检索结果-内蒙古大学图书馆

International Conference on computing, Mathematics and engineering Technologies (iCoMET)

作者： Syed Muslim Jameel Abdul Rehman Gilal Syed Sajjad Hussain Rizvi Mobashar Rehman Manzoor Ahmed Hashmani Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Malaysia High-Performance Cloud Computing Center (HPC3) Center for Research in Data Science (CERDAS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Faculty of Information and Communication Technology (FICT) Universiti Tunku Abdul Rahman Kampar Malaysia Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Department of computer science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Multispectral Image Analysis (MIA) is an exciting area of research and famous due to its utilization in geo monitoring, such as environmental change detection and region classification. Currently, many other areas are also emerging and are utilizing the multispectral imagery for improving the existing business processes and ensuring the reliability in the existing business methods. However, the process of generating and analyzing multispectral data needs more sophisticated techniques and methods. Also, the multispectral data possesses some temporal and structural inconsistencies, which make the existing multispectral analysis a more challenging task. Therefore, this study surveys the practical implication and possible issues in multispectral image analysis. For that reason, this study aims to investigate the two simple research questions and follow a structured analysis approach to achieve the research objectives. The rest of the paper is structured as; Section II briefly explains the systematic methodology adopted for this survey. Section III is relevant to research finding in response to the RQ1 and RQ2. Finally, Section IV presents the conclusion of this study. In future work, we will sharpen this study towards Skin Disease Analysis using Multispectral Imagery.

关键词： Image analysis Agriculture Diseases Machine learning Quality assessment Remote sensing

来源：评论

学校读者我要写书评

暂无评论

GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

引用

arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

引用

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

A Scalable Color-Based Caching Scheme in Telco-CDNs

A Scalable Color-Based Caching Scheme in Telco-CDNs

引用

International Conference on Network and Service Management

作者： Anh-Tu Ngoc Tran Thanh-Dang Diep Takuma Nakajima Masato Yoshimi Nam Thoai High Performance Computing Laboratory Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology VNUHCM Vietnam Service Strategy Sector Strategic Technology Center TIS Inc. Shinjuku-ku Japan

ISBN: (数字)9783903176249

ISBN: (纸本)9781728153964

Internet traffic is growing quickly, and it is majorly contributed by the proliferation of video services. Content Delivery Networks (CDNs) reduce the video traffic by storing replicas of videos in their cache servers. Nonetheless, the cache servers are usually located outside Internet Service Providers (ISPs). This implies that CDNs cannot reduce the video traffic inside ISP networks. To mitigate this issue, many ISPs build their own CDNs called Telco-CDNs. Genetic Algorithm-based caching is deemed the best approach in terms of traffic reduction. However, it is not practical since its computation time to generate content allocations is extremely long even when using a cluster. A color-based approach was devised to help overcome the drawback at the expense of its increase in traffic. Nevertheless, in case the number of content categories or requests proliferates quickly, the approach also has the same limitation like the Genetic Algorithm-based caching. To resolve the limitation, we propose two novel techniques to hamper the increase in the computation time. One is able to cope with the situation when the number of content categories increases while the other can deal with the circumstance when the number of requests rises. The empirical results show that the computation time is reduced 5x for the former and 7x for the latter at the expense of 1% and 12% increase in traffic for a problem of 5,000 contents, respectively.

关键词： Xenon Optical fibers

来源：评论

学校读者我要写书评

暂无评论

A framework for faster porting of scientific applications between heterogeneous clouds 1st

引用

1st International Conference on Infrastructure, Technologies and Applications, SCITA 2017

作者： Ahmed, Waseem Khan, Mohsin Khan, Adeel Ahmed Mehmood, Rashid Algarni, Abdullah Albeshri, Aiiad Katib, Iyad Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India High Performance Computing Center King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9783319941790

The emergence of pay-as-you-use compute clouds has enabled scientists to experiment with the latest processor architectures and accelerators. However, the lack of standardization in cloud computing, more specifically in the interoperability context, makes the task of portability of applications between clouds challenging. Two main tasks that users of multi-vendor clouds will need to perform are porting cost analysis and faster source-to-source translation. Cost analysis is essential to help evaluate the feasibility and cost of portability. And any automation of the source-to-source translation step will help developers perform the translation faster while taking advantage of platform-specific features. This paper presents a framework that assists a developer in performing these two tasks. The first task is achieved using the Maintainability Analyzer module which generates unique funnel shaped patterns that give an insight about the maintainability of an application and its potential for porting. Different scientific applications from various domains, that were developed using different programming paradigms, were evaluated using this module. For the second task, a set of modules use a knowledge repository to perform source-to-source translations while ensuring the maintainability of the generated code. The framework has been tested with different architecture and library combinations with promising results. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Cost benefit analysis

来源：评论

学校读者我要写书评

暂无评论

Study and analysis of hadoop cluster optimization based on configuration properties

Study and analysis of hadoop cluster optimization based on c...

引用

2017 Innovations in Power and Advanced computing Technologies, i-PACT 2017

作者： Reza, Motahar Tripathy, Badrinath Ranjan, Harsh Anant Kumar, G. High Performance Computing Lab School of Computer Science and Engineering National Institute of Science and Technology Berhampur761008 India

ISBN: (纸本)9781509056828

Hadoop framework consists of multiple components. One set of configuration parameters may not be suitable for all types of applications. Hence, for optimum performance, each framework parameter needs to be carefully chosen and adjusted. This paper highlights some of the important configuration properties related to Block size, memory allocation, CPU allocation, Number of MapReduce jobs, Job scheduling and JVM which should be tuned for improved Hadoop application performance. The result of some Benchmark problems are analyzed. © 2017 IEEE.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

performance evaluation of container-based high performance computing ecosystem using OpenPOWER 32nd

Performance evaluation of container-based high performance c...

