Updated index.rst

index.rst

@@ -308,7 +308,7 @@ Yes. All these drafts revolve around the fundamental philosophical/mathematical
            1.2 Alphanumeric Text(String Analytics - Longest Repeated Substring-SuffixArray-LongestCommonPrefix, BioPython/ClustalOmega Multiple Sequence Alignment, Sequence Mining, Minimum Description Length, Entropy, Support Vector Machines, Knuth-Morris-Pratt string match, Needleman-Wunsch alignment, Longest common substring, KNN clustering, KMeans clustering, Decision Tree, Bayes, Edit Distance, Earth Mover Distance, Linear Complexity Relaxed Word Mover Distance, PrefixSpan - astronomical,binary,numeric and generic encoded string datasets - astronomical datasets and algorithmic usecases include (*) USGS Earthquakes and NOAA HURDAT2 datasets (*) Cosmology - Deep Field Space Telescope Visuals - Hubble and WMAP imagery - AstroPy-AstroQuery interface of JPL Horizon Ephemeris service and AstroML astronomical machine learning algorithms integration (*) SkyField-AstroPy JPL Ephemeris queries for positions of celestial bodies (*) Maitreya 8t - encoded strings of celestial bodies obtained from ephemeris corresponding to various extreme weather events (*) Ephemeris Search for astronomical events in SkyField-AstroPy (*) correlation of terrestrial climate events and gravitational influence of solar system N-body orbit choreographies-Syzygies,Conjunctions,Quadratures - implementation of N-Body equation solver to gauge gravitational accelerations of solar system bodies on Earth-Moon barycenter on days of extreme weather events (*) correlation of extreme weather events and celestial bodies by Sequence mining of historic (Hurricane and Earthquake) astronomical datasets to get Class Association Rules (*) prediction of extreme weather and seismic events from N-Body angular separation and gravitational acceleration computed from Sequence Mined Class Association Rules),
            1.3 Audio-speech(Speech-to-Text and recursive lambda function growth,Graph Edit Distance),
            1.4 Audio-music(Music Information Retrieval-MIR, mel frequency cepstral coefficients, Learning weighted automata from music notes waveform, Graph Edit Distance between weighted automata, Equivalence of Weighted automata by Table filling, Kullback-Leibler and Jensen-Shannon divergence, Novelty detection and Originality of a score by waveform distance, AI music synthesis by functions-automata-fractals and polynomial interpolations of training music waveforms, AI music synthesis by Virtual Piano from random 12-notes string, Deep Learnt Automata, Dynamic Time Warping distance similarity between music timeseries, Music synthesis from sum of damped sinusoids, Weierstrass Function - Fractal Fourier summation, Music evoked autobiographical memories, Normalized Compression Distance-Kolmogorov Complexity, Contours of Functional MRI medical imageing for music stimuli - https://openneuro.org/datasets/ds000171/versions/00001) - AI Music Synthesizer from mathematical functions is the converse of Learning weighted automata from music notes wherein innate fractal self-similar structure of music is exploited by machine learning to churn out music - JS Bach + Fractals = New Music - https://www.nytimes.com/1991/04/16/science/j-s-bach-fractals-new-music.html, https://link.springer.com/chapter/10.1007/978-3-642-78097-4_3. Learning a polynomial from music waveform as against weighted automaton learning (graph structure of music) could extract algebraic structure of music - NeuronRain implements a Degree 5 (Quintic) polynomial learner for music waveforms - Unsolvability of Quintic polynomial (Degree >= 5) by Abel-Ruffini Theorem intuitively means roots of polynomial learnt from music waveform could not be expressed as formulae on radicals - tough nut to crack and could be irreducible. Earth Mover Distance Triple Sequence from moves of Towers of Hanoi Single Bin Sorted LIFO histogram exhibits a Collatz-like Chaotic structure suitable for Music and Financial Timeseries modelling ending always in (0,0,0) for 3 buckets.
