The Globus Toolkit （GT） has been developed since the late 1990s to support the development of serviceoriented distributed computing applications and infrastructures. Core GT components address, within a common frame...The Globus Toolkit （GT） has been developed since the late 1990s to support the development of serviceoriented distributed computing applications and infrastructures. Core GT components address, within a common framework, fundamental issues relating to security, resource access, resource management, data movement, resource discovery, and so forth. These components enable a broader ＂Globus ecosystem＂ of tools and components that build on, or interoperate with, GT functionality to provide a wide range of useful application-level functions. These tools have in turn been used to develop a wide range of both ＂Grid＂ infrastructures and distributed applications. I summarize here the principal characteristics of the recent Web Services-based GT4 release, which provides significant improvements over previous releases in terms of robustness, performance,, usability, documentation, standards compliance, and functionality. I also introduce the new ＂dev.globus＂ community development process, which allows a larger community to contribute to the development of Globus software.展开更多
Robust face representation is imperative to highly accurate face recognition. In this work, we propose an open source face recognition method with deep representation named as VIPLFaceNet, which is a lO-layer deep con...Robust face representation is imperative to highly accurate face recognition. In this work, we propose an open source face recognition method with deep representation named as VIPLFaceNet, which is a lO-layer deep convolu- tional neural network with seven convolutional layers and three fully-connected layers. Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training time and 60% testing time, but achieves 40% drop in error rate on the real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean accuracy on LFW us- ing one single network. An open-source C＋＋ SDK based on VIPLFaceNet is released under BSD license. The SDK takes about 150ms to process one face image in a single thread on an i7 desktop CPU. VIPLFaceNet provides a state-of-the-art start point for both academic and industrial face recognition applications.展开更多
基金Work on Giobus has been supported in part by the Mathematical, Information, and Computational Sciences Division subprogram of the 0ffice of Advanced Scientific Computing Research, U.S. Department of Energy, under Contract W-31-109-Eng-38, by the National Science Foundation （NSF）＇s 0ffice of Cyberinfrastructure and other programs, and by IBM, DARPA, NASA, Microsoft, the UK Engineering and Physical Sciences Research Council and Department of Trade and Industry, and the Swedish Research Council. I report here on the work of many talented colleagues, as detailed at www.globus.org. The core team is currently based primarily at Argonne National Lab, U. Chicago, the USC Information Sciences Institute, U. Edinburgh, the Royal Institute of Technology, the National Center for Supercomputing Applications, and Univa Corporation, but many others have also contributed to Globus code, documentation, and testing, and/or made our work worthwhile by using the software.
文摘The Globus Toolkit （GT） has been developed since the late 1990s to support the development of serviceoriented distributed computing applications and infrastructures. Core GT components address, within a common framework, fundamental issues relating to security, resource access, resource management, data movement, resource discovery, and so forth. These components enable a broader ＂Globus ecosystem＂ of tools and components that build on, or interoperate with, GT functionality to provide a wide range of useful application-level functions. These tools have in turn been used to develop a wide range of both ＂Grid＂ infrastructures and distributed applications. I summarize here the principal characteristics of the recent Web Services-based GT4 release, which provides significant improvements over previous releases in terms of robustness, performance,, usability, documentation, standards compliance, and functionality. I also introduce the new ＂dev.globus＂ community development process, which allows a larger community to contribute to the development of Globus software.
基金This work was partially supported by the National Basic Research Program of China （973 Program） （2015CB351802）, and the National Natural Science Foundation of China （Grant Nos. 61402443, 61390511, 61379083, 61222211）.
文摘Robust face representation is imperative to highly accurate face recognition. In this work, we propose an open source face recognition method with deep representation named as VIPLFaceNet, which is a lO-layer deep convolu- tional neural network with seven convolutional layers and three fully-connected layers. Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training time and 60% testing time, but achieves 40% drop in error rate on the real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean accuracy on LFW us- ing one single network. An open-source C＋＋ SDK based on VIPLFaceNet is released under BSD license. The SDK takes about 150ms to process one face image in a single thread on an i7 desktop CPU. VIPLFaceNet provides a state-of-the-art start point for both academic and industrial face recognition applications.
