cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6075/j0g15zb3
title: Wildfire Data Analysis: Predicting Risk and Severity in San Diego County. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Major wildfires have grown in intensity and frequency not only in southern California, but also across the world, in the last few years. Property loss, damages and costs associated with these wildfires can be especially significant to residents, communities, public space, and the environment. Gas and electric companies have begun to proactively take down power grids as a preventative measure, a significant disruptor to everyday life. In this work, we analyze factors that affect wildfires and how changes in these factors over time affect wildfire behaviors and risks. Specifically, we took daily weather observations and associated fire measurements (indication of fire and the acres burned) in San Diego county over the course of the past 20 years, hypothesizing that changing weather conditions are the primary drivers of wildfires. We validate this theory by providing evidence that wildfire risk and severity are correlated with evapotranspiration (a measure of ground dryness), available vegetation, ambient temperature and wind speed. Using machine learning, we develop a robust model that detects the incidence and acres burned of a wildfire with a testing validation Recall score of 77%. Additionally, we pose a method to estimate the economic value of a region with associated “high” wildfire risks. Residents, firefighters, and public policy officials may leverage the results of this project for effective management and mitigation of wildfire risks.
DOI: 10.6075/j06m35b6
title: Neural Correspondence Mapping. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: A Python library for merging disparate neuroscience data into a common coordinate system, to help facilitate semi-automatic hypothesis generation to speed research discovery.
DOI: 10.6075/j03f4phc
title: Time Series Forecasting. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: From the onset of the COVID-19 pandemic, a dramatic change in traffic patterns has been observed across the country due to travel and other restrictions imposed by government agencies and health experts. The causes for these abrupt changes can be at least partially attributed to the severity of the pandemic, the widespread increase in remote work and online learning, business closures, etc. Considering these changes, we hypothesize that the performance of static time series models used for traffic forecasting will degrade beginning in early 2020. Dynamic models that do not rely solely on historical information will better forecast day-to-day traffic and be able to learn long-term changes in traffic patterns. We present a graph convolutional recurrent neural network that captures both the inherent spatial and temporal complexities present in traffic forecasting. The algorithm is part of a larger deep learning library for time series modeling being developed for the open-source community.
DOI: 10.15482/usda.adc/1529181
title: USDA Database for the Flavonoid Content of Selected Foods, Release 3.3 (March 2018)

description: The database contains values for 506 food items for five subclasses of flavonoids:

FLAVONOLS:Quercetin, Kaempferol, Myricetin, Isorhamnetin
FLAVONES:
Luteolin, Apigenin
FLAVANONES:
Hesperetin, Naringenin, Eriodictyol
FLAVAN-3-OLS:
(+)-Catechin, (+)-Gallocatechin, (-)-Epicatechin, (-)-Epigallocatechin, (-)-Epicatechin 3-gallate, (-)-Epigallocatechin 3-gallate, Theaflavin, Theaflavin 3-gallate, Theaflavin 3'-gallate, Theaflavin 3,3' digallate, Thearubigins
ANTHOCYANIDINS:
Cyanidin, Delphinidin, Malvidin, Pelargonidin, Peonidin, Petunidin
DOI: 10.6075/j079431x
title: Classy Data Analysis: Mapping the U.S. Non-Profit Space. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: In this project, we build this social space from the ground up. We use a combination of government IRS form 990 (returns for nonprofits) data along with external textual information (i.e. social media) to create a robust semantic space.
DOI: 10.6075/j0833s7r
title: Prescribed Fire Optimization. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Insufficient application of wildfire prevention techniques has resulted in a dangerous buildup of wildfire fuel. To reduce this build up, fire districts will conduct prescribed burns on areas in their zone of authority. One obstacle to executing prescribed burns is the inability to accurately quantify the risk of a candidate prescribed burn. Currently the preburn risk appraisal is solely dependent on the experience of fire scientists. This project brings a data-driven approach to quantifying the risk of a candidate prescribed burn. This approach utilizes logistic regression to produce calibrated risk scores. The logistic regression is trained on data generated by a fire simulation software called QUIC-Fire. Atmospheric conditions such as wind speed, wind direction, and surface moisture are the features which the model uses to produce a risk score. A proof-of-concept user interface was developed to show how risk scores could be integrated into the go/no-go decision-making process of a prescribed burn. The end goal is for BurnPro3D to integrate the risk scores into their user interface.
