Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection

Community detection is an essential task in network analysis as it helps identify groups and patterns within a network. High-speed community detection algorithms are necessary to analyze large-scale networks in a reasonable amount of time. Researchers have made significant contributions in the development of high-speed community detection algorithms, particularly in the area of label-propagation based disjoint community detection. These algorithms have been proven to be highly effective in analyzing large-scale networks in a reasonable amount of time. However, it is important to evaluate the performance and accuracy of these existing methods to determine which algorithm is best suited for a particular type of network and specific research problem. In this report, we investigate the RAK, COPRA, and SLPA, three label-propagation-based static community discovery techniques. We pay close attention to each algorithm's minute details as we implement both its single-threaded and multi-threaded OpenMP-based variants, making any necessary adjustments or optimizations and obtaining the right parameter values. The RAK algorithm is found to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with 12 threads was 6.75x faster than the sequential non-strict RAK. The COPRA algorithm works well with a single label for road networks and max labels of 4-16 for other classes of graphs. The SLPA algorithm performs well with increasing memory size, but overall doesn't offer a favourable return on investment. The RAK algorithm is recommended for label-propagation based disjoint community detection.Comment: 11 pages, 1 tabl

Structural learning for large scale image classification

To leverage large-scale collaboratively-tagged (loosely-tagged) images for training a large number of classifiers to support large-scale image classification, we need to develop new frameworks to deal with the following issues: (1) spam tags, i.e., tags are not relevant to the semantic of the images; (2) loose object tags, i.e., multiple object tags are loosely given at the image level without their locations in the images; (3) missing object tags, i.e. some object tags are missed due to incomplete tagging; (4) inter-related object classes, i.e., some object classes are visually correlated and their classifiers need to be trained jointly instead of independently; (5) large scale object classes, which requires to limit the computational time complexity for classifier training algorithms as well as the storage spaces for intermediate results. To deal with these issues, we propose a structural learning framework which consists of the following key components: (1) cluster-based junk image filtering to address the issue of spam tags; (2) automatic tag-instance alignment to address the issue of loose object tags; (3) automatic missing object tag prediction; (4) object correlation network for inter-class visual correlation characterization to address the issue of missing tags; (5) large-scale structural learning with object correlation network for enhancing the discrimination power of object classifiers. To obtain enough numbers of labeled training images, our proposed framework leverages the abundant web images and their social tags. To make those web images usable, tag cleansing has to be done to neutralize the noise from user tagging preferences, in particularly junk tags, loose tags and missing tags. Then a discriminative learning algorithm is developed to train a large number of inter-related classifiers for achieving large-scale image classification, e.g., learning a large number of classifiers for categorizing large-scale images into a large number of inter-related object classes and image concepts. A visual concept network is first constructed for organizing enumorus object classes and image concepts according to their inter-concept visual correlations. The visual concept network is further used to: (a) identify inter-related learning tasks for classifier training; (b) determine groups of visually-similar object classes and image concepts; and (c) estimate the learning complexity for classifier training. A large-scale discriminative learning algorithm is developed for supporting multi-class classifier training and achieving accurate inter-group discrimination and effective intra-group separation. Our discriminative learning algorithm can significantly enhance the discrimination power of the classifiers and dramatically reduce the computational cost for large-scale classifier training

An Interactive Bio-inspired Approach to Clustering and Visualizing Datasets

In this work, we present an interactive visual clustering approach for the exploration and analysis of datasets using the computational power of Graphics Processor Units (GPUs). The visualization is based on a collective behavioral model that enables cognitive amplification of information visualization. In this way, the workload of understanding the representation of information moves from the cognitive to the perceptual system. The results enable a more intuitive, interactive approach to the discovery of knowledge. The paper illustrates this behavioral model for clustering data, and applies it to the visualization of a number of real and synthetic datasets

On the Real-Time Performance, Robustness and Accuracy of Medical Image Non-Rigid Registration

Three critical issues about medical image non-rigid registration are performance, robustness and accuracy. A registration method, which is capable of responding timely with an accurate alignment, robust against the variation of the image intensity and the missing data, is desirable for its clinical use. This work addresses all three of these issues. Unacceptable execution time of Non-rigid registration (NRR) often presents a major obstacle to its routine clinical use. We present a hybrid data partitioning method to parallelize a NRR method on a cooperative architecture, which enables us to get closer to the goal: accelerating using architecture rather than designing a parallel algorithm from scratch. to further accelerate the performance for the GPU part, a GPU optimization tool is provided to automatically optimize GPU execution configuration.;Missing data and variation of the intensity are two severe challenges for the robustness of the registration method. A novel point-based NRR method is presented to resolve mapping function (deformation field) with the point correspondence missing. The novelty of this method lies in incorporating a finite element biomechanical model into an Expectation and Maximization (EM) framework to resolve the correspondence and mapping function simultaneously. This method is extended to deal with the deformation induced by tumor resection, which imposes another challenge, i.e. incomplete intra-operative MRI. The registration is formulated as a three variable (Correspondence, Deformation Field, and Resection Region) functional minimization problem and resolved by a Nested Expectation and Maximization framework. The experimental results show the effectiveness of this method in correcting the deformation in the vicinity of the tumor. to deal with the variation of the intensity, two different methods are developed depending on the specific application. For the mono-modality registration on delayed enhanced cardiac MRI and cine MRI, a hybrid registration method is designed by unifying both intensity- and feature point-based metrics into one cost function. The experiment on the moving propagation of suspicious myocardial infarction shows effectiveness of this hybrid method. For the multi-modality registration on MRI and CT, a Mutual Information (MI)-based NRR is developed by modeling the underlying deformation as a Free-Form Deformation (FFD). MI is sensitive to the variation of the intensity due to equidistant bins. We overcome this disadvantage by designing a Top-to-Down K-means clustering method to naturally group similar intensities into one bin. The experiment shows this method can increase the accuracy of the MI-based registration.;In image registration, a finite element biomechanical model is usually employed to simulate the underlying movement of the soft tissue. We develop a multi-tissue mesh generation method to build a heterogeneous biomechanical model to realistically simulate the underlying movement of the brain. We focus on the following four critical mesh properties: tissue-dependent resolution, fidelity to tissue boundaries, smoothness of mesh surfaces, and element quality. Each mesh property can be controlled on a tissue level. The experiments on comparing the homogeneous model with the heterogeneous model demonstrate the effectiveness of the heterogeneous model in improving the registration accuracy

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters