Improving Traffic Safety Through Video Analysis in Jakarta, Indonesia

This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta. The pipeline transforms raw traffic video footage into databases that are ready to be used for traffic analysis. By analyzing these patterns, the city of Jakarta will better understand how human behavior and built infrastructure contribute to traffic challenges and safety risks. The results of this work should also be broadly applicable to smart city initiatives around the globe as they improve urban planning and sustainability through data science approaches.Comment: 6 pages; LaTeX; Presented at NeurIPS 2018 Workshop on Machine Learning for the Developing World; Presented at NeurIPS 2018 Workshop on AI for Social Goo

Perceptions of Political, Academic, and Corporate Leaders: Higher Education Accountability in Georgia

The purpose of this study was to examine Georgia political, academic, and corporate leaders’ perceptions of higher education accountability. A case study design was used to gain in-depth information. Data were collected through semi-structured interviews with 23 participants. The findings of the study included the following: Nearly every participant believed the mission and purpose higher education involved providing students with the skills and abilities needed to obtain gainful employment, and thereby make a positive impact on the economic development of the state of Georgia. Approximately half of the participants believed higher education should cultivate an engaged citizenry. No consensus was reached regarding the definition of accountability or the purpose of higher education accountability. However, nearly half of the participants used the words responsible or responsibility as part of the definition of accountability and almost half felt the purpose of higher education accountability was to demonstrate a return on investment to stakeholders. Only one evidence, graduation rates, was identified as an acceptable and valid reflection of accountability by more than half of the participants. The majority of the participants stated the best way to share accountability evidence to stakeholders was to improve the quality, type, and methods for communicating that information. The majority of participants stated the most important step higher education could take to improve performance accountability was to work to improve communication with stakeholders. Based on the findings of this study, a few conclusions can be drawn. Political, academic, and corporate leaders agree in most areas related to higher education accountability. The common ground among stakeholders is encouraging. Stakeholders believe that the mission and purpose of higher education is to give students the skills to obtain employment. However, corporate leaders do not appear to believe the purpose of higher education is to create engaged citizens like political and academic leaders seem to. All stakeholders agreed colleges and universities must enact accountability measures and be prepared to demonstrate those measures. To accomplish this, communication must improve since stakeholders feel this will result in a better understanding of higher education accountability expectations and outcomes

New methods for fixed-margin binary matrix sampling, Fréchet covariance, and MANOVA tests for random objects in multiple metric spaces

2022 Summer.Includes bibliographical references.Many approaches to the analysis of network data essentially view the data as Euclidean and apply standard multivariate techniques. In this dissertation, we refrain from this approach, exploring two alternate approaches to the analysis of networks and other structured data. The first approach seeks to determine how unique an observed simple, directed network is by comparing it to like networks which share its degree distribution. Generating networks for comparison requires sampling from the space of all binary matrices with the prescribed row and column margins, since enumeration of all such matrices is often infeasible for even moderately sized networks with 20-50 nodes. We propose two new sampling methods for this problem. First, we extend two Markov chain Monte Carlo methods to sample from the space non-uniformly, allowing flexibility in the case that some networks are more likely than others. We show that non-uniform sampling could impede the MCMC process, but in certain special cases is still valid. Critically, we illustrate the differential conclusions that could be drawn from uniform vs. nonuniform sampling. Second, we develop a generalized divide and conquer approach which recursively divides matrices into smaller subproblems which are much easier to count and sample. Each division step reveals interesting mathematics involving the enumeration of integer partitions and points in convex lattice polytopes. The second broad approach we explore is comparing random objects in metric spaces lacking a coordinate system. Traditional definitions of the mean and variance no longer apply, and standard statistical tests have needed reconceptualization in terms of only distances in the metric space. We consider the multivariate setting where random objects exist in multiple metric spaces, which can be thought of as distinct views of the random object. We define the notion of Fréchet covariance to measure dependence between two metric spaces, and establish consistency for the sample estimator. We then propose several tests for differences in means and covariance matrices among two or more groups in multiple metric spaces, and compare their performance on scenarios involving random probability distributions and networks with node covariates

