Morten Mossige

Multi-cycle assignment problems address scenarios where a series of general assignment problems has to be solved sequentially. Subsequent cycles can differ from previous ones due to changing availability or creation of tasks and agents, which makes an upfront static schedule infeasible and introduces uncertainty in the task-agent assignment process. We consi

Andre Maeder, Vesselin G. Gueorguiev

The growth of the density fluctuations is considered to be an important cosmological test. In the standard model, for a matter dominated universe, the growth of the density perturbations evolves with redshift z like (1/{1+z))^s with s=1. This is not fast enough to form galaxies and to account for the observed present-day inhomogeneities. This problem is usua

Rachel Carr, Jonathon Coleman, Mikhail Danilov, Giorgio Gratta

We present neutrino-based options for verifying that the nuclear reactors at North Korea's Yongbyon Nuclear Research Center are no longer operating or that they are operating in an agreed manner, precluding weapons production. Neutrino detectors may be a mutually agreeable complement to traditional verification protocols because they do not require acces

Haiyang Yang, Wei You, Jialu Wang, Junwu Huang

In ferromagnetic solids, even in absence of magnetic field, a transverse voltage can be generated by a longitudinal temperature gradient. This thermoelectric counterpart of the Anomalous Hall effect (AHE) is dubbed the Anomalous Nernst effect (ANE). Expected to scale with spontaneous magnetization, both these effects arise because of the Berry curvature at t

Ravinda Gunaratne, Daniel Wilson, Mark Flegg, Radek Erban

This work investigates multi-resolution methodologies for simulating dimer models. The solvent particles which make up the heat bath interact with the monomers of the dimer either through direct collisions (short-range) or through harmonic springs (long-range). Two types of multi-resolution methodologies are considered in detail: (a) describing parts of the

Jinbo Xu

Contact-assisted protein folding has made very good progress, but two challenges remain. One is accurate contact prediction for proteins lack of many sequence homologs and the other is that time-consuming folding simulation is often needed to predict good 3D models from predicted contacts. We show that protein distance matrix can be predicted well by deep le

Xiao-Gang He, Yu-Ji Shi, Wei Wang

Analyses of heavy mesons and baryons hadronic charmless decays using the flavor SU(3) symemtry can be formulated in two different forms. One is to construct the SU(3) irreducible representation amplitude (IRA) by decomposing effective Hamiltonian, and the other is to draw the topological diagrams (TDA). In the flavor SU(3) limit, we study various $B/D\to PP,

Conrad Watt, Petar Maksimović, Neelakantan R. Krishnaswami, Philippa Gardner

We introduce Wasm Logic, a sound program logic for first-order, encapsulated WebAssembly. We design a novel assertion syntax, tailored to WebAssembly's stack-based semantics and the strong guarantees given by WebAssembly's type system, and show how to adapt the standard separation logic triple and proof rules in a principled way to capture WebAssembl

Semyon Yakubovich

New sequences of orthogonal polynomials with ultra-exponential weight functions are discovered. In particular, it gives an explicit solution to the Ditkin-Prudnikov problem (1966). The 3-term recurrence relations, explicit representations, generating functions and Rodrigues type formulae are derived. The method is based on differential properties of the invo

Daniel Aloni, Yotam Soreq, Mike Williams

We present a novel data-driven method for determining the hadronic interaction strengths of axion-like particles (ALPs) with QCD-scale masses. Using our method, it is possible to calculate the hadronic production and decay rates of ALPs, along with many of the largest ALP decay rate to exclusive final states. To illustrate the impact on QCD-scale ALP phenome

James Thompson

Using the tools of stochastic analysis, we prove various gradient estimates and Harnack inequalities for Feynman-Kac semigroups with possibly unbounded potentials. One of the main results is a derivative formula which can be used to characterize a lower bound on Ricci curvature using a potential.

