Threads are essential to Java programming, but learning to use them effectively is a nontrivial task. This new edition of the classic Java Threads shows you how to take full advantage of Java s threading facilities and brings you up-to-date with the watershed changes in Java 2 Standard Edition version 5.0 (J2SE 5.0). It provides a thorough, step-by-step approach to threads programming.
To use the ATLTrace tool:
Debug an MFC or ATL project select Start from the Debug menu.
Select MFC/ATL Trace Tool in the Tools menu.
Expand the tree control list in the Trace List window. Here you will see the running application, any modules within that application, and the trace categories for each module.
Customize, for each process, module, and category, which information is displayed in the output window. The Trace level control in the Process group is related to the ATLTRACE2 level only those ATLTRACE2 messages with a level equal to or greater than the setting in the Trace level control will be displayed in the output window.
Select Apply to put your settings into effect.
You can save your settings, and load them the next time you debug the application use the Save and Load buttons.
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
Hibernate: A Developer s Notebook shows you how to use Hibernate to automate persistence: you write natural Java objects and some simple configuration files, and Hibernate automates all the interaction between your objects and the database. You don t even need to know the database is there, and you can change from one database to another simply by changing a few statements in a configuration file. If you ve needed to add a database backend to your application, don t put it off. It s much more fun than it used to be, and Hibernate: A Developer s Notebook shows you why.
