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Analyzed about 8 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
1150. (THEORY and FEATURE) NeuronRain CoronaVirus 2019 analyzer (R and Python - GEM,R Logit,Chaotic ERSIR Cellular Automaton Graph) - related to all sections on Space Filling,Chaos,Cellular Automata,Social Network Analysis,Central Limit Theorem,Cybercrime analytics,Ramsey Theory - 5 July 2021, 6 July 2021, 8 July 2021
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 5 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1143. (THEORY and FEATURE) Recursive Lambda Function Growth EventNet GEM - 2 different research articles collated - related to 752, 1138, 1139 and all sections of Intrinsic Merit, EventNet Causal and Graphical Event Models - 8 June 2021 -------------------------------------------------------------------------------------------------------------------------------------------- 1.RecursiveLambdaFunctionGrowth_EventNetGEM.py has been executed on merged abstracts of 2 different research articles on Collatz conjecture and Pseudorandom number generators (but both are indirectly related to Chaotic maps and Fractals) - https://www.math.auckland.ac.nz/~king/745/collatz.pdf and https://www.researchgate.net/publication/332583614_Pseudo-random_number_generators_based_on_the_Collatz_conjecture - which reasonably extracts the crux of the two articles and finds random walks connecting the abstracts. 2.There are some irrelevant word vertices which could be because of deficient word sense ambiguation (Primitive Lesk and PyWSD) and non-scientific nature of WordNet which is more linguistic and might therefore necessitate a Concept Ontology specific to Theoretical and Experimental Science. 3.Cause-effect random walks extracted from the previous 2 articles on Collatz conjecture and Pseudorandom number generators have prominent word vertices "simulation", "graph", "mathematics" which convey the common thread connecting the two articles. Full text merge of the 2 articles might increase the accuracy but cause slowdown.
Srinivasan Kannan
More... over 5 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1143. (THEORY and FEATURE) Recursive Lambda Function Growth EventNet GEM - 2 different research articles collated - related to 752, 1138, 1139 and all sections of Intrinsic Merit, EventNet Causal and Graphical Event Models - 8 June 2021 -------------------------------------------------------------------------------------------------------------------------------------------- 1.RecursiveLambdaFunctionGrowth_EventNetGEM.py has been executed on merged abstracts of 2 different research articles on Collatz conjecture and Pseudorandom number generators (but both are indirectly related to Chaotic maps and Fractals) - https://www.math.auckland.ac.nz/~king/745/collatz.pdf and https://www.researchgate.net/publication/332583614_Pseudo-random_number_generators_based_on_the_Collatz_conjecture - which reasonably extracts the crux of the two articles and finds random walks connecting the abstracts. 2.There are some irrelevant word vertices which could be because of deficient word sense ambiguation (Primitive Lesk and PyWSD) and non-scientific nature of WordNet which is more linguistic and might therefore necessitate a Concept Ontology specific to Theoretical and Experimental Science. 3.Cause-effect random walks extracted from the previous 2 articles on Collatz conjecture and Pseudorandom number generators have prominent word vertices "simulation", "graph", "mathematics" which convey the common thread connecting the two articles. Full text merge of the 2 articles might increase the accuracy but cause slowdown.
Srinivasan Kannan
More... over 5 years ago
1142. (THEORY and FEATURE) SETH is 100% False - Draft Proof - Edit distance-Earth mover distance reduction and order preservation - illustration - related to 752,1135,1136 and all sections on Earth Mover Distance, Edit Distance, BigData and String analytics, String complexity measures, Histogram analytics and Set partitions, Merit of Large Scale Visuals, Embedding of Strings in Syllable vector space - 26 May 2021
Srinivasan Kannan
More... over 5 years ago
Section 1139 updated
Srinivasan Kannan
More... over 5 years ago
Reference 34.101 updated
Srinivasan Kannan
More... over 5 years ago
1139. (THEORY and FEATURE) Recursive Lambda Function Growth EventNet GEM - CoronaVirus 2019 GEM usecase - 5 May 2021 - related to 752, 1138 and all sections of Intrinsic Merit, EventNet Causal and Graphical Event Models
Srinivasan Kannan
More... over 5 years ago
------------------------------------------------------------------------------------------------------------------------------------------ 1139. (THEORY and FEATURE) Recursive Lambda Function Growth EventNet GEM - CoronaVirus 2019 GEM usecase - 5 May 2021 - related to 752, 1138 and all sections of Intrinsic Merit, EventNet Causal and Graphical Event Models ------------------------------------------------------------------------------------------------------------------------------------------ 1.RecursiveLambdaFunctionGrowth_EventNetGEM.py is executed on a text file containing abstract of a research article on COVID19 (Is the 2019 novel coronavirus related to a spike of cosmic rays? - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529058/) and a textgraph created by it is analyzed for random walk causality (having noun-verb-noun patterns fitting actor-action-actor cause-effect template). [As an astronomical aside 2 peak waves of COVID19 tantalizingly coincided with 2 notable celestial conjunctions of planets in December 2019 and February 2021 apart from Solar eclipses in 2019 and 2020 (which are known for malevolent radiations) and an abnormal comet passing tangential to earth (Borisov 2019)] 2.Following are some of the notable random walks in textgraph EventNet GEM showing actor-action causalities from python-src/testlogs/RecursiveLambdaFunctionGrowth_EventNetGEM.log.5May2021.xz: ... ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'], ['determined', 'magnetic', 'sunspot', 'field', 'space', 'cosmic', 'extended'] ... 3.Nested loop for verb filtering has been optimized.
