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Unraveling the Mechanism of Near-Infrared Thermally Activated Delayed Fluorescence of TPA-Based Molecules: Effect of Hydrogen Bond Steric Hindrance

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JOURNAL OF PHYSICAL CHEMISTRY A 2021年第14期125卷 2905-2912页

作者： Leng, Can You, Sheng Si, Yubing Qin, Hai-Mei Liu, Jie Huang, Wei-Qing Li, Keqin Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Peoples R China Xiamen Univ Coll Chem & Chem Engn Fujian Prov Key Lab Theoret & Computat Chem Xiamen 361005 Peoples R China Hunan Univ Sch Phys & Elect Dept Appl Phys Changsha 410082 Peoples R China Zhengzhou Univ Coll Chem Zhengzhou 450001 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

A recently synthesized novel molecule (named CAT-1) exhibits intriguing near-infrared (NIR) thermally activated delayed fluorescence (TADF) close to 1000 nm wavelength;however, the mechanism behind these intrinsic properties is not fully understood. Herein, we unravel that the fluorescence emission spectrum with a broad wavelength range (770-950 nm) of CAT-1 is primarily induced by hydrogen bond steric hindrance based on density functional theory and Marcus theory. It is found that the hydrogen bond steric hindrance plays a critical role in inhibiting the twist of the configuration of different excited states, which leads to the minor driving force for fast electron trapping between the excited states, as well as small internal reorganization energy caused by less changed geometric configuration. Furthermore, such steric hindrance will cause a more distorted plane, resulting in a less favorable electron delocalization. A faster reverse intersystem crossing (RISC) rate is then obtained due to the nearly unchanged conformation between excited states caused by steric hindrance, although the spin-orbit coupling is small. Consequently, the NIR TADF with a longer wavelength can be emitted in CAT-1. This work shows that the hydrogen bond steric hindrance can fine-tune the electronic interactions of the donor and acceptor units to control the TADF.

关键词： Electrical energy Noncovalent interactions Quantum mechanics Excited states Molecules

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LELNER: A Lightweight and Effective Low-resource Named Entity Recognition model

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KNOWLEDGE-BASED SYSTEMS 2022年 251卷

作者： Zhang, Zhanjun Zhang, Haoyu Wan, Qian Liu, Jie Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China Def Innovat Inst Beijing Peoples R China

Named entity recognition aims to find the target entity from the input sentence and determine the category it belongs to. Low-resource means that the training data used by the model is scarce. In order to improve the performance of the model with a small quantity of labeled data, previous works propose the concept of the trigger and introduce trigger information. Despite their excellent performance, these methods still have shortcomings in trigger representation generation, information fusion and model training. To remedy these deficiencies, this paper proposes the LELNER model including information interaction module and information fusion network. The information interaction module realizes the interaction between trigger and sentence, which leads to trigger representation containing more entity information. The information fusion network perfectly merges the trigger representation into the sentence sequence. As a result, the network has a better fusion effect than previous nonlinear fusion methods. In terms of model training, this paper designs a one-step training method to replace the two-step training method used in previous models, which provides convenience for the entire training process. Experimental results show that the proposed LELNER model achieves state-of-the-art results on three public datasets BC5CDR, CONLL and SemEval-lap.(1) (C)2022 Elsevier B.V. All rights reserved.

关键词： Named entity recognition Information interaction Information fusion One-step training

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A region-based hypergraph network for joint entity-relation extraction

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KNOWLEDGE-BASED SYSTEMS 2021年 228卷 107298-107298页

作者： Wan, Qian Wei, Luona Chen, Xinhai Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China

In joint entity and relation extraction, the input document is divided into multiple potential entity regions and context regions, where the characteristics of entities and their relations can often be reflected in the context. Therefore, an effective joint modeling method designed toward the features of different regions can lead to superior performance of joint entity and relation extraction. Previous works tend to implement in-depth modeling only for potential entity regions, ignoring the importance of contextual information for joint entity and relation extraction. In this paper, we propose a Region-based Hypergraph Network (RHGN) for joint entity and relation extraction. The RHGN introduces the concept of regional hypernodes for the first time, and proposes a cooperative method of GCN and BiLSTM to generate hypernodes for each region. Then, a region-based relation hypergraph is constructed for fairly and efficiently aggregate the features of all regions in the sentence. In order to initialize and update the features of the edges and hypernodes in the hypergraph, a Sequence-Enhanced Graph (SEG) unit is designed. Finally, we perform comparison experiments with existing competitive models on three public datasets: the CoNLL04, SciERC and ADE datasets. Experimental results demonstrate that our model achieves a significant improvement over the previous models on both entity recognition and relation extraction, and it also shows superior performance for dataset with nested entities. Extensive additional experiments further confirm the effectiveness of our approach. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Information extraction Named entity recognition Relation extraction Graph network Deep learning

