The Covid-19 crisis pushed many of us to change our working styles. As universities all over Germany changed to an online version, video streaming became a daily necessecity for students to follow the lectures and for professor to be able to evaluate the students' presentations. We hear very often that the stay-home orders amend Covid-19 crisis had an immediate positive impact on the air-pollution and the environment in general but the question is if our new adapted life-style keeps this positive impacts also on the longer runs. Therefore, it is very interesting to answer questions like: "What are the CO2 costs of video streaming?". Professor Wolfgang Konen takes a look at this very important question in this article (unfortunately only in German).


I am very happy to announce that on Wednesday, April 8th 2020, the PhD-colloquium of Samineh Bagheri, which I had the honor to supervise at TH Köln, successfully took place. Her thesis “Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems” deals with state-of-the art optimization algorithms supported by RBF surrogate models.  In normal times we would have been all traveling to Leiden University and would have made the ceremony in the prestigious Leiden senate hall. But times are not normal. Due to Covid-19 it was impossible to proceed as planned. But Leiden University is very innovative, and they decided to do their first-time ever fully-online (read more...)

 

We are happy to inform you that a new version of the SACOBRA package is available on CRAN. SACOBRA is a self-adjusting surrogate assisted optimization framework designed for expensive black-box constrained problems.

This version of SACOBRA can handle black-box equality constraints  as described in this paper. Also the online model selection algorithm described in an award-winning paper is available to use in our recent release of SACOBRA. Additionally, the implementation of all 24 G-problems (the most commonly used test-suite for black-box constrained optimization problems in optimization community) is directly available in our package. If you have used SACOBRA 1.1 you might have experienced repeated warning messages coming from nloptr package, this issue is also resolved for the moment.

 

You can easily install SACOBRA by running the following line in your R command line:

install.packages("SACOBRA",dependencies=T)

 

If you have any interesting or challenging constrained optimization problems that you want to solve efficiently, apply SACOBRA and let us know about your experience.

The GroupLearn proposal of Prof. Konen is one of the three proposals from TH Köln that wins a Stifterverband Fellowship in 2020.  This year only 10 of the 87 submitted Senior Fellowship proposals were selected for funding by Stifterverband. These fellowships were awarded with 50.000 Euro funding.

Stifterverband NRW awards Fellowships for Innovations in Digital University Teaching for purposes like redesign of university teaching modules using digital technologies and the development of digitally supported teaching and examination formats.

 

GroupLearn - Group-based learning in the digital learning world MathWeb

In order to promote group work among students, Prof. Wolfgang Konen wants to create with "GroupLearn for MathWeb" a space where students can support and exchange ideas. "GroupLearn", is a digital learning world that is intended to provide a fun and impactful environment to practice mathematical problems. Two main functionalities of GroupLearn can be listed as follows:

  • The Exercise Compiler is designed to provide an interactive, user-friendly environment in which students design new exercises and their solutions for MathWeb that other students can solve. (The picture above shows a possible layout of the exercise editor.)
  • The Discussion Forum is intended to provide a place for students and learning coaches to exchange solutions, questions, and problems.

 

Electrocardiogram with Anomalies

Markus Thill, who is PhD student in our CIOP group, is working on developing new anomaly detection algorithms since 2017 under supervision of Prof. Wolfgang Konen. The field of anomaly detection, which is today often tackled with machine learning algorithms, became a hot topic in the last years. Thill's recent paper titled "Anomaly detection in electrocardiogram readings with stacked LSTM networks" won the best paper award at the ITAT conference. LSTM (Long Short-Term Memory) networks are a special form of recurrent neural networks and thus belong to the group of deep learning algorithms.

ITAT (Information technologies -- Applications and Theory) is an annual European conference being held in Slovakia. This year ITAT took place from 21 till 23 of September and hosted a number of interesting talks focusing on time series analysis topics including anomaly detection, forecasting etc. " Word guessing game with a social robotic head " was one of the three invited talks in this conference.

A figure taken form the paper which shows the performance of LSTM-AD compared to other state-of-the-art algorithms

Thill uses a deep learning approach to tackle the challenging task of anomaly detection. This method, called LSTM-AD, is described in detail in this paper and shows very promising results compared to the state-of-the-art (see figure above). A distinguishing feature of LSTM-AD is that it learns unsupervised what an anomaly is, just by analysing sufficient data with predominantly normal behaviour. Thill's method is applied to electrocardiograms (heart beat recordings) and can detect anomalies in these electrocardiograms with great reliability.