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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
Sections 1463 and 1468 updated
K.Srinivasan
More... almost 3 years ago
Updated Requirements.txt
Srinivasan Kannan
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+1468. (THEORY and FEATURE) Meaning Representation by Textgraphs - Inferring Knowledge Graph of a text from REBEL relation extraction transformers model - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 26 December 2023
Srinivasan Kannan
More... almost 3 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1468. (THEORY and FEATURE) Meaning Representation by Textgraphs - Inferring Knowledge Graph of a text from REBEL relation extraction transformers model - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 26 December 2023 -------------------------------------------------------------------------------------------------------------------------------------------- 1468.1 New source file KnowledgeGraph.py has been added to NeuronRain AstroInfer repositories which derives the boiler plate code for REBEL relation extraction from a text at https://huggingface.co/Babelscape/rebel-large, gets triplets of subject-predicate-object and translates them to edges of a knowledge graph. 1468.2 Knowledge graph inference has been gaining much traction of-late due to advances in Large Language Models (LLM) - OpenAI, Langchain-Neo4j, REBEL relation extraction etc., 1468.3 NeuronRain implementation of knowledge graphs (or textgraphs as they were known earlier) is based on two algorithms - (1) Recursive Gloss Overlap (2) Recursive Lambda Function Growth - which depend on WordNet and ConceptNet ontologies and grow a deep-learnt textgraph from meaning of the text and convert the vertices and edges of textgraphs into an AVL tree of lambda function composition expressions. Relation extraction in above two algorithms is based on WordNet-ConceptNet edges. 1468.4 Section 249 describes a simplier algorithm to parse a textgraph from text by quadratic algorithm below: 1468.4.1 combine all possible WordNet paths between each pair of words (x,y) in the text 1468.4.2 O(n^2) WordNet paths having common edges are merged creating a knowledge graph of word vertices whose edges are labelled by WordNet relations. 1468.4.3 Earlier algorithm reverses the process of graph traversals by All pair shortest paths(APSP),BFS,DFS and randomwalks as paths-to-graph vis-a-vis usual graph-to-paths: graph is reconstructed bottom-up by merging paths having common vertices. 1468.5 LLM Knowledge Graph discovery is dependent on relations extracted by transformer sequence attention models that infer triplets of subject-predicate-object from flat text without going deeper into the meanings of the word tokens. 1468.6 The relations extracted by REBEL algorithm earlier are formatted as labelled NetworkX edges and written to DOT file KnowledgeGraph.dot (logs: testlogs/KnowledgeGraph.log.27December2023) 1468.7 REBEL requires huge installation preprocessing of upto ~3GBs which include PyTorch, transformers, NVIDIA dependencies.
Srinivasan Kannan
More... almost 3 years ago
Updated NeuronRain_Research_Celestial_Pattern_Mining.txt
Srinivasan Kannan
More... almost 3 years ago
Section 1451 updated
K.Srinivasan
More... almost 3 years ago
Section 1463 updated
K.Srinivasan
More... almost 3 years ago
Section 1408 updated
Srinivasan Kannan
More... almost 3 years ago
Section 1350 updated
Srinivasan Kannan
More... almost 3 years ago
Section 1463 updated
Srinivasan Kannan
More... almost 3 years ago
+1464. (THEORY and FEATURE) Complement Diophantines - Implementation of algorithmic version of Circuit Range Avoidance(AVOID) - related to 323,345,471,735,1160,1280,1314 and all sections on Complement Diophantines, Complement Functions, Complementary Sets, Quadratic Forms, Riemann Zeta Function, Patterns in Primes, Polynomial Reconstruction Problem, Error correcting codes, Computational Learning Theory - Multivariate Polynomial Reconstruction over Finite Fields, Lagrange-Taylor-Barycentric interpolation, Jones-Sato-Wada-Wiens Theorem, Prime-Composite Complementation, Prime Number Theorem, Ulam Spiral, Ramsey 2-coloring of integers, Hilbert Tenth Problem, Unique Factorization , Matiyasevich-Robinson-Davis-Putnam Theorem, Post's Correspondence Problem, Question-Answering, Circuit Range Avoidance (AVOID), One Way Functions - 11 December 2023
