January 2019 arXiv papers — page 25

Christian Moulsdale, Pierre A. Pantaleón, Ramon Carrillo-Bastos, Yang Xian

We present theoretically the thermal Hall effect of magnons in a ferromagnetic lattice with a Kekul\'e-O coupling (KOC) modulation and a Dzyaloshinskii-Moriya interaction (DMI). Through a strain-based mechanism for inducing the KOC modulation, we identify four topological phases in terms of the KOC parameter and DMI strength. We calculate the thermal magnon

Yiheng Wei, Jiachang Wang, Peter W Tse, Yong Wang

The paper focuses on the numerical approximation of nabla fractional order systems with the conditions of nonzero initial instant and nonzero initial state. First, the inverse nabla Laplace transform is developed and the equivalent infinite dimensional frequency distributed models of discrete fractional order system are introduced. Then, resorting the nabla

Yiheng Wei, Jiachang Wang, Tianyu Liu, Yong Wang

This paper is concerned with the stabilization problem of singular fractional order systems with order $\alpha\in(0,2)$. In addition to the sufficient and necessary condition for observer based control, a sufficient and necessary condition for output feedback control is proposed by adopting matrix variable decoupling technique. The developed results are more

Takayuki Tatekawa

In modern cosmology, the precision of the theoretical prediction is increasingly required. In cosmological $N$-body simulations, the effect of higher-order Lagrangian perturbation on the initial conditions appears in terms of statistical quantities of matter density field. We have considered the effect of third-order Lagrangian perturbation (3LPT) on the ini

Temitayo Ajayi, Taewoo Lee, Andrew Schaefer

Submodularity is a key property in discrete optimization. Submodularity has been widely used for analyzing the greedy algorithm to give performance bounds and providing insight into the construction of valid inequalities for mixed-integer programs. In recent years, researchers started to study approximate submodularity, with a primary focus on providing perf

Shiwei Liu, Decebal Constantin Mocanu, Mykola Pechenizkiy

Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a wide range of tasks. Its outstanding performance is guaranteed by the long-term memory ability which matches the sequential data perfectly and the gating structure controlling the information flow. However, LSTMs are prone to be memory-bandwidth limited in realistic applications an

Yiheng Wei, Da-Yan Liu, Peter W. Tse, Yong Wang

Taylor series is a useful mathematical tool when describing and constructing a function. With the series representation, some properties of fractional calculus can be revealed clearly. This paper investigates two typical applications: Lebiniz rule and Laplace transform. It is analytically shown that the commonly used Leibniz rule cannot be applied for Caputo

Ying Wen, Yaodong Yang, Rui Luo, Jun Wang

Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential consequences of others' behaviors so as to plan ahead. It is known that humans use such reasoning ability recursively by considering what others believe about their own beliefs. In this

Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet, Bernhard Schölkopf

Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance. If we see these implicit models as dynamical systems, some of these issues are caused by being unable to control their behavior in a meaningful way d

Yu-Peng Liu, Bo-Yuan Ning, Le-Cheng Gong, Tsu-Chien Weng

A method was developed to calculate the free energy of 2D materials on substrates and was demonstrated by the system of graphene and {\gamma}-graphyne on copper substrate. The method works at least 3 orders faster than state-of-the-art algorithms, and the accuracy was tested by molecular dynamics simulations, showing that the precision for calculations of th

Layth M. Alabdulsada

The aim of this paper is to introduce and investigate a new class of functions called weakly almost contra-$T^*$-continuity which is defined as a function from an operator topological space $(X, \tau, T)$ into an arbitrary topological space $(Y, \delta)$. Furthermore, some new characterizations, several basic propositions are proved and some relevant counter

Xiao-Yun Zhou, Jian-Qing Zheng, Peichao Li, Guang-Zhong Yang

Deep Convolutional Neural Networks (DCNNs) are used extensively in medical image segmentation and hence 3D navigation for robot-assisted Minimally Invasive Surgeries (MISs). However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dime

