Four short links: 13 Feb 2017

Urban Attractors, Millimetre-Scale Computing, Ship Small Code, and C++ Big Data

By Nat Torkington
February 13, 2017
Four short links.
  1. Urban Attractors: Discovering Patterns in Regions of Attraction in CitiesWe 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.
  2. Millimetre-Scale Deep LearningAnother 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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  4. 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.
  5. Thrilldistributed big data batch computations on a cluster of machines … in C++. (via Harris Brakmic)
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