DOI: 10.6075/j0qj7ft8
title: Neuronal Circuit and Synapse Analysis with Deep Neural Networks. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Biomedical image segmentation has led to numerous breakthroughs in neuroscience research by helping scientists map the brain. Brain mapping could be the key to understanding the progression of neurodegenerative diseases. Currently, the most accurate neural segmentations and synapse detections are annotated manually. However, manual annotation of synapses is unsustainable given the very large size of electron microscopy datasets of the brain. Deep learning tools such as CDeep3M can produce automated synapse segmentations on serial block-face scanning electron microscopy (SBEM), but the segmentations are relatively inaccurate. This is due to the numerous barriers including the inherent heterogeneity of neurons and their synapses and the limited tools for assessing and analyzing ultrastructure in molecularly defined synapses. Our developed solution for improving the automated segmentations of synapses, known as CDense3M, could help neuroscientists discover new insights in neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. We implement and apply two different modeling components to perform 3D segmentations and labeling to improve the accuracy of synaptic density segmentations. The first modeling component creates different algorithms for 3D concatenation, labeling, and filtering. As for 3D filtering, we also implement multiple models that use erosion and determine the statistical thresholds for 3D vesicle filtering on 3D objects. In each step of our 3D modeling, the new data is based off of previous modeling and as a result the data in each step of modeling becomes the new data for the next step. The evaluation for our image processing including 3D labeling and 2D and 3D vesicle filtering models is through the visualization and using the biological behavior of known neuronal structures. This improves the accuracy of defining the presynaptic areas based on the vesicle functions. The second modeling component applies flood-filling networks, a class of 3D convolutional neural networks, to perform cell-body segmentation on a 3D image volume. We train a custom flood-filling network model on membrane segmentations produced by the CDeep3M automated segmentation tool. The membrane model is more generalizable at segmenting cell bodies than the standard FIB-25 model trained on raw electron microscopy data from fruit flies. The improvement in the accuracy of presynaptic area recognition from applying both modeling components results in a 10% increase in precision and recall for the voxel-wise classification of synapses in a 3D image volume of the Nucleus accumbens from lab mice. This capstone project was conducted to fulfill the requirements for the Master of Advanced Study Degree in Data Science and Engineering at the University of California, San Diego.
DOI: 10.6075/j0q52ps1
title: Automatic Floorplan Reconstruction from RGB-D Images. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: This project aims to develop approaches to automatically reconstruct an accurate floor plan of a house with multiple rooms using a LiDAR-enabled smartphone. Previous work in this area, such as those described in research papers on the models FloorNet, Floor-SP, and 4D Spatio-Temporal ConvNets, have guided us in formulating our approach. We propose to accomplish this task via a combination of human annotation and deep learning segmentation models. We have developed tools to facilitate rapid human annotation of key features (such as windows, walls, and corners). The human annotations will be complemented by annotations generated by a deep learning segmentation model that can identify and locate doors. Finally, we have designed a cloud computing architecture that can store these annotations and build a digital floor plan of the 3D scene.
DOI: 10.6075/j0d21vxp
title: Analytics Pipeline for Left and Right Ventricle Segmentation on Cardiac MRI using Deep Learning. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: This is the capstone project for MAS DSE program. The goal of the capstone project is to complete an end to end analysis of a large dataset with big data characteristics. The​ ​goal​ ​of​ ​this​ ​project​ ​is​ ​to​ use magnetic resonance imaging(MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV & RV) segmentation. For the left ventricle, we map both the epicardium and endocardium surfaces. The endocardium segment is the area of the left ventricle contained by the inside of the wall of the left ventricle, and is the most reliable way to calculate the ejection fraction of the heart. The left ventricle epicardium segment is the area contained by the outside of the wall of the left ventricle, and once the contours of the inside and outside of the wall are known, the contours of the myocardium, the actual wall, can be calculated. The contours of the myocardium are used in detecting the severity of damage done by heart attacks. For the right ventricle, we segment the endocardium. Since the process of analyzing cardiac images is time consuming and labor intensive, our project aims to apply deep learning to this process to achieve consistent segmentation results efficiently. We also compare the performance of 2D UNET, 3D UNET and Densenet models across various datasets. The datasets used in this project are Sunnybrook Cardiac dataset, Automated Cardiac Diagnosis Challenge dataset, MICCAI 2012 Right Ventricle Segmentation challenge dataset and CAP 2011 Left Ventricle Segmentation challenge dataset.
