March 2012

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This is a collection of government files I have collected concerning the conscientious objector (CO) process in the United States.  Many of these files are outdated; whatever analysis they provide is probably no longer relevant.  They are probably not of interest to you, unless you are doing some serious historical work.

Government Accountability Office (GAO) Reports

  • GAO-07-1196 (2007) “Number of Formally Reported Applications for Conscientious Objectors is Small Relative to the Total Size of the Armed Forces”
  • GAO/NSIAD-94-35 (1993) “Conscientious Objectors – Number of Applications Remained Small During the Persian Gulf War”
  • GAO/NSIAD-98-199 (1998) -”Gender Issues – Changes Would be Needed to Expand Selective Service Registration to Women”

Defense Technical Information Center (DTIC) Publications

Department of Defense Regulations

Publications by Military Law Journals (Army Lawyer and Military Law Review)

These are full journals, not just the relevant article.  Do a search for “conscientious objector” to find the relevant section.

One common myth about conscientious objectors in the US is that they are reservists who took the government’s money to pay for college but then refused to fulfill their end of the bargain.  This graph, however, shows that most conscientious objectors are in fact full-time, active duty personnel:

active duty and reservist conscientious objectorsThe data was obtained from two Government Accountability Office (GAO) reports on conscientious objection.  Report GAO/NSIAD-94-35 covers conscientious objection during the First Persian Gulf War, and report GAO-07-1196 covers conscientious objection during Operation Iraqi Freedom.

EDIT: WordPress seems to garble the code sections on occasion for no good reason.  If you want to run the code, you should download the original file instead.  Sorry.

This is a tutorial for how to use Hidden Markov Models (HMMs) in Haskell.  We will use the Data.HMM package to find genes in the second chromosome of Vitis vinifera: the wine grape vine. Predicting gene locations is a common task in bioinformatics that HMMs have proven good at.

The basic procedure has three steps.  First, we create an HMM to model the chromosome.  We do this by running the Baum-Welch training algorithm on all the DNA.  Second, we create an HMM to model transcription factor binding sites.  This is where genes are located.  Finally, we use Viterbi’s algorithm to determine which HMM best models the DNA at a given location in the chromosome.  If it’s the first, this is probably not the start of a gene.  If it’s the second, then we’ve found a gene!

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