@article{KamionkaMartensChouetal.2010, author = {Kamionka, Thomas and Martens, Michael and Chou, Kang Wei and Curcic, Michael and Drews, Andr{\´e} and Sch{\"u}tz, Gisela and Tyliszczak, Tolek and Stoll, Hermann and Van Waeyenberge, Bartel and Meier, Guido}, title = {Magnetic Antivortex-Core Reversal by Circular-Rotational Spin Currents}, series = {Physical Review Letters}, volume = {105}, journal = {Physical Review Letters}, number = {13}, publisher = {American Physical Society}, issn = {0031-9007}, doi = {10.1103/PhysRevLett.105.137204}, year = {2010}, language = {en} } @phdthesis{Stuhlsatz2010, author = {Stuhlsatz, Andr{\´e}}, title = {Machine learning with Lipschitz classifiers}, publisher = {Otto-von-Guericke-Universit{\"a}t Magdeburg}, address = {Magdeburg}, doi = {10.25673/5050}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-201104192945}, year = {2010}, abstract = {complex image, like for example that of a known person, and to distinguish it from other objects within half a second. While for a solution of this task the brain has access to a massive parallelism and a vast, hierarchically organized, and auto-associative memory, common computer architectures are just able to a sequential processing of information stored in a non auto-associative memory. Even modern, parallelly operating, multi-processor systems are far away from the performance of our brain. However, nowadays, it is possible to solve complex and memory extensive pattern recognition problems, like the recognition of handwritten digits or the transcription of speech, satisfactorily with a common computer by the use of modern statistical and algorithmic learning approaches. One of the most successful pattern recognition methods is the so-called Support Vector Machine (SVM). The SVM is based on the learning paradigm of structural risk minimization, which outperforms empirical approaches if only few data is available for solving the considered classification problem. Although the SVM has proven very good recognition performances in many cases, the SVM also comes up with limitations, for example if specific a priori knowledge shall be used. In particular, the increasing complexity of applications requires a high adaptivity of the classification method to the specific problem. Also concerning this point, the SVM is limited due to a restricted variety of implementable classification functions. The objective of the present thesis is the development of new learning algorithms for the classification of patterns, that on the one hand overcome the limitations of the SVM, but on the other hand are based on the same theoretical concepts facilitating the good performance of the SVM. Two new algorithms will be presented that are justified by a theoretical generalization of the SVM, and which will be utilized for the first time for a practical implementation. In contrast to the SVM, the new methods make accessible a much larger function class for constructing a classifier. This is an important prerequisite for flexible adaptation of the classifier to difficult classification tasks with particular requirements as well as for the integration of a priori knowledge about the problem at hand. In this work, the way to implementable algorithms leads across different mathematical reformulations of the original problem. Starting with the theoretical generalization of the SVM, it results a restricted optimization problem that is difficult to solve in general. In a first step, this problem is expressed in terms of a restricted minimax-problem by a modification of the suitable classification functions to a still very large function class consisting of (affine-)linear combinations of at least one-time continuously differentiable functions. In the next step, the minimax-problem is converted into a so-called Semi-Infinite Problem (SIP). It turns out, that this particular mathematical problem is appropriate in order to obtain a solution of the original problem for the considered function class using well-known optimization methods. To further exploit the problem structure, an equivalent dual problem is derived from the SIP. Therefore, we prove a duality theorem about the equality of the optimal values of the dual and the original problem. For solving the dual problem, a multilevel iterative approach is developed from which the proposed algorithms follow by pursuing different solution strategies. Moreover, all sub-optimization methods of any stage necessary for an implementation in software are developed. Namely, these are an adapted interior-point-method, a simulated annealing based search heuristics and a particular gradient decent approach. Furthermore, options are depicted for an improvement of efficiency for future implementations. Besides the emphasis on the theoretical development of new learning methods and their practical implementations, all algorithms were implemented in the MATLAB(R) programming environment for the experimental part of the present thesis. Hence, they are also available for further research purposes in future. For the first time, classification results are explored and evaluated in comparison to the SVM on different data sets. As test data, an artificial 2d-dataset as well as two real-world datasets were used. In the concluding experiment, a scenario is prototypically considered to which the SVM is only inadequately applicable and which shall precisely prove the capability of the new methods in that case. It follows, regarding the considered datasets, the proposed learning methods reach comparably good classification accuracy like the SVM in standard applications. Moreover, the particular benefit of the new methods is reflected theoretically and experimentally in the ability to solve classification problems using decision functions that are not accessible to SVMs. Thereby, the underlying ideas, which make the SVM excel compared to other approaches with respect to generalization performances in case of few available learning information, are adequately transported into the proposed new environment. This opens the way for a design and a use of new classifiers that have not been implementable in a robust and generalizing basic concept so far.