C4I
Seminar: Friday, Jan 24, 1:30pm, Engr. Bldg., room 4705
Funding
Opportunity: Commonwealth Research Commercialization Fund
[Update]
Mark
Pullen & Nicholas Clark Receive Funding from Sierra Nevada
Corp. and Air
Force Research Lab.
Kun
Sun & Max Albanese Receives Funding from University of
Washington and US
Dept. of the Army
C4I
Seminar: Friday, Jan 24, 1:30pm,
Engr. Bldg., room 4705
Please
mark your calendars and plan to attend the C4I Seminar Series on
Friday, Jan
24, from 1:30 pm to 2:30 pm in the Engineering Building, room
4705. Dr. Wei Sun
discusses “Efficient User
Assets Management
by Trade-based Asset Blocks and Dynamic Junction Tree for Combo
Prediction
Markets.” For further information contact Deb Schenaker,
(703) 993-3682,
email: [log in to unmask],
or visit
our website http://c4i.gmu.edu/.
Abstract
We
have often heard that the collective wisdom of an informed and
diverse group
usually out-performs individual experts on forecasting and
estimation tasks.
The key question is how best to aggregate those diverse judgments.In 2010, it wasn't known
whether any system
could reliably beat the simple average.Mason is among the teams that reliably did so for two years
in IARPA's
ACE forecasting challenge. In June 2013 we closed down our
geopolitical
prediction market to create SciCast, a new and improved science
&
technology market. This talk discusses ongoing improvements to the
SciCast
forecasting engine.
Unlike
other prediction markets, SciCast allows forecasters make
conditional
forecasts: the chance that China's lunar rover would deploy can be
made to depend
on a successful soft lunar landing. To avoid a combinatorial
explosion, SciCast
uses Bayesian networks as the underlying probability model.But tracking the joint
probability structure
is not enough: markets also must track assets for each user,
awarding users for
correct forecasts and ensuring there is no possible world where
they go
negative.Previously, we
tracked assets
using the same junction tree structure as the joint probability
model. This
approach provides fast computation of the minimum value and
expected value.
However, it wastes a lot of space: the majority of users trade
sparsely
relative to the total number of questions, and even more sparsely
compared to
the whole joint probability space.Therefore
most of the asset junction tree remains untouched. Worse, every
time a question
is added or resolved, we have to update the asset tree for all
users, just in
case.
We
think a trade-based method can overcome this problem and be
computationally
efficient as well. It turned out that we can build asset blocks
involving the
questions being traded only, then collect them in an organized
manner such as
merging sub-block to its super set. Further, when computing user
score and
cash, we can construct an asset junction tree dynamically, based
on the collection
of asset blocks then use the asset junction tree for efficient
computations.
When questions are resolved, it is straightforward to update
user's asset
blocks accordingly. Basically, for any asset block which contains
the resolved
questions, we realize the resolving state and truncate the block.
In
this presentation, I will explain in detail how the trade-based
asset blocks
are built and how to construct the corresponding asset junction
tree
dynamically. Computational examples will be demonstrated and
compared with
other alternative methods. For general questions about prediction
markets or
other background knowledge, please visit https://scicast.org/.
Speaker
Information
Dr.
Wei Sun is a research assistant professor of the Sensor Fusion Lab
and the C4I
Center at George Mason University, where he works on stochastic
modeling,
probabilistic reasoning, optimization, decision support systems,
data fusion
and general operations research. Dr. Sun focuses his research on
inference
algorithm for hybrid Bayesian networks, nonlinear filtering, and
information
fusion. He is an expert in Bayesian inference and developer of
several
efficient inference algorithms. He is also a contributor/committer
of the
open-source Matlab BN toolbox. Applications of his research
include tracking,
fusion, bioinformatics, classification, diagnosis, etc.Prior to joining GMU, Dr. Wei
Sun was a
Senior Analyst with United Airlines, Inc. and a professional
Electrical
Engineer in China. He is the recipient of the GMU’s Academic
Excellence Award
in 2003 and PhD Fellowship during 2003-2007.
Funding
Opportunity: Commonwealth
Research Commercialization Fund [Update]
[I
included this last week.It
is a
limited-submission program.If
you wish
to submit a proposal, please forward to Keith Bushey
([log in to unmask]) no later
January 22nd the following information:the names of the PI, a title, target program and a brief
abstract (2000
characters in laymen's term) with relevant references from the PIs
along with
your curriculum vitae.PIs
will be
notified by noon on January 24.]
In
this second of two FY2014 solicitations, CRCF offers six programs
targeting
Virginia's public and private colleges and universities, the
private sector,
nonprofit research institutions, and political subdivisions.
Programs offered
this round are: Commercialization, Eminent Researcher Recruitment,
Facilities
Enhancement Loan, Matching Funds, SBIR Matching Funds, and STTR
Matching Funds.
Details on these programs, including eligibility requirements and
submission
caps, are provided in program-specific guidelines.
Deadlines: 01/31/14:letter of intent
02/21/14:application deadline CRCF
award recipients from previous solicitations: www.cit.org/initiatives/crcf-awards/
Mark
Pullen & Nicholas Clark Receive
Funding from Sierra Nevada Corp. and Air Force Research Lab
Mark
Pullen and Nicholas Clark of the C4I Center received $88K from the
Sierra
Nevada Corporation and the Air Force Research Laboratory for their
project
“Academic PlugFest Follow-On Project”.
Kun
Sun & Max Albanese Receive
Funding from University of Washington and US Dept. of the Army
Kun
Sun and Max Albanese of the Center for Secure Information Systems
received
$115K from the University of Washington and the U.S. Department of
the Army for
his project “Modeling and Analysis of Moving Target Defense
Mechanisms in
MANET”.
[This
notice was included in my News announcements for 01/13/14, but did
not include
Max Albanese as one of the PIs.The
Office
of Sponsored Programs is using a new format for announcing awards,
and
this has made it more difficult for me to identify all the PIs on
an award, as
well as the organization associated with the award.Please let me know if an
announcement omits
relevant information.]