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Analyzed about 22 hours ago. based on code collected 2 days ago.
Nov 05, 2025 — Nov 05, 2026
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Section 1633 updated
Srinivasan Kannan
More... about 1 year ago
+1633. (THEORY and FEATURE) Nachiketas Formal LLM - Integration of Google Ngrams V3 dataset (Ngrams.dev) based fill-in-the-blanks sentence synthesizer - Changes to Wikipedia Question-Answering algorithm for better accuracy 29 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550,1568,1572,1573,1574,1575,1576,1578,1579,1583,1585,1587,1595,1611,1620,1621,1632 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression, Drug discovery, Universal Chomsky Sentence Synthesizer, Formal AI, Perplexity in Statistical AI - 20 August 2025
Srinivasan Kannan
More... about 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1633. (THEORY and FEATURE) Nachiketas Formal LLM - Integration of Google Ngrams V3 dataset (Ngrams.dev) based fill-in-the-blanks sentence synthesizer - Changes to Wikipedia Question-Answering algorithm for better accuracy 29 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550,1568,1572,1573,1574,1575,1576,1578,1579,1583,1585,1587,1595,1611,1620,1621,1632 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression, Drug discovery, Universal Chomsky Sentence Synthesizer, Formal AI, Perplexity in Statistical AI - 20 August 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1633.1 python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to import SentenceSynthesizer_Ngrams.get_ngrams_and_synthesize_sentence() from NeuronRain Grafit repositories for optional statistical Markov k-order model text restoration based on massive Ngrams.dev Google Books Ngrams V3 dataset of English ngrams. 1633.2 Function def WikipediaRLFGTransformersQuestionAnswering(question,questionfraction=1,maxanswers=1,keywordsearch=False,wsheading=True,answerslice=1,answerfraction=1,bothvertices_intersection=True,sentence_type="xtag_node34_triplets",number_of_random_walks=10,number_of_words_per_sentence=5,number_of_cores_per_random_walk=5,std_sentence_PoS_dict={"ADJ":[],"PROPN":[],"NOUN":[],"AUX":[],"ADP":[],"ADV":[],"VERB":[],"DET":[],"PRON":[],"CCONJ":[],"NUM":[],"SYM":[],"X":[]},blanks=False,perplexity_algorithm="WordNet",treenode_type="PoS",sentence_tuple_array=False,sentence_PoS_array=None,randomwalk_to_sentence_template_ratio=10,user_defined_PoS2Vocabulary_dict=None,transformers_enabled=False,ngrams_sentence_synthesis=False,pairwise_ngram=False) has been updated to add boolean flags ngrams_sentence_synthesis and pairwise_ngram for Ngrams sentence synthesis. 1633.3 Invocations of make_sentence2() have been updated to include pairwise_ngram and ngrams_sentence_synthesis arguments accordingly: naturallanguageanswer = make_sentence2(random_walk,sentence_PoS_tuple_array=sentence_PoS_array,treenode_type="tag",user_defined_PoS2Vocabulary_dict=user_defined_PoS2Vocabulary_dict,ngrams_sentence_synthesis=ngrams_sentence_synthesis,pairwise_ngram=pairwise_ngram,number_of_words_per_sentence=number_of_words_per_sentence) ... def make_sentence2(randomwalkvertices,sentence_PoS_tuple_array=[],treenode_type="tag",max_synth_sentences=10,user_defined_PoS2Vocabulary_dict=None,max_words_per_PoS=1000,ngrams_sentence_synthesis=False,pairwise_ngram=False,number_of_words_per_sentence=3): 1633.4 Random walk vertices within make_sentence2() are cast to a set() for uniqueness. Blank marker for unfilled PoS slots has been changed to " * " from "----" or PoS name for Ngrams.dev REST API wildcard convention compliance. 1633.5 Clause for ngrams_sentence_synthesis implements subclasses for : (*) STATISTICAL - considering a randomwalk pairwise and computing itertools.product() of all possible ngram sentences formed from cartesian product of pairwise ngrams (which have to be later checked for Part-of-speech correctness) (*) STATISTICAL - considering entire randomwalk as a single semi-filled wildcard sentence having " * " between each pair of vertices on randomwalk (*) FORMAL and STATISTICAL - filling the semi-filled sentence template derived from inorder traversal of grammar trees after fusing together consecutive blank markers. 1633.6 WikipediaRLFGTransformersQuestionAnswering() is invoked twice for pairwise_ngram=False and pairwise_ngram=True: WikipediaRLFGTransformersQuestionAnswering(question,wsheading=False,answerslice=0.01,bothvertices_intersection=False,sentence_type="textgraph_random_walk",number_of_words_per_sentence=5,std_sentence_PoS_dict={},number_of_cores_per_random_walk=3,number_of_random_walks=3,blanks=False,treenode_type="tag",sentence_tuple_array=True, sentence_PoS_array=sentencePoSarray, randomwalk_to_sentence_template_ratio=3,user_defined_PoS2Vocabulary_dict=None,ngrams_sentence_synthesis=True,pairwise_ngram=False) WikipediaRLFGTransformersQuestionAnswering(question,wsheading=False,answerslice=0.01,bothvertices_intersection=False,sentence_type="textgraph_random_walk",number_of_words_per_sentence=5,std_sentence_PoS_dict={},number_of_cores_per_random_walk=3,number_of_random_walks=3,blanks=False,treenode_type="tag",sentence_tuple_array=True, sentence_PoS_array=sentencePoSarray, randomwalk_to_sentence_template_ratio=3,user_defined_PoS2Vocabulary_dict=None,ngrams_sentence_synthesis=True,pairwise_ngram=True) 1633.7 Logs for earlier updates are at python-src/NeuronRainApps/QuestionAnswering/testlogs/QuestionAnswering.log.20August2025
Srinivasan Kannan
More... about 1 year ago
Updated Requirements.txt
Srinivasan Kannan
More... about 1 year ago
Section 608 updated
Srinivasan Kannan
More... about 1 year ago
Section 1631 updated
Srinivasan Kannan
More... about 1 year ago
ChatGPT Query on Mercury-Venus conjunctions
Srinivasan Kannan
More... about 1 year ago
Section 1631 updated
Srinivasan Kannan
More... about 1 year ago
Section 1631 updated
Srinivasan Kannan
More... about 1 year ago
1631. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 12 August 2025 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 1/7/2025-11/8/2025 for Sun-Mercury-Venus - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567,1581,1590 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, Global warming,Clausius-Clapeyron equation,Navier-Stokes equation
Srinivasan Kannan
More... about 1 year ago
---------------------------------------------------------------------------------------------------------------------------------------- 1631. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 12 August 2025 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 1/7/2025-11/8/2025 for Sun-Mercury-Venus - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567,1581,1590 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, Global warming,Clausius-Clapeyron equation,Navier-Stokes equation ---------------------------------------------------------------------------------------------------------------------------------------- (Disclaimer: Same as Disclaimer in Section 1548)
Srinivasan Kannan
More... about 1 year ago
Section 1630 updated
Srinivasan Kannan
More... over 1 year ago
1630. (THEORY and FEATURE) GIS Urban Sprawl Analytics - WorldPop Population Estimates - India - Spatial Distribution of Population (2015-2030) 2025 - Constrained Raster Dataset - Population Estimate of Chennai Metropolitan Area Bounding Box (for year 2025) - 7 August 2025 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623 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... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1630. (THEORY and FEATURE) GIS Urban Sprawl Analytics - WorldPop Population Estimates - India - Spatial Distribution of Population (2015-2030) 2025 - Constrained Raster Dataset - Population Estimate of Chennai Metropolitan Area Bounding Box (for year 2025) - 7 August 2025 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1630.