Abstract: Urban heat island is intensified by anthropogenic activities and heat in conjunction with the built-up urban area, which absorbs more solar radiation during daytime and releases more heat during nighttime than rural areas. Air cooling systems in Singapore, as one of the anthropogenic heat sources, reject heat into the vicinity and consequently affect urban microclimate. In this paper, a new urban heat island modeling tool is developed to simulate stack effect of split type air-conditioners on high rise buildings and solar radiation induced thermal environment. By coupling the Computational Fluid Dynamics (CFD) program with the solar radiation model and perform parallel computing of conjugate heat transfer, the tool ensures both accuracy and efficiency in simulating air temperature and air relative humidity. The annual cycle of sun pathway in Singapore is well simulated and by decreasing the absorptivity or increasing the reflectivity and thermal conductivity of the buildings, the thermal environment around buildings could be improved.

Abstract: Heaving airfoils can provide invaluable physical insight regarding the flapping flight of birds and insects. We examine the thrust-generation mechanism of oscillating foils, by coupling two-dimensional simulations with multi-objective optimization algorithms. We show that the majority of the thrust originates from the creation of low pressure regions near the leading edge of the airfoil. We optimize the motion of symmetric airfoils exploiting the Knoller-Betz-Katzmayr effect, to attain high speed and lower energy expenditure. The results of the optimization indicate an inverse correlation between energy-efficiency, and the heaving-frequency and amplitude for a purely-heaving airfoil.

Abstract: We focus on a surface evolution problem where the surface is represented as a narrow-band level-set and the local surface speed is defined by a relation to the direct visibility of a source plane above the surface. A level-set representation of the surface can handle complex evolutions robustly and is therefore a frequently encountered choice. Ray tracing is used to compute the visibility of the source plane for each surface point. Commonly, rays are traced directly through the level-set and the already available (hierarchical) volume data structure is used to efficiently perform intersection tests. We present an approach that performs ray tracing on a temporarily generated explicit surface mesh utilizing modern hardware-tailored single precision ray tracing frameworks. We show that the overhead of mesh extraction and acceleration structure generation is compensated by the intersection performance for practical resolutions leading to an at least three times faster visibility calculation. We reveal the applicability of single precision ray tracing by attesting a sufficient angular resolution in conjunction with an integration method based on an up to twelve times subdivided icosahedron.

Abstract: The input workload of an irregular application must be evenly distributed among its threads to enable cutting-edge performance. To address this need in OpenMP, several loop scheduling strategies were proposed. While having this ever-increasing number of strategies at disposal is helpful, it has become a non-trivial task to select the best one for a particular application. Nevertheless, this challenge becomes easier to be tackled when existing scheduling strategies are extensively evaluated. Therefore, in this paper, we present a performance and scalability evaluation of the recently-proposed loop scheduling strategy named Smart Round-Robin (SRR). To deliver a comprehensive analysis, we coupled a synthetic kernel benchmarking technique with several rigorous statistical tools, and considered OpenMP's Static and Dynamic loop schedulers as our baselines. Our results unveiled that SRR performs better on irregular applications with symmetric workloads and coarse-grained parallelization, achieving up to 1.9x and 1.5x speedup over OpenMP's Static and Dynamic schedulers, respectively.

Abstract: Effect of small molecules, as they penetrate into a polymer system, is investigated via molecular dynamics simulations. It is found that small spherical particles reduce the glass transition temperature and thus introduce a softening of the material. Results are compared to experimental findings for the effect of different types of small molecules such as water, acetone and ethanol on the glass transition temperature of a polyurethane-based shape memory polymer. Despite the simplicity of the simulated model, MD results are found to be in good qualitative agreement with experimental data.

Abstract: The simulation of the behavior of the Human Brain is one of the most important challenges today in computing. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulation need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Ca capacitance on neurons’ morphology. This is carried out using the Hines Algorithm. Although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on NVIDIA GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. To evaluate the impact of the optimizations on real inputs, we have used 6 different morphologies in terms of size and branches. Our studies have proven that the optimizations proposed in the present work can achieve a high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two K80 NVIDIA GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling even when dealing with number of neurons.

