Euromaidan: Time to Draw Conclusions

Empirical Algorithmics: draw your own conclusions

In an empirical comparisons of algorithms we might compare run times over a set of benchmark problems to decide which one is fastest, i.e. an algorithmic horse race. Ideally we would like to download source code for the algorithms, compile and then run on our machine. Sometimes code isn't available to download and sometimes resource isn't available to implement all the algorithms we want to stu...

Hierarchical statistical techniques are necessary to draw reliable conclusions from analysis of isolated cardiomyocyte studies

Aims It is generally accepted that post-MI heart failure (HF) changes a variety of aspects of sarcoplasmic reticular Ca2+ fluxes but for some aspects there is disagreement over whether there is an increase or decrease. The commonest statistical approach is to treat data collected from each cell as independent, even though they are really clustered with multiple likely similar cells from each he...

Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches

Developing state-of-the-art approaches for specific tasks is a major driving force in our research community. Depending on the prestige of the task, publishing it can come along with a lot of visibility. The question arises how reliable are our evaluation methodologies to compare approaches? One common methodology to identify the stateof-the-art is to partition data into a train, a development ...

The role of heterosexual anal intercourse for HIV transmission in developing countries: are we ready to draw conclusions?

How to Draw the Minimum