-           1.5 Visuals-images(Compressed Sensing,ImageNet ImageGraph algorithm, Graph Edit Distance between FaceGraphs of segmented images, GIS Remote Sensing Analytics, Weather analytics, Climate analytics, Clustering Analytics of celestial bodies in sky imagery from planetarium software and their correlation to extreme weather events - visual analogue of textual astronomical datasets, Modularity-Community Detection, Urban planning analytics (3D UGM - Digital Elevation Models from GHSL BUILT-H,BUILT-V and BUILT-S datasets - Mapping and 3D modelling using quadrotor drone and GIS software - https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00436-8, 2D UGM - Dynamic Facegraph, Cellular Automata and Polya Urn Urban Growth [by Learnt Replacement matrix] Models), Automatic Delineation of Urban Growth Boundaries-from (*) VIIRS NightsLights contour segmentation - high night lights points to urbanization (*) Suburban Commuting patterns - busy traffic (e.g Google Maps traffic busy markers, OpenStreetMap GPS Traces, Suburban-Metro rail traffic) is proportional to urbanization (*) OSMnx OpenStreetMap Road Density analytics - Road density and Road gravity increase proportional to urbanization (*) 3D UGM Digital Elevation Models of Built-up surface - skyscrapers indicate Central Business District and urbanization, Gini Coefficient of Inequality, Moran's I measure of Urban Sprawl Dispersion-Diffusion Factor, Canny Edge Detection-Transportation Network Lattice Grid, Ocean Floor Bathymetry GIS, Machine Learning models of Urban Extent-NASA SEDAC GPW,Facebook HRSL,European Union GHSL R2019A-R2022A and NASA VIIRS NightLights, USGS LandSat9 TIRS-2/OLI-2 imagery, Population Estimation Models from GIS imagery - Verhulste and Ricker, Voronoi Tessellation, Delaunay Triangulation, GMSH Trimesh-Quadmesh, Preferential attachment, Face and Handwriting Recognition, Neural network clustering, DBSCAN Clustering, DICOM-Medical imageing-ECG-MRI-fMRI-EEG-CTSCAN-PET-Doppler-XRay, Convex Hull, Patches Extraction-RGB and 2-D, Segmentation, Random forests, Autonomous Driving-LIDAR point cloud data, Flood vulnerability detection from GIS and LiDAR DEM, Drone Aerial Imagery Analytics, Astronomy-Cosmology Datasets-Deep Field Visuals from Space Telescopes) - GHSL rasters are mosaics created from Symbolic Machine Learning which is quite akin to Multiple Sequence Alignment and Class Association Rules based learning implemented for Astronomical Pattern Mining in NeuronRain. GDP and other socioeconomic indicators can be estimated from GIS Imagery analytics - Examples: (1) Electricity consumption for Residential-Industrial-Commercial purposes can be estimated from VIIRS NightLights (2) Infrastructure (Built-up volume and surface) can be estimated from GHSL rasters and OSMnx Road network density statistics (3) Foodgrain production can be estimated from radiance of waterbodies and vegetation
+           1.5 Visuals-images(Compressed Sensing,ImageNet ImageGraph algorithm, Graph Edit Distance between FaceGraphs of segmented images, GIS Remote Sensing Analytics, Weather analytics, Climate analytics, Clustering Analytics of celestial bodies in sky imagery from planetarium software and their correlation to extreme weather events - visual analogue of textual astronomical datasets, Modularity-Community Detection, Urban planning analytics (3D UGM - Digital Elevation Models from GHSL BUILT-H,BUILT-V and BUILT-S datasets - Mapping and 3D modelling using quadrotor drone and GIS software - https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00436-8, 2D UGM - Dynamic Facegraph, Cellular Automata and Polya Urn Urban Growth [by Learnt Replacement matrix] Models), Automatic Delineation of Urban Growth Boundaries-from (*) VIIRS NightsLights contour segmentation - high night lights points to urbanization (*) Suburban Commuting patterns - busy traffic (e.g Google Maps traffic busy markers, OpenStreetMap GPS Traces, Suburban-Metro rail traffic) is proportional to urbanization - bottlenecks in live traffic classification (slow to fast) should in principle correspond to betweenness centrality or a minimum cut computed from transportation network graph - an example of betweenness centrality based mincut estimation as an alternative to augmenting path mincut - http://bit.kuas.edu.tw/~jihmsp/2015/vol6/JIH-MSP-2015-05-016.pdf (*) OSMnx OpenStreetMap Road Density analytics - Road density and Road gravity increase proportional to urbanization (*) 3D UGM Digital Elevation Models of Built-up surface - skyscrapers indicate Central Business District and urbanization, Gini Coefficient of Inequality, Moran's I measure of Urban Sprawl Dispersion-Diffusion Factor, Canny Edge Detection-Transportation Network Lattice Grid, Ocean Floor Bathymetry GIS, Machine Learning models of Urban Extent-NASA SEDAC GPW,Facebook HRSL,European Union GHSL R2019A-R2022A and NASA VIIRS NightLights, USGS LandSat9 TIRS-2/OLI-2 imagery, Population Estimation Models from GIS imagery - Verhulste and Ricker, Voronoi Tessellation, Delaunay Triangulation, GMSH Trimesh-Quadmesh, Preferential attachment, Face and Handwriting Recognition, Neural network clustering, DBSCAN Clustering, DICOM-Medical imageing-ECG-MRI-fMRI-EEG-CTSCAN-PET-Doppler-XRay, Convex Hull, Patches Extraction-RGB and 2-D, Segmentation, Random forests, Autonomous Driving-LIDAR point cloud data, Flood vulnerability detection from GIS and LiDAR DEM, Drone Aerial Imagery Analytics, Astronomy-Cosmology Datasets-Deep Field Visuals from Space Telescopes) - GHSL rasters are mosaics created from Symbolic Machine Learning which is quite akin to Multiple Sequence Alignment and Class Association Rules based learning implemented for Astronomical Pattern Mining in NeuronRain. GDP and other socioeconomic indicators can be estimated from GIS Imagery analytics - Examples: (1) Electricity consumption for Residential-Industrial-Commercial purposes can be estimated from VIIRS NightLights (2) Infrastructure (Built-up volume and surface) can be estimated from GHSL rasters and OSMnx Road network density statistics (3) Foodgrain production can be estimated from radiance of waterbodies and vegetation
