Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.

Violations of Bell's inequality for Gaussian states with homodyne detection and nonlinear interactions

We show that homodyne measurements can be used to demonstrate violations of Bell's inequality with Gaussian states, when the local rotations used for these types of tests are implemented using nonlinear unitary operations. We reveal that the local structure of the Gaussian state under scrutiny is crucial in the performance of the test. The effects of finite detection efficiency are thoroughly studied and shown to only mildly affect the revelation of Bell violations. We speculate that our approach may be extended to other applications such as entanglement distillation where local operations are necessary elements besides quantum entanglement.

A study of the biological effects of modulated 6 MV radiation fields

The delivery of spatially modulated radiation fields has been shown to impact on in vitro cell survival responses. To study the effect of modulated fields on cell survival, dose response curves were determined for human DU-145 prostate, T98G glioma tumour cells and normal primary AGO-1552 fibroblast cells exposed to modulated and non-modulated field configurations delivered using a 6 MV Linac with multi-leaf collimator. When exposed to uniform fields delivered as a non-modulated or modulated configuration, no significant differences in survival were observed with the exception of DU-145 cells at a dose of 8 Gy (p = 0.024). Survival responses were determined for exposure to non-uniform-modulated beams in DU-145 and T98G and showed no deviation from the survival response observed following uniform non-modulated exposures. The results of these experiments indicate no major deviation in response to modulated fields compared to uniform exposures.

Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.