Passivity Analysis of Dynamic Neural Networks with Different Time-scales

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.     

Improving Neural Networks for Mechanism Kinematic Chain Isomorphism Identification

Detection of isomorphism among kinematic chains is essential in mechanical design, but difficult and computationally expensive. It has been shown that both traditional methods and previously presented neural networks still have a lot to be desired in aspects such as simplifying procedure of identification and adapting automatic computation. Therefore, a new algorithm based on a competitive Hopfield network is developed for automatic computation in the kinematic chain isomorphism problem. The neural approach provides directly interpretable solutions and does not demand tuning of parameters. We have tested the algorithm by solving problems reported in the recent mechanical literature. Simulation results show the effectiveness of the network that rapidly identifies isomorphic kinematic chains.     

Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

Fast and accurate pricing of discretely monitored barrier options by numerical path integration

Barrier options are financial derivative contracts that are activated or deactivated according to the crossing of specified barriers by an underlying asset price. Exact models for pricing barrier options assume continuous monitoring of the underlying dynamics, usually a stock price. Barrier options in traded markets, however, nearly always assume less frequent observation, e.g. daily or weekly. These situations require approximate solutions to the pricing problem. We present a new approach to pricing such discretely monitored barrier options that may be applied in many realistic situations. In particular, we study daily monitored up-and-out call options of the European type with a single underlying stock. The approach is based on numerical approximation of the transition probability density associated with the stochastic differential equation describing the stock price dynamics, and provides accurate results in less than one second whenever a contract expires in a year or less. The flexibility of the method permits more complex underlying dynamics than the Black and Scholes paradigm, and its relative simplicity renders it quite easy to implement.     

Brokers and bricoleurs: entrepreneurship in Wales' online music scene

 The power of some new entrants to the music industry derives from their position as brokers in computer-mediated environments. Brokers act instrumentally to exploit their position within a network which, in turn, depends on their ability to build and sustain links (and, in computer-mediated environments, hyperlinks). Bricolage in computer-mediated entrepreneurship refers to the intuitive manipulation of resources in order to achieve (perhaps tacit) goals. Without careful stewardship of the new intellectual wealth thus created, bricolage may profit neither the individual nor the wider community. Accepted 14 March 2002     

Formation of Self-Assembled Sidewall Nanowires on Shallow Patterned GaAs (100)

The formation of "sidewall nanowires" on shallow patterned mesa strips with a modulation depth of only 35 nm on GaAs (100) was demonstrated using molecular beam epitaxy. While self-assembled GaAs sidewall nanowire formation is observed near mesa strips running along [011], relatively thinner AlAs/GaAs layers are formed on identical mesa strips running along [01-1]. Cross-sectional atomic force microscopy (XAFM) on (011) and (01-1) and AFM on (100) are used to understand the formation of the different morphology of the nanostructures, depending on the direction of the mesas. The data indicates that anisotropic surface diffusion of adatoms, resulting from the characteristic (2times4) GaAs (100) surface reconstruction, is responsible for the sidewall nanowire formation and for the different morphology observed along different directions     

ESW激波吹灰器在余热锅炉的应用

介绍了ESW激波吹灰器的工作原理及其在茂名分公司Ⅲ套催化裂化装置余热锅炉的应用情况。应用结果表明，使用ESW激波吹灰器吹灰效果明显，吹灰后余热锅炉排烟温度可下降29℃，过热蒸汽产量增加7t/h，锅炉自产饱和蒸汽产量增加2t/h，有效提高了余热锅炉的传热效率和过热能力，节能效果显著。对应用中出现的一些问题提出了改进建议。     

Quality of manual data collection in Java software: an empirical investigation

Data collection, both automatic and manual, lies at the heart of all empirical studies. The quality of data collected from software informs decisions on maintenance, testing and wider issues such as the need for system re-engineering. While of the two types stated, automatic data collection is preferable, there are numerous occasions when manual data collection is unavoidable. Yet, very little evidence exists to assess the error-proneness of the latter. Herein, we investigate the extent to which manual data collection for Java software compared with its automatic counterpart for the same data. We investigate three hypotheses relating to the difference between automated and manual data collection. Five Java systems were used to support our investigation. Results showed that, as expected, manual data collection was error-prone, but nowhere near the extent we had initially envisaged. Key indicators of mistakes in manual data collection were found to be poor developer coding style, poor adherence to sound OO coding principles, and the existence of relatively large classes in some systems. Some interesting results were found relating to the collection of public class features and the types of error made during manual data collection. The study thus offers an insight into some of the typical problems associated with collecting data manually; more significantly, it highlights the problems that poorly written systems have on the quality of visually extracted data.     

Two topographic maps for data visualisation

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [Bishop et al. (1997) Neurl Comput 10(1): 215–234]. But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts [Hinton (2000) Technical report GCNU TR 2000-004, Gatsby Computational Neuroscience Unit, University College, London]. We show visualisation results on some real data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data. We compare these mappings on real and artificial data sets. Responsible editor: Soumen Chakrabarti.