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Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

Energy consumption has always been one of the main design problems in heterogeneous distributed systems, whether for large cluster computer systems or small handheld terminal devices. And as energy consumption explodes for complex performance, many efforts and work are focused on minimizing the schedule length of parallel applications that meet the energy consumption constraints currently. In prior studies, a pre-allocation method based on dynamic voltage and frequency scaling (DVFS) technology allocates unassigned tasks with minimal energy consumption. However, this approach does not necessarily result in minimal scheduling length. In this paper, we propose an enhanced scheduling algorithm, which allocates the same energy consumption for each task by selecting a relatively intermediate value among the unequal allocations. Based on the two real-world applications (Fast Fourier transform and Gaussian elimination) and the randomly generated parallel application, experiments show that the proposed algorithm not only achieves better scheduling length while meeting the energy consumption constraints, but also has better performance than the existing parallel algorithms.

The objective of this paper is to explain the notion of fine-grain transformations (FGTs), showing how they can be used as prototypical building blocks for constructing refactorings of a design-level system description. FGT semantics are specified in terms of pre- and postconditions which, in turn, also determines the sequential dependency relationships between them. An algorithm is provided which uses sequential dependency relationships to convert an FGT-list to a set of so-called FGT-DAGs. It is shown how to compute the precondition of such ordered collections of FGTs. The paper introduces a new approach to deal with refactoring pre- and postconditions by defining them at two different levels. To give these concepts syntactical form, we rely on the Prolog formats used by an FGT-based refactoring prototype tool. An example is provided to illustrate the various concepts and to demonstrate that, because of their simplicity, well-defined pre-post semantics and their intuitive nature, FGTs provide a pragmatic basis for building refactorings.

The tetrad representation theorem, due to Spirtes, Glymour, and Scheines (1993), gives a graphical condition necessary and sufficient for the vanishing of an individual tetrad difference in a recursive path model with uncorrelated errors. In this paper, we generalize their result from individual tetrad differences to sets of tetrad differences of a certain form, and we simplify their proof. The generalization allows tighter constraints to be placed on the set of models compatible with given data and thereby facilitates the search for parsimonious models for large data sets.

Post hoc assignment of patterns determined by all pairwise comparisons in microarray experiments with multiple treatments has been proven to be useful in assessing treatment effects. We propose the usage of transitive directed acyclic graphs (tDAG) as the representation of these patterns and show that such representation can be useful in clustering treatment effects, annotating existing clustering methods, and analyzing sample sizes. Advantages of this approach include: (1) unique and descriptive meaning of each cluster in terms of how genes respond to all pairs of treatments; (2) insensitivity of the observed patterns to the number of genes analyzed; and (3) a combinatorial perspective to address the sample size problem by observing the rate of contractible tDAG as the number of replicates increases. The advantages and overall utility of the method in elaborating drug structure activity relationships are exemplified in a controlled study with real and simulated data.

Age-related Macular Degeneration (AMD) is the prime reason for vision impairment observed in major countries worldwide. Hence an accurate early detection of the disease is vital for more research in this area. Also, having a thorough eye diagnosis to detect AMD is a complex job. This paper introduces a Directed Acyclic Graph (DAG) structure-based Convolutional Neural network (CNN) architecture to better classify Dry or Wet AMD. The DAG architecture can combine features from multiple layers to provide better results. The DAG model also has the capacity to learn multi-level visual properties to increase classification accuracy. Fine tuning of DAG-based CNN model helps in improving the performance of the network. The training and testing of the proposed model are carried out with the Mendeley data set and achieved an accuracy of 99.2% with an AUC value of 0.9999. The proposed model also obtains better results for other parameters such as precision, recall and F1-score. Performance of the proposed network is also compared to that of the related works performed on the same data set. This shows ability of the proposed method to grade AMD images to help early detection of the disease. The model also performs computationally efficient for real-time applications as it does the classification process with few learnable parameters and fewer Floating-Point Operations (FLOPs).

The introduction of Optical Coherence Tomography (OCT) in ophthalmology has resulted in significant progress in the early detection of glaucoma. Traditional approaches to identifying retinal diseases comprise an analysis of medical history and manual assessment of retinal images. Manual diagnosis is time-consuming and requires considerable human expertise, without which, errors could be costly to human sight. The use of artificial intelligence such as machine learning techniques in image analysis has been gaining ground in recent years for accurate, fast and cost-effective diagnosis from retinal images. This work proposes a Directed Acyclic Graph (DAG) network that combines Depthwise Convolution (DC) to decisively recognize early-stage retinal glaucoma from OCT images. The proposed method leverages the benefits of both depthwise convolution and DAG. The Convolutional Neural Network (CNN) information obtained in the proposed architecture is processed as per the partial order over the nodes. The Grad-CAM method is adopted to quantify and visualize normal and glaucomatous OCT heatmaps to improve diagnostic interpretability. The experiments were performed on LFH_Glaucoma dataset composed of 1105 glaucoma and 1049 healthy OCT scans. The proposed faster hybrid Depthwise-Directed Acyclic Graph Network (D-DAGNet) achieved an accuracy of 0.9995, precision of 0.9989, recall of 1.0, F1-score of 0.9994 and AUC of 0.9995 with only 0.0047 M learnable parameters. Hybrid D-DAGNet enhances network training efficacy and significantly reduces learnable parameters required for identification of the features of interest. The proposed network overcomes the problems of overfitting and performance degradation due to accretion of layers in the deep network, and is thus useful for real-time identification of glaucoma features from retinal OCT images.