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Radio frequency interference mitigation using pseudoinverse learning autoencoders

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research in Astronomy and Astrophysics 2020年第8期20卷 121-128页

作者： Hong-Feng Wang Mao Yuan Qian Yin Ping Guo Wei-Wei Zhu Di Li Si-Bo Feng Image Processing and Pattern Recognition Laboratory School of Artificial IntelligenceBeijing Normal UniversityBeijing 100875China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Information Management Dezhou UniversityDezhou 253023China Image Processing and Pattern Recognition Laboratory School of System ScienceBeijing Normal UniversityBeijing 100875China Institute for Astronomical Science Dezhou UniversityDezhou 253023China University of Chinese Academy of Sciences Beijing 100049China Hanvon Technology Co. LtdBeijing 100193China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa

Radio frequency interference(RFI)is an important challenge in radio *** comes from various sources and increasingly impacts astronomical observation as telescopes become more *** this study,we propose a fast and effective method for removing RFI in pulsar *** use pseudo-inverse learning to train a single hidden layer auto-encoder(AE).We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra,leaving real pulsar *** method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels,which could also contain useful astronomical information.

关键词： pulsars:general methods:numerical methods:data analysis

Selecting Stickers in Open-Domain Dialogue through Multitask Learning

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arXiv 2022年

作者： Zhang, Zhexin Zhu, Yeshuang Fei, Zhengcong Zhang, Jinchao Zhou, Jie The CoAI Group DCST China Institute for Artificial Intelligence China State Key Lab of Intelligent Technology and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua University Beijing100084 China Pattern Recognition Center WeChat AI Tencent Inc China

With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a comprehensive understanding of both dialogues and stickers, as well as the relationship between the two types of modalities. To tackle these challenges, we propose a multitask learning method comprised of three auxiliary tasks to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. Extensive experiments conducted on a recent challenging dataset show that our model can better combine the multimodal information and achieve significantly higher accuracy over strong baselines. Ablation study further verifies the effectiveness of each auxiliary task. Our code is available at https://***/nonstopfor/Sticker-Selection. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation

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arXiv 2022年

作者： Ke, Pei Zhou, Hao Lin, Yankai Li, Peng Zhou, Jie Zhu, Xiaoyan Huang, Minlie The CoAI group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Pattern Recognition Center WeChat AI Tencent Inc. China Tsinghua University China

Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. Unsupervised metrics can only provide a task-agnostic evaluation result which correlates weakly with human judgments, whereas supervised ones may over-fit task-specific data with poor generalization ability to other datasets. In this paper, we propose an unsupervised reference-free metric called CTRLEval, which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks. On top of these tasks, the metric assembles the generation probabilities from a pre-trained language model without any model training. Experimental results show that our metric has higher correlations with human judgments than other baselines, while obtaining better generalization of evaluating generated texts from different models and with different qualities Copyright © 2022, The Authors. All rights reserved.

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

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An Optimizing Parameters and Feature Selection in SVM Based ...

16th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

Deep Learning Technology for Face Forgery Detection: A Survey

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arXiv 2024年

作者： Ma, Lixia Yang, Puning Xu, Yuting Yang, Ziming Li, Peipei Huang, Huaibo School of Economics and Management University of Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition CASIA Beijing China Centerfor Research on Intelligent Perception and Computing CASIA Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

Currently, the rapid development of computer vision and deep learning has enabled the creation or manipulation of high-fidelity facial images and videos via deep generative approaches. This technology, also known as deepfake, has achieved dramatic progress and become increasingly popular in social media. However, the technology can generate threats to personal privacy and national security by spreading misinformation. To diminish the risks of deepfake, it is desirable to develop powerful forgery detection methods to distinguish fake faces from real faces. This paper presents a comprehensive survey of recent deep learning-based approaches for facial forgery detection. We attempt to provide the reader with a deeper understanding of the current advances as well as the major challenges for deepfake detection based on deep learning. We present an overview of deepfake techniques and analyse the characteristics of various deepfake datasets. We then provide a systematic review of different categories of deepfake detection and state-of-the-art deepfake detection methods. The drawbacks of existing detection methods are analyzed, and future research directions are discussed to address the challenges in improving both the performance and generalization of deepfake detection. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Description-Enhanced Label Embedding Contrastive Learning for Text Classification

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arXiv 2023年

作者： Zhang, Kun Wu, Le Lv, Guangyi Chen, Enhong Ruan, Shulan Liu, Jing Zhang, Zhiqiang Zhou, Jun Wang, Meng School of Computer and Information Hefei University of Technology Anhui Hefei230029 China AI Lab Lenovo Research Beijing100094 China The School of Computer Science and Technology University of Science and Technology of China Hefei230026 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Ant Group CO. Ltd Hangzhou310007 China

