NYU Data Science newsletter – July 19, 2016

GROUP CURATION: N/A

Of all the data we create and share, perhaps none is more important — or more sensitive — than data about our health. The wearable tech revolution has given us, as patients and individuals, control – but we also must think about what we, and others, do with the data that we collect.

Andreas Weigend, author of Data for the People, Professor at Stanford and Berkeley, Director of the Social Data Lab, and former Chief Scientist at Amazon, keynoted the recent NeuroTech San Francisco conference. Following is a link to his interview with Unity Stoakes of StartUp Health, where he discusses the reality of social data.

Patent application no. 14/451,356 has one goal: to outrun the speed demons of Wall Street. … And if it works as advertised, one of the most illustrious names in the hedge-fund business [Renaissance Technologies] could gain exclusive U.S. rights to a weapon capable of thwarting even the most predatory of high-speed traders.

The greatest advantage self-driving cars hold over outdated humans is the ability to tune out distractions. No buzzing phone, yelling kids, or lovely daydream will divert attention from their primary task. That doesn’t mean they can’t get overwhelmed with information in much the same way you do.

The fully autonomous vehicles that companies like Google, Ford, and Baidu are furiously developing all rely on light detection and ranging (LIDAR) to see and map the world. Those maps are key, because they provide crucial context for the vehicles and let them focus their sensors and computing power on temporary obstacles like cars, pedestrians, and cyclists.

At the most powerful companies in Silicon Valley, small teams of anonymous, hardcore music fans race to solve the record industry’s toughest problem.

The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has been addressed mainly in terms of exact replicability or numerical equivalence, ignoring the wider issue of the reproducibility of conclusions through equivalent, extended or alternative methods. [full text]

When disaster strikes, those affected often turn to social media to request aid, offer assistance, or share other information in real time. In recent years, data scientists have begun analyzing millions of Facebook posts and tweets in order to study the collective response before, during and after a crisis.

In the face of this mountain of information, however, it can be hard to identify the most relevant posts and trends. But thanks to a close collaboration between social science and software engineering, University of Colorado Boulder researchers Leysia Palen and Kenneth Anderson are innovating new ways to find the underlying human behaviors hidden within noisy data.

“The trick is understanding the potential of large-volume social media information along with its limits,” says Palen, chair of the Department of Information Science in the College of Media, Communication and Information at CU Boulder. “Just because we have a lot of data doesn’t mean that we have all the answers.”

Scientists at the Montreal Neurological Institute and Hospital have used a powerful tool to better understand the progression of late-onset Alzheimer’s disease (LOAD), identifying its first physiological signs.

Led by Dr. Alan Evans, a professor of neurology, neurosurgery and biomedical engineering at the Neuro, the researchers analyzed more than 7,700 brain images from 1,171 people in various stages of Alzheimer’s progression using a variety of techniques including magnetic resonance imaging (MRI) and positron emission tomography (PET). Blood and cerebrospinal fluid were also analyzed, as well as the subjects’ level of cognition.

Marine conservation researcher Ben Halpern calls himself an “ocean optimist.” But getting there wasn’t easy — it’s taken more than eight years of research to reassure himself that maybe, just maybe, our seas are not doomed. How did he get there? By collecting and studying data from the oceans, and synthesizing billions of data points into maps that reveal previously untold stories in swirls of blue, green, orange and red. Here’s a look at some of the reasons Halpern sees hope beneath the waves.

I am thrilled to announce that the Gordon and Betty Moore Foundation has provided a significant grant in order to help move Numba and Dask to version 1.0 and graduate them into robust community-supported projects.

Additional funding announcements:

University library Data Curation Network supported by the Sloan Foundation (16 May 2016)

In several markets, firms compete not for consumer expenditure but instead for consumer attention. We model and characterize how households allocate their scarce attention in arguably the largest market for attention: the Internet. Our characterization of household attention allocation operates along three dimensions: how much attention is allocated, where that attention is allocated, and how that attention is allocated. Using click-stream data for thousands of U.S. households, we assess if and how attention allocation on each dimension changed between 2008 and 2013, a time of large increases in online offerings. We identify vast and expected changes in where households allocate their attention (away from chat and news towards video and social media), and yet we simultaneously identify remarkable stability in how much attention is allocated and how it is allocated. Specifically, we identify (i) persistence in the elasticity of attention according to income and (ii) complete stability in the dispersion of attention across sites and in the intensity of attention within sites. We illustrate how this finding is difficult to reconcile with standard models of optimal attention allocation and suggest alternatives that may be more suitable. We conclude that increasingly valuable offerings change where households go online, but not their general online attention patterns. This conclusion has important implications for competition and welfare in other markets for attention.

odo takes two arguments, a source and a target for a data transfer. … It efficiently migrates data from the source to the target through a network of conversions.

840 registered participants as of July 22.