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In the treatment of epilepsy with intracranial electroencephalogram(iEEG), the recognition accuracy is low, and it is
difficult to find the correlation between channels because of the large amount of channel numbers and time series data. In
order to solve these problems, we propose a novel EEG feature prepresentation method for seizure detection based on the
Log Mel-Filterbank energy feature. We propose to adapt the Mel-Filterbank energy to EEG features with logrithm transform

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A novel single-image rain removal method is proposed based on multi-scale cascading image generation (MSCG). In particular, the proposed method consists of an encoder extracting multi-scale features from images and a decoder generating de-rained images with a cascading mechanism. The encoder ensembles the convolution neural networks using the kernels with different sizes, and integrates their outputs across different scales.

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Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation.

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Image restoration and image enhancement are critical image processing tasks since good image quality is mandatory for many image applications. We are particularly interested in the restoration of ill-exposed images. These effects are caused by sensor limitation or optical arrangement. They prevent the details of the scene from being adequately represented in the captured image. We proposed a deep neural network model due to the number of uncontrolled variables that impact the acquisition.

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