This article was originally published on the Motley Fool. Flying a plane with solar energy alone may seem like a far-fetched idea, but it's now a reality. In 2016, a prototype plane built by Solar Impulse with a single pilot flew around the world on nothing but solar energy, a first for humanity. A number of companies are trying to take that test flight from prototype to commercial viability. A realistic commercial application for solar-powered aircraft could be drones flying high above Earth where energy requirements to maintain altitude are lower.

Community detection is considered for a stochastic block model graph of n vertices, with K vertices in the planted community, edge probability p for pairs of vertices both in the community, and edge probability q for other pairs of vertices. The main focus of the paper is on weak recovery of the community based on the graph G, with o(K) misclassified vertices on average, in the sublinear regime $n^{1-o(1)} \leq K \leq o(n).$ A critical parameter is the effective signal-to-noise ratio $\lambda=K^2(p-q)^2/((n-K)q)$, with $\lambda=1$ corresponding to the Kesten-Stigum threshold. We show that a belief propagation algorithm achieves weak recovery if $\lambda>1/e$, beyond the Kesten-Stigum threshold by a factor of $1/e.$ The belief propagation algorithm only needs to run for $\log^\ast n+O(1) $ iterations, with the total time complexity $O(|E| \log^*n)$, where $\log^*n$ is the iterated logarithm of $n.$ Conversely, if $\lambda \leq 1/e$, no local algorithm can asymptotically outperform trivial random guessing. Furthermore, a linear message-passing algorithm that corresponds to applying power iteration to the non-backtracking matrix of the graph is shown to attain weak recovery if and only if $\lambda>1$. In addition, the belief propagation algorithm can be combined with a linear-time voting procedure to achieve the information limit of exact recovery (correctly classify all vertices with high probability) for all $K \ge \frac{n}{\log n} \left( \rho_{\rm BP} +o(1) \right),$ where $\rho_{\rm BP}$ is a function of $p/q$.

The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification (including parameterizations). Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms.

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: 1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; 2) the ML model can be used to improve predictions of the same RSO at future epochs; and 3) the ML model based on a RSO can be applied to other RSOs that share some common features.

The price of oil has hit $70 a barrel for the first time since 2014. Brent crude, the international benchmark for oil prices, jumped after the Organisation of Petroleum Exporting Countries (OPEC) said it would continue to limit supplies.

We develop a 2D travel time tomography method which regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. We further propose to learn optimal slowness dictionaries using dictionary learning, in parallel with the inversion. This patch regularization, which we call the local model, is integrated into the overall slowness map, called the global model. Where the local model considers small-scale variations using a sparsity constraint, the global model considers larger-scale features which are constrained using $\ell_2$-norm regularization. This local-global modeling strategy with dictionary learning has been successful for image restoration tasks such as denoising and inpainting, where diverse image content is recovered from noisy or incomplete measurements. We use this strategy in our locally-sparse travel time tomography (LST) approach to model simultaneously smooth and discontinuous slowness features. This is in contrast to conventional tomography methods, which constrain models to be exclusively smooth or discontinuous. We develop a $\textit{maximum a posteriori}$ formulation for LST and exploit the sparsity of slowness patches using dictionary learning. We demonstrate the LST approach on densely, but irregularly sampled synthetic slowness maps.

Ocado is to test a humanoid maintenance assistant in its warehouses, in the online grocery specialist's latest move to reduce reliance on human workers. Artificial Intelligence has various definitions, but in general it means a program that uses data to build a model of some aspect of the world. This model is then used to make informed decisions and predictions about future events. The technology is used widely, to provide speech and face recognition, language translation, and personal recommendations on music, film and shopping sites. In the future, it could deliver driverless cars, smart personal assistants, and intelligent energy grids.

The global market for intelligent buildings, or smart buildings as they're also called, is projected to reach $31.74 billion by 2022, at a compound annual growth rate of 33.7%, according to Research and Markets. Artificial intelligence, which is enabling this market, has the potential to revolutionize energy management and the commercial building sector. According to a new whitepaper by Navigant, the management of commercial locations has been transformed by the accessibility of new data streams and the tools to analyze and operationalize associated information. In this new paradigm, business insights and actionable priorities are automatically delivered to corporate and site-level energy managers. In addition, customers can ask technical questions 24/7 in their own terms and get understandable answers without laborious software training or data science skills.

Panasonic may be best known for consumer electronics, but it has started moving into high-tech urban design in recent years. The company is now building "smart city" infrastructure near Denver, Colorado, with the goal of turning the area into a "smart city" by 2026. The initiative is part of a larger Panasonic program Panasonic called CityNow. Although the definition of a "smart city" varies depending on who you ask, the term typically describes a metro area that prioritizes the use of technology in its infrastructure. On a 400-acre swath of empty land near the Denver Airport, the company has installed free WiFi, LED street lights, pollution sensors, a solar-powered microgrid, and security cameras.

Now available in new colors and with Dell Mobile Connect, which lets you wirelessly tether a smartphone, the Dell XPS 13 is the smallest and most powerful 13-inch laptop in the world. LAS VEGAS--What happens in Vegas doesn't always stay in Vegas -- at least hopefully not for a bunch of the products showcased here last week. Some of the technology that debuts at CES, the annual consumer electronics show, turns out to be "vaporware," that is, it will never make a commercial debut for one reason or another. But the following ten products should have a chance vying for space in your home or driveway. Beginning this summer, Lenovo's VR headset doesn't require a connection to a device, like a smartphone, laptop, or game console.