For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and potentially more flexible option would be grid topology reconfiguration, as already partially exploited by Coreso (European RSC) and RTE (French TSO) operations. Beyond previous work on branch switching, bus reconfigurations are a broader class of action and could provide some substantial benefits to route electricity and optimize the grid capacity to keep it within safety margins. Because of its non-linear and combinatorial nature, no existing optimal power flow solver can yet tackle this problem. We here propose a new framework to learn topology controllers through imitation and reinforcement learning. We present the design and the results of the first "Learning to Run a Power Network" challenge released with this framework. We finally develop a method providing performance upper-bounds (oracle), which highlights remaining unsolved challenges and suggests future directions of improvement.

Prototype of the Force 12 Xplorer being tested near Victoria, British Columbia. It uses a rigid ... [ ] wingsail for propulsion. It's been a great year for Open Ocean Robotics, a British Columbia-based startup that makes solar-powered drones that can gather environmental data in real time and help address a multitude of issues. During 2019, Open Ocean Robotics won a most-promising startup award from the National Community for Angels, Incubators, and Accelerators; $100,000 in a Spring Impact Investor Challenge; and was a finalist in a New Ventures BC Competition, to name a few. So how do you follow that up for 2020?

Machine learning (ML) is shaping the cyberworld and how users interact with organizations. It has made incredible impacts on the anti-fraud industry and strengthened present security solutions. Learn what Cyxtera's machine learning experts are seeing now and predicting for the future of machine learning – and how the technology can be leveraged by both attackers and security teams. Adversarial machine learning is a technique in which algorithms are fed malicious input in an attempt to fool them into making analysis mistakes. Criminals looking to exploit algorithms can already use this technique to a limited extent, and Cyxtera's experts foresee adversarial machine learning being increasingly leveraged by criminals, making it more prevalent in the future of fraud attacks.

Deep learning has met increasing hype in the last few years, and with lots of practical success. But does that necessarily indicate an exponential growth in AI over the next few years? "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run" In order to understand what's to come, we need to figure out where we are exactly in the development of not only deep learning, but AI in general. The Gartner hype curve gives some perspective about the adoption of a technology in the enterprise over a five- to 10-year perspective. One way to look at AI is to stop considering it as an invention where we endlessly invent new techniques, but instead view it as more are a discovery where mankind is slowly (and quite empirically) discovering, step by step, the behaviour of self-learning systems.

There is no longer any credible reason to deny our part in the climate crisis. We are now facing the destruction of vital ecosystems, and every year 12.6 million people die because of environmental pollution. Cutting edge smart city technologies may be our most useful weapon in the fight against the climate crisis, helping us to reduce our impact on the planet in future, and alleviate the damage we have already done. Part two of this series will focus on how smart cities can help us tackle the looming climate crisis, and which technologies will be used to ensure cities continue to be sustainable as our planet and population dramatically change. Once we have planned out cities that are adaptable and better suited to our needs, we can start implementing smart technologies to overhaul unsustainable utilities, transport and energy systems.

The steel industry has great impacts on the economy and the environment of both developed and underdeveloped countries. The importance of this industry and these impacts have led many researchers to investigate the relationship between a country's steel consumption and its economic activity resulting in the so-called intensity of use model. This paper investigates the validity of the intensity of use model for the case of Iran's steel consumption and extends this hypothesis by using the indexes of economic activity to model the steel consumption. We use the proposed model to train support vector machines and predict the future values for Iran's steel consumption. The paper provides detailed correlation tests for the factors used in the model to check for their relationships with the steel consumption. The results indicate that Iran's steel consumption is strongly correlated with its economic activity following the same pattern as the economy has been in the last four decades.

