
CHECKLIST
PRESENTERS: The poster session will be held Thursday, October 16, 2003.
POSTER BOARD DIMENSIONS: 4ft. high, 8ft. wide (flat).
Push pins will be provided.
Recommended guidelines for creating your poster
Do's and Don'ts of Poster Presentations
Alphabetized by first author listed. Presenting author in bold font.
Bagging Bayesian Networks: Investigating Determinants of Disaster Risk
Kobi Abayomi, Dr. Upmanu Lall, Dr. Andrew Gelman, Columbia University
kaa71@columbia.edu
A common quantification of Risk, of a disaster, under spatial independence,
is as a probabilistic product over exposed elements and their vulnerability
to the disaster. A nontrivial joint quantification of Risk, from multiple
disasters, involves the determination of dependencies between the elements
and vulnerabilites. A Bayesian Network (BN)  a Directed Acyclic Graph (DAG)
where the joint [probability] distribution is the product of marginal,
conditionally independent distributions  can be applied to the problem of
divining dependency structure. We investigate BN learning on a composite
global disaster dataset of large dimension ($n=15600,k>6$) using the DEAL
algorithm  which reduces the NPcomplete problem by using a heuristic
search with random restarts. The DEAL algorithm is highly sensitive to
perturbations in the learning set  to improve accuracy, we apply Bayesian
Aggregating (Bagging) across many learned networks.
On the Development of an Inexact Newton Trust Region InteriorPoint
Algorithm for LargeScale Nonlinear Programming Problems
Dr. Miguel Argaez, Dr. Leticia Velazquez, Jaime Hernandez Jr., University of
TexasEl Paso
jaimeh@utep.edu
We present an algorithm for solving largescale nonlinear programming
problems. We use interiorpoint methodology, a trust region globalization
strategy, and conjugate gradient, with Steihaug's ending conditions, to find
a solution of the problem. Some preliminary numerical results are presented.
Compact Routing in the Name Independent Model
Marta Arias, Lenore J. Cowen, Kofi A. Laing, Rajmohan Rajaraman, Orjeta
Taka, Tufts University
laing@eecs.tufts.edu
This poster discusses compact routing in the name independent model first
introduced by Awerbuch et al. for adaptive routing in dynamic networks.
Compact routing is the problem of finding good tradeoffs between the amount
of space used to store routing tables and the length of the paths the
routing algorithm specifies. For space we focus on the maximum space
required per node in a network, and for path lengths we consider the
stretch, which is defined as the maximum ratio of the length of a path taken
between a pair of nodes, to the length of the optimal path between those two
nodes. Our work presents solutions in the nameindependent model, referring
to our assumption that the graph may not be relabelled in a way that encodes
topological information. A compact routing scheme that uses
$\tilde{O}(n^{1/2})$sized local routing tables, $O(\log^2 n)$sized packet
headers, and stretch bounded by 5 is obtained. Alternative schemes reduce
the packet header size to $O(\log n)$ at cost of either increasing the
stretch to 7, or increasing the table size to $\tilde{O}(n^{2/3})$. For
smaller tablesize requirements, the ideas in these schemes are generalized
to a scheme that uses $O(\log^2 n)$sized headers,
$\tilde{O}(k^2n^{2/k})$sized tables, and achieves a stretch of $\min\{ 1 +
(k1)(2^{k/2}2), 16k^2+4k \}$, improving the best previouslyknown
nameindependent scheme due to Awerbuch and Peleg.
Fermion Monte Carlo Calculations for the Beryllium atom
Alan AspuruGuzik, Malvin H. Kalos, William A. Lester, Jr., University of
California, Berkeley
alan@aspuru.com
Most calculatins of atoms and molecules using Monte Carlo methods present a
"sign problem" for which the most common solution is to apply the fixednode
approximation. An extensive body of work by several authors has shown that
this approximation is very effective, but the fixednode error cannot be
estimated a priori. An exact Monte Carlo method for Fermions has been
recently proposed by Kalos and Pederiva(1) and extended to molecular systems
by Kalos and Hood. We apply the method to the ground state of the Beryllium
atom.
The Effects of Pedagogical Agent Gender and Ethnicity on Student Motivation
Amy L. Baylor, E Shen, Yanghee Kim, Xiaoxia Huang, Florida State University
baylor@coe.fsu.edu
Recent empirical studies have shown that animated pedagogical agents have
great potential for serving as effective humanlike mentors in
computerbased learning environments (e.g., Atkinson, 2002; Baylor, 2002;
Moreno, Mayer & Lester, 2001). One critical factor in determining the
potential effectiveness in support of learning is the extent to which they
are perceived by learners as viable mentors. In two controlled studies we
examined the effect of pedagogical agent image, by gender and ethnicity, on
undergraduates' perceptions and motivation toward learning.
