TY - JOUR A1 - Klemz, Boris A1 - Rote, Günter T1 - Linear-Time Algorithms for Maximum-Weight Induced Matchings and Minimum Chain Covers in Convex Bipartite Graphs JF - Algorithmica N2 - A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandstädt et al. in Theor. Comput. Sci. 381(1–3):260–265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before. KW - dynamic programming KW - graph algorithm KW - induced matching KW - chain cover KW - convex bipartite graph KW - certifying algorithm Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267876 SN - 1432-0541 VL - 84 IS - 4 ER - TY - THES A1 - Herbort, Oliver T1 - Encoding Redundancy for Task-dependent Optimal Control : A Neural Network Model of Human Reaching T1 - Redundante Repräsentationen als Grundlage aufgabenbezogener optimaler Steuerung:Ein neuronales Netzwerk Modell menschlicher Zeigebewegungen N2 - The human motor system is adaptive in two senses. It adapts to the properties of the body to enable effective control. It also adapts to different situational requirements and constraints. This thesis proposes a new neural network model of both kinds of adaptivity for the motor cortical control of human reaching movements, called SURE_REACH (sensorimotor unsupervised learning redundancy resolving control architecture). In this neural network approach, the kinematic and sensorimotor redundancy of a three-joint planar arm is encoded in task-independent internal models by an unsupervised learning scheme. Before a movement is executed, the neural networks prepare a movement plan from the task-independent internal models, which flexibly incorporates external, task-specific constraints. The movement plan is then implemented by proprioceptive or visual closed-loop control. This structure enables SURE_REACH to reach hand targets while incorporating task-specific contraints, for example adhering to kinematic constraints, anticipating the demands of subsequent movements, avoiding obstacles, or reducing the motion of impaired joints. Besides this functionality, the model accounts for temporal aspects of human reaching movements or for data from priming experiments. Additionally, the neural network structure reflects properties of motor cortical networks like interdependent population encoded body space representations, recurrent connectivity, or associative learning schemes. This thesis introduces and describes the new model, relates it to current computational models, evaluates its functionality, relates it to human behavior and neurophysiology, and finally discusses potential extensions as well as the validity of the model. In conclusion, the proposed model grounds highly flexible task-dependent behavior in a neural network framework and unsupervised sensorimotor learning. N2 - Das motorische System des Menschen ist in zweierlei Hinsicht anpassungsfähig. Es passt sich den Eigenschaften des Körpers an, um diesen effektiv zu kontrollieren. Es passt sich aber auch unterschiedlichen situationsabhängigen Erfordernissen und Beschränkungen an. Diese Dissertation stellt ein neues neuronales Netzwerk Modell der motor-kortikalen Steuerung von menschlichen Zeigebewegungen vor, das beide Arten von Anpassungsfähigkeit integriert (SURE_REACH, Sensumotorische, unüberwacht lernende, redundanzauflösende Kontrollarchitektur). Das neuronale Netzwerk speichert kinematische und sensumotorische Redundanz eines planaren, dreigelenkigen Armes in aufgabenunabhängigen internen Modellen mittels unüberwachter Lernverfahrenen. Vor der Ausführung einer Bewegung bereitet das neuronale Netzwerk einen Bewegungsplan vor. Dieser basiert auf den aufgabenunabhängigen internen Modells und passt sich flexibel äu"seren, aufgabenabhängigen Erfordernissen an. Der Bewegungsplan wird dann durch propriozeptive oder visuelle Regelung umgesetzt. Auf diese Weise erklärt SURE_REACH Bewegungen zu Handzielen die aufgabenabhängige Erfordernisse berücksichtigen, zum Beispiel werden kinematische Beschränkungen miteinbezogen, Erfordernisse nachfolgender Aufgaben antizipiert, Hindernisse vermieden oder Bewegungen verletzter Gelenke reduziert. Desweiteren werden zeitliche Eigenschaften menschlicher Bewegungen oder die Ergebnisse von Primingexperimenten erklärt. Die neuronalen Netzwerke bilden zudem Eigenschaften motor-kortikaler Netzwerke ab, zum Beispiel wechselseitig abhängige Raumrepräsentationen, rekurrente Verbindungen oder assoziative Lernverfahren. Diese Dissertation beschreibt das neue Modell, vergleicht es mit anderen Modellen, untersucht seine Funktionalität, stellt Verbindungen zu menschlichem Verhalten und menschlicher Neurophysiologie her und erörtert schlie"slich mögliche Erweiterungen und die Validität des Models. Zusammenfassend stellt das vorgeschlagene Model eine Erklärung für flexibles aufgabenbezogenes Verhalten auf ein Fundament aus neuronalen Netzwerken und unüberwachten sensumotorischen Lernen. KW - Bewegungssteuerung KW - Motorisches Lernen KW - Redundanz KW - Neuronales Netz KW - Optimale Kontrolle KW - Computersimulation KW - Populationscodes KW - dynamisches Programmieren KW - flexibles Verhalten KW - population codes KW - dynamic programming KW - flexible behavior Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-26032 ER - TY - JOUR A1 - Bischler, Thorsten A1 - Kopf, Matthias A1 - Voss, Bjoern T1 - Transcript mapping based on dRNA-seq data JF - BMC Bioinformatics N2 - Background: RNA-seq and its variant differential RNA-seq (dRNA-seq) are today routine methods for transcriptome analysis in bacteria. While expression profiling and transcriptional start site prediction are standard tasks today, the problem of identifying transcriptional units in a genome-wide fashion is still not solved for prokaryotic systems. Results: We present RNASEG, an algorithm for the prediction of transcriptional units based on dRNA-seq data. A key feature of the algorithm is that, based on the data, it distinguishes between transcribed and un-transcribed genomic segments. Furthermore, the program provides many different predictions in a single run, which can be used to infer the significance of transcriptional units in a consensus procedure. We show the performance of our method based on a well-studied dRNA-seq data set for Helicobacter pylori. Conclusions: With our algorithm it is possible to identify operons and 5'- and 3'-UTRs in an automated fashion. This alleviates the need for labour intensive manual inspection and enables large-scale studies in the area of comparative transcriptomics. KW - transcriptional start site KW - dynamic programming KW - RNA-seq KW - differential KW - segmentation KW - transcriptional uni KW - transcriptome KW - reveals KW - model Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-116663 SN - 1471-2105 VL - 15 IS - 122 ER -