Srinivasan Kannan
More... almost 3 years ago
---------------------------------------------------------------------------------------------------------------------------- 1464. (THEORY and FEATURE) Complement Diophantines - Implementation of algorithmic version of Circuit Range Avoidance(AVOID) - related to 323,345,471,735,1160,1280,1314 and all sections on Complement Diophantines, Complement Functions, Complementary Sets, Quadratic Forms, Riemann Zeta Function, Patterns in Primes, Polynomial Reconstruction Problem, Error correcting codes, Computational Learning Theory - Multivariate Polynomial Reconstruction over Finite Fields, Lagrange-Taylor-Barycentric interpolation, Jones-Sato-Wada-Wiens Theorem, Prime-Composite Complementation, Prime Number Theorem, Ulam Spiral, Ramsey 2-coloring of integers, Hilbert Tenth Problem, Unique Factorization , Matiyasevich-Robinson-Davis-Putnam Theorem, Post's Correspondence Problem, Question-Answering, Circuit Range Avoidance (AVOID), One Way Functions --------------------------------------------------------------------------------------------------------------------------- 1464.1 This commit implements new function complement_range_avoid_infinitely_often(function,funcdomain,universalrange) that translates the circuit range avoidance problem into an algorithm for recursive sets only - algorithms are recursive functions guaranteed to terminate (by MRDP theorem recursively enumerable sets are diophantine and recursively enumerable but not recursive - RE \ R sets have diophantine equations but can't be found due to undecidability) by accepting 3 arguments - function to be range avoided or complemented, domain of the function and universal range over which avoidance has to be found. 1464.2 Function to be complemented is passed on as a lambda function which is reflected by inspect.getsource(). Argument funcdomain set is iterated in loop and the lambda function is evaluated for each element of the domain set to get the function range (funcrange). 1464.3 Range avoidance is found by set difference (complement) of universalrange and funcrange. 1464.4 Range avoidance complement for following two diophantines is found : complement_range_avoid_infinitely_often(lambda x: x*x - x + 1,list(range(10)),list(range(100))) complement_range_avoid_infinitely_often(lambda x: x*x,list(range(10)),list(range(100))) 1464.5 Range avoidance for 2 diophantines x*x - x + 1 and x*x are: ------------------------------------------------------------ function to be complemented: complement_range_avoid_infinitely_often(lambda x: x*x - x + 1,list(range(10)),list(range(100))) universal range: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99] function domain: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} function range: {1, 3, 7, 73, 43, 13, 21, 57, 31} range avoidance: {0, 2, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99} ------------------------------------------------------------ function to be complemented: complement_range_avoid_infinitely_often(lambda x: x*x,list(range(10)),list(range(100))) universal range: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99] function domain: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} function range: {0, 1, 64, 4, 36, 9, 16, 49, 81, 25} range avoidance: {2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99} 1464.6 From 1280.2 even a meagre hardness of circuit range avoid is sufficient for existence of one way functions. Function version of range avoid implemented in this update is a stronger notion - all elements in range avoided for a function are found and they can be interpolated to a complement polynomial or a partial function by lagrange four square theorem. Complexity of function complement_range_avoid_infinitely_often() = hardness(function_to_complement) + hardness(set_difference) which could be arbitrarily hard. From MIP* = RE (749.2), quantum MIP* algorithms do exist for previous undecidability limitation due to MRDP theorem.
