Octave is a scientific programming language with powerful mathematics-oriented syntax, built-in plotting and visualization tools. It is free software, runs on GNU/Linux, macOS, BSD, and Windows. And, last, but not least: It is drop-in compatible with many Matlab scripts.
Isn’t it already amazing? Here is the link to help page: http://jweaton.org/?page_id=48
The article “Model-based Methods for Continuous and Discrete Global Optimization” by T. Bartz-Beielstein and M. Zaefferer is available online: http://www.sciencedirect.com/science/article/pii/S1568494617300546
A preprint can be downloaded from “Cologne Open Science”: urn:nbn:de:hbz:832-cos4-4356
The use of surrogate models is a standard method for dealing with complex real-world optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article takes this development into consideration. The first part presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part examines discrete optimization problems. Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in continuous and discrete application domains.
The “Ten Simple Rules for Reproducible Computational Research” are easy to reach for R users. Read more about it in Joris Muller’s Blog.
Here is an interesting WIRED article, which states that “science itself—through its systems of publication, funding, and advancement—had become biased toward generating a certain kind of finding: novel, attention grabbing, but ultimately unreliable. The incentives to produce positive results were so great […] that some scientists were simply locking their inconvenient data away.”
The article is about Brian Arthur Nosek, the co-founder and director of the Center for Open Science. The Center for Open Science supports researchers to plan reproducibility projects.
“It is not enough for the pioneers of AI and ML to share their code. The industry and the world needs a new open source model where AI and ML trained engines themselves are open sourced along with the data, features and real world performance details…” Read more: https://techcrunch.com/2017/01/28/ais-open-source-model-is-closed-inadequate-and-outdated/
Research performed at the SPOTSeven Lab @th_koeln takes not only the scientific content, but also its impact to the society into account. Our research is inspired by our partners from academia, industry, and society. We are advocating open source tools such as R (https://cran.r-project.org). Open Science (@rOpenSci) can be characterized as follows:
“Open science is the practice of making various elements of scientific research — data & methods, code & software, and results & publications — readily accessible to anyone. While this has great potential for advancing research (in addition to education, public policy, & commercial innovation) as a whole, there are both technical and social challenges preventing this practice from being more widespread. Social challenges stem largely from the dichotomy between what is best for an individual researcher and what is best for the community.”
Read more: https://ropensci.org/about/