Networks beyond pairwise interactions: Structure and dynamics

The talk describes the £ 120M UK ‘e-Science’ initiative and begins by defining what is meant by the term e-Science. The majority of the £ 120M, some £ 85M, is for the support of large-scale e-Science projects in many areas of science and engineering. The infrastructure needed to support such projects needs to allow sharing of distributed and heterogeneous computational and data resources and support effective collaboration between groups of scientists. Such an infrastructure is commonly referred to as the Grid. The remaining funds, some £ 35M, constitute the e-Science ‘Core Program’ and is intended to encourage the development of robust and generic Grid middleware in collaboration with industry. The key elements of the Core Program will be described including the construction of a UK e-Science Grid. In addition, the talk will include a description of the pilot projects that have so far received funding. These span a range of disciplines from particle physics and astronomy to engineering and healthcare. We conclude with some remarks about the need to develop a data architecture for the Grid that will allow federated access to relational databases as well as flat files. H. Zima et al. (Eds.): ISHPC 2002, LNCS 2327, p. 6, 2002. c © Springer-Verlag Berlin Heidelberg 2002 SPEC HPC2002: The Next High-Performance Computer Benchmark

High Performance Computing

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

Parametric deep energy approach for elasticity accounting for strain gradient effects

Using line-of-sight Michelson Doppler Imager (MDI) magnetograms of 89 active regions and Solar Geophysical Data (SGD) flare reports, we explored, for the first time, the magnitude scaling correlations between three parameters of magnetic fields and the flare productivity of solar active regions. These parameters are (1) the mean value of spatial magnetic gradients at strong-gradient magnetic neutral lines, ()NL; (2) the length of strong-gradient magnetic neutral lines, LGNL; and (3) the total magnetic energy, (Bz) dA, dissipated in a layer of 1 m during 1 s over the active region's area. The MDI magnetograms of active regions used for our analysis are close to the solar central meridian (within ±10°). The flare productivity of active regions was quantified by the soft X-ray flare index for different time windows from the time interval of the entire disk passage down to +1 day from the time of the analyzed magnetogram. Our results explicitly indicate positive correlations between the parameters and the overall flare productivity of active regions, and imminent flare production as well. The correlations confirm the dependence of flare productivity on the degree of nonpotentiality of active regions.

The statistical relationship between the photospheric magnetic parameters and the flare productivity of active regions

With the rapid development of human society, the urbanization of the world's population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus providing them with a more convenient and comfortable living environment. The new urban framework, the smart city, provides excellent opportunities to meet these challenges, while solving urban problems at the same time. At this stage, many countries are actively responding to calls for smart city development plans. This paper investigates the current stage of the smart city. First, it introduces the background of smart city development and gives a brief definition of the concept of the smart city. Second, it describes the framework of a smart city in accordance with the given definition. Finally, various intelligent algorithms to make cities smarter, along with specific examples, are discussed and analyzed.

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A Survey on Algorithms for Intelligent Computing and Smart City Applications

We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either "horizontally" - by adding more prediction-specific parameters, or "vertically" - by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future.

/pdf/multivariate-time-series-dataset-for-space-weather-data-21gf7bs82v.pdf

Multivariate time series dataset for space weather data analytics

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

Convolutional Neural Networks (CNN) have gained great success in many artificial intelligence tasks However, finding a good set of hyperparameters for a CNN remains a challenging task It usually takes an expert with deep knowledge, and trials and errors Genetic algorithms have been used in hyperparameter optimizations However, traditional genetic algorithms with fixed-length chromosomes may not be a good fit for optimizing deep learning hyperparameters, because deep learning models have variable number of hyperparameters depending on the model depth As the depth increases, the number of hyperparameters grows exponentially, and searching becomes exponentially harder It is important to have an efficient algorithm that can find a good model in reasonable time In this article, we propose to use a variable length genetic algorithm (GA) to systematically and automatically tune the hyperparameters of a CNN to improve its performance Experimental results show that our algorithm can find good CNN hyperparameters efficiently It is clear from our experiments that if more time is spent on optimizing the hyperparameters, better results could be achieved Theoretically, if we had unlimited time and CPU power, we could find the optimized hyperparameters and achieve the best results in the future

Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm.

Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges, one of which is to increase the depth of the neural networks. Such deeper networks not only increase training times, but also suffer from vanishing gradients problem while training. In this work, we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates. We perform experiments on VGG-19 and Resnet models (Resnet-18 and Resnet-34), and study the impact of amplification parameters on these models in detail. Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time.

/pdf/gradient-amplification-an-efficient-way-to-train-deep-neural-4hhegjydbd.pdf

Sunitha Basodi

Papers

A Survey on Algorithms for Intelligent Computing and Smart City Applications

Multivariate time series dataset for space weather data analytics

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm.

Gradient Amplification: An Efficient Way to Train Deep Neural Networks