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Nov 05, 2025 — Nov 05, 2026
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------------------------------------------------------------------------------------------------------------------------------- 1268. (THEORY and FEATURE) Astronomy Dataset Analytics - Angular separation of solar system planets during Extreme Weather Events on Earth - an N-Body gravitational pull correlation study - 15 July 2022 - related to 731,1206,1236,1255,1266,1267 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Reciprocal Recommender Systems based Matrimony analytics in Bipartite Social Networks, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms -------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------- 1268. (THEORY and FEATURE) Astronomy Dataset Analytics - Angular separation of solar system planets during Extreme Weather Events on Earth - an N-Body gravitational pull correlation study - 15 July 2022 - related to 731,1206,1236,1255,1266,1267 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Reciprocal Recommender Systems based Matrimony analytics in Bipartite Social Networks, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms ------------------------------------------------------------------------------------------------------------------------------- 1268.1 EphemerisSearch.py has been updated to define new function extreme_weather_events_n_body_analytics() which iterates through datetime(s) of extreme weather events (North Atlantic Hurricanes,Tropical Cyclones,Earthquakes,Typhoons) and computes the angular separation between solar system planets pairwise for each cataclysm. 1268.2 Patterns in pairwise angular separations between solar system planets are prima facie correlational evidences towards N-Body gravitational influence on terrestrial weather disturbances. 1268.3 Two sets of occurrences of deadliest natural disasters: (*) Days of 21st century Tropical storms - sourced from https://en.wikipedia.org/wiki/List_of_the_deadliest_tropical_cyclones and (*) Days of Top 10 highly studied Earthquakes in seismology literature - sourced from https://en.wikipedia.org/wiki/Lists_of_earthquakes have been analyzed for syzygies and proximities in angular separations of solar system planets as observed from earth during disasters (references: 1266.16,1266.17,1266.18). ------------------------------------------------------------------------------------------------------------------------------ 1268.4 Following are pairwise angular separations for 21st century Tropical storms of Sun-Moon-Venus-Earth 4-body system and Mercury-Venus-Earth 3-body system: ------------------------------------------------------------------------------------------------------------------------------ Angular separations for Sun-Moon : [<Angle 19deg 07' 47.6">, <Angle 155deg 22' 20.7">, <Angle 134deg 27' 32.6">, <Angle 26deg 59' 51.6">, <Angle 49deg 19' 28.1">, <Angle 12deg 45' 45.4">, <Angle 107deg 32' 09.8">, <Angle 160deg 54' 55.2">, <Angle 151deg 43' 25.6">, <Angle 141deg 05' 03.8">, <Angle 06deg 27' 40.2">, <Angle 19deg 07' 47.6">, <Angle 53deg 28' 49.9">, <Angle 28deg 31' 32.0">] Angular separations for Sun-Venus : [<Angle 43deg 57' 46.6">, <Angle 29deg 10' 59.9">, <Angle 36deg 59' 32.2">, <Angle 44deg 14' 36.8">, <Angle 07deg 03' 56.2">, <Angle 45deg 56' 16.8">, <Angle 11deg 34' 06.9">, <Angle 02deg 10' 39.4">, <Angle 29deg 51' 11.0">, <Angle 29deg 31' 15.2">, <Angle 47deg 03' 42.9">, <Angle 43deg 57' 46.6">, <Angle 28deg 15' 36.0">, <Angle 40deg 23' 01.5">] Angular separations for Moon-Venus : [<Angle 25deg 56' 18.5">, <Angle 126deg 18' 14.4">, <Angle 171deg 25' 52.8">, <Angle 71deg 12' 28.8">, <Angle 42deg 17' 33.0">, <Angle 57deg 57' 37.0">, <Angle 95deg 59' 25.6">, <Angle 158deg 54' 48.7">, <Angle 174deg 59' 17.0">, <Angle 169deg 35' 55.4">, <Angle 53deg 28' 04.3">, <Angle 25deg 56' 18.5">, <Angle 25deg 21' 49.9">, <Angle 12deg 06' 55.7">] Angular separations for Mercury-Venus : [<Angle 26deg 45' 07.7">, <Angle 48deg 40' 16.5">, <Angle 55deg 20' 24.4">, <Angle 34deg 25' 52.6">, <Angle 26deg 55' 31.1">, <Angle 27deg 17' 48.4">, <Angle 23deg 39' 