NYU Data Science newsletter – April 8, 2016

GROUP CURATION: N/A

To upend current barriers to sharing clinical data and insights, we need a framework that not only accounts for choices made by trial participants but also qualifies researchers wishing to access and analyze the data.

For years, the federal government, states and some cities have enthusiastically made vast troves of data open to the public. … A project coming out of the M.I.T. Media Lab on Monday seeks to ease that challenge and to make the value of government data available to a wider audience. The project, called Data USA, bills itself as “the most comprehensive visualization of U.S. public data.” It is free, and its software code is open source, meaning that developers can build custom applications by adding other data.

Northeastern’s Alessandro Vespig­nani, Stern­berg Family Dis­tin­guished Uni­ver­sity Pro­fessor of physics, com­puter sci­ence, and health sci­ences, has teamed up with an inter­dis­ci­pli­nary group of sci­en­tists to develop an inno­v­a­tive method to map how tweets about large-scale social events spread. Using mas­sive Twitter datasets and sophis­ti­cated quan­ti­ta­tive mea­sures, it tracks how infor­ma­tion about polit­ical protests, large busi­ness acqui­si­tions, and other “col­lec­tive phe­nomena” gather momentum, peak, and fall over time, from city to city, and where the impetus comes from for that trajectory.

For Andrej Karpathy, a Stanford PhD candidate who makes software that can imitate us, landing a job after graduation was easy.

This course introduces the fundamental concepts and ideas in natural language processing (NLP), otherwise known as computational linguistics. … We consider word-level, syntactic, and semantic processing from both a linguistic and an algorithmic perspective, aiming to get up to speed with current research in the area. The course focuses on modern quantitative techniques in NLP — using large corpora, statistical models for acquisition, disambiguation, and parsing — and the construction of representative systems. (course website)

If artificial intelligence has its way, discounting could disappear, thanks to software that tells retailers exactly what and how many products to buy, and when to put them on sale to sell them at full price. Online shopping could become a conversation, where the shopper describes the dress of their dreams, and, in seconds, an AI-powered search engine tracks down the closest match. Designers, merchandisers and buyers could all work alongside AI, to predict what customers want to wear, before they even know themselves.

The goal of this workshop is to bring together experts from different fields of machine learning, statistics, data science, social sciences and social activism to explore the opportunities for machine learning in applications with social impact. The workshop will consist of: 1) invited presentations from the leading practitioners in the field, and 2) a series of 20 minute presentations on research that fits the theme of machine learning for social good; broadly construed, this could be machine learning related social good applications, or new machine learning methods or theory of particular interest for social good applications.

Deadline for submissions is Sunday, May 1.

Also, 2016 Workshop on Human Interpretability in Machine Learning at ICML 2016 (same May 1 deadline)

Fundamental research problems exist in how to fuse data, how to identify the relevant portions of the data, how assess change in the data, how to sample the data, and how to visualize the data. These issues must be met to advance social theorizing and improve policy analysis. This year’s SBP-BRiMS challenge problem invites you to take part in addressing one or more of these challenges.

Using at least one of four political event datasets (GDELT, KEDS, ICEWS, Phoenix) and one other data set (which may be a second one of these event datasets, or any other relevant dataset), this year’s challenge problem asks participants to address any issue of interest to you or your team that involves events and their distribution over time or space. All entries must have both a strong social theory, political theory or policy perspective and a strong methodology perspective.

Deadline for abstract submissions is Sunday, May 1.

In this series I want to go over some of the Deep Learning techniques that are used to build conversational agents, starting off by explaining where we are right now, what’s possible, and what will stay nearly impossible for at least a little while. This post will serve as an introduction, and we’ll get into the implementation details in upcoming posts.

To push beyond the limitations of current DBMSs and support interactivity, we developed the ForeCache visual exploration system (see Figure 1). ForeCache uses a client-server architecture: The user interacts with a visualization interface running on the client machine (i.e., the user’s laptop), and the client retrieves the corresponding data by issuing requests to a DBMS running on a remote server. For its extensive support for scientific analysis operations, we use the array-based DBMS SciDB as our back-end. To further boost back-end performance, ForeCache includes a server-side middleware layer inserted in front of the DBMS, which pre-fetches data into a main memory cache in anticipation of the user’s future interactions.

So you want to write an R client for a web API? This document walks through the key issues involved in writing API wrappers in R. If you’re new to working with web APIs, you may want to start by reading “An introduction to APIs” by zapier.

Dbpatterns is a service that allows you to create, share, explore database models on the web. GitHub: https://github.com/fatiherikli/dbpatterns.

Beginning today, all Earth imagery from a prolific Japanese remote sensing instrument operating aboard NASA’s Terra spacecraft since late 1999 is now available to users everywhere at no cost.

The public will have unlimited access to the complete 16-plus-year database for Japan’s Ministry of Economy, Trade and Industry (METI) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument, which images Earth to map and monitor the changing surface of our planet. ASTER’s database currently consists of more than 2.95 million individual scenes.

Researchers are preparing to release technology designed to overcome the challenges of coping with large amounts of geospatial data. The Web-based system makes it easier to layer blocks of information, allowing a wide variety of users to quickly understand and share complex data sets.

The National Science Foundation’s (NSF’s) Geospatial Building Blocks (GABBs) project is creating a system for hosting, processing, analyzing and sharing geospatial information. The system is built on HUBzero, an open source platform developed at Purdue University that lets individuals build feature-rich websites to advance research and education.
– See more at: http://www.afcea.org/content/?q=Article-building-geodata-block-block#sthash.bOugdulN.dpuf