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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
Section 1259,1260 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
+--------------------------------------------------------------------------------------------------------------------------------------------- +1260. (THEORY and FEATURE) People Analytics - Unique Identification - Fingerprint recognition - 17 June 2022 - related to 834,1223,1237 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Product Homotopy, Unique identification, Homeomorphism and Topological Deformations, Merit of Large Scale Visuals, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, 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, Cybercrime analytics, Forensics-Predictive Policing, Fraud Analytics, Birthday Paradox, Contextual Name Parsing, PIPL Name syllable based unique person search, Collision resitant hash functions, Non Fungible Tokens +--------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1260. (THEORY and FEATURE) People Analytics - Unique Identification - Fingerprint recognition - 17 June 2022 - related to 834,1223,1237 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Product Homotopy, Unique identification, Homeomorphism and Topological Deformations, Merit of Large Scale Visuals, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, 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, Cybercrime analytics, Forensics-Predictive Policing, Fraud Analytics, Birthday Paradox, Contextual Name Parsing, PIPL Name syllable based unique person search, Collision resitant hash functions, Non Fungible Tokens -------------------------------------------------------------------------------------------------------------------------------------------- 1260.1 Various forms of unique identification namely Face, Physique, Handwriting, Gender have been either theoretically analyzed or implemented in NeuronRain through graph theoretic and topological similarity algorithms based on Product Homotopy, Graph Isomorphism, Dynamic time warping timeseries similarity (of human physique mobility), Graph Edit Distance, Netrd Graph distances, TriMesh-QuadMesh and Topological homeomorphic deformations, Voronoi-Delaunay tessellation-triangulation,Maximum Common Subgraph,Approximate Topological Matching among others. 1260.2 This update defines a new function fingerprint_recognition() in DeepLearning_ConvolutionNetwork_BackPropagation.py for matching two images of fingerprints (fingerprint1.jpeg and fingerprint2.jpeg) based on Kaggle academic research dataset FVC2000 for fingerprint deeplearning - https://www.kaggle.com/datasets/peace1019/fingerprint-dataset-for-fvc2000-db4-b?resource=download thereby adding fingerprint dimension to unique identification arsenal. 1260.3 Function fingerprint_recognition() earlier defined computes contour segmentation by chain approximation for two fingerprints, finds the Hausdorff topological distance between two sets of contour polynomials belonging to either, and clusters the fingerprint contour polynomials of the two images by KMeans clustering algorithm in ImageGraph_Keras_Theano.py (contours_kmeans_clustering()) 1260.4 Human fingerprints are topologically significant as every fingerprint contains clusters of curves or family of polynomials of almost similar curvature. Contour polynomials-Graph structure from fingerprints could be extracted by watershed, voronoi-delaunay and chain approximation algorithms in image_segmentation() and image_segmentation_contours() functions of ImageGraph_Keras_Theano.py 1260.5 DeepLearning_ConvolutionNetwork_BackPropagation.py has been refactored as below: 1260.5.1 TopologicalRecognition has been changed from a boolean flag to a string of 3 values - "Fingerprint", "Face" or "Handwriting" which are used within 3 clauses for invocation of face, handwriting or fingerprint recognition routines accordingly 1260.5.2 Graphics for drawing K-Means clustering of contours has been changed from OpenCV drawContours() to MatPlotLib plot which differentiates the color coded clusters well. 1260.5.3 MatPlotLib show() invocations have been changed to savefig() to write the images for watershed segmets, chain approximation contour segments, Voronoi-Delaunay, K-Means clustered contours as JPEG files. 