Thu Nov 7 02:12:16 2024
EVENTS
 FREE
SOFTWARE
INSTITUTE

POLITICS
JOBS
MEMBERS'
CORNER

MAILING
LIST

NYLXS Mailing Lists and Archives
NYLXS Members have a lot to say and share but we don't keep many secrets. Join the Hangout Mailing List and say your peice.

DATE 2016-11-01

LEARN

2024-11-07 | 2024-10-07 | 2024-09-07 | 2024-08-07 | 2024-07-07 | 2024-06-07 | 2024-05-07 | 2024-04-07 | 2024-03-07 | 2024-02-07 | 2024-01-07 | 2023-12-07 | 2023-11-07 | 2023-10-07 | 2023-09-07 | 2023-08-07 | 2023-07-07 | 2023-06-07 | 2023-05-07 | 2023-04-07 | 2023-03-07 | 2023-02-07 | 2023-01-07 | 2022-12-07 | 2022-11-07 | 2022-10-07 | 2022-09-07 | 2022-08-07 | 2022-07-07 | 2022-06-07 | 2022-05-07 | 2022-04-07 | 2022-03-07 | 2022-02-07 | 2022-01-07 | 2021-12-07 | 2021-11-07 | 2021-10-07 | 2021-09-07 | 2021-08-07 | 2021-07-07 | 2021-06-07 | 2021-05-07 | 2021-04-07 | 2021-03-07 | 2021-02-07 | 2021-01-07 | 2020-12-07 | 2020-11-07 | 2020-10-07 | 2020-09-07 | 2020-08-07 | 2020-07-07 | 2020-06-07 | 2020-05-07 | 2020-04-07 | 2020-03-07 | 2020-02-07 | 2020-01-07 | 2019-12-07 | 2019-11-07 | 2019-10-07 | 2019-09-07 | 2019-08-07 | 2019-07-07 | 2019-06-07 | 2019-05-07 | 2019-04-07 | 2019-03-07 | 2019-02-07 | 2019-01-07 | 2018-12-07 | 2018-11-07 | 2018-10-07 | 2018-09-07 | 2018-08-07 | 2018-07-07 | 2018-06-07 | 2018-05-07 | 2018-04-07 | 2018-03-07 | 2018-02-07 | 2018-01-07 | 2017-12-07 | 2017-11-07 | 2017-10-07 | 2017-09-07 | 2017-08-07 | 2017-07-07 | 2017-06-07 | 2017-05-07 | 2017-04-07 | 2017-03-07 | 2017-02-07 | 2017-01-07 | 2016-12-07 | 2016-11-07 | 2016-10-07 | 2016-09-07 | 2016-08-07 | 2016-07-07 | 2016-06-07 | 2016-05-07 | 2016-04-07 | 2016-03-07 | 2016-02-07 | 2016-01-07 | 2015-12-07 | 2015-11-07 | 2015-10-07 | 2015-09-07 | 2015-08-07 | 2015-07-07 | 2015-06-07 | 2015-05-07 | 2015-04-07 | 2015-03-07 | 2015-02-07 | 2015-01-07 | 2014-12-07 | 2014-11-07 | 2014-10-07

Key: Value:

Key: Value:

MESSAGE
DATE 2016-11-04
FROM Christopher League
SUBJECT Subject: [Learn] Fitch/Sankoff
From learn-bounces-at-nylxs.com Fri Nov 4 19:03:15 2016
Return-Path:
X-Original-To: archive-at-mrbrklyn.com
Delivered-To: archive-at-mrbrklyn.com
Received: from www.mrbrklyn.com (www.mrbrklyn.com [96.57.23.82])
by mrbrklyn.com (Postfix) with ESMTP id 78F75161312;
Fri, 4 Nov 2016 19:03:14 -0400 (EDT)
X-Original-To: learn-at-nylxs.com
Delivered-To: learn-at-nylxs.com
Received: from liucs.net (contrapunctus.net [174.136.110.10])
by mrbrklyn.com (Postfix) with ESMTP id 55703160E77
for ; Fri, 4 Nov 2016 13:44:41 -0400 (EDT)
Received: from localhost (static-100-38-172-234.nycmny.fios.verizon.net
[100.38.172.234]) by liucs.net (Postfix) with ESMTPSA id 0B850E097
for ; Fri, 4 Nov 2016 13:44:37 -0400 (EDT)
From: Christopher League
To: learn-at-nylxs.com
In-Reply-To: <1038aae4-3618-884b-d13b-a58ebca89437-at-panix.com>
References: <20161102182751.GA10998-at-www.mrbrklyn.com>
<87k2cl7184.fsf-at-contrapunctus.net>
<2ab889a4-74bd-7670-b872-04bb8af301ce-at-panix.com>

