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Analyzed about 2 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
Section 1620 updated
K.Srinivasan
More... over 1 year ago
Section 1620 updated
Srinivasan Kannan
More... over 1 year ago
Section 1620 updated
Srinivasan Kannan
More... over 1 year ago
Section 1620 updated
Srinivasan Kannan
More... over 1 year ago
1620. (THEORY and FEATURE) Nachiketas Formal LLM - Changes to Wikipedia Question-Answering algorithm for better accuracy 26 - 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 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 - 12 June 2025
Srinivasan Kannan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1620. (THEORY and FEATURE) Nachiketas Formal LLM - Changes to Wikipedia Question-Answering algorithm for better accuracy 26 - 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 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 - 12 June 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1620.1 python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to change 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) to include additional argument transformers_enabled (set to True or False) for making transformers Q-K-V computation optional and disable them by default. This is to isolate heavy computation involved in transformers iterations which are Statistical AI code fragments and make them independent of Formal AI code implementing Nachiketas LLM that does not depend on training dataset and focuses more on language grammar. 1620.2 Function WikipediaRLFGTransformersQuestionAnswering() is invoked twice - for dictionary PoS sentence template and array PoS sentence template - but with wsheading flag set to False so that wikipedia search result summary sentences are parsed internally instead of just result headings which could increase readability of synthesized sentences: ,,, WikipediaRLFGTransformersQuestionAnswering(question,wsheading=False,answerslice=0.01,bothvertices_intersection=False,sentence_type="textgraph_random_walk",number_of_words_per_sentence=50,std_sentence_PoS_dict=sentencePoSdict,number_of_cores_per_random_walk=5,number_of_random_walks=3,blanks=False,treenode_type="tag",user_defined_PoS2Vocabulary_dict=kaggle_corpus_PoS2Vocabulary_dict) ... WikipediaRLFGTransformersQuestionAnswering(question,wsheading=False,answerslice=0.01,bothvertices_intersection=False,sentence_type="textgraph_random_walk",number_of_words_per_sentence=50,std_sentence_PoS_dict={},number_of_cores_per_random_walk=5,number_of_random_walks=5,blanks=False,treenode_type="tag",sentence_tuple_array=True, sentence_PoS_array=sentencePoSarray, randomwalk_to_sentence_template_ratio=3,user_defined_PoS2Vocabulary_dict=kaggle_corpus_PoS2Vocabulary_dict) 1620.3 Sentences synthesized for query "which is the capital city of India" are logged to QuestionAnswering.log.12June2025. Following are the wikipedia search result sentences for this query which are converted to textgraphs and random walks from those textgraphs are filled into PoS slots of PennTreeBank inorder traversed grammar tree templates: wssummarysentences: ['This is a list of locations which have served as capital cities in India', ' The current capital city is New Delhi, which replaced Calcutta in 1911', ''] 1620.4 Difference between how Universal Chomsky Sentence Synthesizer (SentenceSynthesizer.py in NeuronRain Grafit repositories - section 1611)and QuestionAnswering.py earlier are obvious from logs - (*) SentenceSynthesizer.py finds all possible english sentences in set CHOMSKY (which have blanks only for proper nouns) for a sentence template without worrying about human readability while (*) QuestionAnswering.py tries to maximize meaning and context by filling the PoS slots in sentence template from textgraph randomwalks as much as it could (any PoS slot could be blank) which are part of set MEANINGFUL - Both are Formal AI implementations and do not depend on n-gram context statistics of a word. 1620.5 QuestionAnswer.py and SentenceSynthesizer.py in tandem could minimize the blank PoS slots and maximize meaning of synthesized sentences - intersection of set of sentences synthesized by QuestionAnswer.py and SentenceSynthesizer.py has maximum context and meaning as it is exactly the set {CHOMSKY intersection MEANINFUL}.
