Statistical image modelling using interscale phase relationships of complex wavelet coefficients

A novel method for modelling the statistics of 2D photographic images useful in image restoration is defined. The new method is based on the Dual Tree Complex Wavelet Transform (DT-CWT) but a phase rotation is applied to the coefficients to create complex coefficients whose phase is shift-invariant at multiscale edge and ridge features. This is in addition to the magnitude shift invariance achieved by the DT-CWT. The increased correlation between coefficients adjacent in space and scale provides an improved mechanism for signal estimation. © 2006 IEEE.

Derivative Kernels for Noise Robust ASR

Recently there has been interest in combined gen- erative/discriminative classifiers. In these classifiers features for the discriminative models are derived from generative kernels. One advantage of using generative kernels is that systematic approaches exist how to introduce complex dependencies beyond conditional independence assumptions. Furthermore, by using generative kernels model-based compensation/adaptation tech- niques can be applied to make discriminative models robust to noise/speaker conditions. This paper extends previous work with combined generative/discriminative classifiers in several directions. First, it introduces derivative kernels based on context- dependent generative models. Second, it describes how derivative kernels can be incorporated in continuous discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high- dimensional features of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task.

Biologically-inspired object recognition system with features from complex wavelets

In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in [2] (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.

Speech development: Toddlers don't mind getting it wrong

A recent study has found that toddlers do not compensate for an artificial alteration in a vowel they hear themselves producing. This raises questions about how young children learn speech sounds. © 2012 Elsevier Ltd.

Effects of testosterone undecanoate as a male contraceptive candidate on rat immunological features

Testosterone undecanoate (TU) is under phase III clinical trial as a hormonal male contraceptive in China. Sex hormones can modulate the immune system. Female hormonal contraceptives may affect SIV/HIV-1 transmission. To evaluate the safety of TU and to understand whether long-term use of TU for a male contraceptive affects users' immunological features, adult male rats were treated for a 32-week TU-treated phase at the dose of 20 mg TU/kg body weight and a 24-week recovery phase. The reproductive and immunological parameters of 4-6 rats in each subgroup were examined at the stated time point. The mean sperm count and viability in the treated rats were significantly suppressed (p < 0.01). In the TU-treated group: the mean blood leukocyte and lymphocyte counts; the proliferation indexes of T cells from peripheral blood mononuclear cells (PBMC) and spleen; and, of B cells from spleen, as well as the mean counts of blood T, NK, and B cells decreased in comparison with those of control group. These decreases were not significant (p > 0.01). Similarly, the mean serum IgM, IgG, and IgA levels and complement activity in TU-treated rats were lower than those in control group (p > 0.01), and the changes in the antibody levels of the examined genital secretions were not significant (p > 0.01). The changes in the thickness of urethra epithelium, and in secretory component (SC) expression in genitals were not observed in the treated group. These results demonstrated that long-term supraphysiological TU injection did not obviously affect the examined rat immunological parameters.

Statistical analysis of sequence features of introns with positive transcriptional regulation in yeast genes

A great deal of experimental studies have shown that many introns of eukaryotic genes function as regulators of transcription. However, comprehensive studies of this problem have not yet been conducted. After checking the transcription frequencies of some Saccharomyces cerevisiae (yeast), genes and their introns, a remarkable phenomenon was discovered that generally the introns of the genes with higher transcription frequencies are longer, and the introns of the genes with lower transcription frequencies are shorter. This suggests that the longer introns of genes with higher transcription frequencies may contain some characteristic sequence structures, which could enhance the transcription of genes. Therefore, two sets of introns of yeast genes were chosen for further study. The transcription frequencies of the first set of genes are higher (>30), and those of the second set of genes are lower (less than or equal to10). Some oligonucleotides are detected by statistically comparative analyses of the occurrence frequencies of oligonucleotides (mainly tetranucleotides and pentanucleotides), whose occurrence frequencies in the first set of introns; are significantly higher than those in the second set of introns, and are also significantly higher than those in the exons flanking the introns of the first set. Some of these extracted oligonucleotides are the same as the regulatory elements of transcription revealed by experimental analyses. Besides, the distributions of these extracted oligonucleotides in the two sets of introns and the exons show that the sequence structures of the first set of introns are favorable for transcription of genes.

Upheaval Buckling Assessment Based on Pipeline Features

Upheaval buckling (UHB) is a common design issue for high temperature buried pipelines. This paper highlights some of thekey issues affecting out-of-straightness (OOS) assessment of pipelines. The following factors are discussed; uplift resistancesoil models, uplift resistance in cohesive soils, uplift mobilisation, ratcheting, uplift resistance at low H/D ratios and thecorrect methodology for load factor selection. A framework for determining ratcheting mobilisation is proposed. Furtherresearch is required to verify and validate this proposed framework. UHB assessment of three different diameter pipelineswere carried out using finite element SAGE PROFILE package incorporating pipeline mobilisation and the results arecompared with semi-analytical formulation proposed by Palmer et al. 1990. The paper also presents a summary of as-laidpipeline features based on projects over the past 10 years.

A variational perspective on noise-robust speech recognition

Model compensation methods for noise-robust speech recognition have shown good performance. Predictive linear transformations can approximate these methods to balance computational complexity and compensation accuracy. This paper examines both of these approaches from a variational perspective. Using a matched-pair approximation at the component level yields a number of standard forms of model compensation and predictive linear transformations. However, a tighter bound can be obtained by using variational approximations at the state level. Both model-based and predictive linear transform schemes can be implemented in this framework. Preliminary results show that the tighter bound obtained from the state-level variational approach can yield improved performance over standard schemes. © 2011 IEEE.

Improving reverberant VTS for hands-free robust speech recognition

Model-based approaches to handling additive background noise and channel distortion, such as Vector Taylor Series (VTS), have been intensively studied and extended in a number of ways. In previous work, VTS has been extended to handle both reverberant and background noise, yielding the Reverberant VTS (RVTS) scheme. In this work, rather than assuming the observation vector is generated by the reverberation of a sequence of background noise corrupted speech vectors, as in RVTS, the observation vector is modelled as a superposition of the background noise and the reverberation of clean speech. This yields a new compensation scheme RVTS Joint (RVTSJ), which allows an easy formulation for joint estimation of both additive and reverberation noise parameters. These two compensation schemes were evaluated and compared on a simulated reverberant noise corrupted AURORA4 task. Both yielded large gains over VTS baseline system, with RVTSJ outperforming the previous RVTS scheme. © 2011 IEEE.

Derivative kernels for noise robust ASR

Recently there has been interest in combining generative and discriminative classifiers. In these classifiers features for the discriminative models are derived from the generative kernels. One advantage of using generative kernels is that systematic approaches exist to introduce complex dependencies into the feature-space. Furthermore, as the features are based on generative models standard model-based compensation and adaptation techniques can be applied to make discriminative models robust to noise and speaker conditions. This paper extends previous work in this framework in several directions. First, it introduces derivative kernels based on context-dependent generative models. Second, it describes how derivative kernels can be incorporated in structured discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high-dimensional feature-spaces of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task. © 2011 IEEE.