Real-Time CNN Training and Compression for Neural-Enhanced Adaptive Live Streaming.

We propose a real-time convolutional neural network (CNN) training and compression method for delivering high-quality live video even in a poor network environment. The server delivers a low-resolution video segment along with the corresponding CNN for super resolution (SR), after which the client applies the CNN to the segment in order to recover high-resolution video frames. To generate a trained CNN corresponding to a video segment in real-time, our method rapidly increases the training accuracy by promoting the overfitting property of the CNN while also using curriculum-based training. In addition, assuming that the pretrained CNN is already downloaded on the client side, we transfer only residual values between the updated and pretrained CNN parameters. These values can be quantized with low bits in real time while minimizing the amount of loss, as the distribution range is significantly narrower than that of the updated CNN. Quantitatively, our neural-enhanced adaptive live streaming pipeline (NEALS) achieves higher SR accuracy and a lower CNN compression loss rate within a constrained training time compared to the state-of-the-art CNN training and compression method. NEALS achieves 15 to 48% higher quality of the user experience compared to state-of-the-art neural-enhanced live streaming systems.

Predictions about reward outcomes in rhesus monkeys.

Human infants and nonhuman animals respond to surprising events by looking longer at unexpected than expected situations. These looking responses provide core cognitive evidence that nonverbal minds make predictions about possible outcomes and detect when these predictions fail to match reality. We propose that this phenomenon has crucial parallels with the processes of reward prediction error, indexing the difference between expected and actual reward outcomes. Most work on reward prediction errors to date involves neurobiological techniques that cannot be implemented in many relevant populations, so we developed a novel behavioral task to assess monkeys' predictions about reward outcomes using looking time responses. In Study 1, we tested how semi-free-ranging monkeys (n = 210) responded to positive error (more rewards than expected), negative error (less rewards than expected), and a number control. We found that monkeys looked longer at a given reward when it was unexpectedly large or small, compared to when the same quantity was expected. In Study 2, we compared responses in the positive error condition in monkeys ranging from infancy to old age (n = 363), to assess lifespan changes in sensitivity to reward predictions. We found that adolescent monkeys showed heightened responses to unexpected rewards, similar to patterns seen in humans, but showed no changes during aging. These results suggest that monkeys' looking responses can be used to track their predictions about rewards, and that monkeys share some developmental signatures of reward sensitivity with humans, providing a new approach to access cognitive processes underlying reward-based decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).