Srinivasan Kannan
More... over 1 year ago
+1611. (THEORY and FEATURE) Nachiketas Formal LLM - Universal Chomsky Sentence Synthesizer - Changes to Wikipedia Question-Answering algorithm for better accuracy 25 - 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 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 - 10 June 2025
Srinivasan Kannan
More... over 1 year ago
Section 1576 updated
K.Srinivasan
More... over 1 year ago
Section 1608
K.Srinivasan
More... over 1 year ago
Section 1604 updated
K.Srinivasan
More... over 1 year ago
Section 1608 updated
K.Srinivasan
More... over 1 year ago
Section 1481 updated
K.Srinivasan
More... over 1 year ago
Section 1608 updated
Srinivasan Kannan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1608. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Aggregate computation - GHSL R2025A UNDP GeoHub Residential Population Estimates Raster Dataset - Population Estimate of Chennai Metropolitan Area Bounding Box (for year 2023) - 23 May 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 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1608.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to add a small code fragment to compute total of an urban sprawl statistic for all pixels within a raster bounding box. New variable urbanssprawlstatisticsaggregate has been introduced,computed as a sum of all long-lat pixel values and returned from urban_sprawl_from_raster(). 1608.2 GHSL R2025A UNDP GeoHub Residential Population Estimate raster dataset from https://undpgeohub.blob.core.windows.net/userdata/9426cffc00b069908b2868935d1f3e90/datasets/GHS_POP_E2025_GLOBE_R2023A_54009_1000_V1_0_20240828143933.tif/GHS_POP_E2025_GLOBE_R2023A_54009_1000_V1_0_20240828143933_band1.tif?sv=2025-05-05&ss=b&srt=o&se=2026-05-22T09%3A47%3A02Z&sp=r&sig=RVLa6svZ4sE%2BqVsxhc09iHtmTmsgNJVxNNkIOBOUkxc%3D has been downloaded and population estimates for bounding box http://bboxfinder.com/#12.439259,79.271851,13.568572,80.351257 are computed and aggregated to arrive at total residential population within the longitude-latitude bounding box: #Chennai Metropolitan Area Sprawl Bounding Box 4 - http://bboxfinder.com/#12.439259,79.271851,13.568572,80.351257 - R2025A r1data=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/GHS_POP_E2025_GLOBE_R2023A_54009_1000_V1_0_20240828143933_band1.tif",dt="Residential Population Estimates R2025A") 1608.3 Aggregate residential population estimate in year 2023 for earlier Chennai metropolitan area bounding box from testlogs/GISUrbanSprawlAnalytics.log.23May2025 is 15086806.94303749: urban_sprawl_from_raster(): urbansprawlstatisitcsaggregate = [15086806.94303749] 1608.4 Error bounds in GHSL datasets are discussed in https://www.tandfonline.com/doi/full/10.1080/17538947.2024.2390454#abstract - ".... Empirical evidence suggests that GHSL estimates are the most accurate in the public domain today, e.g. achieving an IoU of 0.98 for the water class, 0.92 for the built-up class, and 0.8 for the non-residential class at 10 m resolution. At 100 m resolution, we find that the MAE of built-up surface estimates corresponds to 6% of the grid cell area, the MAE for the building height estimates is 2.27 m, and we find a total allocation accuracy of 83% for resident population ...." - which amounts to 17% error in earlier resident population estimate 15086806.94303749 (or 83% accurate). Adjusting for 17% error plus or minus gives a range: [maximum 17651564.12335386 , minimum 12522049.762721116] for year 2023 and lower range 12.5 million is obviously discarded as it is close to 2011 Chennai metropolitan area population estimate of 12.2 million from 1533.6 which reduces to a population estimate of 17.6 million for earlier bounding box in 2023. 1608.5 Population estimate from GIS raster basically translates to following formula based on 1323.6 and 1424.3 - Average Built volume (BUVOL) = Average Building height (ANBH) * Average Built surface area (BUSURF): BUVOLx = ANBHx ∗ BUSURFx Resident population estimate = BUVOLx * residents_per_unit_volume
Srinivasan Kannan
More... over 1 year ago
Section 1350 updated
K.Srinivasan
More... over 1 year ago
Grok Think reasoning engine prompt - https://x.com/i/grok/share/J5psYbZwAGkAxzm2vmE0gyWVh
K.Srinivasan
More... over 1 year ago
Section 385 updated
K.Srinivasan
More... over 1 year ago
Section 1582 updated
K.Srinivasan
More... over 1 year ago
Section 1533 updated
K.Srinivasan
More... over 1 year ago
Updated index.rst
K.Srinivasan
More... over 1 year ago
Section 1582 updated
K.Srinivasan
More... over 1 year ago
Section 1582 updated
K.Srinivasan
More... over 1 year ago
Updated BestPractices.txt
K.Srinivasan
More... over 1 year ago
Section 1604 updated
K.Srinivasan
More... over 1 year ago
1605. (THEORY and FEATURE) Social Network Analysis - GitHub code search - Personal Access Tokens obfuscated and NeuronRainFeatures text file created - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555,1571,1592 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,Talent Analytics,Code search of opensource repositories,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox - 16 May 2025