1260.5.4 Directory paths for earlier JPEG images written have been made relative by prefixing ./ and new folder RemoteSensingGIS/ has been created under python-src/testlogs 1260.5.5 Following are the image files and logs for this update: (logs - clusters and Hausdorff distance between fingerprints) testlogs/DeepLearning_ConvolutionNetwork_BackPropagation.log.17June2022 (k-means clustering) testlogs/fingerprint1-contourkmeansclustered.jpg (Voronoi-Delaunay) testlogs/fingerprint1-contourlabelled.jpg (Chain approximation contours) testlogs/fingerprint1-contoursegmented.jpg (Fingerprint1) testlogs/fingerprint1.jpeg (k-means clustering) testlogs/fingerprint2-contourkmeansclustered.jpg (Voronoi-Delaunay) testlogs/fingerprint2-contourlabelled.jpg (Chain approximation contours) testlogs/fingerprint2-contoursegmented.jpg (Fingerprint2) testlogs/fingerprint2.jpeg (DOT) testlogs/RemoteSensingGIS/fingerprint1_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot (Watershed) testlogs/RemoteSensingGIS/fingerprint1_segmented.jpg (DOT) testlogs/RemoteSensingGIS/fingerprint2_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot (Watershed) testlogs/RemoteSensingGIS/fingerprint2_segmented.jpg 1260.6 Computational Hardness of unique identification has been studied in theory through collision resistance of hash functions, Minimal Perfect Hash Functions, birthday paradox et al. Designing a dispute-resistant foolproof Unique ID scheme for population and social networks is still a farcry. An example digitally signed unique id string digest for a person: <10-digit fingerprint>:<retinal scan>:<physique or face photo>:<written signature>:<voice>:<ip address or geocoded address>:<timestamp> though theoretically comprehensive, exponentially hard and collision-resistant suffers from lacunae - worn out biometrics-voicemetrics,signature forgery,prosthetics,cosmetic surgically modified eye,face and physiques might mar its efficacy essentially making a compelling case for non-biometric Non Fungible Tokens (unique private event,art and music) as unique id(s).
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
Sections 1165 and 1252 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
Spark 3.0.1 Configuation File - Performance Tunables
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
Largest Integer Factorized by NeuronRain updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
+--------------------------------------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------- +1259. (THEORY and FEATURE) Computational Geometric Factorization - 20610 bits huge integer quadcore benchmarks - Spark 3.0.1 + Python 3.7.5 - 10 June 2022 - related to 1254 and all sections on Computational Geometric +Factorization, Set Partition to Tile cover reduction by Integer Factorization, Integer Partitions, Histogram Analytics, Neuro fictitious cryp tocurrency proof-of-work and Money changing problem +--------------------------------------------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1259. (THEORY and FEATURE) Computational Geometric Factorization - 20610 bits huge integer quadcore benchmarks - Spark 3.0.1 + Python 3.7.5 - 10 June 2022 - related to 1254 and all sections on Computational Geometric Factorization, Set Partition to Tile cover reduction by Integer Factorization, Integer Partitions, Histogram Analytics, Neuro fictitious cryptocurrency proof-of-work and Money changing problem ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1259.1 Depth of Nick's class non-uniform circuit (Sequential Iteration) in DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py has been set to O((logN)^k) instead of O((logN)^2k) to remove confusion on depth of the circuit. After this change the pseudocode for polylogarithmic factorization is: Sequentially Iterate for O((logN)^k) steps { allocate O(N/(logN)^k)) rasterized hyperbolic arc bow segments to each parallel processor (theoretically O(N/(logN)^k) multicores-PRAMS are necessary per segment by definition of Nick's class for polysize,logdepth circuit) by parallelize() for sequential O(logN) binary search with in each processor } which effectively equals O(logN^k) * O(logN) = O((logN)^(k+1)) parallel time (or) logdepth of the non-uniform circuit is (k+1) for O(N/(logN)^k) multicores. 1259.2 Huge 20610 bits integer has been factorized by DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py in quadcore after earlier change and some optimizations to Spark config because of following exception for large integers which causes Spark crash. Faulthandler python package has been imported and enabled: ================================================================= py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, ksrinivasan-Acer-One-14-Z422, executor driver): java.net.SocketException: Connection reset at java.net.SocketInputStream.read(SocketInputStream.java:210) at java.net.SocketInputStream.read(SocketInputStream.java:141) at java.io.BufferedInputStream.fill(BufferedInputStream.java:246) at java.io.BufferedInputStream.read(BufferedInputStream.java:265) at java.io.DataInputStream.readInt(DataInputStream.java:387) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:628) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:621) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:456) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:941) at