The Experts below are selected from a list of 110685 Experts worldwide ranked by ideXlab platform

Leslie Conrad - One of the best experts on this subject based on the ideXlab platform.

Lei Ying - One of the best experts on this subject based on the ideXlab platform.

  • Fascinate fast cross layer dependency inference on multi layered networks
    Knowledge Discovery and Data Mining, 2016
    Co-Authors: Chen Chen, Hanghang Tong, Lei Xie, Lei Ying
    Abstract:

    Multi-layered networks have recently emerged as a new network model, which naturally finds itself in many high-impact application domains, ranging from critical inter-dependent infrastructure networks, biological systems, organization-level collaborations, to cross-platform e-commerce, etc. Cross-layer dependency, which describes the dependencies or the associations between nodes across different layers/networks, often plays a central role in many data mining tasks on such multi-layered networks. Yet, it remains a daunting task to accurately know the cross-layer dependency a prior. In this paper, we address the problem of inferring the missing cross-layer dependencies on multi-layered networks. The key idea behind our method is to view it as a collective collaborative filtering problem. By formulating the problem into a regularized optimization model, we propose an effective algorithm to find the local optima with linear complexity. Furthermore, we derive an online algorithm to accommodate newly arrived nodes, whose complexity is just linear wrt the size of the neighborhood of the new node. We perform extensive empirical evaluations to demonstrate the effectiveness and the efficiency of the proposed methods.

  • KDD - Fascinate: Fast Cross-Layer Dependency Inference on Multi-layered Networks
    Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016
    Co-Authors: Chen Chen, Hanghang Tong, Lei Ying, Qing He
    Abstract:

    Multi-layered networks have recently emerged as a new network model, which naturally finds itself in many high-impact application domains, ranging from critical inter-dependent infrastructure networks, biological systems, organization-level collaborations, to cross-platform e-commerce, etc. Cross-layer dependency, which describes the dependencies or the associations between nodes across different layers/networks, often plays a central role in many data mining tasks on such multi-layered networks. Yet, it remains a daunting task to accurately know the cross-layer dependency a prior. In this paper, we address the problem of inferring the missing cross-layer dependencies on multi-layered networks. The key idea behind our method is to view it as a collective collaborative filtering problem. By formulating the problem into a regularized optimization model, we propose an effective algorithm to find the local optima with linear complexity. Furthermore, we derive an online algorithm to accommodate newly arrived nodes, whose complexity is just linear wrt the size of the neighborhood of the new node. We perform extensive empirical evaluations to demonstrate the effectiveness and the efficiency of the proposed methods.

Chen Chen - One of the best experts on this subject based on the ideXlab platform.

  • Fascinate fast cross layer dependency inference on multi layered networks
    Knowledge Discovery and Data Mining, 2016
    Co-Authors: Chen Chen, Hanghang Tong, Lei Xie, Lei Ying
    Abstract:

    Multi-layered networks have recently emerged as a new network model, which naturally finds itself in many high-impact application domains, ranging from critical inter-dependent infrastructure networks, biological systems, organization-level collaborations, to cross-platform e-commerce, etc. Cross-layer dependency, which describes the dependencies or the associations between nodes across different layers/networks, often plays a central role in many data mining tasks on such multi-layered networks. Yet, it remains a daunting task to accurately know the cross-layer dependency a prior. In this paper, we address the problem of inferring the missing cross-layer dependencies on multi-layered networks. The key idea behind our method is to view it as a collective collaborative filtering problem. By formulating the problem into a regularized optimization model, we propose an effective algorithm to find the local optima with linear complexity. Furthermore, we derive an online algorithm to accommodate newly arrived nodes, whose complexity is just linear wrt the size of the neighborhood of the new node. We perform extensive empirical evaluations to demonstrate the effectiveness and the efficiency of the proposed methods.

  • KDD - Fascinate: Fast Cross-Layer Dependency Inference on Multi-layered Networks
    Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016
    Co-Authors: Chen Chen, Hanghang Tong, Lei Ying, Qing He
    Abstract:

    Multi-layered networks have recently emerged as a new network model, which naturally finds itself in many high-impact application domains, ranging from critical inter-dependent infrastructure networks, biological systems, organization-level collaborations, to cross-platform e-commerce, etc. Cross-layer dependency, which describes the dependencies or the associations between nodes across different layers/networks, often plays a central role in many data mining tasks on such multi-layered networks. Yet, it remains a daunting task to accurately know the cross-layer dependency a prior. In this paper, we address the problem of inferring the missing cross-layer dependencies on multi-layered networks. The key idea behind our method is to view it as a collective collaborative filtering problem. By formulating the problem into a regularized optimization model, we propose an effective algorithm to find the local optima with linear complexity. Furthermore, we derive an online algorithm to accommodate newly arrived nodes, whose complexity is just linear wrt the size of the neighborhood of the new node. We perform extensive empirical evaluations to demonstrate the effectiveness and the efficiency of the proposed methods.