DOI: 10.6075/j0js9nz5
title: Social Media Data Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: This project focuses on studying how Twitter images impact the narrative of hashtags. A lot of research in Twitter data has been focused on separate textual content without media attachments. Images attached to a tweet provide an additional dimension to help understand a tweet’s context and the user’s general opinion. The assumption is that the visual images reinforce the opinion that was presented in the text. The project aims at finding specific patterns in tweets where media files (images) are used to change the narrative of the corresponding hashtag and co-occurred hashtags. This is achieved by studying the topic of solo hashtag and co-occurred hashtags without or with associated media files. Media file as a visual channel is a powerful medium and can change the subject and original purpose of a hashtag for a given audience. A small number of media tweets (images) associated with a hashtag can have a higher influential impact on an observer/user than the same or even higher number of tweets without media. There are multiple benefactors to the findings from this project. 1- Election organizer as part of a campaign can study and detect endorsing and opposing trends and act by counter measures using similar techniques. 2- Social Media platform and specially Twitter itself can detect patterns and potentially restrict the behavior. 3- Journalists can report to general public on how a potential small group of influencers can sway a narrative and push various agendas.
DOI: 10.6075/j03f4ptd
title: The Impact of Covid-19 on Air Traffic: Spatiotemporal Forecasting with Deep Learning and Benchmarks. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Many global industries have been affected by the COVID 19 pandemic, the airline industry being one of the most heavily hit. As a result, flight trends around the world have drastically deviated from their normal patterns. This presents an interesting scenario to explore, especially with the transience and abnormality of effects of the Covid data. The phenomenon also created uncertainty for both passengers and airline companies, especially due to the multiple waves of virus mutations prompting the following questions: How should airlines plan future flights? When should passengers schedule their travels? In other words, given a country’s COVID situation, how should an airline/passengers plan ahead? To solve this problem, we present a solution in the form of an air traffic forecast using baseline models: AR and ARIMA, and deep learning models: LSTM (Long Short Term Memory), LSTM-mis-regression, LSTM-quantile-regression, and other models that capture the temporal complexity of such data. These algorithms are developed for time series analysis that are available to the open-source community, and can better serve airlines and passengers.
DOI: 10.6075/j0r49qxv
title: Wildfire Smoke Detection Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Wildfires have caused significant damages throughout the state of California. In 2018 alone, it is estimated that approximately $150 billion of damages were caused by wildfires . The devastating impact of these fires in recent years has intensified the need for hardened infrastructure systems and more advanced fire prevention and mitigation efforts. This project explores improving a pre-existing, image classification model for smoke detection, called SmokeyNet , originally developed by the San Diego Supercomputer Center at the University of California, San Diego. SmokeyNet uses images that are captured from a network of cameras across Southern California known as the High Performance Wireless Research and Education Network (HPWREN) to predict and identify fire smoke. Improvements were explored by 1) incorporating supplementary geostationary satellite fire detection model data (Wildfire Automated Biomass Burning Algorithm, WFABBA) to increase model accuracy, and 2) programmatically geolocating the approximate origin of a fire from an image to aid in the emergency response process. Weather data was analyzed as an additional supplementary dataset to the SmokeyNet model, but retraining and retesting was not fully complete. From the ensemble of the multiple fire models, it resulted in a slight boost to overall accuracy. When programmatically geolocating the fire, by using triangulation, location approximations were able to be calculated within 3 miles of actuals; however, a small sample size of 10 was used and further data would need to be gathered and evaluated.
DOI: 10.6075/j0v986kf
title: Opioid Overdose Data Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Opioid Overdose Data Analysis project aims on using machine learning technique extracting information from news article, and presenting the information on a user interactive map interface. This interface provides user an exploration playground on finding interesting location distribution pattern on Opioid overdose information. This project was conducted under course DSE 260B (DSE MAS capstone project).
DOI: 10.6075/j08c9tsc
title: Amazon Recommender System. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Recommender systems are algorithms that suggest relevant items to users based on data. They generate large revenue for the modern e-commerce industry. 35% of Amazon web sales were generated through their recommended items [source: McKinsey]. This study aims to construct an apparel recommender system for Amazon users through user-rating history, product images and product title text. Multiple deep learning models were built on both readily-available and engineered datasets resulting in a multi-step recommender system. Tableau and a web app are used to display results, along with evaluation measurements.
