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At least 1,081 records · Page 60

Microbes to Biomes at Berkeley Lab

Microbes are the Earth's most abundant and diverse form of life. Berkeley Lab's Microbes to Biomes initiative -- which will take advantage of research expertise at the Joint Genome Institute, Advanced Light Source, Molecular Foundry, and the new computational science facility -- is designed to explore and reveal the interactions of microbes with one another and with their environment. Microbes power our planet’s biogeochemical cycles, provide nutrients to our plants, purify our water and are integral components in keeping the human body free of disease and may hold the key to the Earth’s future.

54 ENVIRONMENTAL SCIENCES↗

Scalable Out-of-Core Solvers on Xeon Phi Cluster

This paper documents the implementation of a distributive out-of-core (OOC) solver for performing LU and Cholesky factorizations of a large dense matrix on clusters of many-core programmable co-processors. The out-of- core algorithm combines both the left-looking and right-looking schemes aimed to minimize the movement of data between the CPU host and the co-processor, optimizing data locality as well as computing throughput. The OOC solver is built to align with the format of the ScaLAPACK software library, making it readily portable to any existing codes using ScaLAPACK. A runtime analysis conducted on Beacon (an Intel Xeon plus Intel Xeon Phi cluster which composed of 48 nodes of multi-core CPU and MIC) at the Na- tional Institute for Computational Sciences is presented. Comparison of the performance on the Intel Xeon Phi and GPU clusters are also provided.

D'Azevedo, Ed F↗

Proceedings of Synthetic Biology: Engineering, Evolution and Design (SEED) Conference 2015

Synthetic Biology is an emerging discipline that seeks to accelerate the process of engineering biology. As such, the tools are broadly applicable to application areas, including chemicals and biofuels, materials, medicine and agriculture. A characteristic of the field is to look holistically at cellular design, from sensing and genetic circuitry to the manipulation of cellular processes and actuators, to controlling metabolism, to programming multicellular behaviors. Further, the types of cells that are manipulated are broad, from in vitro systems to microbes and fungi to mammalian and plant cells and living animals. Many of the projects in synthetic biology seek to move biochemical functions across organisms. The field is highly interdisciplinary with faculty and students spread across departments that focus on engineering (biological, chemical, electrical, mechanical, civil, computer science) and basic science (biology and systems biology, chemistry, physics). While there have been many one-off workshops and meeting on synthetic biology, the 2014 Synthetic Biology: Engineering, Evolution and Design (SEED) was the first of an annual conference series that serves as a reliable place to pull together the involved disciplines in order to organize and exchange advances in the science and technology in the field. Further, the SEED conferences have a strong focus on industry, with many companies represented and actively participating. A number of these companies have started major efforts in synthetic biology including large companies (e.g., Pfizer, Novartis, Dow, Dupont, BP, Total), smaller companies have recently gone public (e.g., Amyris, Gevo, Intrexon), and many start-ups (e.g., Teslagen, Refactored Materials, Pivot, Genomatica). There are a number of loosely affiliated Synthetic Biology Centers, including ones at MIT, Boston University, UCSD, UCSF, UC-Berkeley, Imperial College, Oxford, and ETH. SEED 2015 will serve as the primary meeting at which international synthetic biology centers and related infrastructure (synthesis/software/foundries) meet to discuss technology, standards, and education. SEED2015 will be the second in an annual series of meeting held to bring researchers from industry and academia in the area of Synthetic Biology. The first SEED conference was highly successful, attracting 285 attendees with varying backgrounds from academia, industry and government. The SEED series provides leadership in the development of the field of synthetic biology and serves to broaden the participants in the field by appealing to broad sectors in industry and providing a means for young investigators and those outside of the field to participate. Further, the series closely integrates with groups such as the SBCC to provide a means by which the synthetic biology community can communicate with policy makers. Further, we will pursue making the meeting the center for the exchange of educational materials as centers for synthetic biology emerge globally. Proceedings will be published each year in the journal ACS Synthetic Biology. After each SEED meeting, surveys are distributed to assess the success of the conference and to help guide changes year-to-year. The diverse application areas further extend the expertise needed from people in areas such as plant biology, agriculture and soil science, environmental science, medicine, and the chemical industry. These areas could have a widespread impact on society in a number of ways. For example, the CRISPR/Cas9 system that serves to immunize bacteria from phage has provided the fundamental chemistry that is used to edit the genomes of diverse organisms, including human stem cells, crop plants, and livestock animals.