Hong-yan Liu - One of the best experts on this subject based on the ideXlab platform.

  • Fascinated Nanofiber Yarns: From Experiment to Industrialization.
    Recent Patents on Nanotechnology, 2020
    Co-Authors: Hong-yan Liu
    Abstract:

    Background Bubble electrospinning patent has been commercially used for the massproduction of various nanofibers, but its application to the fabrication of nanofiber yarns is less studied. We assume that there is great potential in this direction. Objective This paper focuses on bubble electrospinning with an emphasis on new technologies for the fabrication of Fascinated nanofiber yarns by the bubble electrospinning. Methods The paper begins with the mechanism of the bubble electrospinning to introduce how it produces Fascinated nanofiber yarns experimentally, then the industrialization of Fascinated nanofiber yarns is illustrated. Results The bubble electrospinning is extremely suitable for the fabrication of Fascinated nanofiber yarns with a hierarchical structure, and the hierarchy can be designed biomimetically according to some natural fibers. Conclusion This paper sheds light on both experimental study and industrial applications of Fascinated nanofiber yarns.

  • A novel method for fabrication of Fascinated nanofiber yarns
    Thermal Science, 2015
    Co-Authors: Hong-yan Liu
    Abstract:

    Potential applications of nanofibers as a new-generation of material will be realized if suitable nanofiber yarns become available. Electrospinning has been widely accepted as a feasible technique for the fabrication of continuous nanofiber yarns. However its low output limited its industrial applications. This paper presents a new processing approach to fabrication of Fascinated nanofiber yarns which possess excellent properties of nanofibers while enhancing its mechanical strength by the core yarn.

Gert Kienast - One of the best experts on this subject based on the ideXlab platform.

  • Report on final demonstration. Fascinate deliverable D6.3.1
    2013
    Co-Authors: Graham Thomas, Javier Ruiz Hidalgo, Georg Thallinger, Gert Kienast, Oliver Schreer, Robert Oldfield, Jean François Macq, Martin Prins
    Abstract:

    The objective of WP6 in the Fascinate project is to organise a series of convincing demonstrations that raise awareness of the project in the broadcast and media industry, as well as providing focal points for the technical work of the project. This document reports on the third and final public demonstration of Fascinate technology, held at MediaCity UK, at the premises of the University of Salford. The centrepiece of the demonstration was the use of the end-to-end Fascinate chain being used to capture, deliver and display a live music and dance performance staged in the University’s Digital Performance Lab. The performance ran three times during the day, with each show being preced ed by a 30-minute presentation to introduce the project and explain the various aspects of the technol ogy that were about to be demonstrated. This was accompanied by a set of stand-alone demonstrations that ran throughout the day, giving more in-depth insights into various results from the project. The event also resulted in several press publications, which are also listed in this deliverable. During the demonstrations, audio and video data were captured to support the evaluation tasks during the remainder of the project, and for use for research beyond the end of the project.

  • the Fascinate production scripting engine
    Conference on Multimedia Modeling, 2012
    Co-Authors: Rene Kaiser, Wolfgang Weiss, Gert Kienast
    Abstract:

    In the realm of a format agnostic live event broadcast system, the Fascinate Scripting Engines are software components that automate taking decisions on what is visible and audible at each playout device and prepare the audiovisual content streams for display. Essentially, they act together as a Virtual Director with the production team possibly steering it via a backend user interface. We present an architecture for this real-time system and describe interfaces to other production components. Details of subcomponents of the distributed engine, design decisions and technology choices are discussed.

  • MMM - The Fascinate production scripting engine
    Lecture Notes in Computer Science, 2012
    Co-Authors: Rene Kaiser, Wolfgang Weiss, Gert Kienast
    Abstract:

    In the realm of a format agnostic live event broadcast system, the Fascinate Scripting Engines are software components that automate taking decisions on what is visible and audible at each playout device and prepare the audiovisual content streams for display. Essentially, they act together as a Virtual Director with the production team possibly steering it via a backend user interface. We present an architecture for this real-time system and describe interfaces to other production components. Details of subcomponents of the distributed engine, design decisions and technology choices are discussed.