We study the problem of learning the dependency graph between random processes in a Vector Autoregressive (VAR) model from samples when a subset of the variables are latent. We show that the dependencies among the observed processes can be identified successfully under some conditions on the VAR model. Moreover, we can recover the length of all directed paths between any two observed processes ...

PURPOSE To examine the association of personal values, the built environment, and auto availability with walking for transportation. SETTING Participants were drawn from 11 U.S. metropolitan areas with good transit services. SUBJECTS 865 adults who had recently made or were contemplating making a residential move. MEASURES Respondents reported if walking was their primary mode for nine tr...

Purpose: To evaluate the implications of differences between contours drawn manually and contours generated automatically by deformable image registration for four-dimensional (4D) treatment planning. Methods and Materials: In 12 lung cancer patients intensity-modulated radiotherapy (IMRT) planning was performed for both manual contours and automatically generated (‘‘auto’’) contours in mid and...

Discrete line juxtaposed halftoning creates color halftones with discrete colorant lines of freely selectable rational thicknesses laid-out side by side. Screen elements are made of parallelogram screen tiles incorporating the discrete colorant lines. The repetition of discrete colorant lines from one screen element to the next may create auto moiré artifacts. By decomposing each supertile into...

Hypervolume contribution is an important concept in evolutionary multi-objective optimization (EMO). It involves hypervolume-based EMO algorithms and hypervolume subset selection algorithms. Its main drawback that it computationally expensive high-dimensional spaces, which limits its applicability to many-objective optimization. Recently, R2 indicator variant (i.e., R2HVC indicator) proposed ap...

Vector space embeddings can be used as a tool for learning semantic relationships from unstructured text documents. Among others, earlier work has shown how in a vector space of entities (e.g. different movies) fine-grained semantic relationships can be identified with directions (e.g. more violent than). In this paper, we use stacked denoising auto-encoders to obtain a sequence of entity embed...