description: Download the input data files listed below by following the instructions found here: https://nijianmo.github.io/amazon/index.html - Clothing_Shoes_and_Jewelry_5.json.gz (Go to the "'Small' subsets for experimentation" section of the document and click on "5-core" and next to the entry "Clothing, Shoes and Jewelry") - Clothing_Shoes_and_Jewelry.json.gz - meta_Clothing_Shoes_and_Jewelry.json.gz Output data files: - NLP_output_0603.csv (This file is the output from TF-IDF weighted word2vec model. The csv featured products, recommended products and the euclidean distance between the two vectors.) - Shoes_for_100_users_per_10_products_prediction.csv - RESULT_image_recommended_product_50k_for_100JH_ver3_all.csv
DOI: 10.6075/j0zp4692
title: Predicting Effects of SARS-CoV-2 Variant Mutations on Spike Protein Dynamics and Mechanism. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Python, MDTraj, and Nautilus
description: The COVID-19 pandemic has wrecked global havoc and caused the deaths of millions. Scientists are working around the clock to understand the mechanisms through which the SARS-CoV-2 virus infects the human body, and they are fortunately aided by complex molecular dynamics simulations. The scientists at the Amaro Lab at the University of California San Diego have simulated the spike protein of the SARS-CoV-2 virus. It is understood that the spike is responsible for binding with the ACE2 receptor on the human cell when it is in the open state, with its receptor binding domain in the up position. The aim of this project is to use machine learning techniques to aid the Amaro Lab in using their simulation data to understand the mechanisms by which the SARS-CoV-2 spike protein enters into the open state and infects the human cell. Ultimately a Stochastic Gradient Descent classifier was successfully trained to predict when the spike was in the open state with a precision and recall that is greater than 0.99. This success indicates that machine learning can be used to mine spike simulation data for insights into the substructures relevant to SARS-CoV-2 infection and discover therapeutic targets for drug development.
DOI: 10.6075/j0b856n6
title: Alzheimer's Disease Data Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Brain tissue has long been studied and detailed. Currently there exist both open source and proprietary tools to convert 2 dimensional images from stacks or voxels into 3 dimensional reconstructions. In cases where image qualities are low, or the structure of the brain has altered, images must be pre-processed before rendering can take place. We have created stand-alone models based trained on mouse brain samples that, when combined with our image processing pipeline, model retraining, and 3 dimensional rendering, improve image segmentation and component prediction by nearly 30%. In addition our rendered images are manipulate-able in real time, with component selection, zoom pan and tilt manipulation as well as luminosity and opacity controls. Additionally, to ensure the pre-trained models can be optimized for any environment, we have created a custom tool allowing for humans to interrupt the pipeline, retrain the model in real time, and re-deployed on any image database. Our pipeline is inherently scalable and is written to be deployed in a remote computer cluster environment allowing for thousands of pixels to be evaluated, modeled and modified without artificial thresholds caused by individual computing resources. Our pipeline has been tested on multiple datasets of differing qualities and initial deployment on human tissue samples has been started by our advising team.
DOI: 10.6075/j0zp461g
title: Volumetric Segmentation in Electron Microscopy Brain Imaging. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Advances in volume electron microscopy imaging have enabled the accumulation of large-scale, high-resolution biological data. In the neuroscience domain, the potential for these volumes to characterize the dense network of intertwining structures of the brain along with its subcellular components is within reach, but still limited in many ways by the size and complexity of the analysis. We have utilized the high-speed storage and GPU resources provided by the Pacific Research Platform (PRP) and CHASE-CI, managed by the Kubernetes engine, Nautilus, to implement and validate a 3D U-Net for multi-class volumetric segmentation trained on a scarcely labeled mouse brain dataset. A deep learning internal zero-shot superresolution was evaluated on downsampled volumes to simulate its effect on pixel classification after x-y resolution boosting. In addition to traditional model performance metrics, a volume rendering tool was created to visualize voxel-level predictions to better understand problematic structures and subcellular features. These models and tools extend the neuroimaging reconstruction framework, NeuroKube.
DOI: 10.6075/j0668d28
title: Fire Weather Data Analysis: Modeling Fire Potential Index and High Risk Fire Events. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Over the course of two quarters, our Data Science and Engineering MAS capstone group teamed up with the San Diego utility company, San Diego Gas and Electric, on improving their method for assessing fire risk potential. The Fire Potential Index (FPI) is a risk assessment mechanism and calculation leveraged in various localized forms across different areas by organizations and fire scientists for preventing and controlling wildfires. The San Diego Gas & Electric company leverages a parametric FPI calculation taking into account vegetation, live and dead fuel moisture, and weather elements for predicting risk of significant fire incident in case of an ignition. This risk information is then assessed and applied to determine the day’s required operating protocols, for preventative measures. Being an electric company, SDG&E have begun to proactively shut down power grids as a form of preventative measures on days of elevated FPI values. The task of this project is to apply data science principles to further optimize the San Diego fire risk calculation and 7 day forecast, specifically through development of a machine learning algorithm, flexible data design for incorporation of new features such as solar radiation from mesowest.utah.edu, and model validation procedure from GeoMAC Wildfire. Our goal is for SDG&E to deploy the final results of our project as a tool for communicating to fire fighters, public officials and San Diego residents on potential wildfire risks, and for mitigating wildfire risks with effective company measures.