- Mapping Twitter Topic Networks (Pew Internet) — Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation. (via Washington Post)
- yasp — a fully functional web-based assembler development environment, including a real assembler, emulator and debugger. The assembler dialect is a custom which is held very simple so as to keep the learning curve as shallow as possible.
- The 12-Factor App — twelve habits of highly successful web developers, essentially.
- Fast Approximation of Betweenness Centrality through Sampling (PDF) — Betweenness centrality is a fundamental measure in social network analysis, expressing the importance or influence of individual vertices in a network in terms of the fraction of shortest paths that pass through them. Exact computation in large networks is prohibitively expensive and fast approximation algorithms are required in these cases. We present two efficient randomized algorithms for betweenness estimation.
Downloading Kindle Highlights, Balanced Photos, Long Form, and Crap Regulation
- bookcision — bookmarklet to download your Kindle highlights. (via Nelson Minar)
- Algorithm for a Perfectly Balanced Photo Gallery — remember this when it comes time to lay out your 2013 “Happy Holidays!” card.
- Long Stories (Fast Company Labs) — Our strategy was to still produce feature stories as discrete articles, but then to tie them back to the stub article with lots of prominent links, again taking advantage of the storyline and context we had built up there, making our feature stories sharper and less full of catch-up material.
- Massachusetts Software Tax (Fast Company Labs) — breakdown of why this crappily-written law is bad news for online companies. Laws are the IEDs of the Internet: it’s easy to make massively value-destroying regulation and hard to get it fixed.
Algorithmic Optimisation, 3D Scanners, Corporate Open Source, and Data Dives
- Unhappy Truckers and Other Algorithmic Problems — Even the insides of vans are subjected to a kind of routing algorithm; the next time you get a package, look for a three-letter letter code, like “RDL.” That means “rear door left,” and it is so the driver has to take as few steps as possible to locate the package. (via Sam Minnee)
- Fuel3D: A Sub-$1000 3D Scanner (Kickstarter) — a point-and-shoot 3D imaging system that captures extremely high resolution mesh and color information of objects. Fuel3D is the world’s first 3D scanner to combine pre-calibrated stereo cameras with photometric imaging to capture and process files in seconds.
- Corporate Open Source Anti-Patterns (YouTube) — Brian Cantrill’s talk, slides here. (via Daniel Bachhuber)
- Hacking for Humanity) (The Economist) — Getting PhDs and data specialists to donate their skills to charities is the idea behind the event’s organizer, DataKind UK, an offshoot of the American nonprofit group.
Distributed Browser-Based Computation, Streaming Regex, Preventing SQL Injections, and SVM for Faster Deep Learning
- WeevilScout — browser app that turns your browser into a worker for distributed computation tasks. See the poster (PDF). (via Ben Lorica)
- sregex (Github) — A non-backtracking regex engine library for large data streams. See also slide notes from a YAPC::NA talk. (via Ivan Ristic)
- Bobby Tables — a guide to preventing SQL injections. (via Andy Lester)
- Deep Learning Using Support Vector Machines (Arxiv) — we are proposing to train all layers of the deep networks by backpropagating gradients through the top level SVM, learning features of all layers. Our experiments show that simply replacing softmax with linear SVMs gives significant gains on datasets MNIST, CIFAR-10, and the ICML 2013 Representation Learning Workshop’s face expression recognition challenge. (via Oliver Grisel)
The importance of data science tools that let organizations easily combine, deploy, and maintain algorithms
Data science often depends on data pipelines, that involve acquiring, transforming, and loading data. (If you’re fortunate most of the data you need is already in usable form.) Data needs to be assembled and wrangled, before it can be visualized and analyzed. Many companies have data engineers (adept at using workflow tools like Azkaban and Oozie), who manage1 pipelines for data scientists and analysts.
A workflow tool for data analysts: Chronos from airbnb
A raw bash scheduler written in Scala, Chronos is flexible, fault-tolerant2, and distributed (it’s built on top of Mesos). What’s most interesting is that it makes the creation and maintenance of complex workflows more accessible: at least within airbnb, it’s heavily used by analysts.
Job orchestration and scheduling tools contain features that data scientists would appreciate. They make it easy for users to express dependencies (start a job upon the completion of another job), and retries (particularly in cloud computing settings, jobs can fail for a variety of reasons). Chronos comes with a web UI designed to let business analysts3 define, execute, and monitor workflows: a zoomable DAG highlights failed jobs and displays stats that can be used to identify bottlenecks. Chronos lets you include asynchronous jobs – a nice feature for data science pipelines that involve long-running calculations. It also lets you easily define repeating jobs over a finite time interval, something that comes in handy for short-lived4 experiments (e.g. A/B tests or multi-armed bandits).
Comparing Algorithms, Programming & Visual Arts, Data Brokers, and Your Brain on Ebooks
- mlcomp — a free website for objectively comparing machine learning programs across various datasets for multiple problem domains.
- Printing Code: Programming and the Visual Arts (Vimeo) — Rune Madsen’s talk from Heroku’s Waza. (via Andrew Odewahn)
- What Data Brokers Know About You (ProPublica) — excellent run-down on the compilers of big data about us. Where are they getting all this info? The stores where you shop sell it to them.
- Subjective Impressions Do Not Mirror Online Reading Effort: Concurrent EEG-Eyetracking Evidence from the Reading of Books and Digital Media (PLOSone) — Comprehension accuracy did not differ across the three media for either group and EEG and eye fixations were the same. Yet readers stated they preferred paper. That preference, the authors conclude, isn’t because it’s less readable. From this perspective, the subjective ratings of our participants (and those in previous studies) may be viewed as attitudes within a period of cultural change.