NYU Data Science newsletter – September 16, 2016

GROUP CURATION: N/A

“In order to understand when charts started appearing in papers, we collected data on the number of charts in editions of The New York Times over its history. We checked a weekday, September edition of the paper every five years from 1855 to 2015. We chose the Times because of its rich archives and because we found it to be representative of trends in the newspaper industry.”

Our first JOSS submission (paper? package?) is about to be accepted and I wanted to enthuse about the process a bit. … With JOSS the focus is on the software itself, not on ephemera associated with the software.

No wonder we are still talking about reproducibility. We may all be having different conversations. ““Reproducibility,” “replication,” and related terms are used in many different ways across, and even within, disciplines. The economist Michael Clemens identified more than 70 different terms used in the social science literature to describe “replication” and related concepts.”

Also in reproducibility and open science:

Publishing Open Source Research Software in JOSS – an experience report (September 14, C. Titus Brown, Living in an Ivory Basement blog)

Julian Wolfson Named Reproducibility Editor for Leading Statistics Journal (August 31, University of Minnesota, School of Public Health)

Why scientists must share their research code (September 13, Nature News & Comment, Monya Baker, Victoria Stodden)

1-day Reproducibility Conference Coming to Columbia University December 2016 (December 09, Columbia University, Office of the Executive Vice President for Research)

Members of Congress and some industry experts raised concerns last week over policies governing the commercial remote sensing industry. Regulation of the industry is drawing congressional attention amid concerns that U.S. companies are facing increasing competition from foreign firms and may fall behind as innovators in an economic sector that the United States has led.

Amid general agreement that changes are needed, speakers at a 7 September hearing of a subcommittee of the House of Representatives Committee on Science, Space, and Technology differed over how broad an overhaul the Land Remote Sensing Policy Act of 1992 needs.

In “Ego-net Community Mining Applied to Friend Suggestion,” co-authored by Googlers Silvio Lattanzi, Vahab Mirrokni, Ismail Oner Sebe, Ahmed Taei, Sunita Verma and myself, we explore how social networks can provide better friend suggestions to users, a challenging practical problem faced by all social network platforms

Driving southbound on the 101 freeway through San Francisco, one may notice the following billboard. “Self-driving education,” it reads. Those three words connect to several ideas close to Udacity’s mission: help learners acquire new skills to pursue tech careers of their desire, and let them complete courses at their own pace (as long as they pay the monthly fee.)

Now the punny billboard has a triple entendre: Udacity has formally launched its Self-Driving Car Engineer Nanodegree that aims to teach students subjects including computer vision, sensor fusion and controllers. Students will also get to run their code on an autonomous vehicle owned by Udacity.

Machine learning used to take place behind the scenes: Amazon mined your clicks and purchases for recommendations, Google mined your searches for ad placement, and Facebook mined your social network to choose which posts to show you. But now machine learning is on the front pages of newspapers, and the subject of heated debate. Learning algorithms drive cars, translate speech, and win at Jeopardy! What can and can’t they do? Are they the beginning of the end of privacy, work, even the human race? This growing awareness is welcome, because machine learning is a major force shaping our future, and we need to come to grips with it. Unfortunately, several misconceptions have grown up around it, and dispelling them is the first step. Let’s take a quick tour of the main ones.

Today (at 06:30 New York time) Gaia released it’s DR1 data, and in particular the T-GAS sample of stars with five-parameter solutions and photometry. What a great day it was! I assembled with Kathryn Johnston (Columbia), David Spergel (Princeton), Adrian Price-Whelan (Princeton), Ruth Angus (Columbia), Keith Hawkins (Columbia), and others to get, play with, and make figures from the new data. Many amusing things happened, and this blog post will not capture them all.

Tel Aviv, Israel Biological and environmental networks, social, technological and economic networks.

Deadline for abstract submission is Tuesday, 25 September 2016.

22,000+ exhibition records linked to artist IDs, curator IDs; 1929 – 1989

With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first.