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arXiv 2024年

作者： Wang, Shengbin Li, Guihui Chen, Zhaoyun Wang, Peng Dou, Menghan Zheng, Haiyong Wang, Zhimin Gu, Yongjian Wu, Yu-Chun Guo, Guo-Ping Origin Quantum Computing Company Limited Anhui Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Intelligent Information Sensing and Processing Lab College of Electronic Engineering Ocean University of China Shandong Qingdao266000 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China College of Physics and Optoelectronic Engineering Ocean University of China Shandong Qingdao266100 China

Solving combinatorial optimization problems using variational quantum algorithms (VQAs) represents one of the most promising applications in the NISQ era. However, the limited trainability of VQAs could hinder their scalability to large problem sizes. In this paper, we improve the trainability of variational quantum eigensolver (VQE) by utilizing convex interpolation to solve portfolio optimization. The idea is inspired by the observation that the Dicke state possesses an inherent clustering property. Consequently, the energy of a state with a larger Hamming distance from the ground state intuitively results in a greater energy gap away from the ground state energy in the overall distribution trend. Based on convex interpolation, the location of the ground state can be evaluated by learning the property of a small subset of basis states in the Hilbert space. This enlightens naturally the proposals of the strategies of close-to-solution initialization, regular cost function landscape, and recursive ansatz equilibrium partition. The successfully implementation of a 40-qubit experiment using only 10 superconducting qubits demonstrates the effectiveness of our proposals. Furthermore, the quantum inspiration has also spurred the development of a prototype greedy algorithm. Extensive numerical simulations indicate that the hybridization of VQE and greedy algorithms achieves a mutual complementarity, combining the advantages of both global and local optimization methods. Our proposals can be extended to improve the trainability for solving other large-scale combinatorial optimization problems that are widely used in real applications, paving the way to unleash quantum advantages of NISQ computers in the near future. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

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OpenAUC: Towards AUC-Oriented Open-Set Recognition

arXiv

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arXiv 2022年

作者： Wang, Zitai Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China BDKM University of Chinese Academy of Sciences China Peng Cheng Laboratory China

Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes (open-set). To fix this issue, Open-Set Recognition (OSR), whose goal is to make correct predictions on both close-set samples and open-set samples, has attracted rising attention. In this direction, the vast majority of literature focuses on the pattern of open-set samples. However, how to evaluate model performance in this challenging task is still unsolved. In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden’s index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction. (2) Novelty detection AUC, which measures the ranking performance between close-set and open-set samples, ignores the close-set performance. To fix these issues, we propose a novel metric named OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise formulation that evaluates open-set performance and close-set performance in a coupling manner. Further analysis shows that OpenAUC is free from the aforementioned inconsistency properties. Finally, an end-to-end learning method is proposed to minimize the OpenAUC risk, and the experimental results on popular benchmark datasets speak to its effectiveness. Project Page: https://***/wang22ti/OpenAUC. © 2022, CC BY.

关键词： Forecasting

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Author Correction: Full reconstruction of simplicial complexes from binary contagion and Ising data

引用

Nature communications 2022年第1期13卷 3692页

作者： Huan Wang Chuang Ma Han-Shuang Chen Ying-Cheng Lai Hai-Feng Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. School of Internet Anhui University Hefei 230601 China. School of Physics and Material Science Anhui University Hefei 230601 China. School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 USA. The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. haifengzhang1978@***.

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Graphene based functional devices: A short review

arXiv

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arXiv 2018年

作者： Wang, Rong Jiang, Lijun Ren, Xingang Yan, Ze Sha, Wei E.I. Shan, Guangcun Department of Electrical and Electronic Engineering University of Hong Kong Pokfulam Road Hong Kong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230039 China School of Instrumentation Science & Opto-electronics Engineering Beihang University Beijing100191 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China School of Instrumentation Science & Opto-electronics Engineering Beihang University Beijing100191 China California NanoSystem Institute Department of Chemistry & Biochemistry University of California Los AngelesCA90095 United States

Graphene is an ideal 2D material system bridging electronic and photonic devices. It also breaks the fundamental speed and size limits by electronics and photonics, respectively. Graphene offers multiple functions of signal transmission, emission, modulation, and detection in a broad band, high speed, compact size, and low loss. Here, we will have a brief view of graphene based functional devices at microwave, terahertz, and optical frequencies. Their fundamental physics and computational models will be discussed as well. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphene

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Two weight Zpk -codes, p odd prime

arXiv

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arXiv 2017年

作者： Shi, Minjia Sepasdar, Zahra Solé, Patrick Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Mathematical Sciences of Anhui University Hefei230601 China Department of Pure Mathematics Ferdowsi University of Mashhad P. O. Box 1159 Mashhad91775 Iran CNRS/LTCI Telecom ParisTech University of Paris-Saclay Paris75 013 France Department of Mathematics Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

We show that regular homogeneous two-weight Zpk -codes where p is odd and k ≥ 2 with dual Hamming distance at least four do not exist. The proof relies on existence conditions for the strongly regular graph built on ... 详细信息

关键词： Hamming distance

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Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

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BMC bioinformatics 2019年第1期20卷 616页

作者： Haicang Zhang Qi Zhang Fusong Ju Jianwei Zhu Yujuan Gao Ziwei Xie Minghua Deng Shiwei Sun Wei-Mou Zheng Dongbo Bu Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. University of Chinese Academy of Sciences Beijing China. Center for Quantitative Biology School of Mathematical Sciences Center for Statistical Sciences Peking University Beijing China. College of Life Science and Technology Huazhong University of Science and Technology Wuhan China. Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. dwsun@***. Institute of Theoretical Physics Chinese Academy of Sciences Beijing China. zheng@***. Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. dbu@***. University of Chinese Academy of Sciences Beijing China. dbu@***.

Following publication of the original article [1], the author explained that there are several errors in the original article.

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GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state

arXiv

引用

arXiv 2022年

作者： Bian, Junyi Huang, Xiaodi Zhou, Hong Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia Atypon Systems LLC United Kingdom Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization. In particular, GoSum encodes sentence states in reinforcement learning by building a heterogeneous graph for each input document at different discourse levels. An edge in the graph reflects the discourse hierarchy of a document for restraining the semantic drifts across section boundaries. We evaluate GoSum on two datasets of scientific articles summarization: PubMed and arXiv. The experimental results have demonstrated that GoSum achieve state-of-the-art results compared with strong baselines of both extractive and abstractive models. The ablation studies further validate that the performance of our GoSum benefits from the use of discourse information. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Corrigendum to “Integrated production and transportation on parallel batch machines to minimize total weighted delivery time” [Computers and Operations Research Volume 102, pages 39–51]

引用

Computers & Operations Research 2019年 105卷 248-248页

作者： Zhao-hong Jia Xue-xue Zhuo Joseph Y.-T. Leung Kai Li Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui 230039 P.R. China Department of Computer Science New Jersey Institute of Technology Newark NJ 07102 USA School of Management Hefei University of Technology Hefei Anhui 230009 P.R. China

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Customized Conversational Recommender Systems

arXiv

引用

arXiv 2022年

作者： Li, Shuokai Zhu, Yongchun Xie, Ruobing Tang, Zhenwei Zhang, Zhao Zhuang, Fuzhen He, Qing Xiong, Hui Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China WeChat Search Application Department Tencent China King Abdullah University of Science and Technology Thuwal Saudi Arabia Institute of Artificial Intelligence Beihang University Beijing100191 China Sklsde School of Computer Science Beihang University Beijing100191 China Artificial Intelligence Thrust The Hong Kong University of Science and Technology Guangzhou China The Key Lab of Intelligent Information Processing of Chinese Academy of Sciences China

Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-machine interactive system, it is essential for CRS to improve the user experience. However, most CRS methods neglect the importance of user experience. In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context. (2) Identifying fine-grained intentions, even for the same utterance, different users have diverse finegrained intentions, which are related to users' inherent preference. Based on the observations, we propose a novel CRS model, coined Customized Conversational Recommender System (CCRS), which customizes CRS model for users from three perspectives. For human-like dialogue services, we propose multi-style dialogue response generator which selects context-aware speaking style for utterance generation. To provide personalized recommendations, we extract user's current fine-grained intentions from dialogue context with the guidance of user's inherent preferences. Finally, to customize the model parameters for each user, we train the model from the meta-learning perspective. Extensive experiments and a series of analyses have shown the superiority of our CCRS on both the recommendation and dialogue service. © 2022, CC BY-SA.

关键词： Recommender systems

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Nitrogen vertical distribution by canopy reflectance spectrum in winter wheat

引用

IOP Conference Series: Earth and Environmental Science 2014年第1期17卷

作者： W J Huang Q Y Yang D L Peng L S Huang D Y Zhang G J Yang Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing 100094 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei 230039 Anhui China Beijing Agriculture Information Technology Research Center Beijing 100097 China

Nitrogen is a key factor for plant photosynthesis, ecosystem productivity and leaf respiration. Under the condition of nitrogen deficiency, the crop shows the nitrogen deficiency symptoms in the bottom leaves, while excessive nitrogen will affect the upper layer leaves first. Thus, timely measurement of vertical distribution of foliage nitrogen content is critical for growth diagnosis, crop management and reducing environmental impact. This study presents a method using bi-directional reflectance difference function (BRDF) data to invert foliage nitrogen vertical distribution. We developed upper-layer nitrogen inversion index (ULNI), middle-layer nitrogen inversion index (MLNI) and bottom-layer nitrogen inversion index (BLNI) to reflect foliage nitrogen inversion at upper layer, middle layer and bottom layer, respectively. Both ULNI and MLNI were made by the value of the ratio of Modified Chlorophyll Absorption Ration Index to the second Modified Triangular Vegetation Index (MCARI/MTVI2) referred to as canopy nitrogen inversion index (CNII) in this study at ±40° and ±50°, and at ±30° and ±40° view angles, respectively. The BLNI was composed by the value of nitrogen reflectance index (NRI) at ±20° and ±30° view angles. These results suggest that it is feasible to measure foliage nitrogen vertical-layer distribution in a large scale by remote sensing.

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