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An intelligent agent-based method for task allocation in competitive cloud environments

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An intelligent agent-based method for task allocation in com...

作者： Kong, Yan Zhang, Minjie Ye, Dayong Zhu, Jinxiu Choi, Junho Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Software Engineering University of Wollongong Wollongong2522 Australia Research Institute of Ocean and offshore engineering Hohai Universtiy Nantong226300 China Division of Undeclared Majors Chosun University Gwangju61452 Korea Republic of

In market-based cloud environments, both resource consumers and providers are self-interested;additionally, they can come and leave the environment freely. Therefore, the environment is competitive and uncertain. Because of the competition, participants may cheat in making deals, and this represents that the environment is insecure to resource providers who intend to earn profits through renting their resources to the tasks of resource consumers. Against this, in this paper, intelligent agents are designed to strategically quote for the tasks that they are interested in, on behalf of resource providers. Agents could quote according to the messages it obtained and the information learnt and predicted from the messages, to minimize the influence of insecure factors, such as cheating, competition, and dynamism. The experimental evaluation shows that the proposed method outperforms both a well-known multiresource negotiation-based task allocation method and a max-sum belief propagation–based method. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Intelligent agents

Discrimination of quantum states under locality constraints in the many-copy setting

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arXiv 2020年

作者： Cheng, Hao-Chung Winter, Andreas Yu, Nengkun Department of Electrical Engineering Graduate Institute of Communication Engineering Department of Mathematics National Taiwan University Tapei106 Taiwan Center for Quantum Science and Engineering National Taiwan University Tapei106 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Quantum Computing Center New Taipei City236 Taiwan ICREA-Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Física Teòrica: Informaciò i Fenòmens Quàntics Departament de Física Universitat Autònoma de Barcelona Barcelona08193 Spain Computer Science Department Stony Brook University NY United States Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

We study quantum hypothesis testing between orthogonal states under restricted local measurements in the many-copy scenario. For testing arbitrary multipartite entangled pure state against its orthogonal complement state via the local operation and classical communication (LOCC) operation, we prove that the optimal average error probability always decays exponentially in the number of copies. Second, we provide a sufficient condition for the LOCC operations to achieve the same performance as the positive-partial-transpose (PPT) operations. We further show that testing a maximally entangled state against its orthogonal complement and testing extremal Werner states both fulfill the above-mentioned condition. Hence, we determine the explicit expressions for the optimal average error probability, the optimal trade-off between the type-I and type-II errors, and the associated Chernoff, Stein, Hoeffding, and strong converse exponents. Then, we show an infinite asymptotic separation between the separable (SEP) and PPT operations by providing a pair of states constructed from an unextendible product basis (UPB). The quantum states can be distinguished perfectly by PPT operations, while the optimal error probability, with SEP operations, admits an exponential lower bound. On the technical side, we prove this result by providing a quantitative version of the well-known statement that the tensor product of UPBs is a UPB. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum entanglement

Ensemble super-resolution with a reference dataset

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arXiv 2019年

作者： Jiang, Junjun Yu, Yi Wang, Zheng Tang, Suhua Hu, Ruimin Ma, Jiayi School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Peng Cheng Laboratory Shenzhen China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Communication Engineering and Informatics University of Electro-Communications Tokyo182-8585 Japan National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan430072 China Electronic Information School Wuhan University Wuhan430072 China

By developing sophisticated image priors or designing deep(er) architectures, a variety of image Super-Resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component superresolvers). Based on the assumption that better component superresolver should have larger ensemble weight when performing SR reconstruction, we present a Maximum A Posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Specially, we introduce a reference dataset, which is composed of High-Resolution (HR) and Low-Resolution (LR) image pairs, to measure the super-resolution abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR image should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art non-deep learning based methods, four latest deep learning based methods and one ensemble learning based method, and prove its effectiveness and superiority on three public datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

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arXiv 2019年

作者： Simpson, Amber L. Antonelli, Michela Bakas, Spyridon Bilello, Michel Farahani, Keyvan Van Ginneken, Bram Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc Golia-Pernicka, Jennifer Heckers, Stephan H. Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Vorontsov, Eugene Maier-Hein, Lena Jorge Cardoso, M. Department of Surgery Memorial Sloan Kettering Cancer Center Centre for Medical Image Computing University College London Center for Biomedical Image Computing and Analytics University of Pennsylvania Division of Cancer Treatment and Diagnosis National Cancer Institute Department of Pathology Radboud University Medical Center Division of Biostatistics German Cancer Research Center Department of Electrical Engineering and Computer Science Vanderbilt University Department of Informatics Technische Universität München Google DeepMind Imaging Biomarkers and Computer-aided Diagnosis Lab Radiology and Imaging Sciences National Institutes of Health Clinical Center Department of Radiology Memorial Sloan Kettering Cancer Center Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center Department of Radiology Stanford University Department of Computer Science and Software Engineering École Polytechnique de Montréal Division of Computer Assisted Medical Interventions German Cancer Research Center Department of Imaging and Biomedical Engineering King's College London

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

A family of maximum margin criterion for adaptive learning

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arXiv 2018年

作者： Cheng, Miao Liu, Zunren Zou, Hongwei Tsoi, Ah Chung Department of Computer Science Qingdao University Qingdao China Division of Information Technology Chongqing Branch of China Merchants Bank Chongqing China Department of Computer Science and Software Engineering University of Wollongong Australia

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data sets have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Data mining

Pre-screening for non-diagnostic coronary computed tomography angiography

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European heart journal. Imaging methods and practice 2023年第2期1卷 qyad026页