<1038aae4-3618-884b-d13b-a58ebca89437-at-panix.com>
User-Agent: Notmuch/0.21 (http://notmuchmail.org) Emacs/25.1.1
(x86_64-unknown-linux-gnu)
Date: Fri, 04 Nov 2016 13:44:31 -0400
Message-ID: <87mvhf87i8.fsf-at-contrapunctus.net>
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary="=-=-="
X-Mailman-Approved-At: Fri, 04 Nov 2016 19:03:11 -0400
Subject: [Learn] Fitch/Sankoff
X-BeenThere: learn-at-nylxs.com
X-Mailman-Version: 2.1.17
Precedence: list
List-Id:
List-Unsubscribe: ,

List-Archive:
List-Post:
List-Help:
List-Subscribe: ,

Errors-To: learn-bounces-at-nylxs.com
Sender: "Learn"

--=-=-=
Content-Type: multipart/alternative; boundary="==-=-="

--==-=-=
Content-Type: text/plain


Attached is a sample output image from my Sankoff implementation in
Haskell. Set up a public git repo here:


CL


--==-=-=
Content-Type: text/html; charset=utf-8











Attached is a sample output image from my Sankoff implementation in Haskell. Set up a public git repo here: https://git.liucs.net/league/phylogeny


CL





--==-=-=--

--=-=-=
Content-Type: image/png
Content-Disposition: inline; filename=sankoff-ex1.png
Content-Transfer-Encoding: base64
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--=-=-=
Content-Type: text/plain; charset="us-ascii"
MIME-Version: 1.0
Content-Transfer-Encoding: 7bit
Content-Disposition: inline

_______________________________________________
Learn mailing list
Learn-at-nylxs.com
http://lists.mrbrklyn.com/mailman/listinfo/learn

