1,060 research outputs found
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the
development and operation of the smart city. As an emerging building block,
multi-sourced urban data are usually integrated as urban knowledge graphs
(UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction
models. However, existing UrbanKGs are often tailored for specific downstream
prediction tasks and are not publicly available, which limits the potential
advancement. This paper presents UUKG, the unified urban knowledge graph
dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically,
we first construct UrbanKGs consisting of millions of triplets for two
metropolises by connecting heterogeneous urban entities such as administrative
boroughs, POIs, and road segments. Moreover, we conduct qualitative and
quantitative analysis on constructed UrbanKGs and uncover diverse high-order
structural patterns, such as hierarchies and cycles, that can be leveraged to
benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs,
we implement and evaluate 15 KG embedding methods on the KG completion task and
integrate the learned KG embeddings into 9 spatiotemporal models for five
different USTP tasks. The extensive experimental results not only provide
benchmarks of knowledge-enhanced USTP models under different task settings but
also highlight the potential of state-of-the-art high-order structure-aware
UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban
knowledge graphs and broad smart city applications. The dataset and source code
are available at https://github.com/usail-hkust/UUKG/.Comment: NeurIPS 2023 Track on Datasets and Benchmark
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Photocatalytic nitrogen reduction to ammonia: Insights into the role of defect engineering in photocatalysts
Engineering of defects in semiconductors provides an effective protocol for improving photocatalytic N2 conversion efficiency. This review focuses on the state-of-the-art progress in defect engineering of photocatalysts for the N2 reduction toward ammonia. The basic principles and mechanisms of thermal catalyzed and photon-induced N2 reduction are first concisely recapped, including relevant properties of the N2 molecule, reaction pathways, and NH3 quantification methods. Subsequently, defect classification, synthesis strategies, and identification techniques are compendiously summarized. Advances of in situ characterization techniques for monitoring defect state during the N2 reduction process are also described. Especially, various surface defect strategies and their critical roles in improving the N2 photoreduction performance are highlighted, including surface vacancies (i.e., anionic vacancies and cationic vacancies), heteroatom doping (i.e., metal element doping and nonmetal element doping), and atomically defined surface sites. Finally, future opportunities and challenges as well as perspectives on further development of defect-engineered photocatalysts for the nitrogen reduction to ammonia are presented. It is expected that this review can provide a profound guidance for more specialized design of defect-engineered catalysts with high activity and stability for nitrogen photochemical fixation
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