On the energy leakage of discrete wavelet transform

The energy leakage is an inherent deficiency of discrete wavelet transform (DWT) which is often ignored by researchers and practitioners. In this paper, a systematic investigation into the energy leakage is reported. The DWT is briefly introduced first, and then the energy leakage phenomenon is described using a numerical example as an illustration and its effect on the DWT results is discussed. Focusing on the Daubechies wavelet functions, the band overlap between the quadrature mirror analysis filters was studied and the results reveal that there is an unavoidable tradeoff between the band overlap degree and the time resolution for the DWT. The dependency of the energy leakage to the wavelet function order was studied by using a criterion defined to evaluate the severity of the energy leakage. In addition, a method based on resampling technique was proposed to relieve the effects of the energy leakage. The effectiveness of the proposed method has been validated by numerical simulation study and experimental study

Effects of bearing clearance on the chatter stability of milling process

In the present study, the influences of the bearing clearance, which is a common fault for machines, to the chatter stability of milling process are examined by using numerical simulation method. The results reveal that the presence of bearing clearance could make the milling process easier to enter the status of chatter instability and can shift the chatter frequency. In addition, the spectra analysis to vibration signals obtained under the instable milling processes show that the presence of bearing clearance could introduce more frequency components to the vibration responses but, however, under both the stable and instable milling processes, the generated frequency components will not violate the ideal spectra structures of the vibration responses of the milling process, which are usually characterized by the tooth passing frequency and its associated higher harmonics for the stable milling process and by the complex coupling of the tooth passing frequency and the chatter frequency for the instable milling process. This implies that, even under the case with bearing clearance fault, the stability of the milling process can still be determined by viewing the frequency spectra of the vibration responses. Moreover, the phenomena of the chatter frequency shift and the generation of more components provide potential ways to detect the bearing clearance in machines. (C) 2010 Elsevier Ltd. All rights reserved

Bayesian nonparametric models for name disambiguation and supervised learning

This thesis presents new Bayesian nonparametric models and approaches for their development, for the problems of name disambiguation and supervised learning. Bayesian nonparametric methods form an increasingly popular approach for solving problems that demand a high amount of model flexibility. However, this field is relatively new, and there are many areas that need further investigation. Previous work on Bayesian nonparametrics has neither fully explored the problems of entity disambiguation and supervised learning nor the advantages of nested hierarchical models. Entity disambiguation is a widely encountered problem where different references need to be linked to a real underlying entity. This problem is often unsupervised as there is no previously known information about the entities. Further to this, effective use of Bayesian nonparametrics offer a new approach to tackling supervised problems, which are frequently encountered. The main original contribution of this thesis is a set of new structured Dirichlet process mixture models for name disambiguation and supervised learning that can also have a wide range of applications. These models use techniques from Bayesian statistics, including hierarchical and nested Dirichlet processes, generalised linear models, Markov chain Monte Carlo methods and optimisation techniques such as BFGS. The new models have tangible advantages over existing methods in the field as shown with experiments on real-world datasets including citation databases and classification and regression datasets. I develop the unsupervised author-topic space model for author disambiguation that uses free-text to perform disambiguation unlike traditional author disambiguation approaches. The model incorporates a name variant model that is based on a nonparametric Dirichlet language model. The model handles both novel unseen name variants and can model the unknown authors of the text of the documents. Through this, the model can disambiguate authors with no prior knowledge of the number of true authors in the dataset. In addition, it can do this when the authors have identical names. I use a model for nesting Dirichlet processes named the hybrid NDP-HDP. This model allows Dirichlet processes to be clustered together and adds an additional level of structure to the hierarchical Dirichlet process. I also develop a new hierarchical extension to the hybrid NDP-HDP. I develop this model into the grouped author-topic model for the entity disambiguation task. The grouped author-topic model uses clusters to model the co-occurrence of entities in documents, which can be interpreted as research groups. Since this model does not require entities to be linked to specific words in a document, it overcomes the problems of some existing author-topic models. The model incorporates a new method for modelling name variants, so that domain-specific name variant models can be used. Lastly, I develop extensions to supervised latent Dirichlet allocation, a type of supervised topic model. The keyword-supervised LDA model predicts document responses more accurately by modelling the effect of individual words and their contexts directly. The supervised HDP model has more model flexibility by using Bayesian nonparametrics for supervised learning. These models are evaluated on a number of classification and regression problems, and the results show that they outperform existing supervised topic modelling approaches. The models can also be extended to use similar information to the previous models, incorporating additional information such as entities and document titles to improve prediction