HaiGang Li, YanYan Li, ZhuoLun Yang

In the perfect conductivity problem of composite material, the gradient of solutions can be arbitrarily large when two inclusions are located very close. To characterize the singular behavior of the gradient in the narrow region between two inclusions, we capture the leading term of the gradient and give a fairly sharp description of such asymptotics.

V. V. Zemlyanov, N. S. Kirsanov, M. R. Perelshtein, D. I. Lykov

Unitary Fourier transform lies at the core of the multitudinous computational and metrological algorithms. Here we show experimentally how the unitary Fourier transform-based phase estimation protocol, used namely in quantum metrology, can be translated into the classical linear optical framework. The developed setup made of beam splitters, mirrors and phase

Lukas Burkhalter, Anwar Hithnawi, Alexander Viand, Hossein Shafagh

A growing number of devices and services collect detailed time series data that is stored in the cloud. Protecting the confidentiality of this vast and continuously generated data is an acute need for many applications in this space. At the same time, we must preserve the utility of this data by enabling authorized services to securely and selectively access

Sangchul Hahn, Heeyoul Choi

After deep generative models were successfully applied to image generation tasks, learning disentangled latent variables of data has become a crucial part of deep generative model research. Many models have been proposed to learn an interpretable and factorized representation of latent variable by modifying their objective function or model architecture. To

Juan A. Barceló, Carlos Castro, Teresa Luque, Cristóbal J. Meroño

We present a uniqueness result in dimensions $2$ and $3$ for the inverse fixed angle scattering problem associated to the Schrödinger operator $-Δ+q$, where $q$ is a small real valued potential with compact support in the Sobolev space $W^{β,2}$ with $β>0.$ This result improves the known result, due to Stefanov, in the sense that almost no regularity is requ

David Plankensteiner, Christian Sommer, Michael Reitz, Helmut Ritsch

Cavity-embedded quantum emitters show strong modifications of free space radiation properties such as an enhanced decay known as the Purcell effect. The central parameter is the cooperativity $C$, the ratio of the square of the coherent cavity coupling strength over the product of cavity and emitter decay rates. For a single emitter, $C$ is independent of th

Xingkang He, Wenchao Xue, Xiaocheng Zhang, Haitao Fang

This paper considers the distributed filtering problem for a class of stochastic uncertain systems under quantized data flowing over switching sensor networks. Employing the biased noisy observations of the local sensor and interval-quantized messages from neighboring sensors successively, an extended state based distributed Kalman filter (DKF) is proposed f

Hayoung Eom, Heeyoul Choi

Convolutional neural networks (CNNs) have achieved remarkable performance in many applications, especially in image recognition tasks. As a crucial component of CNNs, sub-sampling plays an important role for efficient training or invariance property, and max-pooling and arithmetic average-pooling are commonly used sub-sampling methods. In addition to the two

Qiao Zheng, Hervé Delingette, Nicholas Ayache

We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generat

Vida Dujmović, Fabrizio Frati, Daniel Gonçalves, Pat Morin

We show that if a planar graph $G$ has a plane straight-line drawing in which a subset $S$ of its vertices are collinear, then for any set of points, $X$, in the plane with $|X|=|S|$, there is a plane straight-line drawing of $G$ in which the vertices in $S$ are mapped to the points in $X$. This solves an open problem posed by Ravsky and Verbitsky in 2008. I

Mathieu Kohli

In this paper we study the notion of geodesic curvature of smooth horizontal curves parametrized by arc lenght in the Heisenberg group, that is the simplest sub-Riemannian structure. Our goal is to give a metric interpretation of this notion of geodesic curvature as the first corrective term in the Taylor expansion of the distance between two close points of

Vida Dujmović, Pat Morin

We show that, if a $n$-vertex triangulation $T$ of maximum degree $Δ$ has a dual that contains a cycle of length $\ell$, then $T$ has a non-crossing straight-line drawing in which some \emph{collinear set} of $Ω(\ell/Δ^4)$ vertices lie on a line. Using the current lower bounds on the length of longest cycles in 3-regular 3-connected graphs, this implies that