scala.collection.Iterator.foreach$(Iterator.scala:941) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:315) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2139) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) =============================================================== 1259.3 Following parameters in /etc/sysctl.conf have been tuned for performance though they did not resolve earlier exception: =============================================================== net.ipv4.tcp_tw_reuse = 1 net.ipv4.tcp_tw_recycle = 1 net.core.somaxconn = 1024 net.core.rmem_max = 262144 vm.max_map_count = 768000 net.core.wmem_max = 262144 net.ipv4.tcp_syn_retries = 20 =============================================================== 1259.4 New Apache Arrow memory manager in Spark 3.0.1 which replaces old Python Virtual Machine - Java Virtual Machine marshalling for Py4J from Spark 3.0.x has been enabled in spark-defaults.conf along with other timeouts and heartbeat timer interval values. But they still do not resolve connection reset exceptions for large integers (especially above 20500 bits when this exception occurs more often): =============================================================== spark.network.timeout 1000 spark.network.timeoutInterval 1000 spark.storage.blockManagerSlaveTimeoutMs 10000 spark.executor.heartbeatInterval 1000 spark.sql.execution.arrow.pyspark.enabled true =============================================================== 1259.5 Following is the first factor pair printed (11 and huge factor) in 768255100634 nanoseconds (~768 seconds) for 20610 bits random integer (largest factorized by NeuronRain so far) from python-src/testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.20610bits.log.10June2022 for logdepth 1437: ('factors of ', 92898239992911111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111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'(', 20609.135623850543, ' bits integer) are:') ('Factors are: (', 11, ',', 84452945448101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101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') (at ', 'Fri, 10 Jun 2022 09:13:57 GMT', ')') nanoseconds elapsed so far in finding all factors: 768255100634 1259.6 Number of multicores required and size of the circuit could be derived for logdepth k=1437: O(N/(logN)^1437) = 2^20610)/(log(20610))^1437 = 2^20610 --------------- (for log(20610) = 14.33) 2^(1437*14.33) = 2^(20610-20594) = O(2^(16)) = 65536 approximately which is not unusual for cloud computing mapreduce frameworks (though software version is under the purview of mapreduce class MRC) and supercomputers which are under true Nick's class - Fugaku Supercomputer has 158976 parallel processors
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
+--------------------------------------------------------------------------------------------------------------------------------------------- +1258. (THEORY and FEATURE) Intrinsic Merit of Music - Kolmogorov Complexity approximation by Normalized Compression Distance (NCD) - implementation - 8 June 2022 - related to 110,1257 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Timeseries analysis, Majority Voting Vs Intrinsic - Condorcet Jury Theorem variants, Astronomical Datasets, Multiple Sequence Alignment of Strings, Handwriting Recognition +---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
Section 1255,1257 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
SEABED2030 Ocean GIS imagery of Dwaraka-Prabhas Patan
Krishna iResearch - NeuronRain - K.Srinivasan
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1257. (THEORY and FEATURE) Medical Imageing and Music Information Retrieval (MIR) - fMRI imagery analysis for music-evoked autobiographical memories - 7 June 2022 - related to 228,385,410,587,588,548,656,868,1218,1223 and all sections on merit of large scale visuals, medical imageing, Evocation WordNet, ThoughtNet and Psychology of Evocation, Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Timeseries analysis, Majority Voting Vs Intrinsic - Condorcet Jury Theorem variants
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1257. (THEORY and FEATURE) Medical Imageing and Music Information Retrieval (MIR) - fMRI imagery analysis for music-evoked autobiographical memories - 7 June 2022 - related to 228,385,410,587,588,548,656,868,1218,1223 and all sections on merit of large scale visuals, medical imageing, Evocation WordNet, ThoughtNet and Psychology of Evocation, Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Timeseries analysis, Majority Voting Vs Intrinsic - Condorcet Jury Theorem variants --------------------------------------------------------------------------------------------------------------------------------------------- 1257.1 Function medical_imageing() in ImageGraph_Keras_Theano.py has been updated to include a clause for fMRI imagery sourced from various experimental study datasets on sensory stimuli affecting human brain, especially how an audio-visual stimulus evokes autobiographical memories of the past. With this support for ECG,MRI and fMRI medical imagery analysis have been added in NeuronRain. 1257.2 Distinguishing human enjoyable music from the rest is the cornerstone of fMRI research having far-reaching implications for music industry, music creativity or music search-music discovery if all 12-note music melody waveforms numbering 68 billion have been already found (from 1223.13). "Is the set of enjoyable music (majority voted, liked by lot) = quality music (naturally meritorious, not voted)" is another open problem in intrinsic merit of music and fits into the realm of Condorcet Jury Theorem variants for bounds on Correctness of Group Decision (Black-Ladha). 1257.3 In this update, an fMRI image for music stimulus (UCDavis dataset imagery - testlogs/medical_imageing/fMRI_MusicStimulus_UCDavis_janata_brain_lg.jpg) on human subjects (music genre of enjoyable-familiar nature evoking memories) has been analyzed by image_segmentation() and its Chain approximation contours,Voronoi-Delaunay Tessellation-Triangulation,Facegraph,Watershed segments are computed as below: (Logs) testlogs/ImageGraph_Keras_Theano.MedicalImageing.fMRI_MusicGenre.7June2022 (fMRI image from 1257.6) testlogs/medical_imageing/fMRI_MusicStimulus_UCDavis_janata_brain_lg.jpg (facegraph DOT) testlogs/medical_imageing/fMRI_MusicStimulus_UCDavis_janata_brain_lg_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot (Chain approximation contours) testlogs/medical_imageing/fMRI_MusicStimulus_UCDavis_janata_brain_lg_contoursegmented_7June2022.jpg (Voronoi-Delaunay) testlogs/medical_imageing/fMRI_MusicStimulus_UCDavis_janata_brain_lg_medical_imageing-contourlabelled.jpg (Watershed segmented) testlogs/medical_imageing/fMRI_MusicStimulus_UCDavis_janata_brain_lg_segmented.jpg 1257.4 Evocation of Memories expressed as texts have been earlier studied and implemented in NeuronRain by various gadgets - ThoughtNet Hypergraph Evocation,Contextual Multi Armed Bandits,Reinforcement Learning and Evocation WordNet - while audio-visual evocation is the best possible formulation of ThoughtNet evocation wherein thoughts are audio-visualized and stored in ThoughtNet Hypergraph hyperedges and hypervertices (Example in Section 387). 1257.5 If number of all possible music waveform melodies are upperbounded by 68 billion in 12-note scale, number of fMRI visuals of human brain for music sensory stimuli are upperbounded as well at 68 billion thereby narrowing down the size of fMRI dataset.
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1253 updated - Optimal alignment lattice walk enumeration algorithm
Krishna iResearch - NeuronRain - K.Srinivasan
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+-------------------------------------------------------------------------------------------------------------------------------------------- +1256. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Bug resolutions and additional Urban sprawl Facegraph complexity measures for Automatic Urban Delineation - 3 June 2022 - related to 1226 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, 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 electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH-Polya-DynamicFacegraph Urban Growth Models, Preferential attachment,Random Graphs,Dynamic Graphs,NeuronRain CoronaVirus 2019 analyzer (R and Python - GEM,R Logit,Chaotic ERSIR Cellular Automaton Graph) +--------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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1255.19 Dating of Mahabharata war by Planetarium software - 3067 BC - [Narahari Achar] - https://www.researchgate.net/publication/237283001_Date_of_the_Mahbhrata_War_based_on_Simulations_using_Planetarium_Software 1255.20 Dating of Mahabharata war - 3136 BC - https://jayasreesaranathan.blogspot.com/2021/11/published-my-new-book-mahabharata-3136.html - dated in Dvapara yuga and based on Surya Siddhanta sidereal calendar without relying on Planetarium software (7200 years ayanamsa to-and-fro oscillation of maximum 27 degrees - separation between 0 degree Aries and Vernal equinox - as opposed to tropical 26000 year cycle of precesssional vernal equinox of Milankovitch at the rate of 50 arcseconds per year anticlockwise) - Limitations of Planetarium software and Theory of Comet hit on Earth-Moon in 3136 BC, a catastrophe causing Earth-Moon orbits to wobble recorded in Mahabharata verses.
