Enormous success has been achieved with deep neural networks consisting of standard linear-convolutions followed by simple non-linear mapping functions. In this paper, we add easily-computed non-linear local and global statistics to deep architectures, augmenting the information available at each layer. This additional information is then used in an identical manner to current processing. The summary statistics, which can be as simple as calculating within-channel variance, introduces little run-time computational overhead and can be instantiated with few extra parameters. All standard training procedures can be used without modification for training these augmented networks. We show, through extensive testing with ResNet on ImageNet, performance improvements across a wide range of network sizes. Additionally, we provide a detailed study of where within the deep networks these statistics are most effective.

A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page-load process. Recent work, based on an adaptive triangulation of the target image, has performed well at extreme compression rates: 200 bytes or less. Gains have been shown, in terms of PSNR and SSIM, over both JPEG and WebP standards. However, qualitative assessments and preservation of semantic content can be less favorable. We present a novel method to significantly improve the reconstruction quality of the original image that requires no changes to the encoded information. Our neural-based decoding triples the amount of semantic-level content preservation while also improving both SSIM and PSNR scores. In addition, by keeping the same encoding stream, our solution is completely inter-operable with the original, and remains suitable for small-device deployment.

A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page load process to improve responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore an active research direction. In this work, we concentrate on extreme compression rates, where the size of the image is typically 200 bytes or less. First, we propose a novel approach for image compression that, unlike commonly used methods, does not rely on block-based statistics. We use an approach based on an adaptive triangulation of the target image, devoting more triangles to high entropy regions of the image. Second, we present a novel algorithm for encoding the triangles. The results show favorable statistics, in terms of PSNR and SSIM, over both the JPEG and the WebP standards.

This paper exposes the concurrency control problem in groupware when it is implemented as a distributed system. Traditional concurrency control methods cannot be applied directly to groupware because system interactions include people as well as computers. Methods, such as locking, serialization, and their degree of optimism, are shown to have quite different impacts on the interface and how operations are displayed and perceived by group members. The paper considers both human and technical considerations that designers should ponder before choosing a particular concurrency control method. It also reviews our work-in-progress designing and implementing a library of concurrency schemes in GROUPKIT, a groupware toolkit.