Srinivasan Kannan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------------------- 1605. (THEORY and FEATURE) Social Network Analysis - GitHub code search - Personal Access Tokens obfuscated and NeuronRainFeatures text file created - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555,1571,1592 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,Talent Analytics,Code search of opensource repositories,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox - 16 May 2025 ------------------------------------------------------------------------------------------------------------------------------------------- 1605.1 python-src/SocialNetworkAnalysis_PeopleAnalytics.py has been updated to obfuscate GitHub personal access tokens (PAT) by reading them through OS environment variable GITHUB_PAT - harcoded PATs posed a security vulnerability. GITHUB_PAT has to be exported from commandline before GitHub codesearch. 1605.2 New text file NeuronRainFeatures.txt has been created and regular expression search results within text design documents in each of NeuronRain repositories for query FEATURE are collated and written to NeuronRainFeatures.txt which serves as unified list of NeuronRain features. 1605.3 NeuronRain conceptual graph DOT file GitHubCodeSearch_NeuronRainConceptualGraph.dot has been updated. More information in logs - testlogs/SocialNetworkAnalysis_PeopleAnalytics.log.16May2025
Srinivasan Kannan
More... over 1 year ago
Section 1604 updated
K.Srinivasan
More... over 1 year ago
+1604. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 7 - Non-linear dynamics fit of while loop LHS-RHS ratio timeseries by SINDy Lorenz model - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596,1597,1601,1602,1603 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction, Sparse Indentification of Non-Linear Dynamics (SINDy) - 12 May 2025
K.Srinivasan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------- 1604. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 7 - Non-linear dynamics fit of while loop LHS-RHS ratio timeseries by SINDy Lorenz model - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596,1597,1601,1602,1603 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction, Sparse Indentification of Non-Linear Dynamics (SINDy) - 12 May 2025 ------------------------------------------------------------------------------------------------------------------------------- 1604.1 python-src/MatiyasevichRH.py has been updated to reduce maximum number of iterations to 3000. Earlier invocation of SINDy_fit() has been replaced by SINDy_fit_lorenz() which has been recently defined in sections 1601,1602 and 1603 within SINDy.py in the context of stock quote timeseries predictive modelling and fits Lorenz attractor training dataset to SINDy model that predicts future evolution of arbitrary dataset. This change makes SINDy model fit of Matiyasevich LHS-RHS ratio series to more accurately align with a chaotic Lorenz model behaviour. 1604.2 LHS-RHS ratio timeseries of Matiyasevich Riemann Hypothesis while loop condition has been predicted by SINDy_fit_lorenz() and all 3 trends (ChaosPRG timeseries, LHSRHS timeseries, SINDy Lorenz prediction of LHSRHS ratio timeseries) are plotted in single graphic with labelled legend. SINDy Lorenz predictions have been divided by 100 to get fractions in 0 to 1 range. 1604.3 Following are the logs and graph plots for SINDy Lorenz fit of Matiyasevich while loop LHS-RHS ratio timeseries: testlogs/MatiyasevichRH.LHSRHSRatio.12May2025.jpg testlogs/MatiyasevichRH.LHSRHSRatio.zoomed.12May2025.jpg (earlier graphic magnified) testlogs/MatiyasevichRH.log.12May2025 1604.4 As can be observed from testlogs/MatiyasevichRH.LHSRHSRatio.12May2025.jpg, ChaosPRG Logistic Map tightly upperbounds Matiyasevich LHS-RHS ratio timeseries for 3000 iterations while SINDy Lorenz model provides a less-tight upperbound of similarly shaped logistic map curve (scaled down by factor of 100). But both chaotic timeseries upperbound while loop LHS-RHS timeseries for 3000 iterations. 1604.5 It has to be noted that ChaosPRG timeseries are generated by NeuronRain Logistic map implementation invoked with following parameters and initial conditions: logisticseq=numpy.array(ChaosAttractor.ChaosPRG(algorithm="Logistic", seqlen=maxiterations, radix=3.5699340, initialcondition=0.000001, prime=104729, seed=complex(1+0j))) while SINDy Lorenz model data are obtained by integral of following Lorenz system partial-differential equations - https://en.wikipedia.org/wiki/Lorenz_system: dx/dt = σ(y - x), dy/dt = rx - y - xz, dz/dt = xy - bz. where x, y, and z represent variables related to the convection, s (or σ), r, and b are parameters, and t is time. In testlogs/MatiyasevichRH.LHSRHSRatio.12May2025.jpg LHS-RHS ratio timeseries is independently upperbounded by both (1) Logistic map (2) Lorenz model non-linear chaotic timeseries for 3000 iterations (or) LHS stays below RHS and Riemann Hypothesis is true for 3000 iterations of while loop.
K.Srinivasan
More... over 1 year ago
Section 1602 updated
K.Srinivasan
More... over 1 year ago