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arXiv 2019年

作者： Liu, Xuejing Li, Liang Wang, Shuhui Zha, Zheng-Jun Su, Li Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China

Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image region (proposal) and the query is unknown in the training stage. In referring expressions, people usually describe a target entity in terms of its relationship with other contextual entities as well as visual attributes. However, previous weakly supervised REG methods rarely pay attention to the relationship between the entities. In this paper, we propose a knowledge-guided pairwise reconstruction network (KPRN), which models the relationship between the target entity (subject) and contextual entity (object) as well as grounds these two entities. Specifically, we first design a knowledge extraction module to guide the proposal selection of subject and object. The prior knowledge is obtained in a specific form of semantic similarities between each proposal and the subject/object. Second, guided by such knowledge, we design the subject and object attention module to construct the subject-object proposal pairs. The subject attention excludes the unrelated proposals from the candidate proposals. The object attention selects the most suitable proposal as the contextual proposal. Third, we introduce a pairwise attention and an adaptive weighting scheme to learn the correspondence between these proposal pairs and the query. Finally, a pairwise reconstruction module is used to measure the grounding for weakly supervised learning. Extensive experiments on four large-scale datasets show our method outperforms existing state-of-the-art methods by a large margin1. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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Multifractal temporally weighted detrended partial cross-correlation analysis of two non-stationary time series affected by common external factors

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Physica A: Statistical Mechanics and its Applications 2021年 573卷

作者： Bao-Gen Li Dian-Yi Ling Zu-Guo Yu Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia

When common factors strongly influence two cross-correlated time series recorded in complex natural and social systems, the results will be biased if we use multifractal detrended cross-correlation analysis (MF-DXA) without considering these common factors. In order to better study the time series of such cases, we extend the multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group (Wei et al., 2017) and propose multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA) to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors in this paper. To test the performance of MF-TWDPCCA, we apply it and multifractal partial cross-correlation analysis (MF-DPXA) proposed by Qian et al. (2015) on simulated series. Numerical tests on artificially simulated series demonstrate that MF-TWDPCCA can more accurately detect the intrinsic cross-correlations for two simultaneously recorded series than MF-DPXA and MF-TWXDFA. To further show the utility of MF-TWDPCCA, we apply it on time series from stock markets and find that there exists significantly multifractal power-law cross-correlation between stock returns. In addition, a new partial cross-correlation coefficient is defined to quantify the level of intrinsic cross-correlation between two time series.

关键词： Partial cross-correlation Multifractal temporally weighted detrended partial cross-correlation analysis Partial cross-correlation coefficient

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Color Space Volume and Superpixel Based Leukocyte Image Segmentation

Color Space Volume and Superpixel Based Leukocyte Image Segm...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Xiaogen Zhou Zuoyong Li Chuansheng Wang Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou P.R. China College of Mathematics and Computer Science Fuzhou University Fuzhou P.R. China School of Computer Science and Technology Harbin University of Science and Technology Harbin P.R. China

In this paper, we propose a novel method based on color space volume and multimodal histogram thresholding (MHT) for leukocyte segmentation in human blood smear images under the rapid staining condition. The proposed method first defines a new color space volume to highlight the leukocyte's nucleus region, and it then uses the MHT method to extract the nucleus. Then, the proposed method takes the central region in the superpixel segmentation result as an initial leukocyte region, and it then uses the MHT method to extract the leukocyte region. Finally, the cytoplasm is obtained by subtracting the nucleus region from the leukocyte region. Experimental results on a dataset containing 300 leukocyte images under rapid staining condition show that the proposed method provides a higher segmentation accuracy than the counterparts.

关键词： Image segmentation Image color analysis Histograms White blood cells Clustering algorithms Shape

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Blockchain-based intelligent network management for 5g and beyond 3

Blockchain-based intelligent network management for 5g and b...

