The trifecta of IoT, Cloud and Analytics have been transforming many aspects of our lives and business. Cities, healthcare, transportation, farming, fitness, home, manufacturing and utilities have been the key beneficiaries of this fast-emerging paradigm. While consumer devices like Fitbit and Amazon Alexa get lot of attention from the media, the commercial buildings have been quietly turning into Software Defined Buildings (SDB). By doing so they are not only lowering the operational cost of the building, but also foster smarter cities, better safety, and occupant comfort. As such they have become an important market segment in the IoT space.

If we actually end up discovering aliens then they'll probably just wipe us all out, Stephen Hawking has said. When we made contact with any aliens it would probably be like when the Native Americans first met Christopher Columbus. And, in that case, things "didn't turn out so well" for the people being visited, Professor Hawking has said. Stephen Hawking made the warning in a film posted online, Stephen Hawking's Favorite Places. It showed him taking a spacecraft across the cosmos, visiting different locations across the universe.

Today on In Case You Missed It: A French company just introduced a hydrogen fuel cell train that it plans to install on a line in Germany in 2017. The train can carry 300 passengers reaching speeds of 87 miles an hour, all while emitting water rather than the usual diesel fumes that go along with such routes. We think the Casper insomnia chatbot is probably just a PR stunt by the company, but it may also be functional so you're guess is as good as ours as to why a mattress company would want to talk about people (likely their own customers) who struggle with going to sleep at night. If you're interested, the truck clock video is here. As always, please share any interesting tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

Over the next decade, mobile, the Internet of Things, machine learning, robotics, and blockchain technologies will change a great deal about how the oil and gas industry works. Five technologies will change the oil and gas industry: mobile will speed oilfield transactions, increase efficiency, and improve safety by removing people from harm's way; the Internet of Things (IoT) will reduce the cost of repairs; machine learning will provide ever more optimal solutions to field challenges; robotics will upend the question of who does the work, and blockchain will make contracting faster and smoother than ever before. Adopting these technologies will be a challenge for many in our industry, requiring a change in mind-set. Engineers tend to focus less on investing for the future than on fixing what's broken now, as do companies trying to maximize their return on investment. But investments in these transformative technologies now will mean less to fix in the future, and more time to innovate, operate, and develop resources as fully as possible--which is what we're all trying to do, correct?

Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of fragments required for chemical accuracy is still prohibitively expensive for ab-initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab-initio potential energy surfaces have improved, especially non-linear models based on neural networks (NN) which can reproduce ab-initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NN's, and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size and intelligently sample chemical space. NN's reduce, by a factor in excess of $10^6$ the computational overhead of the MBE and reproduce the accuracy of ab-initio calculations without specialized force fields. We show they are remarkably general, providing comparable accuracy with drastically different chemical embeddings. To assess this we test a new chemical embedding which can be inverted to predict molecules with desired properties.

We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm for simulating draws from Hawkes processes whose levels of excitation are stochastic processes, and propose a hybrid Markov chain Monte Carlo approach for model fitting. Our sampling procedure scales linearly with the number of required events and does not require stationarity of the point process. A modular inference procedure consisting of a combination between Gibbs and Metropolis Hastings steps is put forward. We recover expectation maximization as a special case. Our general approach is illustrated for contagion following geometric Brownian motion and exponential Langevin dynamics.

Legacy IIoT devices can be modernized utilizing edge of network devices to send data to the Azure IoT hub and Machine Learning. This can create cost and efficiency improvements and reduced downtime. I will try to quickly explain the issue of liquid loading and slow legacy communications. Keep in mind there are many other issues that can be alleviated with this solution and there is no way I could mention them all. Oil & Gas Wells can "Load Up" with liquid reducing production and possibly incurring costly intermediation to relieve the issue.

For all its promise of delivering a bold future, the green energy industry is still decidedly low tech when it comes to moving merchandise. Solar panels are still sold door-to-door, a slow and expensive process that has impeded the industry's growth. Now, a California startup thinks it has a solution, using sophisticated data science to find consumers likely to adopt solar power and let them know just how much they can save on their electric bills. PowerScout, a machine-learning-enabled eCommerce platform for solar energy, aims to eliminate marketing costs that, according to CEO Attila Toth, can exceed the cost of the actual equipment for some green-power vendors. "This is very absurd--very crazy," Toth says.

The goal of graph clustering is to group the nodes into clusters of high similarity. Applications of graph clustering, also known as community detection [1, 2], include but are not limited to graph signal processing [3-11], multivariate data clustering [12-14], image segmentation [15, 16], and network vulnerability assessment [17]. Spectral clustering [12-14] is a popular method for graph clustering, which we refer to as spectral graph clustering (SGC). It works by transforming the graph adjacency matrix into a graph Laplacian matrix [18], computing its eigendecomposition, and performing K-means clustering [19] on the eigenvectors to partition the nodes into clusters. Although heuristic methods have been proposed to automatically select the number of clusters [12,13,20], rigorous theoretical justifications on the selection of the number of eigenvectors for clustering are still lacking and little is known about the capabilities and limitations of spectral clustering on graphs. Based on a recent development of clustering reliability analysis for SGC under the random interconnection model (RIM) [21], we propose a novel automated model order selection (AMOS) algorithm for SGC. AMOS works by incrementally increasing the number of clusters, estimating the quality of identified clusters, and providing a series of clustering reliability tests.

U.S. stocks inched higher Tuesday in another cautious day of trading as investors kept an eye on central banks in the U.S. and Japan. Healthcare and household goods companies led the way, while energy companies slipped. Major market indexes were higher all day but returned most of those gains at the close of trading. They rose just enough to cancel out Monday's small losses. Drug companies helped healthcare stocks make modest gains, while Exxon Mobil fell on reports that it's being investigated by securities regulators.