Baoyi Chen, Carsten Greiner

Charmonia with different transverse momentum $p_T$ usually comes from different mechanisms in the relativistic heavy ion collisions. This work tries to review the theoretical studies on quarkonium evolutions in the deconfined medium produced in p-Pb and Pb-Pb collisions. The charmonia with high $p_T$ are mainly from the initial hadronic collisions, and there

Mikhail I. Belishev, Aleksei F. Vakulenko

Let $\Omega$ be a smooth compact oriented 3-dimensional Riemannian manifold with boundary. A quaternion field is a pair $q=\{\alpha,u\}$ of a function $\alpha$ and a vector field $u$ on $\Omega$. A field $q$ is {\it harmonic} if $\alpha, u$ are continuous in $\Omega$ and $\nabla\alpha={\rm rot\,}u,\,{\rm div\,}u=0$ holds into $\Omega$. The space ${\mathscr Q

Anton Andronic, Peter Braun-Munzinger, Markus K. Köhler, Krzysztof Redlich

Calculations and predictions are presented within the framework of the statistical hadronization model for transverse momentum spectra of the charmonium states J/$\psi$, $\psi(2S)$ and $X(3872)$ produced in nucleus-nucleus collisions at LHC energies. The results are confronted with available data and exhibit very good agreement by using particle flow profile

E. Maghsoodi, H. Hassanabadi, Won Sang Chungb

In this paper, we investigate effect of the generalized uncertainty principle (GUP) under the doubly special relativity (DSR) on the thermodynamics properties of the topological charged black hole in Anti-de Sitter (AdS) space only in the spherical horizon case have. Our study is based on a heuristic analysis of a particle which is captured by the black hole

Liutong Du, Lihua Li, Ping Zhang

We consider the vector perturbation (VP) precoder design for multiuser multiple-input single output (MU-MISO) broadcast channel systems which is robust to power scaling factor errors. VP precoding has so far been developed and analyzed under the assumption that receivers could have known the power scaling factor in advance of tranmission perfectly, which is

Yiheng Wei, YangQuan Chen, Qing Gao, Yong Wang

This paper focuses on the equivalent expression of fractional integrals/derivatives with an infinite series. A universal framework for fractional Taylor series is developed by expanding an analytic function at the initial instant or the current time. The framework takes into account of the Riemann-Liouville definition, the Caputo definition, the constant ord

Gaël Foëx

The main goals of the present work are (i) to study the substructure content of a representative, X-ray selected sample of 31 galaxy clusters, as traced by the spatial distribution of their red-sequence galaxies, and (ii) to compare it to that observed in the intracluster medium distribution. Our substructure indicators are the asymmetry test, $\beta$, the r

Xiaolei Liu, Xiaosong Zhang, Kun Wan, Qingxin Zhu

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory

Ahmed H. Shahin, Karim Amer, Mustafa A. Elattar

The prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an e

Yu Li, Qianqian Wang, Yuanji Xu, Wenhui Xie

Recent discovery of superconductivity in YbRh$_2$Si$_2$ has raised particular interest in its pairing mechanism and gap symmetry. Here we propose a phenomenological theory of its superconductivity and investigate possible gap structures by solving the multiband Eliashberg equations combining realistic Fermi surfaces from first-principles calculations and a q

Carsten Hartmann, Omar Kebiri, Lara Neureither, Lorenz Richter

We propose an adaptive importance sampling scheme for the simulation of rare events when the underlying dynamics is given by a diffusion. The scheme is based on a Gibbs variational principle that is used to determine the optimal (i.e. zero-variance) change of measure and exploits the fact that the latter can be rephrased as a stochastic optimal control probl

Sayyed-Ali Hossayni, Yousef Alizadeh-Q, Vahid Tavana, Seyed M. Hosseini Nejad

Forensic Document Analysis (FDA) addresses the problem of finding the authorship of a given document. Identification of the document writer via a number of its modalities (e.g. handwriting, signature, linguistic writing style (i.e. stylome), etc.) has been studied in the FDA state-of-the-art. But, no research is conducted on the fusion of stylome and signatu

Xiaolei Liu, Yuheng Luo, Xiaosong Zhang, Qingxin Zhu

Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from adversarial samples generated to attack them. However, most of the generation approaches either assume that the attacker has full knowledge of the neural network model or are limited

