Hanghang Tong wants to help people as they go about their daily lives, do their jobs, interact with infrastructure and conduct research. Yet most of the people Tong's work benefits may never know it. Tong applies his expertise -- large-scale data mining and machine learning -- to research networks, including those involved in online social interactions, electrical power grids, infrastructure and transportation. On a smaller-scale example of networks, Tong focuses on graphs, which are people or other nodes that are linked together in varied and complex ways. Hanghang Tong, an assistant professor of computer science in Arizona State University's Ira A. Fulton Schools of Engineering, studies networks and how to improve them in a wide range of applications through the design of novel algorithms.

Sometimes the future shows up so fast it hits us in the face, like a brick wall in a VR headset. Other times the miraculous promises of technology--the rearrange ment of our very DNA, the blockchain- enabled toppling of Facebook--are frustratingly slow to arrive. But either way, the future is coming, and we should be ready. In the following pages we lay out a series of predictions, starting with some changes that are immediately upon us. Then, looking down the road, we get ever-bolder in our prognostications, year by not-so-far-off year. Hackers who disable power grids and detonate gas pipelines have been the villains of popular cyberparanoia for decades.

There are two great times to make money in stock markets: the postcrash rebound and the end-of-cycle excess. Oil and technology fit the pattern perfectly in the past two years. Since the oil-price low of January 2016, the global oil and tech sectors have both made more than 80%, including dividends, beating the wider market's 53% return hands down. The oil sector was merely rebounding as oil prices tripled from their lows, but tech stocks were being led to heady heights by giddy enthusiasm for a bright future. More extreme proxies for the commodity and tech cycles have done even better.

Could artificial intelligence upend concepts of nuclear deterrence that have helped spare the world from nuclear war since 1945? Stunning advances in AI--coupled with a proliferation of drones, satellites, and other sensors--raise the possibility that countries could find and threaten each other's nuclear forces, escalating tensions. Lt. Col. Stanislav Petrov settled into the commander's chair in a secret bunker outside Moscow. His job that night was simple: Monitor the computers that were sifting through satellite data, watching the United States for any sign of a missile launch. It was just after midnight, Sept. 26, 1983.

Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes. However, the ability to optimally distinguish various classes of quantum data is an important primitive in quantum information science. In this work, we train near-term quantum circuits to classify data represented by non-orthogonal quantum probability distributions using the Adam stochastic optimization algorithm. This is achieved by iterative interactions of a classical device with a quantum processor to discover the parameters of an unknown non-unitary quantum circuit. This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs. Notably we use universal circuit topologies, with a theoretically motivated circuit design, which guarantees that our circuits can in principle learn to perform arbitrary input-output mappings. Our numerical simulations show that shallow quantum circuits could be trained to discriminate among various pure and mixed quantum states exhibiting a trade-off between minimizing erroneous and inconclusive outcomes with comparable performance to theoretically optimal POVMs. We train the circuit on different classes of quantum data and evaluate the generalization error on unseen mixed quantum states. This generalization power hence distinguishes our work from standard circuit optimization and provides an example of quantum machine learning for a task that has inherently no classical analogue.

We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between multiple alternative acquisition functions and traditional local optimization at each step. This is combined with a novel stopping condition based on expected regret. This pairing allows us to obtain the best characteristics of both local and Bayesian optimization, making efficient use of function evaluations while yielding superior convergence to the global minimum on a selection of optimization problems, and also halting optimization once a principled and intuitive stopping condition has been fulfilled.

RT antgrasso: Artificial Intelligence can make the Smart Grid smarter and reduce the need for Utilities to add Power Plants.

Japan is poised to flood the Pacific Ocean with one million tons of radioactive water contaminated by the Fukushima nuclear plant. Storage space at the abandoned facility is running dangerously low as officials race to secure the nearly 160 tons of contaminated water produced at the plant per day. As space for tanks dwindles the Japanese government and the plant's owner Tokyo Electric Power Company (Tepco) may decide to dump treated water into the ocean. Japan is poised to flood the Pacific Ocean with one million tons of radioactive water contaminated by the Fukushima nuclear plant. This image shows the transportation of one of the plant's large steel storage tanks Tepco plans to secure 1.37 million tons of storage capacity by the end of 2020, but it has not yet decided on a plan for after 2021.

RoboFly is only slightly bigger than a real fly. A new type of flying robot is so tiny and lightweight -- it weighs about as much as a toothpick -- it can perch on your finger. The little flitter is also capable of untethered flight and is powered by lasers. This is a big leap forward in the design of diminutive airborne bots, which are usually too small to support a power source and must trail a lifeline to a distant battery in order to fly, engineers who built the new robot announced in a statement. Their insect-inspired creation is dubbed RoboFly, and like its animal namesake, it sports a pair of delicate, transparent wings that carry it into the air. But unlike its robot precursors, RoboFly ain't got no strings to hold it down.