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Coexistence of Photoelectric Conversion and Storage in van der Waals Heterojunctions

Physical Review Letters 2021年第21期127卷 217401-217401页

作者： Yucheng Jiang Anpeng He Run Zhao Yu Chen Guozhen Liu Hao Lu Jinlei Zhang Qing Zhang Zhuo Wang Chen Zhao Mingshen Long Weida Hu Lin Wang Yaping Qi Ju Gao Quanying Wu Xiaotian Ge Jiqiang Ning Andrew T. S. Wee Cheng-Wei Qiu Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou Jiangsu 215009 People’s Republic of China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 China State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences 500 Yu Tian Road Shanghai 200083 China School of Materials Science and Engineering Shanghai University Shanghai 200444 China Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA School for Optoelectronic Engineering Zaozhuang University Shandong 277160 China Vacuum Interconnected Nanotech Workstation Suzhou Institute of Nano-tech and Nano-Bionics (SINANO) Suzhou Jiangsu 215123 People’s Republic of China Department of Physics National University of Singapore Singapore 117551 Singapore

Van der Waals (vdW) heterojunctions, based on two-dimensional (2D) materials, have great potential for the development of ecofriendly and high-efficiency nanodevices, which shows valuable applications as photovoltaic cells, photodetectors, etc. However, the coexistence of photoelectric conversion and storage in a single device has not been achieved until now. Here, we demonstrate a simple strategy to construct a vdW p−n junction between a WSe2 layer and quasi-2D electron gas. After an optical illumination, the device stores the light-generated carriers for up to seven days, and then releases a very large photocurrent of 2.9 mA with bias voltage applied in darkness; this is referred to as chargeable photoconductivity (CPC), which completely differs from any previously observed photoelectric phenomenon. In normal photoconductivity, the recombination of electron-hole pairs occurs at the end of their lifetime; in contrast, infinite-lifetime photocarriers can be generated and stored in CPC devices without recombination. The photoelectric conversion and storage are completely self-excited during the charging process. The ratio between currents in full- and empty-photocarrier states below the critical temperature reaches as high as 109, with an external quantum efficiency of 93.8% during optical charging. A theoretical model developed to explain the mechanism of this effect is in good agreement with the experimental data. This work paves a path toward the high-efficiency devices for photoelectric conversion and storage.

关键词： Electrical conductivity Photoconductivity Solid-solid interfaces Two-dimensional electron gas Van der Waals systems

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Fine-tuning by curriculum learning for non-autoregressive neural machine translation

arXiv

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arXiv 2019年

作者： Guo, Junliang Tan, Xu Xu, Linli Qin, Tao Chen, Enhong Liu, Tie-Yan Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Microsoft Research

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Curricula

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Illuminating Recommendation by Understanding the Explicit Item Relations

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Journal of computer Science & technology 2018年第4期33卷 739-755页

作者： Qi Liu Hong-Ke Zhao Le Wu Zhi Li En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027 China School of Computer and Information Hefei University of Technology Hefei 230009 China School of Software Engineering University of Science and Technology of China Hefei 230051 China

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

关键词： recommender system item relation recommendation interpretability

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Synchronous Motion Generation of Multiple Continuum Robots Based on a Jacobian-Estimation Strategy

Synchronous Motion Generation of Multiple Continuum Robots B...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Ning Tan Ruikun Hu Yuyang Wu Xinyu Zhang Fenglei Ni Zhenglong Sun School of Computer Science and Engineering Sun Yat-sen University Guangzhou PR China State Key Laboratory of Robotics and Systems (HIT) Harbin PR China Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education (East China Normal University) Shanghai PR China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9781665405362

In this paper, a novel model-free control scheme is proposed for synchronous motion control of multiple continuum robots. The control problem is formulated as two convex optimization routines in an adaptive and model-free closed-loop framework. The proposed approach is heading to address cooperative task execution for multiple continuum robots. The model-free feedback control guarantees the high portability for a wide range of continuum robots. Simulation studies are conducted on two kinds of tasks, which are cooperative task execution with two robots and simultaneously tracking control with three robots. Besides, the superiority of proposed approach is validated through comparisons with other methods. Simulation results prove the high accuracy and efficiency of this model-free feedback control scheme.

关键词： Jacobian matrices Adaptation models Tracking Simulation Conferences Convex functions Feedback control

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Conformally flat Lorentzian hypersurfaces in R_1~4 with three distinct principal curvatures

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Science China Mathematics 2018年第5期61卷 897-916页

作者： Xiaozhen Wang Changping Wang Zhenxiao Xie School of Mathematics and Computer Science Fujian Normal University Fujian Provincial Key Laboratory of Mathematical Analysis and Its Application Fujian Normal University Department of Mathematics China University of Mining and Technology (Beijing)

A three dimensional Lorentzian hypersurface x : M_1~3→ R_1~4 is called conformally flat if its induced metric is conformal to the flat Lorentzian metric, and this property is preserved under the conformal transformation of R_1~4. Using the projective light-cone model, for those whose shape operators have three distinct real eigenvalues, we calculate the integrability conditions by constructing a scalar conformal invariant and a canonical moving frame in this paper. Similar to the Riemannian case, these hypersurfaces can be determined by the solutions to some system of partial differential equations.

关键词： conformally flat Lorentzian hypersurfaces conformal geometry of Lorentzian space forms integrability equations

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Towards efficient processing and learning with spikes: New approaches for multi-spike learning

arXiv

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arXiv 2020年

作者： Yu, Qiang Li, Shenglan Tang, Huajin Wang, Longbiao Dang, Jianwu Chen Tan, Kay Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Technology Zhejiang University China Co. Ltd. Tianjin China Department of Computer Science City University of Hong Kong City University of Hong Kong Shenzhen Research Institute

Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing. However, efficient processing and learning of discrete spikes still remains as a challenging problem. In this paper, we make our contributions towards this direction. A simplified spiking neuron model is firstly introduced with effects of both synaptic input and firing output on membrane potential being modeled with an impulse function. An event-driven scheme is then presented to further improve the processing efficiency. Based on the neuron model, we propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks including association, classification, feature detection. In addition to efficiency, our learning rules demonstrate a high robustness against strong noise of different types. They can also be generalized to different spike coding schemes for the classification task, and notably single neuron is capable of solving multi-category classifications with our learning rules. In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented, and find a new phenomenon of losing selectivity. In contrast, our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied. Moreover, our rules can not only detect features but also discriminate them. The improved performance of our methods would contribute to neuromorphic computing as a preferable choice. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Semi-supervised recurrent complex-valued convolution neural network for polsar image classification 39

Semi-supervised recurrent complex-valued convolution neural ...

引用

39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

作者： Zhao, Feng Ma, Gaini Xie, Wen Liu, Hanqiang Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public Security Xi'an710121 China School of Communications and Information Engineering Xi'An University of Posts and Telecommunications Xi'an710121 China School of Computer Science Shaanxi Normal University Xi'an710119 China

This paper presents a novel semi-supervised terrain classification method of polarimetric synthetic aperture radar (PolSAR) image based on complex-valued convolution neural network (CV-CNN). Our proposed method only needs a small number of labeled samples to achieve good classification results. First, a Wishart classifier is used to find highly reliable samples in PolSAR data. Then, a new semi-supervised deep recurrent CV-CNN (RCVCNN) classification model has been proposed to improve PolSAR image classification accuracy and effectively solve network overfitting. Finally, a real PolSAR dataset is used to verify the effectiveness of our algorithm. Compared with the other three state-of-the-art methods, the proposed one show improvements in accuracy and better consistency. © 2019 IEEE.

关键词： Image classification

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LAMOST CCD camera-control system based on RTS2

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Research in Astronomy and Astrophysics 2018年第5期18卷 69-82页

作者： Yuan Tian Zheng Wang Jian Li Zi-Huang Cao Wei Dai Shou-Lin Wei Yong-Heng Zhao Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of SciencesBeijing 100101 China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and TechnologyKunming 650500 China University of Chinese Academy of Sciences Beijing 100049 China

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST） is the largest existing spectroscopic survey telescope, having 32 scientific charge-coupled-device（CCD） cameras for acquiring spectra. Stability and automation of the camera-control software are essential, but cannot be provided by the existing system. The Remote Telescope System 2 nd Version（RTS2） is an open-source and automatic observatory-control system. However, all previous RTS2 applications were developed for small telescopes. This paper focuses on implementation of an RTS2-based camera-control system for the 32 CCDs of LAMOST. A virtual camera module inherited from the RTS2 camera module is built as a device component working on the RTS2 framework. To improve the controllability and robustness, a virtualized layer is designed using the master-slave software paradigm, and the virtual camera module is mapped to the 32 real cameras of LAMOST. The new system is deployed in the actual environment and experimentally tested. Finally, multiple observations are conducted using this new RTS2-frameworkbased control system. The new camera-control system is found to satisfy the requirements for automatic camera control in LAMOST. This is the first time that RTS2 has been applied to a large telescope, and provides a referential solution for full RTS2 introduction to the LAMOST observatory control system.

关键词： telescopes - techniques imaging spectroscopy - methods observational - instrumentation detectors

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

引用

Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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SPECT bone imaging thyroid lesion segmentation

引用

Journal of Physics: Conference Series 2021年第1期1994卷

作者： Mingyang-Luo Qiang-Lin Tongtong-Li Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou 730030 Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou 730124 School of Mathematics and Computer Science Northwest Minzu University Lanzhou 730030

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83.

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