Colloidal III–V Nitride Quantum Dots

Colloidal quantum dots (QDs) have attracted intense attention in both fundamental studies and practical applications. To date, the size, morphology, and composition-controlled syntheses have been successfully achieved in II–VI semiconductor nanocrystals. Recently, III-nitride semiconductor quantum dots have begun to draw significant interest due to their promising applications in solid-state lighting, lasing technologies, and optoelectronic devices. The quality of nitride nanocrystals is, however, dramatically lower than that of II–VI semiconductor nanocrystals. In this review, the recent development in the synthesis techniques and properties of colloidal III–V nitride quantum dots as well as their applications are introduced

The concept and connotation of smart tourism from the perspective of rational choice

Although the term “smart tourism” originated from western countries, it has “taken root and flourished” in China. Current understanding in domestic industry and academia not only reflects the reality of the development of smart tourism in China, but also encourages new research into its conceptualization and strategy. Based on the understanding and analysis of the original mainstream technology application theory, this paper proposes new ideas on the concept and connotations of smart tourism from the perspective of rational choice theory. It concludes that the core characteristic of smart tourism is that it encourages the tourism subject to make the most rational choice

Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.Comment: Accepted in the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2023

MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection

Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. However, this is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space for fusion, and they can be roughly divided into 1) early fusion of raw points that aims at augmenting the 3D point cloud at the early input stage, and 2) late fusion of BEV (bird-eye view) maps that merges LiDAR and camera BEV features before the detection head. While both have their merits in enhancing the representation power of the combined features, this single-level fusion strategy is a suboptimal solution to the aforementioned challenge. Their major drawbacks are the inability to interact the multi-granularity semantic features from two distinct modalities sufficiently. To this end, we propose a novel framework that focuses on the multi-scale progressive interaction of the multi-granularity LiDAR and camera features. Our proposed method, abbreviated as MDMSFusion, achieves state-of-the-art results in 3D object detection, with 69.1 mAP and 71.8 NDS on nuScenes validation set, and 70.8 mAP and 73.2 NDS on nuScenes test set, which rank 1st and 2nd respectively among single-model non-ensemble approaches by the time of submission