Surface-plasmon-enhanced near-field radiative heat transfer between planar surfaces with a thin-film plasmonic coupler. (arXiv:1911.10315v1 [physics.app-ph])

In last decade, there have been enormous efforts to experimentally show the near-field enhancement of radiative heat transfer between planar structures. Several recent experiments also have striven to achieve further enhanced heat transfer with the excitation of coupled surface polaritons by introducing nanostructures on both emitter and the receiver; however, these symmetric structures are hardly…

Opportunities and Outcomes for Postdocs in Canada. (arXiv:1911.10320v1 [astro-ph.IM])

Currently, postdoctoral fellow (PDF) researchers in Canada face challenges due to the precarious nature of their employment and their overall low compensation and benefits coverage. This report presents three themes, written as statements of need, to support an inclusive and thriving PDF community. These themes are the need for better terms of employment and conditions,…

Real-time Terahertz Wave Channeling via Multifunctional Metagratings: A Sparse Array of All-Graphene Scatterers. (arXiv:1911.10323v1 [physics.app-ph])

Acquiring full control over a large number of diffraction orders can be strongly attractive in the case of realizing multifunctional devices such as multichannel reflectors. Recently, the concept of metagrating has been introduced which enables obtaining the desired diffraction pattern through a sparse periodic array of engineered scatterers. In this letter, for the first time,…

Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an all-in-line system in a single-shot. (arXiv:1911.10326v1 [physics.optics])

We propose a simple all-in-line single-shot scheme for diagnostics of ultrashort laser pulses, consisting of a multi-mode fiber, a nonlinear crystal and a CCD camera. The system records a 2D spatial intensity pattern, from which the pulse shape (amplitude and phase) are recovered, through a fast Deep Learning algorithm. We explore this scheme in simulations…

Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values. (arXiv:1911.10273v1 [cs.LG])

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted…

Training Modern Deep Neural Networks for Memory-Fault Robustness. (arXiv:1911.10287v1 [cs.LG])

Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging. In this paper, we investigate the solution of reducing the supply voltage of the memories used in the system, which results in bit-cell faults. We explore the robustness of state-of-the-art DNN architectures towards such…

Many-body calculations for periodic materials via quantum machine learning. (arXiv:1911.10330v1 [physics.comp-ph])

A state-of-the-art method that combines a quantum computational algorithm and machine learning, so-called quantum machine learning, can be a powerful approach for solving quantum many-body problems. However, the research scope in the field was mainly limited to organic molecules and simple lattice models. Here, we propose a workflow of quantum machine learning applications for periodic…

The generalised buoyancy/inertial forces and available energy of axisymmetric compressible stratified vortex motions. (arXiv:1911.10333v1 [physics.flu-dyn])

Adiabatic and inviscid axisymmetric perturbations to a stable reference vortex in gradient wind balance gives rise to two kinds of restoring forces: a generalised buoyancy force aligned with the reference pressure gradient, proportional to the perturbation density, and a radial inertial/centrifugal force proportional to the squared angular momentum perturbation. In this paper, it is shown…

A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution. (arXiv:1911.10293v1 [cs.LG])

As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse…

Oscillator Circuit for Spike Neural Network with Sigmoid Like Activation Function and Firing Rate Coding. (arXiv:1911.10351v1 [physics.app-ph])

The study presents an oscillator circuit for a spike neural network with the possibility of firing rate coding and sigmoid-like activation function. The circuit contains a switching element with an S-shaped current-voltage characteristic and two capacitors; one of the capacitors is shunted by a control resistor. The circuit is characterised by a strong dependence of…