02.2">, <Angle 15deg 51' 03.3">, <Angle 46deg 43' 02.6">, <Angle 14deg 49' 04.4">, <Angle 49deg 46' 07.9">, <Angle 26deg 45' 07.7">, <Angle 11deg 04' 25.7">, <Angle 56deg 38' 59.7">] ------------------------------------------------------------------------------------------------------------------------------ 1268.5 Following are pairwise angular separations for Top 10 Earthquakes of Sun-Moon-Earth, Mars-Jupiter-Saturn-Earth systems: ------------------------------------------------------------------------------------------------------------------------------ Angular separations for Sun-Moon : [<Angle 69deg 50' 21.3">, <Angle 91deg 21' 43.4">, <Angle 172deg 17' 32.3">, <Angle 122deg 13' 27.1">, <Angle 64deg 48' 34.3">, <Angle 175deg 09' 29.0">, <Angle 135deg 01' 03.0">, <Angle 159deg 42' 43.8">, <Angle 135deg 18' 23.5">, <Angle 27deg 19' 28.1">] Angular separations for Jupiter-Saturn : [<Angle 174deg 16' 50.1">, <Angle 140deg 34' 08.6">, <Angle 81deg 18' 02.0">, <Angle 12deg 58' 35.3">, <Angle 106deg 42' 10.3">, <Angle 91deg 42' 19.8">, <Angle 153deg 42' 20.0">, <Angle 156deg 25' 30.7">, <Angle 177deg 42' 21.2">, <Angle 158deg 22' 32.9">] Angular separations for Mars-Saturn : [<Angle 147deg 08' 00.1">, <Angle 30deg 26' 09.0">, <Angle 124deg 52' 46.6">, <Angle 154deg 45' 04.9">, <Angle 36deg 57' 00.2">, <Angle 171deg 36' 50.7">, <Angle 178deg 35' 59.3">, <Angle 61deg 46' 19.2">, <Angle 79deg 49' 42.8">, <Angle 82deg 21' 37.6">] Angular separations for Jupiter-Mars : [<Angle 27deg 20' 42.2">, <Angle 170deg 53' 56.1">, <Angle 43deg 35' 24.5">, <Angle 142deg 02' 32.4">, <Angle 69deg 45' 14.5">, <Angle 96deg 19' 42.6">, <Angle 27deg 07' 46.8">, <Angle 141deg 34' 54.5">, <Angle 97deg 52' 51.6">, <Angle 119deg 15' 48.2">] 1268.6 Sun-Moon almost-Syzygies and Mercury-Venus proximities (mostly <30 degrees) are prominent in 1268.4. Jupiter-Saturn,Sun-Moon almost-Syzygies are prominent in 1268.5. More details in testlogs/EphemerisSearch.log.15July2022.
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1267 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Updated Requirements.txt
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1267 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------ 1267. (THEORY and FEATURE) Astronomical Dataset Analytics - Planetarium Ephemeris Search - Longitude-Latitude and AstroPy support - 13 July 2022 - related to 731,1206,1236,1255,1266 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Reciprocal Recommender Systems based Matrimony analytics in Bipartite Social Networks, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms ------------------------------------------------------------------------------------------------------------------------------ 1267.1 EphemerisSearch.py has been updated to compute and search for Longitude-Latitude degree position of solar system bodies which is more compatible than Right Ascension-Declination convention. New match function latlon_match() has been implemented in addition to radec_match() which invokes SkyField ecliptic_latlon() for every observed solar system planet to compute their Longitude-Latitude(s). 1267.2 Functions sky_on_datetime() and planetarium_search() take an additional parameter "position" which is valued {"latlon","radec"} to choose between radec_match() and latlon_match() within planetarium_search() and to print radec() or ecliptic_latlon() within sky_on_datetime(). 1267.3 latlon_match() also computes the pairwise degree separation between solar system bodies by SkyField separation_from() which is crucial for solving an N-Body system particularly for finding correlations between (*) Sun-Moon-Earth 3-Body syzygies (180 degree separation - reference 1266.16) (*) Sun-Moon-Venus Lapses 3-Body System and Tropical storms (reference - 1266.18). 1267.4 The planetary positions from SkyField ephemeris are compared to planetary positions from Maitreya 8t ephemeris and AstroPy ephemeris in planetarium_search() by subprocess.call() of maitreya8t and astropy_ephemeris(). AstroPy (for advanced astronomy and cosmology computations) has been integrated and positions of solar system bodies are printed within astropy_ephemeris().
