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2,151 results on '"Xu, Nuo"'

107. Learning Architectures from an Extended Search Space for Language Modeling

Author

Li, Yinqiao, Hu, Chi, Zhang, Yuhao, Xu, Nuo, Jiang, Yufan, Xiao, Tong, Zhu, Jingbo, Liu, Tongran, and Li, Changliang

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Statistics - Machine Learning
Abstract

Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. In this paper, we extend the search space of NAS. In particular, we present a general approach to learn both intra-cell and inter-cell architectures (call it ESS). For a better search result, we design a joint learning method to perform intra-cell and inter-cell NAS simultaneously. We implement our model in a differentiable architecture search system. For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and WikiText data, with a new state-of-the-art on PTB. Moreover, the learned architectures show good transferability to other systems. E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity recognition (NER) tasks and CoNLL chunking task, indicating a promising line of research on large-scale pre-learned architectures., Comment: ACL 2020

Published
2020

108. Distinguish Confusing Law Articles for Legal Judgment Prediction

Author

Xu, Nuo, Wang, Pinghui, Chen, Long, Pan, Li, Wang, Xiaoyan, and Zhao, Junzhou

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public. In practice, confusing charges are frequent, because law cases applicable to similar law articles are easily misjudged. For addressing this issue, the existing method relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network to automatically learn subtle differences between confusing law articles and design a novel attention mechanism that fully exploits the learned differences to extract compelling discriminative features from fact descriptions attentively. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN., Comment: This work has been accepted by the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)

Published
2020

132. The crystal structure of (S, R p)-4–benzhydrylideneamino-12-(4-tert-butyl oxazolin-2-yl)[2.2]paracyclophane, C36H36N2O

Author

Chen Zhen, Sun Yanwen, Chen Chao, Xu Nuo, Wang Xiaozhong, and Li Sijun

Subjects
2258478, Physics, QC1-999, Crystallography, QD901-999
Abstract

C36H36N2 ${\mathrm{C}}_{36}{\mathrm{H}}_{36}{\mathrm{N}}_{2}$ O, orthorhombic, P212121 ${2}_{1}{2}_{1}{2}_{1}$ (no. 19), a = 11.849(2) Å, b = 14.373(3) Å, c = 17.183(3) Å, V = 2926.4(10) Å3 ${\mathrm{\mathring{\text{A}}}}^{3}$ , Z = 4, Rgt ${R}_{\mathrm{g}\mathrm{t}}$ (F) = 0.0514, w Rref ${R}_{\mathrm{r}\mathrm{e}\mathrm{f}}$ (F2 ${F}^{2}$ ) = 0.1272, T = 296(2) K.

Published
2023
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136. MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

Author

Xu, Nuo, Wang, Pinghui, Chen, Long, Tao, Jing, and Zhao, Junzhou

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods., Comment: Accepted by IJCAI 2019

Published
2019
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139. Intermolecular donor–acceptor stacking to suppress triplet exciton diffusion for long-persistent organic room-temperature phosphorescence.

Author

Ma, Jiajia, Dou, Jiawen, Xu, Nuo, Wang, Guo, Duan, Yuai, Liao, Yi, Yi, Yuanping, and Geng, Hua

Subjects
PHOSPHORESCENCE, PHOSPHORESCENCE spectroscopy, PHOSPHORIMETRY, DENSITY functional theory, CHARGE transfer, CRYSTAL surfaces
Abstract

Controlling triplet states is crucial to improve the efficiency and lifetime of organic room temperature phosphorescence (ORTP). Although the intrinsic factors from intramolecular radiative and non-radiative decay have been intensively investigated, the extrinsic factors that affect triplet exciton quenching are rarely reported. Diffusion to the defect sites inside the crystal or at the crystal surface may bring about quenching of triplet exciton. Here, the phosphorescence lifetime is found to have a negative correlation with the triplet exciton diffusion coefficient based on the density functional theory (DFT)/time-dependent density functional theory (TD-DFT) calculations on a series of ORTP materials. For systems with a weak charge transfer (CT) characteristic, close π–π stacking will lead to strong triplet coupling and fast triplet exciton diffusion in most cases, which is detrimental to the phosphorescence lifetime. Notably, for intramolcular donor–acceptor (D–A) type systems with a CT characteristic, intermolecular D–A stacking results in ultra-small triplet coupling, thus contributing to slow triplet diffusion and long phosphorescence lifetime. These findings shed some light on molecular design toward high-efficiency long persistent ORTP. [ABSTRACT FROM AUTHOR]

Published
2024
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148. Feminism and Bar in the 1960s to 1970s in America

Author

Xu, Nuo, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Holl, Augustin, editor, Chen, Jun, editor, and Guan, Guiyun, editor

Published
2022
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149. Abstains from Prediction: Towards Robust Relation Extraction in Real World

Author

Zhao, Jun, Zhang, Yongxin, Xu, Nuo, Gui, Tao, Zhang, Qi, Chen, Yunwen, Gao, Xiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Liu, Yang, editor, Che, Wanxiang, editor, Feng, Yang, editor, Qiu, Xipeng, editor, Rao, Gaoqi, editor, and Chen, Yubo, editor

Published
2022
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150. An Exploration of Prompt-Based Zero-Shot Relation Extraction Method

Author

Zhao, Jun, Hu, Yuan, Xu, Nuo, Gui, Tao, Zhang, Qi, Chen, Yunwen, Gao, Xiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Liu, Yang, editor, Che, Wanxiang, editor, Feng, Yang, editor, Qiu, Xipeng, editor, Rao, Gaoqi, editor, and Chen, Yubo, editor

Published
2022
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