--=-=-=--

  1. 2016-11-01 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] how is it indexing in cuda
  2. 2016-11-01 Ruben Safir <ruben.safir-at-my.liu.edu> Re: [Learn] not adequately speced of explained
  3. 2016-11-01 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] how is it indexing in cuda
  4. 2016-11-01 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] not adequately speced of explained
  5. 2016-11-02 Christopher League <league-at-contrapunctus.net> Re: [Learn] Fitch Algorithm - C++
  6. 2016-11-02 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Fitch Algorithm - C++
  7. 2016-11-02 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] how is it indexing in cuda
  8. 2016-11-02 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fitch Algorithm - C++
  9. 2016-11-02 IEEE Computer Society <csconnection-at-computer.org> Subject: [Learn] Hear Google's John Martinis Take on Quantum Computing at
  10. 2016-11-02 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] opencl
  11. 2016-11-02 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] scheduled for tommorw
  12. 2016-11-02 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] threads tutorial
  13. 2016-11-03 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Fitch Algorithm - C++
  14. 2016-11-03 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Fitch Algorithm - C++
  15. 2016-11-03 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Fitch Algorithm - C++
  16. 2016-11-03 Christopher League <league-at-contrapunctus.net> Re: [Learn] huffman code
  17. 2016-11-03 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] huffman code
  18. 2016-11-03 Ruben Safir <ruben.safir-at-my.liu.edu> Re: [Learn] huffman code
  19. 2016-11-03 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fitch algorithm from the beginning
  20. 2016-11-03 From: <mrbrklyn-at-panix.com> Subject: [Learn] huffman code
  21. 2016-11-03 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Phenology meeting
  22. 2016-11-03 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] relevant hackathon
  23. 2016-11-03 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] relevant hackathon
  24. 2016-11-04 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] huffman code
  25. 2016-11-04 Christopher League <league-at-contrapunctus.net> Subject: [Learn] Fitch/Sankoff
  26. 2016-11-05 Christopher League <league-at-contrapunctus.net> Re: [Learn] Fwd: templates within templates
  27. 2016-11-05 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Re: const T vs T const
  28. 2016-11-05 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Template Library files and Header linking troubles
  29. 2016-11-05 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: templates within templates
  30. 2016-11-06 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Fwd: templates within templates
  31. 2016-11-06 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Fwd: templates within templates
  32. 2016-11-06 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Fwd: templates within templates
  33. 2016-11-06 Christopher League <league-at-contrapunctus.net> Re: [Learn] Fwd: templates within templates
  34. 2016-11-06 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Fwd: templates within templates
  35. 2016-11-06 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Fwd: templates within templates
  36. 2016-11-06 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Fwd: templates within templates
  37. 2016-11-06 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] GNU Parallel 20161022 ('Matthew') released [stable]
  38. 2016-11-07 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] templates and ostream for future reference
  39. 2016-11-08 Christopher League <league-at-contrapunctus.net> Re: [Learn] C++ signature ambiguity
  40. 2016-11-08 Ruben Safir <ruben.safir-at-my.liu.edu> Re: [Learn] C++ signature ambiguity
  41. 2016-11-08 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] C++ signature ambiguity
  42. 2016-11-08 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Invitation: Phylogeny meeting -at- Weekly from 10:15 to
  43. 2016-11-08 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] Fwd: [nylug-talk] RSVP open: Wed Nov 16,
  44. 2016-11-09 Christopher League <league-at-contrapunctus.net> Re: [Learn] merge sort parallel hw
  45. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] merge sort parallel hw
  46. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] merge sort parallel hw
  47. 2016-11-09 Christopher League <league-at-contrapunctus.net> Re: [Learn] merge sort parallel hw
  48. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] mergesort tutorial
  49. 2016-11-09 Christopher League <league-at-contrapunctus.net> Re: [Learn] mergesort tutorial
  50. 2016-11-09 Christopher League <league-at-contrapunctus.net> Re: [Learn] namespace and external files confusion
  51. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] namespace and external files confusion
  52. 2016-11-09 From: "Carlos R. Mafra" <crmafra-at-gmail.com> Re: [Learn] Question about a small change
  53. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] =?utf-8?q?C++_call_of_overloaded_=E2=80=98track=28int*=26?=
  54. 2016-11-09 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: lost arguments
  55. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: [dinosaur] Dating origins of dinosaurs,
  56. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] merge sort parallel hw
  57. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] mergesort tutorial
  58. 2016-11-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] namespace and external files confusion
  59. 2016-11-10 Christopher League <league-at-contrapunctus.net> Re: [Learn] merge sort parallel hw
  60. 2016-11-10 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] merge sort parallel hw
  61. 2016-11-10 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] merge sort parallel hw
  62. 2016-11-10 Ruben Safir <ruben.safir-at-my.liu.edu> Re: [Learn] merge sort parallel hw
  63. 2016-11-10 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] [Hangout-NYLXS] mergesort tutorial
  64. 2016-11-10 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] Fwd: [Hangout-NYLXS] ease your mind- everything in the
  65. 2016-11-10 Ruben Safir <ruben.safir-at-my.liu.edu> Subject: [Learn] Fwd: [Hangout-NYLXS] R Programming Workshop
