Randomness is critical for many information processing applications, including numerical modeling and cryptography [1,2]. Device-independent quantum random number generation [3] (DIQRNG) based on the loophole free violation of Bell inequality [4][5][6][7] produces unpredictable genuine randomness without any device assumption and is therefore an ultimate goal in the field of quantum information science [8][9][10]. However, due to formidable technical challenges, there were very few reported experimental studies of DIQRNG [11][12][13][14], which were vulnerable to the adversaries. Here we present a fully functional DIQRNG against the most general quantum adversaries [15][16][17]. We construct a robust experimental platform that realizes Bell inequality violation with entangled photons with detection and locality loopholes closed simultaneously. This platform enables a continuous recording of a large volume of data sufficient for security analysis against the general quantum side information and without assuming independent and identical distribution.Lastly, by developing a large Toeplitz matrix (137.90 Gb × 62.469 Mb) hashing technique, we demonstrate that this DIQRNG generates 6.2469 × 10 7 quantum-certified random bits in 96 hours (or 181 bits/s) with uniformity within 10 −5 . We anticipate this DIQRNG may have profound impact on the research of quantum randomness and information-secured applications.
Abstract. High-spatial-resolution and long-term climate data are
highly desirable for understanding climate-related natural processes. China
covers a large area with a low density of weather stations in some (e.g.,
mountainous) regions. This study describes a 0.5′ (∼ 1 km)
dataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs)
and precipitation (PRE) for China in the period of 1901–2017. The dataset
was spatially downscaled from the 30′ Climatic Research Unit (CRU) time
series dataset with the climatology dataset of WorldClim using delta spatial
downscaling and evaluated using observations collected in 1951–2016 by 496
weather stations across China. Prior to downscaling, we evaluated the
performances of the WorldClim data with different spatial resolutions and
the 30′ original CRU dataset using the observations, revealing that their
qualities were overall satisfactory. Specifically, WorldClim data exhibited
better performance at higher spatial resolution, while the 30′ original CRU
dataset had low biases and high performances. Bicubic, bilinear, and
nearest-neighbor interpolation methods employed in downscaling processes
were compared, and bilinear interpolation was found to exhibit the best
performance to generate the downscaled dataset. Compared with the
evaluations of the 30′ original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5′ dataset downscaled by bilinear interpolation) decreased by 35.4 %–48.7 % for TMPs and by 25.7 % for PRE. The root-mean-square error decreased by 32.4 %–44.9 % for TMPs and by 25.8 % for PRE. The Nash–Sutcliffe efficiency coefficients increased by
9.6 %–13.8 % for TMPs and by 31.6 % for PRE, and correlation
coefficients increased by 0.2 %–0.4 % for TMPs and by 5.0 % for PRE. The new dataset could provide detailed climatology data and annual trends of all climatic variables across China, and the results could be evaluated well using observations at the station. Although the new dataset was not evaluated before 1950 owing to data unavailability, the quality of the new
dataset in the period of 1901–2017 depended on the quality of the original
CRU and WorldClim datasets. Therefore, the new dataset was reliable, as the
downscaling procedure further improved the quality and spatial resolution of
the CRU dataset and was concluded to be useful for investigations related
to climate change across China. The dataset presented in this article has
been published in the Network Common Data Form (NetCDF) at
https://doi.org/10.5281/zenodo.3114194 for precipitation (Peng,
2019a) and https://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m
(Peng, 2019b) and includes 156 NetCDF files compressed in zip
format and one user guidance text file.
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