Bielefeld University

About: Bielefeld University is a education organization based out in Bielefeld, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Population & Quantum chromodynamics. The organization has 10123 authors who have published 26576 publications receiving 728250 citations. The organization is also known as: University of Bielefeld & UNIVERSITAET BIELEFELD.

A multilayer multiconfigurational time-dependent Hartree approach for quantum dynamics on general potential energy surfaces.

Abstract: The multiconfigurational time-dependent Hartree (MCTDH) approach facilitates multidimensional quantum dynamics calculations by representing the wavepacket in an optimal set of time-dependent basis functions, called single-particle functions Choosing these single-particle functions to be themselves multidimensional wavefunctions which are represented using a MCTDH representation, a multilayer MCTDH scheme has been constructed and used for quantum dynamics calculations treating up to 1000 degrees of freedom rigorously [Wang and Thoss, J Chem Phys 199, 1289 (2003)] The present work gives a practical scheme which facilitates the application of the multilayer MCTDH approach, which previously has only been employed to study systems described by model-type Hamiltonians, to molecular systems described by more complicated Hamiltonians and general potential energy surfaces A multilayer extension of the correlation discrete variable representation (CDVR) scheme employed in MCTDH calculations studying quantum dynamics on general potential energy surfaces is developed and tested in a simple numerical application The resulting multilayer MCTDH/CDVR approach might offer a perspective to rigorously describe the quantum dynamics of larger polyatomic systems

GABI-Kat SimpleSearch: new features of the Arabidopsis thaliana T-DNA mutant database

Abstract: T-DNA insertion mutants are very valuable for reverse genetics in Arabidopsis thaliana. Several projects have generated large sequence-indexed collections of T-DNA insertion lines, of which GABI-Kat is the second largest resource worldwide. User access to the collection and its Flanking Sequence Tags (FSTs) is provided by the front end SimpleSearch (http://www.GABI-Kat.de). Several significant improvements have been implemented recently. The database now relies on the TAIRv10 genome sequence and annotation dataset. All FSTs have been newly mapped using an optimized procedure that leads to improved accuracy of insertion site predictions. A fraction of the collection with weak FST yield was re-analysed by generating new FSTs. Along with newly found predictions for older sequences about 20 000 new FSTs were included in the database. Information about groups of FSTs pointing to the same insertion site that is found in several lines but is real only in a single line are included, and many problematic FST-to-line links have been corrected using new wet-lab data. SimpleSearch currently contains data from � 71 000 lines with predicted insertions covering 62.5% of the 27 206 nuclear protein coding genes, and offers insertion allele-specific data from 9545 confirmed lines that are available from the Nottingham Arabidopsis Stock Centre.

Memristor-based neural networks

Abstract: The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (Some figures may appear in colour only in the online journal)

Reducing the space requirement of suffix trees

Abstract: SUMMARY We show that suffix trees store various kinds of redundant information. We exploit these redundancies to obtain more space efficient representations. The most space efficient of our representations requires 20 bytes per input character in the worst case, and 10.1 bytes per input character on average for a collection of 42 files of different type. This is an advantage of more than 8 bytes per input character over previous work. Our representations can be constructed without extra space, and as fast as previous representations. The asymptotic running times of suffix tree applications are retained. Copyright © 1999 John Wiley & Sons, Ltd.