Srinivasan Kannan
More... almost 3 years ago
1463. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 8 December 2023 - N-Body gravitational acceleration analysis of date range 21 November 2023-8 January 2024 - 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 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... almost 3 years ago
---------------------------------------------------------------------------------------------------------------------------------------- 1463. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 8 December 2023 - N-Body gravitational acceleration analysis of date range 28 November 2023-8 January 2024 - 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 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 ---------------------------------------------------------------------------------------------------------------------------------------- 1463.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to introduce ratelimit retry logic (which can be used universally as design pattern) within gaussian_ensemble_forecast_rainfall_timeseries(predEWEparams=None,days=5,forecast_seasonal_rainfall=20,historic_training_timeseries=None,maxiterations=100). Boolean variable ratelimiterror is set to True and is iteratively set to False when retry is not necessary and continues looping till retry succeeds. This code is in addition to already in-built TIMEOUT=30 seconds attribute in JPL Horizon ephemeris. 1463.2 Following new date ranges from 21/11/2023 to 8/1/2024 are searched in ephemeris by climate_analytics() and gaussian_ensemble_forecast_rainfall_timeseries() for 4 two-body systems (Mercury-Jupiter,Mercury-Mars,Mercury-Venus,Sun-Moon) with angular separation bounds set to maximum 60 degrees and their gravities are plotted: climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2023,11,21,17,30,00),'dateto':(2024,1,8,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Jupiter",'angularsepbounds':('0d','60d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2023,11,21,17,30,00),'dateto':(2024,1,8,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Mars",'angularsepbounds':('0d','60d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2023,11,21,17,30,00),'dateto':(2024,1,8,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Venus",'angularsepbounds':('0d','60d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2023,11,21,17,30,00),'dateto':(2024,1,8,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Moon",'angularsepbounds':('0d','60d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) gaussian_ensemble_forecast_rainfall_timeseries(predEWEparams={'datefrom':(2023,11,21,17,30,00),'dateto':(2024,1,8,17,30,00),'loc':'@0','bodyconjunctions':"Mars-Venus-Mercury-Sun-Jupiter",'angularsepbounds':('0d','60d')},days=5,forecast_seasonal_rainfall=35,historic_training_timeseries=nem_rainfall_timeseries) 1463.3 Following are the gravity plot images for 2 date ranges (49 days - 21/11/2023-8/1/2024 and 38 days - 2/12/2023-8/1/2024) - especially a steep peak in mercury gravity around 21 December 2023 that could have a cooling effect due to change in specific humidity conducive for low pressure troughs. This anomalous fluctuation in mercury gravity (Gravity anomalies on mercury - MESSENGER probe - https://www.nasa.gov/image-article/gravity-anomalies/ ) especially is still unexplained and beyond scope of NeuronRain: testlogs/GISWeatherAnalytics.Gravity.Mercury-Jupiter.2023-11-21#2024-1-8.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Jupiter.2023-12-2#2024-1-8.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Mars.2023-11-21#2024-1-8.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Mars.2023-12-2#2024-1-8.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2023-11-21#2024-1-8.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2023-12-2#2024-1-8.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Moon.2023-11-21#2024-1-8.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Moon.2023-12-2#2024-1-8.jpg testlogs/GISWeatherAnalytics.log.8December2023 1463.4 Matching pairwise 2-body system angular separation bound dates are as follows (less than 60 degrees apart) in 2 date ranges 3/12/2023-24/12/2023 with peak mercury gravity around 21/12/2023 and 27/12/2023-7/1/2023: Angular separation of Mercury-Jupiter on 2023-12-03 17:30:00.000: [59d14m40.62811775s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-03 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-04 17:30:00.000: [54d55m29.15970191s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-04 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-05 17:30:00.000: [50d27m25.07448926s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-05 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-06 17:30:00.000: [45d50m05.04938389s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-06 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-07 17:30:00.000: [41d03m08.83736106s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-07 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-08 17:30:00.000: [36d06m20.35435758s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-08 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-09 17:30:00.000: [30d59m29.19375542s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-09 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-10 17:30:00.000: [25d42m32.13772073s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-10 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-11 17:30:00.000: [20d15m35.07873474s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-11 