Magpie is a deep learning tool for multi-label text classification. It learns on the training corpus to assign labels to arbitrary text and can be used to predict those labels on unknown data. It has been developed at CERN to assign subject categories to High Energy Physics abstracts and extract keywords from them.

This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have emerged in academic literature. Based on a review of published literature technical aspects of heat pump flexibility, fields of application and control approaches are structured and discussed. Three main categories of applications using heat pumps in a smart grid context have been identified: First stable and economic operation of power grids, second the integration of renewable energy sources and third operation under variable electricity prices. In all fields heat pumps - when controlled in an appropriate manner - can help easing the transition to a decentralized energy system accompanied by a higher share of prosumers and renewable energy sources. Predictive controls are successfully used in the majority of studies, often assuming idealized conditions. Topics for future research have been identified including: a transfer of control approaches from simulation to the field, a detailed techno-economic analysis of heat pump systems under smart grid operation, and the design of heat pump systems in order to increase flexibility are among the future research topics suggested.

In this paper, we consider multi-pursuer single-superior-evader pursuit-evasion differential games where the evader has a speed that is similar to or higher than the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work. The proposed algorithm uses the well-known Apollonius circle mechanism to define the capture region of the learning pursuer based on its location and the location of the superior evader. The proposed algorithm uses the Apollonius circle with a developed formation control approach in the tuning mechanism of the fuzzy logic controller (FLC) of the learning pursuer so that one or some of the learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism used by the proposed algorithm also makes the Apollonius circles of each two adjacent pursuers intersect or be at least tangent to each other so that the capture of the superior evader can occur. The proposed algorithm is a decentralized algorithm as no communication among the pursuers is required. The only information the proposed algorithm requires is the position and the speed of the superior evader. The proposed algorithm is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games. The simulation results show the effectiveness of the proposed algorithm.

Tired of rejections? Cath Chapman and Tim Slade offer a simple way to fight back

All academics aim to publish in high impact journals. However, many leading scientific and medical journals reject more than 80% of the manuscripts they receive, making rejection the biggest barrier to publication in high quality journals. We propose a novel solution to this problem. It involves very little extra work by submitting authors, is applicable to a wide range of circumstances (such as flawed study, lack of broad interest to the field, or highly critical assessors), and is scalable to meet the needs of academics from various disciplines. To be submitted on receipt of a manuscript rejection, the rejection of rejection letter (box) aims to significantly improve the publication rates of participating academics by overcoming the leading barrier to publication—manuscript rejection. An electronic copy of the letter is available from the authors on request.

Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.

This post is to announce the start of a new mathematics journal, to be called Discrete Analysis. While in most respects it will be just like any other journal, it will be unusual in one important way: it will be purely an arXiv overlay journal. That is, rather than publishing, or even electronically hosting, papers, it will consist of a list of links to arXiv preprints. Other than that, the journal will be entirely conventional: authors will submit links to arXiv preprints, and then the editors of the journal will find referees, using their quick opinions and more detailed reports in the usual way in order to decide which papers will be accepted.

Part of the motivation for starting the journal is, of course, to challenge existing models of academic publishing and to contribute in a small way to creating an alternative and much cheaper system. However, I hope that in due course people will get used to this publication model, at which point the fact that Discrete Analysis is an arXiv overlay journal will no longer seem interesting or novel, and the main interest in the journal will be the mathematics it contains.

The LOCKSS Program, based at Stanford University Libraries, provides libraries and publishers with award-winning, low-cost, open source digital preservation tools to preserve and provide access to persistent and authoritative digital content.