TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs

Given a large graph, how can we determine similarity between nodes in a fast and accurate way? Random walk with restart (RWR) is a popular measure for this purpose and has been exploited in numerous data mining applications including ranking, anomaly detection, link prediction, and community detection. However, previous methods for computing exact RWR require prohibitive storage sizes and computational costs, and alternative methods which avoid such costs by computing approximate RWR have limited accuracy. In this paper, we propose TPA, a fast, scalable, and highly accurate method for computing approximate RWR on large graphs. TPA exploits two important properties in RWR: 1) nodes close to a seed node are likely to be revisited in following steps due to block-wise structure of many real-world graphs, and 2) RWR scores of nodes which reside far from the seed node are proportional to their PageRank scores. Based on these two properties, TPA divides approximate RWR problem into two subproblems called neighbor approximation and stranger approximation. In the neighbor approximation, TPA estimates RWR scores of nodes close to the seed based on scores of few early steps from the seed. In the stranger approximation, TPA estimates RWR scores for nodes far from the seed using their PageRank. The stranger and neighbor approximations are conducted in the preprocessing phase and the online phase, respectively. Through extensive experiments, we show that TPA requires up to 3.5x less time with up to 40x less memory space than other state-of-the-art methods for the preprocessing phase. In the online phase, TPA computes approximate RWR up to 30x faster than existing methods while maintaining high accuracy.Comment: 12pages, 10 figure

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Given a time-evolving graph, how can we track similarity between nodes in a fast and accurate way, with theoretical guarantees on the convergence and the error? Random Walk with Restart (RWR) is a popular measure to estimate the similarity between nodes and has been exploited in numerous applications. Many real-world graphs are dynamic with frequent insertion/deletion of edges; thus, tracking RWR scores on dynamic graphs in an efficient way has aroused much interest among data mining researchers. Recently, dynamic RWR models based on the propagation of scores across a given graph have been proposed, and have succeeded in outperforming previous other approaches to compute RWR dynamically. However, those models fail to guarantee exactness and convergence time for updating RWR in a generalized form. In this paper, we propose OSP, a fast and accurate algorithm for computing dynamic RWR with insertion/deletion of nodes/edges in a directed/undirected graph. When the graph is updated, OSP first calculates offset scores around the modified edges, propagates the offset scores across the updated graph, and then merges them with the current RWR scores to get updated RWR scores. We prove the exactness of OSP and introduce OSP-T, a version of OSP which regulates a trade-off between accuracy and computation time by using error tolerance {\epsilon}. Given restart probability c, OSP-T guarantees to return RWR scores with O ({\epsilon} /c ) error in O (log ({\epsilon}/2)/log(1-c)) iterations. Through extensive experiments, we show that OSP tracks RWR exactly up to 4605x faster than existing static RWR method on dynamic graphs, and OSP-T requires up to 15x less time with 730x lower L1 norm error and 3.3x lower rank error than other state-of-the-art dynamic RWR methods.Comment: 10 pages, 8 figure

EACOF: A Framework for Providing Energy Transparency to enable Energy-Aware Software Development

Making energy consumption data accessible to software developers is an essential step towards energy efficient software engineering. The presence of various different, bespoke and incompatible, methods of instrumentation to obtain energy readings is currently limiting the widespread use of energy data in software development. This paper presents EACOF, a modular Energy-Aware Computing Framework that provides a layer of abstraction between sources of energy data and the applications that exploit them. EACOF replaces platform specific instrumentation through two APIs - one accepts input to the framework while the other provides access to application software. This allows developers to profile their code for energy consumption in an easy and portable manner using simple API calls. We outline the design of our framework and provide details of the API functionality. In a use case, where we investigate the impact of data bit width on the energy consumption of various sorting algorithms, we demonstrate that the data obtained using EACOF provides interesting, sometimes counter-intuitive, insights. All the code is available online under an open source license. http://github.com/eaco