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-12 17:30:00.000: [14d38m55.55329517s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-12 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-13 17:30:00.000: [8d53m08.37392694s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-13 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-14 17:30:00.000: [3d00m03.26608996s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-14 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-15 17:30:00.000: [3d07m28.60857718s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-15 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-16 17:30:00.000: [9d15m02.76070618s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-16 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-17 17:30:00.000: [15d29m14.39694215s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-17 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-18 17:30:00.000: [21d47m27.55265838s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-18 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-19 17:30:00.000: [28d07m56.96999903s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-19 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-20 17:30:00.000: [34d28m58.35362405s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-20 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-21 17:30:00.000: [40d48m47.82883938s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-21 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-22 17:30:00.000: [47d05m45.02724418s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-22 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-23 17:30:00.000: [53d18m16.20920661s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-23 17:30:00.000 Angular separation of Mercury-Jupiter on 2023-12-24 17:30:00.000: [59d24m57.2685321s] Angular separation of Mercury-Jupiter matches bounds for date: 2023-12-24 17:30:00.000 Angular separation of Mercury-Venus on 2023-12-27 17:30:00.000: [57d22m05.29802302s] Angular separation of Mercury-Venus matches bounds for date: 2023-12-27 17:30:00.000 Angular separation of Mercury-Venus on 2023-12-28 17:30:00.000: [53d19m06.79328525s] Angular separation of Mercury-Venus matches bounds for date: 2023-12-28 17:30:00.000 Angular separation of Mercury-Venus on 2023-12-29 17:30:00.000: [49d25m47.72273879s] Angular separation of Mercury-Venus matches bounds for date: 2023-12-29 17:30:00.000 Angular separation of Mercury-Venus on 2023-12-30 17:30:00.000: [45d42m19.06669459s] Angular separation of Mercury-Venus matches bounds for date: 2023-12-30 17:30:00.000 Angular separation of Mercury-Venus on 2023-12-31 17:30:00.000: [42d08m42.59224257s] Angular separation of Mercury-Venus matches bounds for date: 2023-12-31 17:30:00.000 Angular separation of Mercury-Venus on 2024-01-01 17:30:00.000: [38d44m52.40541636s] Angular separation of Mercury-Venus matches bounds for date: 2024-01-01 17:30:00.000 Angular separation of Mercury-Venus on 2024-01-02 17:30:00.000: [35d30m36.45753408s] Angular separation of Mercury-Venus matches bounds for date: 2024-01-02 17:30:00.000 Angular separation of Mercury-Venus on 2024-01-03 17:30:00.000: [32d25m37.93337247s] Angular separation of Mercury-Venus matches bounds for date: 2024-01-03 17:30:00.000 Angular separation of Mercury-Venus on 2024-01-04 17:30:00.000: [29d29m36.47999377s] Angular separation of Mercury-Venus matches bounds for date: 2024-01-04 17:30:00.000 Angular separation of Mercury-Venus on 2024-01-05 17:30:00.000: [26d42m09.25948913s] Angular separation of Mercury-Venus matches bounds for date: 2024-01-05 17:30:00.000 Angular separation of Mercury-Venus on 2024-01-06 17:30:00.000: [24d02m51.82646704s] Angular separation of Mercury-Venus matches bounds for date: 2024-01-06 17:30:00.000 Angular separation of Mercury-Venus on 2024-01-07 17:30:00.000: [21d31m18.84274187s] Angular separation of Mercury-Venus matches bounds for date: 2024-01-07 17:30:00.000 1463.5 It is notable that Cyclone Michaung (https://en.wikipedia.org/wiki/Cyclone_Michaung - 988 millibar - sustained windspeed of 100 kmph) rapidly intensified during 3-4 December 2023 when Mercury-Jupiter angular separation started to decrease and they are within 3 degrees during 14-15 December 2023. 1463.6 From 1451.9 - https://arxiv.org/pdf/1901.11426.pdf - "..... In addition to the total water vapour content, the specific humidity, q, will also fall as gravity increases and this can have an important dynamical effect. The specific humidity is defined as the ratio of the mass of water vapour to the total mass of air and in terms of pressures it may be written q = e p − e(1 − ) ≈ e p , (12) where is the ratio of the molar mass of water vapour to that of dry air and the approximation giving e/p holds for a dilute atmosphere. Since pressure scales with α but e does not (it does not depend on α at lowest order) we expect that the specific humidity will fall as gravity increases, scaling roughly as 1/α ....." - alpha is the scaling factor for variable gravity. Specific humidity is inversely proportional to variable gravity, affecting rainfall patterns. 1463.7 While conventional fluid mechanics equations of weather models (IFS,GFS etc.,) have been successful in predicting trajectories of storms (which assume constant gravitational constant), precise prediction of how and when storms get formed is still elusive and change in humidity due to variable gravity - caused potentially by extraterrestial factors - fills in the missing link.
