Motivation: In this paper we present the latest release of EBIC, a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding support for big data, making it possible to efficiently run large genomic data mining analyses. Additional enhancements include integration with R and Bioconductor and an option to remove influence of missing value on the final result. Results: EBIC was applied to datasets of different sizes, including a large DNA methylation dataset with 436,444 rows. For the largest dataset we observed over 6.6 fold speedup in computation time on a cluster of 8 GPUs compared to running the method on a single GPU. This proves high scalability of the algorithm. Availability: The latest version of EBIC could be downloaded from http://github.com/EpistasisLab/ebic . Installation and usage instructions are also available online.

Nico, Emil, and Moritz founded ReRun with the mission of making powerful visualization tools free and easily accessible for roboticists. Nico and Emil talk about how these powerful tools help debug the complex problem scopes faced by roboticists. Nikolaus West Co-Founder & CEO Niko is a second-time founder and software engineer with a computer vision background from Stanford. Emil Ernerfeldt Co-Founder & CTO Emil fell in love with coding over 20 years ago and hasn't looked back since. He's the creator of egui, an easy-to-use immediate mode GUI in Rust, that we're using to build Rerun.

It's been an exciting time for IBM. We recently made the biggest software acquisition in history. Very rarely have I seen any big organization move so quickly and decisively to embrace open source and build a prescriptive methodology to modernize IT workloads. A key part of this strategy is Cloud Pak for Data, our modern Data and AI platform. We are extremely excited for this release, as it brings to a head three key areas we've been building for over the last year and a half: Red Hat integration, new key built-in capabilities and a heavy focus on open source.

In this post I will go over the technical aspects of automatic forecast generation, as well as some of the organizational considerations that will arise when deciding to go with an automatic forecast generating system. As I said earlier, in many fields, including my field of retail demand forecasting, most commercial forecasting packages do perform automatic forecast generation. Several open source packages do so as well, most notably Rob Hyndman's auto.arima() Both the commercial products and the open source packages that I mentioned work based on the idea of using information criteria to choose the best forecasting model: You fit a bunch of models, and then select the model with the lowest AIC, BIC, AICc, etc….(typically this is done in lieu of out of sample validation -- see this presentation for details). There is however a major caveat: all of these methods work within a single family of models.

Software development has gone through massive paradigm shifts over the past decade. Once limited to developers with years of study or access to expensive servers, web development has now become a trade where bootcamps crank out developers in a matter of weeks. We are rapidly approaching our next paradigm shift, which will be AI-based code generation. When we reach that inflection point, web development will have officially died, and the labor force is woefully unprepared. Here are some of the paradigm shifts that have brought us to this point. WordPress launched on May 27th, 2003.