Patrick Müller, Andre Lohmann, Johannes Richter, Oleg Derzhko

We use the rotation-invariant Green's function method (RGM) and the high-temperature expansion (HTE) to study the thermodynamic properties of the Heisenberg antiferromagnet on the pyrochlore lattice. We discuss the excitation spectra as well as various thermodynamic quantities, such as spin correlations, uniform susceptibility, specific heat and static and d

Changgong Zhang, Fangneng Zhan, Hongyuan Zhu, Shijian Lu

A large amount of annotated training images is critical for training accurate and robust deep network models but the collection of a large amount of annotated training images is often time-consuming and costly. Image synthesis alleviates this constraint by generating annotated training images automatically by machines which has attracted increasing interest

Yonatan Geifman, Ran El-Yaniv

We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated reject option. Existing rejection mechanisms are based mostly on a threshold over the prediction confidence of a pre-trained network. In contrast, SelectiveNet is trained to optimize

Stefano Marmi, Corinna Ulcigrai, Jean-Christophe Yoccoz

We introduce two Diophantine conditions on rotation numbers of interval exchange maps (i.e.m) and translation surfaces: the \emph{absolute Roth type condition} is a weakening of the notion of Roth type i.e.m., while the \emph{dual Roth type} condition is a condition on the \emph{backward} rotation number of a translation surface. We show that results on the

S. F. Ebadzadeh, H. Goudarzi, M. Khezerlou

Growth of graphene on monolayer transition-metal dichalcogenides presents opening on band gap and giant spin-orbit coupling which paves the way to achieve a useful hybrid structure for electronics and spintronics applications. Increase of the atomic number of transition-metal results in a large SOC, where eventually a band inversion appears in graphene-$WSe_

Patria Rachman Hakim, A Hadi Syafrudin, Sartika Salaswati, Satriya Utama

As of any other satellite images, LAPAN-A3/IPB multispectral images suffered from both geometric and radiometric distortions which need to be corrected. LAPAN as satellite owner has developed image preprocessing algorithm to process raw image into systematically corrected image. This research aims to evaluate the performance of the developed algorithm, parti

Julyan Arbel, Olivier Marchal, Hien D. Nguyen

We investigate the sub-Gaussian property for almost surely bounded random variables. If sub-Gaussianity per se is de facto ensured by the bounded support of said random variables, then exciting research avenues remain open. Among these questions is how to characterize the optimal sub-Gaussian proxy variance? Another question is how to characterize strict sub

Ricards Marcinkevics, Steven Kelk, Carlo Galuzzi, Berthold Stegemann

The classification of time series data is a well-studied problem with numerous practical applications, such as medical diagnosis and speech recognition. A popular and effective approach is to classify new time series in the same way as their nearest neighbours, whereby proximity is defined using Dynamic Time Warping (DTW) distance, a measure analogous to seq

S. Kang, K. R. Moore, J. P. McGilligan, R. Mott

We demonstrate a novel way to form and deplete a vapor-cell magneto-optic trap (MOT) using a reversible, solid-state alkali-metal source (AMS) via an applied polarized voltage. Using ~100 mW of electrical power, a trapped-atom number of 5x10^6 has been achieved starting from near zero and the timescales of the MOT formation and depletion of ~1 s. This fast,

Raphael Yuster

Erd\H{o}s and Hanani proved that for every fixed integer $k \ge 2$, the complete graph $K_n$ can be almost completely packed with copies of $K_k$; that is, $K_n$ contains pairwise edge-disjoint copies of $K_k$ that cover all but an $o_n(1)$ fraction of its edges. Equivalently, elements of the set $\C(k)$ of all red-blue edge colorings of $K_k$ can be used to

Chen Tessler, Yonathan Efroni, Shie Mannor

A policy is said to be robust if it maximizes the reward while considering a bad, or even adversarial, model. In this work we formalize two new criteria of robustness to action uncertainty. Specifically, we consider two scenarios in which the agent attempts to perform an action $a$, and (i) with probability $\alpha$, an alternative adversarial action $\bar a

