MALA-within-Gibbs samplers for high-dimensional distributions with sparse conditional structure

Markov chain Monte Carlo (MCMC) samplers are numerical methods for drawing samples from a given target probability distribution. We discuss one particular MCMC sampler, the MALA-within-Gibbs sampler, from the theoretical and practical perspectives. We first show that the acceptance ratio and step size of this sampler are independent of the overall problem dimension when (i) the target distribution has sparse conditional structure, and (ii) this structure is reflected in the partial updating strategy of MALA-within-Gibbs. If, in addition, the target density is blockwise log-concave, then the sampler's convergence rate is independent of dimension. From a practical perspective, we expect that MALA-within-Gibbs is useful for solving high-dimensional Bayesian inference problems where the posterior exhibits sparse conditional structure at least approximately. In this context, a partitioning of the state that correctly reflects the sparse conditional structure must be found, and we illustrate this process in two numerical examples. We also discuss trade-offs between the block size used for partial updating and computational requirements that may increase with the number of blocks

A Faddeev-Niemi Solution that Does Not Satisfy Gauss' Law

Faddeev and Niemi have proposed a reformulation of SU(2) Yang-Mills theory in terms of a U(1) gauge theory with 8 off-shell degrees of freedom. We present a solution to Faddeev and Niemi's formulation which does not solve the SU(2) Yang-Mills Gauss constraints. This demonstrates that the proposed reformulation is inequivalent to Yang-Mills, but instead describes Yang-Mills coupled to a particular choice of external charge.Comment: 10 pages, no figure

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Novel Retinal Imaging Technologies

Newly-developed imaging techniques show extensive promise and potential to improve early detection, accurate diagnosis, and management of retinal diseases. Optical coherernce tomography angiography (OCTA), photoacoustic imaging (PAI), and molecular imaging (MI) are all new and promising imaging modalities. As these imaging instruments have advanced, they have enabled visualization of the retina at an unprecedented resolution. Published studies have established the efficacy of these modalities in the assessment of common retinal diseases, such as age-related macular degeneration, diabetic retinopathy, and retinal vascular occlusions. Each of these systems is built upon different principles and all have different limitations. In addition, the three imaging modalities have complementary features and thus can be integrated in to a multimodal imaging system, which will be more powerful in future

Two-dimensional mapping of triaxial strain fields in a multiferroic BiFeO3 thin film using scanning x-ray microdiffraction

The dramatically enhanced polarizations and saturation magnetizations observed in the epitaxially constrained BiFeO3 (BFO) thin films with their pronounced grain-orientation dependence have attracted much attention and are attributed largely to the constrained in-plane strain. Thus, it is highly desirable to directly obtain information on the two-dimensional (2D) distribution of the in-plane strain and its correlation with the grain orientation of each corresponding microregion. Here the authors report a 2D quantitative mapping of the grain orientation and the local triaxial strain field in a 250 nm thick multiferroic BFO film using a synchrotron x-ray microdiffraction technique. This direct scanning measurement demonstrates that the deviatoric component of the in-plane strain tensor is between 5x10(-3) and 6x10(-3) and that the local triaxial strain is fairly well correlated with the grain orientation in that particular region. (c) 2007 American Institute of Physics.X1145Nsciescopu

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first "patch-wise" network acts as an auto-encoder that extracts the most salient features of image patches while the second "image-wise" network performs classification of the whole image. The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields 95 % accuracy on the validation set compared to previously reported 77 % accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018Comment: 10 pages, 5 figures, ICIAR 2018 conferenc

Spin 3 cubic vertices in a frame-like formalism

Till now most of the results on interaction vertices for massless higher spin fields were obtained in a metric-like formalism using completely symmetric (spin-)tensors. In this, the Lagrangians turn out to be very complicated and the main reason is that the higher the spin one want to consider the more derivatives one has to introduce. In this paper we show that such investigations can be greatly simplified if one works in a frame-like formalism. As an illustration we consider massless spin 3 particle and reconstruct a number of vertices describing its interactions with lower spin 2, 1 and 0 ones. In all cases considered we give explicit expressions for the Lagrangians and gauge transformations and check that the algebra of gauge transformations is indeed closed.Comment: 17 pades, no figure

Adomian's decomposition method for electromagnetically induced transparency

[[abstract]]We developed the Adomian's decomposition method to work for the electromagnetically induced transparency (EIT) problem. The method is general and capable to solve the coupled nonlinear partial differential equations for a light pulse passing through a three-level -type coherent medium. This EIT system is described by the coupled Maxwell-Schrödinger equations and optical Bloch equations. In the weak probe field case, the results agree with perturbation solutions and experimental data. In the stronger probe field case while perturbation may fail, our results reproduce experimental data well. With the techniques of spatial and time partitions, we extend the decomposition method that will be versatile for the investigation of the light pulse propagating through a coherent atomic medium.[[fileno]]2010136010034[[department]]物理