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With increasing livestock production due to high demand for consumption, the planted area of green fodder, an essential livestock supplement, has grown rapidly and will continue to grow in China. However, the climate feedback of this rapid land cover conversion is still unclear. Using multisource data (e.g. remote sensing observation and meteorological data), we compared the land surface temperature of green fodder plantation areas and native grassland in the northeastern Tibetan Plateau. The green fodder area was detected to be cooler than the native grassland by −0.54 ± 0.98 °C in the daytime throughout the growing season. The highest magnitude (−1.20 ± 1.68 °C) of cooling was observed in August. A nonradiative process, indicated by the energy redistribution factor, dominated the cooling effects compared to the radiative process altered by albedo variation. The results indicate the potential cooling effects of increasing green fodder area on native grassland, highlighting the necessity of investigating climate feedback from anthropogenic land use change, including green fodder expansion.

 

Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG datasets benefit a variety of downstream tasks such as KG completion and link prediction. However, existing KG embedding methods fell short to provide a systematic solution for the global consistency of knowledge representation. We developed a mathematical language for KG based on an observation of their inherent algebraic structure, which we termed as Knowledgebra. By analyzing five distinct algebraic properties, we proved that the semigroup is the most reasonable algebraic structure for the relation embedding of a general knowledge graph. We implemented an instantiation model, SemE, using simple matrix semigroups, which exhibits state-of-the-art performance on standard datasets. Moreover, we proposed a regularization-based method to integrate chain-like logic rules derived from human knowledge into embedding training, which further demonstrates the power of the developed language. As far as we know, by applying abstract algebra in statistical learning, this work develops the first formal language for general knowledge graphs, and also sheds light on the problem of neural-symbolic integration from an algebraic perspective. 

As a class of approximate measurement approaches, sketching algorithms have significantly improved the estimation of network flow information using limited resources. While these algorithms enjoy sound error-bound analysis under worst-case scenarios, their actual errors can vary significantly with the incoming flow distribution, making their traditional error bounds too "loose" to be useful in practice. In this paper, we propose a simple yet rigorous error estimation method to more precisely analyze the errors for posterior sketch queries by leveraging the knowledge from the sketch counters. This approach will enable network operators to understand how accurate the current measurements are and make appropriate decisions accordingly (e.g., identify potential heavy users or answer "what-if" questions to better provision resources). Theoretical analysis and trace-driven experiments show that our estimated bounds on sketch errors are much tighter than previous ones and match the actual error bounds in most cases.