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Level attraction and PT symmetry in indirectly coupled microresonators

Physical Review A 2020年第4期102卷 043527-043527页

作者： Zhao-Hui Peng Chun-Xia Jia Yu-Qing Zhang Ji-Bing Yuan Le-Man Kuang Institute of Modern Physics and Department of Physics College of Physics and Electronic Science Hunan University of Science and Technology Xiangtan 411201 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China College of Physics and Electronic Engineering and Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang 421008 China

Benefiting from the tunability of their phase delay and gain, we show the possibility of realizing level attraction, parity-time (PT)-, and anti-PT-symmetric photonic structures with indirectly coupled active and passive microresonators mediated by drop-filter waveguides. We find that level attraction originates from two mechanisms, i.e., exceptional point and resonance, and it may emerge not only for dissipative coupling but also coherent coupling and hybrid coupling after considering the gain and loss of microresonators, which confirms the universality of level attraction in the coupled photonic system. On the other hand, the transition from PT to anti-PT symmetry or vice versa may be realizable by tuning their phase delay and gain; thus the indirectly coupled microresonators may provide an excellent platform for exhibiting the unique features of PT and anti-PT symmetry. Furthermore, we also reveal the intrinsic relationship of the line shape and phase behavior of the reflection spectrum with PT symmetry, anti-PT symmetry, and level attraction.

关键词： Nanophotonics Optics & lasers Quantum optics Optical microcavities Waveguides Quantum theory

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Video Captioning With Temporal And Region Graph Convolution Network

Video Captioning With Temporal And Region Graph Convolution ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xinlong Xiao Yuejie Zhang Rui Feng Tao Zhang Shang Gao Weiguo Fan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Managerment and Engineering Shanghai University of Finance and Econnomics Shanghai China School of Information Technology Deakin University Victoria Australia Department of Business Analytics University of Iowa Iowa USA

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Video captioning aims to generate a natural language description for a given video clip that includes not only spatial information but also temporal information. To better exploit such spatial-temporal information attached to videos, we propose a novel video captioning framework with Temporal Graph Network (TGN) and Region Graph Network (RGN). TGN mainly focuses on utilizing the sequential information of frames that most of existing methods ignore. RGN is designed to explore the relationships among salient objects. Different from previous work, we introduce Graph Convolution Network (GCN) to encode frames with their sequential information and build a region graph for utilizing object information. We also particularly adopt a stack GRU decoder with a coarse-to-fine structure for caption generation. Very promising experimental results on two benchmark datasets (MSVD and MSR-VTT) show the effectiveness of our model.

关键词： Decoding Feature extraction Convolution Task analysis Vocabulary Electron tubes Computational modeling

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Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Structural Neural Networks

arXiv

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arXiv 2021年

作者： Gao, Hanning Wu, Lingfei Zhang, Hongyun Wei, Zhihua Hu, Po Xu, Fangli Long, Bo Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University 4800 Cao’an Road Shanghai201804 China *** Silicon Valley Research Center 675 E Middlefield Rd Mountain ViewCA94043 United States Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University School of Computer Central China Normal University Hubei Wuhan China Squirrel AI Learning NY United States *** Beijing China

Considering a collection of RDF triples, the RDF-to-text generation task aims to generate a text description. Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence. Nevertheless, these approaches fail to clearly model the local and global structural information between and within RDF triples. Moreover, the previous methods also face the non-negligible problem of low faithfulness of the generated text, which seriously affects the overall performance of these models. To solve these problems, we propose a model combining two new graph-augmented structural neural encoders to jointly learn both local and global structural information in the input RDF triples. To further improve text faithfulness, we innovatively introduce a reinforcement learning (RL) reward based on information extraction (IE). We first extract triples from the generated text using a pretrained IE model and regard the correct number of the extracted triples as the additional RL reward. Experimental results on two benchmark datasets demonstrate that our proposed model outperforms the state-of-the-art baselines, and the additional reinforcement learning reward does help to improve the faithfulness of the generated text. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Cycle extendability of hamiltonian strongly chordal graphs

arXiv

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arXiv 2020年

作者： Rong, Guozhen Li, Wenjun Wang, Jianxin Yang, Yongjie School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Faculty of Human and Business Sciences Saarland University Saarbrücken Germany

In 1990, Hendry conjectured that all Hamiltonian chordal graphs are cycle extendable. After a series of papers confirming the conjecture for a number of graph classes, the conjecture is yet refuted by Lafond and Seamone in 2015. Given that their counterexamples are not strongly chordal graphs and they are all only 2-connected, Lafond and Seamone asked the following two questions: (1) Are Hamiltonian strongly chordal graphs cycle extendable? (2) Is there an integer k such that all k-connected Hamiltonian chordal graphs are cycle extendable? Later, a conjecture stronger than Hendry’s is proposed. In this paper, we resolve all these questions in the negative. On the positive side, we add to the list of cycle extendable graphs two more graph classes, namely, Hamiltonian 4-FAN-free chordal graphs where every induced K5 − e has true twins, and Hamiltonian {4-FAN, A}-free chordal graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Hamiltonians

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Learning Latent Semantic Attributes for Zero-Shot Object Detection

Learning Latent Semantic Attributes for Zero-Shot Object Det...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Kang Wang Lu Zhang Yifan Tan Jiajia Zhao Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineer Institute Beijing China

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

Zero-shot Object Detection (ZSD) aims to locate and classify instances of unseen categories. Existing methods focus on learning the mapping from visual space to semantic space, while the learning of discriminative representations for ZSD has not gained enough attention. In this paper, we demonstrate the necessity to learn discriminative semantic representations for ZSD, and propose a new end-to-end framework for this task. Our framework is able to learn discriminative semantic representations in an augmented space introduced for both user-defined and latent attributes, and refine the user-defined attributes with the help of unseen and external classes. The proposed method is extensively evaluated on two challenging ZSD datasets, and the experimental results show that our method significantly outperforms several existing methods.

关键词： Visualization Conferences Semantics Object detection Tools Boosting Task analysis

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Cloud-based data-sharing scheme using verifiable and CCA-secure re-encryption from indistinguishability obfuscation 14th

Cloud-based data-sharing scheme using verifiable and CCA-sec...

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14th International Conference on information Security and Cryptology, Inscrypt 2018

作者： Zhang, Mingwu Jiang, Yan Shen, Hua Li, Bingbing Susilo, Willy School of Computers Hubei University of Technology Wuhan China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong Australia

ISBN: (纸本)9783030142339

A cloud-based re-encryption scheme allows a semi-trusted cloud proxy to convert a ciphertext under delegator’s public-key into a ciphertext of delegatee’s. However, for an untrusted cloud proxy, as the re-encryption program was outsourced on the cloud, the cloud can debug the program and might have illegal activities in practice, such as monitoring the program executing, returning an incorrect re-encryption ciphertext, or colluding with the participants to obtain the sensitive information. In this work, we propose a construction of cloud-based verifiable re-encryption by incorporating new cryptographic primitives of indistinguishability obfuscation and puncturable pseudorandom functions, which can achieve the master-secret security even if the proxy colludes with the delegatee. Furthermore, our scheme can provide the white-box security in re-encryption procedure to implement the sensitive-data protection in the presence of white-box access, and it resists on chosen-ciphertext attacks in both the first-level encryption and the second-level encryption. The decryption is very efficient since it only requires several symmetric PRF operations, which can be deployed and applied in the light-weight security device such as Mobile Phones (MPs), Wireless Body Area Networks (WBANs) and nodes in Internet-of-Things (IoTs). © 2019, Springer Nature Switzerland AG.

关键词： Data obfuscation

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Automatic Generation Method of Ancient Poetry Based on LSTM

Automatic Generation Method of Ancient Poetry Based on LSTM

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Hanshuang Zhang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

This paper mainly focuses on the literary genre of ancient poetry with a certain rhythm and cadence, and proposes a novel automatic generation model of ancient poetry. The model uses about 300,000 Tang poems and Song poems as training data, uses One-hot encoding to process data, and uses long short-term memory networks (LSTMs) to learn the semantics of ancient poetry texts and conduct research across a single RNN structure. According to the user's requirements, the model can automatically calculate the most relevant coherent words in the context of the selected context, and generate common ancient poetry and specified words. The method of BLEU automatic evaluation and manual evaluation finally demonstrates the effectiveness of the experiment.

关键词： Industrial electronics Semantics Training data Manuals Rhythm Encoding Periodic structures

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Correction to: Parallel optimization of the ray-tracing algorithm based on the HPM model

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The Journal of Supercomputing 2021年第9期77卷 10333-10333页

作者： Jun-Feng, Wang Gang-Yi, Ding Yi-Ou, Wang Yu-Gang, Li Fu-Quan, Zhang School of Computer Science and Technology Beijing Institute of Technology Beijing People’s Republic of China Beijing Institute of Science and Technology Information Beijing People’s Republic of China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou People’s Republic of China

A correction to this paper has been published: https://***/10.1007/s11227-021-03680-0

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On Automatic Detection of Central Serous Chorioretinopathy and Central Exudative Chorioretinopathy in Fundus Images

On Automatic Detection of Central Serous Chorioretinopathy a...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Wen Leiting Chen Lifeng Qiao Yu Deng Siying Dai Junjing Chen Chuan Zhou School of Computer Science and Engineering Key Laboratory of Digital Media Technology of Sichuan Province Institute of Electronic and Information Engineering in Guangdong School of Medicine University of Electronic Science and Technology of China Provincial People’s Hospital King’s College London London UK

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Automatic detection of chorioretinopathy plays an important role in clinical practice, but the detection of a major chorioretinopathy of central serous chorioretinopathy based on fundus photography images has rarely been studied, let alone distinguishing it from another chorioretinopathy of central exudative chorioretinopathy. Due to the high degree of similarity between the two chorioretinopathies on fundus images, it is difficult for the latest automatic methods to accurately distinguish between them. In this study, we design a deep neural network with two branches for different classification tasks, where the first one is to distinguish the normal and abnormal while the other is to classify the two chorioretinopathies. We manage to improve the classification accuracy by combining focal loss and discriminative loss. Extensive experiments are conducted for comparison between our method and other universal classification models using a private retinal fundus dataset. The results demonstrate that our method achieves the best performance with 97.69%, 99.58% and 98.87% on the accuracy, precision and sensitivity, respectively.

关键词： Deep learning Feature extraction Retina Photography Support vector machines computer architecture Pathology

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A survey of facial expression recognition based on deep learning

A survey of facial expression recognition based on deep lear...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Heng Wei Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

Facial expression recognition is the key research direction in many fields such as machine vision, pattern recognition and artificial intelligence. lt has become a research hotspot of many scholars and experts. This paper gives a comprehensive overview of deep expression recognition from image preprocessing, common expression recognition networks, and common expression databases. Then we review the current design and training strategies of deep neural networks based on static images and dynamic images, and discuss their advantages and limitations. Finally, the future challenges and corresponding opportunities in the field are analyzed, as well as the future direction of deep learning expression recognition systems.

关键词： Deep learning Training Image recognition Databases Face recognition Neural networks Training data

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