NetNews Usenet Archive 1992 #19/NN_1992_19.iso/spool/comp/ai/neuraln/3312

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Subject: Neuron Digest V9 #40 (jobs + misc discussion)
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Date: 26 Aug 92 04:27:30 GMT
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Neuron Digest   Wednesday, 26 Aug 1992
                Volume 9 : Issue 40

Today's Topics:
                     Administrivia - ND back on-line
                              Job available
                       neural networks in control
                 Neural Nets + KBS-type expert systems?
     JOB ANNOUNCEMENT: Post-Doc in machine learning/computer vision
                                Job Offer
                    postdoctoral positions available
                           IEEE-NNC Standards


Send submissions, questions, address maintenance, and requests for old
issues to "[e-mail]". The ftp archives are
available from cattell.psych.upenn.edu (128.91.2.173). Back issues
requested by mail will eventually be sent, but may take a while.

----------------------------------------------------------------------

Subject: Administrivia - ND back on-line
From:    "Neuron-Digest Moderator, Peter Marvit" <[e-mail]>
Date:    Wed, 26 Aug 92 00:19:14 -0500

The Neuron Digest summer holiday is over, almost in time for the new
academic year. In deference to past submissions with dates attached, the
next few issues will be timely postings including job announcements,
conferences, and calls for papers.  Thereafter, in addition to regular
submissios (especially interesting questios and discussions from you, the
reader *hint, hint*), the backlog of paper and technical report
announecments will be published.

In addition, according to tradition, September will mark a new volume
number.

: Peter Marvit, Neuron Digest Moderator
: Courtesy of Psychology Department, University of Pennsylvania
: [e-mail]


------------------------------

Subject: Job available
From:    Tuan Duong <[e-mail]>
Date:    Thu, 23 Jul 92 09:42:03 -0800


We have a job opening for an engineer with neural-net and VLSI design
experience - MS/PHD or equivalent experience.

We currently work with (analog, digital, and optical) neural net
and concurrent processing devices and hardware systems. We build special-
purpose and general-purpose analog, digital, mixed-signal, and
opto-electronic chips. We develop neural net algorithms that suit our
applications and our hardware.

For over 7 years we have been a leader in hardware neural nets .

If you're interested, please get in touch with:

Harry Langenbacher 818-354-9513 [e-mail]
Concurrent Processing Devices Group, FAX [phone]
JPL,  M/S 302-231, 4800 Oak Grove Dr, Pasadena CA 91109        USA



------------------------------

Subject: neural networks in control
From:    [e-mail]
Date:    Fri, 24 Jul 92 14:46:15 -0100

Recently I received in the list a list of references on neural networks
applied in the field of signal processing. I am particularly interested in
the field of neural networks in control, and if some reader of this list
could send me a similar list on the applications on control it could be
very useful for me and one of my students that is starting a Ph. D. on this
field. Thanks in advance.

My e-mail: [e-mail]

JBarreto



------------------------------

Subject: Neural Nets + KBS-type expert systems?
From:    [e-mail] (Richard Herrell)
Date:    Fri, 24 Jul 92 13:15:28 -0500

ND Readers,

        I am interested in using a combination of neural networks and KBS type
expert systems in creating a general sort of game player.  I have found brief
information on the subject of neural network/ expert system combinations ( Byte
Magazine Jan, 1991 "Putting The Experts to Work", author Marge Sherald ),
but nothing of extreme detail.  If anyone knows of a good source of information
in this area, please e-mail me.

        --Richard Herrell

        [e-mail]


------------------------------

Subject: JOB ANNOUNCEMENT: Post-Doc in machine learning/computer vision
From:    "Gregory J. Wolff" <[e-mail]>
Date:    Wed, 29 Jul 92 15:54:44 -0800


POST-DOCTORAL POSITION AVAILABLE

The Machine Learning and Perception Group at The Ricoh California
Research Center seeks an exceptionally talented candidate for a one-year
post-doctoral position in either of the following two areas:

1) Computational learning theory
The ideal candidate will have knowledge of theories of learning and
generalization (Vapnik-Chervonenkis dimension, Kolmogorov methods,
Probably Approximately Correct methods,...), information theory (Akaike
and Fisher information, Cramer-Rao bounds,...), and connectionist learning
methods (backpropagation, Boltzmann learning,...).  C programming ability
and experience with parallel and connectionist learning systems is highly
desirable.  The successful candidate will have access to extremely powerful
learning machines at Ricoh, such as an Adaptive Solutions, Inc. CNAPS
Neurocomputer and an in-house giga-connection update/second Boltzmann
learning machine, for fundamental and applied studies of computational
learning theory.