I. Gonzalez-Adalid Pemartin, V. Martin-Mayor, G. Parisi, J. J. Ruiz-Lorenzo

We study the problem of glassy relaxations in the presence of an external field in the highly controlled context of a spin-glass simulation. We consider a small spin glass in three dimensions (specifically, a lattice of size L=8, small enough to be equilibrated through a Parallel Tempering simulations at low temperatures, deep in the spin glass phase). After

Liana David, Claus Hertling

A $(TE)$-structure $\nabla$ over a complex manifold $M$ is a meromorphic connection defined on a holomorphic vector bundle over $\mathbb{C}\times M$, with poles of Poincaré rank one along $\{ 0 \} \times M.$ Under a mild additional condition (the so called unfolding condition), $\nabla$ induces a multiplication on $TM$ and a vector field on $M$ (the Euler fi

Torsten Keßler, Sergej Rjasanow, Steffen Weißer

This paper presents a grid-free simulation algorithm for the fully three-dimensional Vlasov--Poisson system for collisionless electron plasmas. We employ a standard particle method for the numerical approximation of the distribution function. Whereas the advection of the particles is grid-free by its very nature, the computation of the acceleration involves

Yuji Roh, Geon Heo, Steven Euijong Whang

Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional

Sumit Mukherjee, Anik Rudra, Debarshi Das, Shiladitya Mal

We investigate quantum violation of macrorealism for multilevel spin systems under the condition of coarsening of measurement times -- i.e., when measurement times have experimental indeterminacy. This is studied together with the effect of coarsening of measurement outcomes for which individual outcomes cannot be unambiguously discriminated. In our treatmen

Halil Mutuk

In this work we applied a feed forward neural network to solve Blasius equation which is a third-order nonlinear differential equation. Blasius equation is a kind of boundary layer flow. We solved Blasius equation without reducing it into a system of first order equation. Numerical results are presented and a comparison according to some studies is made in t

Krystal Guo, Tony Huynh, Marco Macchia

We determine the exact value of the biclique covering number for all grid graphs.

Axel Maas, Sebastian Raubitzek, Pascal Törek

Gauge invariance requires even in the weak interactions that physical, observable particles are described by gauge-invariant composite operators. Such operators have the same structure as those describing bound states, and consequently the physical versions of the $W^\pm$, the $Z$, and the Higgs should have some kind of substructure. To test this consequence

Ioannis Mavromatis, Andrea Tassi, Robert J. Piechocki, Andrew Nix

The field of parallel network simulation frameworks is evolving at a great pace. That is also because of the growth of Intelligent Transportation Systems (ITS) and the necessity for cost-effective large-scale trials. In this contribution, we will focus on the INET Framework and how we re-factor its single-thread code to make it run in a multi-thread fashion.

Divya Rawat, Mayukh Pahari, J S Yadav, Pankaj Jain

In this work, we present a $\sim$90 ks continuous monitoring of the Galactic micro-quasar GRS 1915+105 with AstroSat when the source undergoes a major transition from a non-variable, $χ$ class (similar to radio-quiet $χ$ class) to a structured, large amplitude, periodic heartbeat state (similar to $ρ$ class). We show that such transition takes place via an i

Frank Soboczenski, Michael D. Himes, Molly D. O'Beirne, Simone Zorzan

Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmosphere's temperature structure and composition from a

Jill-Jênn Vie, Hisashi Kashima

Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some student, one can optimize instruction. Existing methods are either based on temporal latent variable models, or factor analysis with tempor

Daniel Reiche, Marty Oelschläger, Kurt Busch, Francesco Intravaia

The dissipative properties of spatially nonlocal conductors are investigated in the context of quantum friction acting on an atom moving above a macroscopic body. The focus is on an extended version of the hydrodynamic model for the bulk material's electromagnetic response. It is shown that the standard hydrodynamic description is inadequate for evaluati

