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In this paper, we propose to learn a discriminative and share-able feature transformation filter bank to transform local image patches (represented as raw pixel values) into features for scene image classification. The learned filters are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative and(More)
Neurogenesis in the dentate gyrus (DG) declines severely by middle age, potentially because of age-related changes in the DG microenvironment. We hypothesize that providing fresh glial restricted progenitors (GRPs) or neural stem cells (NSCs) to the aging hippocampus via grafting enriches the DG microenvironment and thereby stimulates the production of new(More)
We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. They are able to produce satisfactory labeling results for visually dissimilar pixels. However, CNNs struggle in visually similar pixels due to using their limited context. As shown in Figure 1, the sand(More)
Maintenance of neurogenesis in adult hippocampus is important for functions such as mood and memory. As exposure to unpredictable chronic stress (UCS) results in decreased hippocampal neurogenesis, enhanced depressive- and anxiety-like behaviors, and memory dysfunction, it is believed that declined hippocampal neurogenesis mainly underlies the behavioral(More)
Acute seizure (AS) activity in old age has an increased predisposition for evolving into temporal lobe epilepsy (TLE). Furthermore, spontaneous seizures and cognitive dysfunction after AS activity are often intense in the aged population than in young adults. This could be due to an increased vulnerability of inhibitory interneurons in the aged hippocampus(More)
In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks(More)
In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for(More)
Greatly waned neurogenesis, diminished microvasculature, astrocyte hypertrophy and activated microglia are among the most conspicuous structural changes in the aged hippocampus. Because these alterations can contribute to age-related memory and mood impairments, strategies efficacious for mitigating these changes may preserve cognitive and mood function in(More)