High-resolution socioeconomic data are crucial for place-based policy design and implementation, but it remains scarce for many developing cities and countries. We show that an easily accessible and timely updated neighborhood attribute, restaurant, when combined with machine-learning models, can be used to effectively predict a range of socioeconomic attributes. This approach allows us to collect training samples from representative neighborhoods and then use our trained model to infer unsampled neighborhoods in the city in a granular, timely, and low-cost manner. The good cross-city transferability performance of our model can also help bridge the “data gap” between cities, by training the model in cities with rich survey data and then applying it to cities where such data are unavailable.

 

Predicting neighborhoods’ socioeconomic attributes using restaurant data

Lei Dong, Carlo Ratti, and Siqi Zheng
PNAS

Source: www.pnas.org

Consciousness is a famously hard problem, so Hod Lipson is starting from the basics: with self-aware robots that can help us understand how we think.

Source: www.quantamagazine.org

While rich medical, behavioral, and socio-demographic data are key to modern data-driven research, their collection and use raise legitimate privacy concerns. Anonymizing datasets through de-identification and sampling before sharing them has been the main tool used to address those concerns. We here propose a generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset. On 210 populations, our method obtains AUC scores for predicting individual uniqueness ranging from 0.84 to 0.97, with low false-discovery rate. Using our model, we find that 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes. Our results suggest that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR and seriously challenge the technical and legal adequacy of the de-identification release-and-forget model.

 

Estimating the success of re-identifications in incomplete datasets using generative models
Luc Rocher, Julien M. Hendrickx & Yves-Alexandre de Montjoye
Nature Communicationsvolume 10, Article number: 3069 (2019)

Source: www.nature.com

Designing collective behaviors for robot swarms is a difficult endeavor due to their fully distributed, highly redundant, and ever-changing nature. To overcome the challenge, a few approaches have been proposed, which can be classified as manual, semi-automatic, or automatic design. This paper is intended to be the manifesto of the automatic off-line design for robot swarms. We define the off-line design problem and illustrate it via a possible practical realization, highlight the core research questions, raise a number of issues regarding the existing literature that is relevant to the automatic off-line design, and provide guidelines that we deem necessary for a healthy development of the domain and for ensuring its relevance to potential real-world applications.

 

Automatic Off-Line Design of Robot Swarms: A Manifesto

Mauro Birattari, et al.

Front. Robot. AI, 19 July 2019

Source: www.frontiersin.org