Koijam Manihar Singh, Kangujam Priyokumar Singh

In trying to explain the present accelerated expansion of the universe in the light of a five-dimensional Brans-Dicke theory, it is found that the fifth dimension itself here acts as a source of dark energy. It may be taken as a curvature-induced form of dark energy, in one case of which it behaves similar to that form of dark energy arising out of the cosmo

Yucheng Liu, Parastoo Sadeghi

In this paper, we study the information-theoretic converse for the index coding problem. We generalize the definition for the alignment chain, introduced by Maleki et al., to capture more flexible relations among interfering messages at each receiver. Based on this, we derive improved converse results for the single-server index coding problem. Compared to t

Tao Yu, Chuanpu Liu, Haiming Yu, Yaroslav M. Blanter

We theoretically investigate the interlayer dipolar and exchange couplings for an array of metallic magnetic nanowires grown on top of an extended ultrathin yttrium iron garnet film. The calculated interlayer dipolar coupling agrees with observed anticrossings [Chen \emph{et al.}, Phys. Rev. Lett. \textbf{120}, 217202 (2018)], concluding that the interlayer

Shiwei Liu, Decebal Constantin Mocanu, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei

Artificial Neural Networks (ANNs) have emerged as hot topics in the research community. Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-increasing model size and the unprecedented growth in the data volumes. Particularly for microarray data, the very-high dimensionality and the small number

Davide Grossi, Simon Rey

The Poison Game is a two-player game played on a graph in which one player can influence which edges the other player is able to traverse. It operationalizes the notion of existence of credulously admissible sets in an argumentation framework or, in graph-theoretic terminology, the existence of non-trivial semi-kernels. We develop a modal logic (poison modal

Tianwen Luo, Tao Tao, Liqun Zhang

We show the existence of finite kinetic energy solution with prescribed kinetic energy to the 2d Boussinesq equations with diffusive temperature on torus.

Zhen Wang, Yuan-Hai Shao, Lan Bai, Chun-Na Li

In this paper, we propose a general model for plane-based clustering. The general model contains many existing plane-based clustering methods, e.g., k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector clustering (TWSVC) and its extensions. Under this general model, one may obtain an appropriate clustering method for specific purpos

Songyang Zhang, Weimin Lei

The multipath transmission scheme can work as an effective way to provide better quality of experiments to end users. Two key research points in the multipath real time video transmission context are congestion control and packet scheduling. As Utility maximization theory shows, to provide better satisfaction to end users is to provide higher throughput and

Ari Arapostathis, Hassan Hmedi, Guodong Pang, Nikola Sandrić

We study the ergodic properties of a class of controlled stochastic differential equations (SDEs) driven by $\alpha$-stable processes which arise as the limiting equations of multiclass queueing models in the Halfin-Whitt regime that have heavy-tailed arrival processes. When the safety staffing parameter is positive, we show that the SDEs are uniformly ergod

Nikolaos Fountoulakis, Dieter Mitsche, Tobias Müller, Markus Schepers

In this paper we consider the existence of Hamilton cycles and perfect matchings in a random graph model proposed by Krioukov et al.~in 2010. In this model, nodes are chosen randomly inside a disk in the hyperbolic plane and two nodes are connected if they are at most a certain hyperbolic distance from each other. It has been previously shown that this model

Chaouki Ben Issaid, Mohamed-Slim Alouini, Raul Tempone

Estimating the left tail of quadratic forms in Gaussian random vectors is of major practical importance in many applications. In this paper, we propose an efficient and robust importance sampling estimator that is endowed with the bounded relative error property. This property significantly reduces the number of simulation runs required by the proposed estim

John D. Barrow, Andronikos Paliathanasis

We consider the Szekeres universe with an inhomogeneous dust fluid and a homogeneous and isotropic ghost matter source with equation of state $p_{g}=\left( \gamma-1\right) \rho_{g},$ where $\gamma$ is a constant. The field equations determine two families of spacetimes which describe homogeneous Kantowski-Sachs universes and inhomogeneous Friedmann universe.

