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Complexity-Efficient Coherent Physical Cell Identity Detection Method for Cellular IoT Systems
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Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
Published 2023-01-01“…We explore the potential of the Gaussian mixture model (GMM), an unsupervised machine-learning method, to identify coherent physical structures in the interstellar medium. …”
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Predicting Coherent Turbulent Structures via Deep Learning
Published 2022-04-01“…Some regions exhibit a coherent physical behavior in turbulent flow, satisfying specific properties; these regions are denoted as coherent structures. …”
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The roles of contact conformity, temperature and displacement amplitude on the lubricated fretting wear of a steel-on-steel contact
Published 2016-01-01“…The roles of the various parameters in controlling the wear behaviour are presented in a coherent physical framework.…”
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Relationship between Women`s Body Dissatisfaction, Sense of Coherence and Physical Activity
Published 2018-05-01“…Keywords: women, body shape dissatisfaction, sense of coherence, physical activity…”
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Large-scale Globally Propagating Coronal Waves
Published 2015-09-01“…This review focuses on demonstrating how the numerous observational findings of the last one and a half decades can be used to constrain our models of large-scale coronal waves, and how a coherent physical understanding of these disturbances is finally emerging.…”
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Fine-scale distribution and spatial variability of benthic invertebrate larvae in an open coastal embayment in Nova Scotia, Canada.
Published 2014-01-01“…We postulate that in a system with no strong oceanographic features, the scale of spatially coherent physical forcing (e.g. tidal periodicity) can regulate the formation or maintenance of larval patches; however, swimming ability may modulate it.…”
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Topological Generality and Spectral Dimensionality in the Earth Mineral Dust Source Investigation (EMIT) Using Joint Characterization and the Spectral Mixture Residual
Published 2023-04-01“…Specifically, for a mosaic of 20 spectrally diverse scenes, we find: (1) a generalized three-endmember (Substrate, Vegetation, Dark; SVD) spectral mixture model is capable of capturing the preponderance (99% in three dimensions) of spectral variance with low misfit (99% pixels with <3.7% RMSE); (2) manifold learning (UMAP) is capable of identifying spatially coherent, physically interpretable clustering relationships in the spectral feature space; (3) UMAP yields results that are at least as informative when applied to the MR as when applied to raw reflectance; (4) SVD fraction information usefully contextualizes UMAP clustering relationships, and vice-versa (JC); and (5) when EMIT data are convolved to spectral response functions of multispectral instruments (Sentinel-2, Landsat 8/9, Planet SuperDove), SVD fractions correlate strongly across sensors, but UMAP clustering relationships for the EMIT hyperspectral feature space are far more informative than for simulated multispectral sensors. …”
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Correlation in catalysts enables arbitrary manipulation of quantum coherence
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Interconnections and continuity among planning documents of Kota Lama Semarang development within the context of urban heritage conservation
Published 2022“…On the other hand, the results of development projects indicated that there are no coherent physical improvements in the area. The study was conducted to show whether there are consistencies or mismatches between the three planning documents. …”
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Applications of Deep Learning to Scientific Inverse Problems
Published 2022“…SymAE embeds the data into explanatory latent coordinates corresponding to either coherent physical information or nuisance information. …”
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Quantum-Spacetime Symmetries: A Principle of Minimum Group Representation
Published 2024-01-01“…(v) From the line element as operator level, a coherent physical state of spin 2 can be obtained and naturally related to the metric tensor. …”
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