The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The application of soft computing methods for MPPT of PV system: A technological and status review

With the availability of powerful and low cost computing power, maximum power point tracking (MPPT) that utilizes soft computing (SC) techniques are attracting substantial interests from the PV communities. Due to their flexibility and ability to handle non-linear problems, robust SC-based MPPT schemes can be developed. Furthermore, the adaptive in nature SC algorithms is suitable in handling adverse environmental conditions such as partial shading and rapid changes in irradiance. To date, there are several works on MPPT using SC from which we select approximately 45 published works that are directly related to MPPT. However, information on these methods are scattered and there appears to be an absence for a comprehensive review paper on this topic. This work summarizes the current technology and status of SC MPPT as reported in various literature. It also provides an evaluation on the performance of various SC methods based on several criteria, namely PV array dependency, convergence time, ability to handle partial shading conditions, algorithm complexity and hardware/practical implementation. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for researchers, as well as providing a direction for future research in this area

NETKIT: a software component-based approach to programmable networking

While there has already been significant research in support of openness and programmability in networks, this paper argues that there remains a need for generic support for the integrated development, deployment and management of programmable networking software. We further argue that this support should explicitly address the management of run-time reconfiguration of systems, and should be independent of any particular programming paradigm (e.g. active networking or open signaling), programming language, or hardware/ operating system platform. In line with these aims, we outline an approach to the structuring of programmable networking software in terms of a ubiquitously applied software component model that can accommodate all levels of a programmable networking system from low-level system support, to in-band packet handling, to active networking execution environments to signaling and coordination

A generalized maximum entropy approach to bregman co-clustering and matrix approximation

Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic co-clustering approach applicable to empirical joint probability distributions was proposed. In many situations, co-clustering of more general matrices is desired. In this paper, we present a substantially generalized co-clustering framework wherein any Bregman divergence can be used in the objective function, and various conditional expectation based constraints can be considered based on the statistics that need to be preserved. Analysis of the coclustering problem leads to the minimum Bregman information principle, which generalizes the maximum entropy principle, and yields an elegant meta algorithm that is guaranteed to achieve local optimality. Our methodology yields new algorithms and also encompasses several previously known clustering and co-clustering algorithms based on alternate minimization