A Recursive Method for Determining the One-Dimensional Submodules of Laurent-Ore Modules

We present a method for determining the one-dimensional submodules of a Laurent-Ore module. The method is based on a correspondence between hyperexponential solutions of associated systems and one-dimensional submodules. The hyperexponential solutions are computed recursively by solving a sequence of first-order ordinary matrix equations. As the recursion proceeds, the matrix equations will have constant coefficients with respect to the operators that have been considered.Comment: To appear in the Proceedings of ISSAC 200

Fault tolerance in an inner-outer solver: a GVR-enabled case study

Abstract. Resilience is a major challenge for large-scale systems. It is particularly important for iterative linear solvers, since they take much of the time of many scientific applications. We show that single bit flip errors in the Flexible GMRES iterative linear solver can lead to high computational overhead or even failure to converge to the right answer. Informed by these results, we design and evaluate several strategies for fault tolerance in both inner and outer solvers appropriate across a range of error rates. We implement them, extending Trilinos' solver library with the Global View Resilience (GVR) programming model, which provides multi-stream snapshots, multi-version data structures with portable and rich error checking/recovery. Experimental results validate correct execution with low performance overhead under varied error conditions

Towards Understanding the Adoption and Social Experience of Digital Wallet Systems

For millions around the globe, digital wallets are replacing cash and credit cards. These services support user-to-user payments, and add a social component to transactions. However, there is little understanding of the key factors behind digital wallets’ rapid growth in US (Venmo) and China (WeChat Pay). What are the factors that led to their success? How social relationships play a role in their adoption? We conduct a mixed methods study, using a comprehensive survey (N=879) and semi-structured interviews (N=41) to explore the interplay of the two roles of these digital wallets, i.e., a payment system and a social platform. Our analysis suggests that the network effect does benefit their adoption and retention, but through different mechanisms. In return, transaction activities performed in digital wallets help strengthen existing social ties. We also present design implications for future social payment services

Tin Nanoparticles Encapsulated Carbon Nanoboxes as High-Performance Anode for Lithium-Ion Batteries

One of the crucial challenges for applying Sn as an anode of lithium-ion batteries (LIBs) is the dramatic volume change during lithiation/delithiation process, which causes a rapid capacity fading and then deteriorated battery performance. To address this issue, herein, we report the design and fabrication of Sn encapsulated carbon nanoboxes (denoted as Sn@C) with yolk@shell architectures. In this design, the carbon shell can facilitate the good transport kinetics whereas the hollow space between Sn and carbon shell can accommodate the volume variation during repeated charge/discharge process. Accordingly, this composite electrode exhibits a high reversible capacity of 675 mAh g−1 at a current density of 0.8 A g−1 after 500 cycles and preserves as high as 366mAh g−1 at a higher current density of 3 A g−1 even after 930 cycles. The enhanced electrochemical performance can be ascribed to the crystal size reduction of Sn cores and the formation of polymeric gel-like layer outside the electrode surface after long-term cycles, resulting in improved capacity and enhanced rate performance

System log pre-processing to improve failure prediction

Log preprocessing, a process applied on the raw log be-fore applying a predictive method, is of paramount impor-tance to failure prediction and diagnosis. While existing fil-tering methods have demonstrated good compression rate, they fail to preserve important failure patterns that are cru-cial for failure analysis. To address the problem, in this paper we present a log preprocessing method. It consists of three integrated steps: (1) event categorization to uni-formly classify system events and identify fatal events; (2) event filtering to remove temporal and spatial redundant records, while also preserving necessary failure patterns for failure analysis; (3) causality-related filtering to com-bine correlated events for filtering through apriori associ-ation rule mining. We demonstrate the effectiveness of our preprocessing method by using real failure logs collected from the Cray XT4 at ORNL and the Blue Gene/L system at SDSC. Experiments show that our method can preserve more failure patterns for failure analysis, thereby improv-ing failure prediction by up to 174%

Tin Nanoparticles Encapsulated Carbon Nanoboxes as High-Performance Anode for Lithium-Ion Batteries

One of the crucial challenges for applying Sn as an anode of lithium-ion batteries (LIBs) is the dramatic volume change during lithiation/delithiation process, which causes a rapid capacity fading and then deteriorated battery performance. To address this issue, herein, we report the design and fabrication of Sn encapsulated carbon nanoboxes (denoted as Sn@C) with yolk@shell architectures. In this design, the carbon shell can facilitate the good transport kinetics whereas the hollow space between Sn and carbon shell can accommodate the volume variation during repeated charge/discharge process. Accordingly, this composite electrode exhibits a high reversible capacity of 675 mAh g−1 at a current density of 0.8 A g−1 after 500 cycles and preserves as high as 366 mAh g−1 at a higher current density of 3 A g−1 even after 930 cycles. The enhanced electrochemical performance can be ascribed to the crystal size reduction of Sn cores and the formation of polymeric gel-like layer outside the electrode surface after long-term cycles, resulting in improved capacity and enhanced rate performance

Extensive sequencing of seven human genomes to characterize benchmark reference materials

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCode WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly