Neuron Digest V6 #42
Neuron-Digest Moderator Peter Marvit
Volume 6 : Issue 42
Today's Topics:
Public Data
Re: Public Data (machine learning)
Genetic make-up of brains
A questin about neural inhibition
Some parameters in Kohonen's network + Hello
RE: Neuron Digest V6 #40
Phone Number Given Incorrectly for GA Course Info
POPLOG Conference announcement (UK)
Send submissions, questions, address maintenance and requests for old issues to
"neuron-...@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request"
Use "ftp" to get old issues from hplpm.hpl.hp.com (15.255.176.205).
------------------------------------------------------------
Subject: Public Data
From: jc...@unix.cis.pitt.edu (John J Cierniakoski)
Organization: Univ. of Pittsburgh, Computing & Information Services
Date: 19 Jun 90 18:53:45 +0000
(This is the third time I am attempting to send this message, so if
all 3 appear at the same time I apologize.)
How can I get a copy, either machine readable or paper, of
"Quinlan's Mushroom Data"?
Did Quinlan use ID3 to analyze the Mushroom Data and then report
the results? If so, then where is the report?
How can I get a copy of the data Mingers used for his research
reported in the recent issues of Machine Learning? I got Fisher's
Iris data in an appendix to the Classification Algorithms book by
Mike James and I got a complete description of the generated Breiman
et. al data in their CART book. But there are a few other datasets
that Mingers used which I cannot find.
I wrote a computer program to analyze data and I am using it to analyze
a file describing work-related injuries. I wish to make the data and
program available to the public in the future but I do not wish to be
bothered with the distribution myself. Is there some organization that
handles such matters? Someone mentioned Stanford CS Dept. but when I
phoned them asking about public data all of the people I talked with did
not know what I was talking about.
From: John Cierniakoski
------------------------------
Subject: Re: Public Data (machine learning)
From: bra...@cs.utexas.edu (Bradley L. Richards)
Organization: U. Texas CS Dept., Austin, Texas
Date: 19 Jun 90 20:05:07 +0000
>How can I get a copy, either machine readable or paper, of
>"Quinlan's Mushroom Data"?
>
>I wrote a computer program to analyze data and I am using it to analyze
>a file describing work-related injuries. I wish to make the data and
>program available to the public in the future but I do not wish to be
>bothered with the distribution myself. Is there some organization that
>handles such matters?
UCI maintains an extensive set of publically available data sets suitable
for machine learning implementations, including the "mushroom" data. They
are also interested in new data sets with documentation.
When I wrote the librarian, David Aha, he sent the following information
on the database. To save him a flood of queries, I'm appending his
response below. This is a very long file, but since your questions are
probably of general interest I decided to post it in its entirity. It
contains all the information you need to access the UCI database. Folks
not interested in machine learning data should hit "j" now....
---------------------------------------------------------------------------
Bradley L. Richards uucp: cs.utexas.edu!bradley
bra...@cs.utexas.edu CompuServe: 75216,1744
---------------------------------------------------------------------------
===============================================================================
This is the UCI Repository Of Machine Learning Databases
7 February 1990
ics.uci.edu: /usr2/spool/ftp/pub/machine-learning-databases
Site Librarian: David W. Aha (a...@ics.uci.edu)
47 databases (5884K plus 1 offline database of unknown size)
===============================================================================
Included in this directory are data sets that have been or can be used to
evaluate learning algorithms. Each data file (*.data) consists of
individual records described in terms of attribute-value pairs. See the
corresponding *.names file for voluminous documentation. (Some files
_generate_ databases; they do not have *.data files.)
The contents of this repository can be remotely copied to other network
sites via ftp. Both the userid and password are "anonymous". As of
today, I've uncompressed the data files. However, they are usually in a
compressed state: use the "binary" command to ftp in order to tell it
that the file being transferred has been compressed. Otherwise, ftp will
assume that it is an ASCII file and will not transfer it properly.
Compressed files, whose filenames are postpended with ".Z", can be
uncompressed using the "uncompress" and "uncompressdir" functions.
Notes:
1. We're always looking for additional databases. Please send yours, with
documentation. Thanks. Current documentation requirements are located
in file DOC-REQUIREMENTS. Complaints and suggestions for improvements
are welcome anytime.
2. There is also the "undocumented" sub-directory which contains six
databases that require attention before being incorporated into the
repository. You are welcome to access them.
3. Ivan Bratko has asked me to restrict the access on the databases he
donated from the Ljubljana Oncology Institute. These databases, under
the breast-cancer, lymphography, and primary-tumor directories, are
unreadable to you. However, we are allowed to share them with academic
institutions upon request. If used, these databases (like several
others) require providing proper citations be made in published articles
that use them. The citation requirements can be found in each database's
corresponding documentation file.
4. Finally, I'm maintaining a list of CORRESPONDENTS and TRANSACTIONS.
Perhaps someone on your site is listed among the CORRESPONDENTS and
can provide you with some of these databases and related information.
(I have corresponded with over 75 people so far concerning these
databases.) TRANSACTIONS is a log of my correspondence with others,
which should enlighten you as to what problems we're having, etc.
David W. Aha
Repository Librarian
- ----------------------------------------------------------------------
Brief Overview of Databases:
Quick Listing:
1. annealing
2. audiology
3. autos
4. breast-cancer (restricted access)
5-6. chess-end-games
7. cpu-performance
8. echocardiogram
9. glass
10. hayes-roth
11-14. heart-disease
15. hepatitis
16. iris
17. labor-negotiations
18-19. led-display-creator
20. lymphography (restricted access)
21. mushroom
22. primary-tumor (restricted access)
23. shuttle-landing-control
24-25. soybean
26. spectrometer
27-34. thyroid-disease
35. university
36. voting-records
37-38. waveform domain
39-46. Undocumented databases: sub-directory undocumented
1. Bradshaw's flare data
2. Pat Langley's data generator
3. David Lewis's information retrieval (IR) data collection (offline)
4. Mike Pazzani's economic sanctions database
5. Ross Quinlan's latest version of the thyroid database
6. Philippe Collard's database on cloud cover images
7. Mary McLeish & Matt Cecile's database on horse colic
8. Paul O'Rorke's database containing theorems from Principia Mathematica
47. Nine small EBL domain theories and examples in sub-directory ebl
Quick Summaries of Each Database:
1. Annealing data (unknown source)
-- Documentation: On everything except database statistics
-- Background information on this database: unknown
-- Many missing attribute values
2. Audiology data (Baylor College)
-- Documentation: On everything except database statistics
-- Non-standardized attributes (differs between instances)
-- All attributes are nominally-valued
3. Automobile data (1985 Ward's Automotive Yearbook)
-- Documentation: On everything except statistics and class distribution
-- Good mix of numeric and nominal-valued attributes
-- More than 1 attribute can be used as a class attribute in this database
4. Breast cancer database (Ljubljana Oncology Institute)
-- Documentation: On everything except database statistics
-- Well-used database
-- 286 instances, 2 classes, 9 attributes + the class attribute
5-6. Chess endgames data creator
1. king-rook-vs-king-knight
-- Documentation: limited (nothing on class distribution, statistics)
-- This concerns king-knight versus king-rook end games
-- The database creator is coded in Common Lisp
2. king-rook-vs-king-pawn
-- Documentation: sufficient
-- This concerns king-rook versus king-pawn end games
-- Originally described by Alen Shapiro
7. Computer hardware described in terms of its cycle time, memory size, etc.
and classified in terms of their relative performance capabilities (CACM
4/87)
-- Documentation: complete
-- Contains integer-valued concept labels
-- All attributes are integer-valued
8. Echocardiogram database (Reed Institute, Miami)
-- Documentation: sufficient
-- 13 numeric-valued attributes
-- Binary classification: patient either alive or dead after survival period
9. Glass Identification database (USA Forensic Science Service)
-- Documentation: completed
-- 6 types of glass
-- Defined in terms of their oxide content (i.e. Na, Fe, K, etc)
-- All attributes are numeric-valued
10. Hayes-Roth and Hayes-Roth's database
-- Described in their 1977 paper
-- Topic: human subjects study
11-14. Heart Disease databases (Sources listed below)
-- Documentation: extensive, but statistics and missing attribute
information not yet furnished (perhaps later)
-- 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach
-- 13 of the 75 attributes were used for prediction in 2 separate
tests, each of which achieved approximately 75%-80% classification
accuracy
-- The chosen 13 attributes are all continuously valued
15. Hepatitis database (G.Gong: CMU)
-- Documentation: incomplete
-- 155 instances with 20 attributes each; 2 classes
-- Mostly Boolean or numeric-valued attribute types
16. Iris Plant database (Fisher, 1936)
-- Documentation: complete
-- 3 classes, 4 numeric attributes, 150 instances
-- 1 class is linearly separable from the other 2, but the other 2 are
not linearly separable from each other (simple database)
17. Labor relations database (Collective Bargaining Review)
-- Documentation: no statistics
-- Please see the labor directory for more information
18-19. LED display domains (Classification and Regression Trees book)
-- Documentation: sufficient, but missing statistical information
-- All attributes are Boolean-valued
-- Two versions: 7 and 24 attributes
-- Optimal Baye's rate known for the 10% probability of noise problem
-- Several ML researchers have used this domain for testing noise tolerancy
-- We provide here 2 C programs for generating sample databases
20. Lymphography database (Ljubljana Oncology Institute)
-- Documentation: incomplete
-- CITATION REQUIREMENT: Please use (see the documentation file)
-- 148 instances; 19 attributes; 4 classes; no missing data values
21. Mushrooms in terms of their physical characteristics and classified
as poisonous or edible (Audobon Society Field Guide)
-- Documentation: complete, but missing statistical information
-- All attributes are nominal-valued
-- Large database: 8124 instances (2480 missing values for attribute #12)
22. Primary Tumor database (Ljubljana Oncology Institute)
-- Documentation: incomplete
-- CITATION REQUIREMENT: Please use (see the documentation file)
-- 339 instances; 18 attributes; 22 classes; lots of missing data values
23. Shuttle Landing Control database
-- tiny, 15-instance database with 7 attributes per instance; 2 classes
-- appears to be well-known in the decision-tree community
24-25. Soybean data (Michalski)
-- Documentation: Only the statistics is missing
-- (2 sizes)
-- Michalski's famous soybean disease databases
26. Low resolution spectrometer data (IRAS data -- NASA Ames Research Center)
-- Documentation: no statistics nor class distribution given
-- LARGE database...and this is only 531 of the instances
-- 98 attributes per instance (all numeric)
-- Contact NASA-Ames Research Center for more information
27-34. Thyroid patient records classified into disjoint disease classes
(Garavan Institute)
-- Documentation: as given by Ross Quinlan
-- 6 databases from the Garavan Institute in Sydney, Australia
-- Approximately the following for each database:
-- 2800 training (data) instances and 972 test instances
-- plenty of missing data
-- 29 or so attributes, either Boolean or continuously-valued
-- 2 additional databases, also from Ross Quinlan, are also here
-- hypothyroid.data and sick-euthyroid.data
-- Quinlan believes that these databases have been corrupted
-- Their format is highly similar to the other databases
35. University data (Lebowitz)
-- Documentation: scant; we've left it in its original (LISP-readable) form
-- 285 instances, including some duplicates
-- At least one attribute, academic-emphasis, can have multiple values
per instance
-- The user is encouraged to pursue the Lebowitz reference for more
information on the database
36. Congressional voting records classified into Republican or Democrat (1984
United Stated Congressional Voting Records)
-- Documentation: completed
-- All attributes are Boolean valued; plenty of missing values; 2 classes
-- Also, their is a 2nd, undocumented database containing 1986 voting
records here. (will be)
37-38. Waveform data generator (Classification and Regression Trees book)
-- Documentation: no statistics
-- CART book's waveform domains
-- 21 and 40 continuous attributes respectively
-- difficult concepts to learn, but known Bayes optimal classification
rate of 86% accuracy
39-46. Undocumented databases: see the sub-directory named undocumented
1. Bradshaw's flare data
2. Pat Langley's data generator
3. David Lewis's information retrieval (IR) data collection (offline)
4. Mike Pazzani's economic sanctions database
5. Ross Quinlan's latest version of the thyroid database
6. Philippe Collard's database on cloud cover images
7. Mary McLeish & Matt Cecile's database on horse colic
8. Paul O'Rorke's database containing theorems from Principia Mathematica
47. Nine simple small EBL domain theories and examples in sub-directory ebl
1. cup
2. deductive.assumable (contains three domain theories)
3. emotion
4. ice
5. pople
6. safe-to-stack
7. suicide
------------------------------
Subject: Genetic make-up of brains
From: king...@hpwrc02.hp.com
Date: Mon, 25 Jun 90 18:40:38 -0700
Would anyone like to speculate about which part of our brain comes from
our fathers, and which part comes from our mothers? Is it half and half?
Does the right half come from Mom and the left half from Dad? Visa-versa?
Or, considering that humans have 23 genes which cross over during
reproduction, then the Mom and Dad parts could be separated by a 23
dimensional hyperplane. But what are the dimensions? Space? Neurons?
Synapses? Axons, dendrites or thresholds? Cell adhesion molecules?
Has anyone seen studies of the genetics of intelligence, akin to Mendels
studies of peas? Or, has anyone seen studies of the distribution of brain
genes along a chromosome? Has anyone mapped the genes for brains?
And why does everyone have a cortex, a cerebellum, cortical columns and a
corpus callosum? Why doesn't the material we inherit from our parents
just make a blob?
Kingsley Morse
king...@hpwrc02.hp.com
------------------------------
Subject: A questin about neural inhibition
From: "DAVE MCKEE" <mc...@tisss.radc.af.mil>
Date: 26 Jun 90 10:51:00 -0400
I would like to pose a question to the list about inhibiting neurons. As
I look at various photographs and diagrams of biological neurons, it
strikes me that dendritic branches or input connections to neurons are
not simply a collection that is summed into the central body of the cell.
All of the neural net models I have seen in papers and proceedings use
the basic summation of the activation signals coming into the input
dendrites, perhaps multiplied by some weighting factor. It seems to me,
however, that the inhibiting neurons might not simply be a negative
activation acting to cancel some positive one, but instead act as a "main
switch" that shuts off or attenuates to some degree, whole branches of
input dendrites, while leaving the other branches unaffected. I have yet
to see this kind of idea employed in any neural models to date, but I
would be very appreciative if anyone does know of such models and could
give me references.
To better get an flavor of what I am talking about I will attempt to
draw a crude picture here:
_____
_____\ Inhibitor input (top branch is attenuated)
______\ |
_______\ | _______
\______v_________________ / \
\_____/ \________
________________________/ \neuron /
________/ \_______/
_______/
______/ (lower branch is unaffected)
_____/
The overall effect would appear to be the ability to modify the input
paradigm of the node. As the inhibitor moves further back in the tree
branching of the input connections to the node, the effects would be more
and more subtle. Obviously the complexity of such a structure is many
orders of magnitude over the straightforward NN's that have been thus far
explored, but I think that this particular implementation should not be
ignored.
Digressing a moment, I have been thinking about creating a standard for a
software simulation/hardware description language for neural nets (I work
with simulation languages and HDL's , specifically VHDL). I think
perhaps the best way to create a standard modeling/specification language
for neural nets would be to implement a subset of the C language and
define some basic structural types that allows the flexibility for most
any network configuration, but also gives the means to translate that
structure into real hardware. This has been successfully done with the
VHSIC/VLSI Hardware Description Language (VHDL).
The C subset would be compilable by any C compiler with the proper
library packages, but eventually accepted tools that check the syntax of
the standard could be built. These tools would not be restricted to C,
but only have to process the standard itself.
The language would have to model the structure of how nodes are
connected, while the behavioral modeling capabilities of the language
would describe how the nodes, the synaptic junctions (weight changes),
"growth" functions (new connections, new nodes, connections or nodes
being destroyed ), etc. would evolve. One eventual goal would be to
translate this information to actual design layout on a CAD station.
For correspondence please feel free to E-mail me at:
mc...@tisss.radc.af.mil until July 13, 1990 at which time I will be
leaving Rome Air Development Center. After that date I can be reached by
Snail Mail at:
David T. McKee
1821 Calibre Place
Apt 204
Raleigh, NC 27604
I can be reached now at: 369A Steadman Rd.
Lee Center, NY 13363
######################################################################
David T. McKee # "The opinions expressed within
Software Engineer # are totally mine, I accept
Microelectronics Reliability Division # full responsibility for them
Rome Air Development Center # unless, of course, they cause
Griffiss AFB # any liability whatsoever, in
Rome, NY 13363 # which case I've never seen
# them before!"(just kidding)
######################################################################
------------------------------
Subject: Some parameters in Kohonen's network + Hello
From: JJ Merelo <jme...@ugr.es>
Date: 28 Jun 90 11:48:00 +0200
I am trying to software-implement Kohonen's network, and I have met some
parameters, k1 and k2 on the gain factor alfa. Does anybody know how to
vary them, and which range is suitable?
Please, write back
JJ
==================
Date: 26 Jun 90 12:01 +0200
From: JJ Merelo <jme...@ugr.es>
To: neuron-...@hplabs.hp.com
Message-ID: <44*jme...@ugr.es>
Subject: Introduction
Return-Receipt-To: JJ Merelo <jme...@ugr.es>
My name is JJ Merelo, I am working in Granada University. Our
grooup is called CSIP and we are more prone to the hardware stuff, but I
am myself concerned with software. I have already implemented a Kohonen
network, that is being used for Spanish speech r ecognition. The source
code is available in C, should anyone be interested. That's all by now.
JJ
------------------------------
Subject: RE: Neuron Digest V6 #40
From: livingston_d%frgen...@smithkline.com (David Livingstone, Med. Chem., Ext 3856)
Date: Mon, 02 Jul 90 10:01:28 -0400
>I am interested in applications of Neural Networks in
> protein structure prediction,
> chemical reaction product prediction,
> drug interaction effects ... and the like.
Here are a few more on protein structure prediction:
[1] H.Bohr, J.Bohr, S.Brunak, R.J.M.Cotterill, B.Lautrup, L.Norskov, O.H.Olsen
and S.B.Petersen, FEBS Lett., 241 (1988) 223-228.
[2] M.J.McGregor, T.P.Flores and M.J.E.Sternberg, Protein Eng., 2 (1989)
521-526
[3] H.Bohr, J.Bohr, S.Brunak, R.M.J.Cotterill, H.Fredholm, B.Lautrup and
S.B.Petersen, FEBS Lett., 261 (1990) 43-46
And one which carries out discriminant analysis on structure-activity data:
[4] T.Aoyama, Y.Suzuki and H.Ichikawa, J.Med.Chem., 33 (1990) 905-908
I have prepared a paper on the use of a neural net as a dimension
reduction device for the display of multivariate data used in
Quantitative Structure-Activity Relationships. If anyone would
like a pre-print please send me your postal address (the figures
won't go by E-mail).
David Livingstone.
Organization: Medicinal Chemistry,
SmithKline Beecham Pharmaceuticals,
The Frythe,
Welwyn.
Herts.
AL6 9AR
England.
E-Mail: Livingston_d%frgen...@smithkline.com (Internet)
------------------------------
Subject: Phone Number Given Incorrectly for GA Course Info
From: "Dave Goldberg (dgol...@ua1vm.ua.edu)" <DGOL...@UA1VM.ua.edu>
Date: Tue, 03 Jul 90 06:06:56 -0500
For those of you seeking information regarding the five-day short course
entitled "Genetic Algorithms in Search, Optimization, and Machine
Learning" to be presented at Stanford University's Western Institute in
Computer Science on August 6-10, the wrong phone number was given
previously. Contact Joleen Barnhill, Western Institute in Computer
Science, PO Box 1238, Magalia, CA 95954, (916)873-0575.
The course, presented by John Koza and myself, includes in-depth coverage
of GA mechanics, theory and application in search, optimization, and
machine learning. Students will be encouraged to solve their own
problems in hands-on computer workshops monitored by the course
instructors. New material on Walsh functions, Boltzmann tournament
selection, Koza's genetic programming, messy genetic algorithms (mGAs),
and the theory of real-coded GAs and virtual alphabets will be presented
in a classroom setting for the first time. I hope to see some of you
there. Dave Goldberg
------------------------------
Subject: POPLOG Conference announcement (UK)
From: PO...@vax.oxford.ac.uk
Date: Thu, 05 Jul 90 10:07:30 +0000
FROM: Jocelyn Paine,
Department of Experimental Psychology,
South Parks Road,
Oxford OX1 3UD.
JANET: POPX @ UK.AC.OX.VAX
Phone: (0865) 271444 - messages.
(0865) 271339 - direct.
*******************************************
* *
* POPLOG USERS' GROUP CONFERENCE 1990 *
* *
* JULY 17TH - 18TH *
* *
* OXFORD *
* *
*******************************************
Why am I posting news about a Poplog conference to the neural net
digests? After all, Poplog is an implementation of Pop-11, Prolog, Lisp,
and ML - all very conventional and symbolic AI languages. Well, following
from work done by David Young at Sussex, you can now buy from Integral
Solutions Limited (Poplog's commercial distributors) the "Poplog Neural"
package. This allows you to design neural nets of various kinds; display
them graphically using Poplog's windowing system; build fast production
versions; and integrate what you've designed with existing code written
in Pop-11, Prolog, Lisp, or ML.
So if you need to build a mixed net/symbolic program, Poplog is well
worth considering. And you get the convenience of a rather nice
development environment for your nets; plus the four languages I've
mentioned, a built-in editor, a window manager, and the object-oriented
"Flavours" package.
If you want to find out more about Poplog Neural, and Poplog in general,
this year's User Group conference, PLUG90, is the place to do it. We
still have places left at PLUG90, and can accept bookings if made
quickly. The conference will be held in Oxford on the 17th and 18th of
July; accomodation is provided in Keble College, and talks themselves
will be in Experimental Psychology. Registration will open at 11 am on
the 17th, with the conference proper beginning at 2; it will close at
about 4 on the 18th. There will be a rather good conference dinner on the
night of the 17th (main course: duck in lime and ginger sauce).
The price is #75 to members of PLUG and #95 to non-members (#15
non-residential without dinner; #37 non-residential with dinner).
Integral Solutions Limited, who distribute Poplog commercially, has
generously paid for three free places. These will be offered to academic
members of PLUG who have not attended a PLUG conference before, and who
have difficulty raising funds. All three are still available.
~~~~~
This is the provisional list of talks:
"Poplog Neural",
Colin Shearer, Integral Solutions Ltd. (30 mins)
A demonstration of ISL's new neural-networking system.
It's implemented in Poplog, does its number crunching in Fortran,
and allows you to build and test nets by drawing on a window.
Fully interfaceable with Pop-11, Prolog, Lisp and ML.
"Pop9X - The Standard",
Steve Knight, Hewlett-Packard Labs. (1 hour)
Gives a review of the BSI standardisation process and
the progress of the Pop standard - the language YOU will be
writing soon (ish).
"Assembly code translation in Prolog"
Ian O'Neill, Program Validation. (30 mins)
State of the art assembly language translation in Prolog.
"THESEUS: a production-system simulation of the spinning behaviour of
an orb-web spider",
Nick Gotts, Oxford University Zoology Department. (30 mins)
As well as giving a demo, Nick will talk about his experiences
of using AlphaPop: Theseus runs on a Macintosh.
"MODEL: From Package to Language",
James Anderson, Reading University. (1 hour)
A six year old software package for model based vision
goes up in flames under the heat of self-criticism - to be replaced
by a language.
TALK INCLUDES VIDEO PRESENTATION
"GRIT - General Real-time Interactive Ikbs Toolset",
Mark Swabey, Avonicom. (30 mins)
Mark will talk about GRIT, but also about his experiences (nice
and otherwise) of Poplog as a development environment.
"IVE - Interactive Visual Environment"
Anthony Worrall, Reading University. (30 mins)
The software environment that beat the pants off MODEL
(based on MODEL and PWM and quite a lot of other things besides.)
TALK INCLUDES VIDEO PRESENTATION
"Embedded Systems",
Rob Zancanato, Cambridge Consultants. (30 mins)
Poplog for embedded systems - especially MUSE for designing
real-time controllers. We hope to have a demo of one such self-
contained system.
"Doing representation theory in Prolog",
John Fitzgerald, Oxford University Maths Department. (30 mins)
Representation theory is part of the study of (mathematical)
groups. Prolog copes surprisingly well with such a geometric
topic.
"Building User Interfaces with Flavours",
Chris Price,
Department of Computer Science, University College of Wales. (30 mins)
Object-oriented user-interface design, using Poplog's OO flavours
package and window manager.
"TPM - a graphical Prolog debugger",
Dick Broughton, Expert Systems Limited. (1 hour)
Dick will show how a debugger should be designed: with TPM, you can
display Prolog proof trees as trees, rewind and fast forward
execution, zoom in and out, watch the "cut" prune branches, and
generally do everything you can't do with 'spy'.
"Processing of Road Accident Data",
Jiashu Wu, UCL Transport Studies. (30 mins)
UCL use Poplog for an EMYCIN-based expert system which advises
on accident blackspots, taking 'raw' accident data from
incident reports. They like Poplog because it's an "open
system": its jobs include fuzzy matching, stats, and handling very
big databases.
"Faust - an online fault-diagnosis system",
David Cockburn, Electricity Research and Development Centre. (30 mins)
(to be confirmed)
"Design for testability",
Lawrence Smith, SD Scicon. (30 mins)
(to be confirmed)
A system for advising the users of CAD packages on loopholes in
testability.
Something on the future of Poplog
Integral Solutions. (1 hour)
(details awaited)
~~~~~
And this is the provisional timetable:
Accomodation is provided in Keble College, Parks Road, Oxford. Luggage
can be left there from mid-day on the 17th.
The conference itself will be in the Department of Experimental
Psychology, South Parks Road.
July 17th
- ---------
Registration and coffee: 11:00 - 12:30
Lunch: 12:30 - 2:00
Talks: 2:00 - 3:30
Tea: 3:30 - 4:00
Talks: 4:00 - 6:00
(Depart for Keble).
Keble bar opens from 6 to 11 pm.
Dinner starts at 7.
July 18th
- ---------
Talks: 9:00 - 10:30
Coffee: 10:30 - 11:00
Talks: 11:00 - 1:00
Lunch: 1:00 - 2:00
Talks: 2:00 - 3:30
Tea: 3:30 - 4:00
------------------------------
End of Neuron Digest [Volume 6 Issue 42]
****************************************