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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 1561 updated
K.Srinivasan
More... over 1 year ago
Section 1590 updated
K.Srinivasan
More... over 1 year ago
Section 1590 updated
K.Srinivasan
More... over 1 year ago
Section 1590 updated
K.Srinivasan
More... over 1 year ago
1590. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 14 March 2025 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 25/2/2025-30/4/2025 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 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
Srinivasan Kannan
More... over 1 year ago
---------------------------------------------------------------------------------------------------------------------------------------- 1590. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 14 March 2025 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 25/2/2025-30/4/2025 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 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 ---------------------------------------------------------------------------------------------------------------------------------------- (Disclaimer: Same as Disclaimer in Section 1548)
Srinivasan Kannan
More... over 1 year ago
Section 1486 updated
K.Srinivasan
More... over 1 year ago
Section 1486 updated
K.Srinivasan
More... over 1 year ago
Section 1589 updated
K.Srinivasan
More... over 1 year ago
Updated Requirements.txt
K.Srinivasan
More... over 1 year ago
Section 1589 updated
K.Srinivasan
More... over 1 year ago
1589. (THEORY and FEATURE) Nachiketas Formal LLM - Therapeutic Data Commons and PrimeKG medical knowlege graph integration - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1543,1585 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, Drug Discovery - 26 February 2025
K.Srinivasan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------- 1589. (THEORY and FEATURE) Nachiketas Formal LLM - Therapeutic Data Commons and PrimeKG medical knowlege graph integration - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1543,1585 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, Drug Discovery - 26 February 2025 -------------------------------------------------------------------------------------------------------------------------------- 1589.1 python-src/KnowledgeGraph.py has been updated to import pandas, PyTDC (Therapeutic Data Commons) libraries for processing PrecisionMedicineKnowledgeGraph or PrimeKG - https://github.com/mims-harvard/PrimeKG - medical queries to Nachiketas Formal LLM could be answered by replacing textgraphs with medicine-specific knowledge graphs and computing randomwalks on them. 1589.2 Function def create_PrimeKG_knowledge_graph(mode="CSV",query='y_type=="drug"|x_type=="drug"|relation=="indication"',chunksz=1000,numberofrows=100,maximumedges=100000) has been defined which imports PrimeKG data either as CSV file and creates a NetworkX knowledge graph or Graph instantiated directly from PyTDC (which is still in development). 1589.3 Earlier function takes - the mode (CSV or Graph), query expression for knowledge graph, chunk size and number of rows for batched processing of KG dataset, maximumedges for limiting the size of knowledge graph - as arguments. Mode "CSV" reads the PrimeKG dataset by commandline: wget -O kg.csv https://dataverse.harvard.edu/api/access/datafile/6180620 (1 GB sized CSV file) and reads the CSV into DataFrame chunks converting them to relation triples from which NetworkX PrimeKG graph object is instantiated. Mode "Graph" instantiates PrimeKG object from PyTDC and gets the NetworkX object from it which at present is quite heavy and causes core dumps as PyTDC is still in development. 1589.4 CSV mode therefore has been opted and query on all graph edges concerning "drugs" have been mined from PrimeKG CSV dataset. Results are logged to KnowledgeGraph.PrimeKG.log.26February2025 by commandline: /home/ksrinivasan/venv/bin/python3.12 KnowledgeGraph.py 2>&1 > testlogs/KnowledgeGraph.PrimeKG.log.26February2025 - An example relation triples (edges) of the form Drug-Relation-Disease in PrimeKG graph for fibromyalgia from the logs are (Amitriptyline belongs to class of Tricyclic Anti-Depressants or TCA while Pregabalin or Lyrica belongs to class of anti-convulsant analgesics) - for default query 'y_type=="drug"|x_type=="drug"|relation=="indication"': PrimeKG edge triples: ('Milnacipran', 'indication', 'fibromyalgia') PrimeKG edge triples: ('Trolnitrate', 'off-label use', 'fibromyalgia') PrimeKG edge triples: ('Paroxetine', 'off-label use', 'fibromyalgia') PrimeKG edge triples: ('Fluoxetine', 'off-label use', 'fibromyalgia') PrimeKG edge triples: ('Amitriptyline', 'off-label use', 'fibromyalgia') PrimeKG edge triples: ('Cyclobenzaprine', 'off-label use', 'fibromyalgia') PrimeKG edge triples: ('Tramadol', 'off-label use', 'fibromyalgia') PrimeKG edge triples: ('Pregabalin', 'indication', 'fibromyalgia') PrimeKG edge triples: ('Duloxetine', 'indication', 'fibromyalgia') PrimeKG edge triples: ('Pramipexole', 'off-label use', 'fibromyalgia') PrimeKG edge triples: ('Acetaminophen', 'off-label use', 'fibromyalgia') 1589.5 Query parameter in create_PrimeKG_knowledge_graph() can be arbitrarily configured to get the required edges from the knowledge graph.
