Accelerating Iterative Computations for Large-Scale Data Processing

Recent advances in sensing, storage, and networking technologies are creating massive amounts of data at an unprecedented scale and pace. Large-scale data processing is commonly leveraged to make sense of these data, which will enable companies, governments, and organizations, to make better decisions and bring convenience to our daily life. However, the massive amount of data involved makes it challenging to perform data processing in a timely manner. On the one hand, huge volumes of data might not even fit into the disk of a single machine. On the other hand, data mining and machine learning algorithms, which are usually involved in large-scale data processing, typically require time-consuming iterative computations. Therefore, it is imperative to efficiently perform iterative computations on large computer clusters or cloud using highly-parallel and shared-nothing distributed systems. This research aims to explore new forms of iterative computations that reduce unnecessary computations so as to accelerate large-scale data processing in a distributed environment. We propose the iterative computation transformation for well-known data mining and machine learning algorithms, such as expectation-maximization, nonnegative matrix factorization, belief propagation, and graph algorithms (e.g., PageRank). These algorithms have been used in a wide range of application domains. First, we show how to accelerate expectation-maximization algorithms with frequent updates in a distributed environment. Then, we illustrate the way of efficiently scaling distributed nonnegative matrix factorization with block-wise updates. Next, our approach of scaling distributed belief propagation with prioritized block updates is presented. Last, we illustrate how to efficiently perform distributed incremental computation on evolving graphs. We will elaborate how to implement these transformed iterative computations on existing distributed programming models such as the MapReduce-based model, as well as develop new scalable and efficient distributed programming models and frameworks when necessary. The goal of these supporting distributed frameworks is to lift the burden of the programmers in specifying transformation of iterative computations and communication mechanisms, and automatically optimize the execution of the computation. Our techniques are evaluated extensively to demonstrate their efficiency. While the techniques we propose are in the context of specific algorithms, they address the challenges commonly faced in many other algorithms

Co-ClusterD: A Distributed Framework for Data Co-Clustering with Sequential Updates

Abstract-Co-clustering has emerged to be a powerful data mining tool for two-dimensional co-occurrence and dyadic data. However, co-clustering algorithms often require significant computational resources and have been dismissed as impractical for large data sets. Existing studies have provided strong empirical evidence that expectation-maximization (EM) algorithms (e.g., k-means algorithm) with sequential updates can significantly reduce the computational cost without degrading the resulting solution. Motivated by this observation, we introduce sequential updates for alternate minimization co-clustering (AMCC) algorithms which are variants of EM algorithms, and also show that AMCC algorithms with sequential updates converge. We then propose two approaches to parallelize AMCC algorithms with sequential updates in a distributed environment. Both approaches are proved to maintain the convergence properties of AMCC algorithms. Based on these two approaches, we present a new distributed framework, Co-ClusterD, which supports efficient implementations of AMCC algorithms with sequential updates. We design and implement Co-ClusterD, and show its efficiency through two AMCC algorithms: fast nonnegative matrix tri-factorization (FNMTF) and information theoretic co-clustering (ITCC). We evaluate our framework on both a local cluster of machines and the Amazon EC2 cloud. Empirical results show that AMCC algorithms implemented in Co-ClusterD can achieve a much faster convergence and often obtain better results than their traditional concurrent counterparts

Characterizing the Biology of Lytic Bacteriophage vB_EaeM_φEap-3 Infecting Multidrug-Resistant Enterobacter aerogenes

Carbapenem-resistant Enterobacter aerogenes strains are a major clinical problem because of the lack of effective alternative antibiotics. However, viruses that lyze bacteria, called bacteriophages, have potential therapeutic applications in the control of antibiotic-resistant bacteria. In the present study, a lytic bacteriophage specific for E. aerogenes isolates, designated vB_EaeM_φEap-3, was characterized. Based on transmission electron microscopy analysis, phage vB_EaeM_φEap-3 was classified as a member of the family Myoviridae (order, Caudovirales). Host range determination revealed that vB_EaeM_φEap-3 lyzed 18 of the 28 E. aerogenes strains tested, while a one-step growth curve showed a short latent period and a moderate burst size. The stability of vB_EaeM_φEap-3 at various temperatures and pH levels was also examined. Genomic sequencing and bioinformatics analysis revealed that vB_EaeM_φEap-3 has a 175,814-bp double-stranded DNA genome that does not contain any genes considered undesirable for the development of therapeutics (e.g., antibiotic resistance genes, toxin-encoding genes, integrase). The phage genome contained 278 putative protein-coding genes and one tRNA gene, tRNA-Met (AUG). Phylogenetic analysis based on large terminase subunit and major capsid protein sequences suggested that vB_EaeM_φEap-3 belongs to novel genus “Kp15 virus” within the T4-like virus subfamily. Based on host range, genomic, and physiological parameters, we propose that phage vB_EaeM_φEap-3 is a suitable candidate for phage therapy applications

