The goal of this challenge is to test the potential of Reinforcement Learning (RL) to control electrical power transmission, in the most cost-effective manner, while keeping people and equipment safe from harm. Solving this challenge may have very positive impacts on society, as governments move to decarbonize the electricity sector and to electrify other sectors, to help reach IPCC climate goals. Existing software, computational methods and optimal powerflow solvers are not adequate for real-time network operations on short temporal horizons in a reasonable computational time. With recent changes in electricity generation and consumption patterns, system operation is moving to become more of a stochastic rather than a deterministic control problem. In order to overcome these complexities, new computational methods are required. The intention of this challenge is to explore RL as a solution method for electricity network control. There may be under-utilized, cost-effective flexibility in the power network that RL techniques can identify and capitalize on, that human operators and traditional solution techniques are unaware of or unaccustomed to. An RL agent that can act in conjunction, or in parallel with human network operators, will optimize grid security and reliability, allowing more renewable resources to be connected while minimizing the cost and maintaining supply to customers, and preventing damage to electrical equipment. Another aim of the project is to broaden the audience for the problem of electricity network control and to foster collaboration between experts in both the power systems community and the wider RL/ML community.

We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture, and we then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions. We show how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and $5^{th}$ percentile user rates throughout a range of network configurations.

TOKYO/SAN, FRANCISCO – Tesla Inc.'s stock surged 20 percent on Monday in its largest one-day gain since 2013, fueled by a quarterly profit at Panasonic's battery business with the U.S. carmaker and an investor report predicting its shares would rise more than ten-fold by 2024. Shares of Tesla have rallied by over 30 percent since the car maker run by Chief Executive Elon Musk posted its second consecutive quarterly profit last Wednesday, which was viewed as a milestone for the company competing against established heavyweights including General Motors Co. and BMW. The stock is up over 300 percent since early June, helped by Tesla's better-than-expected financial results and ramped up production at its new car factory in Shanghai. Monday's rise came after Panasonic Corp. reported the first quarterly profit in its U.S. battery business with Tesla, which followed years of production troubles and delays. "We are catching up as Tesla is quickly expanding production," Panasonic Chief Financial Officer Hirokazu Umeda told an earnings briefing, referring to battery cell production. "Higher production volume is helping to push down materials costs and erase losses."

GreenWaves Technologies, a fabless semiconductor vendor focused on ultra-low power edge-based AI processing, recently announced a new member of its GAP IoT application processor family, the GAP9. This latest member combines architectural enhancements using Global Foundries 22-nm FDX process to deliver a peak cluster memory bandwidth of 41.6 Gbytes/s and up to 50 GOPS combined compute power at an overall power consumption of 50 mW. GAP9 lets OEMs embed machine learning and signal processing capabilities into battery-powered or energy-harvesting devices.

People like to call millennials "lazy" when in fact we're just a bunch of tech-savvy innovators who increasingly show that you don't have to do everything the same way your parents or grandparents did. Case in point: You don't have to have cable. You don't actually have to call people on the phone, ever. And splitting monthly bills with strangers can actually be normal. Adults born in the 1980s and early 1990s have practically become experts at finding alternatives to everyday tasks so they can get more done with less physical exertion.

It is the first boat to be showcased at CES in Las Vegas, but the state-of-the-art Sea Ray SLX-R 400e is far from a traditional water vessel. The 40-foot yacht is equip with auto-docking capabilities and allows passengers to communicate with it using gestures and voice commands through a new'Future Helm'. It seats 22 people and comes with a lithium battery pack that can power the craft's high-performance engines in order to save energy. Steve Langlais, Sea Ray president, said: 'CES presents a unique opportunity to debut the new SLX-R 400e in front of an audience that will truly appreciate its beauty, capabilities and incredible suite of new technologies.' 'This pioneering new model, which will be available in 2020, showcases the kind of unique, advanced technologies that are worthy of the world's largest consumer electronics show.'

DOE/Argonne National Laboratory researchers have turned to the power of machine learning and artificial intelligence to dramatically accelerate battery discovery. The press release likens designing new batteries with the best molecular building blocks for battery components to trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together – or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material. As described in two new papers, the Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.

Rest of Europe 8.4 Asia Pacific 8.4.1

Focus is vital to thrive in any career, and data science is no different. Since being a proficient data scientist requires various skills, developers get perplexed and fail to concentrate on the core of the data science. To understand effective ways for flourishing in data science landscape, we interviewed Arihant Jain for our weekly column My Journey In Data Science. Jain is a Staff Data Scientist at General Electric. He has 5 years of experience in the data domain while working at Genpact, RBL Bank, Vodafone, and GE. Jain is a mechanical engineer-turned-data scientist by choice.

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power gri ds, and transportation systems. Similar to other information systems, a significant threat to indust rial control systems is the attack from cyberspace--the offensive maneuvers launched by "anon ymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusio n detection systems that serve as the first line of defense by monitoring and reporting potenti ally malicious activities. Classical machine-learning-based intrusion detection methods usua lly generate prediction models by learning modest-sized training samples all at once. Such approac h is not always applicable to industrial control systems, as industrial control systems must proces s continuous control commands with limited computational resources in a nonstop way. To satisf y such requirements, we propose using online learning to learn prediction models from the control ling data stream. W e introduce several state-of-the-art online learning algorithms categorical ly, and illustrate their efficacies on two typically used testbeds--power system and gas pipeline. Fur ther, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance pro blem that is pervasive in industrial intrusion detection systems. Our experimental results ind icate that the proposed algorithm can achieve an overall improvement in the detection rate of cybe rattacks in industrial control systems. Modern industrial control systems are microprocessor-equ ipped devices and associated communication networks used to monitor and operate physica l equipment in the industrial environment.