National Institute of Standards and Technology における超伝導研究及び生活

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

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Properties Of Concrete

The Globalization of Ethics: Universal Declaration of Human Rights

We say that a network coding scheme is strongly 1-secure if a source node s can multicast n field elements {m1, · · · ,mn} to a set of sink nodes {t1, · · · , tq} in such a way that any single edge leaks no information on any S ⊂ {m1, · · · ,mn} with |S| = n − 1, where n = mintimax-flow(s, ti) is the maximum transmission capacity. We also say that a strongly h-secure network coding scheme is strongly (h + 1)secure if any h + 1 edges leak no information on any S ⊂ {m1, · · · ,mn} with |S| = n − (h + 1). In this paper, we show the first explicit algorithm which can construct strongly k-secure network coding schemes. In particular, it runs in polynomial time for fixed k.

Information Theoretic Security

This paper presents the results of an experimental study on the compressive behavior of circular and noncircular concrete columns confined by polyethylene terephthalate (PET) fiber-reinforced polymer (FRP). The main objective of this study is to observe the compressive behavior of PET FRP–confined noncircular concrete columns, in which the cross-sectional geometry significantly influences the effectiveness of FRP confinement. For this purpose, 54 specimens were tested under monotonic axial compression. The parameters considered were the cross-sectional shape (i.e., circular, square, and rectangular), the corner radius in case of square and rectangular specimens, and the number of FRP layers. The results of this study showed that, unlike the bilinear stress-strain response of square and rectangular concrete columns confined by other FRP materials, PET FRP–confined square and rectangular concrete columns exhibit a distinct trilinear relationship. Owing to the large rupture strain (LRS) characteristi...

Compressive Behavior of PET FRP–Confined Circular, Square, and Rectangular Concrete Columns

Lateral response of PET FRP-confined concrete

Effect of waste electronic plastic and silica fume on mechanical properties and thermal performance of concrete

Compressive behavior of basalt FRP-confined circular and non-circular concrete specimens

The improved strength and ductility of fiber-reinforced polymer (FRP) confined concrete is a result of external confinement, which places an effective constraint on its lateral dilation. Fo...

Shahzad Saleem

Papers

Compressive Behavior of PET FRP–Confined Circular, Square, and Rectangular Concrete Columns

Lateral response of PET FRP-confined concrete

Effect of waste electronic plastic and silica fume on mechanical properties and thermal performance of concrete

Compressive behavior of basalt FRP-confined circular and non-circular concrete specimens

Dilation Characteristics of PET FRP–Confined Concrete