  66. 2016-11-10 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Paleocast phenogenetic tree building
  67. 2016-11-11 Christopher League <league-at-contrapunctus.net> Re: [Learn] merge sort parallel hw
  68. 2016-11-12 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] HW of mergesort in parallel
  69. 2016-11-13 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] merge sort in parallel assignment
  70. 2016-11-14 Christopher League <league-at-contrapunctus.net> Re: [Learn] merge sort in parallel assignment
  71. 2016-11-14 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] merge sort in parallel assignment
  72. 2016-11-14 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] merge sort parallel hw
  73. 2016-11-14 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] CUDA and video
  74. 2016-11-14 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] PNG Graphic formats and CRCs
  75. 2016-11-15 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: PNG coding
  76. 2016-11-15 ruben safir <ruben.safir-at-my.liu.edu> Subject: [Learn] Fwd: PNG Coding
  77. 2016-11-16 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Fwd: lost arguments
  78. 2016-11-16 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] relevant hackathon
  79. 2016-11-16 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] C++ Workshop Announcement
  80. 2016-11-16 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] Fwd: Re: ref use
  81. 2016-11-16 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] ref use
  82. 2016-11-16 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] why use a reference wrapper int his example
  83. 2016-11-17 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] [Hangout-NYLXS] at K&R now
  84. 2016-11-17 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: [Hangout-NYLXS] Fwd: PNG Coding
  85. 2016-11-18 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] C++ workshop and usenet responses
  86. 2016-11-19 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: ref use
  87. 2016-11-20 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] when is the constructor called for an object
  88. 2016-11-21 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: creating a binary tree
  89. 2016-11-21 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: hidden static
  90. 2016-11-21 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: ISBI 2017 Call for Abstracts and Non-Author
  91. 2016-11-21 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: PNG coding
  92. 2016-11-21 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Re: the new {} syntax
  93. 2016-11-21 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: when is the constructor called for an object
  94. 2016-11-21 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: when is the constructor called for an object
  95. 2016-11-21 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: [dinosaur] Eoconfuciusornis feather keratin and
  96. 2016-11-21 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] look what I found
  97. 2016-11-22 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Cuccuency book
  98. 2016-11-22 ruben safir <ruben.safir-at-my.liu.edu> Subject: [Learn] declare a func or call an object
  99. 2016-11-22 Ruben Safir <ruben.safir-at-my.liu.edu> Subject: [Learn] Fwd: Re: Using CLIPS as a library
  100. 2016-11-23 Ruben Safir <ruben.safir-at-my.liu.edu> Subject: [Learn] Fwd: Simple C++11 Wrapper for CLIPS 6.30
  101. 2016-11-23 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Parrelel Programming HW2 with maxpath
  102. 2016-11-24 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] great research news for big data
  103. 2016-11-24 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] mapping algorithms
  104. 2016-11-24 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Todays meeting
  105. 2016-11-25 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: [dinosaur] Flightless theropod phylogenetic variation
  106. 2016-11-26 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Note to self for Thursday
  107. 2016-11-26 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fitch etc
  108. 2016-11-26 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Note to self for Thursday
  109. 2016-11-26 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] operator<<() overloading details and friend
  110. 2016-11-27 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] 130 year old feathers analysis
  111. 2016-11-27 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: ACM/SPEC ICPE 2017 - Call for Tutorial Proposals
  112. 2016-11-27 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: ACM/SPEC ICPE 2017 - Call for Workshop Proposals
  113. 2016-11-27 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: CfP 22nd Conf. Reliable Software Technologies,
  114. 2016-11-27 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Seeking contributors for psyche-c
  115. 2016-11-29 Christopher League <league-at-contrapunctus.net> Re: [Learn] Look at this exciting output by my test program
  116. 2016-11-29 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Look at this exciting output by my test program
  117. 2016-11-29 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Look at this exciting output by my test program
  118. 2016-11-29 Christopher League <league-at-contrapunctus.net> Re: [Learn] Quantum Entanglement
  119. 2016-11-29 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Quantum Entanglement
  120. 2016-11-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Here is the paper I was talking out
  121. 2016-11-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Look at this exciting output by my test program
  122. 2016-11-29 nylxs <mrbrklyn-at-optonline.net> Subject: [Learn] Look at this exciting output by my test program
  123. 2016-11-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Quantum Entanglement
  124. 2016-11-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] The Death of PBS
  125. 2016-11-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] witmer lab ohio and 3d imaging
  126. 2016-11-30 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] phylogenetic crawler

NYLXS are Do'ers and the first step of Doing is Joining! Join NYLXS and make a difference in your community today!