From one basic face shape, eight agent images differing by ethnicity (Black,
White), gender (male, female), and realism (cartoon, realistic) were
constructed by a graphic artist. After validating their operational
effectiveness, these images were implemented as animated pedagogical agents
within the MIMIC (Multiple Intelligent Mentors Instructing Collaboratively)
agentbased research environment. Importantly, each agent was implemented
within MIMIC with identical animations, scripts, and computergenerated
voices, differing *only* by image.
In the first "choice" study, 167 participants (99 White and 68 Black) were
presented with all eight agent images simultaneously and were asked to
select the agent from which they would like to learn, and then learn from
it. Results indicated that African American students were more significantly
likely to choose an African American agent (although White students were not
more likely to choose a White agent), and females were more likely to choose
a cartoon agent. Openended responses revealed that Black students were much
more likely to choose an agent that they could 'better relate to' in terms
of ethnicity and gender. After students actually worked with the agent that
they chose, other differences emerged. Across all students, Black agents
were perceived to be significantly more interesting and able to keep
learner's attention after they were chosen. Female agents were perceived as
more enthusiastic, interesting, having a personality, and essential for
information to make sense once they were chosen. Realistic agents were
perceived as more enthusiastic and making instruction more interesting.
In the second experimental study, 273 participants (163 White and 110 Black)
were randomly assigned to one of four agent conditions in a twofactor
(agent gender x agent ethnicity) betweensubjects design. Results indicated
that when students had an agent of the *same* ethnicity, they reported
significantly higher satisfaction with learning from the agent and perceived
the agent as more motivating and helpful. Interestingly, no matter which
agent they received, female students evaluated it overall more positively
than males in terms of friendliness, helpfulness, and personal warmth.
A MultiAgent System to Improve the (RAS) of Large HighPerformance
Computational Clusters
Nina Berry, Jim Brandt, and Ann Gentile, Sandia National Laboratories; Rose
Yao, University of Nebraska, Lincoln
ryao@unlnotes.unl.edu
Large computational clusters rely on different levels of "RAS"  Reliability
(infrequency of problems), Availability (usability during failures or
maintenance), and Serviceability (ease of maintenance and problem
diagnosis). Current methods of ensuring "RAS"; use predefined events that
signals a single central management node which invokes the appropriate
scripts. This design is very limited in the complexity of the situation it
can handle and in its scalability.
The MAS for RAS project is focused on developing software to address RAS
problems in large computational clusters by distributing agents with
decisionmaking capabilities on each node in the cluster. Each RAS entity
will run on its own service processor, therefore the performance of the node
will not be compromised. To simulate this situation, we are currently using
Zaurus PDAs acting as "service processers"; to run our software.
The MAS for RAS software has several advantages over existing systems.
First, it is a decentralized system, which means the RAS of the entire
cluster is no longer dependent on one node. Second, the software agents will
be independent and is capable of making local and global decisions. This
makes the system quasi selfhealing and minimizes the work of the system
administrator. For those reasons, our system can handle more complex
situations and is easily scalable to a large cluster.
TextConstrained Speaker Recognition Using Hidden Markov Models
Kofi A. Boakye, University of California, Berkeley
kaboakye@icsi.berkeley.edu
This poster presents a possible application of a textdependent speaker
recognition system within the unconstrained domain of telephone conversation
speech, as contained in the Switchboard I corpus, a standard corpus in the
speaker recognition community. The system utilizes wordlevel Hidden Markov
Models to generate likelihood scores for key words among the backchannel,
filled pause, and discourse marker categories.
Examining CrossGenerational Collaboration in a Visual Programming
Environment
Jamika D. Burge, Virginia Polytechnic Institute and State University
jaburge@vt.edu
Our research investigates how community members work together to use
computing technology in an effort to understand more about their community
and its members. These members can also share their ideas and opinions about
how to make their community better. Specifically, we are interested in how
elderly community members collaborate with student community members as they
work together in a visual programming environment. From our research, we
expect to understand community collaboration at two levels. First, we want
to study novice users in and across generational pairings. Want hope to
understand how senior community members influence the younger student
community members, and vice versa. Secondly, we want to investigate roles
and how they are reciprocated during programming activities. So, instead of
assigning specific roles to the elderstudent pairings, we are interested in
examining how people fall into their roles naturally.
