We advocate the position that unsupervised learning of rich representations requires careful consideration of an issue that usually receives only cursory attention: The definition of a statistical ‘event’, or ‘sample’. Data sets are presumed to have been generated by sampling from some probability distribution that is to be estimated, but there is no general canonical way to select a model for a given data set and define the correspondence between the various components of its joint random variable and particular subsets, or more generally, features, of the data. Any attempt to automate this choice must confront the fact that without a definition of ‘event’, this exercise cannot be formulated as a statistical learning problem. We introduce two supplementary criteria, information at a distance and information contrast , in order to clear this impasse, and show anecdotal results from using each. We argue that this issue also arises (whether recognized or not) in automated learning of feature hierarchies to form a rich representations, because distinct events are selected at one level of the hierarchy and bound together to form joint events at the next level.
She said: "The question now is, what will that treaty look like? Will there be a weak form that focuses on marine litter and waste management? Or will there be a resolution that includes the full life cycle of plastics including extraction and production right through to remediation of legacy pollution?"
To globally characterize the effect of cytosine methylation on transcription factor binding, we systematically analyzed binding specificities of full-length transcription factors and extended DNA binding domains to unmethylated and CpG-methylated DNA by using methylation-sensitive SELEX (systematic evolution of ligands by exponential enrichment). We evaluated binding of 542 transcription factors and identified a large number of previously uncharacterized transcription factor recognition motifs. Binding of most major classes of transcription factors, including bHLH, bZIP, and ETS, was inhibited by mCpG. In contrast, transcription factors such as homeodomain, POU, and NFAT proteins preferred to bind methylated DNA. This class of binding was enriched in factors with central roles in embryonic and organismal development.