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Biweekly links for 11/13/2009

by Michael Nielsen on November 13, 2009
  • Polymath and the origin of life « Gowers’s Weblog
    • Tim Gowers has some very interesting ideas for an open science project to come up with a simple theoretical model where self-replication organisms are likely to spontaneously arise. In this post he tries to formulate a question or questions such a project could feasibly attack, and discusses what would count as a success.
  • The Law of Unintended Consequences
    • “From 1992 to September 2003, pharmaceutical companies tied up the federal courts with 494 patent suits. That’s more than the number filed in the computer hardware, aerospace, defense, and chemical industries combined. Those legal expenses are part of a giant, hidden “drug tax”–a tax that has to be paid by someone. And that someone, as you’ll see below, is you. You don’t get the tab all at once, of course. It shows up in higher drug costs, higher tuition bills, higher taxes–and tragically, fewer medical miracles.

      So how did we get to this sorry place? It was one piece of federal legislation that you’ve probably never heard of–a 1980 tweak to the U.S. patent and trademark law known as the Bayh-Dole Act. That single law, named for its sponsors, Senators Birch Bayh and Bob Dole, in essence transferred the title of all discoveries made with the help of federal research grants to the universities and small businesses where they were made. “

  • Evil reptilian kitten-eater from another planet – Wikipedia, the free encyclopedia
    • “”Evil reptilian kitten-eater from another planet (Sorry.)” was a pejorative used to refer to then Ontario Liberal Party opposition leader Dalton McGuinty in a press release disseminated by the Progressive Conservative Party of Ontario on September 12, 2003, during the provincial election campaign in Ontario, Canada.”
  • Structure+Strangeness: Power laws and all that jazz, redux
    • Aaron Clauset summarizes his review (with Cosma Shalizi and Mark Newman) of how to fit and validate power-law distributions in empirical data. A lot of phenomena that people think follow power laws… well, don’t.
  • bixo
    • Open source Hadoop-based web crawler. Backed by EMI Music and ShareThis.
  • Math Overflow « Combinatorics and more
    • Gil Kalai’s impressions of Math Overflow, the new question and answer site for mathematicians.
  • Charlie’s Diary: Designing society for posterity
    • “So. You, and a quarter of a million other folks, have embarked on a 1000-year voyage aboard a hollowed-out asteroid. What sort of governance and society do you think would be most comfortable, not to mention likely to survive the trip without civil war, famine, and reigns of terror?”
  • History’s greatest comet hunter discovers 1000th comet
    • A fascinating amateur-professional hybrid model for processing data. A wide field robotic solar observatory takes data, which is then examined by amateurs (and pros), who find comets as they graze the sun. One of the amateurs has discovered more than 150 comets this way, which is an appreciable fraction of all the comets discovered in all of history.
  • Shigeki Murakami;Can comet hunters survive?
    • Despair and elation over new automated telescopes.
  • Barack Obama’s Work in Progress
    • Many interesting tidbits on how Obama writes.
  • Open Knowledge Conference (OKCon) 2010: Call for Proposals
    • “We welcome proposals on any aspect of creating, publishing or reusing content or data that is open in accordance with “
  • Scientific software quality: what would it take to convince software engineers?
    • “what would convince you, as a software engineer, that a climate model is of good software quality or not? I asked this question at the CASCON workshop… No one had an answer. In fact, most people just dismissed the question with a laugh. Is it that silly of a question? I think it’s a great one… I’ve asked a few climate scientists the same question in earnest: what convinces you that climate model software is of good quality or not? The answers have been quite varied. Knowing the history of the model, or the development team, the state of the documentation, whether they’ve seen the model code or not, and generally how open the development is, are some of the things that factor into their assessment.”
  • The Sleep Experiment
    • Beautiful virtual choir on YouTube
  • Rough Type: Nicholas Carr’s Blog: Does my tweet look fat?
    • “…it becomes kind of annoying when somebody actually uses the full 140 characters. Jeez, I’m going to skip that tweet. It’s too long.

      The same thing has happened, of course, with texting. Who sends a 160-character text? A 160-character text would feel downright Homeric. And that’s what a 140-character tweet is starting to feel like, too.

      I think our alphabetic system of writing may be doomed. It doesn’t work well with realtime communication. That’s why people are forced to use all sorts of abbreviations and symbols – the alphabet’s just too damn slow. In the end, I bet we move back to a purely hieroglyphic system of writing, with the number of available symbols limited to what can fit onto a smartphone keypad. Honestly, I think that communicating effectively in realtime requires no more than 25 or 30 units of meaning. “

  • To science!
    • Cheers!
  • The original quasar paper: 3C 273 : A Star-Like Object with Large Red-Shift : Nature
    • 3C 273 is approximately 100 times brighter than our Milky Way; it’s probably about the size of our solar system. It’s 2 billion light years away, and can be seen in a good optical amateur telescope. Other quasars had been seen earlier, but this was the paper that nailed what strange objects they are.
  • NYT’s Keller: “What you can do with less, is less” » Nieman Journalism Lab
    • Lengthy, fascinating remarks from the editor of the New York Times, Bill Keller.
  • The problem with data-driven science
    • “However, data-driven science becomes more messy, methodologically and conceptually, when generation and testing of hypotheses are both based on the same, enormous data sets, and when the hypotheses to be tested are products of an automated search for patterns. Thousand-to-one odds in favor of a hypothesis (based on the usual kind of analysis) don’t mean much when a million hypotheses were screened to find it — but the evidence is the same, so what is the problem?

      In other words, What is so special about starting with a human-generated hypothesis? Bayesian methods suggest what I think is the right answer: To get from probabilistic evidence to the probability of something requires combining the evidence with a prior expectation, a “prior probability”, and human hypothesis generation enables this requirement to be ignored with considerable practical success.”

  • An interview with Alain Connes (pdf)
    • Connes on the future of mathematics, the value of freedom in mathematical research, and much else.

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