CIS Map Drawer Visualization System

U.S. Citizenship and Immigration Services (USCIS) is the government agency that manages immigration to the United States. They are not only working on filing documents, but also analyzing those data they have. They require help from a visualization system to know the trend of immigration among offices across the country over time. We developed a project called CIS Map Drawer that is used to help build a visualization system for USCIS in order to analyze immigration data easily. This system makes use of a map view, a calendar visual representation, line charts, pixel drawing and other analysis tools to improve the efficiency of analysis process. Data is collected and updated by USCIS office. Map Drawer represents those data and provides users analysis tools for deeper understanding. Methods such as diverse colors are used both on the map and pixel drawing to improve the performance of this system. Our system is designed in a way to fit diverse monitor resolutions to avoid distortion. Some modules are currently under progress and more modules will be achieved to improve the visualization system in both layout and functions

On uncertainty quantification of eigenvalues and eigenspaces with higher multiplicity

We consider generalized operator eigenvalue problems in variational form with random perturbations in the bilinear forms. This setting is motivated by variational forms of partial differential equations with random input data. The considered eigenpairs can be of higher but finite multiplicity. We investigate stochastic quantities of interest of the eigenpairs and discuss why, for multiplicity greater than 1, only the stochastic properties of the eigenspaces are meaningful, but not the ones of individual eigenpairs. To that end, we characterize the Fr\'echet derivatives of the eigenpairs with respect to the perturbation and provide a new linear characterization for eigenpairs of higher multiplicity. As a side result, we prove local analyticity of the eigenspaces. Based on the Fr\'echet derivatives of the eigenpairs we discuss a meaningful Monte Carlo sampling strategy for multiple eigenvalues and develop an uncertainty quantification perturbation approach. Numerical examples are presented to illustrate the theoretical results

Evolution of sperm morphology in a crustacean genus with fertilization inside an open brood pouch

Sperm is the most fundamental male reproductive feature. It serves the fertilization of eggs and evolves under sexual selection. Two components of sperm are of particular interest, their number and their morphology. Mode of fertilization is believed to be a key determinant of sperm length across the animal kingdom. External fertilization, unlike internal, favors small and numerous sperm, since sperm density is thinned out in the environment. Here, we study the evolution of sperm morphology in the genus Daphnia, where fertilization occurs in a receptacle, the brood pouch, where sperm can constantly be flushed out by a water current. Based on microscopic observations of sperm morphologies mapped on a phylogeny with 15 Daphnia and 2 outgroup species, we found that despite the internal fertilization mode, Daphnia have among the smallest sperm recorded, as would be expected with external fertilization. Despite being all relatively small compared to other arthropods, sperm length diverged at least twice, once within each of the Daphnia subgenera Ctenodaphnia and Daphnia. Furthermore, species in the latter subgenus also lost the ability of cell compaction by extracellular encapsulation and have very polymorphic sperm with long, and often numerous, filopodia. We discuss the different strategies that Daphnia evolved to achieve fertilization success in the females’ brood pouch

Web-based Visual Analytics for Social Media Data

Social media data provides valuable information about different events, trends and happenings around the world. Visual data analysis tasks for social media data have large computational and storage space requirements. Due to these restrictions, subdivision of data analysis tools into several layers such as Data, Business Logic or Algorithms, and Presentation Layer is often necessary to make them accessible for variety of clients. On server side, social media data analysis algorithms can be implemented and published in the form of web services. Visual Interface can then be implemented in the form of thin clients that call these web services for data querying, exploration, and analysis tasks. In our work, we have implemented a web-based visual analytics tool for social media data analysis. Initially, we extended our existing desktop-based Twitter data analysis application named “ScatterBlog” to create web services based API that provides access to all the data analysis algorithms. In the second phase, we are creating web based visual interface consuming these web services. Some major components of the visual interface include map view, content lens view, abnormal event detection view, Tweets summary view and filtering / visual query module. The tool can then be used by parties from various fields of interest, requiring only a browser to perform social media data analysis tasks

Social Media Analytics Reporting Toolkit

With the fast growth of social media services, vast amount of user-generated content with time-space stamps are produced everyday. Considerable amount of these data are publicly available online, some of which collectively convey information that are of interest to data analysts. Social media data are dynamic and unstructured by nature, which makes it very hard for analysts to efficiently and effectively retrieve useful information. Social Media Analytics Reporting Toolkit (SMART), a system developed at Purdue VACCINE lab, aims to support such analyzing. The current framework collects real-time Twitter messages and visualizes volume densities on a map. It uses Latent Dirichilet Allocation (LDA) to extract regional topics and can optionally apply Seasonal-Trend decomposition using Loess (STL) to detect abnormal events. While Twitter has a fair amount of active users, they account for a small portion of total active social media users. Data generated by many other social media services are not currently utilized by SMART. Therefore, my work focused on expanding data sources of SAMRT system by creating means to collect data from other sources such as Facebook and Instagram. During a test run using a collection of 88 specified keywords in search, over two million Facebook posts were collected in one week. Besides, current SMART framework utilizes only one topic model, i.e. LDA, which is considered to be slower than Non-negative Matrix Factorization (NMF) model, thus I also put my effort into integrating NMF algorithm into the system. The improved SMART system can be used to fulfill a variety of analyzing tasks such as monitoring regional social media responses from different sources in disastrous events, detecting user reported crimes and so on. SMART is currently an ongoing and promising project that can be further improved by integrating new features

Introduction to Interactive Visual Decision Analytics Minitrack

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