Implementing an Algorithm to Solve Sequential Testing Procedure
Kalatu Davies, Rice University
kdavies@rice.edu
Cervical precancer is a disease that affects many women across the world
and if left untreated it can lead to infertility and cancer of the cervix.
Thus it is very important to be able to properly diagnose the disease in the
precancerous stage. There is a standard testing sequence that is currently
being used for the detection and treatment of precervical cancer. We want
to use sequential decision analysis to determine if the current standard of
care is optimal and the decision rules for each stage of a disease testing
sequence. This optimal decision procedure has been solved in the past using
backward induction methods for a two stage, binary procedure. However, this
method poses many computational limitations. Thus, the main objective of my
research is to develop a method for implementing an algorithm to solve the
sequential decision problem in relation to disease screening, specifically
for cervical precancer. This algorithm may be applicable to many other
disease screening procedures.
Bifurcations and Simulations of JefferyHamel Flows
Jessica Deshler, University of New Mexico
deshler@math.unm.edu
Nearly all industrial machines which involve fluid flow have a point at
which the fluid must flow from a small area to a larger area, or more
simply, through an expanding channel. For a particular wedge angle and at
some critical Reynolds number a bifurcation in the flow occurs and the flow
changes from pure outflow to flow with regions of outflow and inflow.
Clearly this limits the throughput of the channel and thus the efficiency of
any machine with such a design. The more we understand the behavior of these
flows, the better able we will be to build efficient machines. Simulation of
this radial and twodimensional flow is done using MPSalsa, a finite element
CFD code developed at Sandia National Laboratories, which solves the
NavierStokes system of equations on the geometry of the wedge. The two
dimensional simulations are validated via comparison to experimental data
and results from basic numerical codes written to solve the JefferyHamel
system of equations. By comparing numerical, computational and experimental
data, these results are perhaps among the most complete descriptions of
JefferyHamel flows. These results may also have future implications in the
determination of the selection mechanism as JefferyHamel flows exhibit
multiple solutions.
Bivariate Mean Residual Lifetime Function
Musie Ghebremichael, Javier Rojo, Rice University
musie@stat.rice.edu
In survival analysis the additional lifetime that an object
survives past a time is called the residual life function of the object.
Mathematically speaking if the lifetime of the object is described by a
random variable then the random variable is called the residual life
random variable. The quantity is called the mean residual lifetime (mrl)
function or the life expectancy at age .
There are numerous situations where the bivariate mrl function is
important. Times to death or times to initial contraction of a disease may
be of interest for twin studies in humans. The time to deterioration level
or the time to reaction of a treatment may be of interest in pairs of lungs,
kidneys, breasts, eyes or ears of humans. In reliability, the distribution
of the life lengths of a particular pair of components in a system may be of
interest. Because of the dependence among the event times, we cannot use the
univariate mrl function on each event times in order to assess the aging
process. A bivariate mrl function is useful in analyzing the joint
distribution of two event times where there is dependence between the event
times.
In recent years, though a considerable attention has been paid to
the univariate mrl function, relatively little research has been devoted to
the analysis of bivariate mrl function. The purpose of my work is to extend
and apply the concept of mrl functions to a problem that arise in a
bivariate survival analysis.
A solver for the maximumweight independent set problem
Illya V. Hicks, Jeffrey S. Warren, Texas A&M University
jwarren@tamu.edu
For a weighted simple graph G=(V,E), the maximumweight independent set
(MWIS) problem is that of finding an independent set of vertices such that
the sum of the weights of these vertices is maximum among all independent
sets of the graph. The problem is famously NPhard. Yet, because of its
numerous applications (e.g., in coding theory and computer vision) and its
relationship to other interesting and difficult computational problems
(e.g., the minimum coloring problem), it is worthwhile to develop exact
algorithms that can solve the problem on small graphs in a reasonable amount
of time. Balas and Xue developed a branchandbound algorithm for the MWIS
problem that solves the MWIS problem quickly on a chordal subgraph, finds a
larger subgraph on which the solution is still optimal, and then uses the
resulting subgraph to make branching decisions. We, too, find subgraphs for
which the MWIS problem is easily solved and use them to make branching
decisions. However, we construct a highweight independent set first and
then build the subgraph around it. Our goal is to produce larger subgraphs
than do Balas and Xue, thereby producing fewer child nodes at every
branching instance. Herein, we present our algorithm and compare its
performance to other algorithms for the MWIS problem, with special attention
to the BalasXue algorithm. We also discuss supplementary upper bound
computations for these two algorithms, noting their effect on
branchandbound tree size and run time.