2) Computer vision and understanding
The ideal candidate will have knowledge and experience with visual feature
detection methods, motion estimation, shape from shading, adaptive visual
processing, model based recognition, and be an accomplished C
programmer.  A knowledge of lip reading and speech recognition is highly
desirable.

A completed dissertation is expected.

The Ricoh California Research Center
Nestled at the base of the panoramic Palo Alto Hills, The Ricoh California
Research Center is adjacent to hundreds of acres of wildland and is within
walking distance of Stanford University and easy driving distance of
Berkeley, Santa Cruz and other San Francisco Bay Area research centers.
Research areas at CRC include Machine Learning and Perception; Color
Image Processing; Document Analysis; Remote Diagnostics and Parallel
Image Processing.  There is an extensive UNIX-based network of SUN,
Silicon Graphics and other workstations, graphics capability, and an in-
house learning machine mentioned above.  We expect to have an ASI
Neurocomputer by the time the post-doc has arrived.  Accounts on
Connection Machine and MassPar supercomputers are also possible.

Please send a letter describing research interests and curriculum vita and one
or two representative papers/conference papers/dissertation chapters to the
address below.  (Please do not have letters of recommendation sent at this
time; neither should you send your vita by e-mail.)

Questions may be addressed to:  [e-mail] or the address below.
Send application to:

Dr. David G. Stork
Ricoh California Research Center
2882 Sand Hill Road Suite 115
Menlo Park, CA 94025-7022                       attn:  Post-doc position

Ricoh Corporation, an equal opportunity/affirmative action employer.
M/F/H/V.  Must be able to legally work in the U.S. on a full time basis.


------------------------------

Subject: Job Offer
From:    "Norman Packard" <[e-mail]>
Date:    Wed, 12 Aug 92 15:43:35 -0700

                            Prediction Company
                          Financial Forecasting
                             August 12, 1992

Prediction Company is a small Santa Fe, NM based startup firm
utilizing the latest nonlinear forecasting technologies for prediction
and computerized trading of derivative financial instruments. The
senior technical founders of the firm are Doyne Farmer and Norman
Packard, who have worked for over ten years in the fields of chaos
theory and nonlinear dynamics. The technical staff includes other
senior researchers in the field. The company has the backing of a
major technically based trading firm and their partner, a major
European bank.

There is currently one opening at the company for a senior computer
scientist to provide leadership in that area for a staff of physical
scientists and mathematicians with strong programming backgrounds. The
job responsibilities include software design and implementation,
support of deployed software systems for trading, management of a UNIX
workstation network and infusion of computer science technologies into
the firm.

The successful applicant will be an experienced and talented C and C++
programmer with architectural skills, UNIX knowledge and an advanced
degree in computer science or a related discipline. Experience in a
production environment, in support of products or mission critical
in-house software systems (preferably in the financial industry) is
required. Knowledge of and experience with top down design methods,
written specifications, formal tes methods and source code control is
highly desirable, as is familiarity with data base and
wide-area-networking technologies.

Applicants should send resumes to Prediction Company, 234 Griffin Street,
Santa Fe, NM 87501 or to Laura Barela at [e-mail].


------------------------------

Subject: postdoctoral positions available
From:    scott@cpl_mmag.nhrc.navy.mil (Scott Makeig)
Date:    Tue, 18 Aug 92 07:39:09 -0800


              Opportunities for post doctoral research
          at the Naval Health Research Center, San Diego.
       Cognitive Performance and Psychophysiology Department
         __________________________________________________

    Our laboratory is developing alertness and  attention  moni-
    toring  systems  based on human psychophysiological measures
    (EEG, ERP, EOG, ECG), through ongoing research at basic  and
    exploratory  development  levels.  We have openings for post
    doctoral fellows in signal processing / neural network esti-
    mation  and  human cognitive psychophysiology.  We are espe-
    cially interested  in  the  relation  of  oscillatory  brain
    dynamics  to  attention  and alertness.  Our research is not
    classified.