Brice Bastian, Stefan Hohenegger

Recent studies (arXiv:1610.07916, arXiv:1711.07921, arXiv:1807.00186) of six-dimensional supersymmetric gauge theories that are engineered by a class of toric Calabi-Yau threefolds $X_{N,M}$, have uncovered a vast web of dualities. In this paper we analyse consequences of these dualities from the perspective of the partition functions $\mathcal{Z}_{N,M}$ (or

M. L. Savchenko, D. A. Kozlov, N. N. Vasilev, Z. D. Kvon

Surface states of topological insulators (TIs) have been playing the central role in the majority of outstanding investigations in low-dimensional electron systems for more than 10 years. TIs based on high-quality strained HgTe films demonstrate a variety of subtle physical effects. The strain leads to a bulk band gap but limits a maximum HgTe strained film

Sebastian Bodenstedt, Martin Wagner, Lars Mündermann, Hannes Kenngott

Purpose The course of surgical procedures is often unpredictable, making it difficult to estimate the duration of procedures beforehand. A context-aware method that analyses the workflow of an intervention online and automatically predicts the remaining duration would alleviate these problems. As basis for such an estimate, information regarding the current

Sebastian Bodenstedt, Dominik Rivoir, Alexander Jenke, Martin Wagner

For many applications in the field of computer assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical workflow analysis are a prerequisite. Often machine learning based approaches serve as basis for surgical workflow ana

Sebastian Schleißinger

In this note we regard non-commutative probability theory with operator-valued expectation. We show that the moment generating functions of distributions coming from monotone increment processes of unitary random variables yield biholomorphic mappings on certain higher dimensional unit balls.

Xi Chen, Ali Ghadirzadeh, Mårten Björkman, Patric Jensfelt

Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objectives. Generally, in such formulations, there is no single optimal policy which optimizes all the objectives simultaneously, and instead, a numb

Timur Aslyamov, Iskander Akhatov

Lee-Yang and Fisher zeros are crucial for the study of phase transitions in the grand canonical and the canonical ensembles, respectively. However, these powerful methods do not cover the isothermal-isobaric ensemble (NPT ensemble), which reflects the conditions of many experiments. In this work we present a theory of the phase transitions in terms of the ze

Shuiqin Zheng, Xuanke Zeng, Lang Zha, Huancheng Shangguan

Several laws are found for the Diffractive Deep Neural Networks (D2NN). They reveal the inner product of any two light fields in D2NN is invariant and the D2NN act as a unitary transformation for optical fields. If the output intensities of the two inputs are separated spatially, the input fields must be orthogonal. These laws imply that the D2NN is not only

Igor Nikitin

It is shown that the models of white hole interacting with external matter can be made stable by introduction of a negative central mass. Similar results are obtained for the models of white hole, interacting with null shells, with radial flows of matter and with photon gas. In realistic models, a naked timelike singularity corresponding to the negative mass

Mohammad Reza Mehdizadeh, Amir Hadi Ziaie

Static solutions representing wormhole configurations in Einstein-Cartan theory ({\sf ECT}) in the presence of electric charge are obtained. The solutions are described by a constant redshift function with matter content consisting of a Weyssenhoff fluid along with an anisotropic matter and energy momentum tensor ({\sf EMT}) of the electric field which toget

Bal Krishna Yadav, Murli Manohar Verma

We explore the scalar field obtained under the conformal transformation of the spacetime metric $g_{μν}$ from the Jordan frame to the Einstein frame in $f(R)$ gravity. This scalar field is the result of the modification in the gravitational part of the Einstein's general relativistic theory of gravity. For $f(R)=\frac{R^{1+δ}}{R_{c}^δ}$, we find the effe

Harvey B. Meyer, Konstantin Ottnad, Tobias Schulz

One of the most challenging tasks in lattice calculations of baryon form factors is the analysis and control of excited-state contaminations. Taking the isovector axial form factors of the nucleon as an example, both a dispersive representation and a calculation in chiral effective field theory show that the excited-state contributions become dominant at fix

