MainstreamBIO Multi-actor Innovation Platforms
A report outlining the findings of the mapping exercise and the activities related to the formation of the MIPs, as well as their baseline operational model conducted during T1.1.
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IntelliScheduler: an edge-cloud computing environment hybrid deep learning framework for task scheduling based on learning. [PDF]
Raju LR +5 more
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A cognitive internet of things resource allocation method based on multi-agent reinforcement learning algorithm. [PDF]
Wang R, Shen Y, Wang D, Li W.
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DRL-Based Beam Split Alleviation for Movable Antenna-Enabled Near-Field Wideband Communications. [PDF]
Zhang T, Jiang R, Dai H, Zhou C, Xu Y.
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A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion. [PDF]
Zhong K +5 more
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Policy lessons regarding medical security for rare diseases inChina: Insights from Zhejiang Province through the multiplestreams framework. [PDF]
Wang M, Rong C, Wang P.
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Deep deterministic policy gradient based routing protocol for UWSNs. [PDF]
Gola KK, Chakraborty S.
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A multi-actor analysis of the QoE environment
2010 9th Conference of Telecommunication, Media and Internet, 2010The concept of Quality of Experience (QoE) comprises quality as perceived by the end user. The implementation of technologies that allow to provide a high and constant level of QoE involves an extensive network of actors, each having its own strategy and chasing different goals.
Tobias Heger, Thomas Monath, Mario Kind
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Multi‐actor systems and ethics
International Transactions in Operational Research, 2010AbstractThis paper looks at implicit and explicit ethical aspects of research and decision making in complex issues that are characterized by combined systems and multi‐actor complexities. These social–technical issues, called multi‐actor systems, are so complex that researchers and decision makers need to reduce these complexities.
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