引用

32nd International Conference on high performance computing, ISC high performance 2017

作者： Kuity, Animesh Peddoju, Sateesh Kumar High Performance Computing Lab Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India

ISBN: (纸本)9783319676296

Container-based high performance computing (HPC) has started gaining popularity due to its almost negligible performance penalty compared to the BareMetal hardware. Although HPC hardware architectures and programming models are continuously evolving, the platform models are suffering from the HPC community’s awareness. Power-awareness, hardware and application-aware co-design along with security related concerns have attracted the most in recent time to empower the platform models. Otherside, the OpenPOWER ecosystems have stepped into our life to fulfill the thirst of exploiting the last drop of a performance benefit from our invested system. It has Power8 compliant processor with a larger cache, big fat instructions and data path accompanied by the popular coherent accelerator. In this paper, we have proposed a container-based HPC ecosystem established using OpenPOWER machine. The performance of the designed and developed ecosystem is evaluated stressing on different subcomponents of the system such as processor, memory, IO, and interconnect. Finally, the results are compared with the performance of the equivalent environments made of virtual machines and BareMetal hardware. © Springer International Publishing AG 2017.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster

Energy Load Forecasting using Deep Learning Approach-LSTM an...

引用

International Conference on Emerging Applications of Information Technology (EAIT)

作者： Sumit Kumar Lasani Hussain Sekhar Banarjee Motahar Reza B. Tech CSE 3rd Year Students High Performance Computing Lab School of Computer Science and Engineering National Institute of Science & Technology Berhampur India

Electric load forecasting has a significant role in power grids in order to facilitate the decision making process of energy generation & consumption. Long term forecasting is not feasible as there might be an uncertainty in the prediction because of irregular increase in the demand for power with the growing population and dependency on electric power. Since the behaviour of electric load time series is very much non-linear and seasonal, Neural Networks are best suited model for learning the Non-Linear behaviour within the data and for forecasting purpose. This paper deals with the Recurrent Neural Networks based Models: Long-Short-Term-Memory (LSTM) and Gated-Recurrent-Unit (GRU) to deal with this challenge. Observations have been made based on the distributed implementation of various configurations of LSTM-RNN & GRU-RNN on spark clusters for hyper parameter tuning purpose and deploying best suited configuration with least RMSE value using apache memos resource management.

关键词： Load modeling Predictive models Logic gates Computational modeling Load forecasting Forecasting Sparks

来源：评论

学校读者我要写书评

暂无评论

Laser-Damage Attack Against Optical Attenuators in Quantum Key Distribution

引用

Physical Review Applied 2020年第3期13卷 034017-034017页

作者： Anqi Huang Ruoping Li Vladimir Egorov Serguei Tchouragoulov Krtin Kumar Vadim Makarov Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People’s Republic of China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Faculty of Photonics and Optical Information ITMO University 199034 Kadetskaya line 3b St. Petersburg Russia QGLex Incorporated 105 Schneider Road Suite 111 Ottawa Ontario K2K 1Y3 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia

Many quantum key distribution systems employ a laser followed by an optical attenuator to prepare weak coherent states in the source. Their mean photon number must be precalibrated to guarantee the security of key distribution. Here we experimentally show that this calibration can be broken with a high-power laser attack. We test four fiber-optic attenuator types used in quantum key distribution systems, and find that two of them exhibit a permanent decrease in attenuation after laser damage. This results in higher mean photon numbers in the prepared states and may allow an eavesdropper to compromise the key.

关键词： Light-matter interaction Photon statistics Photonics Quantum communication Quantum cryptography Quantum optics Devices Fiber lasers

来源：评论

学校读者我要写书评

暂无评论

Referral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study

引用

The Lancet Digital Health 2021年第1期3卷 e29-e40页

作者： Tham, Yih-Chung Anees, Ayesha Zhang, Liang Goh, Jocelyn Hui Lin Rim, Tyler Hyungtaek Nusinovici, Simon Hamzah, Haslina Chee, Miao-Li Tjio, Gabriel Li, Shaohua Xu, Xinxing Goh, Rick Tang, Fangyao Cheung, Carol Yim-Lui Wang, Ya Xing Nangia, Vinay Jonas, Jost B Gopinath, Bamini Mitchell, Paul Husain, Rahat Lamoureux, Ecosse Sabanayagam, Charumathi Wang, Jie Jin Aung, Tin Liu, Yong Wong, Tien Yin Cheng, Ching-Yu Singapore Eye Research Institute Singapore National Eye Centre Singapore Duke-NUS Medical School Singapore Institute of High Performance Computing A*STAR Singapore School of Chemical and Biomedical Engineering Division of Bioengineering Nanyang Technological University Singapore Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region Hong Kong Beijing Institute of Ophthalmology Beijing Ophthalmology and Visual Science Key Lab Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing China Suraj Eye Institute Nagpur India Department of Ophthalmology Medical Faculty Mannheim of the Ruprecht-Karis-University Heidelberg Mannheim Germany Centre for Vision Research Department of Ophthalmology The Westmead Institute for Medical Research The University of Sydney Westmead Hospital Westmead NSW Australia Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore

Background: In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment. Methods: In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any disease-related visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC). Findings: In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94·2% (95% CI 93·0–95·3;sensitivity 90·7% [87·0–93·6];specificity 86·8% [85·6–87·9]). The AUC for moderate or worse disease-related visual impairment was 93·9% (95% CI 92·2–95

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：