Coherent structures and modeling: Some background comments

Coherent structures are discussed as a sequence of events (identifiable motions) in the flow which convert significant amounts of mechanical energies of the mean flow stream, into turbulent fluctuations. The use of structure information in modeling is also discussed

Identity Analytics And Belief Structures

Personal identity is an important topic in information systems in general and data analytics in particular.  Normally associated with digital security and privacy, the scope of identity is much greater and affects most aspects of everyday life.  Related subjects are behavioral tracking, personal-identifiable information (PII), privacy data relevance, data repurposing, identity theft, and homeland security.  The purpose of this paper is to establish a context for using analytics to combine evidence to categorize certain subjects based on belief structures.  &nbsp

Bridging the Data Divide: Understanding State Agency and University Research Partnerships within SLDS

This report examines this question through an analysis of state agency-university researcher partnerships that exist in State Longitudinal Data Systems (SLDS). Building state agency-university researcher partnerships is an important value of SLDS. To examine state agency-university researcher partnerships within SLDS, our analysis is guided by the following set of questions based on 71 interviews conducted with individuals most directly involved with SLDS efforts in Virginia, Maryland, Texas and Washington. The findings from this analysis suggest that each state’s SLDS organization and governance structure includes university partners in differing ways. In general, stronger partnership efforts are driven by legislative action or executive-level leadership. Regardless of structure, the operation of these partnerships is shaped by the agency’s previous experience and cultural norms surrounding the value and inclusion of university researchers

Bridging the Data Divide: Understanding State Agency and University Research Partnerships within SLDS

This report examines this question through an analysis of state agency-university researcher partnerships that exist in State Longitudinal Data Systems (SLDS). Building state agency-university researcher partnerships is an important value of SLDS. To examine state agency-university researcher partnerships within SLDS, our analysis is guided by the following set of questions based on 71 interviews conducted with individuals most directly involved with SLDS efforts in Virginia, Maryland, Texas and Washington. The findings from this analysis suggest that each state’s SLDS organization and governance structure includes university partners in differing ways. In general, stronger partnership efforts are driven by legislative action or executive-level leadership. Regardless of structure, the operation of these partnerships is shaped by the agency’s previous experience and cultural norms surrounding the value and inclusion of university researchers

Arabidopsis nucleolar protein database (AtNoPDB)

The Arabidopsis Nucleolar Protein Database (http://bioinf.scri.sari.ac.uk/cgi-bin/atnopdb/home) provides information on 217 proteins identified in a proteomic analysis of nucleoli isolated from Arabidopsis cell culture. The database is organized on the basis of the Arabidopsis gene identifier number. The information provided includes protein description, protein class, whether or not the plant protein has a homologue in the most recent human nucleolar proteome and the results of reciprocal BLAST analysis of the human proteome. In addition, for one-third of the 217 Arabidopsis nucleolar proteins, localization images are available from analysis of full-length cDNA–green fluorescent protein (GFP) fusions and the strength of signal in different parts of the cell—nucleolus, nucleolus-associated structures, nucleoplasm, nuclear bodies and extra-nuclear—is provided. For each protein, the most likely human and yeast orthologues, where identifiable through BLASTX analysis, are given with links to relevant information sources

Sparse Linear Identifiable Multivariate Modeling

In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully Bayesian hierarchy for sparse models using slab and spike priors (two-component delta-function and continuous mixtures), non-Gaussian latent factors and a stochastic search over the ordering of the variables. The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable computational complexity. We attribute this mainly to the stochastic search strategy used, and to parsimony (sparsity and identifiability), which is an explicit part of the model. We propose two extensions to the basic i.i.d. linear framework: non-linear dependence on observed variables, called SNIM (Sparse Non-linear Identifiable Multivariate modeling) and allowing for correlations between latent variables, called CSLIM (Correlated SLIM), for the temporal and/or spatial data. The source code and scripts are available from http://cogsys.imm.dtu.dk/slim/.Comment: 45 pages, 17 figure