Pinterest is now the fourth most popular social network site after Facebook, Twitter, and LinkedIn in the United States, offering its own suite of functions. This study investigated why individuals use specific features of Pinterest such as pinning, creating, liking, following, commenting, inviting, sharing, checking, searching, and browsing different categories. An online survey ( N = 113) revealed that a brand new set of gratifications (specific to digital media) predicted a large number of user behaviors in Pinterest. The results showcased the predictive value of affordance-based gratifications in shaping specific user behaviors on social-media.
Hollow nanostructures are widely used in chemistry, materials, bioscience, and medicine, but their fabrication remains a great challenge. In particular, there is no effective strategy for their assembly and interconnection. We bring pottery, the oldest and simplest method of fabricating hollow containers, into the nanoscale. By exploiting the liquid nature of the xylene template, fullerene hollow nanostructures of tailored shapes, such as bowls, bottles, and cucurbits, are readily synthesized. The liquid templates permit stepwise and versatile manipulation and hence, modular assembly of nodes and junctions leads to interconnected hollow systems. As a proof-of-concept, we create multi-compartment nano-containers, with different nanoparticles isolated in the separate pockets. This methodology expands the synthetic freedom for hollow nanostructures, building a bridge from isolated hollow units to interconnected hollow systems.
Distant supervision is an effective method to generate large scale labeled data for relation extraction, which assumes that if a pair of entities appears in some relation of a Knowledge Graph (KG), all sentences containing those entities in a large unlabeled corpus are then labeled with that relation to train a relation classifier. However, when the pair of entities has multiple relationships in the KG, this assumption may produce noisy relation labels. This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly. Specifically, we make use of the type information and the translation law derived from typical KG embedding model to learn embeddings for certain sentence patterns. As the supervision signal is only determined by the two aligned entities, neither hard relation labels nor extra noise-reduction model for the bag of sentences is needed in this way. The experiments show that the approach performs well in current distant supervision dataset.
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