Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona , by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FriendsPersona. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue.
Light activated shape memory polymers (LASMPs) are relatively new kinds of smart materials and have significant technological applications ranging from biomedical devices to aerospace technology. EVA films doped with spiropyran with contents ranging from 0.1% to 3% show efficient UV activated shape memory behaviors if the fixed shape deformation is limited within 80%. For EVA films containing 3% spiropyran, UV irradiation causes a decrease in EVA modulus of about 44%. FT-IR and solid (13)C NMR in association with UV-vis absorption analysis demonstrate that UV irradiation transforms spiropyran from the SP form to the MC form, meanwhile, it induces an increase in the molecular mobility in the amorphous phase of EVA. Thus, the spiropyran-doped EVA films act as LASMPs via a mechanism of light induced plasticization. Light activated spiropyran acts as a plasticizer to EVA.
Over the past decades, photonics has transformed many areas in both fundamental research and practical applications. In particular, we can manipulate light in a desired and prescribed manner by rationally designed subwavelength structures. However, constructing complex photonic structures and devices is still a time-consuming process, even for experienced researchers. As a subset of artificial intelligence, artificial neural networks serve as one potential solution to bypass the complicated design process, enabling us to directly predict the optical responses of photonic structures or perform the inverse design with high efficiency and accuracy. In this review, we will introduce several commonly used neural networks and highlight their applications in the design process of various optical structures and devices, particularly those in recent experimental works. We will also comment on the future directions to inspire researchers from different disciplines to collectively advance this emerging research field.
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