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1255 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1266 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1266 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Updated Requirements.txt
Krishna iResearch - NeuronRain - K.Srinivasan
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+------------------------------------------------------------------------------------------------------------------------------ +1266. (THEORY and FEATURE) Astronomical Dataset Analytics - Planetarium Ephemeris Search - SkyField implementation - 7 July 2022 - related to 731,1206,1236,1255 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Reciprocal Recommender Systems based Matrimony analytics in Bipartite Social Networks, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem +and Gravity Induced Terrestrial Extreme Weather Events, Astronomical Algorithms +------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------ 1266. (THEORY and FEATURE) Astronomical Dataset Analytics - Planetarium Ephemeris Search - SkyField implementation - 7 July 2022 - related to 731,1206,1236,1255 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Reciprocal Recommender Systems based Matrimony analytics in Bipartite Social Networks, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem and Gravity Induced Terrestrial Extreme Weather Events, Astronomical Algorithms ------------------------------------------------------------------------------------------------------------------------------ 1266.1 New python 3.7.x script EphemerisSearch.py has been added to NeuronRain AsFer repositories which imports SkyField python astronomical algorithms implementation defines a class EphemerisSearch containing following member functions: ephem.sky_on_datetime((2022,7,7,1,15,30),"earth","mars") ephem.astronomical_event_to_search(planetradecdict) ephem.planetarium_search((2016,6,29,1,15,30),(2022,7,6,1,15,30)) 1266.2 __init__() of EphemerisSearch.py downloads a NASA JPL Ephemeris (DE421 in this example - de421.bsp) and loads a timescale based on the ephemeris. 1266.3 sky_on_datetime() accepts a datetime tuple (Year-Month-Day-Hour-Minute-Second shown in earlier example invocation) and converts it to Julian Ephemeris Date (denoted JD or JED) by utc(). Two parameters "earth" and "mars" in earlier invocation correspond to planet observed from (Earth) and the planet observed (Mars). Instantaneous Right Ascension and Declination of a planetary body is available by a call of observe() on observed from planet to the observed planet at the timepoint argument supplied to at(). Function observe() returns an astrometric object from which right ascension and declination are obtained by radec(). 1266.4 Function planetarium_search() does the main computation by searching the ephemeris for all possible dates with in a range matching the query dictionary of planets-to-radec. This functionality of searching the dates of astronomical events is inherently supported by find_discrete() function of SkyField by doing a callback to a boolean function (radec_match()) passed on as argument which turns "True" whenever there is a match to ephemeris data on a specific date. 1266.5 Above facility notwithstanding, use of find_discrete() has been made optional by an if-else clause within planetarium_search() because of multiple internal exceptions within find_discrete() callstack of SkyField and an equivalent native bruteforce search is performed within a datetime range loop and a boolean function radec_match() returns True or False depending on ephemeris match for query on that date. Any new algorithm better than bruteforce search of astronomical objects to locate an event could be a breakthrough (Example: Given planetary ephemeris data for an event algorithm has to find the date of the event efficiently without bruteforce search, exploiting Chebyshev polynomial representation of ephemeris). 1266.6 Ephemeris for datetime "3 July 2016 1 hour 15 minutes 30 seconds" is computed by sky_on_datetime() for all solar system planets and made into a query dictionary planetstoradecdict by astronomical_event_to_search(). Earlier ephemeris data is searched in two date ranges "29 June 2016 - 6 July 2022" and "29 June 2016BC - 6 July 2022" and match is found in the former as "planetarium_search(): found matching datetime = 2016-07-03 01:16:38 TT" while the latter throws exception "Exception: ephemeris segment only covers dates 1899-07-28 23:59:18Z through 2053-10-08 23:58:51Z UT" because of DE421 year limitations: ephem.planetarium_search((2016,6,29,1,15,30),(2022,7,6,1,15,30)) ephem.planetarium_search((-2016,6,29,1,15,30),(2022,7,6,1,15,30),step_days=100) 1266.7 Arguments step_days is similar to find_discrete() for periodicity filtering of dates to search. In earlier example ephemeris match is computed every 100 days within the range instead of iterating for every day. Full logs of the computation and search are at testlogs/EphemerisSearch.log.7July2022. 1266.8 EphemerisScript.py has multiple uses across NeuronRain - Search of an astronomical event mentioned in an archaeological artifact, N-Body Choreography - Computation of Separation between astronomical bodies (by separation_from() API), Climate Analytics - analyzing gravitational correlations between periodicities of celestial bodies and terrestrial weather events.