Controlling internal barrier in low loss BaTiO3 supercapacitors

Supercapacitor behavior has been reported in a number of oxides including reduced BaTiO3 ferroelectric ceramics. These so-called giant properties are however not easily controlled. We show here that the continuous coating of individual BaTiO3 grains by a silica shell in combination with spark plasma sintering is a way to process bulk composites having supercapacitor features with low dielectric losses and temperature stability. The silica shell acts both as an oxidation barrier during the processing and as a dielectric barrier in the final composite

Observational Constraints on First-Star Nucleosynthesis. I. Evidence for Multiple Progenitors of CEMP-no Stars

We investigate anew the distribution of absolute carbon abundance, $A$(C) $= \log\,\epsilon$(C), for carbon-enhanced metal-poor (CEMP) stars in the halo of the Milky Way, based on high-resolution spectroscopic data for a total sample of 305 CEMP stars. The sample includes 147 CEMP-$s$ (and CEMP-r/s) stars, 127 CEMP-no stars, and 31 CEMP stars that are unclassified, based on the currently employed [Ba/Fe] criterion. We confirm previous claims that the distribution of $A$(C) for CEMP stars is (at least) bimodal, with newly determined peaks centered on $A$(C)$=7.96$ (the high-C region) and $A$(C)$=6.28$ (the low-C region). A very high fraction of CEMP-$s$ (and CEMP-r/s) stars belong to the high-C region, while the great majority of CEMP-no stars reside in the low-C region. However, there exists complexity in the morphology of the $A$(C)-[Fe/H] space for the CEMP-no stars, a first indication that more than one class of first-generation stellar progenitors may be required to account for their observed abundances. The two groups of CEMP-no stars we identify exhibit clearly different locations in the $A$(Na)-$A$(C) and $A$(Mg)-$A$(C) spaces, also suggesting multiple progenitors. The clear distinction in $A$(C) between the CEMP-$s$ (and CEMP-$r/s$) stars and the CEMP-no stars appears to be $as\ successful$, and$likely\ more\ astrophysically\ fundamental$, for the separation of these sub-classes as the previously recommended criterion based on [Ba/Fe] (and [Ba/Eu]) abundance ratios. This result opens the window for its application to present and future large-scale low- and medium-resolution spectroscopic surveys.Comment: 26pages, 7 figures, and 3 Tables ; Accepted for publication in ApJ; added more data and corrected minor inconsistencies existed in the compiled data of the previous studie

Trapping a Free-propagating Single-photon into an Atomic Ensemble as a Quantum Stationary Light Pulse

Efficient photon-photon interaction is one of the key elements for realizing quantum information processing. The interaction, however, must often be mediated through an atomic medium due to the bosonic nature of photons, and the interaction time, which is critically linked to the efficiency, depends on the properties of the atom-photon interaction. While the electromagnetically induced transparency effect does offer the possibility of photonic quantum memory, it does not enhance the interaction time as it fully maps the photonic state to an atomic state. The stationary light pulse (SLP) effect, on the contrary, traps the photonic state inside an atomic medium with zero group velocity, opening up the possibility of the enhanced interaction time. In this work, we report the first experimental demonstration of trapping a free-propagating single-photon into a cold atomic ensemble via the quantum SLP (QSLP) process. We conclusively show that the quantum properties of the single-photon state are preserved well during the QSLP process. Our work paves the way to new approaches for efficient photon-photon interactions, exotic photonic states, and many-body simulations in photonic systems

Rice genetic marker database: An identification of single nucleotide polymorphism (SNP) and quantitative trait loci (QTL) markers

The National Academy of Agricultural Science (NAAS) has developed a web-based genetic marker system to provide information about SNP and QTL markers in rice. The SNP marker database provides 7,227 SNP markers including location information on chromosomes by using genetic map. It allows users to access a detailed characterization table of 12,829 potential SNPs in 3,356 genes. The QTL marker database provides 175 QTL markers information with 942 polymorphic markers on each of the12 chromosomes in rice. Users are assisted in tracing any new structures of the chromosomes and gene positional functions through comparisons using specific SNP and QTL markers