Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peak-shifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition, and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset, and demonstrates robust accuracy and identification abilities.

Deep neural networks are a powerful technique for learning complex functions from data. However, their appeal in real-world applications can be hindered by an inability to quantify the uncertainty of predictions. In this paper, the generation of prediction intervals (PI) for quantifying uncertainty in regression tasks is considered. It is axiomatic that high-quality PIs should be as narrow as possible, whilst capturing a specified portion of data. In this paper we derive a loss function directly from this high-quality principle that requires no distributional assumption. We show how its form derives from a likelihood principle, that it can be used with gradient descent, and that in ensembled form, model uncertainty is accounted for. This remedies limitations of a popular model developed on the same high-quality principle. Experiments are conducted on ten regression benchmark datasets. The proposed quality-driven (QD) method is shown to outperform current state-of-the-art uncertainty quantification methods, reducing average PI width by around 10%.

There are more than 1 million drones registered in the U.S. Most of them belong to people flying them for fun, but a growing number are used commercially. Companies including Amazon, UPS, Google and DHL are already exploring ways to deliver packages with drones instead of trucks. Our new research has measured how that shift would change how the U.S. uses energy, and the resulting environmental effects. Companies including Amazon, UPS, Google and DHL are already exploring ways to deliver packages with drones instead of trucks. We found that in some cases using electric-powered drones rather than diesel-powered trucks or vans could reduce energy use and greenhouse gas emissions.

This paper explores the influences of the growing field of artificial intelligence (AI) on the software development process. Several techniques and their potential positive effects on multiple areas of software development will be explored. The main focus is on requirements engineering, the development process, testing and deployment, and the human factor in the field of software development as a whole. A number of scenarios concerning the future influence of AI on software engineering are presented. Software development has been a strongly evolving discipline from the moment of its inception.

Continuing with my series of 2018 predictions in the energy and utilities industry launched last week with prediction 1 on the rise of RenTechs, be sure to read prediction 2 on self-powering communities and prediction 3 on the role AI and robotics will play for utilities. Because renewable energy is rapidly becoming more affordable, communities are starting to see the possibilities of implementing community owned and operated renewable generation facilities. We see two types of communities forming. The first are physical communities--be it cities, towns, or campuses--in which decisions to implement renewable or green energy are made by mayors, counselors or facility owners. Virtual communities, on the other hand, form peer-to-peer groups in which members can trade energy and share their energy assets in a peer-to-peer network.

Scientific and engineering processes produce massive high-dimensional data sets that are generated as highly non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold can facilitate better understanding of the underlying process, and ultimately their optimization. We show that off-the-shelf non-linear spectral dimensionality methods, such as Isomap, fail for such data, primarily due to the presence of strong temporal correlation among observations belonging to the same process pathways. We propose a novel method, Entropy-Isomap, to address this issue. The proposed method is successfully applied to morphology evolution data of the organic thin film fabrication process. The resulting output is ordered by the process variables. It allows for low-dimensional visualization of the morphological pathways, and provides key insights to guide subsequent design and exploration.

One of AWS' most highly decorated partners has arrived in Australia, bagging Origin Energy as its foundation client.

We analyse a linear regression problem with nonconvex regularization called smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis for Gaussian random data. We propose an approximate message passing (AMP) algorithm considering nonconvex regularization, namely SCAD-AMP, and analytically show that the stability condition corresponds to the de Almeida--Thouless condition in spin glass literature. Through asymptotic analysis, we show the correspondence between the density evolution of SCAD-AMP and the replica symmetric solution. Numerical experiments confirm that for a sufficiently large system size, SCAD-AMP achieves the optimal performance predicted by the replica method. Through replica analysis, a phase transition between replica symmetric (RS) and replica symmetry breaking (RSB) region is found in the parameter space of SCAD. The appearance of the RS region for a nonconvex penalty is a significant advantage that indicates the region of smooth landscape of the optimization problem. Furthermore, we analytically show that the statistical representation performance of the SCAD penalty is better than that of L1-based methods, and the minimum representation error under RS assumption is obtained at the edge of the RS/RSB phase. The correspondence between the convergence of the existing coordinate descent algorithm and RS/RSB transition is also indicated.

In the year 1900, the world was in the midst of a machine revolution. As electrical power became more ubiquitous, tasks once done by hand were now completed quickly and efficiently by machine. Sewing machines replaced needle and thread. A hundred years later, in the year 2000, machines were again pushing the boundaries of what was possible. Humans could now work in space, thanks to the International Space Station.

Conventional models, while still solid, are no longer up to the heightened challenges of the present. Exponentially improving technologies for the Internet of Things (IoT) and artificial intelligence are enabling urban developments with much higher levels of efficiency and flexibility to conserve resources, promote security, and boost the quality of life. The key development is not the technologies themselves, but their integration around a holistic view of urbanization that enables a series of smart services. Instead of focusing on single services, or specific buildings or highways, leading organizations around the world are using IoT and analytics to optimize infrastructure generally and evolve with changing needs. While getting there will take a great deal of investment and expertise, the result will be places where residents thrive in unexpected ways in their personalized urban developments.