Vector Quantization Codebook Design and Application Based on the Clonal Selection Algorithm

In the area of digital image compression, the vector quantization algorithm is a simple, effective and attractive method. After the introduction of the basic principle of the vector quantization and the classical algorithm for vector quantization codebook design, the paper, based on manifold distance, presents a clonal selection code book design method, using disintegrating method to produce initial code book and then to obtain the final code book through optimization with the clonal selection cluster method based on the manifold distance. Through experiment, based on manifold distance, compared the clonal selection codebook design algorithm (MDCSA) with the hereditary codebook design algorithm and LBG algorithm. According to the result of the experiment, MDCSA is more suitable for the evolution algorithm of the image compression

Sample Size Calculation for Controlling False Discovery Proportion

The false discovery proportion (FDP), the proportion of incorrect rejections among all rejections, is a direct measure of abundance of false positive findings in multiple testing. Many methods have been proposed to control FDP, but they are too conservative to be useful for power analysis. Study designs for controlling the mean of FDP, which is false discovery rate, have been commonly used. However, there has been little attempt to design study with direct FDP control to achieve certain level of efficiency. We provide a sample size calculation method using the variance formula of the FDP under weak-dependence assumptions to achieve the desired overall power. The relationship between design parameters and sample size is explored. The adequacy of the procedure is assessed by simulation. We illustrate the method using estimated correlations from a prostate cancer dataset

An empirical comparison of several recent epistatic interactions detection methods

ABSTRACT Motivation: Many new methods have recently been proposed for detecting epistatic interactions in GWAS data. There is however no in-depth independent comparison of these methods yet. Results: Five recent methods-TEAM, BOOST, SNPHarvester, SNPRuler, and Screen and Clean (SC)-are evaluated here in terms of power, type-1 error rate, scalability, and completeness. In terms of power, TEAM performs best on data with main effect and BOOST performs best on data without main effect. In terms of type-1 error rate, TEAM and BOOST have higher type-1 error rates than SNPRuler and SNPHarvester. SC does not control type-1 error rate well. In terms of scalability, we tested the five methods using a dataset with 100,000 SNPs on a 64-bit Ubuntu system, with Intel (R) Xeon(R) CPU 2.66GHz, 16G memory. TEAM takes ∼36 days to finish and SNPRuler reports heap allocation problems. BOOST scales up to 100,000 SNPs and the cost is much lower than that of TEAM. SC and SNPHarvester are the most scalable. In terms of completeness, we study how frequently the pruning techniques employed by these methods incorrectly prune away the most significant epistatic interactions. We find that, on average, 20% of datasets without main effect and 60% of datasets with main effect are pruned incorrectly by BOOST, SNPRuler, and SNPHarvester

Study on particle plugging in propagating fractures based on CFD-DEM

In the drilling and completion process of fractured formations, wellbore stability is a key factor affecting the safety of drilling and completing engineering. Previous studies have demonstrated that propping moderately and plugging fractures with soluble particles can improve formation fracture pressure. When it comes to particle transport in 3D rough propagation fractures, the interactions between particle-fracture-fluid need to be considered. Meanwhile, size-exclusion, particle bridging/strain effects all influence particle transport behavior and ultimately particle plugging effectiveness. However, adequate literature review shows that fracture plugging, and fracture propagation have not been considered together. In this study, a coupled CFD-DEM method was put forward to simulate the particle plugging process of propagating fracture, and the effects of positive pressure difference, fracture roughness, particle concentration, and particle shape on the plugging mechanism were examined. It is concluded through the study that: 1) Positive pressure difference too large will lead to excessive fracture aperture, making the particles unable to form effective plugging in the middle of the fracture; positive pressure difference too small will lead to fracture aperture too small, making particles unable to enter into and plug the fracture. 2) No matter how the concentration, particle size and friction coefficient change, they mainly affect the thickness of the plugging layer, while the front end of the particle is still dominated by single-particle bridging, and double-particles bridging and multiple-particles bridging are hardly ever seen. For the wellbore strengthening approaches, such as stress cages, fracture tip sealing, etc., specific analysis should be carried out according to the occurrence of extended fractures. For example, for fractures with low roughness, the particles rarely form effective tight plugging in the middle of the fracture, so it is more suitable for fracture tip sealing; For the fracture with high roughness, if the positive pressure difference is controlled properly to ensure reasonable fracture extension, the particle plugging effect will be good, and the stress cage method is recommended for borehole strengthening

The search for optimal oxygen saturation targets in critically ill: Patients observational data from large ICU databases

Ministry of Education, Singapore under its Academic Research Funding Tier

Towards exploratory hypothesis testing and analysis

10.1109/ICDE.2011.5767907Proceedings - International Conference on Data Engineering745-75

Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function

Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings