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Fusing magnitude and phase features with multiple face models for robust face recognition

Frontiers of Computer Science 2018年第6期12卷 1173-1191页

作者： Yan LI Shiguang SHAN Ruiping WANG Zhen CUI Xilin CHEN Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology (ICT)CASBeijing100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

High accuracy face recognition is of great importance for a wide variety of real-world applications. Although significant progress has been made in the last decades, fully automatic face recognition systems have not yet approached the goal of surpassing the human vision system, even in controlled conditions. In this paper, we propose an approach for robust face recognition by fusing two complementary features: one is Gabor magnitude of multiple scales and orientations and the other is Fourier phase encoded by spatial pyramid based local phase quantization (SPLPQ). To reduce the high dimensionality of both features, block-wise fisher discriminant analysis (BFDA) is applied and further combined by score-level fusion. Moreover, inspired by the biological cognitive mechanism, multiple face models are exploited to further boost the robustness of the proposed approach. We evaluate the proposed approach on three challenging databases, i.e., FRGC ver2.0, LFW, and CFW-p, that address two face classification scenarios, i.e., verification and identification. Experimental results consistently exhibit the complementarity of the two features and the performance boost gained by the multiple face models. The proposed approach achieved approximately 96% verification rate when FAR was 0.1% on FRGC ver2.0 Exp.4, impressively surpassing all the best known results.

关键词： face recognition fisher discriminant analysis fusion Gabor magnitude feature multiple face models spatial pyramid based local phase quantization

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Warm up cold-start advertisements: Improving CTR predictions via learning to learn ID embeddings

arXiv

引用

arXiv 2019年

作者： Pan, Feiyang Li, Shuokai Ao, Xiang Tang, Pingzhong He, Qing Institute of Computing Technology Chinese Academy of Sciences IIIS Tsinghua University Key Lab of Intelligent Information Processing of Chinese Academy of Sciences University of Chinese Academy of Sciences China

Click-through rate (CTR) prediction has been one of the most central problems in computational advertising. Lately, embedding techniques that produce low-dimensional representations of ad IDs drastically improve CTR prediction accuracies. However, such learning techniques are data demanding and work poorly on new ads with little logging data, which is known as the cold-start problem. In this paper, we aim to improve CTR predictions during both the cold-start phase and the warm-up phase when a new ad is added to the candidate pool. We propose Meta-Embedding, a meta-learningbased approach that learns to generate desirable initial embeddings for new ad IDs. The proposed method trains an embedding generator for new ad IDs by making use of previously learned ads through gradient-based meta-learning. In other words, our method learns how to learn better embeddings. When a new ad comes, the trained generator initializes the embedding of its ID by feeding its contents and attributes. Next, the generated embedding can speed up the model fitting during the warm-up phase when a few labeled examples are available, compared to the existing initialization methods. Experimental results on three real-world datasets showed that Meta-Embedding can significantly improve both the cold-start and warm-up performances for six existing CTR prediction models, ranging from lightweight models such as Factorization Machines to complicated deep models such as PNN and DeepFM. All of the above apply to conversion rate (CVR) predictions as well. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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DeepACE: Automated chromosome enumeration in metaphase cell images using deep convolutional neural networks

arXiv

引用

arXiv 2019年

作者： Xiao, Li Luo, Chunlong Luo, Yufan Yu, Tianqi Tian, Chan Qiao, Jie Zhao, Yi Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Peking University Third Hospital Beijing China Beijing China

Chromosome enumeration is an important but tedious procedure in karyotyping analysis. In this paper, to automate the enumeration process, we developed a chromosome enumeration framework, DeepACE, based on the region based object detection scheme. Firstly, the ability of region proposal network is enhanced by a newly proposed Hard Negative Anchors Sampling to extract unapparent but important information about highly confusing partial chromosomes. Next, to alleviate serious occlusion problems, we novelly introduced a weakly-supervised mechanism by adding a Template Module into classification branch to heuristically separate overlapped chromosomes. The template features are further incorporated into the NMS procedure to further improve the detection of overlapping chromosomes. In the newly collected clinical dataset, the proposed method outperform all the previous method, yielding an mAP with respect to chromosomes as 99.45, and the error rate is about 2.4%. Copyright © 2019, The Authors. All rights reserved.

关键词： Chromosomes

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How to write high-quality news on social network? Predicting news quality by mining writing style

arXiv

引用

arXiv 2019年

作者： Yang, Yuting Cao, Juan Lu, Mingyan Li, Jintao Lin, Chia-Wen Key Laboratory of Intelligent Information Processing Center for Advanced Computing Research Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Department of Electrical Engineering National Tsing Hua University Taiwan

Rapid development of Internet technologies promotes traditional newspapers to report news on social networks. However, people on social networks may have different needs which naturally arises the question: whether can we analyze the influence of writing style on news quality automatically and assist writers in improving news quality? It's challenging due to writing style and 'quality' are hard to measure. First, we use 'popularity' as the measure of 'quality'. It is natural on social networks but brings new problems: popularity are also influenced by event and publisher. So we design two methods to alleviate their influence. Then, we proposed eight types of linguistic features (53 features in all) according eight writing guidelines and analyze their relationship with news quality. The experimental results show these linguistic features influence greatly on news quality. Based on it, we design a news quality assessment model on social network (SNQAM). SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to. Copyright © 2019, The Authors. All rights reserved.

关键词： Linguistics

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A Compact co-Firefly Algorithm for Matching Ontologies

A Compact co-Firefly Algorithm for Matching Ontologies

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Xingsi Xue Junfeng Chen College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Ontologies can provide the shared knowledge for the intelligent systems to implement their collaboration. However, the heterogeneous entities seriously hampers two ontologies' communication. It is necessary to determine the entity mappings between two ontologies, so-called ontology matching. Recently, Swarm intelligent Algorithm (SIA) becomes the suitable methodology to determine the ontology alignment. Being inspired by the succeed of Firefly Algorithm (FA) in many domains, in this paper, a Compact co-Firefly Algorithm (CCFA) is proposed to optimize the ontology alignment. The experiment uses the benchmark of Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparing results show that CCFA-based matcher significantly outperforms other SIA-based matchers in terms of recall, precision and f-measure at 5% significance level.

关键词： Ontologies Optimization intelligent systems

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An acupoint-originated human interstitial fluid circulatory network

引用

Chinese Medical Journal 2021年第19期134卷 2365-2369页

作者： Hong-Yi Li Fang Wang Min Chen Zhi-Jian Zheng Ya-Jun Yin Jun Hu Hua Li Andreas Sammer Georg Feigl Norbert Maurer Chao Ma Fu-Sui Ji Department of Cardiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Radiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Accupuncture Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Engineering Mechanics Tsinghua UniversityBeijing 10084China Key Lab of Interfacial Physics and Technology Shanghai Institute of Applied PhysicsChinese Academy of SciencesShanghai 201210China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201210China Key Lab of Intelligent Information Processing Institute of Computing Technology and University of Chinese Academy of SciencesBeijing 100190China Ordination Dr.Sammer Dr.med.univ.Andreas SammerGrazAustria Institute of Anatomy and Clinical Morphology University of Witten/HerdeckeWittenGermany University Clinic for Physical Medicine Rehabilitation and Occupational MedicineMedical University of ViennaViennaAustria Institute of Basic Medical Sciences Chinese Academy of Medical SciencesDepartment of Human AnatomyHistology and EmbryologySchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China Chinese Institute for Brain Research Beijing 100005China

To the Editor:Since 1027,when the Tian Sheng Bronze Statue showing acupoints and meridians was first introduced for medical practitioners,it has evolved to the currently used acupoints and meridians atlas in traditional Chinese medicine(TCM).[1]To date,the acupoints represent defined areas on the body surface relative to certain *** acupoints in the extremity endings would connect anatomically with diverse associated visceral organs or tissues,forming a Meridian and Collateral *** on the atlas,each main meridian is usually a virtual line connecting a group of adjacent acupoints that correlate with certain visceral *** modern medical science,the anatomical structures of main meridians remain largely unclarified.

关键词： organs originated connecting

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An Efficiency Enhanced Graphene/n-Si Schottky Junction for Solar Cells

引用

Chinese Physics Letters 2018年第7期35卷 58-61页

作者：李刚程宏伟郭丽芳王开鹰程再军 MicroNano System Research Center College of Information and Computer & Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan 030024 School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen 361024

A novel and facile oxidation-induced self-doping process of graphene-silicon Schottky junction by nitric acid（HNO3） vapor is reported. The HNO3 oxidation process makes graphene p-type self-doped, and leads to a higher built-in potential and conductivity to enhance charge transfer and to suppress charge carrier recombination at the graphene-silicon Schottky junction. After the HNO3 oxidation process, the open-circuit voltage is increased from the initial value of 0.36 V to the maximum value of 0.47 V, the short-circuit current is greatly increased from 0.80μA to 7.71μA, and the ideality factor is optimized from 4.4 to 1.0. The enhancement of the performance of graphene-Si solar cells may be due to oxidation-induced p-type self-doping of graphene-Si junctions.

关键词： Graphene

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Predicting compositional time series via autoregressive Dirichlet estimation

引用

中国科学（信息科学） 2018年第9期61卷 268-270页

作者： Ganbin ZHOU Ping LUO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

In recent years,compositional time series (CTS) prediction has become a widely applied data analysis method for modeling tactile sequence data [1],hydrological time series data using a four-stage algorithm (denoising,decomposition,components prediction and ensemble) [2],and daily and monthly extreme temperature data [3,4].

关键词：

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Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design

arXiv

引用

arXiv 2021年

作者： Huang, Chongwen Yang, Zhaohui Alexandropoulos, George C. Xiong, Kai Wei, Li Yuen, Chau Zhang, Zhaoyang Debbah, Mérouane The College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China The ZJU-UIUC Institute Zhejiang University Haining314400 China The Centre for Telecommunications Research Department of Engineering King's College London WC2R 2LS United Kingdom The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The Singapore University of Technology and Design Singapore487372 Singapore CentraleSupélec University Paris-Saclay Gif-sur-Yvette 91192 France The Lagrange Mathematical and Computing Research Center Paris75007 France

Wireless communication in the TeraHertz band (0.1-10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops. Copyright © 2021, The Authors. All rights reserved.

关键词： Beamforming

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

引用

arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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