A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Measuring conflict and agreement between two prioritized knowledge bases in possibilistic logic.

In this paper we investigate the relationship between two prioritized knowledge bases by measuring both the conflict and the agreement between them.First of all, a quantity of conflict and two quantities of agreement are defined. The former is shown to be a generalization of the well-known Dalal distance which is the hamming distance between two interpretations. The latter are, respectively, a quantity of strong agreement which measures the amount ofinformation on which two belief bases “totally” agree, and a quantity of weak agreement which measures the amount of information that is believed by onesource but is unknown to the other. All three quantity measures are based on the weighted prime implicant, which represents beliefs in a prioritized belief base. We then define a degree of conflict and two degrees of agreement based on our quantity of conflict and quantities of agreement. We also consider the impact of these measures on belief merging and information source ordering.

A framework for managing uncertain inputs: An axiomization of rewarding

The success postulate in belief revision ensures that new evidence (input) is always trusted. However, admitting uncertain input has been questioned by many researchers. Darwiche and Pearl argued that strengths of evidence should be introduced to determine the outcome of belief change, and provided a preliminary definition towards this thought. In this paper, we start with Darwiche and Pearl’s idea aiming to develop a framework that can capture the influence of the strengths of inputs with some rational assumptions. To achieve this, we first define epistemic states to represent beliefs attached with strength, and then present a set of postulates to describe the change process on epistemic states that is determined by the strengths of input and establish representation theorems to characterize these postulates. As a result, we obtain a unique rewarding operator which is proved to be a merging operator that is in line with many other works. We also investigate existing postulates on belief merging and compare them with our postulates. In addition, we show that from an epistemic state, a corresponding ordinal conditional function by Spohn can be derived and the result of combining two epistemic states is thus reduced to the result of combining two corresponding ordinal conditional functions proposed by Laverny and Lang. Furthermore, when reduced to the belief revision situation, we prove that our results induce all the Darwiche and Pearl’s postulates as well as the Recalcitrance postulate and the Independence postulate.

Watermarking for Multimedia Security using Complex Wavelets

This paper investigates the application of complex wavelet transforms to the field of digital data hiding. Complex wavelets offer improved directional selectivity and shift invariance over their discretely sampled counterparts allowing for better adaptation of watermark distortions to the host media. Two methods of deriving visual models for the watermarking system are adapted to the complex wavelet transforms and their performances are compared. To produce improved capacity a spread transform embedding algorithm is devised, this combines the robustness of spread spectrum methods with the high capacity of quantization based methods. Using established information theoretic methods, limits of watermark capacity are derived that demonstrate the superiority of complex wavelets over discretely sampled wavelets. Finally results for the algorithm against commonly used attacks demonstrate its robustness and the improved performance offered by complex wavelet transforms.

Effect of temporal separation on synchronization in rhythmic performance

A variety of short time delays inserted between pairs of subjects were found to affect their ability to synchronize a musical task. The subjects performed a clapping rhythm together from separate sound-isolated rooms via headphones and without visual contact. One-way time delays between pairs were manipulated electronically in the range of 3 to 78 ms. We are interested in quantifying the envelope of time delay within which two individuals produce synchronous per- formances. The results indicate that there are distinct regimes of mutually coupled behavior, and that `natural time delay'o¨delay within the narrow range associated with travel times across spatial arrangements of groups and ensembleso¨supports the most stable performance. Conditions outside of this envelope, with time delays both below and above it, create characteristic interaction dynamics in the mutually coupled actions of the duo. Trials at extremely short delays (corresponding to unnaturally close proximity) had a tendency to accelerate from anticipation. Synchronization lagged at longer delays (larger than usual physical distances) and produced an increasingly severe deceleration and then deterioration of performed rhythms. The study has implications for music collaboration over the Internet and suggests that stable rhythmic performance can be achieved by `wired ensembles' across distances of thousands of kilometers.

Allelic depletion of grem1 attenuates diabetic kidney disease.

OBJECTIVE: Gremlin (grem1) is an antagonist of the bone morphogenetic protein family that plays a key role in limb bud development and kidney formation. There is a growing appreciation that altered grem1 expression may regulate the homeostatic constraints on damage responses in diseases such as diabetic nephropathy. RESEARCH DESIGN AND METHODS: Here we explored whether knockout mice heterozygous for grem1 gene deletion (grem1(+/-)) exhibit protection from the progression of diabetic kidney disease in a streptozotocin-induced model of type 1 diabetes. RESULTS: A marked elevation in grem1 expression was detected in the kidneys and particularly in kidney tubules of diabetic wild-type mice compared with those of littermate controls. In contrast, diabetic grem1(+/-) mice displayed a significant attenuation in grem1 expression at 6 months of diabetes compared with that in age- and sex-matched wild-type controls. Whereas the onset and induction of diabetes were similar between grem1(+/-) and wild-type mice, several indicators of diabetes-associated kidney damage such as increased glomerular basement membrane thickening and microalbuminuria were attenuated in grem1(+/-) mice compared with those in wild-type controls. Markers of renal damage such as fibronectin and connective tissue growth factor were elevated in diabetic wild-type but not in grem1(+/-) kidneys. Levels of pSmad1/5/8 decreased in wild-type but not in grem1(+/-) diabetic kidneys, suggesting that bone morphogenetic protein signaling may be maintained in the absence of grem1. CONCLUSIONS: These data identify grem1 as a potential modifier of renal injury in the context of diabetic kidney disease.

Mitochondrial translation initiation factor 3 polymorphism and Parkinson's disease

Mitochondrial dysfunction has been proposed to play a role in the pathogenesis of Parkinson s disease (PD) Supportive of this hypothesis several genetic variants that regulate mitochondrial function and homeostasis have been described to alter PD susceptibility A recent report demonstrated association of a single nucleotide polymorphism in the mitochondrial translation initiation factor 3 (MTIF3) gene with PD risk The protein encoded by this nuclear gene is essential for initiation complex formation on the mitochondrial 55S ribosome and regulates translation of proteins within the mitochondria Changes in the function or expression of the MTIF3 protein may result in altered mitochondrial function ATP production or formation of reactive oxygen species thereby affecting susceptibility to PD We examined the association of rs7669 with sporadic PD in three Caucasian case control series (n = 2434) A significant association was observed in the largest series (Norwegian n = 1650) when comparing CC vs CT/TT genotypes with the Irish and US series having a similar but non-significant trend The combined series also revealed an association with risk of PD (P = 0 01) supporting the possible involvement of this gene in PD etiology Published by Elsevier Ireland Ltd

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.

Application of Nonlinear PCA for Fault Detection in Polymer Extrusion Processes

This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.

Bimodal biometric verification based on face and lips

In this paper, we present a novel approach to person verification by fusing face and lip features. Specifically, the face is modeled by the discriminative common vector and the discrete wavelet transform. Our lip features are simple geometric features based on a lip contour, which can be interpreted as multiple spatial widths and heights from a center of mass. In order to combine these features, we consider two simple fusion strategies: data fusion before training and score fusion after training, working with two different face databases. Fusing them together boosts the performance to achieve an equal error rate as low as 0.4% and 0.28%, respectively, confirming that our approach of fusing lips and face is effective and promising.