Srinivasan Kannan
More... over 5 years ago
Updated section 1138
Srinivasan Kannan
More... over 5 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1138. (THEORY and FEATURE) Recursive Lambda Function Growth Textgraph as EventNet Causality Graphical Event Model - implementation - 12 April 2021 - related to 762 and all sections on Intrinsic Merit of Text and Videos, EventNet, Graphical Event Models -------------------------------------------------------------------------------------------------------------------------------------------- 1.New python source file RecursiveLambdaFunctionGrowth_EventNetGEM.py derived from RecursiveLambdaFunctionGrowth.py implements function rlfg_eventnet_graphical_event_model() which takes a Recursive Lambda Function Growth WordNet textgraph as argument and computes all pairs shortest paths between word vertices from textgraph. 2.All pairs shortest paths in the textgraph are filtered for words of verb part-of-speech and top core number words of dense subgraph unsupervised classifier - rationale being paths between word vertices containing lot of verb vertices are approximate chronology and causality indicators of actors and actions while words of top core numbers are keywords of the text - "who does what to whom". 3.This EventNet causality model for texts (mostly news articles) is inspired by EventNet Tensor product algorithm implementation for Merit of Large Scale Visuals which determines causality between frames of video by ImageNet. 4.Textgraph of an example news article is filtered for verb paths - some exceprts from testlogs/RecursiveLambdaFunctionGrowth_EventNetGEM.log.12April2021: ['government', 'unit', 'density', 'size', 'add', 'make', 'propose', 'proposal', 'intention', 'usually', 'area', 'urban', 'city', 'Chennai', 'Tamil', 'Dravidian'] ['government', 'unit', 'density', 'size', 'add', 'make', 'propose', 'proposal', 'intention', 'usually', 'area', 'urban', 'city', 'Chennai', 'Tamil', 'language'] ['influence', 'extend', 'expand'] 5.Existing Graphical Event Models viz, PGEM extract graph dependency from timeseries of events by probabilistic maximum likelihood parent search while previous algorithm is graph theoretic. Start and/or end vertices of paths are (actors) nouns while most intermediary nodes are (actions) verbs.
Srinivasan Kannan
More... over 5 years ago
Updated Section 1136
Srinivasan Kannan
More... over 5 years ago
Section 1137 - references updated
Srinivasan Kannan
More... over 5 years ago
1137. (THEORY and FEATURE) Intrinsic Merit Versus Majority Voting, P=BPP ("Maybe" tends to "Exists") in the context of Condorcet Jury Theorem and its derivatives, Margulis-Russo Threshold for boolean majority, Histogram analytics, Set Partition Theoretical Electronic Voting Machines, Edit distance to Earth Mover Distance reduction, Boolean function composition, KRW Conjecture, Communication complexity, Hardness amplification, Bounded Rationality, Gibbard-Satterthwaite Theorem - related to 400,572,688,1136 and all sections on complexity theoretic aspects of Majority Voting and Intrinsic merit - 31 March 2021
Srinivasan Kannan
More... over 5 years ago
1136 updated - references
Srinivasan Kannan
More... over 5 years ago
1136 updated - references
Srinivasan Kannan
More... over 5 years ago
1136. (THEORY and FEATURE) SETH is 100% False - Draft Proof - alternative Edit distance-Earth mover distance reduction - related to 752,1135 and all sections on Earth Mover Distance, Edit Distance, BigData and String analytics, String complexity measures, Histogram analytics, Merit of Large Scale Visuals, Embedding of Strings in Syllable vector space - 24 March 2021
