Elemente einer Theorie hierarchischer Planung

Zusammenfassung Die Arbeit stellt einen konzeptionellen Rahmen zur Modellierung hierarchischer Planungsprobleme bereit, indem sie die Ebenen der hierarchischen Planung als Stufen eines dynamischen Programms interpretiert. Wesentlich ist hierbei, daß die unteren Ebenen zunächst nur approximativ antizipiert werden und eine genauere Beschreibung erst in nach unten fortschreitender (rollierender) Planung erfolgt. An einigen Beispielen wird gezeigt, wie sich die vorhandenen hierarchischen Modelle in dem allgemeinen Rahmen beschreiben lassen, und es wird angedeutet, welcher Zusammenhang zu hierarchischen Algorithmen, Agency-Fragestellungen und hierarchischen Aushandlungen besteht.     

Unique reconstruction of piecewise-smooth images by minimizing strictly convex nonquadratic functionals

We propose the minimization of a nonquadratic functional or, equivalently, a nonlinear diffusion model to smooth noisy image functionsg: R ⁿ R while preserving significant transitions of the data. The model is chosen such that important properties of the conventional quadratic-functional approach still hold: (1) existence of a unique solution continuously depending on the datag and (2) stability of approximations using the standard finite-element method. Relations with other global approaches for the segmentation of image data are discussed. Numerical experiments with real data illustrate this approach.This work was supported by the ESPRIT project SUBSYM.     

Highly Stable n–i–p Structured Formamidinium Tin Triiodide Solar Cells through the Stabilization of Surface Sn2+ Cations

Improving the performance, reproducibility, and stability of Sn-based perovskite solar cells (PSCs) with n–i–p structures is an important challenge. Spiro-OMeTAD [2,2′,7,7′-tetrakis(N,N-di-p-methoxyphenyl-amine)9,9′-spirobifluorene], a hole transporting material (HTM) with n–i–p structure, requires the oxygen exposure after addition of Li-TFSI [Lithium bis(trifluoromethanesulfonyl)imide] as a dopant to increase the hole concentration. In Sn-based PSC, Sn²⁺ is easily oxidized to Sn⁴⁺ under such a condition, resulting in a sharp decrease in efficiency. Herein, a formamidinium tin triiodide (FASnI₃)-based PSCs fabricated using DPI-TPFB [4-Isopropyl-4′-methyldiphenyliodonium tetrakis(pentafluorophenyl)borate] instead of Li-TFSI are reported as a dopant in Spiro-OMeTAD. The DPI-TPFB enables the fabrication of PSCs with an efficiency of up to 10.9%, the highest among FASnI₃-based PSCs with n–i–p structures. Moreover, ≈80% of the initial efficiency is maintained even after 1,597 h under maximum power point tracking conditions. In particular, the encapsulated device does not show any decrease in efficiency even after holding for 50 h in the 85 °C/85% RH condition. The high efficiency and excellent stability of PSCs prepared by doping with DPI-TPFB are attributed to not only increasing electrical conductivity by acting as a Lewis acid, but also stabilizing Sn²⁺ through coordination with Sn²⁺ on the surface of FASnI₃.     

Pointing in the third-person: an exploration of human motion and visual pointing aids for 3D virtual mirrors

Virtual Reality - A “virtual mirror” is a promising interface for virtual or augmented reality applications in which users benefit from seeing themselves within the environment, such as...     

Double Dissociations in Visual and Spatial Short-Term Memory.

A visual short-term memory task was more strongly disrupted by visual than spatial interference, and a spatial memory task was simultaneously more strongly disrupted by spatial than visual interference. This double dissociation supports a fractionation of visuospatial short-term memory into separate visual and spatial components. In 6 experiments, this interpretation could be defended against alternative explanations in terms of trade-offs in resource allocation between memory tasks and interference tasks, in terms of an involvement of short-term consolidation and long-term memory, in terms of differential phonological-loop and central-executive involvement, and in terms of similarity-based interference. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Gamma oscillations and stimulus selection

More coherent excitatory stimuli are known to have a competitive advantage over less coherent ones. We show here that this advantage is amplified greatly when the target includes inhibitory interneurons acting via GABA(A)-receptor-mediated synapses and the coherent input oscillates at gamma frequency. We hypothesize that therein lies, at least in part, the functional significance of the experimentally observed link between attentional biasing of stimulus competition and gamma frequency rhythmicity.     

Streamline integration using MPI-hybrid parallelism on a large multicore architecture

Streamline computation in a very large vector field data set represents a significant challenge due to the nonlocal and data-dependent nature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programming and execution as applied to streamline integration on a large, multicore platform. With multicore processors now prevalent in clusters and supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice. We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize over seeds and parallelize over blocks, and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing between cores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication and I/O bandwidth than a traditional, nonhybrid distributed implementation.     

Interactive multiscale tensor reconstruction for multiresolution volume visualization

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.     

A framework for regional association rule mining and scoping in spatial datasets

The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. In this paper, we systematically study this problem and address the unique challenges of regional association mining and scoping: (1) region discovery: how to identify interesting regions from which novel and useful regional association rules can be extracted; (2) regional association rule scoping: how to determine the scope of regional association rules. We investigate the duality between regional association rules and regions where the associations are valid: interesting regions are identified to seek novel regional patterns, and a regional pattern has a scope of a set of regions in which the pattern is valid. In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results confirm and validate research results in the study of arsenic contamination, and our work leads to the discovery of novel findings to be further explored by domain scientists.