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Analyzed about 10 hours ago. based on code collected 1 day ago.
Oct 20, 2025 — Oct 20, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Section 1648 updated
Srinivasan Kannan
More... 10 months ago
Section 1648 updated
Srinivasan Kannan
More... 10 months ago
Section 1648 updated
Srinivasan Kannan
More... 10 months ago
Section 1648 updated
Srinivasan Kannan
More... 10 months ago
1648. (THEORY and FEATURE) VoterSATs or Boolean Voter Decision Functions from Multiple choice questionnaire for Boolean majority function+VoterSAT composition to choose between 2 candidates 0 and 1 - related to 651,1213,1215,1217,1221,1226,1227,1426,1484,1485,1563,1564 and all sections on Set Partitions and Histogram Analytics, Merit of Large Scale Visuals, GIS Urban Sprawl Analytics - minimum rectangular bounding box of an urban sprawl, Computational Geometric Integer Factorization,Neuro Cryptocurrency Proof-Of-Work Hardness,Economic Merit,Poverty Alleviation,Money Changing Problem,Integer Linear Programming,Pell Diophantine for Factorization,Perfect Numbers,N-dimensional ball,Lebesgue-Borel measure,Chinese remaindering theorem,Kernel lifting,Shell Turing Machines, DRONA VREPAT Drone Electronic Voting Machines, Condorcet Jury Theorem, 2 candidates binary voting and Majority+VoterSAT boolean composition, Forrelation of boolean functions - 31 December 2025
Srinivasan Kannan
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Section 1426 updated
Srinivasan Kannan
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Section 1522 updated
Srinivasan Kannan
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Section 1489 updated
Srinivasan Kannan
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Section 1646 updated
Srinivasan Kannan
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+------------------------------------------------------------------------------------------------------------------------------ +1646. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 9 - Gaussian Multiplicative Chaos an d while loop LHS-RHS ratio - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596,1597,1601,1602, 1603,1604,1624 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analy tics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries ana lysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorando m generator, CPU Branch Prediction, Sparse Indentification of Non-Linear Dynamics (SINDy), Pattern in Distribution of Primes, Lo ngest Repeated Substring, Suffix arrays, Multifractal, Financial Time series - 23 December 2025 +-------------------------------------------------------------------------------------------------------------------------------
Srinivasan Kannan
More... 10 months ago
------------------------------------------------------------------------------------------------------------------------------ 1646. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 9 - Gaussian Multiplicative Chaos and while loop LHS-RHS ratio - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596,1597,1601,1602,1603,1604,1624 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), Pattern in Distribution of Primes, Longest Repeated Substring, Suffix arrays, Multifractal, Financial Time series - 23 December 2025 ------------------------------------------------------------------------------------------------------------------------------- 1646.1 python-src/MatiyasevichRH.py has been updated to import scipy.stats and function gaussian_multiplicative_chaos() from newly added source file GaussianMultiplicativeChaos.py that implements Gaussian Multiplicative Chaos based on the definition and example implementation from ChatGPT which has been slightly modified - uploaded to testlogs/GaussianMultiplicativeChaos_ChatGPTDefinition.pdf. Gaussian Multiplicative Chaos finds applications across whole gamut of disciplines - Loop Quantum gravity, Financial timeseries (stock markets), Prime distribution, Urban modelling , Fluid mechanics among others. 1646.2 Four new boolean flags have been introduced as below which enable or disable SINdy, Jacob Ladder, Logistic map PRG and Gaussian Multiplicative Chaos - if-else clauses for these flags have been introduced in respective places: sindyfit=False jacobladder=False chaosprg=True gmcfit=True 1646.3 Matiyasevich program while loop has been changed to also plot Gaussian Multiplicative Chaos along with other timeseries. 1646.4 SciPy.stats PearsonR coefficient is computed by pearsonr() instead of NumPy corrcoef() for Logistic map-LHSRHS ratio and GMC-LHSRHS ratio in 2 clauses 1646.5 New source file GaussianMultiplicativeChaos.py implements Gaussian Multiplicative Chaos in a function that augments ChatGPT definition of GMC exp(gamma*X-0.5*gamma*gamma*Variance(X)) for a log correlated Gaussian Field X and multiplies it by X as defined in 1624.10 and plots the function X*exp(gamma*X-0.5*gamma*gamma*Variance(X)). 1646.6 Following are the plots,logs for above code changes: testlogs/GaussianMultiplicativeChaos_ChatGPTDefinition.pdf testlogs/MatiyasevichRH.LHSRHSRatio.GMC.23December2025.jpg testlogs/MatiyasevichRH.log.GMC.23December2025 1646.7 Plot testlogs/MatiyasevichRH.LHSRHSRatio.GMC.23December2025.jpg shows a remarkable similarity between X*exp(gamma*X-0.5*gamma*gamma*Variance(X)) and Matiyasevich while loop LHS-RHS ratio series for first 3000 iterations.
