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Determination of topological invariants of graphene flakes, nanotubes, and fullerenes constitutes a challenging task due to its time-intensive nature and exponential scaling. The invariants can be organized in a form of a combinatorial polynomial commonly known as the Zhang-Zhang (ZZ) polynomial or the Clar covering polynomial. We report here a computer program, ZZPolyCalc, specifically designed to compute ZZ polynomials of large carbon nanostructures. The curse of the exponential scaling is avoided for a broad class of nanostructures by employing a sophisticated bookkeeping algorithm, in which each fragment appearing in the recursive decomposition is stored in the cache repository of molecular fragments indexed by a hash of the corresponding adjacency matrix. Although exponential scaling persists for the remaining nanostructures, the computational time is reduced by a few orders of magnitude owing to efficient use of hash-based fragment bookkeeping. The provided benchmark timings show that ZZPolyCalc allows for treating much larger carbon nanostructures than previously envisioned. Program Title: ZZPolyCalc CPC Library link to program files: https://doi.org/10.17632/vmnr5xfb6t.1 Developer's repository link: https://github.com/quantumint/zzpolycalc Licensing provisions: GPLv3 Programming language: Fortran 2008, C Nature of problem: Calculations of Zhang-Zhang (ZZ) polynomials for carbon nanostructures have traditionally been performed by the application of a recursive algorithm, which scales exponentially with the size of the system. For large nanostructures, the problem often becomes intractable due to prohibitive computational cost. Consequently, ZZ polynomials were reported so far only for small or model systems, for which analytical formulas could be determined; larger systems, such as graphene flakes, fullerenes, or nanotubes, have been intractable with the existing implementations. Solution method: The process of determination of ZZ polynomials, based on recursive decomposition of a given carbon nanostructure, produces many identical fragments. In ZZPolyCalc, we avoid redundant calculations for such fragments by storing all the intermediate data in a hash table and reusing it efficiently whenever necessary. This new algorithm is particularly effective for elongated graphene flakes or carbon nanotubes, where the scaling becomes polynomial, but it also greatly reduces the computational time for other, less regular nanostructures. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) can solve structured multioutput learning problems such as multilabel classification, multiclass classification, and vector regression. SVM training is expensive, especially for large and high-dimensional data sets. The bottleneck of the SVM training often lies in the kernel value computation. In many real-world problems, the same kernel values are used in many iterations during the training, which makes the caching of kernel values potentially useful. The majority of the existing studies simply adopt the least recently used (LRU) replacement strategy for caching kernel values. However, as we analyze in this article, the LRU strategy generally achieves high hit ratio near the final stage of the training but does not work well in the whole training process. Therefore, we propose a new caching strategy called EFU (less frequently used), which replaces the EFU kernel values that enhance least frequently used (LFU). Our experimental results show that EFU often has 20% higher hit ratio than LRU in the training with the Gaussian kernel. To further optimize the strategy, we propose a caching strategy called hybrid caching for the SVM training (HCST), which has a novel mechanism to automatically adapt the better caching strategy in different stages of the training. We have integrated the caching strategy into ThunderSVM, a recent SVM library on many-core processors. Our experiments show that HCST adaptively achieves high hit ratios with little runtime overhead among different problems including multilabel classification, multiclass classification, and regression problems. Compared with other existing caching strategies, HCST achieves 20% more reduction in training time on average. [ABSTRACT FROM AUTHOR]

• We propose modified single-layer nonlinear Convolutional Neural Network (MCNN) based user behavior analysis method to discover context-aware user preferences. The extraction of user behavior features makes full use of the user-centric effect to reduce the feature dimensions. • We subsequently propose a contextual zoom caching algorithm to provide online learning ability for content popularity with diversity according to context-aware user behavior preferences. • Smart-caching makes smart caching decisions on the mobile edge according to user demands within the MEC architecture. It provides good performance in terms of cache hit ratio and stability with low complexity and overhead. With the explosive growth in wireless data traffic, it is difficult to support the massive consumption of backhaul link bandwidth. Mobile edge computing (MEC) uses edge caching to store content closer to the end user in case to reduce the computational overhead and latency. Caching decisions depend on user demand, which is associated with user behavior. Popular content shows the preferences of user behavior. Combining user context behavior to benefit caching, our optimization objective is to maximize the hit rate of contents within limited time. This optimization problem can be modeled as a knapsack problem. Even if the context and content popularity are known for all circumstances, it is still NP-hard. We thus propose a heuristic Smart caching algorithm (Smart-caching) within the MEC paradigm. Smart-caching constructs the user behavior preferences to reflect different user demands for content. Based on this construct, Smart-caching employs contextual zoom caching to find the context-aware content popularity with diversity via partitioning and exploring the context space. Our experiments are implemented with a real large-volume dataset, i.e., China Mobile User Detail Record (UDR). The results show that Smart-caching achieves a better cache hit rate and stability with low overhead compared with other related caching algorithms. [ABSTRACT FROM AUTHOR]

The article reports that Laboratory researchers have demonstrated an approach that uses the Lawrence Livermore Microbial Detection Array (LLMDA) and the Livermore Metagenomic Analysis Toolkit (LMAT) for helping physicians identifying and quantifying microbes to improve patient care.

Abstract: The growing storage capacity of flash memory (up to 640GB) and the proliferation of small mobile devices such as PDAs and mobile phones makes it attractive to build database management systems (DBMSs) on top of flash memory. However, most existing DBMSs are designed to run on hard disk drives. The unique characteristics of flash memory make the direct application of these existing DBMSs to flash memory very energy inefficient and slow. The relatively few DBMSs that are designed for flash suffer from two major short-comings. First, they do not take full advantage of the fact that updates to tuples usually only involve a small percentage of the attributes. A tuple refers to a row of a table in a database. Second, they do not cater for the asymmetry of write versus read costs of flash memory when designing the buffer replacement algorithm. In this paper, we have developed algorithms that address both of these short-comings. We overcome the first short-coming by partitioning tables into columns and then group the columns based on which columns are read or updated together. To this end, we developed an algorithm that uses a cost-based approach, which produces optimal column groupings for a given workload. We also propose a heuristic solution to the partitioning problem. The second short-coming is overcome by the design of the buffer replacement algorithm that automatically determines which page to evict from buffer based on a cost model that minimizes the expected read and write energy usage. Experiments using the TPC-C benchmark [S.T. Leutenegger, D. Dias, A modeling study of the TPC-C benchmark, in: Proceedings of ACM SIGMOD, 1993, pp. 22–31] show that our approach produces up to 40-fold in energy usage savings compared to the state-of-the-art in-page logging approach. [Copyright &y& Elsevier]

Level 3 Communications has reached an agreement with Telekom Malaysia (TM) to provide internet localisation services. [ABSTRACT FROM AUTHOR]

SEACOM has announced a partnership with content delivery company CDNetworks to deploy content clusters and caching nodes on its pan-African IP network in Dar es Salaam, Tanzania. [ABSTRACT FROM AUTHOR]