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Section 1255 updated
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+--------------------------------------------------------------------------------------------------------------------------------------------- +1255. (THEORY and FEATURE) Astronomical Dataset Analytics - Planetarium Ephemeris Search - 1 June 2022 - related to 731,1206,1236 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Reciprocal Recommender Systems based Matrimony analytics in Bipartite Social Networks, Climate and Extreme Weather Event Analytics +---------------------------------------------------------------------------------------------------------------------------------------------
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--------------------------------------------------------------------------------------------------------------------------------------------- 1255. (THEORY and FEATURE) Astronomical Dataset Analytics - Planetarium Ephemeris Search - 1 June 2022 - related to 731,1206,1236 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Reciprocal Recommender Systems based Matrimony analytics in Bipartite Social Networks, Climate and Extreme Weather Event Analytics --------------------------------------------------------------------------------------------------------------------------------------------- 1255.1 MaitreyaEncHoro_RuleSearch.py python Swiss-JPL ephemeris search maitreya8t text client wrapper has been updated for searching sky for a zodiac configuration which simulates visual timeline search for celestial bodies in planetarium software. 1255.2 Astronomical data are rife in ancient scriptures and most archaeological artefacts as religion and astronomy are often intertwined. Scientific corroboration of mythology and historicity of epics requires an exhaustive search of astronomical references in scriptures either through Planetarium software or traditional alternatives. 1255.3 Problem lies in computational cost of this exhaustive search to pinpoint historic timeline of events described in an epic which could have occurred few millenia ago and lack of ephemeris data for that period (possibly hindered by tropical-sidereal conflicts,ayanamsa conundrum among others). Exhaustive search could be optimized to some extent by exploiting orbital periodicity-conjunctions of planets and extrapolating future - e.g Eight planets were conjunct 0 degree of Aries during Kali era birth, Eight planets were conjunct in 1962 in Aquarius. 1255.4 Visual equivalent of earlier textual search script could be far more intensive and might need preprocessing but does not depend on cuspal boundaries : 1255.4.1 Plot visual zodiac (Degrees of Planets,Meteors,Constellations) from astronomical data mentioned in a treatise 1255.4.2 Do a matching image search of previous plot and Visual Planetarium Sky Maps dating back thousands of years - number of sky images to search and match could be in millions. 1255.5 MaitreyaEncHoro_RuleSearch.py has been so far restricted to search of class association rules (which are subsets of 360 degree zodiac), mined from past extreme weather event datasets by GSP Sequence Mining and Sequence Alignment, to predict future climate and seismic events. It has been enhanced in this commit to search a celestial configuration from text file CelestialConfiguration.txt (full 360 degree zodiac - which could be birth data of a mythological personality or event described in scriptures) in the ephemeris within a minimum-maximum range of days-months-years through maitreya8t text client. 1255.6 MaitreyaEncHoro_RuleSearch.py has been upgraded to Python 3.7 by 2to3 + autopep8 upgrade tools. Two boolean flags ClassAssociationRuleSearch and PlanetariumSearch have been introduced to separate CAR (class association rule) search and Planetarium search (read from either MinedClassAssociationRules.txt or CelestialConfiguration.txt) and invoke either substring_find() or a new function match_chart_and_rule(). 