09 BIOMASS FUELS↗

RELAP-7 Code Assessment Plan and Requirement Traceability Matrix

The RELAP-7, a safety analysis code for nuclear reactor system, is under development at Idaho National Laboratory (INL). Overall, the code development is directed towards leveraging the advancements in computer science technology, numerical solution methods and physical models over the last decades. Recently, INL has also been putting an effort to establish the code assessment plan, which aims to ensure an improved final product quality through the RELAP-7 development process. The ultimate goal of this plan is to propose a suitable way to systematically assess the wide range of software requirements for RELAP-7, including the software design, user interface, and technical requirements, etc. To this end, we first survey the literature (i.e., international/domestic reports, research articles) addressing the desirable features generally required for advanced nuclear system safety analysis codes. In addition, the V&V (verification and validation) efforts as well as the legacy issues of several recently-developed codes (e.g., RELAP5-3D, TRACE V5.0) are investigated. Lastly, this paper outlines the Requirement Traceability Matrix (RTM) for RELAP-7 which can be used to systematically evaluate and identify the code development process and its present capability.

42 ENGINEERING↗

Pececillo

Pececillo is a proxy-app for the open source Truchas metal processing code (LA-CC-15-097). It implements many of the physics models used in Truchas: free-surface, incompressible Navier-Stokes fluid dynamics (e.g., water waves); heat transport, material phase change, view factor thermal radiation; species advection-diffusion; quasi-static, elastic/plastic solid mechanics with contact; electomagnetics (Maxwell's equations). The models are simplified versions that retain the fundamental computational complexity of the Truchas models while omitting many non-essential features and modeling capabilities. The purpose is to expose Truchas algorithms in a greatly simplified context where computer science problems related to parallel performance on advanced architectures can be more easily investigated. While Pececillo is capable of performing simulations representative of typical Truchas metal casting, welding, and additive manufacturing simulations, it lacks many of the modeling capabilites needed for real applications.

Carlson, Neil↗

2018 Technology Innovation Program Heartbeat

Heartbeat responds to this problem by focusing instead on the physical behavior of the device being protected, under the hypothesis that malware infection will produce a measurable change in the power consumption state of a device that can be picked up by an outside detector. All code execution uses power, so the execution of malware—especially polymorphic variants—will leave a trace on a power consumption record. The Heartbeat system collects power trace measurements directly from the hardware and so is invisible to malware and resilient to internet service interruption. By collecting power measurement data only during the periodic invocation of a single or of several system functions, Heartbeat will address several challenges that plague current anomaly-based intrusion detection systems and is operational-context agnostic.

97 MATHEMATICS AND COMPUTING↗

Exploiting Literature-Based Discovery to Study Effects of Bullying

Bullying represents an aggressive behavioral problem. It can create severe mental illness in children, adolescents, and adults. Researchers are interested in studying bullying to understand its long-term negative consequences and design better-targeted intervention programs. Broadly researchers in psychology and education have conducted studies on bullying without using advanced analytical features offered by the computer science domain. However, researchers could benefit from power of advanced analytics for an in-depth understanding of bullying. In this paper, we present graph-based advanced analytics by employing a literature-based discovery (LBD) approach to study the direct and indirect consequences of bullying from published literature. In addition, we describe a graph-based study on the evolution of research on bullying consequences in the last two decades. Our graph-based study reveals a summary view of associations of bullying with other diseases and disorders from published literature. Further, we employ literature reference information to quantify the strength of the graph-derived relationships. We demonstrate that LBD is a valuable approach to perform advanced analytics on bullying that helps us to understand the severity of its consequences.