Ryota Gemma, Moritz to Baben, Astrid Pundt, Vassilios Kapaklis

The atomic distribution in amorphous FeZr alloys is found to be close to random, nevertheless, the composition can not be viewed as being homogenous at the nm-scale. The spatial variation of the local composition is identified as the root of the unusual magnetic properties in amorphous Fe$_{1-x}$Zr$_{x}$ alloys. The findings are discussed and generalised wit

Tanya Shreedhar, Sanjit K. Kaul, Roy D. Yates

The next generation of networks must support billions of connected devices in the Internet-of-Things (IoT). To support IoT applications, sources sense and send their measurement updates over the Internet to a monitor (control station) for real-time monitoring and actuation. Ideally, these updates would be delivered at a high rate, only constrained by the sen

Tapani Hyttinen, Gianluca Paolini

We prove that the theory of open projective planes is complete and strictly stable, and infer from this that Marshall Hall's free projective planes $(π^n : 4 \leq n \leq ω)$ are all elementary equivalent and that their common theory is strictly stable and decidable, being in fact the theory of open projective planes. We further characterize the elementar

Peter Ashwin, Sofia Castro, Alexander Lohse

Heteroclinic connections are trajectories that link invariant sets for an autonomous dynamical flow: these connections can robustly form networks between equilibria, for systems with flow-invariant spaces. In this paper we examine the relation between the heteroclinic network as a flow-invariant set and directed graphs of possible connections between nodes.

Pan Wang, Mazhar E. Nasir, Alexey V. Krasavin, Wayne Dickson

Highly efficient information processing in brain is based on processing and memory components called synapses, whose output is dependent on the history of the signals passed through them. Here we have developed an artificial synapse with both electrical and optical memory effects using reactive tunnel junctions based on plasmonic nanorods. In an electronic r

Baruch Meerson

We study large fluctuations of the area $\mathcal{A}$ under a Brownian excursion $x(t)$ on the time interval $|t|\leq T$, constrained to stay away from a moving wall $x_0(t)$ such that $x_0(-T)=x_0(T)=0$ and $x_0(|t|<T)>0$. We focus on wall functions described by a family of generalized parabolas $x_0(t)=T^γ [1-(t/T)^{2k}]$, where $k\geq 1$. Using the optima

Zongze Yang, Zhanbin Yuan, Yufeng Nie, Jungang Wang

This paper is a generalization of the previous work (Yang et.al, J. Comput. Phys. 330 (2017), 863-883) to the 3-D irregular convex domains. The analytical calculation formula of fractional derivatives of finite element basis functions are given and a path searching method is developed to find the integration paths corresponding to the Gaussian points. Moreov

Nondas E. Kechagias

A non-connected neither of finite type Hopf algebra $\mathcal{F}_{0}$ is defined over $\mathbb{Z}/ 2\mathbb{Z}$ and its hom dual turns out to be a tensor product of polynomial algebras. Certain quotient Hopf algebras include the Steenrod and Dyer-Lashof algebras. This setting provides a map between the Steenrod coalgebra and a direct limit of Dyer-Lashof coa

Aaron Bernstein, Danupon Nanongkai

In the {\em distributed all-pairs shortest paths} problem (APSP), every node in the weighted undirected distributed network (the CONGEST model) needs to know the distance from every other node using least number of communication rounds (typically called {\em time complexity}). The problem admits $(1+o(1))$-approximation $\tildeΘ(n)$-time algorithm and a near

Tomas Stolker, Markus J. Bonse, Sascha P. Quanz, Adam Amara

The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and PSF subtraction

Hélène Ruffieux, Anthony C. Davison, Jörg Hager, Jamie Inshaw

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, i.e., predictors associated with several responses. Such a task is critical in statistical genetics, as hotspot genetic variants shape the architecture of the genome by controlling the expression of many gen