University of Potsdam

About: University of Potsdam is a education organization based out in Potsdam, Germany. It is known for research contribution in the topics: Population & Computer science. The organization has 9629 authors who have published 26740 publications receiving 759745 citations. The organization is also known as: Universität Potsdam.

Toward a model of microsaccade generation: The case of microsaccadic inhibition

Abstract: Microsaccades are one component of the small eye movements that constitute fixation. Their implementation in the oculomotor system is unknown. To better understand the physiological and mechanistic processes underlying microsaccade generation, we studied microsaccadic inhibition, a transient drop of microsaccade rate, in response to irrelevant visual and auditory stimuli. Quantitative descriptions of the time course and strength of inhibition revealed a strong dependence of microsaccadic inhibition on stimulus characteristics. In Experiment 1, microsaccadic inhibition occurred sooner after auditory than after visual stimuli and after luminance-contrast than after color-contrast visual stimuli. Moreover, microsaccade amplitude strongly decreased during microsaccadic inhibition. In Experiment 2, the latency of microsaccadic inhibition increased with decreasing luminance contrast. We develop a conceptual model of microsaccade generation in which microsaccades result from fixation-related activity in a motor map coding for both fixation and saccades. In this map, fixation is represented at the central site. Saccades are generated by activity in the periphery, their amplitude increasing with eccentricity. The activity at the central, fixation-related site of the map predicts the rate of microsaccades as well as their amplitude and direction distributions. This model represents a framework for understanding the dynamics of microsaccade behavior in a broad range of tasks.

Challenges and opportunities for integrating lake ecosystem modelling approaches

Abstract: A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others (‘reinventing the wheel’). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available (‘having tunnel vision’). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its ‘leading principle’, there are many opportunities for combining approaches. We take the point of view that a single ‘right’ approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

On the Use of Response Spectral-Reference Data for the Selection and Ranking of Ground-Motion Models for Seismic-Hazard Analysis in Regions of Moderate Seismicity: The Case of Rock Motion

Abstract: The use of ground-motion-prediction equations to estimate ground shak- ing has become a very popular approach for seismic-hazard assessment, especially in the framework of a logic-tree approach. Owing to the large number of existing published ground-motion models, however, the selection and ranking of appropriate models for a particular target area often pose serious practical problems. Here we show how observed ground-motion records can help to guide this process in a sys- tematic and comprehensible way. A key element in this context is a new, likelihood based, goodness-of-fit measure that has the property not only to quantify the model fit but also to measure in some degree how well the underlying statistical model assumptions are met. By design, this measure naturally scales between 0 and 1, with a value of 0.5 for a situation in which the model perfectly matches the sample dis- tribution both in terms of mean and standard deviation. We have used it in combi- nation with other goodness-of-fit measures to derive a simple classification scheme to quantify how well a candidate ground-motion-prediction equation models a par- ticular set of observed-response spectra. This scheme is demonstrated to perform well in recognizing a number of popular ground-motion models from their rock-site- recording subsets. This indicates its potential for aiding the assignment of logic-tree weights in a consistent and reproducible way. We have applied our scheme to the border region of France, Germany, and Switzerland where the M w 4.8 St. Dieearth- quake of 22 February 2003 in eastern France recently provided a small set of ob- served-response spectra. These records are best modeled by the ground-motion- prediction equation of Berge-Thierry et al. (2003), which is based on the analysis of predominantly European data. The fact that the Swiss model of Bay et al. (2003) is not able to model the observed records in an acceptable way may indicate general problems arising from the use of weak-motion data for strong-motion prediction.

SIMD-scan: ultra fast in-memory table scan using on-chip vector processing units

Abstract: The availability of huge system memory, even on standard servers, generated a lot of interest in main memory database engines. In data warehouse systems, highly compressed column-oriented data structures are quite prominent. In order to scale with the data volume and the system load, many of these systems are highly distributed with a shared-nothing approach. The fundamental principle of all systems is a full table scan over one or multiple compressed columns. Recent research proposed different techniques to speedup table scans like intelligent compression or using an additional hardware such as graphic cards or FPGAs. In this paper, we show that utilizing the embedded Vector Processing Units (VPUs) found in standard superscalar processors can speed up the performance of mainmemory full table scan by factors. This is achieved without changing the hardware architecture and thereby without additional power consumption. Moreover, as on-chip VPUs directly access the system's RAM, no additional costly copy operations are needed for using the new SIMD-scan approach in standard main memory database engines. Therefore, we propose this scan approach to be used as the standard scan operator for compressed column-oriented main memory storage. We then discuss how well our solution scales with the number of processor cores; consequently, to what degree it can be applied in multi-threaded environments. To verify the feasibility of our approach, we implemented the proposed techniques on a modern Intel multi-core processor using Intel® Streaming SIMD Extensions (Intel® SSE). In addition, we integrated the new SIMD-scan approach into SAP® Netweaver® Business Warehouse Accelerator. We conclude with describing the performance benefits of using our approach for processing and scanning compressed data using VPUs in column-oriented main memory database systems.