The Development of a Program to Simulate Contact Mode Atomic Force
Microscopy
Divine Kumah
divineknjr@yahoo.com
The atomic force microscope (AFM) is a powerful scanning probe technique
used for highresolution imaging and characterization of nanoscale surfaces
of various materials, including silicon. Atomic force microscopy allows the
researcher to obtain quantitative information on the surface topography and
adhesion activity as well as on the micromechanical properties of the
superficial layers of materials. The atomic force microscope probes the
surface of a sample with a sharp tip a few microns long and around 100
angstroms in radius. The tip is mounted at the free end of a cantilever that
is between 100 and 200 micrometers in length. One major drawback identified
in AFM imaging is the dependence of the image's precision on the shape of
the probe tip. Artifacts are introduced during AFM imaging as a result of
convolution between the tip and the sample. This study aims at providing a
simulation to investigate artifacts in Atomic Force Microscopy. A program
has been developed to simulate AFM in the contact mode to investigate the
effect of tip design on the quality and accuracy of AFM images. Tips of
varying dimensions are used in the simulation program to image a sample
surface which has features suspected to produce artifacts. The images
produced are analyzed and compared to evaluate tipimage convolution. This
program shows promise as a tool to help scientists in the measurement and
characterization fields, separate true images from artificial images in AFM.
Efficient Gradient Estimation for Motor Control Learning
Gregory Lawrence, Noah Cowan, Stuart Russell, University of California,
Berkeley
gregl@cs.berkeley.edu
The task of estimating the gradient of a function in the presence of noise
is central to several forms of reinforcement learning, including policy
search methods. We present two techniques for reducing gradient estimation
errors in the presence of observable {\em input} noise applied to the
control signal. The first method extends the idea of a reinforcement
baseline by fitting a local model to the response function whose gradient is
being estimated; we show how to find the response surface model that
minimizes the variance of the gradient estimate, and how to estimate the
model from data. The second method improves this further by discounting
components of the gradient vector that have high variance. These methods are
applied to the problem of motor control learning, where actuator noise has a
significant influence on behavior. In particular, we apply the techniques to
learn locally optimal controllers for a dartthrowing task using a simulated
threelink arm; we demonstrate that the proposed methods significantly
improve the response function gradient estimate and, consequently, the
learning curve, over existing methods.
Infrastructure for Performance Tuning LAM/MPI Applications
Kathryn Mohror, Karen L. Karavanic, Portland State University
kathryn@cs.pdx.edu
Clusters of workstations are becoming increasingly popular as a lowbudget
alternative for supercomputing power. In these systems, messagepassing is
often used to allow the separate nodes to act as a single computing machine.
Programmers of such systems face a daunting challenge in understanding the
performance bottlenecks of their applications. This is largely due to the
vast amount of performance data that is collected, and the time and
expertise necessary to use traditional parallel performance tools to analyze
that data. Paradyn is a parallel performance tool that addresses these
issues by employing dynamic instrumentation to insert the performance
measurement instructions into the application and by automatically locating
bottlenecks for the programmer. This project implements support for LAM/MPI
into Paradyn. LAM/MPI is one of the two most important implementations of
the Message Passing Interface (MPI), and also includes several newer MPI
features, such as dynamic process creation. As a result of this project,
parallel application programmers will be able to use LAM/MPI and have access
to detailed performance data while developing their applications. This
project will also lay the foundations for future performance tool support
for the newer features of MPI.
Reverse Engineering of Genetic and Protein Networks
Edusmildo Orozco , Dr. D. Bollman, Dr. O. Moreno, University of Puerto Rico
at Mayagez,
eorozco@cs.uprm.edu
The graphical representation of a network consists of a set of nodes and a
set of edges that connect certain pairs of nodes. In a genetic network the
nodes represent genes and an edge from node g1 to node g2 represents the
idea that a change in the activity of gene g1 changes the activity in gene
g2. In a protein network each node represents a protein and an edge from
node p1 to node p2 represents the idea that protein p1 interacts with
protein p2. Traditionally the networks used for such modeling purposes are
Boolean. In such a model, either one gene can affect another or not and
either one protein reacts with another or not. We consider a more general
type of network in which nodes are represented by variables that vary over
finite fields. These networks allow for more realistic models, in which the
effect of one gene on another or the rate of a chemical reaction involving
two proteins can be measured over a full range of discrete values. Although
reverse engineering methods have been applied mostly to genetic methods, it
could be advantageous to apply them to protein networks as well. The reverse
engineering problem is the problem of determining the network, given
experimental data. In this ongoing work we study efficient algorithms for
solving the reverse engineering problem for networks over finite fields.
