Greening Consumer Electronics: Moving Away From Bromine and Chlorine

Presents case studies of seven electronics companies that have engineered environmental solutions that eliminate the use of most brominated and chlorinated chemicals that generate toxic materials. Discusses global standards and regulations

Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist

Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations that are the basis for solving common machine learning problems---are significantly slower in Spark than when done using libraries written in a high-performance computing framework such as the Message-Passing Interface (MPI). To remedy this, we introduce Alchemist, a system designed to call MPI-based libraries from Apache Spark. Using Alchemist with Spark helps accelerate linear algebra, machine learning, and related computations, while still retaining the benefits of working within the Spark environment. We discuss the motivation behind the development of Alchemist, and we provide a brief overview of its design and implementation. We also compare the performances of pure Spark implementations with those of Spark implementations that leverage MPI-based codes via Alchemist. To do so, we use data science case studies: a large-scale application of the conjugate gradient method to solve very large linear systems arising in a speech classification problem, where we see an improvement of an order of magnitude; and the truncated singular value decomposition (SVD) of a 400GB three-dimensional ocean temperature data set, where we see a speedup of up to 7.9x. We also illustrate that the truncated SVD computation is easily scalable to terabyte-sized data by applying it to data sets of sizes up to 17.6TB.Comment: Accepted for publication in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, 201

Relative Abundance Measurements in Plumes and Interplumes

We present measurements of relative elemental abundances in plumes and interplumes. Plumes are bright, narrow structures in coronal holes that extend along open magnetic field lines far out into the corona. Previous work has found that in some coronal structures the abundances of elements with a low first ionization potential (FIP) < 10 eV are enhanced relative to their photospheric abundances. This coronal-to-photospheric abundance ratio, commonly called the FIP bias, is typically 1 for element with a high-FIP (> 10 eV). We have used EIS spectroscopic observations made on 2007 March 13 and 14 over an ~24 hour period to characterize abundance variations in plumes and interplumes. To assess their elemental composition, we have used a differential emission measure (DEM) analysis, which accounts for the thermal structure of the observed plasma. We have used lines from ions of iron, silicon, and sulfur. From these we have estimated the ratio of the iron and silicon FIP bias relative to that for sulfur. From the results, we have created FIP-bias-ratio maps. We find that the FIP-bias ratio is sometimes higher in plumes than in interplumes and that this enhancement can be time dependent. These results may help to identify whether plumes or interplumes contribute to the fast solar wind observed in situ and may also provides constraints on the formation and heating mechanisms of plumes.Comment: 21 pages; 3 tables; 12 figure

Milling plant and soil material in plastic tubes over-estimates carbon and under-estimates nitrogen concentrations

Peer reviewedPostprin

Image segmentation with adaptive region growing based on a polynomial surface model

A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces

Elinvar effect in β−\beta-Ti simulated by on-the-fly trained moment tensor potential

A combination of quantum mechanics calculations with machine learning (ML) techniques can lead to a paradigm shift in our ability to predict materials properties from first principles. Here we show that on-the-fly training of an interatomic potential described through moment tensors provides the same accuracy as state-of-the-art {\it ab inito} molecular dynamics in predicting high-temperature elastic properties of materials with two orders of magnitude less computational effort. Using the technique, we investigate high-temperature bcc phase of titanium and predict very weak, Elinvar, temperature dependence of its elastic moduli, similar to the behavior of the so-called GUM Ti-based alloys [T. Sato {\ it et al.}, Science {\bf 300}, 464 (2003)]. Given the fact that GUM alloys have complex chemical compositions and operate at room temperature, Elinvar properties of elemental bcc-Ti observed in the wide temperature interval 1100--1700 K is unique.Comment: 15 pages, 4 figure

An introduction to crowdsourcing for language and multimedia technology research

Language and multimedia technology research often relies on large manually constructed datasets for training or evaluation of algorithms and systems. Constructing these datasets is often expensive with significant challenges in terms of recruitment of personnel to carry out the work. Crowdsourcing methods using scalable pools of workers available on-demand offers a flexible means of rapid low-cost construction of many of these datasets to support existing research requirements and potentially promote new research initiatives that would otherwise not be possible