Abstract: Computational kinematics is a fundamental tool for the design, simulation, control, optimization and dynamic analysis of multibody systems - mechanical systems whose bodies are connected by joints which allow relative movement. The analysis of complex multibody systems and the need for real time solutions requires the development of kinematic and dynamic formulations that reduces computational cost, the selection and efficient use of the most appropriated solvers and the exploiting of all the computer resources using parallel computing techniques. The topological approach based on group equations and natural coordinates reduces the computation time in comparison with well-known global formulations and enables the use of parallelism techniques which can be applied at different levels: simultaneous solution of equations, use of multithreading routines for each equation, or a combination of both. This paper studies and compares these topological formulation and parallel techniques to ascertain which combination performs better in two applications. The first application is the use of dedicated systems for the real time control of small multibody systems, defined by a few number of equations and small linear systems, so shared-memory parallelism in combination with linear algebra routines is analyzed in a small multicore and in Raspberry Pi. The control of a Stewart platform is used as a case study. The second application is the study of large multibody systems in which the kinematic analysis must be performed several times during the design of multibody systems. A simulator which allows us to control the formulation, the solver, the parallel techniques and size of the problem has been developed and tested in more powerful computational systems with larger multicores and GPU.

Abstract: The growing interest to incorporate new features into mobile devices has increased the number of signal processing applications running over processors designed for mobile computing. A challenging signal processing field is acoustic source localization, which is attractive for applications such as automatic camera steering systems, human-machine interfaces, video gaming or audio surveillance. In this context, the emergence of systems-on-chip (SoC) that contain a small graphics accelerator (or GPU), contributes a notable increment of the computational capacity while partially retaining the appealing low-power consumption of embedded systems. This is the case, for example, of the Samsung Exynos 5422 SoC that includes a Mali-T628 MP6 GPU. This work evaluates an OpenCL-based implementation of a method for sound source localization, namely, the Steered-Response Power with Phase Transform (SRP-PHAT) algorithm, on GPUs of this type. The results show that the proposed implementation can work in real time with high-resolution spatial grids using up to 12 microphones.

Abstract: Complex diseases may result from many genetic variants interacting with each other. For this reason, genome-wide interaction studies (GWIS) are currently performed to detect pairwise SNP interactions. While the computations required here can be completed within reasonable time, it has been inconvenient yet to detect third-order SNP interactions for large-scale datasets due to the cubic complexity of the problem. In this paper we introduce a feasible method for third-order GWIS analysis of genotyping data on a low-cost heterogeneous computing system that combines a Virtex-7 FPGA and a GeForce GTX 780 Ti GPU, with speedups between 70 and 90 against a CPU-only approach and a speedup of approx. 5 against a GPU-only approach. To estimate effect sizes of third-order interactions we employed information gain (IG), a measure that has been applied on a genome-wide scale only for pairwise interactions in the literature yet.

Abstract: This paper presents new algorithmic approaches and optimization techniques for LU factorization and matrix inversion of millions of very small matrices using GPUs. These problems appear in many scientific applications including astrophysics and generation of block-Jacobi preconditioners. We show that, for very small problem sizes, design and optimization of GPU kernels require a mindset different from the one usually used when designing LAPACK algorithms for GPUs. Techniques for optimal memory traffic, register blocking, and tunable concurrency are incorporated in our proposed design. We also take advantage of the small matrix sizes to eliminate the intermediate row interchanges in both the factorization and inversion kernels. The proposed GPU kernels achieve performance speedups vs. CUBLAS of up to 6x for the factorization, and 14x for the inversion, using double precision arithmetic on a Pascal P100 GPU.

Abstract: [No abstract available]

Abstract: We consider how the orthology/paralogy information can be corrected in order to represent a gene tree, a problem that has recently gained interest in phylogenomics. Interestingly, the problem is related to the Minimum CoGraph Editing problem on the relation graph that represents orthology/paralogy information, where we want to minimize the number of edit operations on the given relation graph in order to obtain a cograph. In this paper we provide both theoretical and experimental results on the Minimum CoGraph Editing problem. On the theoretical side, we provide approximation algorithms for bounded degree relation graphs, for the general problem and for the problem restricted to deletion of edges. On the experimental side, we present a genetic algorithm for Minimum CoGraph Editing and we provide an experimental evaluation of the genetic algorithm on synthetic data.

