A fascinating article in New York Times details the fascinating details of the Stuxnet virus, apparently the most successful cyber weapon in recent times.
Given that Industrial Controllers are a part of a everything from factories to missile launch configurations, I believe this is a fascinating area of study for the world’s research scientists including creating variants and defenses for this.
I would be very surprised if 260,000 documents and not even one was a counter-intelligence dis information move. Why was ALL the information stored in one place- maybe Wikileaks would leak the launch codes of the missiles next.
One more data visualization for Tableau– R watchers can not how jjplot by Facebook Analytics and Tableau are replacing GGPLOT 2 as visualization standards- (GGPLOT 2 needs a better GUI maybe using pyqt than the Deducer currently- maybe they can create GGPLOT extensions for Red R yet)
and yes stranger stupid things have happened in diplomacy and intelligence (like India exploding the nuclear bomb on exactly the same date and same place —-surprising CIA, but we are supposed to be on the same side atleast for the next decade) but it would be wrong not to cross reference the cables with the some verification.
Tableau gives great data viz though, but I dont think all 260,000 cables are valid data points (and boy they must really be regretting creating the internet at DARPA and DoD- but you can always blame Al Gore for that)
The utilization of computer models for complex real-world processes requires addressing Uncertainty Quantification (UQ). Corresponding issues range from inaccuracies in the models to uncertainty in the parameters or intrinsic stochastic features.
This Summer school will expose students in the mathematical and statistical sciences to common challenges in developing, evaluating and using complex computer models of processes. It is essential that the next generation of researchers be trained on these fundamental issues too often absent of traditional curricula.
Participants will receive not only an overview of the fast developing field of UQ but also specific skills related to data assimilation, sensitivity analysis and the statistical analysis of rare events.
Theoretical concepts and methods will be illustrated on concrete examples and applications from both nuclear engineering and climate modeling.
The main lecturers are:
Dan Cacuci (N.C. State University): data assimilation and applications to nuclear engineering
Dan Cooley (Colorado State University): statistical analysis of rare events
This short course will introduce the current statistical practice for analyzing extreme events. Statistical practice relies on fitting distributions suggested by asymptotic theory to a subset of data considered to be extreme. Both block maximum and threshold exceedance approaches will be presented for both the univariate and multivariate cases.
Doug Nychka (NCAR): data assimilation and applications in climate modeling
Climate prediction and modeling do not incorporate geophysical data in the sequential manner as weather forecasting and comparison to data is typically based on accumulated statistics, such as averages. This arises because a climate model matches the state of the Earth’s atmosphere and ocean “on the average” and so one would not expect the detailed weather fluctuations to be similar between a model and the real system. An emerging area for climate model validation and improvement is the use of data assimilation to scrutinize the physical processes in a model using observations on shorter time scales. The idea is to find a match between the state of the climate model and observed data that is particular to the observed weather. In this way one can check whether short time physical processes such as cloud formation or dynamics of the atmosphere are consistent with what is observed.
Dongbin Xiu (Purdue University): sensitivity analysis and polynomial chaos for differential equations
This lecture will focus on numerical algorithms for stochastic simulations, with an emphasis on the methods based on generalized polynomial chaos methodology. Both the mathematical framework and the technical details will be examined, along with performance comparisons and implementation issues for practical complex systems.
The main lectures will be supplemented by discussion sessions and by presentations from UQ practitioners from both the Sandia and Los Alamos National Laboratories.
DECISION STATS 