K.Srinivasan
More... over 1 year ago
Section 1217 updated
K.Srinivasan
More... over 1 year ago
Section 1584 updated
K.Srinivasan
More... over 1 year ago
Section 1585 updated
K.Srinivasan
More... over 1 year ago
Section 1486 updated
K.Srinivasan
More... over 1 year ago
Section 1486 updated
Srinivasan Kannan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1588. (THEORY and FEATURE) Nachiketas Formal LLM - Universal Chomsky Sentence Synthesizer - 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 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 - 24 February 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1588.1 As an independent component of Nachiketas Formal LLM, new python source file SentenceSynthesizer.py has been added to NeuronRain Grafit repositories that creates all possible English sentences for inorder-traversed Penn Treebank PoS grammar tree templates (either in dictionary or array format) by filling the PoS slots in the template from a user-defined PoS-to-words corpus (word_pos.csv from https://www.kaggle.com/datasets/ruchi798/part-of-speech-tagging?resource=download) that covers entire PoS-inflected English vocabulary. 1588.2 Earlier universal synthesizer does not depend on textgraph randomwalks, but directly fills the flattened grammar tree template PoS slots by PoS tagged_words dataset lookup thereby creating all possible English sentences for a grammar tree. This script is computationally intensive as its complexity is O(number_of_inorder_traversed_grammar_tree_templates*number_of_PoS_slots_per_template^number_of_words_per_PoS_slot) which is exponential because creating all possible sentences for a flattened grammar tree template by itertools.product() is combinatorially heavy - Example: For 10 grammar tree templates, each having 15 PoS slots per template and 1000 words per PoS slot, number of possible sentences synthesized by this script is: 10*15^1000 an astronomical number of possible Chomsky sentences of length 15 that are grammatically right but not necessarily meaningful. 1588.3 Some PoS slots in the synthesized sentences are marked <blank> because of missing PoS tagged words in the dataset. End result of this script is a list of synthesized sentences which are ranked by their (1) RecursiveGlossOverlap textgraph complexity measures (core numbers and pagerank) (2) textgraph intrinsic merit scores 1588.4 Function def synthesize_chomsky_sentences(sentence_template=[], corpus="../../../../../asfer-github-code/python-src/NeuronRainApps/QuestionAnswering/words_pos.csv",treebankdatasets=["wsj_0002.mrg","wsj_0003.mrg","wsj_0004.mrg","wsj_0005.mrg"],max_templates=2,max_sentences=20,threshold_wordnet_distance=100,dictionary_template=False) takes a sentence template, PoS-words corpus dataset as arguments and internally limits the number of sentences by parameters max_templates and max_sentences. Each synthesized sentence is filtered for meaning by the wordnet distance between each adjacent pair of words in the sentence (upperbounded at threshold_wordnet_distance) computed by find_wordnet_distance(). Earlier wordnet filtering reduces the randomness in Chomsky sentences and makes them more meaningful. Boolean argument dictionary_template chooses between dictionary and array templates. 1588.5 Example synthesized sentences are logged to code/testlogs/SentenceSynthesizer.log.24February2025. Function part_of_speech_mapping() has been defined for validating the synthesized sentence by printing the PoS to word mapping. Utility of this script is to experimentally verify the feasibility of Formal Large Language Models as against statistical LLMs and most importantly this script is "universal" because it can synthesize all possible English sentences if it is executed for indefinite time and only dataset it requires are the PoS grammar trees from a treebank. 1588.6 This update also affects NeuronRain AstroInfer repositories - Debug prints in python-src/WordNetPath.py have been commented.
