Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by training an INR model with fewer weights than the number of image pixels to map the coordinates of the image to corresponding pixel values. While traditional training approaches for INRs are based on enforcing pixel-wise image consistency, we propose to further improve image quality by using a new structural regularizer. We present structural regularization for INR compression (SINCO) as a novel INR method for image compression. SINCO imposes structural consistency of the compressed images to the groundtruth by using a segmentation network to penalize the discrepancy of segmentation masks predicted from compressed images. We validate SINCO on brain MRI images by showing that it can achieve better performance than some recent INR methods.

Mencius is a protocol for general state machine replication that tolerates crash failures. It has high performance in wide-area networks. However, the commit latency of Mencius is limited by the slowest replica. This paper presents Fast Mencius, a crash fault-tolerant state machine replication protocol, which enhances Mencius with Active Revoke and Multi-instance Propose. Active Revoke allows the non-slow replicas to proceed without being delayed by the slowest replica, while Multi-instance Propose enables the slow replicas to have their proposals chosen by the replicated state machine. Our evaluation shows that in presence of slow replicas, Fast Mencius's commit latency is significantly lower than that of Mencius, and it also achieves high throughput.