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Expert elicitations are a promising method for determining how R&D investments are likely to have an impact on technological advance in climate change energy technologies. But, expert elicitations are time-consuming and resource intensive. Thus, we investigate the value of the information gained in expert elicitations. More specifically, given baseline(More)
BACKGROUND Efforts to reduce unhealthy dietary intake behaviors in youth are urgently needed. Theory-based interventions can be effective in promoting behavior change; one promising model is the Theory of Planned Behavior (TPB). PURPOSE The aim of this study was to determine, using a systematic literature review, how the TPB has been applied to(More)
This paper investigates a generalized likelihood ratio (GLR) control chart for detecting sustained changes in the parameters of linear profiles when individual observations are sampled. The control charts usually used for monitoring linear profiles are based on taking a sample of n observations at each sampling time point, where n is large enough that a(More)
Agent-oriented techniques are being increasingly used in a range of networking security applications. In this paper, we introduce FNTAE, a Federated Network Traffic Analysis Engine for real-time network intrusion detection. In FNTAE, each analysis engine is powered with an incremental learning agent, for capturing attack signatures in real-time, so that the(More)
Training data in real world is often presented in random chunks. Yet existing sequential Incremental IDR/QR LDA (s-QR/IncLDA) can only process data one sample after another. This paper proposes a constructive chunk Incremental IDR/QR LDA (c-QR/IncLDA) for multiple data samples incremental learning. Given a chunk of s samples for incremental learning, the(More)
A series of nonplanar tri-s-triazine-based molecules were designed, and their optical, electronic, and charge transport properties as ambipolar host materials for blue electrophosphorescence emitters were explored by density functional theory. The influence of the linkage between tri-s-triazine and carbazole, diphenylamine and triphenylamine, as well as the(More)
Actor-Critic algorithms have been increasingly researched for tackling challenging reinforcement learning problems. These algorithms are usually composed of two distinct learning processes, namely actor (a.k.a, policy) learning and critic (a.k.a, value function) learning. Actor learning is heavily dependent on critic learning; particularly unreliable critic(More)