SOCR APS GDS VTS SK 2025 - Revision history

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2025-04-25T12:42:32Z

‎Resources

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Dinov: /* April 25, 2025 APS GDS Virtual Tutorial Series Presentation */

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‎April 25, 2025 APS GDS Virtual Tutorial Series Presentation

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Dinov: /* APS GDS Virtual Tutorial Series */

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‎APS GDS Virtual Tutorial Series

Dinov: /* APS GDS Virtual Tutorial Series */

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‎APS GDS Virtual Tutorial Series

Dinov: /* APS GDS Virtual Tutorial Series */

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‎APS GDS Virtual Tutorial Series

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 ** [http://socr.umich.edu/HTML5/BrainViewer/data/fMRI_FilteredData_4D.nii.gz Test 4D fMRI data] (nii.gz)
 ** [http://socr.umich.edu/HTML5/BrainViewer/data/PET_FDG_3D_vol.nii.gz Test PET 3D data] (nii.gz)
-* [https://socr.umich.edu/HTML5/BrainViewer/ SOCR BrainViewer)
+* [https://socr.umich.edu/HTML5/BrainViewer/ SOCR BrainViewer]
 * [https://doi.org/10.1515/9783110697827 Book: Data Science - Time Complexity, Inferential Uncertainty, and Spacekime Analytics] and [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU website]
 * [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html TCIU Basic Spacekime Predictive Analytics]

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 ===Resources===
 * Prerequisites
 ** [https://R-project.org base ''R'']
 ** [https://posit.co/download/rstudio-desktop/ ''RStudio'']

@@ Line 32: / Line 32: @@
 ** [https://R-project.org base ''R'']
 ** [https://posit.co/download/rstudio-desktop/ ''RStudio'']
-** [https://cran.r-project.org/web/packages/available_packages_by_name.html appropriate ''R'' packages]
+** [https://cran.r-project.org/web/packages/available_packages_by_name.html appropriate ''R'' packages], e.g., [https://cran.r-project.org/web/packages/TCIU/index.html TCIU ''R'' package]
 * Test Data
 ** [http://socr.umich.edu/HTML5/BrainViewer/data/TestBrain.nii.gz Test sMRI 3D data] (nii.gz)

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 ** [https://posit.co/download/rstudio-desktop/ ''RStudio'']
 ** [https://cran.r-project.org/web/packages/available_packages_by_name.html appropriate ''R'' packages]
 * [https://doi.org/10.1515/9783110697827 Book: Data Science - Time Complexity, Inferential Uncertainty, and Spacekime Analytics] and [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU website]
 * [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html TCIU Basic Spacekime Predictive Analytics]

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 : Complex-time (kime) representation of repeated measurement longitudinal processes paves the way for advanced spacekime statistical inference and artificial intelligence (AI) applications. Extending time into the complex plane offers a unified framework connecting fundamental quantum mechanics principles, statistical dynamics, and machine learning. Kime representation enhances both model-based statistical inference techniques – utilizing classical probability distributions – and model-free AI prediction and classification algorithms – relying on data and generalized functions. Many open mathematical-physics problems emerge from this formulation, including definition and interpretation of a consistent spacekime-metric tensor and classification of alternative time-series to kime-surfaces transformations. Simulations and observed neuroimaging data demonstrate the utility of complex-time representation and the induced spacekime analytics. These methods enable forward prediction by extrapolating processes beyond their observed timespan and facilitate group comparisons based on corresponding kime surfaces. Additionally, they allow for statistical quantification of differences between experimental groups and conditions, support topological kime surface analysis, and enhance AI prediction for repeated measurement longitudinal data.
-: [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html This interactive tutorial will present hands-on the core elements of spacekime analytics]. These include
+: [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html This interactive tutorial will present hands-on the core elements of spacekime analytics], see [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.Rmd TCIU Basic Spacekime Predictive Analytics (Rmd source)]. These include
 :: Importing of repeated measurement longitudinal data,
 :: Numeric (stitching) and analytic (Laplace) kimesurface reconstruction from time-series data,

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 * [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_MappingLongitudinalTimeseries_2_Kimesurfaces.html Strategies for Mapping Repeated Measurement Longitudinal Data (Time-series) to 2D Manifolds (Kimesurfaces)]
 * [https://www.socr.umich.edu/TCIU/HTMLs/Chapter3_Kime_Phase_Problem.html The Kime-Phase Problem and Kime-Phase Tomography (KPT)]
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← Older revision		Revision as of 17:56, 17 April 2025
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	===APS GDS Virtual Tutorial Series===		===APS GDS Virtual Tutorial Series===

−	~~[[Image:ComplexValued_KimeIndexed_Kimesurface.gif\|center\| [https://spacekime.org] ]]~~

	* [https://www.aps.org/ American Physical Society (APS)]		* [https://www.aps.org/ American Physical Society (APS)]

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 ===APS GDS Virtual Tutorial Series===
-[[Image:ComplexValued_KimeIndexed_Kimesurface.gif|500px|center| [https://spacekime.org] ]]
+[[Image:ComplexValued_KimeIndexed_Kimesurface.gif|center| [https://spacekime.org] ]]
 * [https://www.aps.org/ American Physical Society (APS)]