    Please address inquiries to:

    Dr. Scott Makeig
    Naval Health Research Center  email: [e-mail]
    PO Box 85122                  fax:   [phone]
    San Diego, CA 92186-5122      phone: [phone]


------------------------------

Subject: IEEE-NNC Standards
From:    Mary Lou Padgett <[e-mail]>
Date:    Mon, 24 Aug 92 09:52:54 -0600

IEEE-NNC Standards Committee Report

It is the purpose of this column to update you on this activity and to
invite you to participate in forthcoming meetings.  At its June meeting
the IEEE Standards Board formally approved the Project Authorization
Requests (PAR's) submitted by the Working Group on Glossary and Symbols
and by the Working Group on Performance Evaluation, so those two groups
now have their "marching orders." The NNC Standards Committee had a
series of fruitful meetings in conjunction with the Baltimore IJCNN.
Progress made by the various working groups is detailed below.

FUTURE EVENTS

* IJCNN Beijing, Nov. 1-6, 1992
A panel discussion and/or workshop will be conducted by Mary Lou Padgett
early in the meeting.  The formation of an international glossary and
symbology for artificial neural networks will be discussed.

* SimTec/WNN92 Houston, Nov. 4-7, 1992
There will be a Standards Committee Meeting on Friday, Nov. 6, in
conjunction with this conference.  Paper competition awards will be
announced.  Dr. Robert Shelton of NASA/JSC is conducting the Performance
Measure Methodology Contest and Prof. E. Tzanakou of Rutgers is
conducting the Paradigm Comparison Student Paper Contest.

* IEEE-ICNN and IEEE-FUZZ 1993 San Francisco, March 28 - April 1, 1993
A come-and-go meeting of everyone interested in standards will be held
on Sunday, March 27 and individual working group meetings will take
place on Monday and Tuesday evenings, March 28 and 29.

* Proposed New Activity
It has been proposed to form a working group to draft a glossary for
fuzzy systems.  An initial meeting to that end will take place in San
Francisco on March 27 and 28, in conjuction with the conference.  Please
contact either of the undersigned if interested in participating.

Over 400 people and companies are on the interest list for standards.
If you would like to be included, please contact Mary Lou Padgett.


WORKING GROUP REPORTS:

WORKING GROUP ON GLOSSARY AND SYMBOLS
Chair:  Mary Lou Padgett, Auburn University

The Working Group on Glossary and Symbols submitted the following PAR,
which has been approved by IEEE as a formal project for the group.  A
voting group will be constructed in the near future.

Project Title:
Recommended Definition of Terms for Artificial Neural Networks

Scope:
Terminology used to describe and discuss artificial neural networks
including hardware, software and algorithms related to artificial neural
networks.

Purpose:
The subject of artificial neural networks is treated in a wide variety
of textbooks, technical papers, manuals and other publications.  At the
present time, there is no widely accepted guide or standard for the use
of technical terms relating to artificial neural networks.  It is the
purpose of this project to provide a comprehensive glossary of
recommended terms to be used by the authors of future publications in
this field.

Status Report:

The glossary being developed should be usable by everyone interested in
neural networks, so a simple basic structure is desirable.  The draft
glossary proposed by Russell Eberhart meets this requirement, with some
modifications.  To help insure that the finished product is usable and
still specific enough to help in specialized areas, Glossary Special
Interest Group Chairs have been appointed.  The exact scope of their
groups will be discussed in San Francisco.  Eventually, representation
from all major neural networks thrusts and geographic areas should be
included.  People from academia, industry and government in all areas
should be represented.  The first Glossary SIG Chairs are:  Patrick A.
Shoemaker, NOSC; Dale E. Nelson, WPAFB; and Emile Fiesler, IDIAP.  The
glossary will be structured in a modular form, with basic elements
coming first, followed by more specialized subsets.  Your input is
respectfully requested!


WORKING GROUP ON PERFORMANCE METHODOLOGY
Chair:  Robert Shelton, NASA/JSC

The Working Group on Performance Methodology met at the Baltimore IJCNN
to discuss their newly approved project and formulate an agenda.

Project Title:
Guidelines for the Evaluation of the Speed and Accuracy of
Implementations of Feed-Forward Artificial Neural Networks.

Scope:
Artificial neural network implementations which implement supervised
learning through minimization of an error function based on the sum of
the squares of residual errors.

Purpose:
Since 1986, a large number of implementations of the feed-forward
back-error propagation neural network algorithms have been described
with widely varying claims of speed and accuracy.  At present, buyers
and users of software and/or hardware for the purpose of executing such
algorithms have no common set of bench-marks to facilitate the
verification of vendor claims.  The working group proposes to fulfill
this need by assembling a suite of test cases.