Abstract: MicroRNAs (miRNAs) are small non-coding RNA molecules that have an important role in a wide range of biological processes, since they interact with specific mRNAs affecting the expression of the corresponding proteins. The role of miRNA can be deeply influenced by Single Nucleotide Polymorphisms (SNPs), in particular in their seed sites, since these variations may modify their affinity with particular transcripts, but they may also generate novel binding capabilities for specific miRNA binding sites or destroy them. Several computational tools for miRNA-target site predictions have been developed, but the obtained results are often not in agreement, making the study the binding sites hard, and the analysis of SNP effects even harder. For these reasons, we developed a web application called Rank miRNA, which allows to retrieve and aggregate the results of three prediction tools, but also to process and compare new input miRNA sequences, allowing the analysis of how variations impact on their function. Therefore, our tool is also able to predict the impact of SNPs (and any other kind of variations) on miRNA-mRNA binding capability and also to find the target genes of (potentially new) miRNA sequences. We evaluated the performance of Rank miRNA on specific human SNPs, which are likely to be involved in several mental disorder diseases, showing the potentiality of our tool in helping the study of miRNA-target interactions.

Abstract: In high-variation genomics datasets, such as found in metagenomics or complex polyploid genome analysis, error detection and variant calling are impeded by the difficulty in discerning sequencing errors from actual biological variation. Confirming base candidates with high frequency of occurrence is no longer a reliable measure, because of the natural variation and the presence of rare bases. This work employs machine learning models to classify bases into erroneous and rare variations, after preselecting potential error candidates with a weighted frequency measure, which aims to focus on unexpected variations by using the inter-sequence pairwise similarity. Different similarity measures are used to account for different types of datasets. Four machine learning models are tested.

Abstract: The human brain is a complex organ. An important first step to understand the function of such network is to model and to analyze its elements and connections, i.e. the connectome, in order to achieve a comprehensive description of the network. In this work we apply the graph theory formalisms to represent the connectomes. The human brain connectomes are usually derived from neuroimages; then an atlas-free random parcellation is used to define network nodes of individual brain networks. In this network domine, the question of comparison of the structure of networks arises. Such issue may be modeled as a network alignment (NA) problem. The use of different NA approaches, widely applied in molecular biology, has not been explored in relation to MRI connectomics. In this paper, we first defined the problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

Abstract: Chinese segmentation has attracted amounts of attention in natural language processing in recent years and is the basis of web text mining. The article improved statistics-based method EMI, then we use improved approach to detect new words in tourism field. The result demonstrates that our method can detect new words significantly, especially in detecting proper nouns and sentiment words which will be helpful in subsequent tasks such as sentiment analysis and word embedding. In additional, this paper analyze parameters which are influential on the effects of new words detection. At last, the article discussed possible application of new word detection in sentiment analysis.

Abstract: Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are considered as the popular leaning techniques, their performance with kernels are often suffering from time consuming. Recently, linear SVC without kernels not only is shown to obtain competitive performance in most of the cases, but also it is considerably faster in training and testing process. However, a few studies have focused on linear SVM-based ordinal regression models. In this paper, we proposed an efficient solver for training the linear Nonparallel Support Vector Ordinal Regression based on alternating direction method of multipliers (ADMM). Our experiments show that the proposed algorithm is suitable for training large document ordinal regression and efficiently obtained desired results.

Abstract: This paper analyzes the influence of capital operations on the performance of listed companies under different market conditions by combining various capital operation modes with the market value management. Random Forest algorithm is adopted and other machine learning methods are used to compare. We find that capital operation is significantly related to market value management and different capital operations have different effects on companies in different market conditions. In addition, Random Forest algorithm has the highest classification accuracy in different market environments and is more stable under different thresholds. Our findings will help to establish a market or industry benchmark which provides a scientific basis and decision support to the target companies when they operate their capitals.

Abstract: Recent years have witnessed more and more success of hash methods for building efficient classifiers, but less for prediction in machine learning. In this paper, we propose a hash based method for large scale nonparallel support vector machine prediction(HNPSVM). Our key idea of this method is that we use an approximal decision function instead of exact decision function by computing the Hamming distance between hashing the normal to the hyperplane of the classifier and the features. This method benefits nonparallel support vector(NPSVM) prediction in three aspects. First, it enhances the prediction accuracy using an flexible and general method. Second, the proposed HNPSVM reduce storage cost owing to the compact binary hash representation. Last, HNPSVM can speed up the computation of classification function. Moreover, we prove that the classification results of a hash based NPSVM classifier converge to the results of the exact NPSVM classifier as the number of binary hash functions tends to infinity. Several experiments on large scale data sets show the efficient of our method.

Abstract: Recently, two-sided best fitting hyperplane classifier (2S-BFHC) is proposed, which has several significant advantages over previous proximal hyperplane classifiers. Moreover, Concave-Convex Procedure (CCCP) has already been provided to solve the dual problem of 2S-BFHC. In this paper, we solve the primal problem of 2S-BFHC by the alternating direction method of multipliers (ADMM) which is well suited to solve the distributed optimization problem, and we also propose a robust L1-norm two-sided best fitting hyperplane classifier (L1-2S-BFHC) with ADMM, which aims at giving a robust performance for the problem with outliers. Priliminary numerical results demonstrate the effectiveness of proposed methods.

