Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully accelerates simulations by up to 2 billion times in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of $n$ are tested, with $n > 6$ being exceedingly rare. Larger values of $n$ are not tested due to computational burden or the fear of overfitting.

In this work, we present a method to find the top-$k$ most frequent $n$-grams that is 60$\times$ faster for small $n$, and can tackle large $n\geq1024$. Despite the unprecedented size of $n$ considered, we show how these features still have predictive ability for malware classification tasks. More important, large $n$-grams provide benefits in producing features that are interpretable by malware analysis, and can be used to create general purpose signatures compatible with industry standard tools like Yara. Furthermore, the counts of common $n$-grams in a file may be added as features to publicly available human-engineered features that rival efficacy of professionally-developed features when used to train gradient-boosted decision tree models on the EMBER dataset.

Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in general, it has, as a result, become difficult to keep track of what represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications point out problems in today's research practice in applied machine learning, e.g., in terms of the reproducibility of the results or the choice of the baselines when proposing new models.

In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically, we considered 18 algorithms that were presented at top-level research conferences in the last years. Only 7 of them could be reproduced with reasonable effort. For these methods, it however turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques. The remaining one clearly outperformed the baselines but did not consistently outperform a well-tuned non-neural linear ranking method.

Overall, our work sheds light on a number of potential problems in today's machine learning scholarship and calls for improved scientific practices in this area. Source code of our experiments and full results are available at: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation.

We present HotStuff, a leader-based Byzantine fault-tolerant replication protocol for the partially synchronous model.

Once network communication becomes synchronous, HotStuff enables a correct leader to drive the protocol to consensus at the pace of actual (vs. maximum) network delay--a property called responsiveness--and with communication complexity that is linear in the number of replicas. To our knowledge, HotStuff is the first partially synchronous BFT replication protocol exhibiting these combined properties. HotStuff is built around a novel framework that forms a bridge between classical BFT foundations and blockchains. It allows the expression of other known protocols (DLS, PBFT, Tendermint, Casper), and ours, in a common framework.

Our deployment of HotStuff over a network with over 100 replicas achieves throughput and latency comparable to that of BFT-SMaRt, while enjoying linear communication footprint during leader failover (vs. quadratic with BFT-SMaRt).

IoT is considered as one of the key enabling technologies for the fourth industrial revolution, that is known as Industry 4.0. In this paper, we consider the mechatronic component as the lowest level in the system composition hierarchy that tightly integrates mechanics with the electronics and software required to convert the mechanics to intelligent (smart) object offering well defined services to its environment. For this mechatronic component to be integrated in the IoT- based industrial automation environment, a software layer is required on top of it to convert its conventional interface to an IoT compliant one. This layer, that we call IoTwrapper, transforms the conventional mechatronic component to an Industrial Automation Thing (IAT). The IAT is the key element of an IoT model specifically developed in the context of this work for the manufacturing domain. The model is compared to existing IoT models and its main differences are discussed. A model-to-model transformer is presented to automatically transform the legacy mechatronic component to an IAT ready to be integrated in the IoT-based industrial automation environment. The UML4IoT profile is used in the form of a Domain Specific Modeling Language to automate this transformation. A prototype implementation of an In dustrial Automation Thing using C and the Contiki operating system demonstrates the effectiveness of the proposed approach.

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 Hague Declaration aims to foster agreement about how to best enable access to facts, data and ideas for knowledge discovery in the Digital Age. By removing barriers to accessing and analysing the wealth of data produced by society, we can find answers to great challenges such as climate change, depleting natural resources and globalisation.

@ARTICLE{7160638,
author={Shi, X. and Li, Y. and Cao, Y. and Tan, Y.},
journal={Power and Energy Systems, CSEE Journal of},
title={Cyber-physical electrical energy systems: challenges and issues},
year={2015},
month={June},
volume={1},
number={2},
pages={36-42},
abstract={Cyber-physical electrical energy systems (CPEES) combine computation, communication and control technologies with physical power system, and realize the efficient fusion of power, information and control. This paper summarizes and analyzes related critical scientific problems and technologies, which are needed to be addressed with the development of CPEES. Firstly, since the co-simulation is an effective method to investigate infrastructure interdependencies, the co-simulation platform establishment of CPEES and its evaluation is overviewed. Then, a critical problem of CPEES is the interaction between energy and information flow, especially the influence of failures happening in information communication technology (ICT) on power system. In order to figure it out, the interaction is analyzed and the current analysis methods are summarized. For the solution of power system control and protection in information network environment, this paper outlines different control principles and illustrates the concept of distributed coordination control. Moreover, mass data processing and cluster analysis, architecture of communication network, information transmission technology and security of CPEES are summarized and analyzed. By solving the above problems and technologies, the development of CPEES will be significantly promoted.},
keywords={Cyber-physical electrical energy systems;informationcommunication technology;power system;smart grid},
doi={10.17775/CSEEJPES.2015.00017},}

Coordinated Electric Vehicle Charging Control with Aggregator Power Trading and Indirect Load Control
James J.Q. Yu, Junhao Lin, Albert Y.S. Lam, Victor O.K. Li
(Submitted on 4 Aug 2015)

Due to the increasing concern on greenhouse gas emmissions and fossil fuel security, Electric Vehicles (EVs) have attracted much attention in recent years. However, the increasing popularity of EVs may cause stability issues to the power grid if their charging behaviors are uncoordinated. In order to address this problem, we propose a novel coordinated strategy for large-scale EV charging. We formulate the energy trade among aggregators with locational marginal pricing to maximize the aggregator profits and to indirectly control the loads to reduce power network congestion. We first develop a centralized iterative charging strategy, and then present a distributed optimization-based heuristic to overcome the high computational complexity and user privacy issues. To evaluate our proposed approach, a modified IEEE 118 bus testing system is employed with 10 aggregators serving 30 000 EVs. The simulation results indicate that our proposed approach can effectively increase the total profit of aggregators, and enhance the power grid stability. 

Subjects: Systems and Control (cs.SY)