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3rd International Conference on Advanced information and Communications Technologies, AICT 2019

作者： Maksymyuk, Taras Gazda, Juraj Han, Longzhe Jo, Minho Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine Department of Computers and Informatics Technical University of Kosice Kosice Slovakia Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Department of Computer Convergence Software Korea University Sejong Metropolitan City Korea Republic of

ISBN: (纸本)9781728123998

The concept of spectrum and infrastructure sharing appeared to solve the problem by allowing operators to cooperate between each other. However, the current architecture of mobile network, which is mostly based on the centralized management entities is not suitable for the spectrum and infrastructure sharing due to the complexity of billing procedure. Moreover, the operator oriented mobile network is slowing the overall technological development, because operators must balance between capital expenditures and profit margins. Such situation negatively affects users, because they are either experience lower quality of service or forced to pay higher price. In this paper, we propose a new intelligent network architecture, which leverage the blockchain technology to handle relationship between operators and users based on smart contracts. The new unlicensed spectrum sharing algorithm between operators using the virtual cryptocurrency has been developed based on game theory. Simulation results show that proposed algorithm achieves Nash equilibrium between operators within few seconds. © 2019 IEEE.

关键词： Game theory

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Towards Distributed Privacy-Preserving Prediction

Towards Distributed Privacy-Preserving Prediction

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lingjuan Lyu Yee Wei Law Kee Siong Ng Shibei Xue Jun Zhao Mengmeng Yang Lei Liu National University of Singapore School of Engineering University of South Australia Software Innovation Institute Australian National University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University School of Computer Science and Engineering Nanyang Technological University Singapore Unicloud Engine Technology Co. Ltd China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models' predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.

关键词： Privacy Training data Machine learning Predictive models Data models Encryption Noise measurement

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Modeling of graphene based strong coulping nanodevice

Modeling of graphene based strong coulping nanodevice

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Xingang Ren Yong Wang Hao Ren Zhixiang Huang Xianliang Wu Guangdong Provincial Key Laboratory of Nano-Micro Materials Research Peking University Shenzhen Graduate School Shenzhen China Ministry of Education Institute of Physical Science and Information Technology Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

The monolayer graphene has received intensive attention due to its unique characteristics, such as atomic thickness, tunable Fermi level by doping. Here, we will present comprehensive studies from theoretical modeling to application of graphene based nanodevice. Our work indicates that graphene can be considered as bulk material with an effective thickness. In addition, we propose graphene as nanospacer forming a strong coupling sandwiched nanodevice, which realizes strong coupling between localized surface plasmon of metal nanoparticle and surface plasmon of planar metal film. Our work is of critical importance for graphene modeling and applications.

关键词： Graphene Plasmons Conductivity Metals Couplings Permittivity Sensors

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Relay selections for cooperative underlay CR systems with energy harvesting

Relay selections for cooperative underlay CR systems with en...

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作者： Ye, Jia Liu, Zhedong Zhao, Hui Pan, Gaofeng Ni, Qiang Alouini, Mohamed-Slim Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom

In this paper, we consider a cooperative underlay cognitive radio system in which one secondary user (S) transmits its data to a secondary receiver with the assistance of multiple relays. In the considered underlay spectrum sharing scenario, the transmit power levers at S and forwarding relays are adjusted simultaneously according to a given threshold interference power that the primary user can tolerate. We also consider that relays adopt threshold decode-and-forward relay scheme and harvest energy from the primary transmitter with finite energy storage capacity under time switching scheme. Three best relay selection (RS) schemes, optimal source-relay link with wireless energy harvesting (WEH), optimal relay-destination link with WEH and optimal source-relay-destination link with WEH schemes, are proposed and studied. Closed-form analytical expressions for the outage probability of these three best RS schemes are derived, respectively. Finally, our analytical results are verified by Monte-Carlo simulations. © 2015 IEEE.

关键词： Energy harvesting

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Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