JuAe Song

For a map $\varphi : \varGamma \rightarrow \varGamma^{\prime}$ between metric graphs and an isometric action on $\varGamma$ by finite group $K$, $\varphi$ is a $K$-Galois covering on $\varGamma^{\prime}$ if $\varphi$ is a morphism, the degree of $\varphi$ coincides with the order of $K$ and $K$ induces a transitive action on every fibre. We prove that for a

Zhaoyou Wang, Marek Pechal, E. Alex Wollack, Patricio Arrangoiz-Arriola

Modulating the frequency of a harmonic oscillator at nearly twice its natural frequency leads to amplification and self-oscillation. Above the oscillation threshold, the field settles into a coherent oscillating state with a well-defined phase of either $0$ or $\pi$. We demonstrate a quantum parametric oscillator operating at microwave frequencies and drive

Rahul Trivedi, Logan Su, Jesse Lu, Martin F Schubert

Designing modern photonic devices often involves traversing a large parameter space via an optimization procedure, gradient based or otherwise, and typically results in the designer performing electromagnetic simulations of correlated devices. In this paper, we present an approach to accelerate the Generalized Minimal Residual (GMRES) algorithm for the solut

Supriyo Ghosh, Soumen Mondal, Ramkrishna Das, Dhrimadri Khata

We present here new medium resolution spectra ($\lambda/\Delta\lambda$ $\sim$ 1200) of K$-$M giants covering wavelength range 1.50$-$1.80 and 1.95$-$2.45 $\mu$m. The sample includes 72 K0$-$M8 giants from our TIRSPEC observations and all available 35 giants in that spectral range from archival IRTF spectral library. We have calibrated here the empirical rela

Pan Lai, Lingjie Duan, Xiaojun Lin

Large electricity customers (e.g., large data centers) can exhibit huge and variable electricity demands, which poses significant challenges for the electricity suppliers to plan for sufficient capacity. Thus, it is desirable to design incentive and coordination mechanisms between the customers and the supplier to lower the capacity cost. This paper proposes

Abhay Shastry, Sosuke Inui, Charles A. Stafford

Temperature imaging of nanoscale systems is a fundamental problem which has myriad potential technological applications. For example, nanoscopic cold spots can be used for spot cooling electronic components while hot spots could be used for precise activation of chemical or biological reactions. More fundamentally, imaging the temperature fields in quantum c

Bharadwaj Aryasomayajula, Dibakar Sil, Sarbani Palit

Periodicity estimation from an arbitrary length noisy signal is computationally very costly. A recently developed Ramanujan Fat Dictionary is one of the ways to find the hidden components from an arbitrary length (non integral multiple of period) of the signal. This method suffers from high run time due to the lack of information about the period and effect

Altay Zhakatayev, Leila Tlebaldiyeva

In this paper, radiation due to standing wave currents are considered for the electric dipole. Assumptions that wavelength is much smaller than the dipole separation distance, which in turn is much smaller than the distance to the point of observation, were employed. Results indicate that the electric dipole, which now essentially is a linear antenna, emits

Kai Lei, Meng Qin, Bo Bai, Gong Zhang

In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic

Zakaria Mzaouali, Steve Campbell, Morad El Baz

We apply the Wigner function formalism from quantum optics via two approaches, Wootters' discrete Wigner function and the generalized Wigner function, to detect quantum phase transitions in critical spin-$\tfrac{1}{2}$ systems. We develop a general formula relating the phase space techniques and the thermodynamical quantities of spin models, which we apply t

M. F. A. R. Sakti, A. Suroso, F. P. Zen

We present a new twisted rotating black hole solution by performing Demia{\'n}ski-Newman-Janis algorithm to the electrically and dyonically charged black hole with quintessence in Rastall theory of gravity. Using our black hole solution, we argue that Rastall gravity is not equivalent with Einstein gravity. For further explanation, the black hole properties

Carlos Borges, George Biros

We propose domain decomposition preconditioners for the solution of an integral equation formulation of forward and inverse acoustic scattering problems with point scatterers. We study both forward and inverse problems and propose preconditioning techniques to accelerate the iterative solvers. For the forward scattering problem, we extend the domain decompos