Hasan, S M Shamimul↗

DE-SC0016684 - DOE Final Scientific and Technical Report

The Computing Research Association’s Committee on the Status of Women in Computing Research (CRA-W) aims to increase the number of women who participate and succeed in computer science research at every stage of the research pipeline. Achieving these objectives requires that an increasing number of women start and progress to the next stage. The CRA-W Grad Cohort Workshop is focused at the graduate level to maximize the likelihood participants will successfully complete their graduate degrees. Factors such as role models, skills, competence, confidence, mentoring, social support, and development of identity as a researcher help students to persist in graduate school, while factors such as sexism, racism, and dissatisfaction with their graduate program may cause them to leave. We, therefore, help first, second, and third year students build their skills, research strategies, and confidence; meet potential mentors from outside their institutions; develop connections with each other and with their research communities; and inform them about strategies for successful publishing, speaking, and advisor management strategies. By targeting early graduate students, we seek to help them stay in graduate school and in research.

Russell, Erik↗

Sample Complexity of Power System State Estimation using Matrix Completion: Preprint

In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system-a problem known as \emph{sample complexity} analysis in computer science. Motivated by the increasing adoption of distributed energy resources into the distribution-level grids, it becomes imperative to estimate the state of distribution grids in order to ensure stable operation. Traditional power system state estimation techniques mainly focus on the transmission network which involve solving an overdetermined system and eliminating bad data. However, distribution networks are typically \emph{underdetermined} due to the large number of connection points and high cost of pervasive installation of measurement devices. In this paper, we consider the recently proposed state-estimation method for underdetermined systems that is based on \emph{matrix completion}. In particular, a \emph{constrained} matrix completion algorithm was proposed, wherein the standard matrix completion problem is augmented with additional equality constraints representing the physics (namely power-flow constraints). We analyze the sample complexity of this general method by proving an upper bound on the sample complexity that depends directly on the properties of these constraints that can lower number of needed samples as compared to the unconstrained problem. To demonstrate the improvement that the constraints add to state estimation, we test the method on a 141-bus distribution network case study and compare it to the traditional least squares minimization state estimation method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

On Holder-Brascamp-Lieb inequalities for torsion-free discrete Abelian groups

H\"older-Brascamp-Lieb inequalities provide upper bounds for a class of multilinear expressions, in terms of $L^p$ norms of the functions involved. They have been extensively studied for functions defined on Euclidean spaces. Bennett-Carbery-Christ-Tao have initiated the study of these inequalities for discrete Abelian groups and, in terms of suitable data, have characterized the set of all tuples of exponents for which such an inequality holds for specified data, as the convex polyhedron defined by a particular finite set of affine inequalities. In this paper we advance the theory of such inequalities for torsion-free discrete Abelian groups in three respects. The optimal constant in any such inequality is shown to equal $1$ whenever it is finite. An algorithm that computes the admissible polyhedron of exponents is developed. It is shown that nonetheless, existence of an algorithm that computes the full list of inequalities in the Bennett-Carbery-Christ-Tao description of the admissible polyhedron for all data, is equivalent to an affirmative solution of Hilbert's Tenth Problem over the rationals. That problem remains open. Applications to computer science will be explored in a forthcoming companion paper.

Christ, Michael↗

Discrete Graph Structure Learning for Forecasting Multiple Time Series

Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting models, we hypothesize that exploiting the pairwise information among multiple (multivariate) time series also improves their forecast. If an explicit graph structure is known, graph neural networks (GNNs) have been demonstrated as powerful tools to exploit the structure. In this work, we propose learning the structure simultaneously with the GNN if the graph is unknown. We cast the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution. The distribution is parameterized by a neural network so that discrete graphs can be sampled differentiably through reparameterization. Empirical evaluations show that our method is simpler, more efficient, and better performing than a recently proposed bilevel learning approach for graph structure learning, as well as a broad array of forecasting models, either deep or non-deep learning based, and graph or non-graph based.

Shang, Chao↗