Abstract: A method for generating, for a video stream of a first spatial resolution and a first temporal resolution, a first reduced quality steam of a second spatial resolution and a second reduced-quality stream of a second temporal resolution. A first subset of STPs is sampled from the first reduced-quality stream and a second subset of STPs is sampled from the second reduced-quality stream. Using a machine learning model (MLM) the STPs are processed to identify a quality score for each quality-representative STPs that are representative of a quality of the video stream. One or more quality-improving actions for the video stream are identified using the quality scores of the quality-representative STPs.

Abstract: Systems and methods provide improved neural network compression by training, based on training data and an optimization problem, a deep neural network to produce a trained deep neural network by iteratively updating a weight matrix of the deep neural network according to, at each iteration, minimizing a rank value of the weight matrix until a memory capacity metric is satisfied, minimizing a loss function based on the training data and the weight matrix and updating the weight matrix, and terminating the iterations upon the loss function being minimized within the memory capacity metric. Tensor decomposition is used to compress the trained deep neural network based on the rank value and the weight matrix to obtain a trained tensor decomposition format deep neural network. The trained tensor decomposition format deep neural network is retrained with the training data to obtain a fine-tuned trained tensor decomposition format deep neural network.

Abstract: In one embodiment, a method includes accessing a batch B of a plurality of images, wherein each image in the batch is part of a training set of images used to train a vision transformer comprising a plurality of attention heads. The method further includes determining, for each attention head A, a similarity between (1) the output of the attention head evaluated using each image in the batch and the (2) output of each attention head evaluated using each image in the batch. The method further includes determining, based on the determined similarities, an importance score for each attention head; and pruning, based on the importance scores, one or more attention heads from the vision transformer.

Abstract: The present disclosure provides an epoxy resin wave-absorbing composite material and a preparation method thereof. The method includes: heating an epoxy resin to 50° C.˜70° C., and adding carbon black, to obtain a mixture of the epoxy resin and the carbon black; heating the mixture of the epoxy resin and the carbon black to 100° C.˜120° C., adding a curing agent, and stirring and dissolving them to obtain a mixture of the epoxy resin, the carbon black, and the curing agent; and adding surface-treated hollow glass microbeads into the mixture of the epoxy resin, the carbon black, and the curing agent, and curing them to obtain the epoxy resin wave-absorbing composite material.

Abstract: The present disclosure provides an epoxy resin wave-absorbing composite material and a preparation method thereof. The method includes: heating an epoxy resin to 50° C.˜70° C., and adding carbon black, to obtain a mixture of the epoxy resin and the carbon black; heating the mixture of the epoxy resin and the carbon black to 100° C.˜120° C., adding a curing agent, and stirring and dissolving them to obtain a mixture of the epoxy resin, the carbon black, and the curing agent; and adding surface-treated hollow glass microbeads into the mixture of the epoxy resin, the carbon black, and the curing agent, and curing them to obtain the epoxy resin wave-absorbing composite material.