Robots have become central to the cleaning-up operation at Japan's Fukushima nuclear power plant, six years after the tsunami that triggered the nuclear meltdown.

A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statistical challenges, including the design of statistical methods that enable the coherent propagation of probabilities through a (possibly deterministic) computational work-flow. This paper examines the case for probabilistic numerical methods in routine statistical computation. Our focus is on numerical integration, where a probabilistic integrator is equipped with a full distribution over its output that reflects the presence of an unknown numerical error. Our main technical contribution is to establish, for the first time, rates of posterior contraction for these methods. These show that probabilistic integrators can in principle enjoy the "best of both worlds", leveraging the sampling efficiency of Monte Carlo methods whilst providing a principled route to assess the impact of numerical error on scientific conclusions. Several substantial applications are provided for illustration and critical evaluation, including examples from statistical modelling, computer graphics and a computer model for an oil reservoir.

We consider the problem of quantifying the quality of a model selection problem for a graphical model. We discuss this by formulating the problem as a detection problem. Model selection problems usually minimize a distance between the original distribution and the model distribution. For the special case of Gaussian distributions, the model selection problem simplifies to the covariance selection problem which is widely discussed in literature by Dempster [2] where the likelihood criterion is maximized or equivalently the Kullback-Leibler (KL) divergence is minimized to compute the model covariance matrix. While this solution is optimal for Gaussian distributions in the sense of the KL divergence, it is not optimal when compared with other information divergences and criteria such as Area Under the Curve (AUC). In this paper, we analytically compute upper and lower bounds for the AUC and discuss the quality of model selection problem using the AUC and its bounds as an accuracy measure in detection problem. We define the correlation approximation matrix (CAM) and show that analytical computation of the KL divergence, the AUC and its bounds only depend on the eigenvalues of CAM. We also show the relationship between the AUC, the KL divergence and the ROC curve by optimizing with respect to the ROC curve. In the examples provided, we pick tree structures as the simplest graphical models. We perform simulations on fully-connected graphs and compute the tree structured models by applying the widely used Chow-Liu algorithm [3]. Examples show that the quality of tree approximation models are not good in general based on information divergences, the AUC and its bounds when the number of nodes in the graphical model is large. We show both analytically and by simulations that the 1-AUC for the tree approximation model decays exponentially as the dimension of graphical model increases.

Montefiore Institute - Department of Electrical Engineering and Computer Science University of Li ege, L.Wehenkel@ulg.ac.be Abstract--We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems. Experimental results on an updated versions of IEEE-RTS79 and IEEE-RTS96 show high accuracy measured on operational cost, achieved in run-times that are lower in several orders of magnitude than the traditional approach. Unit commitment (UC) is solved daily by Transmission System Operators (TSO) worldwide as part of the market clearing process, to ensure safe operation. Typically, the resulting mathematical problem is either a deterministic or stochastic Mixed Integer-Linear Program (MILP). It is solved accurately for the following day, taking into account all available information on generation and demand, along with exogenous factors such as renewable generation forecast. As intermittent generation capacity is increasing regularly in recent years, more stochasticity is involved in power system operation, affecting the way planning is done not only in the day-ahead time horizon, but in all different time horizons [1], [2], [3]. The complex dependence between the different time-horizons and the high uncertainty in long time-horizons makes long-term planning challenging.

Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Speaker Bio Stuart Gillen is the Director of Business Development at SparkCognition. In this role, he is responsible for driving business engagements, partner development, marketing activities, and go-to market strategy.

World petrol demand will peak within 13 years thanks to the impact of electric cars and more efficient engines, energy experts have predicted. UK-based Wood Mackenzie said it expected the take-up of electric vehicles to cut gasoline demand significantly, particularly beyond 2025 as the battery-powered cars go mainstream. Combined with car manufacturers forced by regulations to produce models that run further on the same amount of oil, a new report by the analysts suggests global gasoline demand is likely to peak by 2030. The UK and France have recently said they will phase out sales of new petrol and diesel cars by 2040. China, the world's biggest car market, is mulling a similar move, which would have a significant impact on oil demand.

Huawei's new Mate 10 Pro takes aim squarely at Samsung, Google and Apple with a large screen, competition-beating big battery and AI baked in. The Mate 10 Pro is the latest in third-largest smartphone manufacturer Huawei's big-screen line, which has become popular for its battery life and multitasking prowess. The new top-end Huawei features a 6in, 18:9 elongated OLED screen, matching its Samsung rivals with tiny bezels at the side, top and bottom, which are only just big enough to fit the front-facing camera and sensors at the top and company logo at the bottom. The back of the phone is curved glass, with metal sides that feel significantly more premium than previous Mate smartphones, matching the level of build-quality and design of rivals. "The Mate 10 shows Huawei can now produce devices that can really compete with the quality of flagship devices like Apple," said Francisco Jeronimo, research director for European mobile devices at research firm IDC.

"How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention. From lengthening battery life of mobile devices to reducing the energy bill of a datacenter, it is important to understand the energy efficiency of CNNs during serving for making an inference, before actually training the model. In this work, we propose NeuralPower: a layer-wise predictive framework based on sparse polynomial regression, for predicting the serving energy consumption of a CNN deployed on any GPU platform. Given the architecture of a CNN, NeuralPower provides an accurate prediction and breakdown for power and runtime across all layers in the whole network, helping machine learners quickly identify the power, runtime, or energy bottlenecks. We also propose the "energy-precision ratio" (EPR) metric to guide machine learners in selecting an energy-efficient CNN architecture that better trades off the energy consumption and prediction accuracy. The experimental results show that the prediction accuracy of the proposed NeuralPower outperforms the best published model to date, yielding an improvement in accuracy of up to 68.5%. We also assess the accuracy of predictions at the network level, by predicting the runtime, power, and energy of state-of-the-art CNN architectures, achieving an average accuracy of 88.24% in runtime, 88.34% in power, and 97.21% in energy. We comprehensively corroborate the effectiveness of NeuralPower as a powerful framework for machine learners by testing it on different GPU platforms and Deep Learning software tools.

A few days after a 7.1-magnitude earthquake struck Mexico City last month, Carnegie Mellon University roboticists were contacted to see if their snake robots could help with search-and-rescue efforts. Mexican rescuers were still trying find people in the rubble of collapsed buildings, and even though several days had passed, they thought it'd be worth trying to bring in the snakebots. Within 24 hours, a team of CMU roboticists had packed their gear and headed out to the disaster site. We spoke with Matt Travers, who was on the ground in Mexico City operating the robots, along with Howie Choset, who heads CMU's Biorobotics Lab where the snake robots are developed, about their experience with using robots in a real disaster and how, although no survivors were found during the rescue missions they assisted with, they learned an enormous amount being on-site. IEEE Spectrum: Were you and your robots ready for a real disaster? Howie Choset: Since the beginning of my adventure into snake robots, I've been interested in search and rescue.

An aquatic robot called Avexis is being tested in Japan ahead of being deployed into the damaged core of the Fukushima Daiichi Nuclear Power Plant. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. An aquatic robot called Avexis is being tested in Japan ahead of being deployed into the damaged core of the Fukushima Daiichi Nuclear Power Plant.