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Entity-relation triple extraction based on relation sequence information

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期238卷

Data overlap is a significant challenge in the task of entity-relation triple extraction. This task includes two research lines, line one first identifies entities and then predicts relations while line two completely shuffles the order. The methods in line two are more conducive to the optimization of the data overlap problem. Recent works have made breakthroughs in dealing with overlapping data, but there are still some defects such as difficulty in convergence and poor performance on datasets with numerous relations. To solve the above problems, we adopt a two-step strategy of first extracting subjects, and then predicting relation- object pairs. Considering the absence of connectivity between the two steps in the conventional method, we adopt the relation sequence as input in both steps and propose the TERS model. The relation sequence can connect two steps and improve the single-step and comprehensive extraction capability of the model. The TERS model consists of two modules. The first module performs information interaction and filters invalid subjects through the Text Relation Attention method. The second module implements multiple iterations of information interaction through the Information Flow method. The combination of these two modules contributes to the strong entity-relation triple extraction capability of our model. We evaluate our method on three public datasets. Extensive experiments show that our TERS model outperforms previous state-of-the-art models in triple extraction and overlapping data processing. Compared with other two-step extraction models, the advantages of our model are more obvious.

关键词： Data overlap Entity-relation triple Relation sequence Text Relation Attention Information Flow

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An improved data-free surrogate model for solving partial differential equations using deep neural networks

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SCIENTIFIC REPORTS 2021年第1期11卷 1-17页

作者： Chen, Xinhai Chen, Rongliang Wan, Qian Xu, Rui Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410000 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China

Partial differential equations (PDEs) are ubiquitous in natural science and engineering problems. Traditional discrete methods for solving PDEs are usually time-consuming and labor-intensive due to the need for tedious mesh generation and numerical iterations. Recently, deep neural networks have shown new promise in cost-effective surrogate modeling because of their universal function approximation abilities. In this paper, we borrow the idea from physics-informed neural networks (PINNs) and propose an improved data-free surrogate model, DFS-Net. Specifically, we devise an attention-based neural structure containing a weighting mechanism to alleviate the problem of unstable or inaccurate predictions by PINNs. The proposed DFS-Net takes expanded spatial and temporal coordinates as the input and directly outputs the observables (quantities of interest). It approximates the PDE solution by minimizing the weighted residuals of the governing equations and data-fit terms, where no simulation or measured data are needed. The experimental results demonstrate that DFS-Net offers a good trade-off between accuracy and efficiency. It outperforms the widely used surrogate models in terms of prediction performance on different numerical benchmarks, including the Helmholtz, Klein-Gordon, and Navier-Stokes equations.

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On the performance of maestro2 high performance network equipment, using new improvement techniques

On the performance of maestro2 high performance network equi...

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23rd IEEE International Performance, Computing, and Communications Conference, Conference Proceedings, IPCCC 2004

作者： Yamagiwa, Shinichi Ferreira, Kevin Campos, Luis Miguel Aoki, Keiichi Ono, Masaaki Wada, Koichi Fukuda, Munehiro Sousa, Leonel PDM and FC Rua Latino Coelho 87 1050-134 Lisboa Portugal Parallel/Distributed Comp. Lab. University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki 305-8573 Japan Comp. and Software Systems UW1-331 University of Washington Bothell 18815 Campus Way NE Bothell WA 98011-8246 IST/INESC-ID Rua Alves Redol 9 1000-029 Lisboa Portugal

Cluster computers have become the vehicle of choice to build high performance computing environments. To fully exploit the computing power of these environments, one must utilize high performance network and protocol technologies, since the communication patterns of parallel applications running on clusters require low latency and high throughput, not achievable by using off-the-shell network technologies. We have developed a technology to build high performance network equipment, called Maestro2. This paper describes the novel techniques used by Maestro2 to extract maximum performance from the physical medium and studies the impact of software-level parameters. The results obtained clearly show that Maestro2 is a promising technology, presenting very good results both in terms of latency and throughput. The results also show the large impact of software overhead in the overall performance of the system and validate the need for optimized communication libraries for high performance computing.

