This paper presents a model-based approach that combines the data-flow and object-oriented computing paradigms to model embedded systems. The rationale behind the approach is that both views are important for modelling purposes in embedded systems environments, and thus a combined and integrated

behavior model for typical navigation tasks. The behavior model together with a model of how the collision avoidance module should "perceive" the environment is used to detect behavior "flaws," diagnose them, and revise the plans to improve their performance. The learning method

bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-based linear transforms are considered. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii

applications include fixed-income pricing models, with a role for intensity-based models of default, as well as a wide range of option-pricing applications. An illustrative example examines the implications of stochastic volatility and jumps for option valuation. This example highlights the impact on option

intensities. We then examine the behavior of the P M and M M using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data

of input text. The transformation does not itself compress the data, but reorders it to make it easy to compress with simple algorithms such as move-to-front coding. Our algorithm achieves speed comparable to algorithms based on the techniques of Lempel and Ziv, but obtains compression close to the best

Abstract — In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion

for long lags. It is possible to characterize lrfld to be ‘long memory ’ and this property is strongest when d is around 1. This result appears to argue against ARCH type specifications based upon squared returns. But our Monte-Carlo study shows that both ARCH type models based on squared returns and those

transformer networks (GTN’s), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility

eing analysed. On the negative side, the semantic correctness of the analysis is seldom established and therefore there is often no formal justification for the program transformations for which the information is used. The semantics based approach [1; 5] is often based on domain theory