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We present GEM-NI -- a graph-based generative-design tool that supports parallel exploration of alternative designs. Producing alternatives is a key feature of creative work, yet it is not strongly supported in most extant tools. GEM-NI enables various forms of exploration with alternatives such as parallel editing, recalling history, branching, merging, comparing, and Cartesian products of and for alternatives. Further, GEM-NI provides a modal graphical user interface and a design gallery, which both allow designers to control and manage their design exploration. We conducted an exploratory user study followed by in-depth one-on-one interviews with moderately and highly skills participants and obtained positive feedback for the system features, showing that GEM-NI supports creative design work well.
Binary relations with certain properties such as biorders, equivalences or difunctional relations can be represented as particular matrices. In order for these properties to be identified usually a rearrangement of rows and columns is required in order to reshape it into a recognisable normal form. Most algorithms performing these transformations are working on binary matrix representations of the underlying relations. This paper presents an approach to use the RLE-compressed matrix representation as a data structure for storing relations to test whether they are biorders in a hopefully more efficient way.
The generation and maintenance of intricate spatiotemporal patterns of gene expression in multicellular organisms requires the establishment of complex mechanisms of transcriptional regulation. Estimations that up to one million enhancers exist in the human genome accentuates the utmost importance of this type of cis-regulatory element for gene regulation. However, surprisingly little is known about the mechanisms used to temporarily or permanently activate or inactivate enhancers during cellular differentiation. The current work addresses the question how enhancer regulation can be achieved.
Using the chemokine (C-C motif) ligand gene Ccl22 as a model, the first example is based on the question how the activation of an enhancer can be prevented in a physiological context. Ccl22 is expressed by myeloid cells, such as dendritic cells, upon exposure to inflammatory stimuli. The expression in other cell types, such as fibroblasts, is prevented by the strong accumulation of H3K9me3 at the enhancer's proximal region. This accumulation is attenuated in myeloid cells through activity of the stimulus-induced demethylase Jmjd2d. To tease out which genomic fragment or fragments in the Ccl22 locus could be responsible for the maintenance of enhancer inactivity, potentially through the recruitment of H3K9 methyltransferases, the enhancer repressing capacity of 1 kb fragments of the gene locus was analysed in retroviral reporter assays. It was found that a fragment adjacent to the Ccl22 enhancer that overlaps with a member of a subfamily of long interspersed nuclear elements (LINEs) showed strong repressive potential on a model enhancer. Subsequent retroviral reporter assays with LINEs from loci of other stimulus-dependent genes identified additional LINE fragments that exhibit strong enhancer repressive capacity. These findings suggest a mechanism for enhancer silencing involving LINEs.
The second example concentrates on the inactivation of an enhancer during colorectal cancer (CRC) progression. The adenoma to carcinoma transition during CRC progression often is accompanied by a downregulation of the tumour suppressor gene EPHB2. The EMT inducing factor SNAIL1 strongly downregulated EPHB2 expression in a CRC cell model. To gain insights into the transcriptional regulation of EPHB2, potential cis-regulatory elements in the EPHB2 upstream region were analysed using reporter assays. A cell-type-specific enhancer was identified and subsequent chromatin analyses revealed a correlation between enhancer chromatin conformation and EPHB2 expression in different CRC cell lines. Additionally, the overexpression of the murine Snail1 induced chromatin changes at the EPHB2 enhancer towards a poised, transcriptionally silent chromatin conformation. Mutational analyses of the minimal enhancer region pinpointed three transcription factor binding motifs to be essential for full enhancer activity. Different binding patterns between CRC cell lines at the TCF/LEF motif were subsequently identified. Furthermore, a switch from TCF7L2 to LEF1 occupancy was found upon overexpression of Snail1 in vitro and in vivo. The generation of LS174T CRC cells overexpressing LEF1 confirmed the involvement of LEF1 in the downregulation of EPHB2 and the competitive displacement of TCF7L2. This part of the work demonstrated that the SNAIL1 induced downregulation of EPHB2 is dependent on the decommissioning of a transcriptional enhancer and led to a hypothetical model involving LEF1 and ZEB1.
In summary, this work highlighted two distinct mechanisms for enhancer regulation. One mechanism is based on enhancer repressive LINE fragments that might prevent stimulus-dependent enhancer activation. In the second, enhancer silencing was shown to be based on a competitive transcription factor binding mechanism.
Communicating Climate Risks. A case study of the International Red Cross/Red Crescent Movement
(2015)
The central theme of the 2014 Annual Report is human thinking.
In an interview, University President Hartmut Ihne and 3Sat moderator Gert Scobel discuss the concept of thought: "Should we be allowed to give up our autonomy voluntarily?"
Our university’s Language Centre Director James Chamberlain examines to what extent thinking varies in different languages.