关键词： Computer networks

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Feedback Chain Network for Hippocampus Segmentation

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2023年第3期19卷 133-133页

作者： Hung, Heyu Cong, Runmin Yang, Lianhe Du, Ling Wang, Cong Kwong, Sam Tiangong Univ Sch Comp Sci & Technol Tianjin Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Huawei Technol Distributed & Parallel Software Lab Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

The hippocampus plays a vital role in the diagnosis and treatment of many neurological disorders. Recent years, deep learning technology has made great progress in the field of medical image segmentation, and the performance of related tasks has been constantly refreshed. In this article, we focus on the hippocampus segmentation task and propose a novel hierarchical feedback chain network. The feedback chain structure unit learns deeper and wider feature representation of each encoder layer through the hierarchical feature aggregation feedback chains and achieves feature selection and feedback through the feature handover attention module. Then, we embed a global pyramid attention unit between the feature encoder and the decoder to further modify the encoder features, including the pairwise pyramid attention module for achieving adjacent attention interaction and the global context modeling module for capturing the long-range knowledge. The proposed approach achieves state-of-the-art performance on three publicly available datasets compared with existing hippocampus segmentation approaches. The code and results can be found from the link of https://***/easymoneysniper183/sematic_seg.

关键词： Deep learning hippocampus segmentation multi-level feature fusion feedback chain

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Message from the program co-chairs

Proceedings - Fifth International Conference on Grid and Coo...

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Proceedings - Fifth International Conference on Grid and Cooperative Computing, GCC 2006 2006年 12-12页

作者： Xiao, Wong Buyya, Rajkumar Liu, Yunhao National Laboratory for Parallel and Distributed Processing Changsha China GRIDS Lab. Dept. of Computer Science and Software Engineering University of Melbourne Australia Dept. of Computer Science Hong Kong University of Science and Technology Hong Kong Hong Kong

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Message from the program co-chairs

Proceedings - Fifth International Conference on Grid and Coo...

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Proceedings - Fifth International Conference on Grid and Cooperative Computing, GCC 2006 - Workshops 2006年 12-12页

作者： Nong, Xiao Buyya, Rajkumar Liu, Yunhao National Laboratory for Parallel and Distributed Processing Changsha China GRIDS Lab. Dept. of Computer Science and Software Engineering University of Melbourne Australia Dept. of Computer Science Hong Kong University of Science and Technology Hong Kong Hong Kong

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Variational Distillation for Multi-View Learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第7期46卷 4551-4566页

作者： Tian, Xudong Zhang, Zhizhong Wang, Cong Zhang, Wensheng Qu, Yanyun Ma, Lizhuang Wu, Zongze Xie, Yuan Tao, Dacheng East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Huawei Technol Distributed & Parallel Software Lab Labs 2012 Hangzhou 518129 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Xiamen Univ Sch Informat Sci & Technol Xiamen 361005 Fujian Peoples R China East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200050 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shenzhen Univ Coll Mech & Control Engn Shenzhen 518060 Peoples R China Normal Univ Chongqing Inst East China Shanghai 200062 Peoples R China JD Exploer Acad Beijing Peoples R China Univ Sydney Camperdown NSW 2050 Australia

Information Bottleneck (IB) provides an information-theoretic principle for multi-view learning by revealing the various components contained in each viewpoint. This highlights the necessity to capture their distinct roles to achieve view-invariance and predictive representations but remains under-explored due to the technical intractability of modeling and organizing innumerable mutual information (MI) terms. Recent studies show that sufficiency and consistency play such key roles in multi-view representation learning, and could be preserved via a variational distillation framework. But when it generalizes to arbitrary viewpoints, such strategy fails as the mutual information terms of consistency become complicated. This paper presents Multi-View Variational Distillation ((MVD)-D-2), tackling the above limitations for generalized multi-view learning. Uniquely, (MVD)-D-2 can recognize useful consistent information and prioritize diverse components by their generalization ability. This guides an analytical and scalable solution to achieving both sufficiency and consistency. Additionally, by rigorously reformulating the IB objective, (MVD)-D-2 tackles the difficulties in MI optimization and fully realizes the theoretical advantages of the information bottleneck principle. We extensively evaluate our model on diverse tasks to verify its effectiveness, where the considerable gains provide key insights into achieving generalized multi-view representations under a rigorous information-theoretic principle.

关键词： Mutual information Task analysis Representation learning Predictive models Optimization Visualization Pattern analysis Multi-view learning information bottleneck mutual information variational inference knowledge distillation

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