Fully Automatic Expression-Invariant Face Correspondence

We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models

Emotional valence and arousal affect reading in an interactive way: neuroimaging evidence for an approach-withdrawal framework

A growing body of literature shows that the emotional content of verbal material affects reading, wherein emotional words are given processing priority compared to neutral words. Human emotions can be conceptualised within a two-dimensional model comprised of emotional valence and arousal (intensity). These variables are at least in part distinct, but recent studies report interactive effects during implicit emotion processing and relate these to stimulus-evoked approach-withdrawal tendencies. The aim of the present study was to explore how valence and arousal interact at the neural level, during implicit emotion word processing. The emotional attributes of written word stimuli were orthogonally manipulated based on behavioural ratings from a corpus of emotion words. Stimuli were presented during an fMRI experiment while 16 participants performed a lexical decision task, which did not require explicit evaluation of a word's emotional content. Results showed greater neural activation within right insular cortex in response to stimuli evoking conflicting approach-withdrawal tendencies (i.e., positive high-arousal and negative low-arousal words) compared to stimuli evoking congruent approach vs. withdrawal tendencies (i.e., positive low-arousal and negative high-arousal words). Further, a significant cluster of activation in the left extra-striate cortex was found in response to emotional than neutral words, suggesting enhanced perceptual processing of emotionally salient stimuli. These findings support an interactive two-dimensional approach to the study of emotion word recognition and suggest that the integration of valence and arousal dimensions recruits a brain region associated with interoception, emotional awareness and sympathetic functions

Data-intensive spatial pattern discovery based on generalized spatial point representations

Geospatial big data consisting of records at the individual level or with fine spatial resolutions, such as geo-referenced social media posts and movement records collected using GPS, provide tremendous opportunities to understand complex geographic phenomena and their space-time dynamics. Such data have been widely used in many real-world applications, such as event detection and population migration analyses. These applications require not only efficient data handling and processing capabilities, but also innovative data models and analytical approaches that satisfy application-specific requirements. The aim of this dissertation research is to establish a suite of innovative methods for analyzing geospatial big data that can be modeled as generalized spatial points while addressing the following key research questions: how to estimate the spatial and spatiotemporal patterns of geographic phenomena from geospatial big data based on spatial point models? How to compare these patterns to gain insights into complex geographic phenomena? How to estimate the computational intensity of the methods? How can cyberGIS be advanced to resolve the computational intensity? Specifically, novel methods are designed in this dissertation research to exploit spatial data characteristics, innovate spatial point pattern analytics, and resolve computational intensity through high-performance spatial algorithms. Such methods are evaluated in the context of several real-world applications, including event detection from social media data and spatial movement pattern detection. Experiment results demonstrated that fine-scale spatial patterns can be revealed from geospatial big data using the proposed approaches. Novel cyberGIS software capabilities are also created as a result of this dissertation research

State and trait characteristics of anterior insula time-varying functional connectivity.

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures

Investigation of antenna pattern constraints for passive geosynchronous microwave imaging radiometers

Progress by investigators at Georgia Tech in defining the requirements for large space antennas for passive microwave Earth imaging systems is reviewed. In order to determine antenna constraints (e.g., the aperture size, illumination taper, and gain uncertainty limits) necessary for the retrieval of geophysical parameters (e.g., rain rate) with adequate spatial resolution and accuracy, a numerical simulation of the passive microwave observation and retrieval process is being developed. Due to the small spatial scale of precipitation and the nonlinear relationships between precipitation parameters (e.g., rain rate, water density profile) and observed brightness temperatures, the retrieval of precipitation parameters are of primary interest in the simulation studies. Major components of the simulation are described as well as progress and plans for completion. The overall goal of providing quantitative assessments of the accuracy of candidate geosynchronous and low-Earth orbiting imaging systems will continue under a separate grant