Srinivasan Kannan
More... 10 months ago
Section 1150 updated
Srinivasan Kannan
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Section 1630 updated
Srinivasan Kannan
More... 10 months ago
Section 1624 updated
Srinivasan Kannan
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Section 1624 updated
Srinivasan Kannan
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Section 1624 updated
Srinivasan Kannan
More... 10 months ago
Updated index.rst
Srinivasan Kannan
More... 10 months ago
Section 1639 updated
Srinivasan Kannan
More... 10 months ago
Section 1630 updated
Srinivasan Kannan
More... 10 months ago
Section 1630 updated
Srinivasan Kannan
More... 10 months ago
Section 1639 updated
Srinivasan Kannan
More... 10 months ago
Section 1645 updated
Srinivasan Kannan
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Section 1624 updated
Srinivasan Kannan
More... 11 months ago
Section 1630 updated
Srinivasan Kannan
More... 11 months ago
Section 1645 updated
Srinivasan Kannan
More... 11 months ago
Section 1645 updated
Srinivasan Kannan
More... 11 months ago
Updated Requirements.txt
Srinivasan Kannan
More... 11 months ago
+1645. (THEORY and FEATURE) Social Network Analysis - DeepDiff similarity of Social Media JSON Profiles - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555,1571,1592,1605 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,Matching in Matrimony-Professional-Personal Social Media Network Profiles,Tree matching - 8 December 2025
Srinivasan Kannan
More... 11 months ago
------------------------------------------------------------------------------------------------------------------------------------------- 1645. (THEORY and FEATURE) Social Network Analysis - DeepDiff similarity of Social Media JSON Profiles - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555,1571,1592,1605 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,Matching in Matrimony-Professional-Personal Social Media Network Profiles,Tree matching - 8 December 2025 ------------------------------------------------------------------------------------------------------------------------------------------- 1645.1 python-src/SocialNetworkAnalysis_PeopleAnalytics.py has been updated to import DeepDiff (https://zepworks.com/deepdiff/current/) python library that is widely used for diff operations over python JSON and dictionary objects 1645.2 Almost all of social media profiles, be it personal-matrimonial-professional, are ingested in JSON nested dictionary format per person. 1645.3 In social networks that require closest matching between 2 profiles (example:- Matrimonial:Bride-Groom, Professional:Candidate-Employer), DeepDiff of 2 JSON profiles might serve as similarity metric. JSON dictionaries have N-ary tree structure (every nested level in JSON object corresponds to a subtree) and this makes profile matching in social networks a N-ary tree matching problem that tries to find if a subtree pattern is common across 2 trees (or nested JSON dictionaries). 1645.4 New function profile_JSON_similarity() has been implemented that invokes DeepDiff() comparison function on 2 JSONs. DeepDistance between two JSON objects is also printed that could be used in ranking of social profiles by similarity. 1645.5 Two GitHub codesearch JSONs and two fictitious matrimonial profiles have been compared by DeepDiff. 1645.6 Logs testlogs/SocialNetworkAnalysis_PeopleAnalytics.log.8December2025 demonstrate JSON DeepDiff of 2 pairs of JSONs. For matrimonial profile match example following is the distance similarity computed by DeepDiff: Profile similarity: {'values_changed': {"root['Personal Data']['Age']": {'new_value': '45', 'old_value': '41'}, "root['Personal Data']['Occupation']": {'new_value': 'IT', 'old_value': 'Banking sector'}, "root['Personal Data']['Income']": {'new_value': '50 LPA', 'old_value': '30 LPA'}, "root['Personal Data']['Star']": {'new_value': 'Rohini', 'old_value': 'Ashwini'}, "root['Preferences']['Drinks']": {'new_value': 'No', 'old_value': 'Yes'}}, 'deep_distance': 0.14705882352941177} 1645.7 In the context of matrimonial profiles, based on DeepDiff results earlier, further qualitative analysis might be required on each of the differing tree node fields.
Srinivasan Kannan
More... 11 months ago
Section 1639 updated
Srinivasan Kannan
More... 11 months ago