Blind Detection of Independent Dynamic Components

In certain applications of independent component analysis (ICA) it any of interest to test hypotheses concerning the number of components or simply to test whether a given number of components is significant relative to a "white noise" null hypothesis. We estimate probabilities of such competing hypotheses for ICA based on dynamic decorrelation. The probabilities are evaluated in the so-called Bayesian information criterion approximation, however, they are able to detect the content of dynamic components as efficient as an unbiased test set estimator. Keywords: Blind Source Separation (BSS), Dynamic Components, Independent Component Analysis (ICA), BIC detection 1

Signal Detection using ICA: Application to Chat Room Topic Spotting

Signal detection and pattern recognition for online grouping huge amounts of data and retrospective analysis is becoming increasingly important as knowledge based standards, such as XML and advanced MPEG, gain popularity. Independent component analysis (ICA) can be used to both cluster and detect signals with weak a priori assumptions in multimedia contexts. ICA of real world data is typically performed without knowledge of the number of non-trivial independent components, hence, it is of interest to test hypotheses concerning the number of components or simply to test whether a given set of components is significant relative to a "white noise" null hypothesis. It was recently proposed to use the so-called Bayesian information criterion (BIC) approximation, for estimation of such probabilities of competing hypotheses. Here, we apply this approach to the understanding of chat. We show that ICA can detect meaningful context structures in a chat room log file

Independent component analysis for understanding multimedia content

Abstract. This paper focuses on using independent component analysis of combined text and image data from web pages. This has potential for search and retrieval applications in order to retrieve more meaningful and context dependent content. It is demon-strated that using ICA on combined text and image features pro-vides a synergistic eect, i.e., the retrieval classication rates in-crease if based on multimedia components relative to single media analysis. For this purpose a simple probabilistic supervised clas-si er which works from unsupervised ICA features is invoked. In addition, we demonstrate the use of the suggested framework for automatic annotation of descriptive key words to images

Modeling text with generalizable Gaussian mixtures

We apply and discuss generalizable Gaussian mixture (GGM) models for textmining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in text data. Finally, we implement a novelty detector based on the density model. 1. INTRODUCTION Information retrieval is a very active research field which is starting to adapt advanced machine learning techniques for solving hard real world problems [17, 18]. Textmining or pattern recognition in text data is used to categorize text according to topic, to spot new topics, and in a broader sense to create more intelligent searches, e.g., by WWW search engines [12, ?, 14]. Textmining proceeds by pattern recognition based on text features, typically document summary statistics. While there are numerous highlevel language models for extr..

Senescence and Inflammatory Markers for Predicting Clinical Progression in Parkinson's Disease: The ICICLE-PD Study.

BACKGROUND: Cognitive decline is a frequent complication of Parkinson's disease (PD) and the identification of predictive biomarkers for it would help in its management. OBJECTIVE: Our aim was to analyse whether senescence markers (telomere length, p16 and p21) or their change over time could help to better predict cognitive and motor progression of newly diagnosed PD patients. We also compared these senescence markers to previously analysed markers of inflammation for the same purpose. METHODS: This study examined the association of blood-derived markers of cell senescence and inflammation with motor and cognitive function over time in an incident PD cohort (the ICICLE-PD study). Participants (154 newly diagnosed PD patients and 99 controls) underwent physical and cognitive assessments over 36 months of follow up. Mean leukocyte telomere length and the expression of senescence markers p21 and p16 were measured at two time points (baseline and 18 months). Additionally, we selected five inflammatory markers from existing baseline data. RESULTS: We found that PD patients had shorter telomeres at baseline and 18 months compared to age-matched healthy controls which also correlated to dementia at 36 months. Baseline p16 levels were associated with faster rates of motor and cognitive decline over 36 months in PD cases, while a simple inflammatory summary score at baseline best predicted cognitive score over this same time period in PD patients. CONCLUSION: Our study suggests that both inflammatory and senescence markers (p16) are valuable predictors of clinical progression in PD patients.This study was supported by a Newcastle upon Tyne Hospital Trust (Brain Research Unit PD0612) grant to GS. ICICLE-PD is funded by Parkinson’s UK (grant no J-0802, G-1301) and supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre in Ageing and Chronic Disease and the Biomedical Research Unit in Lewy Body Dementia based at Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University (CM-R) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (146281). This work was also supported by grants from the Academy of Medical Sciences, UK, the Rosetrees Trust, and the Stevenage Biosciences Catalyst. CHWG is supported by a RCUK/UKRI Research Innovation Fellowship awarded by the Medical Research Council (MR/R007446/1). RAB is an NIHR Senior Investigator (NF-SI-0616-10011) and is supported by the WT/MRC Stem Cell Institute (203151/Z/16/Z

Social change and the family: Comparative perspectives from the west, China, and South Asia

This paper examines the influence of social and economic change on family structure and relationships: How do such economic and social transformations as industrialization, urbanization, demographic change, the expansion of education, and the long-term growth of income influence the family? We take a comparative and historical approach, reviewing the experiences of three major sociocultural regions: the West, China, and South Asia. Many of the changes that have occurred in family life have been remarkably similar in the three settings—the separation of the workplace from the home, increased training of children in nonfamilial institutions, the development of living arrangements outside the family household, increased access of children to financial and other productive resources, and increased participation by children in the selection of a mate. While the similarities of family change in diverse cultural settings are striking, specific aspects of change have varied across settings because of significant pre-existing differences in family structure, residential patterns of marriage, autonomy of children, and the role of marriage within kinship systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45661/1/11206_2005_Article_BF01124383.pd