Srinivasan Kannan
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Section 1265 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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+------------------------------------------------------------------------------------------------------------------------------- +1265. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Automatic Delineation of Urban Growth Boundaries by KMeans Clustering of Contour Polynomials and Canny Edge Detector - Recomputed - 30 June 2022 - related to 1151,1174,1196,1226,1233,1234,1237,1252,1253,1262 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Automatic Delineation of Urban Growth Boundaries, Preferential attachment,Random Graphs,Dynamic Graphs,Separate Chaining Hash tables, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Drone EVMs-Passenger Drones,Wiring Problem +-------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------- 1265. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Automatic Delineation of Urban Growth Boundaries by KMeans Clustering of Contour Polynomials and Canny Edge Detector - Recomputed - 30 June 2022 - related to 1151,1174,1196,1226,1233,1234,1237,1252,1253,1262 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Automatic Delineation of Urban Growth Boundaries, Preferential attachment,Random Graphs,Dynamic Graphs,Separate Chaining Hash tables, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Drone EVMs-Passenger Drones,Wiring Problem ------------------------------------------------------------------------------------------------------------------------------- 1265.1 GISUrbanSprawlAnalytics.py has been updated to add 2 new parameters set to 2 by default (which completes faster within an hour, higher number of clusters and iterations are computationally intensive lasting upto few hours) - number of clusters and number of iterations - to function urban_sprawl_from_segments() which are in turn passed onto contours_kmeans_clustering() for automatic urban growth boundary delineation by clustering of urban sprawl transportation network contour polynomials. 1265.2 Canny Edge Detector algorithm which is recently introduced in image_segmentation() could as well be used for detecting transportation network edges (rail,road) from an urban sprawl GIS map apart from Chain approximation contour polynomial segments subject to the constraint that the transportation network edges (roads and rail tracks) must be mostly straightlines. 1265.3 KMeans clustering of contour polynomials and Canny Edge Detection have been computed for Google Maps and GHSL urban transportation GIS images of Chennai urban sprawl - 1) testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022.jpg and 2) testlogs/GHSL_GIS_ChennaiMetropolitanArea.jpg - by ImageGraph_Keras_Theano.image_segmentation() and GISUrbanSprawlAnalytics.urban_sprawl_from_segments(). Canny edge detector reasonably approximates curved transportation network edges by piecewise axis-parallel straightlines simulating staircase random walks of multiple 90 degree turns between localities on lattice grid. 1265.4 Deficient color-coding in KMeans contours clustering imagery of testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022.jpg has been rectified by earlier recomputation and transportation network contour polynomials have been segregated into 2 clusters (coded by light and dark green color pixels) implicitly delineating a boundary. 1265.5 Following are the imagery and log files affected by this update - Contour Chain Approximation, Canny Edge Detector, KMeans clustering, Voronoi-Delaunay: testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022-contourkmeansclustered.jpg testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022-contourlabelled.jpg testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022-contoursegmented.jpg testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022-cannyedgedetection.jpg testlogs/GHSL_GIS_ChennaiMetropolitanArea-contourkmeansclustered.jpg testlogs/GHSL_GIS_ChennaiMetropolitanArea-cannyedgedetection.jpg testlogs/GISUrbanSprawlAnalytics.log.30June2022.xz 1265.6 Moran's I dispersion and p-norm values for the two images are: testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022.jpg - Moran's I of the Urban Sprawl dispersion: 0.6759297613633821 testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022.jpg - Moran's p-norm of the Urban Sprawl dispersion: 0.0 testlogs/GHSL_GIS_ChennaiMetropolitanArea.jpg - Moran's I of the Urban Sprawl dispersion: 0.8848283189311299 testlogs/GHSL_GIS_ChennaiMetropolitanArea.jpg - Moran's p-norm of the Urban Sprawl dispersion: 0.0