1255.7 New function match_chart_and_rule() has been implemented as a container iterable alternative of string-based substring_find() to check equality of chart data dictionary from maitreya8t and dictionary computed from CelestialConfiguration.txt and return boolean -1 (Dissimilar) or 1 (Similar). Both dictionaries have 12 key-value pairs mapping zodiac divisions to planets housed therein. 1255.8 Maitreya8t and any planetarium software are limited by availability of ephemeris data which seems to be restricted to 3000 BC and older dates are not supported. Maitreya8t provides facilities for astrological matrimonial match by following commandline options: --file2=<str> Open second file for partner chart --partner-vedic Show Vedic partner analysis --partner-composite Show partner composite chart which is culture-oriented orthodox match making complementing reciprocal recommender systems followed in bipartite (Bride-Groom) matrimonial portal software. 1255.9 An example Celestial configuration for 1 June 2022 is searched by MaitreyaEncHoro_RuleSearch.py (PlanetariumSearch=True) and match is identified as per testlogs/MaitreyaEncHoro_RuleSearch.PlanetariumSearch.log.1June2022
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Section 1253 updated
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Section 1253 updated
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Section 1240 updated
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+---------------------------------------------------------------------------------------------------------------------------------------------- +1254. (THEORY and FEATURE) Computational Geometric Factorization - Quadcore benchmarks - Spark 3.0.1 + Python 3.7.x - Nanoseconds perf counter - 27 May 2022 - related to 1214 and all sections on Computational Geometric Factorization,Set Partitions to Exact Square Tile Cover Reduction by Lagrange Sum of 4 Squares - Optimal denominations and Money Changing Problem, Integer Partitions, Equidepth-Equiheight V-Optimal Histograms and Set partition analytics +---------------------------------------------------------------------------------------------------------------------------------------------
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---------------------------------------------------------------------------------------------------------------------------------------------- 1254. (THEORY and FEATURE) Computational Geometric Factorization - Quadcore benchmarks - Spark 3.0.1 + Python 3.7.x - Nanoseconds perf counter - 27 May 2022 - related to 1214 and all sections on Computational Geometric Factorization,Set Partitions to Exact Square Tile Cover Reduction by Lagrange Sum of 4 Squares - Optimal denominations and Money Changing Problem, Integer Partitions, Equidepth-Equiheight V-Optimal Histograms and Set partition analytics --------------------------------------------------------------------------------------------------------------------------------------------- 1254.1 Nick's class Computational Geometric Factorization implementation - DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py - has been updated to print nanoseconds elapsed in factorization which raises the resolution of performance counter from present seconds (Greenwich mean time) by python primitive gmtime() to highest possible nanoseconds elapsed from epoch by times_ns() primitive. 1254.2 Python time_ns() nanoseconds function is imported and time duration perf counter is printed in nanoseconds from start of factorization and nanoseconds elapsed from beginning till each factor is found. 1254.3 20523 bits integer and 107 bits integer have been factorized (20523 bits integer is same as section 1214). Factors and nanosecond perf counters have been printed to python-src/testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log.27May2022 and testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log2.27May2022 1254.4 DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.largestintegerfactorized has been updatedd to 20523 bits integer.
