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Titlebook: Consciousness, Cognition and Crosstalk: The Evolutionary Exaptation of Nonergodic Groupoid Symmetry-; Rodrick Wallace Book 2022 The Author

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21#
發(fā)表于 2025-3-25 05:32:24 | 只看該作者
Book 2022ymmetries driven by fundamental equivalence class algebras..?.Although this is far indeed from the familiar world of physical theory, it should be possible, on the basis of the probability models developed in the book, to develop new statistical tools for the analysis of observational and empirical data regarding cognition and consciousness..
22#
發(fā)表于 2025-3-25 09:08:46 | 只看該作者
23#
發(fā)表于 2025-3-25 12:46:27 | 只看該作者
https://doi.org/10.1007/978-1-349-25487-3s. These slower, but far more complex, systems support several simultaneous, tunable, spotlights, each entraining multiple ‘unconscious’ cognitive modules. Such mechanisms include the immune self/other system, wound-healing, tumor suppression, and gene expression/regulation dyads.
24#
發(fā)表于 2025-3-25 17:19:42 | 只看該作者
Discussion and Conclusions,s. These slower, but far more complex, systems support several simultaneous, tunable, spotlights, each entraining multiple ‘unconscious’ cognitive modules. Such mechanisms include the immune self/other system, wound-healing, tumor suppression, and gene expression/regulation dyads.
25#
發(fā)表于 2025-3-25 22:57:14 | 只看該作者
26#
發(fā)表于 2025-3-26 03:40:21 | 只看該作者
27#
發(fā)表于 2025-3-26 04:38:33 | 只看該作者
28#
發(fā)表于 2025-3-26 08:54:03 | 只看該作者
29#
發(fā)表于 2025-3-26 15:38:06 | 只看該作者
Random Topography in Geophysical Models,nee and the learning objectives. A wide variety of adult, pediatric, and specialized mannequin simulators exist in the marketplace providing the simulation educator with many options to choose from when developing focused curriculum or the overall setup of a medical system’s simulation center.
30#
發(fā)表于 2025-3-26 20:46:57 | 只看該作者
A Theoretical Framework for Multi-sphere Support Vector Data Descriptionta description to the normal data set. We prove that the classification error will be reduced after each iteration in this learning process. Experimental results on 28 well-known data sets show that the proposed multi-sphere SVDD provides lower classification error rate comparing with the standard single-sphere SVDD.
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