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1264 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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+------------------------------------------------------------------------------------------------------------------------------ +1264. (THEORY and FEATURE) Space Archaeology - Edge detection for Airborne-Satellite GIS imagery (LIDAR,SAR,LandSat) by Canny +Edge Detector - related to 1203,1232,1233,1236,1237,1240,1255 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Archaeology-Topological Script Recognition, Product Homotopy, Homeomorphism and Topological Deformations, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching, Archaeoastronomical Dating of Scriptures and Mythologies +------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------ 1264. (THEORY and FEATURE) Space Archaeology - Edge detection for Airborne-Satellite GIS imagery (LIDAR,SAR,LandSat) by Canny Edge Detector - related to 1203,1232,1233,1236,1237,1240,1255 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Archaeology-Topological Script Recognition, Product Homotopy, Homeomorphism and Topological Deformations, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching, Archaeoastronomical Dating of Scriptures and Mythologies ------------------------------------------------------------------------------------------------------------------------------ 1264.1 ImageGraph_Keras_Theano.py has been updated to implement a new function image_segmentation_edge_detection() which internally invokes Canny Edge Detector algorithm of OpenCV2 to detect dominant edges in an image which complements already existing Chain approximation contour segmentation. 1264.2 Canny edge detector applies a Gaussian convolution kernel on an image to magnify prominent pixels along edges of objects in it and thresholds the non-edge pixels which are filtered out leaving only edges in resultant image - following steps summarize Canny Edge Detector: 1264.2.1 Noise filtering by Gaussian 5*5 kernel 1264.2.2 Sobel kernel for each pixel to find its first derivative intensity gradient and direction 1264.2.3 Non-local maxima pixels based on Gradient are removed. 1264.2.4 Hysteresis thresholding to remove edges which are not within minimum value and maximum intensity gradient value 1264.3 Space archaeology (Satellite and Airborne) by GIS infrared imagery from Airborne LIDAR-Synthetic Aperture Radar(SAR) and LandSat satellite imagery has met with much success recently in non-invasively penetrating the superficial ground obstacles (Sand dunes, Trees) to map subterranean artefacts (upto few metres depth) which would have otherwise required random digging, thereby furthering Ground Penetrating Radar (GPR) imageing to next level e.g. new structures discovered underground in Mayan sites,Peru,Iram,Egypt. Canny edge detector is useful in archaeology to identify ancient pathways,drains,viaducts (which are edges) constructed by ancient civilizations. 1264.4 An example costly LIDAR 3D imagery of a Mayan pyramid - testlogs/Mayan_Canaa_Pyramid_Caracol_LIDAR.jpg - invisible earlier and excavated after LIDAR imageing, has been Canny edge detected by earlier function invoked as part of image_segmentation() 1264.5 Following are logs,DOT,image files(Voronoi-Delaunay,Watershed,Chain approximation,Canny Edge Detection) for this update: testlogs/ImageGraph_Keras_Theano.CannyEdgeDetection.log.28June2022 testlogs/Mayan_Canaa_Pyramid_Caracol_LIDAR-cannyedgedetection.jpg testlogs/Mayan_Canaa_Pyramid_Caracol_LIDAR-contourlabelled.jpg testlogs/Mayan_Canaa_Pyramid_Caracol_LIDAR-contoursegmented.jpg testlogs/Mayan_Canaa_Pyramid_Caracol_LIDAR.jpg testlogs/RemoteSensingGIS/Mayan_Canaa_Pyramid_Caracol_LIDAR_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/Mayan_Canaa_Pyramid_Caracol_LIDAR_segmented.jpg
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1263 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1263 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1261 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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1263. (THEORY and FEATURE) People Analytics - Social Network Analysis (Professional) - Chaotic Hidden Markov Model of Career Transition - JSON persistence to MongoDB - 24 June 2022 - related to 14,365,711,753,876,1127,1128,1129,1130,1204 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,Urban Sprawl Analytics, AI Question-Answering