Abstract: In this paper, we have presented a strategy for studying a large observational dataset at different resolutions to obtain a sparse representation in a computationally efficient manner. Such representations are crucial for many applications from modeling and inference to visualization. Resolution here stems from the variation of the correlation strength among the different observation instances. The motivation behind the approach is to make a large dataset as small as possible by removing all the redundant information so that, the original data can be reconstructed with minimal losses of information.Our past work borrowed ideas from multilevel simulations to extract a sparse representaiton. Here, we introduce the use of multi-resolution kernels. We have tested our approach on a carefully designed suite of analytical functions along with gravity and altimetry time series datasets from a section of the Greenland Icesheet. In addition to providing a good strategy for data compression, the proposed approach also finds application in efficient sampling procedures and error filtering in the datasets. The results, presented in the article clearly establish the promising nature of the approach along with prospects of its application in different fields of data analytics in the scientific computing and related domains.

Abstract: To make a Bayesian prediction of the chances of a volcanic hazard impacting a particular region requires an estimate of the mass flow consequent to an eruption, for tens of thousands of input parameters. These inputs include physical parameters, computational factors, and spatial locations. Mass flow estimates can be determined by computer simulations, which are often too slow to be used for all the necessary input evaluations. Statistical emulators provide a very fast procedure for estimating the mass flow, along with a measure of the error in that estimate. But construction of many classical emulators, such as the GAussian Stochastic Process emulator requires inversion of a covariance matrix whose dimension is equal to the number of inputs – again, too slow to be useful. To speed up the emulator construction, some down sample the input space, which ignores expensive and potentially important simulation results. Others propose truncating the covariance to a small-width diagonal band, which is easy to invert. Here we propose an alternative method. We construct a localized emulator around every point at which the mass flow is to be estimated, and tie these localized processes together in a hierarchical fashion. We show how this approach fits into a theory of Gauss-Markov Random Fields, to demonstrate the efficacy of the approach.

Abstract: This paper aims at generating as well as extracting design strategies for a real world problem using an evolutionary learning classifier system. Data mining for a design optimization result as a virtual database specifies design information and discovers latent design knowledge; it is essential for decision making in real world problems. Although we employed several methods from classic statistics to artificial intelligence to obtain design information from optimization results, we may not cognize anything beyond a prepared database. In this study, we have applied an evolutionary learning classifier system as a data mining technique to a real world engineering problem. Consequently, not only it extracted known design information but also it successfully generated design strategies not to extract from the database. The generated design rules do not physically become innovative knowledge because the prepared dataset include Pareto solutions owing to complete exploration to the edge of the feasible region in the optimization. However, this problem is independent of the method; our evolutionary learning classifier system is a useful method for incomplete datasets.

Abstract: In the research field of remote sensing, the recent growth of image sizes draws a remarkable attention for processing these files in a distributed architecture. Divide-and conquer rule is the main attraction in the analysis of scalable algorithm. On the other hand, fault tolerance in data parallelism, is the new aspect of requirement. In this regard, Apache Hadoop architecture becomes a promising and an efficient MapReduce model. In the satellite image processing, large scale images put the limitation on the single computer analysis. Whereas, Hadoop Distributed File System (HDFS) gives a remarkable solution to handle these files through its inherent data parallelism technique. This architecture is well suited for structured data, as the structured data can be equally distributed easily and be accessed selectively in terms of relevancy of data. Images are considered as unstructured matrix data in Hadoop and the whole part of the data is relevant for any processing. Naturally, it becomes a challenge to maintain data locality with equal data distribution. In this paper, we introduce a novel technique, which decrypts the standard format of raw satellite data and localizes the distributed preprocessing step on the equal split of datasets in Hadoop. For this purpose, a suitable modification on the Hadoop interface is proposed. For the case study on scalability of preprocessing steps, Synthetic Aperture Radar (SAR) and Multispectral (MS), are used in distributed environment.

