探讨用于网格的光网络技术

本文简要描述了用于网格的光网络总体要求,探讨了用于网格的高速大容量核心光传送网技术、波长交换技术、多层网络技术和光网络控制和管理技术的一般要求和特殊要求.最后,指出如何解决这些特殊要求将是下一代光网络研究应特别关注的问题.     

e-Science技术综述

e-Science是在全球范围内基于Internet的分布式大规模合作研究,以及能够支持这种合作研究的下一代基础体系结构。e-Science技术将对传统的科学研究方法产生革命性的影响。文章对支撑e-Science应用的基础体系结构进行了讨论,对e-Science中需要解决的关键技术问题及研究方向进行了分析,最后对我国e-Science研究的前景进行了探讨。     

Portfolio and investment risk analysis on global grids

The financial services industry today produces and consumes huge amounts of data and the processes involved in analysing these data have large and complex resource requirements. The need to analyse the data using such processes and get meaningful results in time, can be met only up to a certain extent by current computer systems. Most service providers attempt to increase efficiency and quality of their service offerings by stacking up more hardware and employing better algorithms for data processing. However, there is a limit to the gains achieved by using such an approach. One viable alternative would be to use emerging technologies such as the Grid. Grid computing and its application to various domains have been actively studied by many groups for more than a decade now. In this paper we explore the use of the Grid in the financial services domain; an area which we believe has not been adequately looked into.     

颠覆性信息技术发展及对科研信息化产生的影响

颠覆性技术是具有变革性意义的、会对已有技术产生创新性应用和颠覆性效果的技术。过去 10 年间,一些颠覆性技术已经对国家的科技创新、产业产生了巨大的能效。科学发现与研究的范式经过上千年的演进,共经历了四个阶段,分别是实验观察、理论分析、计算模拟和数据密集型科学研究。这四种范式的融合,共同成为现代科学研究方法的统一体。本文分析了五大类颠覆性信息技术的发展前景,阐述了颠覆性信息技术对计算机体系结构、大数据思维模式以及科研信息化的颠覆性影响。未来随着信息技术与信息化技术将不断发展,使得科研信息化基础设施将成为像水、电、气一样的便利的基础设施,可以随时随地、智能地满足科研人员开展科学研究的需求,人类和机器将会携手取得更加显著的科技突破。     

人工智能对科研信息化的推动作用

随着科技的不断发展,国家相关部门的重视,人工智能掀起了又一轮热潮。国家相关部门发布了关于人工智能的行动实施方案,明确了人工智能的重点研究领域和相关任务,为人工智能的强力有序发展提供了保障。本文概述了人工智能的国家战略以及主要任务,如加快建设文献、语音、图像、视频、地图等多种类数据的海量训练资源库和基础资源服务公共平台,建设支撑超大规模深度学习的新型计算集群,建立完善产业公共服务平台。本文列举了人工智能在各行各业的应用,包括化学,物理学,生物信息学,地质学,药物设计,建筑领域等方面,其中包括深度学习方法的运用,重点强调了人工智能对于科研信息化的巨大推动作用,包括超级计算领域,科学数据领域,无线网络领域,物联网领域,生物信息领域等。之后从政府、科研院所、企业多个层面详细阐述世界各国以及中国在基于人工智能的科研信息化方面的探索,对中国基于人工智能的科研信息化建设方面提出可行性建议。最后,对人工智能的应用范围以及发展前景做了展望。     

青海湖区域重要野生鸟类监测与空间分布格局研究示范应用

青海湖区域具有独特的地理和生态环境,拥有丰富的生物、生态领域的科研资源。长期以来,中国的科研人员在青海湖开展了广泛的科研活动。伴随着科研活动的深入,青海湖区域开展的科研活动开始呈现出多学科融合和交叉的趋势。随着信息技术的发展,科研人员开展科研的手段和方式也发生了变化。科研活动之间的交流,科研信息的获取和处理,出现了许多新的变化。在这种趋势下,科研人员普遍要求一种信息化的科研环境来支持其科研活动,即希望开展基于e-Science的科研活动。报告主要介绍青海湖区域重要野生鸟类资源及疫病监测与风险评估研究e-Science应用。目标是通过跨学科、跨领域、跨单位及国际性的合作研究,结合青海湖区域重要野生鸟类物种鉴别、空间分布格局、疫病监测和风险评估等科学研究活动的开展,研究信息化环境下如何采用先进的信息技术促进科学研究的学科交叉和融合,改善和优化传统的科研方式,提高科研效率。服务于青海湖国家级自然保护区候鸟保护与疫病监测与防治的需求。报告从信息技术如何支持科研活动的开展,包括科学数据的获取、数据的集成和管理、通过对海量数据的分析处理实现知识发现、知识展现等方面,以及信息化环境推动的组织机制创新,具体介绍中国科学院在青海湖区域开展的工作。     

语义网格：语义Web与网格计算的融合

本文对近几年Web服务、语义Web及网格计算等一些新兴的技术进行了简要的回顾，并对它们的背景、特点及相互关系进行了分析，在这基础上，介绍了一种新的网格发展趋势——语义网格，对它的背景、目标、体系结构及其知识层进行了详细描述并简要总结了当前语义网格的研完现状。     

支持e-Science的网格体系结构及原型研究

提出了e-Science网格的虚拟动态分层体系结构(VDHA).VDHA是具有P2P特征的分散的支持e-Science网格的体系结构.VDHA具有可扩展性、自动性、精确和完全服务发现的特点.实现了基于VDHA的验证原型系统VDHA Grid,其具有可扩展的网格信息服务.VDHA_Grid是中国大学e-Science网格项目的核心软件.也讨论了VDHA的一些优点及相关协议.     

Collider physics based on e-Science paradigm of experiment–computing–theory

Researches in the 21st century are characterized by e-Science paradigm, which is the data centric analysis as a unified concept of experiment–computing–theory. In this paper the e-Science paradigm has been realized in collider physics by constructing the unified research environment of experiment–theory and theory–computing as well as that of computing–experiment performed at KISTI (Korea Institute of Science and Technology Information). In other words, the fusion concept of collider physics of experiment, computing and theory has been applied. The goal of this approach is to study collider physics anytime, anywhere for more efficient research process. The construction of e-Science paradigm of experiment, computing and theory in collider physics provides us with new research methodology in computational physics.     

Scientific Data Collections and Distributed Collective Practice

As the basic sciences become increasingly information-intensive, the management and use of research data presents new challenges in the collective activities that constitute scholarly and scientific communication. This also presents new opportunities for understanding the role of informatics in scientific work practices, and for designing new kinds tools and resources needed to support them. These issues of data management, scientific communication and collective activity are brought together at once in scientific data collections (SDCs). What can the development and use of shared SDCs tell us about collective activity, dynamic infrastructures, and distributed scientific work? Using examples drawn from a nascent neuroscience data collection, we examine some unique features of SDCs to illustrate that they do more than act as infrastructures for scientific research. Instead, we argue that they are themselves instantiations of Distributed Collective Practice (DCP), and as such illustrate concepts of transition, emergence, and interdependency that may not be so apparent in other kinds of DCPs. We propose that research into SDCs can yield new insights into institutional arrangements, policymaking, and authority structures in other very large-scale socio-technical networks.