NYU Data Science newsletter – April 6, 2016

GROUP CURATION: N/A

… despite promising results, the extent of coordination between gaze and mouse cursor depends on the user task e.g. text highlighting, pointing or clicking. Moreover, eye and cursor movement are poorly coordinated during cursor inactivity, hence limiting the utility of mouse cursor as an attention measurement tool in a news reading task, where minimal pointing is required. Thus, in our recently-published research paper entitled “Understanding and Measuring User Engagement and Attention in Online News Reading” at the International Conference On Web Search And Data Mining (WSDM 2016), we propose to use viewport time to study user attention instead.

Researchers at ETH Zurich (Swiss Federal Institute of Technology in Zurich) and the University of Bologna have developed PULPino, an open-source processor optimized for low power consumption and application in wearables and the Internet of Things (IoT).

The social sciences are undergoing a dramatic transformation from studying problems to solving them; from making do with a small number of sparse data sets to analyzing increasing quantities of diverse, highly informative data; from isolated scholars toiling away on their own to larger scale, collaborative, interdisciplinary, lab-style research teams; and from a purely academic pursuit focused inward to having a major impact on public policy, commerce and industry, other academic fields, and some of the major problems that affect individuals and societies. In the midst of all this productive chaos, we have been building the Institute for Quantitative Social Science at Harvard, a new type of center intended to help foster and respond to these broader developments. We offer here some suggestions from our experiences for the increasing number of other universities that have begun to build similar institutions and for how we might work together to advance social science more generally.

Amazon has hired nearly all of the people who worked at Orbeus, a small startup with technology for identifying items and people in photos and videos using deep learning, a type of artificial intelligence. Orbeus built an app for iOS and Android that could instantly tag photos, called PhotoTime, as well as the underlying Rekognition application programming interface (API) that developers use for their own apps.

Last week, Dharmendra Modha said goodbye to a computer some six years in the making: a set of 16 interconnected TrueNorth chips built to mimic the ultra-low-energy, highly-parallel operation of the human brain.

On Thursday, a team from IBM Research-Almaden in California hopped in a car and drove the unit some 75 minutes north to the U.S. Department of Energy’s Lawrence Livermore National Laboratory. There, scientists and engineers will evaluate whether the technology could be a useful weapon in their computing arsenal.

A widely derided editorial, a controversial series of articles and delayed corrections have prompted critics to question the direction of the New England Journal of Medicine.

Post-financial crisis changes have made the financial system more stable, but policymakers need to acknowledge that large banks still pose systemic risk, Minneapolis Fed President Neel Kashkari contended Tuesday.

Kashkari, 42, started at the bank earlier this year and quickly made further regulation a top priority. On Monday, he hosted a symposium in Minneapolis called “Ending Too Big to Fail,” where experts floated ideas about how best to make the financial system safer.

If the science community is serious about integrating social science into its thinking and operations — and statements by everyone from Nature and the UK government to Paul Nurse, former president of the Royal Society, indicate that it is — then we social scientists must do more to make this happen.

Our input is necessary because, too often, the reach and influence of research is discovered only with hindsight. Lessons are ‘learned’ only after the social implications of new domains of science and technology have provoked controversy or challenged existing norms.

Also, Restructuring the Social Sciences: Reflections from Harvard’s Institute for Quantitative Social Science (March 21, Political Science Now, Gary King)

What would be your advice to a software engineer who wants to learn machine learning? … This depends a lot on the background of the software engineer. And it depends on which part of machine learning you want to master. So, for the sake of concreteness, let’s assume that we’re talking… (more)

ACM CoNEXT 2016, to be held in Irvine, California, USA, is soliciting proposals for one-day workshops to be co-located with the main conference. The workshops are considered an integral part of the conference and will be fully incorporated into its logistical and budget planning process. Workshop papers will be published in the same set of proceedings as the conference, and available on the ACM Digital Library.

The workshops should be on original and timely topics of relevance to the CoNEXT community, and aimed at fostering lively discussions and technical exchanges among participants.

Deadline for proposing workshops is Friday, April 29.

I recently had the pleasure of speaking on a few panels about analytics to my fellow MBA students and alumni, as well as many Penn undergrads. After these talks, I’ve been asked for my advice on what the best resources are for someone coming from the business world (i.e., non-technical) who wants to develop the skills to become an effective data scientist. This post is an attempt to codify the advice I give and general resources I point people towards. Hopefully, this will make what I have learned accessible to more people and provide some guidance for those who realize that the future belongs to the empirically inclined (see below) but don’t know where to start their journey to becoming part of the club.

pomegranate implements fast, efficient, and extremely flexible probabilistic modelling for Python. It grew out of the YAHMM package where many of the components of hidden Markov models could be rearranged to form other probabilistic models, such as general mixture models and markov chains. pomegranate is flexible enough to allow nesting of these components to form models such as general mixture model hidden Markov models (GMM-HMMs) or Naive Bayes comparing a hidden Markov model to a Markov chain.

This is an online demo with explanation and tutorial on Visual Question Answering. This is not a naive or hello-world model, this model returns close to state-of-the-art without using any attention models, memory networks (other than LSTM) and fine-tuning, which are essential recipe for current best results.

I have tried to explain different parts, and reasoning behind their choices. This is meant to be an interactive tutorial, feel free to change the model parameters and experiment. If you have latest graphics card execution time should be within a minute.

Host your static websites on GitLab.com for free, or on your own GitLab EE instance.

Love impressing your pals with all there is to know about computer science? Or perhaps state geography is more your thing?

MIT Media Lab, in partnership with Deloitte and the data visualization startup Datawheel, has just gone live with perhaps the most extensive tool ever created for mining and visualizing US government open data, called Data USA.