Bioinspired Adaptive-Depth Neural Growth for Deepfake Video Forensics: An Entropy-Guided State-Space Framework. [PDF]
Hussain M, Saeed F, Aldera S.
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Iterative spectral methods for Hamilton-Jacobi-Bellman quasi-variational inequality in finance. [PDF]
Lei M, Lu Z.
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Relating the perceived useful field of view to visual search in 2D Images and 3D volumetric images. [PDF]
Klein DS, Eckstein MP.
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Tracking risk-taking behind the wheel: Which indicator of risky driving is the most useful in driving simulator studies? A pilot study. [PDF]
Baran P +4 more
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Beyond uncertainty in modern active learning for trustworthy AI. [PDF]
Horchani R.
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Automated model selection in population pharmacokinetics using metaheuristic algorithms
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A simple stopping criterion for turbo decoding
IEEE Communications Letters, 2000This article proposes a simple stopping criterion for turbo decoding that extends the existing sign change ratio (SCR) technique. The new sign difference ratio (SDR) criterion counts the sign differences between the a priori information and the extrinsic information. Unlike the SCR, it requires no extra data storage.
B D Woerner
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Maximum number of generations as a stopping criterion considered harmful
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Distributed Stopping Criterion for Ratio Consensus
2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2018Distributed averaging of initial conditions held by multiple agents is a well-studied problem in context of networked systems where coordination amongst the agents is of paramount importance. The asymptotic nature of distributed averaging protocols makes it challenging to implement in practical settings as it does not lend itself to a finite-time ...
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A new stopping criterion for iterative decoding
2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577), 2004We propose a new stopping criterion for iterative decoding of concatenated codes. The new stopping rule observes the a priori probabilities of different symbols and stops further iterations in the decoder whenever the average number of symbols in a trellis section below a certain a priori value, is above a predefined threshold.
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