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Analyzed about 3 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
-------------------------------------------------------------------------------------------------------------------------------------------- 1529. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Realtime Live Air Traffic from FlightRadar24 - FlightRadar24API and pyflightdata - 9 July 2024 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1476 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, Verlhulste-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 Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,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,TomTom and HERE live road traffic API,Autonomous Online Shopping Delivery by Cargo drones -------------------------------------------------------------------------------------------------------------------------------------------- 1529.1 python-src/image_pattern_mining/ImageNet/GISLiveTraffic.py has been updated to import pandas and two python modules - FlightRadar24API and pyflightdata - which are wrappers for https://www.flightradar24.com/ realtime air traffic map captured by GPS. 1529.2 New function def flightradar24_live_air_traffic(zones=["asia"],max_no_of_flights=10) has been defined to get realtime flight data within an array of geographic zones (defaults to "asia") from FlightRadar24API upperbounded by max_no_of_flights. Function get_bounds() returns a bounding box for a geographic zone and data for all flights presently on air are returned by get_flights(). Flights array is looped for max_no_of_flights iterations and text description of flight is printed after translating IATA code by get_airport() along with its present longitude-latitude-altitude 3D coordinate information. 1529.3 Another function def pyflightdata_live_air_traffic(airport_code="MAA") obtains statistics-arrivals-departure-grounding information for an airport code from pyflightdata. JSON data is normalized to a Pandas DataFrame by json_normalize() and pretty printed. 1529.4 Previous functions are invoked for zone "asia" and airport code "MAA". Logs in python-src/image_pattern_mining/ImageNet/testlogs/GISLiveTraffic.log.9July2024 display live 3D locations of first few flights in realtime over "asia" and live arrivals-departures-grounding data for "MAA": flightradar24_live_air_traffic() pyflightdata_live_air_traffic(airport_code="MAA") 1529.5 Flight data is an important indicator of economic might and urbanization (e.g number of longhaul direct flights to international destinations from an urban area is a measure of tourism and industry potential).Realtime flight origin-destination data indirectly constitute a dynamic graph of airports as vertices and flight trajectories as edges - Betweenness centrality measures of air traffic dynamic graph provide insights into flight patterns of airports.
K.Srinivasan
More... over 2 years ago
1528. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 7 July 2024 - N-Body gravitational acceleration and Barometric pressure analysis of date range 2 July 2024-10 August 2024 for Chennai location in NASA JPL Horizons API - Analysis of logs - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules
K.Srinivasan
More... over 2 years ago
Section 1471 updated
K.Srinivasan
More... over 2 years ago
Section 1527 updated
K.Srinivasan
More... over 2 years ago
Section 1350 updated
Srinivasan Kannan
More... over 2 years ago
Section 1527 updated
Srinivasan Kannan
More... over 2 years ago
1527. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 4 July 2024 - N-Body gravitational acceleration and Barometric pressure analysis of date range 2 July 2024-10 August 2024 for Chennai location in NASA JPL Horizons API - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules
Srinivasan Kannan
More... over 2 years ago
---------------------------------------------------------------------------------------------------------------------------------------- 1527. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 4 July 2024 - N-Body gravitational acceleration and Barometric pressure analysis of date range 2 July 2024-10 August 2024 for Chennai location in NASA JPL Horizons API - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules ---------------------------------------------------------------------------------------------------------------------------------------- 1527.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to compute N-Body gravities for 3-body system (Sun-Mercury-Venus) for same date range as 1526 (2 July 2024-10 August 2024) at a different geographic location (Location code 223 - Chennai - Coordinate Center: 80°10′58.8″E, 13°00′00.0″N): climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2024,7,2,17,30,00),'dateto':(2024,8,10,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Mercury-Venus",'angularsepbounds':('0d','30d'),'longlat':['223@399']},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) 1527.2 N-Body Gravity values computed for Sun-Mercury-Venus 3-body system at location 223 are different from those for Hawaii-Mauna Kea in Section 1526 (written to testlogs/GISWeatherAnalytics.log.4July2024) 1527.3 Differences between gravity L2 norms and barometric pressure values (date range 3 July 2024-10 August 2024) computed for Hawaii (testlogs/GISWeatherAnalytics.log.2July2024) and Chennai (testlogs/GISWeatherAnalytics.log.4July2024) JPL Horizons location codes (568 and 223) are captured in GISWeatherAnalytics.logdiff.HawaiiAndChennai.2and4July2024. Small decimal variations in gravity values should have exponential impact on barometric pressure due to Barometric pressure formula (Section 1479.4) and following pressure plots are only representative (gravity plots for both locations appear identical due to small decimal variations getting rounded off): GISWeatherAnalytics.BarometricPressure.Chennai.Mercury-Venus.2024-7-2#2024-8-10.jpg GISWeatherAnalytics.BarometricPressure.Chennai.Sun-Mercury.2024-7-2#2024-8-10.jpg GISWeatherAnalytics.BarometricPressure.Chennai.Sun-Venus.2024-7-2#2024-8-10.jpg 1527.4 Full list of NASA JPL Horizons location codes across the globe has been captured in file testlogs/NASA_JPL_Horizons_API_location_codes.pdf (obtained through *@399 command to Horizons object instantiation)