Agenda:

Forward Propagation Only

The following will comprise a forward propagation system to which the
standard will apply.  Such a system will be a 3-layer (input, hidden,
output), fully connected (sequentially i.e. input to hidden to output),
feed-forward neural network.

Cases of varying sizes will be proposed.  In addition, for each size,
there will be at least one "problem" of the following two types.
     A.  Discrete output
     B.  Continuous output.
A "problem" will consist of a set of I/O pairs which the system will be
required to reproduce.  Sequential, portable e.g. C language computer
code will be distributed which emulates the desired network including
nominal weights and customary sigmoidal transfer functions.  The user of
the standard may make use of the distributed code and weight values as
he or she sees fit.  The determination of weights is deemed to be a
"learning" problem and not within the scope of the part of the standard
described here.  Parity problems were proposed as hard cases for the
discrete output test.  Such problems are sufficiently well understood
that weights could be provided without recourse to the use of learning
algorithms.  Character identification was suggested as a second easier
kind of discrete output problem.  The task of providing good test
problems for the case of continuous output was agreed to be
significantly more complex.  It was suggested that mathematical
combinations of algebraic and transcendental functions could serve as
the basic model, but it was agreed that the determination of the
candidate problems for continuous output would require considerable
additional effort.

     Robert Shelton
     PT4, NASA/JSC
     Houston, TX 77058
     P: [phone]
     [e-mail]


WORKING GROUP ON SOFTWARE AND HARDWARE INTERFACES
Chair: Steven Deiss, Applied Neurodynamics

The NNC Working Group on Software and Hardware Interfaces met at the
Baltimore IJCNN.  The group was evenly divided by interest into an ad
hoc Working Subgroup on Software Interface Standards and an ad hoc
Working Subgroup on Hardware Interface Standards.  The overall working
group persists as an umbrella to integrate current efforts and promote
new interface standards activities.  Future meetings are expected to
discuss PAR submission along with the technical issues.

The Software Group got off to a fast start in Baltimore and several
meetings were held there.  Ten ANN vendors and 15 labs and companies
expressed interest in the task of formalizing selected data format
standards which would be used to store ANN training sets.  Many vendors
have translation tools for importing data to their own environments, but
many research users find it difficult to share data because of use of
unique data formats and paradigm code written early on to accept their
nonstandard formats.  The group reached consensus that a simple standard
training data format is needed, several were discussed, and it was felt
that the task was manageable.   For further information concerning this
project contact:

    Dr. Harold K. Brown
    Florida Institute of Technology
    Dept. of Electrical and Computer Engineering
    Melbourne, FL  32901-6988
    Phone: [phone] x 7556
    Fax:   [phone]
    Email: [e-mail]

The Hardware Group discussed related work on hardware standards that was
carried out under the IEEE Computer Society Microprocessor Standards
Committee and tried to focus on goals for the current group.  In 1989 A
Study Group was formed under the auspices of the MSC to evaluate
Futurebus+ (896) and Scalable Coherent Interface (1596) for
applicability to NN applications.  The group recommended a hybrid
approach while recognizing the longer range potential of a NN specific
interface and interconnect standard.  The present group chose to focus
on 'guidelines' for utilization of existing standards for NN
applications.  It was the consensus that the NN community may not yet be
ready for a real NN hardware interface standard since this is such an
active area of reseach, however, work toward the evolution of such a
standard would appear to be timely.  For further information about this
project or about other areas where interface standards might be
appropriate contact:

    Stephen R. Deiss
    Applied Neurodynamics
    2049 Village Park Wy, #248
    Encinitas, CA  92024-5418
    Phone: [phone]
    Fax:   [phone]
    Email: [e-mail]

Thank you for your support of the IEEE-NNC Neural Networks Standards
Committee.  Please continue to interact with all of the working groups
to help us grow in positive directions, and provide service to the
entire community.  SEE YOU IN SAN FRANCISCO, if not before!

Sincerely,

Professor Walter J. Karplus            Mary Lou Padgett
Chair                                  Vice Chair
IEEE-NNC Standards Committee           IEEE-NNC Standards Committee
UCLA, CS Dept.                         Auburn University, EE Dept.
3723 Boelter Hall                      1165 Owens Road
Los Angeles, CA 90024                  Auburn, AL 36830
P: [phone]                      P: [phone] or 3488
email: [e-mail]             email: [e-mail]



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End of Neuron Digest [Volume 9 Issue 40]
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