In all types of surface code decoders, fully neural network-based high-level decoders offer decoding thresholds that surpass decoder-Minimum Weight Perfect Matching (MWPM), and exhibit strong scalability, making them one of the ideal solutions for addressing surface code challenges. However, current fully neural network-based high-level decoders can only operate serially and do not meet the current latency requirements (below 440 ns). To address these challenges, we first propose a parallel fully feedforward neural network (FFNN) high-level surface code decoder, and comprehensively measure its decoding performance on a computing-in-memory (CIM) hardware simulation platform. With the currently available hardware specifications, our work achieves a decoding threshold of 14.22%, and achieves high pseudo-thresholds of 10.4%, 11.3%, 12%, and 11.6% with decoding latencies of 197.03 ns, 234.87 ns, 243.73 ns, and 251.65 ns for distances of 3, 5, 7 and 9, respectively.

Land use for urbanization has become a global crisis with increasing population and diminishing habitats in the context of climate change. How can human beings modify the inappropriate environment for urban construction? Unprecedented crustal surgery in the forms of hilltop cuts and gully fills is happening over the Loess Plateau of China of 10s km2for new urban area in Yan'an. We use multi-source remote sensing imagery to quantify the spatiotemporal surface changes and ground deformation, to predict the length of time it needs to be stabilized, and to model the consequent stress changes in the hazard-prone loess landscapes. This study demonstrates the capability of surface elevation and displacement measurements from SAR remote sensing in urban construction and resilience.

In the paper by Shiyong Yang et al. , a closed-form power allocation scheme of weighted rate sum maximization in Gaussian broadcast channel was given. Their scheme is indeed optimal, but there are some critical defects in their proof for the optimality. In this paper, we first analyze the defects in their proof and then present a new proof for the optimality of their scheme.