Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation

We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by non-stationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in sleep EEG data is used to illustrate the usefulness of the proposed methodology. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional non-stationary time series without variance lower bounds are established, which could be of independent interest

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

On the dynamics of the South China Sea Warm Current

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 113 (2008): C08003, doi:10.1029/2007JC004427.The South China Sea Warm Current (SCSWC) flows northeastward over the shelf and continental slope in the northern South China Sea (SCS). This current persists in its northeastward direction in all seasons despite the fact that the annually averaged wind stress is decisively southwestward against it. Two major mechanisms have been proposed in previous studies, one attributing it directly to the wind stress forcing within the SCS and the other to the Kuroshio intrusion through the Luzon Strait. In this study we use a simple model to demonstrate that neither of them is the leading forcing mechanism. Instead, the SCSWC is a source- and sink-driven flow induced by the Taiwan Strait Current (TSC), a year-round northward flow through the Taiwan Strait. The two previously suggested mechanisms are important but secondary. The model simulations show that the local wind stress alone would force a current in the opposite direction to the SCSWC. Blocking the Kuroshio intrusion through the Luzon Strait, on the other hand, only weakens the SCSWC. The SCSWC vanishes when the Taiwan Strait is closed in the model.This study has been supported by the U.S. National Science Foundation (OCE-0351055), China’s International Science and Technology Cooperation Program (2006DFB21250), and China’s National Basic Research Priorities Program (2005CB422302)

Molecular detection of Torque teno virus in different breeds of swine

<p>Abstract</p> <p>Background</p> <p>Torque teno virus (TTV), of the <it>Anelloviridae </it>family, <it>Iotatorquevirus </it>genus, is a non-enveloped, single-stranded, and negative sense DNA (ssDNA) virus infecting human and many domestic animals including swines. Very little information is known about the investigations of TTV prevalence in different swine breeds so far.</p> <p>Methods</p> <p>In this study, 208 serum samples collected from seven swine breeds (<it>Rongchang pig</it>, <it>Chenghua pig</it>, <it>Zibet pig</it>, <it>Wild boar</it>, <it>Duroc</it>, <it>Landrace</it>, <it>Large Yorkshire</it>) from two independent farms were detected to determine the prevalence of two swine TTV genogroups, TTV1 and TTV 2, by nested polymerase chain reaction methods, and to analyse prevalence difference among these breeds.</p> <p>Results</p> <p>The results showed that the prevalence of TTV in the seven breeds was 92%-100%. No significant difference (p > 0.05) in TTV infection was observed between different breeds. Interestingly, significantly higher prevalence for TTV1 in <it>Rongchang </it>boars (90%) and for TTV2 in <it>Rongchang </it>sows (95%) were detected, while co-infection rate (43.8%) was lower than other breeds. Sequence analysis showed that the homology of TTV1 and TTV2 were over 90.9% and 86.4% in these breeds, respectively.</p> <p>Conclusions</p> <p>The results indicated that TTV was widely distributed in the seven swine breeds. The prevalence of both TTV genogroups associated with swine breeds and genders. This study also respented the first description of swine TTV prevalence in different swine breeds. It was vitally necessary to further study swine TTV pathogenicity.</p