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BackgroundIndividuals who exhibit large‐magnitude blood pressure (BP) reactions to acute psychological stressors are at risk for hypertension and premature death by cardiovascular disease. This study tested whether a multivariate pattern of stressor‐evoked brain activity could reliably predict individual differences in BP reactivity, providing novel evidence for a candidate neurophysiological source of stress‐related cardiovascular risk.Methods and ResultsCommunity‐dwelling adults (N=310; 30–51 years; 153 women) underwent functional magnetic resonance imaging with concurrent BP monitoring while completing a standardized battery of stressor tasks. Across individuals, the battery evoked an increase systolic and diastolic BP relative to a nonstressor baseline period (M ∆systolic BP/∆diastolic BP=4.3/1.9 mm Hg [95% confidence interval=3.7–5.0/1.4–2.3 mm Hg]). Using cross‐validation and machine learning approaches, including dimensionality reduction and linear shrinkage models, a multivariate pattern of stressor‐evoked functional magnetic resonance imaging activity was identified in a training subsample (N=206). This multivariate pattern reliably predicted both systolic BP (r=0.32; P<0.005) and diastolic BP (r=0.25; P<0.01) reactivity in an independent subsample used for testing and replication (N=104). Brain areas encompassed by the pattern that were strongly predictive included those implicated in psychological stressor processing and cardiovascular responding through autonomic pathways, including the medial prefrontal cortex, anterior cingulate cortex, and insula.ConclusionsA novel multivariate pattern of stressor‐evoked brain activity may comprise a phenotype that partly accounts for individual differences in BP reactivity, a stress‐related cardiovascular risk factor.

Deep learning classification models typically train poorly on classes with small numbers of examples. Motivated by the human ability to solve this task, models have been developed that transfer knowledge from classes with many examples to learn classes with few examples. Critically, the majority of these models transfer knowledge within model feature space. In this work, we demonstrate that transferring knowledge within classifier space is more effective and efficient. Specifically, by linearly combining strong nearest neighbor classifiers along with a weak classifier, we are able to compose a stronger classifier. Uniquely, our model can be implemented on top of any existing classification model that includes a classifier layer. We showcase the success of our approach in the task of long-tailed recognition, whereby the classes with few examples, otherwise known as the "tail" classes, suffer the most in performance and are the most challenging classes to learn. Using classifier-level knowledge transfer, we are able to drastically improve -by a margin as high as 12.6% -the state-of-the-art performance on the "tail" categories.Preprint. Under review.

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Designing Universal embedded hardware architecture for discrete wavelet transform is a challenging problem because of diversity among wavelet kernel filters. In this work, DWT is used for compression application. Wavelet transform divides the information of an image into approximation and details sub signals. The approximation sub signals shows the general trend of pixel values and other three detail sub signals show the vertical, horizontal and diagonal details or changes in the images. If these details are very small (threshold) then they can be set to zero without significantly changing the image. The greater the number of zeros the greater the compression ratio. If the energy retained (amount of retained by an image after compression and decompression) is 100% then the compression is lossless as the image can be reconstructed exactly. The design follows the JPEG2000 standard and can be used for both lossy and lossless compression. The High-performance and memory-efficient pipeline architecture which performs the one-level (2-D) DWT in the 5/3 and 9/7 filters.Index terms -Sub-band coding, discrete wavelet transform (DWT).I.