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Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and(More)
Blastocyst implantation and placentation require molecular and cellular interactions between the uterine endometrium and blastocyst trophectoderm. Previous studies showed that histamine produced in the mouse uterine luminal epithelium interacts with trophoblast histamine type-2 receptors (H2) to initiate blastocyst implantation. However, it is unknown(More)
Cytotrophoblasts of the anchoring villi convert during human placentation from a transporting epithelium to an invasive, extravillous phenotype that expresses a distinct repertoire of adhesion molecules. Developing extravillous trophoblasts accumulate heparin-binding EGF-like growth factor (HB-EGF), a multifunctional cytokine, which binds HER1 and HER4 of(More)
Feather selection is a process that extracts a number of feature subsets which are the most representative of the original meaning from original feature set. It greatly reduces the text processing time and increases the accuracy because of removing some data outliers. With the rapid development of Web 2.0 and the further evolution of the Internet, short(More)
Transient elevation of intracellular calcium (Ca2+(i)) by various means accelerates murine preimplantation development and trophoblast differentiation. Several G-protein-coupled receptors (GPCRs), including the lysophosphatidic acid (LPA) receptor (LPAR), induce Ca2+(i) transients and transactivate the EGF receptor (ErbB1) through mobilization of EGF family(More)
Development of accurate models of complex clinical time series data is critical for understanding the disease, its dynamics , and subsequently patient management and clinical decision making. Clinical time series differ from other time series applications mainly in that observations are often missing and made at irregular time intervals. In this work, we(More)
In this work we develop and test a novel hierarchical framework for modeling and learning multivariate clinical time series data. Our framework combines two modeling approaches: Linear Dynamical Systems (LDS) and Gaussian Processes (GP), and is capable to model and work with time series of varied length and with irregularly sampled observations. We test our(More)
—Feature selection is a process which chooses a subset from the original feature set according to some rules. The selected feature retains original physical meaning and provides a better understanding for the data and learning process. However, few modern feature selection approaches take the advantage of features' context information. Based on this(More)
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to(More)
OBJECTIVE Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for(More)