Srinivasan Kannan
More... over 1 year ago
Section 1587 updated
Srinivasan Kannan
More... over 1 year ago
Section 1350 updated
Srinivasan Kannan
More... over 1 year ago
Grok 3 LLM estimate of CMA population
Srinivasan Kannan
More... over 1 year ago
1587. (THEORY and FEATURE) Nachiketas Formal LLM - semi-filled sentene template populated by closest words on WordNet - Changes to Wikipedia Question-Answering algorithm for better accuracy 20 - 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 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 - 21,22 February 2025
Srinivasan Kannan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1587. (THEORY and FEATURE) Nachiketas Formal LLM - semi-filled sentene template populated by closest words on WordNet - Changes to Wikipedia Question-Answering algorithm for better accuracy 20 - 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 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 - 21,22 February 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1587.1 python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to import path_between() from WordNetPath.py for finding words which are closest in meaning to semi-filled sentence template. 1587.2 Function def make_sentence2(randomwalkvertices,sentence_PoS_tuple_array=[],treenode_type="tag",max_synth_sentences=1000,user_defined_PoS2Vocabulary_dict=None,max_words_per_PoS=1000) has been enhanced to add argument max_words_per_PoS for restricting maximum number of words per PoS that are sourced from user defined PoS tagged_words. 1587.3 New function def get_closest_possible_tokens(rwtexttoks, vocabulary, max_words_per_PoS=1000,maximum_radius=10) has been defined to filter PoS tagged words from user defined vocabulary and choose only those words that are related by WordNet path of maximum radius. 1587.4 Minor issue in filledrwtext has been resolved by replacing [] by ['---'] which otherwise prevented itertools.product() from creating flattened arrays for sentence creation. 1587.5 Function def get_closest_possible_tokens(rwtexttoks, vocabulary, max_words_per_PoS=1000,maximum_radius=10) has been implemented to choose only those words that are close enough on WordNet defined by maximum_radius (WordNet distance found by length of path returned by path_between()) which decreases the randomness of Chomsky sentences and makes them more meaningful. 1587.6 WSJ Penn Treebank datasets have been changed to wsj_0092.mrg and wsj_0090.mrg. Sentence synthesis has been invoked three times: 1) sentence synthesis (manual sentence_PoS_dict) 2) sentence synthesis (sentence_PoS_dict retrieved from treebank) 3) sentence synthesis (sentence_PoS_array retrieved from treebank) and results are logged to testlogs/QuestionAnswering.log.21February2025.xz 1587.7 Earlier logs contain phrases-sentences synthesized to answer computational query: "sum of 1 and 2"
Srinivasan Kannan
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
Section 1586 updated
K.Srinivasan
More... over 1 year ago
Section 1586 updated
K.Srinivasan
More... over 1 year ago
Section 1586 updated
K.Srinivasan
More... over 1 year ago
1586. (THEORY and FEATURE) Equating Computational Complexity and Economic complexity (Intrinsic value of Cryptocurrency) - Neuro Cryptocurrency mining rig Proof of Work - related to 135,562,1140,1215 and all sections on Money Changing Problem,Optimal Denominations,Integer Partitions, Computational Economics - Computational Hardness (Labour) Theory of Value, Histogram and Set Partition analytics, Equidepth-Equiheight Histograms, Separate Chaining Hashtables, Drone EVMs, Space Filling and Teapot Shards, Equal and Unequal Circle packing, Grid filling, Integer Linear Programming and Cellular automata, Quantum Versus Classical Computation, Parallel Pseudorandom Generators, Berry-Esseen Central Limit Theorem - 18 February 2025
K.Srinivasan
More... over 1 year ago