Protein interface prediction using graph convolutional networks

2017 Fall.Includes bibliographical references.Proteins play a critical role in processes both within and between cells, through their interactions with each other and other molecules. Proteins interact via an interface forming a protein complex, which is difficult, expensive, and time consuming to determine experimentally, giving rise to computational approaches. These computational approaches utilize known electrochemical properties of protein amino acid residues in order to predict if they are a part of an interface or not. Prediction can occur in a partner independent fashion, where amino acid residues are considered independently of their neighbor, or in a partner specific fashion, where pairs of potentially interacting residues are considered together. Ultimately, prediction of protein interfaces can help illuminate cellular biology, improve our understanding of diseases, and aide pharmaceutical research. Interface prediction has historically been performed with a variety of methods, to include docking, template matching, and more recently, machine learning approaches. The field of machine learning has undergone a revolution of sorts with the emergence of convolutional neural networks as the leading method of choice for a wide swath of tasks. Enabled by large quantities of data and the increasing power and availability of computing resources, convolutional neural networks efficiently detect patterns in grid structured data and generate hierarchical representations that prove useful for many types of problems. This success has motivated the work presented in this thesis, which seeks to improve upon state of the art interface prediction methods by incorporating concepts from convolutional neural networks. Proteins are inherently irregular, so they don't easily conform to a grid structure, whereas a graph representation is much more natural. Various convolution operations have been proposed for graph data, each geared towards a particular application. We adapted these convolutions for use in interface prediction, and proposed two new variants. Neural networks were trained on the Docking Benchmark Dataset version 4.0 complexes and tested on the new complexes added in version 5.0. Results were compared against the state of the art method partner specific method, PAIRpred [1]. Results show that multiple variants of graph convolution outperform PAIRpred, with no method emerging as the clear winner. In the future, additional training data may be incorporated from other sources, unsupervised pretraining such as autoencoding may be employed, and a generalization of convolution to simplicial complexes may also be explored. In addition, the various graph convolution approaches may be applied to other applications with graph structured data, such as Quantitative Structure Activity Relationship (QSAR) learning, and knowledge base inference

The Vote, The Vote, Nothing But The Vote!: A Survey of Public and Press Reaction to the Woman\u27s Suffrage Movement In Great Britain, 1906-1914

The Times, the mighty organ of the London press, took no notice of a disturbance created by two young women during Sir Edward Grey\u27s speech at the Free Trade Hall in Manchester on October 13, 1905. It should have; with their small banners inscribed with the same words they shouted, Votes for women! Christabel Pankhurst and Annie Kenney with this action set a precedent to be followed fervently by vote-seeking women for the next nine years. The actions of these young women and their followers would be ridiculed by press and public from the time of initiation until the outbreak of the World War, but a strident note of rebellion against the existing order was sounded by this outrageous act in Manchester

Anomalous Spin Dynamics observed by High Frequency ESR in Honeycomb Lattice Antiferromagnet InCu2/3V1/3O3

High-frequency ESR results on the S=1/2 Heisenberg hexagonal antiferromagnet InCu2/3V1/3O3 are reported. This compound appears to be a rare model substance for the honeycomb lattice antiferromagnet with very weak interlayer couplings. The high-temperature magnetic susceptibility can be interpreted by the S=1/2 honeycomb lattice antiferromagnet, and it shows a magnetic-order-like anomaly at TN=38 K. Although, the resonance field of our high-frequency ESR shows the typical behavior of the antiferromagnetic resonance, the linewidth of our high-frequency ESR continues to increase below TN, while it tends to decrease as the temperature in a conventional three-dimensional antiferromagnet decreases. In general, a honeycomb lattice antiferromagnet is expected to show a simple antiferromagnetic order similar to that of a square lattice antiferromagnet theoretically because both antiferromagnets are bipartite lattices. However, we suggest that the observed anomalous spin dynamics below TN is the peculiar feature of the honeycomb lattice antiferromagnet that is not observed in the square lattice antiferromagnet.Comment: 5 pages, 5 figure

Structure and Petrology of the Willimantic Dome and the Willimantic Fault, Eastern Connecticut

Guidebook for field trips in Connecticut and south central Massachusetts: New England Intercollegiate Geological Conference 74th annual meeting, University of Connecticut, Storrs Connecticut , October 2 and 3, 1982: Trip P-