The African American Distributed Multiple Learning Styles System (AADMLSS)
Nicholas Parks, Tanecia K. Simmons, Juan E. Gilbert, Auburn University
gilbert@eng.auburn.edu
Each student has a personal learning style that originates from innate
tendencies and environmental experiences. Because cultural groups often
share common values, the experiences of children growing up with those
values are reflected in their classroom learning behaviors (i.e. cultural
learning style). Therefore, a culturally relevant pedagogy is central to the
academic success of minority students. The research described in this talk
is influenced by the compelling impact of social and cultural issues on
academic performance. Accordingly, the African American Distributed Multiple
Learning Styles System (AADMLSS) was developed to provide Educators with an
easy to use viable alternative, for supplementing their classroom
instruction portfolio, with culture specific elearning tools. AADMLSS
embraces the differences in cultural learning styles by providing a
culturally sensitive, multicurriculum, elearning pedagogical environment,
in an effort to enhance a student's overall learning experience and
classroom performance. In this presentation, we present the infrastructure
and empirical data findings for AADMLSS.
Aerodynamic Simulation of a Falling Paratrooper: Mesh Refinement Techniques
for Higher Accuracy in the Computational Boundary Layer
Victor Udoewa, Tayfun Tezduyar, Rice University
udoewa@rice.edu
Our target is to develop computational techniques for studying aerodynamic
interactions between multiple objects with emphasis on studying the fluid
mechanics and dynamics of an object exiting and separating from an aircraft.
The object could be a paratrooper jumping out of a transport aircraft or a
package of emergency aid dropped from a cargo plane. These are applications
with major practical significance, and what we learn and develop can make a
major impact on this technological area. The computational tools we are
developing are based on the simultaneous solution of the timedependent
NavierStokes equations governing the airflow around the aircraft and the
separating object, as well as the equations governing the motion of that
object. And the computational challenge is to predict the dynamic behavior
and path of the object, so that the separation process is safe and
effective. The gravitational and aerodynamic forces acting on the object
determine its dynamics and path. We are focusing on more accurate
computation of the boundary layer around the aircraft, so that the
aerodynamic forces acting on the paratrooper during the period immediately
following the exit from the aircraft are calculated more accurately.
A Reduced Basis Method for Molecular Dynamics Simulations
Rachel E. Vincent, Dr. Danny C. Sorensen, Rice University; Dr. B. Montgomery
Pettitt, University of Houston
rvincen@caam.rice.edu
Molecular dynamics (MD) simulations allow scientists to computationally
determine atomic positions in a molecule over a specified period of time.
These simulations are computationally expensive and generally require a
large amount of data storage. We expect to reduce computational costs and
storage requirements using singular value decomposition (SVD) analysis. In
any trajectory, whether generated by traditional dynamics methods,
timeaveraged refinements, or a reduced basis set method, classical
principal component analysis may be used to classify and represent the
dominant characteristics of the MD trajectory. Here we augment the classical
principal component analysis with an SVD updating scheme. SVD analysis of
the computed trajectories will be developed to augment abilities to locate
active sites, to identify preferred molecular configurations, and to study
periodic behavior. Preliminary results obtained with respect to our reduced
basis method provide insight into the relationship between the reduced and
standard simulations. Furthermore they suggest that constraints are
necessary to insure the integrity of the simulated molecule.
Durable Wide Area Archival Storage in OceanStore
Hakim Weatherspoon, University of California, Berkeley
hweather@cs.berkeley.edu
Traditional archival media are rapidly being replaced with digital
repositories. This has generated a need for longterm, digital archival
storage. In this poster we describe the architecture of a globalscale,
distributed storage infrastructure that is selfrepairing and resilient to
faults and malicious attacks. This infrastructure employs erasurecoding to
enhance durability, coupled with mechanisms for locationindependent
routing, introspective failure analysis, and automatic repair. The result is
archival storage that has the potential to preserve information
indefinitely. We present results from the prototype archival layer of
OceanStore, currently under construction at Berkeley. The results include
expected throughput, latency, and other application usage details. That is,
we present what is required to use the archival layer of oceanstore for our
everyday storage needs. By building the system we are able to determine the
minimal set of requirements to maintain data reliably in the wide area.
