Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like telling a leaf apart from a rock. But they have struggled to build ...

Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods.

Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their feature learning ...

Having multiple convolutional layers stacked sequentially increases the receptive field (size of the convolutional layer input that the filter is affected by). A max pooling operation (pooling nearby ...

This important work presents a self-supervised method for the segmentation of 3D cells in fluorescent microscopy images, conveniently packaged as a Napari plugin and tested on an annotated dataset.

Uenc and Udec start and end with 2 3 × 3×3 3D convolutional layers, in-between are 5 blocks, each block being defined by two 3 × 3 × 3 3D convolutional layers, followed by a ReLU and group ...

CBC Radio's As It Happens' listening experience is like taking a trip around the world. From the complex headlines of the day to the weird and wacky, As It Happens brings you the story behind the ...

We also introduce hardware implementations for key computational steps in photonic SCNNs, including the nonlinear activation function, the convolutional layer, the fully connected layer, and the ...