作者： Ramtin Hakimjavadi Juan Lu Yeung Yam Girish Dwivedi Gary R Small Benjamin J W Chow Department of Medicine Division of Cardiology University of Ottawa Heart Institute 40 Ruskin Street Ottawa ON K1Y 4W7 Canada. Department of Medicine The University of Western Australia 35 Stirling Highway CRAWLEY Western Australia 6009 Australia. Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway CRAWLEY Western Australia 6009 Australia. Advanced Clinical and Translational Cardiovascular Imaging Harry Perkins Institute of Medical Research 6 Verdun Street Nedlands Western Australia 6009 Australia. Department of Medicine Fiona Stanley Hospital 11 Robin Warren Drive Murdoch Western Australia 6150 Australia. Department of Radiology University of Ottawa 451 Smyth Rd Ottawa ON K1H 8M5 Canada.

Aims:Indiscriminate coronary computed tomography angiography (CCTA) referrals for suspected coronary artery disease could result in a higher rate of equivocal and non-diagnostic studies, leading to inappropriate downstream resource utilization or delayed time to diagnosis. We sought to develop a simple clinical tool for predicting the likelihood of a non-diagnostic CCTA to help identify patients who might be better served with a different test. Methods and results:We developed a clinical scoring system from a cohort of 21 492 consecutive patients who underwent CCTA between February 2006 and May 2021. Coronary computed tomography angiography study results were categorized as normal, abnormal, or non-diagnostic. Multivariable logistic regression analysis was conducted to produce a model that predicted the likelihood of a non-diagnostic test. Machine learning (ML) models were utilized to validate the predictor selection and prediction performance. Both logistic regression and ML models achieved fair discriminate ability with an area under the curve of 0.630 [95% confidence interval (CI) 0.618-0.641] and 0.634 (95% CI 0.612-0.656), respectively. The presence of a cardiac implant and weight >100 kg were among the most influential predictors of a non-diagnostic study. Conclusion:We developed a model that could be implemented at the 'point-of-scheduling' to identify patients who would be best served by another non-invasive diagnostic test.

关键词： cardiac imaging techniques coronary computed tomographic angiography image interpretation non-diagnostic tests prediction model

Corrigendum to “Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques” [Renew Sustain Energy Rev 144 (2021) 11096]

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Renewable and Sustainable Energy Reviews 2021年 145卷

作者： Ghezlane Halhoul Merabet Mohamed Essaaidi Mohamed Ben Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed Riduan Abid Driss Benhaddou Smart Systems Laboratory (SSL) ENSIAS Mohammed V University in Rabat 713 Morocco Department of Engineering Technology University of Houston TX 77204 USA MENTIS Consulting SA 13 rue de Congrès 1000 Brussels Belgium Department of Cyber Systems College of Business and Technology University of Nebraska at Kearney (UNK) Kearney NE 68849 USA Information Technology University Lahore 54000 Pakistan Software Engineering Department Alfaisal University-Riyadh Saudi Arabia Information and Computing Technology (ICT) Division College of Science and Engineering (CSE) Hamad Bin Khalifa University Doha Qatar Department of Computer Science Western Michigan University Kalamazoo MI 49008 USA School of Science and Engineering Alakhawayn University in Ifrane 1005 Morocco

Multi-scale neural networks for retinal blood vessels segmentation

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arXiv 2018年

作者： Zhang, Boheng Huang, Shenglei Hu, Shaohan MOE Key Laboratory of Bioinformatics and Bioinformatics Division TNLIST Department of Automation Tsinghua University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Software Tsinghua University

Existing supervised approaches didn't make use of the low-level features which are actually effective to this task. And another deficiency is that they didn't consider the relation between pixels, which means effective features are not extracted. In this paper, we proposed a novel convolutional neural network which make sufficient use of low-level features together with high-level features and involves atrous convolution to get multi-scale features which should be considered as effective features. Our model is tested on three standard benchmarks - DRIVE, STARE, and CHASE databases. The results presents that our model significantly outperforms existing approaches in terms of accuracy, sensitivity, specificity, the area under the ROC curve and the highest prediction speed. Our work provides evidence of the power of wide and deep neural networks in retinal blood vessels segmentation task which could be applied on other medical images tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

I/Q Imbalances in QAM Communication Systems with Multi-Antenna Receivers and IF Architecture

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I/Q Imbalances in QAM Communication Systems with Multi-Anten...

IEEE Southeastcon

作者： Thomas Yang Douglas Smith Daniel Benincasa Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach Florida Information Exploitation & Operations Division Air Force Research Laboratory Information Directorate NY

ISBN: (纸本)9781538661345

The Inphase/Quadrature phase (I/Q) imbalance is an inevitable problem in wireless communication systems due to imperfections in the transceiver hardware. In this paper, we investigate the effect of I/Q imbalance in Quadrature Amplitude Modulation (QAM) communication systems adopting multiantenna receivers and the IF (Intermediate Frequency) architecture. Two application scenarios are studied: adaptive beamforming and Blind Source Separation (BSS)-based multiuser detection. For both applications, after analyzing the interference introduced by the transceiver I/Q imbalance, we illustrate that, like systems adopting the zero-IF architecture, straightforward digital signal processing algorithms can be applied in systems with the IF architecture as well to correct the effect of I/Q imbalance. The differences between systems adopting the zero-IF and IF architectures are clarified. computer simulation results are presented to confirm the conclusions of theoretic analysis.

关键词： computer architecture Array signal processing Baseband Adaptation models Quadrature amplitude modulation Receiving antennas