Precore Mutation of Hepatitis B Virus May Contribute to Hepatocellular Carcinoma Risk: Evidence from an Updated Meta-Analysis

BACKGROUND: Studies focused on the correlation of mutations in the genome of Hepatitis B Virus (HBV) like Pre-S mutation, Basal Core promoter (BCP), Enhancer II (EnhII), especially Precore mutation, with the risk of hepatocellular carcinoma (HCC) have triggered stiff controversies. With an increasing number of studies in this field recently, we conducted this meta-analysis to appraise the correlations. METHODS: We searched the commonly used databases both in English and Chinese till February 1(st), 2012. Meta-analysis was performed in fixed/random-effects models using STATA 10.0. Publication bias was examined through Egger's test and Begg's funnel plot. RESULTS: In total, 85 case-control studies were included involving 16745 HBV-infected patients, of whom 5781 had HCC. Statistically significant correlations were observed in Precore mutation G1896A (OR = 1.46, 95% confidence interval [CI] = 1.15-1.85, P(OR) = 0.002), G1899A (OR = 3.13, 95%CI = 2.38-4.13, P(OR)<0.001) and Pre-S mutation especially Pre-S1 deletion (OR = 2.94, 95%CI = 2.22 to 3.89) and Pre-S2 deletion (OR = 3.02, 95%CI = 2.03 to 4.50). Similar correlation existed between BCP double mutation A1762T/G1764A, T1753V, C1653T and HCC. In subgroup analysis, the Asians, genotype C or HBeAg positive patients with certain above mutations may be more susceptible to HCC. Besides, the mutations like G1896A and BCP double mutation may be associated with the progression of the liver diseases. CONCLUSIONS: Precore mutation G1896A, G1899A, deletions in Pre-S region as well as the other commonly seen mutations correlated with the increased risk of HCC, especially in Asians and may predict the progression of the liver disease

Eye state recognition method for drivers with glasses

Eye state recognition is a key step in fatigue detection method. However, factors such as occlusion of different types of glasses and changes in lighting conditions may have some impact on eye state recognition. In order to solve these problems, a driver\u27s eye state recognition method based on deep learning is proposed. Firstly, the driver\u27s face images are acquired using an infrared acquisition device. Secondly the multi-task cascaded convolution neural networks are used to detect the face bounding box and feature points of the driver\u27s face image, and then the eye regions are extracted. Finally the Convolution Neural Network (CNN) is adopted to identify the open and closed state of the eyes. Experimental result shows that the proposed method can accurately identify the state of eyes and help to calculate the fatigue parameters of drivers

Decorrelation: sufficient for convolutive blind source separation?

This paper considers blind separation of signal sources in a convolutive mixing environment. It tries to show that decorrelation is sufficient for separation of convolutively mixed sources. Two algorithms are also proposed and tested by computer simulations

De-cumulant based approaches for convolutive blind source separation

This paper studies the blind separation of signal sources (BSS) based on the approach of de-cumulant. It considers the cases where independent signal sources are mixed through convolutive mixing system with unity autochannel frequency responses and causal cross-channel FIR filters. Firstly, it tries to show that the de-cumulant is sufficient for separation. Secondly, novel algorithms are developed based on zero-forcing of cross-cumulant pairs. These algorithms are developed in the time-domain and so there is not the frequency permutation ambiguity problem usually suffered by most of the frequency-domain algorithms. Simulation results are presented to support the validity of the proposed algorithms

Experimental study for the influence of surface characteristics on the fringe patterns

Fringe projection profilometry (FPP) has been widely used for three dimensional (3D) imaging and measurement. The fringe acquisition of FPP mainly depends on the diffuse light from the surface of objects, thus the characteristics of object surface have significant influence on phase calculation. One of the essential factors related to phase precision is modulation index, which has a direct relationship with the surface reflectivity. This paper presents a comparative study which focuses on the modulation index of different materials. The distribution of modulation index for different samples is statistical analyzed, which leads to the conclusion that the modulation index is determined by the diffuse reflectivity rather than the type of materials. This work is helpful to the development of effective de-noising algorithms to improve the measurement accuracy