Biography

Thesis (M.F.A.)--Boston Universit

A multi-method investigation of the association between emotional clarity and empathy.

Higher emotional clarity, the extent to which people unambiguously identify, label, and describe their own emotions, is related to a host of positive intrapersonal factors but its relation to interpersonal factors is unexplored. We hypothesized that emotional clarity would be related to cognitive empathy (i.e., perceiving others' emotions) and to accurately understanding others' negative affect (NA), but not positive affect (PA), in the context of a stressful situation. After completing self-reports of trait emotional clarity and cognitive and affective empathy (i.e., one's emotional reaction to others), participants (N = 94 undergraduate students; i.e., perceivers) viewed a series of video clips of adults (i.e., targets) completing a stressful laboratory task in a previous research study. Before and after the stress task, targets reported their state NA and PA. While viewing the recordings, perceivers rated how they thought the targets were feeling at the corresponding time points. Correspondence between perceivers' and targets' affect ratings were used as indices of the outcome variable, performance-based cognitive empathy. As expected, self-reported emotional clarity was related to the self-reported cognitive, but not affective, empathy. Moreover, perceivers' emotional clarity was related to higher cognitive empathy for NA not PA after the stressful task. Our findings provide preliminary support for the importance of emotional clarity in the ability to accurately understand others' affective experiences, which has important interpersonal implications. (PsycINFO Database Recor

Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis

Polarimetric thermal to visible face verification entails matching two images that contain significant domain differences. Several recent approaches have attempted to synthesize visible faces from thermal images for cross-modal matching. In this paper, we take a different approach in which rather than focusing only on synthesizing visible faces from thermal faces, we also propose to synthesize thermal faces from visible faces. Our intuition is based on the fact that thermal images also contain some discriminative information about the person for verification. Deep features from a pre-trained Convolutional Neural Network (CNN) are extracted from the original as well as the synthesized images. These features are then fused to generate a template which is then used for verification. The proposed synthesis network is based on the self-attention generative adversarial network (SAGAN) which essentially allows efficient attention-guided image synthesis. Extensive experiments on the ARL polarimetric thermal face dataset demonstrate that the proposed method achieves state-of-the-art performance.Comment: This work is accepted at the 12th IAPR International Conference On Biometrics (ICB 2019

Mapping the Space of Genomic Signatures

We propose a computational method to measure and visualize interrelationships among any number of DNA sequences allowing, for example, the examination of hundreds or thousands of complete mitochondrial genomes. An "image distance" is computed for each pair of graphical representations of DNA sequences, and the distances are visualized as a Molecular Distance Map: Each point on the map represents a DNA sequence, and the spatial proximity between any two points reflects the degree of structural similarity between the corresponding sequences. The graphical representation of DNA sequences utilized, Chaos Game Representation (CGR), is genome- and species-specific and can thus act as a genomic signature. Consequently, Molecular Distance Maps could inform species identification, taxonomic classifications and, to a certain extent, evolutionary history. The image distance employed, Structural Dissimilarity Index (DSSIM), implicitly compares the occurrences of oligomers of length up to $k$ (herein $k=9$) in DNA sequences. We computed DSSIM distances for more than 5 million pairs of complete mitochondrial genomes, and used Multi-Dimensional Scaling (MDS) to obtain Molecular Distance Maps that visually display the sequence relatedness in various subsets, at different taxonomic levels. This general-purpose method does not require DNA sequence homology and can thus be used to compare similar or vastly different DNA sequences, genomic or computer-generated, of the same or different lengths. We illustrate potential uses of this approach by applying it to several taxonomic subsets: phylum Vertebrata, (super)kingdom Protista, classes Amphibia-Insecta-Mammalia, class Amphibia, and order Primates. This analysis of an extensive dataset confirms that the oligomer composition of full mtDNA sequences can be a source of taxonomic information.Comment: 14 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:1307.375