Emergence of Superlattice Dirac Points in Graphene on Hexagonal Boron Nitride

The Schr\"odinger equation dictates that the propagation of nearly free electrons through a weak periodic potential results in the opening of band gaps near points of the reciprocal lattice known as Brillouin zone boundaries. However, in the case of massless Dirac fermions, it has been predicted that the chirality of the charge carriers prevents the opening of a band gap and instead new Dirac points appear in the electronic structure of the material. Graphene on hexagonal boron nitride (hBN) exhibits a rotation dependent Moir\'e pattern. In this letter, we show experimentally and theoretically that this Moir\'e pattern acts as a weak periodic potential and thereby leads to the emergence of a new set of Dirac points at an energy determined by its wavelength. The new massless Dirac fermions generated at these superlattice Dirac points are characterized by a significantly reduced Fermi velocity. The local density of states near these Dirac cones exhibits hexagonal modulations indicating an anisotropic Fermi velocity.Comment: 16 pages, 6 figure

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.Comment: Published in Foundations and Trends in Machine Learning Vol 4 Issue 1. See: https://www.nowpublishers.com/article/Details/MAL-08

Large and Small Photovoltaic Powerplants

The installed base of photovoltaic power plants in the United States has roughly doubled every 1 to 2 years between 2008 and 2015. The primary economic drivers of this are government mandates for renewable power, falling prices for all PV system components, 3rd party ownership models, and a generous tariff scheme known as net-metering. Other drivers include a desire for decreasing the environmental impact of electricity generation and a desire for some degree of independence from the local electric utility. The result is that in coming years, PV power will move from being a minor niche to a mainstream source of energy. As additional PV power comes online this will create challenges for the electric grid operators. We examine some problems related to large scale adoption of PV power in the United States. We do this by first discussing questions of reliability and efficiency at the PV system level. We measure the output of a fleet of small PV systems installed at Tucson Electric Power, and we characterize the degradation of those PV systems over several years. We develop methods to predict energy output from PV systems and quantify the impact of negatives such as partial shading, inverter inefficiency and malfunction of bypass diodes. Later we characterize the variability from large PV systems, including fleets of geographically diverse utility scale power plants. We also consider the power and energy requirements needed to smooth those systems, both from the perspective of an individual system and as a fleet. Finally we report on experiments from a utility scale PV plus battery hybrid system deployed near Tucson, Arizona where we characterize the ability of this system to produce smoothly ramping power as well as production of ancillary energy services such as frequency response

Intifada II: The long trail of Arab anti-semitism

A method to report PV system degradation rates without using irradiance data is demonstrated. First, a set of relative degradation rates are determined by comparing daily AC final yields from a group of PV systems relative to the average final yield of all the PV systems. Then, the difference between relative and absolute degradation rates is found from a statistical analysis. This approach is verified by comparing to methods that utilize irradiance data. This approach is significant because PV systems are often deployed without irradiance sensors, so the analysis method described here may enable measurements of degradation using data that were previously thought to be unsuitable for degradation studies