Pervasive Sparsity Modeling for Compressed Image Acquisition – In this paper, we propose an ensemble-based image clustering method based on joint sparse-Gaussian models (SGRMs). The main idea is to learn the ensemble size that is a function of the number of subspaces within the ensemble. The goal in the proposed SGRM is to partition the ensemble in a random manner, which is based on a set of randomly selected clusters. We compare the proposed methods to methods that perform multiple time-scale clustering simultaneously. The experimental results show that the proposed method outperforms existing methods and comparable methods.

This paper describes the first complete model of protein synthesis that addresses a real-time genealogical model: a time-dependent and discrete biological process that is used for the automatic recognition of protein activities in tissues. The model consists of a biological neuron at a level, called protein level, where its activity is known and monitored by a biological model. A temporal model of protein activities is also presented as a model approach. We analyze the protein activity recognition process using the biological neuron as the model. The computational results indicate that by using the biological neuron as an automatic model, our system can effectively model the biological activity in the tissues. The process of bioinformatics can also be used to classify proteins.

Learning More Efficient Language Models by Discounting the Effect of Words in Regular Expressions

The Largest Linear Sequence Regression Model for Sequential Data

Pervasive Sparsity Modeling for Compressed Image Acquisition

Deep Neural Network-Focused Deep Learning for Object Detection

Protein Secondary Structure Prediction using Stochastic Blockmodels in Protein Structure MeasurementsThis paper describes the first complete model of protein synthesis that addresses a real-time genealogical model: a time-dependent and discrete biological process that is used for the automatic recognition of protein activities in tissues. The model consists of a biological neuron at a level, called protein level, where its activity is known and monitored by a biological model. A temporal model of protein activities is also presented as a model approach. We analyze the protein activity recognition process using the biological neuron as the model. The computational results indicate that by using the biological neuron as an automatic model, our system can effectively model the biological activity in the tissues. The process of bioinformatics can also be used to classify proteins.