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------- 1263. (THEORY and FEATURE) People Analytics - Social Network Analysis (Professional) - Chaotic Hidden Markov Model of Career Transition - JSON persistence to MongoDB - 24 June 2022 - related to 14,365,711,753,876,1127,1128,1129,1130,1204 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,Urban Sprawl Analytics, AI Question-Answering ------------------------------------------------------------------------------------------------------------------------------- 1263.1 SocialNetworkAnalysis_PeopleAnalytics_ChaoticHMM.py has been changed to read career profile JSONs from MongoDB instead of flatfile. Streaming_AbstractGenerator.py has been accordingly updated to include a clause for MongoDB-JSON object streaming in addition to existing clause for MongoDB-GIS Visual object streaming - MongoDB client code for database (neuronrainjson) and collections have been made part of the __init__ and __iter__ member functions. 1263.2 An example career transition profile JSON of author SocialNetworkAnalysis_PeopleAnalytics_ChaoticHMM.json has been populated in a MongoDB database neuronrainjson and collection "bucket2" which is read as an iterable by Streaming_AbstractGenerator.py 1263.3 Logs for earlier code changes - testlogs/SocialNetworkAnalysis_PeopleAnalytics_ChaoticHMM.log.24June2022. Merit of Social or Professional Profiles in candidate recruitment or complexity of Human Interviews/Contests/Question-Answering in its broader theoretical HR analytics context, have been central to the discussions throughout NeuronRain theory drafts supra formalized by a supremacy contrast between Hardness and Goodness of Majority voting and Hardness and Goodness of Intrinsic Merit. Recently there have two significant strides in AI - GPT3 and DALL-E - which are respectively *) AI text question-answering and *) text-to-image generation systems (converse of image-to-text ImageNet and could be thought visualizer for ThoughtNet and Autobiographical Memory Evocation) mostly driven by massive datasets and transformer attention sequence pipeline. Computational complexity of Question-Answering (Interview algorithm) has been theoretically analyzed in NeuronRain Design - in https://github.com/shrinivaasanka/Krishna_iResearch_DoxygenDocs/blob/master/kuja27_website_mirrored/site/kuja27/InterviewAlgorithmInPSPACE.pdf as Total Quantified Boolean Formulae(TQBF), a PSPACE-complete problem, and as Linear Threshold Functions-Polynomial Threshold Functions.
Krishna iResearch - NeuronRain - K.Srinivasan
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Requirements.txt updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1262 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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1262. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Moran's I measure of Urban Sprawl Dispersion - 23 June 2022 - related to 1151,1174,1196,1226,1233,1234,1237,1252 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Automatic Delineation of Urban Growth Boundaries, Preferential attachment,Random Graphs,Dynamic Graphs,Separate Chaining Hash tables, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Drone EVMs-Passenger Drones,Wiring Problem
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------ 1261. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Moran's I measure of Urban Sprawl Dispersion - 23 June 2022 - related to 1151,1174,1196,1226,1233,1234,1237,1252 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Automatic Delineation of Urban Growth Boundaries, Preferential attachment,Random Graphs,Dynamic Graphs,Separate Chaining Hash tables, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Drone EVMs-Passenger Drones,Wiring Problem ------------------------------------------------------------------------------------------------------------------------------ 1261.1 GISUrbanSprawlAnalytics.py has been updated to implement statistical measure of Moran's I in a function urban_sprawl_dispersion() called as subroutine from urban_sprawl_from_segments(). Moran's I is proportional to gravity of an urban area and a quantifier of how spatially dispersed an Urban Sprawl GIS (diffusion factor) is in terms of tree canopy(NDVI),building coverage,land usage,transportation network among others. 1261.2 Moran's I ranges from -1 (perfect dispersion) to +1 (lopsided dispersion) with 0 being the inflexion point(random dispersion). Color coded pixels of urban sprawl settlement GIS (SEDAC,HRSL,GHSL) are basically scatterplots of multiple land use segments (29-colored LUIS in 1174.8.5) and Moran's I of those urban sprawl GIS imagery is an analytics measure of equitable distribution of segments in an urban area. 1261.3 Function urban_sprawl_dispersion() internally invokes PySAL (Python Spatial Abstraction Library) implementations of Lat2W,Moran's I and Moran's p-norm and a new function image3D_to_2D() defined within ImageToBitMatrix.py for flattening 3D OpenCV2 array for an image to 2D array of dimensions height*width, entries of which are sum of RGB values of the respective pixel ordinates. 