Regular and stochastic behavior of Parkinsonian pathological tremor signals

Regular and stochastic behavior in the time series of Parkinsonian pathological tremor velocity is studied on the basis of the statistical theory of discrete non-Markov stochastic processes and flicker-noise spectroscopy. We have developed a new method of analyzing and diagnosing Parkinson's disease (PD) by taking into consideration discreteness, fluctuations, long- and short-range correlations, regular and stochastic behavior, Markov and non-Markov effects and dynamic alternation of relaxation modes in the initial time signals. The spectrum of the statistical non-Markovity parameter reflects Markovity and non-Markovity in the initial time series of tremor. The relaxation and kinetic parameters used in the method allow us to estimate the relaxation scales of diverse scenarios of the time signals produced by the patient in various dynamic states. The local time behavior of the initial time correlation function and the first point of the non-Markovity parameter give detailed information about the variation of pathological tremor in the local regions of the time series. The obtained results can be used to find the most effective method of reducing or suppressing pathological tremor in each individual case of a PD patient. Generally, the method allows one to assess the efficacy of the medical treatment for a group of PD patients.Comment: 39 pages, 10 figures, 1 table Physica A, in pres

Formation of Small-Scale Condensations in the Molecular Clouds via Thermal Instability

A systematic study of the linear thermal instability of a self-gravitating magnetic molecular cloud is carried out for the case when the unperturbed background is subject to local expansion or contraction. We consider the ambipolar diffusion, or ion-neutral friction on the perturbed states. In this way, we obtain a non-dimensional characteristic equation that reduces to the prior characteristic equation in the non-gravitating stationary background. By parametric manipulation of this characteristic equation, we conclude that there are, not only oblate condensation forming solutions, but also prolate solutions according to local expansion or contraction of the background. We obtain the conditions for existence of the Field lengths that thermal instability in the molecular clouds can occur. If these conditions establish, small-scale condensations in the form of spherical, oblate, or prolate may be produced via thermal instability.Comment: 16 page, accepted by Ap&S

Stability and function of adult vasculature is sustained by Akt/Jagged1 signalling axis in endothelium.

The signalling pathways operational in quiescent, post-development vasculature remain enigmatic. Here we show that unlike neovascularization, endothelial Akt signalling in established vasculature is crucial not for endothelial cell (EC) survival, but for sustained interactions with pericytes and vascular smooth muscle cells (VSMCs) regulating vascular stability and function. Inducible endothelial-specific Akt1 deletion in adult global Akt2KO mice triggers progressive VSMC apoptosis. In hearts, this causes a loss of arteries and arterioles and, despite a high capillary density, diminished vascular patency and severe cardiac dysfunction. Similarly, endothelial Akt deletion induces retinal VSMC loss and basement membrane deterioration resulting in vascular regression and retinal atrophy. Mechanistically, the Akt/mTOR axis controls endothelial Jagged1 expression and, thereby, Notch signalling regulating VSMC maintenance. Jagged1 peptide treatment of Akt1ΔEC;Akt2KO mice and Jagged1 re-expression in Akt-deficient endothelium restores VSMC coverage. Thus, sustained endothelial Akt1/2 signalling is critical in maintaining vascular stability and homeostasis, thereby preserving tissue and organ function

Search for flavor-changing neutral currents and lepton-family-number violation in two-body D0 decays

Results of a search for the three neutral charm decays, D0 -> mu e, D0 -> mu mu, and D0 -> e e, are presented. This study was based on data collected in Experiment 789 at the Fermi National Accelerator Laboratory using 800 GeV/c proton-Au and proton-Be interactions. No evidence is found for any of the decays. Upper limits on the branching ratios, at the 90% confidence level, are obtained.Comment: 28 pages, 18 figures. Submitted to Physical Review

Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state

Aim This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors. Methods We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient. Results A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11). Conclusion Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states

News from the Muon (g-2) Experiment at BNL

The magnetic moment anomaly a_mu = (g_mu - 2) / 2 of the positive muon has been measured at the Brookhaven Alternating Gradient Synchrotron with an uncertainty of 0.7 ppm. The new result, based on data taken in 2000, agrees well with previous measurements. Standard Model evaluations currently differ from the experimental result by 1.6 to 3.0 standard deviations.Comment: Talk presented at RADCOR - Loops and Legs 2002, Kloster Banz, Germany, September 8-13 2002, to be published in Nuclear Physics B (Proc. Suppl.); 5 pages, 3 figure

Search for direct production of charginos and neutralinos in events with three leptons and missing transverse momentum in √s = 7 TeV pp collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with three electrons or muons and missing transverse momentum is presented. The analysis is based on 4.7 fb−1 of proton–proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in three signal regions that are either depleted or enriched in Z-boson decays. Upper limits at 95% confidence level are set in R-parity conserving phenomenological minimal supersymmetric models and in simplified models, significantly extending previous results