A Spatial Inquiry of the U.S. Opioid Epidemic and Geodemographic Segmentation Systems

The objective of this dissertation research was to explore the use of geodemographic segmentation as a socioeconomic variable to spatially analyze opioid related mortalities and hospital discharges. Opioid data were investigated by three ICD-10 classifications: heroin, other opioids, and other synthetic narcotics. Demographic and spatial characteristics of opioid mortality were examined using data from the Centers for Disease Controls (CDC) National Vital Statistics System mortality (NVSS-M) multiple causes of death dataset via the WONDER database for the year 2017. This was followed by a literature review of previous research that investigated the use of geodemographic segmentation systems in health research.Spatial rules association data mining was used to explore the relationship between county level ESRI Tapestry segmentation and opioid mortality rates from the CDC NVSS-M for the years 2015-2017. These findings were further examined by comparing the results to the 2017 Tennessee opioid mortality and Tapestry data at the ZIP code level. Additional demographic analysis was conducted using county level socioeconomic variables, unemployment, and opioid prescribing rates.Tennessee opioid related hospital discharge and mortality data from the year 2017 were analyzed using rate mapping, ANOVA, descriptive statistics, and spatial rules based association data mining. The rates were associated with ESRI Tapestry LifeMode groupings. The results of the analysis of Tennessees ZIP code level data were compared to the CDCs county level data from 2017 to examine scale dependency of the analysis and data

Feature Detection in Medical Images Using Deep Learning

This project explores the use of deep learning to predict age based on pediatric hand X-Rays. Data from the Radiological Society of North America’s pediatric bone age challenge were used to train and evaluate a convolutional neural network. The project used InceptionV3, a CNN developed by Google, that was pre-trained on ImageNet, a popular online image dataset. Our fine-tuned version of InceptionV3 yielded an average error of less than 10 months between predicted and actual age. This project shows the effectiveness of deep learning in analyzing medical images and the potential for even greater improvements in the future. In addition to the technological and potential clinical benefits of these methods, this project will serve as a useful pedagogical tool for introducing the challenges and applications of deep learning to the Bryant community

LCC-DCU C-C question answering task at NTCIR-5

This paper describes the work for our participation in the NTCIR-5 Chinese to Chinese Question Answering task. Our strategy is based on the “Retrieval plus Extraction” approach. We first retrieve relevant documents, then retrieve short passages from the above documents, and finally extract named entity answers from the most relevant passages. For question type identification, we use simple heuristic rules which can cover most questions. The Lemur toolkit with the OKAPI model is used for document retrieval. Results of our task submission are given and some preliminary conclusions drawn

Learning a Hierarchical Latent-Variable Model of 3D Shapes

We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion. Through the use of skip-connections, our model can successfully learn and infer a latent, hierarchical representation of objects. Furthermore, realistic 3D objects can be easily generated by sampling the VSL's latent probabilistic manifold. We show that our generative model can be trained end-to-end from 2D images to perform single image 3D model retrieval. Experiments show, both quantitatively and qualitatively, the improved generalization of our proposed model over a range of tasks, performing better or comparable to various state-of-the-art alternatives.Comment: Accepted as oral presentation at International Conference on 3D Vision (3DV), 201

Visual Summary of Egocentric Photostreams by Representative Keyframes

Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted by means of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the summaries.Comment: Paper accepted in the IEEE First International Workshop on Wearable and Ego-vision Systems for Augmented Experience (WEsAX). Turin, Italy. July 3, 201