Professor Paul Plöger from the Department of Computer Science explains why robots have tremendous problems understanding complex relationships in open environments.
Rather than focusing solely on our university’s future, the Annual Report links the fascinating theme to the enormous variety of life, research and tuition offered by H-BRS.
Advanced driver assistance systems (ADAS) are technology systems and devices designed as an aid to the driver of a vehicle. One of the critical components of any ADAS is the traffic sign recognition module. For this module to achieve real-time performance, some preprocessing of input images must be done, which consists of a traffic sign detection (TSD) algorithm to reduce the possible hypothesis space. Performance of TSD algorithm is critical.
One of the best algorithms used for TSD is the Radial Symmetry Detector (RSD), which can detect both Circular [7] and Polygonal traffic signs [5]. This algorithm runs in real-time on high end personal computers, but computational performance of must be improved in order to be able to run in real-time in embedded computer platforms.
To improve the computational performance of the RSD, we propose a multiscale approach and the removal of a gaussian smoothing filter used in this algorithm. We evaluate the performance on both computation times, detection and false positive rates on a synthetic image dataset and on the german traffic sign detection benchmark [29].
We observed significant speedups compared to the original algorithm. Our Improved Radial Symmetry Detector is up to 5.8 times faster than the original on detecting Circles, up to 3.8 times faster on Triangle detection, 2.9 times faster on Square detection and 2.4 times faster on Octagon detection. All of this measurements were observed with better detection and false positive rates than the original RSD.
When evaluated on the GTSDB, we observed smaller speedups, in the range of 1.6 to 2.3 times faster for Circle and Regular Polygon detection, but for Circle detection we observed a decreased detection rate than the original algorithm, while for Regular Polygon detection we always observed better detection rates. False positive rates were high, in the range of 80% to 90%.
We conclude that our Improved Radial Symmetry Detector is a significant improvement of the Radial Symmetry Detector, both for Circle and Regular polygon detection. We expect that our improved algorithm will lead the way to obtain real-time traffic sign detection and recognition in embedded computer platforms.
Communicating Sequential Processes (CSP) [7] is a calculus for concurrent systems that has been the basis of subject-oriented business process management (S-BPM) [4]. We use CSPm -- a machine readable dialect of CSP -- to create a sequence of models for a case study on an "Automated Teller Machine" [1]. We use the refinement checker FDR2 to prove that certain models are correct implementations of specifications.
Comparison of the subject-oriented and the Petri net based approach for business process automation
(2015)
The subject-oriented modelling approach [5] significally differs from the classic Petri net based approach of many business process modeling languages like EPC [9], Business Process Model and Notation (BPMN) [11], and also Yet Another Workflow Language (YAWL) [10]. In this work, we compare the two approaches by modeling a case study called "Procure to Pay"[3], a typical business process where some equipment for a construction site is rented and finally paid. The case study is not only modelled but also automated using the Metasonic Suite for the subject-oriented and YAWL for the Petri net based approach.
Extraction of text information from visual sources is an important component of many modern applications, for example, extracting the text from traffic signs on a road scene in an autonomous vehicle. For natural images or road scenes this is a unsolved problem. In this thesis the use of histogram of stroke widths (HSW) for character and noncharacter region classification is presented. Stroke widths are extracted using two methods. One is based on the Stroke Width Transform and another based on run lengths. The HSW is combined with two simple region features– aspect and occupancy ratios– and then a linear SVM is used as classifier. One advantage of our method over the state of the art is that it is script-independent and can also be used to verify detected text regions with the purpose of reducing false positives. Our experiments on generated datasets of Latin, CJK, Hiragana and Katakana characters show that the HSW is able to correctly classify at least 90% of the character regions, a similar figure is obtained for non-character regions. This performance is also obtained when training the HSW with one script and testing with a different one, and even when characters are rotated. On the English and Kannada portions of the Chars74K dataset we obtained over 95% correctly classified character regions. The use of raycasting for text line grouping is also proposed. By combining it with our HSW-based character classifier, a text detector based on Maximally Stable Extremal Regions (MSER) was implemented. The text detector was evaluated on our own dataset of road scenes from the German Autobahn, where 65% precision, 72% recall with a f-score of 69% was obtained. Using the HSW as a text verifier increases precision while slightly reducing recall. Our HSW feature allows the building of a script-independent and low parameter count classifier for character and non-character regions.
This book chapter describes application examples of gas chromatography/mass spectrometry and pyrolysis – gas chromatography/mass spectrometry in failure analysis for the identification of chemical materials like mineral oils and nitrile rubber gaskets. Furthermore, failure cases demanding identification of polymers/copolymers in fouling on the compressor wall of a car air conditioner and identification of fouling on the surface of a bearing race from the automotive industry are demonstrated. The obtained analytical results were then used for troubleshooting and remedial action of the technological process.