Qiulin Lin, Hanling Yi, John Pang, Minghua Chen

This paper studies online optimization under inventory (budget) constraints. While online optimization is a well-studied topic, versions with inventory constraints have proven difficult. We consider a formulation of inventory-constrained optimization that is a generalization of the classic one-way trading problem and has a wide range of applications. We pres

Samuel Huberman, Ryan A. Duncan, Ke Chen, Bai Song

Wavelike thermal transport in solids, referred to as second sound, has until now been an exotic phenomenon limited to a handful of materials at low temperatures. This has restricted interest in its occurrence and in its potential applications. Through time-resolved optical measurements of thermal transport on 5-20 {\mu}m length scales in graphite, we have ma

Ding Jia, Fabio Costa

In theories of communication, it is usually presumed that the involved parties perform actions in a fixed causal order. However, practical and fundamental reasons can induce uncertainties in the causal order. Here we show that a maximal uncertainty in the causal order forbids asymptotic quantum communication, while still enabling the noisy transfer of classi

David Jekel, Weihua Liu

We study $N$-ary non-commutative notions of independence, which are given by trees and which generalize free, Boolean, and monotone independence. For every rooted subtree $\mathcal{T}$ of the $N$-regular tree, we define the $\mathcal{T}$-free product of $N$ non-commutative probability spaces and we define the $\mathcal{T}$-free additive convolution of $N$ no

Roberto da Silva, Pablo Valverde, Luis C. Lamb

We consider a version of the ultimatum game which simultaneously combines reactive and Darwinian aspects with offers in [0,1]. By reactive aspects, we consider the effects that lead the player to change their offer given the previous result. On the other hand, Darwinian aspects correspond to copying a better strategy according to best game payoff when the cu

Norimichi Ukita

Human pose estimation in images and videos is one of key technologies for realizing a variety of human activity recognition tasks (e.g., human-computer interaction, gesture recognition, surveillance, and video summarization). This paper presents two types of human pose estimation methodologies; 1) 3D human pose tracking using motion priors and 2) 2D human po

Cristina White, Daniela Ushizima, Charbel Farhat

Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with suffi

Nikita Porwal, Jaivardhan Sinha, Prasanta Kumar Datta

Micromagnetic simulations studies on Permalloy (Ni80Fe20) nanodot with different shape and edge-to-edge separation (s) down to 25nm arranged in square lattice are reported. We observe the significant variation of spin-wave (SW) dynamics of nanodots of different shapes (triangular, diamond and hexagon) and of fixed dot diameter 100nm with varying s. Modes for

Diego Delle Donne, Guido Tagliavini

We consider an optimization problem posed by an actual newspaper company, which consists of computing a minimum length route for a delivery truck, such that the driver only stops at street crossings, each time delivering copies to all customers adjacent to the crossing. This can be modeled as an abstract problem that takes an unweighted simple graph $G = (V,

Blake Barker, Rafael Monteiro, Kevin Zumbrun

We study by a combination of analytical and numerical Evans function techniques multi-D viscous and inviscid stability and associated transverse bifurcation of planar slow Lax MHD shocks in a channel with periodic boundary conditions. Notably, this includes the first multi-D numerical Evans function study for viscous MHD. Our results suggest that, rather tha

Nikita Porwal, Sucheta Mondal, Samiran Choudhury, Anulekha De

Novel magnetic structures with precisely controlled dimensions and shapes at the nanoscale have potential applications in spin logic, spintronics and other spin-based communication devices. We report the fabrication of two-dimensional bi-structure magnonic crystal in the form of embedded nanodots in a periodic Ni80Fe20 antidot lattice structure (annular anti

Jon Spalding, Shan-Wen Tsai, David K Campbell

We study the ground state of the one-dimensional extended Hubbard model at half-filling using the entanglement entropy calculated by Density Matrix Renormalization Group (DMRG) techniques. We apply a novel curve fitting and scaling method to accurately identify a $2^{nd}$ order critical point as well as a Berezinskii-Kosterlitz-Thouless (BKT) critical point.