Srinivasan Kannan
More... over 5 years ago
1135. (THEORY and FEATURE) SETH is 100% False - Draft Proof - related to 752 and all sections on Earth Mover Distance, Edit Distance, BigData and String analytics, String complexity measures, Histogram analytics, Merit of Large Scale Visuals - 11 March 2021,12 March 2021
Srinivasan Kannan
More... over 5 years ago
1135. (THEORY and FEATURE) SETH is 100% False - Draft Proof - related to 752 and all sections on Earth Mover Distance, Edit Distance, BigData and String analytics, String complexity measures, Merit of Large Scale Visuals - 10 March 2021
Srinivasan Kannan
More... over 5 years ago
1134. (THEORY and FEATURE) Named Entity Recognition - Transformers Attention Model - related to 697,1131,1133 and all sections on Named Entity Recognition - 9 February 2021,11 February 2021
Srinivasan Kannan
More... over 5 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1134. (THEORY and FEATURE) Named Entity Recognition - Transformers Attention Model - related to 697,1131,1133 and all sections on Named Entity Recognition - 9 February 2021,11 February 2021 --------------------------------------------------------------------------------------------------------------------------------------------- 1. NamedEntityRecognition_HMMViterbi_CRF.py has been updated to invoke rlfg_transformers_attention_model() from Recursive Lambda Function Growth python implementation for the textgraph of the observation text based on degree attention per word vertex defined earlier. 2. Perceptron learnt Query,Key and Value weights are printed in Recursive Lambda Function Growth rlfg_transformers_attention_model() routine. 3. Adjacency matrix for textgraph is instantiated by Numeric Python ones() function initialized to all 1s and cast to python list. 4. Textgraph edges (wi,wj) between word vertex wi to word vertex wj of maximum query,key,value weights could be high contributors to meaning of word vertex wj. 5. Test Logs for this code change are: python-src/testlogs/NamedEntityRecognition_HMMViterbi_CRF.log.11February2021 python-src/testlogs/NamedEntityRecognition_HMMViterbi_CRF.log.9February2021
Srinivasan Kannan
More... over 5 years ago
Updated for sections 1132 and 1133 of NeuronRain Design
Srinivasan Kannan
More... almost 6 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1132. (THEORY and FEATURE) People Analytics - Alphabet Vectorspace Embedding - Earth Mover Distance between String Syllable Tensors - related to 752,874,875,1123 and all sections on Embedding in vectorspace, Compressed Sensing, Social Network Analytics, Meaning Representation, Intrinsic Merit of People Profiles/HR Analytics, Set Partition and Histogram analytics - 5 February 2021 --------------------------------------------------------------------------------------------------------------------------------------------- 1. SocialNetworkAnalysis_PeopleAnalytics.py implementation has been updated to compare Earth Mover Distance and Wagner-Fischer Edit distance. 2. Few debug print()s have been added to print EMD and Edit distance measures for 2 strings. EMD computes the Earth Mover Distance between histograms of 2 strings. 3. Each string histogram has single alphabet buckets which reduces Edit Distance to Earth Mover Distance and subquadratic Earth Mover Distance computation (e.g LC-ACT linear complexity approximation of EMD has high accuracy close to 100% depending on iterations) implies SETH is False.