}, language = {en} } @inproceedings{StoehrZielke2023, author = {St{\"o}hr, Marcel and Zielke, Thomas}, title = {Machine Learning for Soft Sensors and an Application in Cement Production}, series = {Smart, Sustainable Manufacturing in an Ever-Changing World Proceedings of International Conference on Competitive Manufacturing (COMA '22)}, volume = {Lecture Notes in Production Engineering}, booktitle = {Smart, Sustainable Manufacturing in an Ever-Changing World Proceedings of International Conference on Competitive Manufacturing (COMA '22)}, editor = {Leipzig, Konrad von and Sacks, Natasha and Mc Clelland, Michelle}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {978-3-031-15604-5}, doi = {10.1007/978-3-031-15602-1_46}, pages = {627 -- 638}, year = {2023}, language = {en} } @phdthesis{Krieter2020, author = {Krieter, Philipp}, title = {Looking Inside - Mobile Screen Recordings as a Privacy Friendly Long-Term Data Source to Analyze User Behavior}, publisher = {Bremen}, address = {Universit{\"a}t Bremen}, doi = {10.26092/elib/103}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:46-elib43189}, year = {2020}, abstract = {Mobile devices are ubiquitous in many societies and shape the way we interact with technology and each other. Research on how we use and perceive technology is essential to understand its impact. This work advances how we can follow user behavior on mobile devices. We combine the strength of two common data sources for tracking on mobile devices, log files, and screen recordings. Log files are suitable for long-term and privacy-friendly analyzation but provide rather general data (e.g. system log files) unless one has access to the source code of the applications or operating systems. Screen recordings are usually used for short-termed analysis (e.g. usability tests) because the analysis is time-consuming, but they provide all activities on the screen in high detail regardless of which application or operating system. This thesis combines both data sources and presents an approach to automatically generate log files from mobile screen recordings. The approach utilizes methods of computer vision and machine learning to automatically process screen recordings and extend their use. Screen recordings reveal virtually everything a user does with a device, making privacy important, especially in user studies. We present a privacy concept and implementation and show how the risk of exposing private data can be reduced, by processing all recordings locally on the mobile devices and anonymizing the resulting log files. In order to apply the developed method in practice, we carry out a study in the context of education and show how log files of screen recordings can complement and extend existing research in learning analytics. This thesis opens up novel perspectives on how we can look at human-computer interaction with mobile devices. We show how to generate long-term log data with high detail and accuracy from mobile screen recordings, in a privacy-friendly way, locally on mobile devices.}, language = {en} } @inproceedings{SteffensGuastavino2014, author = {Steffens, Jochen and Guastavino, Catherine}, title = {Looking back by looking into the future - The role of anticipation and trend effects in retrospective judgments of musical excitement}, series = {Proceedings of the Congress of the International Association of Empirical Aesthetics, New York City (NY), USA}, booktitle = {Proceedings of the Congress of the International Association of Empirical Aesthetics, New York City (NY), USA}, address = {New York}, organization = {IAEA}, year = {2014}, language = {en} } @article{VocksWaechterWuchereretal.2008, author = {Vocks, Silja and W{\"a}chter, Alexandra and Wucherer, Maike and Kosfelder, Joachim}, title = {Look at yourself: can body image therapy affect the cognitive and emotional response to seeing oneself in the mirror in eating disorders?}, series = {European eating disorders review : the journal of the Eating Disorders Association}, volume = {16}, journal = {European eating disorders review : the journal of the Eating Disorders Association}, number = {2}, publisher = {Wiley}, doi = {10.1002/erv.825}, pages = {147 -- 154}, year = {2008}, abstract = {The study examined whether the extent of negative body-related thoughts and emotions evoked by looking in a mirror can be changed by cognitive-behavioural body image therapy (BIT) and whether any remaining cognitive and affective responses dropped faster after the treatment. Females with eating disorders (n = 17) looked in a mirror for 40 minutes under standardised conditions before and after BIT. A control group (CG) (n = 24) was also exposed to their own bodies twice. Cognitive and affective reactions were assessed four times during the session. Hierarchical Linear Modelling revealed that among eating-disordered patients, the entrance levels of negative thoughts and feelings were lower at post compared to pre-BIT, whereas they remained stable in the CG. The extent of reduction of the remaining body image distress was not changed after the treatment. BIT seems to be a potent treatment of negative emotions and cognitions in an in vivo situation.}, language = {en} } @inproceedings{BoegeleinBrinkmannAckermannetal.2018, author = {B{\"o}gelein, Silke and Brinkmann, Fabian and Ackermann, David and Weinzierl, Stefan}, title = {Localization Cues of a Spherical Head Model [Abstract]}, series = {Fortschritte der Akustik - DAGA 2018 - 44. Jahrestagung f{\"u}r Akustik 19.-22. M{\"a}rz 2018, M{\"u}nchen}, booktitle = {Fortschritte der Akustik - DAGA 2018 - 44. Jahrestagung f{\"u}r Akustik 19.-22. M{\"a}rz 2018, M{\"u}nchen}, publisher = {Deutsche Gesellschaft f{\"u}r Akustik e.V.}, isbn = {978-3-939296-13-3}, pages = {347 -- 350}, year = {2018}, language = {en} } @article{MuellerBraun2023, author = {M{\"u}ller, Patrick and Braun, Alexander}, title = {Local performance evaluation of AI-algorithms with the generalized spatial recall index}, series = {tm - Technisches Messen}, volume = {90}, journal = {tm - Technisches Messen}, number = {7-8}, publisher = {De Gruyter}, issn = {2196-7113}, doi = {10.1515/teme-2023-0013}, pages = {464 -- 477}, year = {2023}, language = {en} } @incollection{MacklinVirchow2011, author = {Macklin, Graham and Virchow, Fabian}, title = {Local embedding as a factor in electoral success: the BNP and NPD compared}, series = {The British National Party}, booktitle = {The British National Party}, editor = {Macklin, Graham and Copsey, Nigel}, publisher = {Routledge}, address = {Abingdon, Oxon; New York}, isbn = {9781136820625}, pages = {1 -- 20}, year = {2011}, language = {en} } @inproceedings{BlaettermannVersuemerSteffens2025, author = {Bl{\"a}ttermann, Patrick and Vers{\"u}mer, Siegbert and Steffens, Jochen}, title = {Loadings of Acoustical Metrics on Soundscape Items and their relation to Soundscape Dimensions across different datasets [Abstract]}, series = {DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen}, booktitle = {DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen}, publisher = {Deutsche Gesellschaft f{\"u}r Akustik e.V.}, address = {Berlin}, year = {2025}, subject = {Soundscape}, language = {en} }