Cost function adaptation: a stochastic gradient algorithm for data echo cancellation

A family of stochastic gradient algorithms and their behaviour in the data echo cancellation work platform are presented. The cost function adaptation algorithms use an error exponent update strategy based on an absolute error mapping, which is updated at every iteration. The quadratic and nonquadratic cost functions are special cases of the new family. Several possible realisations are introduced using these approaches. The noisy error problem is discussed and the digital recursive filter estimator is proposed. The simulation outcomes confirm the effectiveness of the proposed family of algorithms.

Adaptive data echo cancellation using cost function adaptation

For a digital echo canceller it is desirable to reduce the adaptation time, during which the transmission of useful data is not possible. LMS is a non-optimal algorithm in this case as the signals involved are statistically non-Gaussian. Walach and Widrow (IEEE Trans. Inform. Theory 30 (2) (March 1984) 275-283) investigated the use of a power of 4, while other research established algorithms with arbitrary integer (Pei and Tseng, IEEE J. Selected Areas Commun. 12(9)(December 1994) 1540-1547) or non-quadratic power (Shah and Cowan, IEE.Proc.-Vis. Image Signal Process. 142 (3) (June 1995) 187-191). This paper suggests that continuous and automatic, adaptation of the error exponent gives a more satisfactory result. The family of cost function adaptation (CFA) stochastic gradient algorithm proposed allows an increase in convergence rate and, an improvement of residual error. As special case the staircase CFA algorithm is first presented, then the smooth CFA is developed. Details of implementations are also discussed. Results of simulation are provided to show the properties of the proposed family of algorithms. (C) 2000 Elsevier Science B.V. All rights reserved.

PAR-2 Regulates Dental Pulp Inflammation Associated with Caries

Protease-activated receptors (PARs) are G-protein-coupled receptors that are activated enzymatically by proteolysis of an N-terminal domain. The cleavage and activation of PARs by serine proteases represent a novel mechanism by which such enzymes could influence the host inflammatory response. The aim of this study was to determine whether PAR-2 expression and activation were increased in dental caries. Using immunohistochemistry, we showed PAR-2 to be localized to pulp cells subjacent to caries lesions, but minimally expressed by healthy pulp tissue. Trypsin and the PAR-2 agonist (PAR2-AP) activated PAR-2 in an in vitro functional assay. Endogenous molecules present in pulp cell lysates from carious teeth specifically activated PAR-2, but those from healthy teeth failed to do so. The activation of PAR-2 in vitro was shown to increase the expression of the pro-inflammatory mediator cyclo-oxygenase-2 (COX-2), providing a mechanism whereby PAR-2 could modulate pulpal inflammation.

Climate Change Adaptation for Conservation in Madagascar

Madagascar's imperilled biota are now experiencing the effects of a new threat—climate change (Raxworthy et al. 2008). With more than 90% endemism among plants, mammals, reptiles and amphibians, the stakes are high. The pristine landscapes that allowed this exceptional biodiversity to survive past climate changes are largely gone. Deforestation has claimed approximately 90% of the island's natural forest (Ingram & Dawson 2005; Harper et al. 2007) and what remains is highly fragmented, providing a poor template for large-scale species range shifts. The impacts of current and future climate change may therefore be much different than past impacts, with profound implications for biodiversity.
We review evidence of past response to climate change, models of future change and projected biological response, developing insights to formulate adaptation actions for reducing extinction in Madagascar's biota. We then explore the cost of implementing actions and examine new income opportunities developing through efforts to mitigate climate change.