Modelling water dynamics with DNDC and DAISY in a soil of the North China Plain: A comparative study

The performance of the DNDC and Daisy model to simulate the water dynamics in a floodplain soil of the North China Plain was tested and compared. While the DNDC model uses a simple cascade approach, the Daisy model applies the physically based Richard's equation for simulating water movement in soil. For model testing a three years record of the soil water content from the Dong Bei Wang experimental station near Beijing was used. There, the effect of nitrogen fertilization, irrigation and straw removal on soil water and nitrogen dynamics was investigated in a three factorial field experiment applying a split-split-plot design with 4 replications. The dataset of one treatment was used for model testing and calibration. Two other independent datasets from further treatments were employed for validating the models. For both models, the simulation results were not satisfying using default parameters. After parameter optimisation and the use of site-specific van Genuchten parameters, however, the Daisy model performed well. But, for the DNDC model, parameter optimisation failed to improve the simulation result. Owing to the fact that many biological processes such as plant growth, nitrification or denitrification depend strongly on the soil water content, our findings bring us to the conclusion that the site-specific suitability of the DNDC model for simulating the soil water dynamics should be tested before further simulation of other processes.     

Anomalies in ontologies with rules

For the development of practical semantic applications, ontologies are commonly used with rule extensions. Prominent examples of semantic applications not only are Semantic Wikis, Semantic Desktops, but also advanced Web Services and agents. The application of rules increases the expressiveness of the underlying knowledge in many ways. Likewise, the integration not only creates new challenges for the design process of such ontologies, but also existing evaluation methods have to cope with the extension of ontologies by rules.Since the verification of Owl ontologies with rule extensions is not tractable in general, we propose to verify ontologies at the symbolic level by using a declarative approach: With the new language Datalog^?, known anomalies can be easily specified and tested in a compact manner. We introduce supplements to existing verification techniques to support the design of ontologies with rule enhancements, and we focus on the detection of anomalies that especially occur due to the combined use of rules and ontological definitions.     

Distributing weights under hierarchical clustering: A way in reducing performance breakdown

This paper proposes a clustering asset allocation scheme which provides better risk-adjusted portfolio performance than those obtained from traditional asset allocation approaches such as the equal weight strategy and the Markowitz minimum variance allocation. The clustering criterion used, which involves maximization of the in-sample Sharpe ratio (SR), is different from traditional clustering criteria reported in the literature. Two evolutionary methods, namely Differential Evolution and Genetic Algorithm, are employed to search for such an optimal clustering structure given a cluster number. To explore the clustering impact on the SR, the in-sample and the out-of-sample SR distributions of the portfolios are studied using bootstrapped data as well as simulated paths from the single index market model. It was found that the SR distributions of the portfolios under the clustering asset allocation structure have higher mean values and skewness but approximately the same standard deviation and kurtosis than those in the non-clustered case. Genetic Algorithm is suggested as a more efficient approach than Differential Evolution for the purpose of solving the clustering problem.     

Shape from Shading Using Probability Functions and Belief Propagation

Shape-from-shading (SFS) aims to reconstruct the three-dimensional shape of an object from a single shaded image. This article proposes an improved framework based on belief propagation for computing SFS. The implementation of the well-known brightness, integrability and smoothness constraints inside this framework is shown. We implement the constraints as probability density functions. For example, the brightness constraint is a two-dimensional probability density function that relates all possible surface gradients at a pixel to their probability given the pixel intensity. A straightforward extension of the framework to photometric stereo is presented, where multiple images of the same scene taken under different lighting conditions are available. The results are promising, especially since the solution is obtained by iteratively applying simple operations on a regular grid of points. The presented framework therefore can be implemented in parallel and is a reasonably likely biological scheme.     

Cognitive agents — a procedural perspective relying on the predictability of Object-Action-Complexes (OACs)

Embodied cognition suggests that complex cognitive traits can only arise when agents have a body situated in the world. The aspects of embodiment and situatedness are being discussed here from the perspective of linear systems theory. This perspective treats bodies as dynamic, temporally variable entities, which can be extended (or curtailed) at their boundaries. We show how acting agents can, for example, actively extend their body for some time by incorporating predictably behaving parts of the world and how this affects the transfer functions. We suggest that primates have mastered this to a large degree increasingly splitting their world into predictable and unpredictable entities. We argue that temporary body extension may have been instrumental in paving the way for the development of higher cognitive complexity as it is reliably widening the cause-effect horizon about the actions of the agent. A first robot experiment is sketched to support these ideas.We continue discussing the concept of Object-Action Complexes (OACs) introduced by the European PACO-PLUS consortium to emphasize the notion that, for a cognitive agent, objects and actions are inseparably intertwined. In another robot experiment we devise a semi-supervised procedure using the OAC-concept to demonstrate how an agent can acquire knowledge about its world. Here the notion of predicting changes fundamentally underlies the implemented procedure and we try to show how this concept can be used to improve the robot’s inner model and behaviour. Hence, in this article we have tried to show how predictability can be used to augment the agent’s body and to acquire knowledge about the external world, possibly leading to more advanced cognitive traits.     

Digit Pipelined Arithmetic for 3-D Massively Parallel Optoelectronic Circuits

A concept for a future integer arithmetic unit suitable for a realization with 3-D optoelectronic very large scale integrated (VLSI) circuits is presented. Due to the use of optical interconnections running vertically to the circuit's surface no pin limitation is given. This allows massively parallelism and a higher throughput performance than in all-electronic solutions. To exploit the potential of optical interconnections in VLSI systems efficiently well-adapted low-level algorithms and architectures have to be developed. This is demonstrated for a pipelined arithmetic unit using a redundant number representation. A transistor layout for the optoelectronic circuits is given as well as a specification for the necessary optical interconnection scheme linking the circuits with free-space optics. It is shown that the throughput can be increased by a factor of 10 to 50 compared to current all-electronic processors by considering state-of-the-art optical and optoelectronic technology. Furthermore we present results we gained by investigations on a first realized optoelectronic VLSI test chip.     

First-order and second-order statistical analysis of 3D and 2D image structure

In the first part of this article, we analyze the relation between local image structures (i.e., homogeneous, edge-like, corner-like or texture-like structures) and the underlying local 3D structure (represented in terms of continuous surfaces and different kinds of 3D discontinuities) using range data with real-world color images. We find that homogeneous image structures correspond to continuous surfaces, and discontinuities are mainly formed by edge-like or corner-like structures, which we discuss regarding potential computer vision applications and existing assumptions about the 3D world. In the second part, we utilize the measurements developed in the first part to investigate how the depth at homogeneous image structures is related to the depth of neighbor edges. For this, we first extract the local 3D structure of regularly sampled points, and then, analyze the coplanarity relation between these local 3D structures. We show that the likelihood to find a certain depth at a homogeneous image patch depends on the distance between the image patch and a neighbor edge. We find that this dependence is higher when there is a second neighbor edge which is coplanar with the first neighbor edge. These results allow deriving statistically based prediction models for depth interpolation on homogeneous image structures.     

Kernel least-squares models using updates of the pseudoinverse

Sparse nonlinear classification and regression models in reproducing kernel Hilbert spaces (RKHSs) are considered. The use of Mercer kernels and the square loss function gives rise to an overdetermined linear least-squares problem in the corresponding RKHS. When we apply a greedy forward selection scheme, the least-squares problem may be solved by an order-recursive update of the pseudoinverse in each iteration step. The computational time is linear with respect to the number of the selected training samples.