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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis


The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.



Lecture "Große Daten, Kleine Geräte" ("Big Data, Small Devices") in the Science Notes

Science Notes Poster

Intelligent fabrics, fitness wristbands, smartphones, cars, factories, and large scientific experiments are recording tremendous data streams. Machine Learning can harness these masses of data, but storing, communicating, and analysing them spends lots of energy. Therefore, small devices should send less, but more meaningful data to a central processor where additional analyses are performed.

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Katharina Morik among the leaders of Germany's "Platform Learning Systems"

WG leaders

Germany ranks among the pioneers in the field of learning systems and Artificial Intelligence. The aim of the Plattform Lernende Systeme initiated by the Federal Ministry of Education and Research is to promote the shaping of Learning Systems for the benefit of individuals, society and the economy. Learning Systems will improve people’s quality of life, strengthen good work performance, secure growth and prosperity and promote the sustainability of the economy, transport systems and energy supply.

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Two Best Paper Awards for Subproject B2

Biosignals 2018

 

Two independent publications from subproject B2 have been honored with a Best Paper Award at different conferences dealing with the automatic classification of nano-objects (e. g. viruses) in noisy sensor data. They provide a valuable contribution to the automatic analysis of medical probes using the PAMONO sensor.

The joint work "Real-Time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks" of Jan Eric Lenssen, Anas Toma, Albert Seebold, Victoria Shpacovitch, Pascal Libuschewski, Frank Weichert, Jian-Jia Chen and Roland Hergenröder received the Best Paper Award of the BIOSIGNALS 2018.

The joint work "Unsupervised Data Analysis for Virus Detection with a Surface Plasmon Resonance Sensor" of Dominic Siedhoff, Martin Strauch, Victoria Shpacovitch and Dorit Merhof received the Best Paper Award of the IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA) 2017. The approach was developed in cooperation with the Department of Image Processing, RWTH Aachen University.

Best PhD thesis and degree awards for Claudia Köllmann and Andrea Bommert

Andrea Bommert und Claudia Kllmann

At the recent yearly anniversary celebration of the TU Dortmund University two members of the CRC 876 received awards for their research:

Andrea Bommert (photo left, project A3) received an award as best in class for her masters degree.

Dr. Claudia Köllmann (photo right, project C4) received a PhD award for her outstanding contribution with the topic Unimodal Spline Regression and Its Use in Various Applications with Single or Multiple Modes:

Research in the field of non-parametric shape constrained regression has been extensive and there is need for such methods in various application areas, since shape constraints can reflect prior knowledge about the underlying relationship. This thesis develops semi-parametric spline regression approaches to unimodal regression. However, the prior knowledge in different applications is also of increasing complexity and data shapes may vary from few to plenty of modes and from piecewise unimodal to accumulations of identically or diversely shaped unimodal functions. Thus, we also go beyond unimodal regression in this thesis and propose to capture multimodality by employing piecewise unimodal regression or deconvolution models based on unimodal peak shapes. More explicitly, this thesis proposes unimodal spline regression methods that make use of Bernstein-Schoenberg-splines and their shape preservation property.

To achieve unimodal and smooth solutions we use penalized splines, and extend the penalized spline approach towards penalizing against general parametric functions, instead of using just difference penalties. For tuning parameter selection under a unimodality constraint a restricted maximum likelihood and an alternative Bayesian approach for unimodal regression are developed. We compare the proposed methodologies to other common approaches in a simulation study and apply it to a dose-response data set.

All results suggest that the unimodality constraint or the combination of unimodality and a penalty can substantially improve estimation of the functional relationship. A common feature of the approaches to multimodal regression is that the response variable is modelled using several unimodal spline regressions. This thesis examines mixture models of unimodal regressions, piecewise unimodal regression and deconvolution models with identical or diverse unimodal peak shapes. The usefulness of these extensions of unimodal regression is demonstrated by applying them to data sets from three different application areas: marine biology, astroparticle physics and breath gas analysis. The proposed methodologies are implemented in the statistical software environment R and the implementations and their usage are explained in this thesis as well.

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