Srinivasan Kannan
More... over 2 years ago
1526. (JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 2 July 2024 - N-Body gravitational acceleration and Barometric pressure analysis of date range 2 July 2024-10 August 2024 and location based gravity computation in NASA JPL Horizons API - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules
Srinivasan Kannan
More... over 2 years ago
---------------------------------------------------------------------------------------------------------------------------------------- 1526. (JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 2 July 2024 - N-Body gravitational acceleration and Barometric pressure analysis of date range 2 July 2024-10 August 2024 and location based gravity computation in NASA JPL Horizons API - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules ---------------------------------------------------------------------------------------------------------------------------------------- 1526.1 python-src/EphemerisSearch.py has been updated to include a user defined parameter userdefinedlatlons in multiple functions (predict_EWE(),extreme_weather_events_n_body_analytics()) for computing location-specific gravity of a celestial body. Argument userdefinedlatlons is either an array of longitude-latitudes or one of the 2300+ location code names on earth published in https://ssd.jpl.nasa.gov/horizons/app.html#/ NASA JPL Horizons web interface. Horizons() object is instantiated by setting location argument to elements of userdefinedlatlons. 1526.2 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to update predict_EWE() invocation accordingly in climate_analytics() which is invoked with value of 'longlat' set to location id 56...@399 or city 568 on Earth to find N-Body gravities for Sun-Mercury-Venus 3-body system for date range 2 July 2024-10 August 2024: climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2024,7,2,17,30,00),'dateto':(2024,8,10,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Mercury-Venus",'angularsepbounds':('0d','30d'),'longlat':['568@399']},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) 1526.3 Location-specific gravity computation makes the N-body model truly a variable-gravity computation dependent on observed location on earth. 1526.4 Following are the gravity,barometric pressure plots and logs for this date range: image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.BarometricPressure.Mercury-Venus.2024-7-2#2024-8-10.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.BarometricPressure.Sun-Mercury.2024-7-2#2024-8-10.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.BarometricPressure.Sun-Venus.2024-7-2#2024-8-10.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2024-7-2#2024-8-10.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2024-7-2#2024-8-10.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Gravity.Sun-Venus.2024-7-2#2024-8-10.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.log.2July2024 1526.5 Following dates match < 30 degrees angular separation of Sun-Mercury and earlier plots show a dip in gravity and an increase in barometric pressure during 2 July 2024-10 August 2024 as observed from location 568 on Earth (which is Maunakea (observatory) - Hawaii): SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-03 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-04 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-05 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-06 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-07 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-08 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-09 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-10 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-11 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-12 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-13 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-14 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-15 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-16 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-17 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-18 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-19 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-20 17:30:00.000 SkyCoord separation(): Angular separation of Sun-Mercury matches bounds for date: 2024-07-21 17:30:00.000
Srinivasan Kannan
More... over 2 years ago
Section 1525 updated
K.Srinivasan
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Section 1525 updated
K.Srinivasan
More... over 2 years ago
Section 1525 updated
K.Srinivasan
More... over 2 years ago
1525. (THEORY and FEATURE) Sequential Optimization to Computational Geoemetric Factorization - Finding all factors - Euclid's Greatest Common Divisor (GCD) Algorithm implementation - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369,1379,1381,1384,1385,1401,1419,1454,1487 and all sections on Integer Factorization (DMRC MapReduce PySpark and NC HPC Rust implementations),Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition,Integer partitions,Diophantine representation - 26 June 2024
