Median network analysis of defectively sequenced entire mitochondrial genomes from early and contemporary disease studies

Sequence analysis of the mitochondrial genome has become a routine method in the study of mitochondrial diseases. Quite often, the sequencing efforts in the search of pathogenic or disease-associated mutations are affected by technical and interpretive pr

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. © 2011 Elsevier B.V.

Robust filtering and smoothing with gaussian processes

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail. © 2011 IEEE.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.

Analytical model of spin filtering effect and its experimental verification in a forward-biased iron-metal-Al2O3-n-type GaAs tunneling structure under optical spin orientation

An analytical model for the spin filtering transport in a ferromagnetic-metal - Al2O3 - n-type semiconductor tunneling structure has been developed, and demonstrated that the ratio of the helicity-modulated photo-response to the chopped one is proportional to the sum of the relative asymmetry in conductance of two opposite spin-polarized tunneling channels and the MCD effect of the ferromagnetic metal film. The performed measurement in an iron-metal/Al2O3/n-type GaAs tunneling structure under the optical spin orientation has verified that all the aspects of the experimental results are very well in accordance with our model in the regime of the spin filtering. After the MCD effect of the iron film is calibrated by an independent measurement, the physical quantity of Delta G(t)/G(t) (Delta G(t) = G(t)(up arrow) - G(t)(down arrow) is the difference of the conductance between two opposite spin tunneling channels, G(t) =( G(t)(up arrow) + G(t)(down arrow))/2 the averaged tunneling conductance), which concerns us most, can be determined quantitatively with a high sensitivity in the framework of our analytical model. Copyright (c) EPLA, 2008.

Finite-difference time-domain analysis of deformed square cavity filters with a traveling-wave-like filtering response by mode coupling

An add-drop filter based on a perfect square resonator can realize a maximum of only 25% power dropping because the confined modes are standing-wave modes. By means of mode coupling between two modes with inverse symmetry properties, a traveling-wave-like filtering response is obtained in a two-dimensional single square cavity filter with cut or circular corners by finite-difference time-domain simulation. The optimized deformation parameters for an add-drop filter can be accurately predicted as the overlapping point of the two coupling modes in an isolated deformed square cavity. More than 80% power dropping can be obtained in a deformed square cavity filter with a side length of 3.01 mu m. The free spectral region is decided by the mode spacing between modes, with the sum of the mode indices differing by 1. (c) 2007 Optical Society of America.

In-plane stray field induced spin-filtering in a two-dimensional electron gas under the modulation of surface ferromagnetic dual-gate

Based upon a hybrid ferromagnet/semiconductor structure consisting of two-dimensional electron gas and a pair of surface ferromagnetic stripes on top, we have theoretically investigated the effect of in-plane stray field omitted frequently in previous studies on the spin-dependent ballistic transport properties in hybrid structure. It is demonstrated here that, in combination with an external-controllable electrostatic modulation, the concerned structure shows a similar function as a lateral spin-polarized resonant tunneling device, where the strong spin-filtering effect occurs and nearly single-mode polarization is anticipated for the proper modulation. More importantly, the spin polarity of transmission electron can be easily transferred from one extreme to the other by switching the magnetization of stripes, showing the promising application as an efficient spin aligner in the developing semiconductor spintronics.

Measurement of the isospin-filtering dd → 4He K+K- reaction at Q = 39 MeV

The total cross-section for the dd → 4HeK+K− reaction has been measured at a beam momentum of 3.7GeV/c, corresponding to an excess energy of 39MeV, which is the maximum possible atthe Cooler Synchrotron COSY-Jülich. A deuterium cluster-jet target and the ANKE forward magnetic spectrometer, placed inside the storage ring, have been employed in this investigation. We find a total cross-section of σtot < 14 pb, which brings into question the viability of investigating the dd → 4He a0(980)reaction as a means of studying isospin violation.

incremental learning of triadic plsa for collaborative filtering

Beijing University of Technology (BJUT); Beijing Municipal Lab of Brain Informatics; Chinese Society of Radiology; National Natural Science Foundation of China (NSFC); State Administration of Foreign Experts Affairs