Massive amounts of satellite data have been gathered over time, holding the potential to unveil a spatiotemporal chronicle of the surface of Earth. These data allow scientists to investigate various important issues, such as land use changes, on a global scale. However, not all land-use phenomena are equally visible on satellite imagery. In particular, the creation of an inventory of the planet's road infrastructure remains a challenge, despite being crucial to analyze urbanization patterns and their impact. Towards this end, this work advances data-driven approaches for the automatic identification of roads based on open satellite data. Given the typical resolutions of these historical satellite data, we observe that there is inherent variation in the visibility of different road types. Based on this observation, we propose two deep learning frameworks that extend state-of-the-art deep learning methods by formalizing road detection as an ordinal classification task. In contrast to related schemes, one of the two models also resorts to satellite time series data that are potentially affected by missing data and cloud occlusion. Taking these time series data into account eliminates the need to manually curate datasets of high-quality image tiles, substantially simplifying the application of such models on a global scale. We evaluate our approaches on a dataset that is based on Sentinel~2 satellite imagery and OpenStreetMap vector data. Our results indicate that the proposed models can successfully identify large and medium-sized roads. We also discuss opportunities and challenges related to the detection of roads and other infrastructure on a global scale.

To overcome the energy and bandwidth limitations of traditional IoT systems, edge computing or information extraction at the sensor node has become popular. However, now it is important to create very low energy information extraction or pattern recognition systems. In this paper, we present an approximate computing method to reduce the computation energy of a specific type of IoT system used for anomaly detection (e.g. in predictive maintenance, epileptic seizure detection, etc). Termed as Anomaly Detection Based Power Savings (ADEPOS), our proposed method uses low precision computing and low complexity neural networks at the beginning when it is easy to distinguish healthy data. However, on the detection of anomalies, the complexity of the network and computing precision are adaptively increased for accurate predictions. We show that ensemble approaches are well suited for adaptively changing network size. To validate our proposed scheme, a chip has been fabricated in UMC65nm process that includes an MSP430 microprocessor along with an on-chip switching mode DC-DC converter for dynamic voltage and frequency scaling. Using NASA bearing dataset for machine health monitoring, we show that using ADEPOS we can achieve 8.95X saving of energy along the lifetime without losing any detection accuracy. The energy savings are obtained by reducing the execution time of the neural network on the microprocessor.

Event ticket price prediction is important to marketing strategy for any sports team or musical ensemble. An accurate prediction model can help the marketing team to make promotion plan more effectively and efficiently. However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.), but also because of the temporal and spatial sparsity in the dataset. For a game/concert, the ticket selling price of one seat is only observable once at the time of sale. Furthermore, some seats may not even be purchased (therefore no record available). In fact, data sparsity is commonly encountered in many prediction problems. Here, we propose a bi-level optimizing deep neural network to address the curse of spatio-temporal sparsity. Specifically, we introduce coarsening and refining layers, and design a bi-level loss function to integrate different level of loss for better prediction accuracy. Our model can discover the interrelations among ticket sale price, seat locations, selling time, event information, etc. Experiments show that our proposed model outperforms other benchmark methods in real-world ticket selling price prediction.

A panel of judges at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19) on Thursday awarded a multi-institutional team led by Lawrence Livermore National Laboratory computer scientists with the conference's Best Paper award. The paper, entitled "Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer," describes the workflow driving a first-of-its-kind multiscale simulation on predictively modeling the dynamics of RAS proteins -- a family of proteins whose mutations are linked to more than 30 percent of all human cancers -- and their interactions with lipids, the organic compounds that help make up cell membranes. Developed as part of the Pilot 2 project in the Joint Design of Advanced Computing for Cancer program, a collaboration between the Department of Energy (DOE) and National Cancer Institute (NCI), the research resulted in a Multiscale Machine-Learned Modeling Infrastructure (MuMMI) that investigators found was scalable to next-generation heterogenous supercomputers such as LLNL's Sierra and Oak Ridge's Summit. Working for more than two years on the pilot project, which is funded by the National Nuclear Security Administration's Advanced Simulation and Computing program, the multidisciplinary team, composed of more than 20 computational scientists, biophysicists, chemists and statisticians from LLNL, Los Alamos National Laboratory, NCI/Frederick National Laboratory for Cancer Research, Oak Ridge National Laboratory (ORNL) and IBM, ran nearly 120,000 simulations on Sierra, using 5.6 million GPU hours of compute time and generating a massive 320 terabytes of data. "I can't begin to describe how happy I am for our team -- it's been a lot of hard work, and to have it recognized at this level is just amazing," said Francesco Di Natale, LLNL computer scientist and the paper's lead author.