Urban Attractors: Discovering Patterns in Regions of Attraction in Cities — We use a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We detect three types of Urban Attractors in Riyadh during the morning period: Global, which are significant places in the city, and Downtown, which are the central business district and Residential attractors. In addition, we uncover what makes these places different in terms of attraction patterns. We used a statistical significance testing approach to rigorously quantify the relationship between Points of Interests (POIs) types (services) and the three patterns of Urban Attractors we detected.
Millimetre-Scale Deep Learning — Another micro mote they presented at the ISSCC incorporates a deep-learning processor that can operate a neural network while using just 288 microwatts.
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Ship Small Diffs (Dan McKinley) — your deploys should be measured in dozens of lines of code rather than hundreds. […] In online systems, you have to ship code to prove that it works. […] Your real problem is releasing frequently. So quotable, so good.
Thrill — distributed big data batch computations on a cluster of machines … in C++. (via Harris Brakmic)