Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy sources such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of a novel approach for nonparametric probabilistic forecasting of wind power that combines a smooth approximation of the pinball loss function with a neural network architecture and a weighting initialization scheme to prevent the quantile cross over problem. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 10%, to 90% prediction intervals which are evaluated using a quantile score and reliability measures. Benchmark models such as the persistence and climatology distributions, multiple quantile regression, and support vector quantile regression are used for comparison where results demonstrate the proposed approach leads to improved performance while preventing the problem of overlapping quantile estimates.

There have been several spectral bounds for the percolation transition in networks, using spectrum of matrices associated with the network such as the adjacency matrix and the non-backtracking matrix. However they are far from being tight when the network is sparse and displays clustering or transitivity, which is represented by existence of short loops e.g. triangles. In this work, for the bond percolation, we first propose a message passing algorithm for calculating size of percolating clusters considering effects of triangles, then relate the percolation transition to the leading eigenvalue of a matrix that we name the triangle-non-backtracking matrix, by analyzing stability of the message passing equations. We establish that our method gives a tighter lower-bound to the bond percolation transition than previous spectral bounds, and it becomes exact for an infinite network with no loops longer than 3. We evaluate numerically our methods on synthetic and real-world networks, and discuss further generalizations of our approach to include higher-order sub-structures.

In this paper, we applied the multifractal detrended fluctuation analysis to the daily means of wind speed measured by 119 weather stations distributed over the territory of Switzerland. The analysis was focused on the inner time fluctuations of wind speed, which could be more linked with the local conditions of the highly varying topography of Switzerland. Our findings point out to a persistent behaviour of all the measured wind speed series (indicated by a Hurst exponent significantly larger than 0.5), and to a high multifractality degree indicating a relative dominance of the large fluctuations in the dynamics of wind speed, especially in the Swiss plateau, which is comprised between the Jura and Alp mountain ranges. The study represents a contribution to the understanding of the dynamical mechanisms of wind speed variability in mountainous regions.

Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the nonparametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure, and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.

Data powers everything we do these days. And that means how we accumulate, disseminate, study, store and act upon data is one of the most important jobs out there. That must be why data science is such a booming business -- one that's expected to reach $16 billion in value by 2025. There's never been a better time to pursue a career in this field. The skills you'll acquire as a data scientist are extremely valuable and could see you employed with companies as diverse as IBM, Coca-Cola, Ford Motors and Uber, as well as countless pro-social and nonprofit endeavors aimed at building a better world.

This week's milestones in the history of technology include the end of life of one of the first examples of artificial intelligence or "giant brains" and its 50th anniversary, patents for the transistor, xerography, and carbon paper, and the first solar-powered mobile phone. At 11:45pm, the power to the Electronic Numerical Integrator and Computer (ENIAC), is removed. For a few years after it started calculating in 1946, it was "the only fully electronic computer working in the U.S." Thomas Haigh, Mark Priestley and Crispin Rope write in ENIAC in Action: Making and Remaking the Modern Computer: Since 1955, When ENIAC punched its last card, its prominence has only grown… ENIAC was as much symbol as machine, producing cultural meanings as well as numbers… In its own small way, ENIAC has returned frequently to the forefront of public awareness over the decades as a symbol of a variety of virtues and vices. Among other things, the ENIAC was a symbol of the computer as a giant brain (see October 8 entry below), giving rise to today's warnings that artificial intelligence "will be able to do everything better than us." Walter H. Brattain and John Bardeen are granted a patent for a three-electrode circuit element utilizing semiconductive materials, otherwise known as the transistor.

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for Gaussian Process (GP) classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.

Electric submersible pumps (ESPs) are one of the go-to mechanisms for artificial lift, with as many as 150,000 ESPs in operation around the world, according to Schlumberger. Although ESPs are remarkably efficient for pumping large volumes, they are highly susceptible to prolonged failures. Post-mortems on ESPs reveal that produced gas and solids have proven to be their undoing. But with the advent of Big Data and machine learning, predicting when an ESP may fail well in advance of any potential mechanical problems is improving. At the Unconventional Resources Technology Conference held in July in Austin, Texas, Devon Energy reported on its recent efforts to do just that.

Contaminated water might have leaked from the damaged Fukushima nuclear reactors after erroneous settings on water gauges lowered groundwater levels nearby, according to the plant operator. Tokyo Electric Power (TEPCO) said the settings on six of the dozens of wells around the reactors were 70 centimetres (three feet) below the requirement. Groundwater at one well briefly sank below the contaminated water inside in May, possibly causing radioactive water to leak into the soil. An underwater robot has captured images inside Japan's crippled Fukushima nuclear plant. The marine robot, is on a mission to study damage and find resources inside the devastated plant.

Tesla is developing a long-haul, electric semi-truck that can drive itself and move in "platoons" that automatically follow a lead vehicle, and is getting closer to testing a prototype. Love exists, but they are not together. Reports swirled over the weekend that the entrepreneur, Elon Musk, and Amber called it quits, after publicizing their couple status in April. TheStreet's Action Alerts PLUS Portfolio Manager Jim Cramer looks at Thursday's trending stocks. Tesla had roughly 63,000 people cancel their order for the companies Model 3 car in the last year. Tesla's greatly anticipated Model 3 will finally start rolling out of the manufacturing plant. CEO, Elon Musk took to Twitter to make the announcement.