Migration of DCE applications into CORBA and SOAP environments

Legacy applications based on the Distributed Computing Environment (DCE) are subject to several significant limitations. As the development and support of DCE wanes, object‐oriented development becomes more desirable, and transmission over HTTP is established as the preferred protocol over the Internet, DCE application managers and developers are pressed to find extensions and alternatives to DCE. This paper briefly discusses several alternative targets for migration of DCE systems, then proceeds to detail, compare, and contrast two preferred candidates: CORBA and SOAP. Although we have found that developing a general migration solution for legacy DCE applications to CORBA or SOAP to be a non‐trivial, long‐term project, developing specific solutions that are based on a general architecture is feasible. Given a short list of reasonable premises, many DCE applications may be ported to technologies such as CORBA and SOAP. Copyright © 2002 John Wiley & Sons, Ltd.     

Generating robust and flexible job shop schedules using genetic algorithms

The problem of finding robust or flexible solutions for scheduling problems is of utmost importance for real-world applications as they operate in dynamic environments. In such environments, it is often necessary to reschedule an existing plan due to failures (e.g., machine breakdowns, sickness of employees, deliveries getting delayed, etc.). Thus, a robust or flexible solution may be more valuable than an optimal solution that does not allow easy modifications. This paper considers the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases.     

Rendering Caustics on Non-Lambertian Surfaces

This paper presents a new technique for rendering caustics on non-Lambertian surfaces. The method is based on an extension of the photon map which removes previous restrictions limiting the usage to Lambertian surfaces. We add information about the incoming direction to the photons and this allows us to combine the photon map with arbitrary reflectance functions. By using a cone-filter we improve the quality of the radiance estimate in particular at discontinuities. Furthermore we introduce balancing of the photon map which not only reduces the memory requirements but also significantly reduces the rendering time. We have used the method to render caustics on surfaces with reflectance functions varying from Lambertian to glossy specular.     

Learning a decision maker's utility function from (possibly) inconsistent behavior

When modeling a decision problem using the influence diagram framework, the quantitative part rests on two principal components: probabilities for representing the decision maker's uncertainty about the domain and utilities for representing preferences. Over the last decade, several methods have been developed for learning the probabilities from a database. However, methods for learning the utilities have only received limited attention in the computer science community.

A promising approach for learning a decision maker's utility function is to take outset in the decision maker's observed behavioral patterns, and then find a utility function which (together with a domain model) can explain this behavior. That is, it is assumed that decision maker's preferences are reflected in the behavior. Standard learning algorithms also assume that the decision maker is behavioral consistent, i.e., given a model of the decision problem, there exists a utility function which can account for all the observed behavior. Unfortunately, this assumption is rarely valid in real-world decision problems, and in these situations existing learning methods may only identify a trivial utility function. In this paper we relax this consistency assumption, and propose two algorithms for learning a decision maker's utility function from possibly inconsistent behavior; inconsistent behavior is interpreted as random deviations from an underlying (true) utility function. The main difference between the two algorithms is that the first facilitates a form of batch learning whereas the second focuses on adaptation and is particularly well-suited for scenarios where the DM's preferences change over time. Empirical results demonstrate the tractability of the algorithms, and they also show that the algorithms converge toward the true utility function for even very small sets of observations.     

Computer-based determination of size and shape in living cells

Measurement of cell volume in living epithelial cells has become an important technique in studies of membrane transport processes that function in cell volume regulation. Planimetry of video images of optical sections enables the measurement of the cross sectional area of each section. Cell volume is calculated from the measured area of each section and the known focus displacements. In the past the measurement of cross section area has been done by manual positioning of a cursor superimposed on the video image. Each experiment generates approximately 200 images in which two or more cells may be analysed. We have developed a computer-based method that uses one image as a template, and allows automated area determination of successive images by template matching and digital image processing. This new method is comparable to the older method in speed and accuracy, but requires much less effort from the experimenter.     

Raman spectroscopy and X-ray diffraction studies of stress effects in PbTiO3 thin films

A systematic study of domain structure and residual stress evolution with film thickness and of phase transition in c/a epitaxial PbTiO(3)/LaAlO(3) films using X-ray diffraction and Raman spectroscopy is reported. Both techniques revealed that the films are under tensile residual stress in the film plane and that a-domains are more stressed than c-domains. The two components of the large A(1)(TO) Ramanmodes are associated with a- and c-domains and their intensity ratio correlates to the volume fraction of a-domains. The evolution of the Raman signature with temperature revealed that the spectrum of a-domains disappears around 480 degrees C, whereas c-domains present an anomaly in their spectrum at 500 degrees C but maintain a well-defined Raman signature up to 600 degrees C.     

Ultrahigh Energy Absorption Multifunctional Spinodal Nanoarchitectures

Nanolattices are promoted as next‐generation multifunctional high‐performance materials, but their mechanical response is limited to extreme strength yet brittleness, or extreme deformability but low strength and stiffness. Ideal impact protection systems require high‐stress plateaus over long deformation ranges to maximize energy absorption. Here, glassy carbon nanospinodals, i.e., nanoarchitectures with spinodal shell topology, combining ultrahigh energy absorption and exceptional strength and stiffness at low weight are presented. Noncatastrophic deformation up to 80% strain, and energy absorption up to one order of magnitude higher than for other nano‐, micro‐, macro‐architectures and solids, and state‐of‐the‐art impact protection structures are shown. At the same time, the strength and stiffness are on par with the most advanced yet brittle nanolattices, demonstrating true multifunctionality. Finite element simulations show that optimized shell thickness‐to‐curvature‐radius ratios suppress catastrophic failure by impeding propagation of dangerously oriented cracks. In contrast to most micro‐ and nano‐architected materials, spinodal architectures may be easily manufacturable on an industrial scale, and may become the next generation of superior cellular materials for structural applications.