Youjun Deng, Jinhong Li, Hongyu Liu

This paper is a continuation and an extension of our recent work [13] on the identification of magnetized anomalies using geomagnetic monitoring, which aims to establish a rigorous mathematical theory for the geomagnetic detection technology. Suppose a collection of magnetized anomalies is presented in the shell of the Earth. By monitoring the variation of t

Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not well understood. In this paper, we seek a crisp, clean and precise characterization of their behavior in nonconvex settings. To this end, we first provide a novel view of adaptive methods as preconditioned SGD, where the preconditioner is estimated in an online manner. By

Georgios Kydonakis, Hao Sun, Lutian Zhao

We generalize the classical Beauville-Narasimhan-Ramanan correspondence to the case of parabolic Higgs bundles with regular singularities and Higgs $V$-bundles. Using this correspondence along with Bott-Morse theoretic techniques we provide an exact component count for moduli spaces of maximal parabolic $\text{Sp}\left( 2n,\mathbb{R} \right)$-Higgs bundles w

Chang-Wei Wu, Dao-Xin Yao

Here, we study the magnetism of the distorted 1T-TcX2 (X=S,Se) based on first-principles calculation. The magnetism originates from the hole doping due to the density of states near the valence band edge having van Hove singularity feature. The calculated results show that the TcS2 monolayer can develop an interesting ferromagnetic (FM) half-metallic phase w

Jaeyoung Kim, Mostafa El-Khamy, Jungwon Lee

Supervised learning based on a deep neural network recently has achieved substantial improvement on speech enhancement. Denoising networks learn mapping from noisy speech to clean one directly, or to a spectrum mask which is the ratio between clean and noisy spectra. In either case, the network is optimized by minimizing mean square error (MSE) between groun

Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar

This work is devoted to the study of modeling geophysical and financial time series. A class of volatility models with time-varying parameters is presented to forecast the volatility of time series in a stationary environment. The modeling of stationary time series with consistent properties facilitates prediction with much certainty. Using the GARCH and sto

Z. L. Tu, J. Hu, F. Y. Wang

Recently, some divergent conclusions about cosmic acceleration were obtained using type Ia supernovae (SNe Ia), with opposite assumptions on the intrinsic luminosity evolution. In this paper, we use strong gravitational lensing systems to probe the cosmic acceleration. Since the theory of strong gravitational lensing is established certainly, and the Einstei

Leonardo Felizardo, Afonso Pinto

In this paper we present a statistical analysis about the characteristics that we intend to influence in the performance of the neural networks in terms of assertiveness in the prediction of Brazilian stock returns. We created a population of architectures for analysis and extracted the sample that had the best assertive performance. It was verified how the

Han-Jie Tao, Yan-Jun Sun, Song-Pei Guo, Wei Hong

We calculate the form factor and cross section of the excited charmonium production process $e^{+}+e^{-}\rightarrow \psi \left ( 2S \right )+\eta _{c}$ by light-cone sum rules. In our method, the form factor depends on the distribution amplitude of $\eta _{c}$ meson. Experimentally, the energy scale of $e^{+}+e^{-}\rightarrow \psi \left ( 2S \right )+\eta _{

T. Hanaguri, S. Kasahara, J. Böker, I. Eremin

FeSe is argued as a superconductor in the Bardeen-Cooper-Schrieffer Bose-Einstein-condensation crossover regime where the superconducting-gap size and the superconducting transition temperature Tc are comparable to the Fermi energy. In this regime, vortex bound states should be well quantized and the preformed pairs above Tc may yield a pseudogap in the quas

Tao Hu, Honggang Qi, Qingming Huang, Yan Lu

Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propose Weakly Supervised Data Augmentation Ne

Chen Jia, Da-Quan Jiang, Youming Li

Detailed balance of a chemical reaction network can be defined in several different ways. Here we investigate the relationship among four types of detailed balance conditions: deterministic, stochastic, local, and zero-order local detailed balance. We show that the four types of detailed balance are equivalent when different reactions lead to different speci

R. P. Ashley, J. Farihi, T. R. Marsh, D. J. Wilson

We present the results of a radial velocity survey of 20 white dwarf plus M dwarf binaries selected as a follow up to a \textit{Hubble Space Telescope} study that aimed to spatially resolve suspected binaries. Our candidates are taken from the list of targets that were spatially unresolved with \textit{Hubble}. We have determined the orbital periods for 16 o