            1.6 Visuals-videos(ImageNet VideoGraph EventNet Tensor products algorithm for measuring Tensor Rank connectivity merits of movies,youtube videos and Large Scale Visuals, Graph Edit Distance between Video EventNet, Sentiment analysis of predictions textgraphs for youtube and movie videos by Empath-MarkovRandomFields Recursive Gloss Overlap Belief Propagation-SentiWordNet, Topological Sort for video summary, Digital watermarking, Drone Aerial Video Streaming Analytics, GIS Imagery Contour graphs for A-Star motion planning and Road Geometry Airspace Drone obstacle avoidance),
            1.7 People(Social and Professional Networks) - experiential and intrinsic(recursive mistake correction tree, Question-Answering in Interviews/Examinations/Contests),
            1.8 People(Social and Professional Networks) - lognormal least energy(inverse lognormal sum of education-wealth-valour,Sports Analytics-Intrinsic Performance Ratings-IPR e.g Elo ratings,Real Plus Minus, Non-perceptive Rankings in Sports, PSPACE-hardness of most games encoded as TQBF, Wealth, Research and Academics),
@@ -778,7 +778,7 @@ sharp thresholds for majority, BQP as VoterSAT, Derandomization, Quantum decoher
 sections on Factorization, KRW communication complexity and Majority Voting - 19 July 2021, 20 July 2021, 21 July 2021, 5 September 2021
 -------------------------------------------------------------------------------------------------------------------------------------------
 1152.1 Quoting 801:
-"...Boolean composition of leaves of Boolean majority circuit and individual VoterSATs has a curious implication when all voters are quantum voters (all VoterSATs are in BQP): By Condorcet Jury Theorem and its later versions by [Black] and [Ladha] and Margulis-Russo sharp threshold at p-bias > 0.5, infinite majority + BQP VoterSAT boolean composition tends to goodness 1 or quantum world derandomizes to P (by phenomena of D6ecoherence, Wavefunction collapse) implying one of the superimposed quantum states of some amplitude (defined in Hilbert space) is chosen for certainty by nature. Majority is in non-uniform NC1 and thus in P which in turn is in BPP and the larger class BQP which implies boolean majority is in BQP. If boolean composition of BQP majority function and BQP voter SATs are relativizable (conjectural assumption: boolean composition is equivalent to oracle access Turing machines) as BQP^BQP (BQP majority function having oracle access to BQP voter SATs) and since BQP is low for itself, BQP^BQP = BQP. By CJT-Black-Ladha-Margulis-Russo threshold theorems for infinite majority quantum boolean composition  tends to 100% goodness or in other words BQP asymptotically dissipates quantum error and derandomizes to P...."
+"...Boolean composition of leaves of Boolean majority circuit and individual VoterSATs has a curious implication when all voters are quantum voters (all VoterSATs are in BQP): By Condorcet Jury Theorem and its later versions by [Black] and [Ladha] and Margulis-Russo sharp threshold at p-bias > 0.5, infinite majority + BQP VoterSAT boolean composition tends to goodness 1 or quantum world derandomizes to P (by phenomena of Decoherence, Wavefunction collapse) implying one of the superimposed quantum states of some amplitude (defined in Hilbert space) is chosen for certainty by nature. Majority is in non-uniform NC1 and thus in P which in turn is in BPP and the larger class BQP which implies boolean majority is in BQP. If boolean composition of BQP majority function and BQP voter SATs are relativizable (conjectural assumption: boolean composition is equivalent to oracle access Turing machines) as BQP^BQP (BQP majority function having oracle access to BQP voter SATs) and since BQP is low for itself, BQP^BQP = BQP. By CJT-Black-Ladha-Margulis-Russo threshold theorems for infinite majority quantum boolean composition  tends to 100% goodness or in other words BQP asymptotically dissipates quantum error and derandomizes to P...."
 1152.2 Conjecture: Boolean composition of BQP majority and BQP voter SATs is relativizable - Draft Proof outline: Compute boolean majority function on a BQP Turing machine whose leaves have oracle access to infinite number of BQP VoterSAT Turing machines. This oracle machine simulates boolean composition as BQP^BQP relativization.
 1152.3 Some peculiar conclusion is arrived at if all VoterSATs depend on Shor's BQP factorization: Every voter factorizes same integer N by Shor's BQP factorization and votes 1 if factors are correct and 0 if factors are wrong. By definition of BQP more than 2/3 of infinite voters factorize N correctly (67%).
 1152.4 But CJT and its variants for infinite majority imply 100% correct factorization because each VoterSAT has p-bias error <= 1/3 and group decision correctness (whether factors of N are right or not) tends to 100% probability implying Shor's BQP factorization derandomizes to P (or) success of BQP factorization is amplified to exact and CJT implies quantum decoherence.