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to compute aggregate population count of following bounding box for recently released WordPop - India Spatial Distribution of Population (2015-2030) 1km resolution R2024B dataset for year 2025 from https://data.worldpop.org/GIS/Population/Global_2015_2030/R2024B/2025/IND/v1/1km_ua/constrained/ind_pop_2025_CN_1km_R2024B_UA_v1.tif downloaded to testlogs/ind_pop_2025_CN_1km_R2024B_UA_v1.tif: #Chennai Metropolitan Area Sprawl Bounding Box 4 - http://bboxfinder.com/#12.439259,79.271851,13.568572,80.351257 - WorldPop r2data=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/ind_pop_2025_CN_1km_R2024B_UA_v1.tif",dt="WorldPop population estimates 2025") 1630.2 Earlier dataset is constrained estimate which means only population within built area pixels are considered and not all pixels within entire rectangle of bounding box. Above dataset is of 1km resolution per pixel of size ~15 MB while there is another 100m resolution pixel dataset of size ~800 MB which could be more accurate as AI algorithms for building detection depend on image resolution for precise building counts and building detection may not work for non-conventional structures. 1630.3 Differences between constrained (only built pixels are chosen by settlement masks) and unconstrained (all pixels) versions in WorldPop datasets are discussed in https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained/ 1630.4 From logs for above dataset - testlogs/GISUrbanSprawlAnalytics.log.7August2025 - aggregate population living only in built pixels of earlier bounding box is 82.45 lakh and from 1630.6 inaccuracies in built area population estimation algorithms could be as high as 50% (for every person estimated in urban cell, 1 person is omitted) which could double the estimate to 82.45*2 = 164.9 lakh dwelling within earlier bounding box concurring with 14 years of compounded annual growth rate (CAGR) projections for Chennai metropolitan area population based on Census 2011 data in 1533.6 (122*1.024^14 = 170 lakh for 2.4% CAGR): urban_sprawl_from_raster(): urbansprawlstatisitcsaggregate = [8245145.5]
Srinivasan Kannan
More... over 1 year ago
Section 1623 updated
Srinivasan Kannan
More... over 1 year ago
Section 1623 updated
Srinivasan Kannan
More... over 1 year ago
Section 1623 updated
Srinivasan Kannan
More... over 1 year ago
Section 1629 updated
Srinivasan Kannan
More... over 1 year ago
Section 1474 updated
Srinivasan Kannan
More... over 1 year ago
Section 1411 updated
Srinivasan Kannan
More... over 1 year ago
Section 1474 updated
Srinivasan Kannan
More... over 1 year ago
Section 1629 updated
Srinivasan Kannan
More... over 1 year ago
Section 1629 updated
Srinivasan Kannan
More... over 1 year ago
Updated BestPractices.txt
Srinivasan Kannan
More... over 1 year ago
Updated BestPractices.txt
Srinivasan Kannan
More... over 1 year ago
Updated Requirements.txt
Srinivasan Kannan
More... over 1 year ago
1629. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.13 + Spark 4.0.0 + OpenJDK 17 + Linux Kernel 6.14 Upgraded Single node clustering benchmark - 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,Circuit Value Problem - 5 August 2025
Srinivasan Kannan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1629. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.13 + Spark 4.0.0 + OpenJDK 17 + Linux Kernel 6.14 Upgraded Single node clustering benchmark - 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,Circuit Value Problem - 5 August 2025 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1629.1 Computational Geometric Factorization Python-Spark MapReduce implementation has been benchmarked on upgraded Python3.13 + Spark 4.0.0 + OpenJDK 17 Java binary configuration for 17 bits integer 99129 by following commandline: ../../../../spark-4.0.0-bin-hadoop3/bin/spark-submit DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py 99129 1 False False all False 2> testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.Python3.13Spark4.0.0Upgrade.log.5August2025 1629.2 Factors found for 99129 are (from python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.factors): {"99129": ["519", "573", "33043", "191", "3", "173"]} 1629.3 Earlier benchmark significantly alters the python-java interaction because all earlier benchmarks were based on legacy JDK 1.8.0_144. All factors of 99129 were found in 6.1 seconds (from nanoseconds/10^9 in testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.Python3.13Spark4.0.0Upgrade.log.5August2025): nanoseconds elapsed so far in finding all factors: 6167543990
Srinivasan Kannan
More... over 1 year ago
Section 1272 updated
Srinivasan Kannan
More... over 1 year ago
Section 1374 updated
Srinivasan Kannan
More... over 1 year ago