PyExperimenter: Easily distribute experiments and track results

PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly. It is intended to be used by researchers in the field of artificial intelligence, but is not limited to those.Comment: Published in Journal of Open Source Softwar

Realistiche Bilder gibt es nicht/There Are No Realistic Images

Non ci sono immagini realistich

Realistiche Bilder gibt es nicht/There Are No Realistic Images

Non ci sono immagini realistich

Towards Green Automated Machine Learning: Status Quo and Future Directions

30 páginasAutomated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. At the same time, AutoML is being criticized for its high resource consumption as many approaches rely on the (costly) evaluation of many machine learning pipelines, as well as the expensive large-scale experiments across many datasets and approaches. In the spirit of recent work on Green AI, this paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly. Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool. Afterward, different strategies on how to design and benchmark an AutoML tool w.r.t. their "greenness", i.e., sustainability, are summarized. Finally, we elaborate on how to be transparent about the environmental footprint and what kind of research incentives could direct the community in a more sustainable AutoML research direction. As part of this, we propose a sustainability checklist to be attached to every AutoML paper featuring all core aspects of Green AutoML. © 2023 The Authors

Rapid extraction and preservation of genomic DNA from human samples

Simple and rapid extraction of human genomic DNA remains a bottle neck for genome analysis and disease diagnosis. Current methods using microfilters require cumbersome, multiple handling steps in part because salt conditions must be controlled for attraction and elution of DNA in porous silica. We report a novel extraction method of human genomic DNA from buccal swab- and saliva samples. DNA is attracted on to a gold-coated microchip by an electric field and capillary action while the captured DNA is eluted by thermal heating at 70 °C. A prototype device was designed to handle 4 microchips, and a compatible protocol was developed. The extracted DNA using microchips was characterized by qPCR for different sample volumes, using different lengths of PCR amplicon, and nuclear and mitochondrial genes. In comparison with a commercial kit, an equivalent yield of DNA extraction was achieved with fewer steps. Room-temperature preservation for one month was demonstrated for captured DNA, facilitating straightforward collection, delivery and handling of genomic DNA in an environment-friendly protocol