1261.4 Urban sprawl diffusion factor (Moran's I dispersion) is computed for (excerpts from testlogs/GISUrbanSprawlAnalytics.log.23June2022.xz): 1) GHSL imagery testlogs/GHSL_GIS_ChennaiMetropolitanArea.jpg: Moran's I of the Urban Sprawl dispersion: 0.8848283189311299 Moran's p-norm of the Urab Sprawl dispersion: 0.0 2) VIIRS imagery NASAVIIRSNightLightsChennaiMetropolitanArea_17November2021.jpg Moran's I of the Urban Sprawl dispersion: 0.99 Moran's p-norm of the Urab Sprawl dispersion: 0.0 1261.5 Invocation of contours_kmeans_clustering() has been included in urban_sprawl_from_segments() analytics pipeline and k-means clustering of transit network contours computed from testlogs/GHSL_GIS_ChennaiMetropolitanArea.jpg show a distinct 3-cluster layering (big,bigger and biggest) of transportation contours from city centre to suburbs coded in 3 colors automatically delineating urban sprawl boundaries based on transportation contour clusters. 1261.6 Following are logs,DOT and imagery for Watershed segments,Chain approximation contour segments,Voronoi-Delaunay,k-means clustering for this commit: testlogs/GHSL_GIS_ChennaiMetropolitanArea-contourkmeansclustered.jpg testlogs/GHSL_GIS_ChennaiMetropolitanArea-contourlabelled.jpg testlogs/GHSL_GIS_ChennaiMetropolitanArea-contoursegmented.jpg testlogs/GISUrbanSprawlAnalytics.log.23June2022.xz testlogs/NASAVIIRSNightLightsChennaiMetropolitanArea_17November2021-contoursegmented.jpg testlogs/RemoteSensingGIS/GHSL_GIS_ChennaiMetropolitanArea_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/GHSL_GIS_ChennaiMetropolitanArea_segmented.jpg
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1253 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
1261. (THEORY and FEATURE) Intrinsic merit of Music - Music Information Retrieval - AI Music Synthesis from Weierstrass Fourier Fractal - related to 110,1223,1224,1257,1258 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants - 21 June 2022
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1261. (THEORY and FEATURE) Intrinsic merit of Music - Music Information Retrieval - AI Music Synthesis from Weierstrass Fourier Fractal - related to 110,1223,1224,1257,1258 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants --------------------------------------------------------------------------------------------------------------------------------------------- 1261.1 AudioToBitMatrix.py has been updated to define a new function weierstrass_fractal_fourier_sinusoids(a,b,n) which implements celebrated Weierstrass Function,a fractal fourier sinusoidal summation that is continuous everywhere and differentiable nowhere which could be a candidate for AI fractal music synthesis by summation of damped sinusoids. This function computes the summation in a loop subject to the constraint (reference: 1261.4) and returns the fourier series as a huge string length of which is tunable depending on parameters a (coefficient),b (radian multiple) and n (maximum exponent). 1261.2 String for Weierstrass function returned from weiestrass_fractal_fourier_sinusoids() is passed on to notes_to_audio() as lambda function to evaluate. Weierstrass Function for an example set of parameters is written to testlogs/AudioToBitMatrix.log.MusicSynthesizer.Weierstrass.log.21June2022 and synthesized music waveform from Weierstrass function for a=0.5,b=5,n=20 is written to function_synthesized_music.wav loadable in Audacity. 1261.3 Music waveforms of arbitrary complexity could be synthesized from Weierstrass function by varying parameters a,b,n. Music waveforms are theoretically summation of multiple periodic waveforms - piecewise or continuous - which may be sinusoidal. Synthesizing music from mathematical functions so far is less of AI and more computational - Synthesizing human enjoyable (or discovering melodies from 68 billion 12-note possibilities - 1223.13) music through AI would hypothetically be as follows: for all a,b,n: { 1261.3.1 Obtain fMRI imagery for Weierstrass Function synthesized music waveform stimulus for a,b,n (or) enumerate all 68 billion 12-note melodies 1261.3.2 Label the fMRI imagery for "enjoyable" autobiographical memory evoking music as "potential candidates" e.g corresponding to "blue" regions of cortex in 1257.6 1261.3.3 Cluster or find similarities in the music waveforms for "potential candidates" - patterns found in music waveforms thereof could be generating functions for enjoyable music or melodies }
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago