A Biologically-Parameterized Feather Model

Feathers, unlike other cutaneous appendages such as hair, fur, or scales have a definite structure. Variation in feather structure creates a wide range of resulting appearances. Collectively, feather structure determines the appearance of the feather coat, which can largely affect the resulting look of a feathered object (bird). In this paper we define the structure of individual feathers using a parameterization based on biological structure and substructures of actual feathers. We show that our parameterization can generate a large variety of feathers at multiple levels of detail and provide an initial step to semi‐automatically generating a wide range of feather coats. his is achieved by specifying an intuitive interpolation between different structures and ages of feathers.     

Effect of exponentially graded base doping on the performance ofGaAs/AlGaAs heterojunction bipolar transistors

The authors have fabricated n-p-n GaAs/AlGaAs heterojunction bipolar transistors (HBTs) with base doping graded exponentially from 5×10¹⁹ cm^-3 at the emitter edge to 5×10¹⁸ cm^-3 at the collector edge. The built-in field due to the exponentially graded doping profile significantly reduces base transit time, despite bandgap narrowing associated with high base doping. Compared to devices with the same base thickness and uniform base doping of 1×10¹⁹ cm^-3 , the cutoff frequency is increased from 22 to 31 GHz and maximum frequency of oscillation is increased from 40 to 58 GHz. Exponentially graded base doping also results ill consistently higher common-emitter current gain than uniform base doping, even though the Gummel number is twice as high and the base resistance is reduced by 40%     

High-reliability GaAs-AlGaAs HBTs by MBE with Be base doping andInGaAs emitter contacts

The authors have developed a modified MBE growth process to produce high-gain n-p-n GaAs-AlGaAs heterojunction bipolar transistors (HBTs) with a mean time to failure (MTTF) of 1.5×10⁸ h at 125°C. Beryllium incorporation and diffusion are controlled through a combination of reduced substrate temperature and increased As/Ga flux ratio during MBE growth, resulting in extremely stable HBT profiles. The authors also demonstrate graded InGaAs surface layers with nonalloyed refractory metal contacts that significantly improve ohmic reliability compared to alloyed AuGe contacts. The ability to produce robust HBTs by MBE is critically important to this technology     

Cost effective second generation AC-modules: Development and testing aspects

In the framework of the European research project PV2GO, a new AC-module inverter was developed, taking into account all relevant aspects from a European market's point of view (standards, market, application, and research and development goals). The project goal was to achieve the overall system costs of 3 Euro per Wp for a modular plug-and-play photovoltaic system. For the photovoltaic-module, a standard 130-Wp Eurosolare module was chosen. The research and development (R&D) goal was to develop an advanced DC-control system consisting of a state-of-the-art programmable digital device and an Application Specific Integrated Circuit (ASIC) for the AC-control of the inverter. According to the topology concept, thermal and magnetic designs were optimized with regard to production technology and packaging for large-scale production. The new AC-modules were tested in a number of field-test sites in various parts of Europe and their reliability was assessed through Highly Accelerated Stress Tests. Efficiency and power quality have been tested in the laboratory. Further in the PV2GO project an optimization study of the manufacturing process of the new generation of AC-modules for high volume output was done. Another task was the pre-certification procedure to assure compliance with the European guidelines and standards.     

Statistical relational learning of trust

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.     

A short and versatile finite element multiscale code for homogenization problems

We describe a multiscale finite element (FE) solver for elliptic or parabolic problems with highly oscillating coefficients. Based on recent developments of the so-called heterogeneous multiscale method (HMM), the algorithm relies on coupled macro- and microsolvers. The framework of the HMM allows to design a code whose structure follows the classical finite elements implementation at the macro level. To account for the fine scales of the problem, elementwise numerical integration is replaced by micro FE methods on sampling domains. We discuss a short and flexible FE implementation of the multiscale algorithm, which can accommodate simplicial or quadrilateral FE and various coupling conditions for the constrained micro simulations. Extensive numerical examples including three dimensional and time dependent problems are presented illustrating the efficiency and the versatility of the computational strategy.     

A spreadsheet approach to facilitate visualization of uncertainty in information

Information uncertainty is inherent in many problems and is often subtle and complicated to understand. Although visualization is a powerful means for exploring and understanding information, information uncertainty visualization is ad hoc and not widespread. This paper identifies two main barriers to the uptake of information uncertainty visualization: firstly, the difficulty of modeling and propagating the uncertainty information; and secondly, the difficulty of mapping uncertainty to visual elements. To overcome these barriers, we extend the spreadsheet paradigm to encapsulate uncertainty details within cells. This creates an inherent awareness of the uncertainty associated with each variable. The spreadsheet can hide the uncertainty details, enabling the user to think simply in terms of variables. Furthermore, the system can aid with automated propagation of uncertainty information, since it is intrinsically aware of the uncertainty. The system also enables mapping the encapsulated uncertainty to visual elements via the formula language and a visualization sheet. Support for such low-level visual mapping provides flexibility to explore new techniques for information uncertainty visualization.     

Design and development of a fuzzy agent-based model to measure interest rate expectations

The financial system, which has been investigated by various researchers, is a rather complicated environment. Most research has only been concerned with quantitative factors (technical indexes), though qualitative factors (e.g., political situation, social conditions, international events, government policies, among others) play a critical role in the financial system environment, determining the regulatory policies within an economy. This paper presents a fuzzy knowledge-based model to measure the qualitative aspects related to one of the most important financial instruments used to regulate an economy, the base interest rate. The development and assessment of the proposed model was based on the Brazilian economy. Evaluation of the results obtained indicates that our approach gives good results when compared with real data and statistical-based forecasting tools. The main advantage of our approach is its capability to forecast long term interest rate expectations when combined with a powerful econometric model.