Yu Cheng, Mo Yu, Xiaoxiao Guo, Bowen Zhou

Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weights of homogeneous-structured task-specifi

Zhiqiang Tan

Consider a logistic partially linear model, in which the logit of the mean of a binary response is related to a linear function of some covariates and a nonparametric function of other covariates. We derive simple, doubly robust estimators of coefficient for the covariates in the linear component of the partially linear model. Such estimators remain consiste

Darren Flynn, Khodr Shamseddine

In this paper, we study the topological structure of the Hahn field whose elements are functions from the additive abelian group of rational numbers to the real numbers field, with well-ordered support. After reviewing the algebraic and order structures of the Hahn field, we introduce different vector topologies that are induced by families of semi-norms and

Ryan McKenna, Daniel Sheldon, Gerome Miklau

Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensi

Zhong Qiu Lin, Alexander Wong

Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little research on how the concept of distillation can be leveraged to distill the knowledge encapsulated in the training data itself into a reduced form. In this study, we explore the co

Nino Arsov, Martin Pavlovski, Ljupco Kocarev

Stacking is a general approach for combining multiple models toward greater predictive accuracy. It has found various application across different domains, ensuing from its meta-learning nature. Our understanding, nevertheless, on how and why stacking works remains intuitive and lacking in theoretical insight. In this paper, we use the stability of learning

Zhao-Yun Chen, Cheng Xue, Si-Ming Chen, Guo-Ping Guo

Deep learning is a modern approach to realize artificial intelligence. Many frameworks exist to implement the machine learning task; however, performance is limited by computing resources. Using a quantum computer to accelerate training is a promising approach. The variational quantum circuit (VQC) has gained a great deal of attention because it can be run o

Anubhav Dwivedi, Nathaniel Hildebrand, Joseph W. Nichols, Graham V. Candler

We study physical mechanisms that trigger transient growth in a high-speed spatially-developing laminar boundary layer that interacts with an oblique shock wave. We utilize an approach based on power-iteration, with the global forward and adjoint linearized equations, to quantify the transient growth in compressible boundary layers with flow separation. For

Yi-Fan Jiang, Thomas P. Devereaux, Hong-Chen Jiang

Recently considerable excitement has arisen due to the experimental observation of a field-induced spin liquid phase in the compound $\alpha$-RuCl$_3$. However, the nature of this putative spin liquid phase and the relevant microscopic model Hamiltonian remain still unclear. In this work, we address these questions by performing large-scale numerical simulat

Gulden Othman

The Majorana Demonstrator is currently searching for neutrinoless double-beta decay in $^{76}$Ge and will demonstrate the feasibility to deploy a tonne-scale experiment in a phased and modular fashion. It consists of two modular arrays of natural and $^{76}$Ge-enriched germanium detectors totaling 44.1 kg, of which 29.7 kg is enriched, located at the 4850' l

Xuan Li, Miao Jin

Wireless rechargeable sensor networks, consisting of sensor nodes with rechargeable batteries and mobile chargers to replenish their batteries, have gradually become a promising solution to the bottleneck of energy limitation that hinders the wide deployment of wireless sensor networks (WSN). In this paper, we focus on the mobile charger scheduling and path

Ming-Wei Chang, Kristina Toutanova, Kenton Lee, Jacob Devlin

Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentation, and sentiment analysis. However, effective usage of such a large context can be difficult to learn, especially in the case where there is limited labeled data available. Buildi

Yuliya Lierler

Answer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing different knowledge representation formalisms. Frequently, several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different perfor

Garrett Beatty, Ethan Kochis, Michael Bloodgood

Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. Three potential sources for informing when to stop active learning are an addi

Marc Denecker, Yuliya Lierler, Miroslaw truszczynski, Joost Vennekens

In Knowledge Representation, it is crucial that knowledge engineers have a good understanding of the formal expressions that they write. What formal expressions state intuitively about the domain of discourse is studied in the theory of the informal semantics of a logic. In this paper we study the informal semantics of Answer Set Programming. The roots of an