A duration dependence test for rational speculative bubbles in the Chinese stock market

The Chinese stock market suffered from great fluctuation in the past two decades, especially in periods 2006-2008 and 2014-2015. The long increasing stock prices and followed sharp decline arise the suspicion that bubbles exist in the Chinese stock market. Therefore, this dissertation aims to investigate whether these abnormal price movements can be attributed to bubbles. This dissertation employs the duration dependence test for detecting rational bubbles, which is suitable for the properties of the Chinese stock market. By investigating real returns of the Chinese stock market index, negative duration dependence in positive runs was found in the full sample period 1997-2019 and the sub-period 1997-2010, but no duration dependence was found in the sub-period 2010-2019. The results suggest the existence of rational bubbles in the Chinese market in the sub-period that covers the first suspicious bubble existence period (2006-2008), while no rational bubble in the sub-period that covers the second suspicious bubble existence period (2014-2015)

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods

Four Dimensional Image Registration For Intravital Microscopy

Increasingly the behavior of living systems is being evaluated using intravital microscopy since it provides subcellular resolution of biological processes in an intact living organism. Intravital microscopy images are frequently confounded by motion resulting from animal respiration and heartbeat. In this paper we describe an image registration method capable of correcting motion artifacts in three dimensional fluorescence microscopy images collected over time. Our method uses 3D B-Spline non-rigid registration using a coarse-to-fine strategy to register stacks of images collected at different time intervals and 4D rigid registration to register 3D volumes over time. The results show that our proposed method has the ability of correcting global motion artifacts of sample tissues in four dimensional space, thereby revealing the motility of individual cells in the tissue

Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D