Srinivasan Kannan
More... almost 3 years ago
1462. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Clustering analysis of NASA WorldView NightLights (11 November 2023) for Chennai Metropolitan Area - 6 December 2023 - 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 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
Srinivasan Kannan
More... almost 3 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1462. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Clustering analysis of NASA WorldView NightLights (11 November 2023) for Chennai Metropolitan Area - 6 December 2023 - 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 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1462.1 Recent NASA NightLights imagery dt.11 November 2023 has been clustered by contour_kmeans implementation in NeuronRain - Contour k-means clustering of the NightLights is lot clearer with well-marked urban clustering delineations of bright pixels compared to DBSCAN clustering of same image (not part of the update): #Chennai Metropolitan Area - NASA WorldView NightLights - 11 November 2023 - kmeans contour segment clustering analysis seg16=ImageGraph_Keras_Theano.image_segmentation("testlogs/NASANightLights_11November2023.jpeg") ImageGraph_Keras_Theano.contours_kmeans_clustering(image,seg16) 1462.2 Above contour kmeans clustering offers a visual clustering and computational geometric comparison of cities as opposed to histogram based comparison in World Bank Open NightLights Dataset till year 2020 - https://worldbank.github.io/OpenNightLights/tutorials/mod5_4_comparing_cities.html 1462.3 NightLights of an urban sprawls are useful not just for urban delineation but to identify high growth suburbs as Voronoi-Delaunay tessellation centroids equidistant with each other in a larger urban area causing a conurbation phenomenon wherein multiple suburbs grow in parallel and coalesce over time - An example polycentric conurbation model of 6 satellite cities (Minjur,Thiruvallur,Thirumazhisai,Kanchipuram,Chengalpattu,Mamallapuram) for Chennai Metropolitan Area of 5904 sqkm (from X handle - https://twitter.com/CMDA_Official) is illustrated in testlogs/CMA_SatelliteCities_IMG_20231205_203829.jpg . 1462.4 Following are the image files (contour segmented, contour kmeans clustering, canny edge detector, watershed segmented),facegraph DOT file and logs for clustering analysis of testlogs/NASANightLights_11November2023.jpeg: testlogs/CMA_SatelliteCities_IMG_20231205_203829.jpg testlogs/GISUrbanSprawlAnalytics.log.6December2023 testlogs/NASANightLights_11November2023-cannyedgedetection.jpg testlogs/NASANightLights_11November2023-contourkmeansclustered.jpg testlogs/NASANightLights_11November2023-contourlabelled.jpg testlogs/NASANightLights_11November2023-contoursegmented.jpg testlogs/NASANightLights_11November2023.jpeg testlogs/RemoteSensingGIS/NASANightLights_11November2023_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/NASANightLights_11November2023_segmented.jpg
Srinivasan Kannan
More... almost 3 years ago
Section 1280 updated
K.Srinivasan
More... almost 3 years ago
Section 1408 updated
Srinivasan Kannan
More... almost 3 years ago
Section 1459 updated
Srinivasan Kannan
More... almost 3 years ago
Section 1408 updated
Srinivasan Kannan
More... almost 3 years ago
Rank correlation example - Google PageRank and Microsoft Bing SPTAG rankings on 24 November 2023 for query "big data machine learning linux kernel cloud"
K.Srinivasan
More... almost 3 years ago
Section 1460 updated
Srinivasan Kannan
More... almost 3 years ago
Section 1460 updated
Srinivasan Kannan
More... almost 3 years ago
Section 1460 updated
K.Srinivasan
More... almost 3 years ago
Section 1449 updated
K.Srinivasan
More... almost 3 years ago
1460. (THEORY and FEATURE) NeuronRainApps - Sports Analytics - Sabermetrics - Concept Implementation of Parallel Cricket (PRICKET) simulator - related to 754 and all sections on Intrinsic Performance Ratings and Sports Analytics, Streaming Histogram analytics, Partition distance measures, Random graph streams, Logic and Game theory, Generalized Geography and PSPACE-completeness, Parity games - 21 November 2023
K.Srinivasan
More... almost 3 years ago