Text Classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially Pre-trained Language Models (PLMs). Usually, these methods concentrate on input sentences and corresponding semantic embedding generation. However, for another essential component: labels, most existing works either treat them as meaningless one-hot vectors or use vanilla embedding methods to learn label representations along with model training, underestimating the semantic information and guidance that these labels reveal. To alleviate this problem and better exploit label information, in this paper, we employ Self-Supervised Learning (SSL) in model learning process and design a novel self-supervised Relation of Relation (R2) classification task for label utilization from a one-hot manner perspective. Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets. Meanwhile, triplet loss is employed to enhance the analysis of differences and connections among labels. Moreover, considering that one-hot usage is still short of exploiting label information, we incorporate external knowledge from WordNet to obtain multi-aspect descriptions for label semantic learning and extend R2-Net to a novel Description-Enhanced Label Embedding network (DELE) from a label embedding perspective. One step further, since these fine-grained descriptions may introduce unexpected noise, we develop a mutual interaction module to select appropriate parts from input sentences and labels simultaneously based on Contrastive Learning (CL) for noise mitigation. Extensive experiments on different text classification tasks reveal that R2-Net can effectively improve classification performance, and DELE can make better

关键词： Classification (of information)

Synchronization of Multi-Weighted Complex Networks with Mixed Variable Delays and Uncertainties Via Impulsive Pinning Control

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SSRN

SSRN 2023年

作者： Fan, Hongguang Shi, Kaibo Wen, Hui Zhao, Yi College of Computer Chengdu University Chengdu610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu610106 China School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China New Engineering Industry College Putian University Fujian Putian351100 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Province University Fujian Putian351100 China College of Mathematical and Statistical Shenzhen University Shenzhen518060 China

In this paper, we examine the synchronization problem in nonlinearly-coupled multi-weighted complex networks that contain uncertainties and varying delays. The complexities of these networks arise from the presence of both internal time-varying delays and multiple coupling time-varying delays. To address this challenge, we employ impulsive pinning control schemes and incorporate the impulsive pinning control ratio in the construction of the Lyapunov function, which allows us to determine the optimal number of pinned nodes at each pulse instant. Our analysis, based on stability theory, provides synchronization criteria that are strongly dependent on the multi-weights and time delays present in the network. Finally, we present numerical simulations of a delayed complex network to demonstrate the validity and efficacy of our derived theoretical results. © 2023, The Authors. All rights reserved.

关键词： Complex networks

Mid-infrared spectrally-pure single-photon states generation from 22 nonlinear optical crystals

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arXiv 2023年

作者： Cai, Wu-Hao Tian, Ying Jin, Rui-Bo Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Sendai980-8577 Japan Graduate School of Engineering Tohoku University 6-6 Aramaki Aza Aoba Aoba-ku Sendai980-8579 Japan Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

We theoretically investigate the preparation of pure-state single-photon source from 14 birefringent crystals (CMTC, THI, LiIO3, AAS, HGS, CGA, TAS, AGS, AGSe, GaSe, LIS, LISe, LGS, and LGSe) and 8 periodic poling crystals (LT, LN, KTP, KN, BaTiO3, MgBaF4, PMN-0.38PT, and OP-ZnSe) in a wavelength range from 1224 nm to 11650 nm. The three kinds of group-velocity-matching (GVM) conditions, the phase matching conditions, the spectral purity, and the Hong-Ou-Mandel interference are calculated for each crystal. This study may provide high-quality single-photon sources for quantum sensing, quantum imaging, and quantum communication applications at the mid-infrared wavelength range. © 2023, CC BY.

关键词： Selenium compounds

Witnessing Entangled Two-Photon Absorption via Quantum Interferometry

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Witnessing Entangled Two-Photon Absorption via Quantum Inter...

CLEO: Fundamental Science, CLEO:FS 2023 - Part of Conference on Lasers and Electro-Optics 2023

作者： Martínez-Tapia, Áulide Corona-Aquino, Samuel Triana-Arango, Freiman You, Chenglong Jin, Rui-Bo Magaña-Loaiza, Omar S. Dong, Shi-Hai U'Ren, Alfred B. de J. León-Montiel, Roberto Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Apartado Postal 70-543 Cd. Mx.04510 Mexico Centro de Investigaciones en Optica AC Apartado Postal 37150 Gto Leon Mexico Quantum Photonics Laboratory Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Research Center for Quantum Physics Huzhou University Huzhou313000 China Laboratorio de Ciencias de la Informacion Cuantica CIC Instituto Politecnico Nacional UPALM CDMXC.P 07700 Mexico

We demonstrate that a two-photon, N00N-state interferometer is insensitive to linear (single-photon) losses, and thus capable to unequivocally certify entangled two-photon absorption (eTPA) in arbitrary-sample transmi...

ISBN: (纸本)9781957171258

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