Srinivasan Kannan
More... almost 6 years ago
1130. (THEORY and FEATURE) People Analytics - HR and Talent Analytics - Chaotic Hidden Markov Model - Career Polynomial of a Profile and Inner Product Space Distance Similarity between 2 people profiles - related to 753,1127,1128,1129 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability of Interview LTF/PTF - 30 December 2020
Srinivasan Kannan
More... almost 6 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1130. (THEORY and FEATURE) People Analytics - HR and Talent Analytics - Chaotic Hidden Markov Model - Career Polynomial of a Profile and Inner Product Space Distance Similarity between 2 people profiles - related to 753,1127,1128,1129 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability of Interview LTF/PTF - 30 December 2020 --------------------------------------------------------------------------------------------------------------------------------------------- 1.This commit implements 3 new functions in python-src/SocialNetworkAnalysis_PeopleAnalytics_ChaoticHMM.py - career_polynomial_inner_product_distance(),normalize(),second() - and polynomial fit of weighted HMM observations (designations) in career_transition_analytics() 2.career_polynomial_inner_product_distance() takes two arrays of points on a polynomial (of same size sampled on similar time points which could be numeric values for designations,remunerations or durations), polyfit()-s the points to two polynomials and computes inner product of the two polynomials which is the clustering similarity measure for two people profiles. 3.second() is a utility function for getting the second element in an array of ordered tuples. Usual cast of array of ordered tuple to dict() and invoking values() on them returns unique values and not a multiset of repetitive values which is redressed by second(). 4.Utility Function normalize() divides a numeric array by maximum element which might be necessary for comparing trends in remunerations,designations and durations of two talent profiles. 5.Apart from these, weighted observations (designations) within Chaotic HMM are as well fitted to a polynomial in career_transition_analytics() 6.All polynomials are of degree 5 and logs testlogs/SocialNetworkAnalysis_PeopleAnalytics_ChaoticHMM.log.30December2020 demonstrate the inner product distance measures and fitted career polynomials through few example inputs. 16553,1 Bot
Srinivasan Kannan
More... almost 6 years ago
Updated BestPractices.txt
Srinivasan Kannan
More... almost 6 years ago
Correction - Section 1128
Srinivasan Kannan
More... almost 6 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1128. (THEORY and FEATURE) People Analytics - HR and Talent Analytics - Chaotic Hidden Markov Model - Career Transition Score of a Profile - Weighted Automaton of Career Transition - related to 753, 1127 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability of Interview LTF/PTF - 23 December 2020 ---------------------------------------------------------------------------------------------------------------------------------------------
Srinivasan Kannan
More... almost 6 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1128. (THEORY and FEATURE) People Analytics - HR and Talent Analytics - Chaotic Hidden Markov Model - Career Transition Score of a Profile - Weighted Automaton of Career Transition - related to 753, 1127 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability of Interview LTF/PTF - 23 December 2020 --------------------------------------------------------------------------------------------------------------------------------------------- 1. This commit implements an important and most fundamental merit measure for People profiles - Career Transition Score - which quantifies and grades the career tenure switchovers of a Professional profile by traversing designations/paygrades in a Career Transition Weighted Automaton State Machine. 2. Section 753 attributes following qualities to career transition ranked by incentivizing choices made: " ... (*) transition from industry to academics is considered nobler than industry-to-industry. (*) transition from academics to industry is majority choice (*) transition from low remuneration to high is a majority choice (*) transition from high remuneration to low is rare (*) transition from employment to entrepreneur is rare and considered less risk-averse (*) transition from low designation to high is majority choice ... " 3.SocialNetworkAnalysis_PeopleAnalytics_ChaoticHMM.py defines a new function career_transition_analytics() which traverses the Observations of the Chaotic Hidden Markov Model,classifies them into states and looksup the state transitions in a predefined Weighted Automaton defined by a dictionary: self.career_statemachine={'academic-academic':2,'work-work':1,'startup-startup':2,'academic-work':1,'work-academic':2,'academic-start up':3,'startup-academic':3,'work-startup':3,'startup-work':1,'academic-business':5,'business-business':6} 4.Previous career state machine presently has 4 classes of designations for states - academic, work, startup (which includes own initiatives of a profile) and business (corporate designations) - and transitions among them are weighted by integers in ascending order of importance and risk aversion. E.g Transition work-work is weighted as 1 which is considered secure while business-business is weighted as 6 because of high risk. 5.Designations (Observations in HMM) are classified into 4 classes above - academic,startup,business,startup - and weighted by increased paygrades/power/independence/authority/experience/totempole: self.designations["academic"]={"Schooling":1,"Graduation":2,"PostGraduation":3,"PostGraduation-ResearchScholar":4} self.designations["startup"]={"Founder-Architect":7} self.designations["business"]={'CEO':10,'CTO':9,'CFO':8} self.designations["work"]={"AssociateSoftwareEngineer":1, "MemberTechStaff":2, "SystemAnalyst":3, "Specialist":4, "Consultant-Archite ct":6, "Consultant":5, "Architect":6} 6.More predefined designations, classes of designations and state transitions could be added to previous dictionaries which might be optionally read from a persisted NoSQL datastore. 7.Observations of Career Chaotic Hidden Markov Model are traversed and weights per state transition and corresponding observation are summed to get a Career Transition Score of a profile which intrinsically rates merit of a profile based on predefined weights. 8.Industry has been divided into startup (for MSMEs, private initiatives) and business (for management profiles of Corporates, Bellwether, Blue Chips, Big Fishes) for weighting the risk.
Srinivasan Kannan
More... almost 6 years ago
Updated Requirements.txt
Srinivasan Kannan
More... almost 6 years ago
Section 666 updated for Election Forecast Models
Srinivasan Kannan
More... almost 6 years ago