------------------------------------------------------------------------------------------------------------------------------- 1460. (THEORY and FEATURE) NeuronRainApps - Sports Analytics - Sabermetrics - Concept Implementation of Parallel Cricket (PRICKET) simulator - related to 754 and all sections on Intrinsic Performance Ratings and Sports Analytics, Streaming Histogram analytics, Partition distance measures, Random graph streams, Logic and Game theory, Generalized Geography and PSPACE-completeness, Parity games - 21 November 2023 ------------------------------------------------------------------------------------------------------------------------------- 1460.1 New source file python-src/NeuronRainApps/ParallelCricket/Pricket.py has been added to NeuronRain AstroInfer repositories which conceptually and minimally implements parallel or semisequential version of the game of cricket (described in Section 754) which is sequentially played (one team chasing the target set by the other). 1460.2 python-src/NeuronRainApps/ParallelCricket/Pricket.py instantiates a Player object defined by class of same name. Player class also defines a function play_shot() for a ball which is simulated by a random angle of the drive played by the batter on the outfield. Outfield of 360 degrees is split into 10 sections of 36 degrees each one per fielder. Ball is deemed to be caught or fielded if the trajectory of the ball is outside (lowerdegree,upperdegree) range for lowerdegree > 0 and upperdegree < 36 i.e 2 adjoining fielders in outfield are able to reach the ball within lowerdegree or upperdegree in two directions. Runs per ball is also chosen at random from an enumerated array [0,1,2,3,4,6] of possible runs per shot. 1460.3 Function def pricket_sabermetrics(number_of_balls=6,team1=None,team2=None,lowerdegree=3,upperdegree=33) loops through 2 deques for team1 and team2 each containing 11 Player objects and invokes play_shot() twice for one player each from both teams thus parallely enabling both teams to bat and bowl by alternate role reversals every second ball. Players are disqualified if shot of the angle is outside predefined degree range. Game stops when either of the team deque is empty by allout. 1460.4 Earlier implementation dynamically traces a Generalized Geography kind-of graph, where playerY of team2 continues from where playerX of team1 stopped in previous ball (and viceversa) thus reducing Cricket to GG in parallel sense. 1460.5 In PRICKET, probability of a player getting out = (36 - upperdegree + lowerdegree)/36. An example commentary transcript of PRICKET for 300 balls and 2 teams of 11 players each is at python-src/NeuronRainApps/ParallelCricket/testlogs/Pricket.log.21November2023 : Team2 allout - Number of balls bowled: 300 Team1 runs: 309 Team2 runs: 360 Team2 wins 1460.6 Multiple scenarios can be simulated by varying the lower and upper degree range for wicket loss. Though a parallel cricket is practically less feasible as fielding-batting sides have to be interchanged every ball, virtual parallel software version of the game is lot more flexible as role alternation every ball is programmable. In parallel cricket, there is no predefined target to chase as both teams are simultaneously at the crease. Present implementation does not cover all possible "out" scenarios - clean bowled, caught and bowled, stumped, runout, LBW are yet to be implemented.
K.Srinivasan
More... almost 3 years ago
Section 1455 updated
K.Srinivasan
More... almost 3 years ago
Updated Requirements.txt
K.Srinivasan
More... almost 3 years ago
1459. (THEORY and FEATURE) Software Analytics - Kernel Analytics and Scheduler Analytics - Enhanced Berkeley Packet Filter (eBPF) support - bpftrace script implementation - related to 561,1191,619,770,906,911,771,1192,1443 and all sections on Program Analysis, AsFer-USBmd-VIRGO-KingCobra FTrace kernel callgraph analysis - Valgrind-Callgrind-KCacheGrind-Flamegraph userspace callgraph analysis, SATURN control flow analysis of VIRGO linux kernel, Software Analytics, Pandemic model of Botnet-Defense adversarial Game theoretic security, Cybercrime analytics, Anti-Malware,Anti-Ransomware,Anti-Theft and Anti-Plagiarism tools, KML, LAKE,Intrusion Prevention-Detection Systems(IDS and IPS),Anomaly Detection - 17 November 2023
K.Srinivasan
More... almost 3 years ago