Mathematical Modeling Framework for American Option Valuation with Financial
Constraints
Donald C. Williams, Rice University
donald@caam.rice.edu
Fundamental to many complex financial derivative securities is the valuation
and optimal exercise of options with Americanstyle exercise features. This
remains one of the most important and intellectually challenging problems
within option pricing theory. This work proposes a direct computational
algorithm for solving the American option valuation problem within an
optimization framework. The algorithm employs a Newton type constrained
nonlinear interiorpoint optimization method for solving the discretized
variational inequality problem that arises. Considerations are made in terms
of numerically approximating, in a stable manner, the governing parabolic
partial differential equation that can become convection dominated in
certain areas of the solution domain. Considerations are also made for
incorporating additional economic constraints within the optimization
framework. Some example computations are presented for special cases of
Americanstyle options with the aim of revealing the general applicability
of the constrained optimization pricing methodology.
Optimization of Trajectories to Mars Using Electric Propulsion
Powtache Williams, Rice University
powwow@rice.edu
Although chemical rocket propulsion is widely used in space transportation,
large amounts of propellant and vehicle mass limit designs for a human
mission to Mars. Electric propulsion, which requires a smaller propellant
load while producing greater speed, is an alternative system that can be
used for manned interplanetary flight. This research investigates the use of
ion electric rockets in previous robotic missions for the design of manned
missions.
Specifically, this research modifies the sequential gradientrestoration
algorithm (SGRA) to a multiplesubarc sequential gradient restoration
algorithm (MSGRA) and includes specifications of ion electric engine (Isp =
3,000 sec, Deep Space 1 Engine) for optimization of maximum payload and
minimum time trajectories. Additional studies will be made on the design of
hybrid launch vehicles, which includes the use of chemical engines for
flight from Earth to lowEarth atmosphere and ion electric engines for
interplanetary space flight for both manned and unmanned missions.
Multimodal SQL  Mobile Access to Databases
DaleMarie Wilson, Auburn University
wilsodc@eng.auburn.edu
The increasing sizes of databases coupled with the decreasing sizes of
mobile computing devices introduce increasing restrictions on information
display and output. The ballooning sales of these devices and the rising
number of Internet users, have led to the onset of pervasive humancentered
computing. New advances in speech and multimodal interfaces complement this
trend. This poster presents Multimodal SQL, a speech interface that allows
users to remotely access their databases using their voices. Multimodal SQL
consists of three major components: a speech interface coded using VoiceXML
and JavaScript; a relational database; and an interface for the presentation
of the results. Multimodal SQL is the first iteration of this software
development cycle and its results are presented visually. The cycle is
projected to conclude in an interface that accepts spoken queries and
returns visual and auditory results. A usability study to determine both the
effectiveness and user satisfaction of the first iteration will be
presented. The results of this study indicate that Multimodal SQL provides
efficient access to databases using voice queries.
Learning rules for the lowdimensional Clifford neural networks
Qing Yi, Bryant York, Portland State University
yiq@cs.pdx.edu
Lowdimensional Clifford algebras include the real numbers, the complex
numbers, and the quaternions. Most neural network theory is applied to the
learning (or approximation) of realvalued functions of a real variable. In
the past decade researchers have begun to study the approximation of
functions defined on the complex numbers and the quaternions. A number of
theoretical results exist; however, the extension of the universality
results from the real numbers to these arbitrary lowdimensional Clifford
algebras has met with some resistance. Because the Clifford algebras are
generally normed, associative algebras (not necessarily commutative),
Clifford analysis poses different problems from real and complex analysis.
The learning rules for multilayer perceptrons using the backpropagation
algorithm are critically dependent on gradientdescent and the notion of
differentiability in the optimization space. In this work, we outline the
essential differences between, real, complex, quaternionic, and Clifford
analysis as they pertain to the development of effective learning rules for
Clifford neural networks. In addition, we demonstrate the effectiveness of
these learning rules for a variety of applications.
Questions regarding the 2003 Tapia Poster Selection process should be directed to:
Brian M. Dennis
Poster Committee Chair
2003 Richard Tapia Celebration of Diversity in Computing Conference
bmd@cs.northwestern.edu
The subject line should read: "Questions about 2003 Tapia Poster Selection".
