Research

In the last decade, multimedia and scientific applications have pushed CPU manufactures to include native support for vector instruction sets. This software presents how to implement efficient condition encoding and fast rule matching strategies using vector instructions. The paper elaborates on Altivec (PowerPC G4 and G5) and SSE2 (Intel P4/Xeon and AMD Opteron) instruction sets producing speedups beyond ninety times when compared to non-vectorized implementations. The code of this post was used to run the experiments described in the IlliGAL 2006001 technical report “Fast Rule Matching for Learning Classifier Systems via Vector Instructions” by Xavier Llorà and Kumara Sastry. The code for fast rule matching can be downloaded here. Please read the README file for further details and instructions. The code is distributed under GPL license. ...

Cecilia Oversdotter Alm is working on an adaptation of active interactive genetic algorithms (see here) to her work on speech synthesis and perception of emotions in expressive storytelling. She needs a version of the active interactive genetic algorithm that works on continuous domains. For that reason I coded a version of UMDAc to replace the cGA currently used for discrete domains. The Java implementation of UMDAc can be found here. In order to run it, you need to download the COLT toolkit . The code is distributed under GPL license. ...

The Journal of New Mathematics and Natural Computation is running the first ot two parts of a special issue on chance discovery (volume 1, number 3). The number, besides including two regular papers, contains the first part of this special issue. The journal page can be found here. The table of contents of this first part of the special issue is: Ruediger Oehlmann, Preface, page 371. Yukio Ohsawa, Data crystallization: chance discovery extended for dealing with unobservable events, page 373. Renate Fruchter, Yukio ohsawa, and Naohiro Matsumura, Knowledge reuse through chance discovery from an enterprise design-build enterprise data store, page 393. Calkin a. s. Montero and Kenji araki, Human chat and self-organized criticality: A chance discovery application, page 407. Ja-min Koo and Sung-bae Cho, Interpreting chance for computer security by viterbi algorithm with edit distance, page 421. Edward Tsang, Sheri Markose and Hakan Er, Chance discovery in stock index option and futures arbitrage, page 435.

Recently, Pier Luca Lanzi and I decided to upgrade the learning classifier systems site he used to maintain. We are greatly thankful to professor David E. Goldberg for kindly accept to host the site at IlliGAL. The new LCS and other GBML site is still in dippers, but do not worry, it will grow soon.

Recently I attended a talk by David Blei about probabilistic models of text and images. His thesis described probabilistic models for the retrieval, organization, and exploration of large information collections, casting this tasks as statistical queries. The abstract of his thesis ca be found here and the thesis itself here.