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Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for(More)
Perinatal exposure to bisphenol A (BPA) has been shown to cause aberrant mammary gland morphogenesis and mammary neoplastic transformation. Yet, the underlying mechanism is poorly understood. We tested the hypothesis that mammary glands exposed to BPA during a susceptible window may lead to its susceptibility to tumorigenesis through a stem cell-mediated(More)
BACKGROUND It has been shown that the application of a lung-protective mechanical ventilation strategy can improve the prognosis of patients with acute lung injury (ALI) or acute respiratory distress syndrome (ARDS). However, the optimal mechanical ventilation strategy for intensive care unit (ICU) patients without ALI or ARDS is uncertain. Therefore, we(More)
Researches indicate that the dysregulation of microRNA (miRNA) is involved in tumorigenesis. Among such dysregulated miRNAs in cancer, miR-145 is reported to be downregulated in multiple cancers. In this study, we demonstrated the downregulation of miR-145 in triple-negative breast cancer (TNBC) tissues and TNBC cell lines by quantitative(More)
Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between(More)
The effectiveness of nearest neighbor search heavily relies on the definition of distance function. Unfortunately, the meaningfulness of the frequently used distance, such as Euclidean distance, fractional distance and so on, will degrade with the increasing dimensionality. This problem, which is called distance concentration or instability, makes NN method(More)
Subunit models provide a powerful yet parsimonious description of neural responses to complex stimuli. They are defined by a cascade of two linear-nonlinear (LN) stages, with the first stage defined by a linear convolution with one or more filters and common point nonlinearity, and the second by pooling weights and an output nonlinearity. Recent interest in(More)
In many problem settings, parameter vectors are not merely sparse, but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependency as “region sparsity”. Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), model parameters as independent a priori, and(More)
In many problem settings, parameter vectors are not merely sparse, but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependency as “region sparsity”. Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), model parameters as independent a priori, and(More)
Dimensionality reduction techniques are widely used in time series data mining. Dimensionality reduction can not only speed up the computation but also lead to improved performance. Most available techniques implement the reduction process without supervised information. This operation can be used to de-noise the insignificance detail, or blur the(More)