K.Srinivasan
More... over 2 years ago
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1525. (THEORY and FEATURE) Sequential Optimization to Computational Geoemetric Factorization - Finding all factors - Euclid's Greatest Common Divisor (GCD) Algorithm implementation - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369,1379,1381,1384,1385,1401,1419,1454,1487 and all sections on Integer Factorization (DMRC MapReduce PySpark and NC HPC Rust implementations),Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition,Integer partitions,Diophantine representation - 26 June 2024 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1525.1 New C++ source file cpp-src/greatest_common_divisor.cpp has been added to NeuronRain AstroInfer repositories which implements Euclid's algorithm to find Greatest Common Divisor (GCD) of two integers. It defines a C++ function "long long euclid_greatest_common_divisor(long long p,long long q)" that takes two integers p and q and returns GCD recursively. 1525.2 Euclid's algorithm finds GCD of two integers a and b recursively as below and is of sequential runtime O(logb): GCD(a,b) = GCD(b,a mod b) 1525.3 Euclid's GCD algorithm is of immense importance in the context of finding all factors of an integer by Computational Geometric Factorization algorithm implemented in NeuronRain (SparkPython MapReduce DMRC and Rust HPC versions) which is based on parallel planar point location on PRAMs or multicores. 1525.4 Usually scope of the term "factorization" is limited to finding pair of integers p and q such that N=pq. Recursive invocation of same factorization algorithm on previously found factors finds all factors of N which implies Computational Geometric Factorization has to be recursively invoked on p and q once integer N is factorized to p and q which is again either in DMRC or NC requiring parallel computing resources. 1525.5 Instead Euclid's GCD algorithm could be invoked on p and q to find further factors of N in sequential logarithmic time without necessity of PRAMs or multicores. 1525.6 As an example GCD of two factors of 94315716930730671 - 913920939 and 103198989 - located by Computational Geometric Factorization are found by euclid_greatest_common_divisor() - from cpp-src/testlogs/greatest_common_divisor.log.26June2024 - which is the third factor of 94315716930730671 found in sequential logarithmic time: Factors of integer N=pq found by Computational Geoemetric Factorization:94315716930730671 euclid_greatest_common_divisor():p=913920939;q=103198989 euclid_greatest_common_divisor():p=103198989;q=88329027 euclid_greatest_common_divisor():p=88329027;q=14869962 euclid_greatest_common_divisor():p=14869962;q=13979217 euclid_greatest_common_divisor():p=13979217;q=890745 euclid_greatest_common_divisor():p=890745;q=618042 euclid_greatest_common_divisor():p=618042;q=272703 euclid_greatest_common_divisor():p=272703;q=72636 euclid_greatest_common_divisor():p=72636;q=54795 euclid_greatest_common_divisor():p=54795;q=17841 euclid_greatest_common_divisor():p=17841;q=1272 euclid_greatest_common_divisor():p=1272;q=33 euclid_greatest_common_divisor():p=33;q=18 euclid_greatest_common_divisor():p=18;q=15 euclid_greatest_common_divisor():p=15;q=3 euclid_greatest_common_divisor():p=3;q=0 Greatest Common Divisor of 913920939 and 103198989 is (which is also a factor of 94315716930730671):3 ... Greatest Common Divisor of 304640313 and 34399663 is (which is also a factor of 94315716930730671):1 1525.7 Earlier Euclid's GCD optimization is the best choice when the first two factors found by Computational Geometric Factorization are not coprime and have common divisor. Repetitive calls to euclid_greatest_common_divisor() finds more and more factors if not all.Earlier iteration stops after finding 3rd factor because 304640313 and 34399663 are coprime.
K.Srinivasan
More... over 2 years ago
Section 1524 updated - 2
K.Srinivasan
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Section 1524 updated
K.Srinivasan
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Section 1523 updated
K.Srinivasan
More... over 2 years ago
Updated Requirements.txt
Srinivasan Kannan
More... over 2 years ago
Section 1524 updated
Srinivasan Kannan
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1524. (THEORY and FEATURE) Music Information Retrieval(MIR) - AI Music Synthesis by Random walks on Music Weighted Automata Directed Graph - PySynth integration - 20 June 2024 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441,1458,1478 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, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries, Ramsey Theory-Van Der Waerden Theorem for coloring of sequences, Pumping Lemma, Normalized Compression Distance-Kolmogorov complexity, Merit of Large Scale Visuals, Recurrent Neural Networks,Neo-Riemannian Theory of Music,Attack-Decay-Sustain-Release model
Srinivasan Kannan
More... over 2 years ago
