We introduce a simple and efficient lossless image compression algorithm. We
store a low resolution version of an image as raw pixels, followed by several
iterations of lossless super-resolution. For lossless super-resolution, we
predict the probability of a high-resolution image, conditioned on the
low-resolution input, and use entropy coding to compress this super-resolution
operator. Super-Resolution based Compression (SReC) is able to achieve
state-of-the-art compression rates with practical runtimes on large datasets.
Code is available online at this https URL

介绍了一种简单高效的无损图像压缩算法，存储低分辨率版本，再经过多次无损超分辨率加工，采用熵编码来压缩超分辨率操作符，能够在大型数据集上以实用的运行时间实现最先进的压缩率。

通过超分辨率进行无损图像压缩

Lossless Image Compression through Super-Resolution

The increasing popularity of real-world recommender systems produces data
continuously and rapidly, and it becomes more realistic to study recommender
systems under streaming scenarios. Data streams present distinct properties
such as temporally ordered, continuous and high-velocity, which poses
tremendous challenges to traditional recommender systems. In this paper, we
investigate the problem of recommendation with stream inputs. In particular, we
provide a principled framework termed sRec, which provides explicit
continuous-time random process models of the creation of users and topics, and
of the evolution of their interests. A variational Bayesian approach called
recursive meanfield approximation is proposed, which permits computationally
efficient instantaneous on-line inference. Experimental results on several
real-world datasets demonstrate the advantages of our sRec over other
state-of-the-arts.

本文提出 sRec 框架，使用变分贝叶斯方法来处理流式推荐系统数据，并在真实数据集上的实验结果表明其优于其他流推荐系统的算法。