Cluster interpretation after dimensionality reduction (DR) is a ubiquitous part of exploring multidimensional datasets. DR results are frequently represented by scatterplots, where spatial proximity encodes similarity among data samples. In the literature, techniques support understanding scatterplots’ organization visualizing importance features for cluster definition with layout enrichment st...

We propose a novel semi-supervised learning approach for classification of histopathology images. employ strong supervision with patch-level annotations combined co-training loss to create framework. Co-training relies on multiple conditionally independent and sufficient views the data. separate hematoxylin eosin channels in pathology images using color deconvolution two each slide that can par...

State-space search is paramount for intelligent decision making when long-term thinking needed. We introduce dominance and contrastive analysis methods, which enable reasoning about the relative advantages among different courses of action. This re-shapes how agents reason leads to new families state-space algorithms.

We present an eye-tracking experiment investigating the time course with which listeners derive pragmatic inferences from contextual information. We used as a test case the construction “It looks like an X” pronounced either with (a) a nuclear pitch accent on the final noun, or (b) a contrastive L+H* pitch accent and a rising boundary tone, a contour that can support a complex contrastive infer...