---------------------------------------------------------------------------------------------------------------------------------------------- 1524. (THEORY and FEATURE) Music Information Retrieval(MIR) - AI Music Synthesis by Random walks on Music Weighted Automata Directed Graph - PySynth integration - 20 June 2024 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441,1458,1478 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, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries, Ramsey Theory-Van Der Waerden Theorem for coloring of sequences, Pumping Lemma, Normalized Compression Distance-Kolmogorov complexity, Merit of Large Scale Visuals, Recurrent Neural Networks,Neo-Riemannian Theory of Music,Attack-Decay-Sustain-Release model ----------------------------------------------------------------------------------------------------------------------------------------------1524.1 New source file MusicSynthesizer_AddOn.py has been implemented to integrate third-party music synthesizers as add-on(s). Function pysynth_synthesize_notes(virtual_piano_notes,duration,filename) has been defined as a wrapper for PySynth - https://github.com/mdoege/PySynth - python music synthesizer to convert a notes sequence into PySynth notes format and synthesize a WAV file(PySynth_music.wav). 1524.2 python-src/music_pattern_mining/AudioToBitMatrix.py has been updated to import pysynth and pysynth_synthesize_notes() from MusicSynthesizer_AddOn.py and invoke it from music_synthesis(). 1524.3 Function notes_to_audio() is invoked to synthesize new music from random walks on Music Weighted Automaton (equivalent to a Second Order Recurrent Neural Network - 2-RNN = WFA - Sections 1434.9, 1435.8) learnt from a music clip of JSBach Flute Sonata. 1524.4 PySynth page at https://mdoege.github.io/PySynth/ contains information on various PySynth variants (A,B,E,S,C,D,P,samp,beeper) 1524.5 Following are the affected music files and logs: modified: music21_percussion_for_piano.midi modified: music21_percussion_for_piano.wav modified: music21_piano_with_percussions.wav modified: virtual_piano_music.WesternClassical.wav MusicSynthesizer_AddOn.py PySynth_music.wav testlogs/AudioToBitMatrix.log.PySynth.20June2024
Srinivasan Kannan
More... over 2 years ago
Section 1523 updated
Srinivasan Kannan
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Section 1523 updated
Srinivasan Kannan
More... over 2 years ago
Section 1523 updated
Srinivasan Kannan
More... over 2 years ago
Section 1523 updated
Srinivasan Kannan
More... over 2 years ago
Section 1522 updated
Srinivasan Kannan
More... over 2 years ago
Updated Requirements.txt
Srinivasan Kannan
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1523. (JIRA,THEORY and FEATURE) NeuronRainApps - Textgraph Transformers Question-Answering Bot - Enhancements to Wikipedia Question-Answering Bot and optional support for OpenAI Question-Answering prompt - related to 249,707,829,1133,1134,1204,1263,1269,1284,1285,1469 and all sections on Intrinsic Merit of Texts, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains - 17 June 2024
Srinivasan Kannan
More... over 2 years ago
------------------------------------------------------------------------------------------------------------------------------- 1523. (JIRA,THEORY and FEATURE) NeuronRainApps - Textgraph Transformers Question-Answering Bot - Enhancements to Wikipedia Question-Answering Bot and optional support for OpenAI Question-Answering prompt - related to 249,707,829,1133,1134,1204,1263,1269,1284,1285,1469 and all sections on Intrinsic Merit of Texts, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains - 17 June 2024 ------------------------------------------------------------------------------------------------------------------------------- 1523.1 python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to define a new function OpenAIQuestionAnswering() to query OpenAI ChatGPT and get prompt response. This function requires an API Key generated by OpenAI account (atleast a free account with $5 credit) and environment variable OPENAI_API_KEY to be set - OpenAI python installation (https://github.com/openai/openai-python) is optional and at present prompt responds with error (from logs - python-src/NeuronRainApps/QuestionAnswering/testlogs/QuestionAnswering.log.OpenAI.17June2024): openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}} 1523.2 Function AIQuestionAnswering(question,questionfraction=5,maxanswers=1,keywordsearch=False,wsheading=False,answersliced=1,answerfraction=20) has been renamed as WikipediaRLFGTransformersQuestionAnswering(question,questionfraction=5,maxanswers=1,keywordsearch=False,wsheading=True,answersliced=1,answerfraction=5) and debug print() statements have been uncommented. 1523.3 Sentence creation by make_sentence() has been extended to sorted Key and Value weights in addition to sorted Query weights. Primitive deep-learnt sentence creation from WordNet in make_sentence() has been updated to replace hardcoded "is a(n)" relation by results of lowest common hypernyms function of WordNet - https://www.nltk.org/howto/wordnet_lch.html. Primitive sentence creation by make_sentence() is less human-readable and similar to relation triplets of REBEL in section 1469 composing semi-natural language sentences of the form x R y for relation R. 1523.4 Logs for earlier Wikipedia Q&A code changes with wsheading set to True - NeuronRainApps/QuestionAnswering/testlogs/QuestionAnswering.log.Wikipedia.17June2024 - describe the degree attention transformer perceptron iterations (Logs for wsheading=False are huge due to size of wikipedia search results). 1523.5 Sorted Degree attention edge weights - Query(Q),Key(K),Value(V) - have been cast to absolute values after transformer iterations to learn Q-K-V as perceptrons caused negative signed values that were sorted wrongly as lowest. Only a user defined fraction (answerfraction) of Q-K-V edges are used in deep-learnt sentence creation of make_sentence(). 1523.6 WordNet path_between() computation for top ranked edges in sorted Query-Key-Weights arrays has been commented due to lowest_common_hypernyms() invocation in make_sentence() earlier. 1523.7 This update resolves NeuronRain JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-40
Srinivasan Kannan
More... over 2 years ago