Constrained Multi-scale Dense Connections for Accurate Biome...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawei Zhang Yanchun Zhang Shanfeng Zhu Xiaowei Xu Shanghai key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China College of Engineering and Science Victoria University Melbourne Australia ISTBI and Shanghai Institute of Artificial Intelligence Algorithms Fudan University Shanghai China Nanjing University Institute of Artificial Intelligence Biomedicine Nanjing China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Guangdong Provincial People’s Hospital Guangdong Cardiovascular Institute Guangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical image segmentation plays a critical role in clinical diagnosis and medical intervention. Recently, a variety of deep neural networks have boosted the biomedical image segmentation performance with a large margin, which adopts dense connections to explore rich representations in multiple scales. In multi-scale dense connections, features from all or most scales are fused or iteratively aggregated. In this paper, we propose constrained multi-scale dense connections (CMDC) for accurate biomedical image segmentation, which only fuse features from the nearest scales containing the most relevant appearance or semantic information. Based on CMDC, we further construct constraint multi-scale dense networks (CMD-Net) by applying CMDC to existing segmentation networks. Experiments across various architectures (including FCN-8s, U-Net, and DeepLabV3) and datasets (including GlaS, CRAG, KID, and ECS) demonstrate that CMD-Net not only outperforms existing schemes on both accuracy and efficiency but also can be easily generalized to a variety of segmentation networks. In addition, CMD-Net achieves state-of-the-art performance on two instance segmentation datasets, GlaS and CRAG.

关键词： Decoding Image segmentation Image resolution Biomedical imaging Glands Fuses Cancer

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Self-repairing high entropy oxides

arXiv

引用

arXiv 2021年

作者： Liu, Zongwen Huang, Pengru Sun, Lixian Liu, Yanping Qu, Jiangtao Cairney, Julie Zheng, Zhong Wang, Zhiming M. Khan, Naveed A. Lai, Zhiping Fu, Li Teng, Bing Zhou, Cuifeng Zhao, Hong Xu, Fen Xiong, Pan Zhu, Junwu Yuan, Peng Tsoutas, Kosta Akhavan, Behnam Bilek, Marcela M. Ringer, Simon P. Novoselov, Kostya S. School of Chemical and Biomolecular Engineering The University of Sydney NSW2006 Australia The University of Sydney Nano Institute The University of Sydney NSW2006 Australia Department of Materials Science and Engineering National University of Singapore 117575 Singapore Guangxi Key Laboratory of Information Materials Guangxi Collaborative Innovation Center of Structure and Property for New Energy and Materials School of Material Science & Engineering Guilin University of Electronic Technology Guilin541004 China School of Physics and Electronics Hunan Key Laboratory for Super-Microstructure and Ultrafast Process Central South University Hunan410083 China School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia The Australian Centre for Microscopy and Microanalysis The University of Sydney NSW2006 Australia Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China Advanced Membranes and Porous Materials Center Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia State Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an710072 China College of Physics Qingdao University Qingdao266071 China Key Laboratory for Soft Chemistry and Functional Materials Ministry Education Nanjing University of Science and Technology Nanjing210094 China CAS Key Laboratory of Mineralogy and Metallogeny Guangdong Provincial Key laboratory of Mineral Physics and Materials Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou510640 China School of Physics The University of Sydney NSW2006 Australia School of Biomedical Engineering The University of Sydney NSW2006 Australia

All biological organisms, from plants to living creatures, can heal minor wounds and damage. The realization of a similar self-healing capacity in inorganic materials has been a design target for many decades. This would represent a breakthrough in materials engineering, enabling many novel technological applications, since such materials would be able to resist damage caused by electromagnetic irradiation and/or mechanical impact. Here we demonstrate that a high-entropy oxide is intrinsically capable of undergoing an autonomous self-repairing process. Transmission electron microscopy revealed that the spinel structure of (AlCoCrCu0.5FeNi)3O4 can regrow and repair itself at the atomic level when damaged. Density functional theory calculations reveal that the extra enthalpy stored in the high entropy material during fabrication can be released to effectively heal macroscopic defects by regrowing into a partially ordered state. This extraordinary self-repairing phenomenon makes this new material highly desirable as a coating, enabling structures used in harsh environments to better withstand damage, such as cosmic irradiation in space, nuclear irradiation in nuclear power facilities, or tribological damage. Most importantly, our results set the general design principles for the synthesis of self-repairing materials. Copyright © 2021, The Authors. All rights reserved.

关键词： Entropy

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Direct speech-to-image translation

arXiv

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arXiv 2020年

作者： Li, Jiguo Zhang, Xinfeng Jia, Chuanmin Xu, Jizheng Zhang, Li Wang, Yue Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Video Technology School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China Peng Cheng Lab Shenzhen China Bytedance Inc

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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