Abstract: Malware has been the primary threat to computer and network for years.Traditionally, supervised learning methods are applied to detect malware. But supervised learning models need a great number of labeled samples to train models beforehand, and it is impractical to label enough malicious code manually. Insufficient training samples yields imperfect detection models and satisfactory detection result could not be obtained as a result. In this paper, we bring out a new algorithm call collaborative SVM based on semi-supervised learning and independent component analysis. With collaborative SVM, only a few labeled samples is needed while the detection result keeps in a high level. Besides, we propose a general framework with independent components analysis, with which to reduce the restricted condition of collaborative train. Experiments prove the efficiency of our model finally.

Abstract: Many scientific works have focused on developing propagation models that predict forest fires behavior. These models require a precise knowledge of the environment where the fire is taking place. Geographical Information Systems allow us determining and building the different information layers that define the terrain and the fire. These data, along with meteorological information from weather services, enables the simulation based on real conditions. However, fire spread prediction models require a set of input parameters that, in some cases, are difficult to know or even estimate precisely. Therefore, a framework, based on a genetic algorithm calibration stage, was introduced to reduce the uncertainty in the input parameters and improve the accuracy of the predictions. This stage is implemented using a MPI master/worker scheme and an OpenMP parallel version of the fire spread simulator. Additionally, the whole system is run using suitable automatic worker-assignment and core-allocation policies to respect the existing time restrictions, inherent to this real-world problem. This paper details the process of obtaining the necessary input data as well as the parallel evolutionary framework that delivers the final prediction. A real case study is presented to illustrate the way this framework works.

Abstract: Hydraulic fracturing is a procedure of injecting high pressure fluid into the wellbore in order to break shell rock and facilitate gas flow. It is a very costly procedure and, if not conducted properly, it may lead to environment contamination. To avoid costs associated with pumping fluid outside the perspective (gas rich) zone and improve one’s knowledge about the reservoir rock, microseismic monitoring can be applied. The method involves recording seismic waves, which are induced by fractured rock, by an array of sensors distributed in a wellbore nearby or on the surface. Combining geological and geophysical knowledge of region with signal processing technics, one can locate induced fractures allowing for real-time process monitoring and rock properties evaluation. In Poland perspective shell formation is located very deep -about 4km from the surface. Additionally overlaying rock formations strongly attenuate and disperse seismic waves. Therefor signal recorded by a surface array of sensors is very weak. Signal from a seismic event can be orders of magnitude lower than noise. To recover signal connected with fractured rock one needs to use methods utilizing coherence of signals. An example of such analysis procedure is presented.

Abstract: This study introduces a web-based visual analytic framework to better understand the software structures of large-scale environmental models. The framework integrates data management, software structures analysis, and web-based visualizations. A system for the Community Land Model (CLM) is developed to demonstrate the capability of the proposed framework. It consists of three major components: (1) a Fortran-syntax analysis tool that decomposes CLM source code into simpler forms; (2) an application tier that further analyzes and converts the preprocessed data into meaningful software structural information; (3) a web-based front end that is developed using state-of-the-art web technologies and visualization toolkit (e.g., D3.js). The framework provides users with easy access to the internal structures of complex environmental models. Currently, the prototype system is being used by CLM modelers and field scientists to tackle different environmental research problems.

Abstract: The authors propose a three-tier framework model that can be used to conceptualise and study the overall economic and ecological impact of large-scale applied environmental modelling services. While some of these services (such as weather forecast) have clear, near-universally accepted positive net impact on society, there are many emerging candidate services whose sustainability requires systematic cost-benefit analysis. We think that the proposed model should facilitate both performing this analysis, and verifying and communicating the results e.g. to funding agencies or general public. The tiers we propose consist of: 1. Economical and ecological operational costs (OPEX) that are directly related to the execution of the modelling steps (computing and data processing). These include power consumption, salaries and office costs of the IT service staff – and the associated environmental impacts. 2. The economic and environmental capital expenditures (CAPEX) that consists of IT infrastructure investments (either direct, on-premises ones or ones at Cloud service providers’ presemises), investments in buildings and other infrastructure, immaterial costs (e.g. software development or license fees) and so on. 3. The environmental impact of the real-world application of the new modelling services. The impact assessment should consider both the direct impact (reduced risks, optimised logistics) as well as the possible indirect impacts (increased volume of shipments due to reduced costs and risks offsetting per-shipment optimisation). We will present some examples of how to link these analysis components together in specific use cases, ranging from disaster risk reduction to international logistics. We will also discuss the challenges related to timescales involved: for example, OPEX represents an immediate cost, whereas the third-tier impact of disaster preparedness for a natural hazard scenario with very long return period (“100 year earthquake”) introduces considerable uncertainties to short-term cost-benefit calculations.