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Section 1236 updated
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Seabed 2030 Bathymetry GIS of Ramsethu Depth Relief
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Section 1236 updated
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RamSethu Ocean Depth Imagery (PDF) - Google Maps
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1253. (THEORY and FEATURE) Merit of Large Scale Visuals - GIS Urban Sprawl Analytics - Automatic Delineation of Urban Growth Boundaries from Transportation Network GIS 2 - KMeans Clustering of Contour Polynomials - 24 May 2022 - related to 714,1151,1174,1196,1226,1233,1234,1237,1252 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, 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 electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Automatic Delineation of Urban Growth Boundaries, Preferential attachment,Random Graphs,Dynamic Graphs,Separate Chaining Hash tables, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Drone EVMs-Passenger Drones,Wiring Problem
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-------------------------------------------------------------------------------------------------------------------------------------------- 1253. (THEORY and FEATURE) Merit of Large Scale Visuals - GIS Urban Sprawl Analytics - Automatic Delineation of Urban Growth Boundaries from Transportation Network GIS 2 - KMeans Clustering of Contour Polynomials - 24 May 2022 - related to 714,1151,1174,1196,1226,1233,1234,1237,1252 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, 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 electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Automatic Delineation of Urban Growth Boundaries, Preferential attachment,Random Graphs,Dynamic Graphs,Separate Chaining Hash tables, Unmanned Aerial Vehicle Navigation-Autonomous Drone Delivery-Drone EVMs-Passenger Drones,Wiring Problem --------------------------------------------------------------------------------------------------------------------------------------------- 1253.1 New clustering function contours_kmeans_clustering(imagefile,segment,number_of_clusters=3,maxiterations=3) has been defined in ImageGraph_Keras_Theano.py which implements K-Means Clustering of contour polynomials not only useful for automatic delineation of urban boundaries from GIS transportation network imagery but also in analysis of any contour segmented large scale visual. 1253.2 Function contours_kmeans_clustering() is a Python 3.7.x verbatim translation of C++ k-means unsupervised clustering implementation in NeuronRain AstroInfer (centroids of contour clusters are with reference to a predefined contour similar to cpp-src/asferkmeansclustering.cpp which computes centroids based on a reference string) and redefines the Euclidean distance metric as topological Hausdorff distance amongst pair of contour polynomials in place of string edit distance. 1253.3 Pasting Lemma-Path Homotopy-Product Homotopy formalism for Shape Recognition (Inscription Decipherment-Handwriting-Face-Physique-Gender) in earlier sections (714) is a proper subset ("Very similar") of Contour Polynomial clustering ("Similar") of a visual focusing only on homeomorphic deformation of shapes - set of deformations are cluster subsets. 1253.4 Usual clustering algorithms are limited to delineation of point sets while contours are sets of polynomials (or algebraic varieties) defining a topological orientation. Clustering of polynomials or curves is less studied in Bigdata mining. This requires definition of Euclidean distance as topological or polynomial distance,revised from usual Levenshtein distance for texts. 1253.5 Transportation Network Google Maps imagery of Chennai Metropolitan area testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022.jpg which includes Live traffic layer (red busy clrcles - a visual urbanization measure of dynamic floating population centred around a city) has been clustered by K-Means (3 clusters and 3 iterations) and contour polynomials of transportation network are colored in RGB channels proportional to the cluster id they have been classified into (written to ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022-contourkmeansclustered.jpg). 1253.6 Voronoi Tessellation and Delaunay Triangulation have been made optional by boolean flags voronoi_delaunay=False in following segmentation functions in ImageGraph_Keras_Theano.py and GISUrbanSprawlAnalytics.py as plots by multiple segmentation algorithms obfuscate and overlay previous layers: (*) def urban_sprawl_from_segments(image,segment,maximum_population_density=None,sqkmtocontourarearatio=0,legend=None,sqkmareatopopulationratio=6.22,voronoi_delaunay=False) (*) def image_segmentation(imagefile,voronoi_delaunay=False) 1253.7 Following are the contour segmented files created by this commit: (*) Voronoi-Delaunay set to False - testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022-contourlabelled.jpg (*) Plot for KMeans clustering of contour polynomials - testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022-contourkmeansclustered.jpg - 3 color coded clusters of transportation network contours (*) Contour polynomial cluster dictionary is printed to Logs (comprehensive listing of 3 clusters of contour polynomials computed from testlogs/ChennaiMetropolitanAreaTransitNetwork_GoogleMaps_20May2022.jpg which could be candidate delineators, better than graphics plot earlier as color differentiation for 3 clusters in drawContours is not upto mark) - testlogs/GISUrbanSprawlAnalytics.log.24May2022 - road network polynomials (wires connecting localities) are discernible by their almost straightline alignment (pixel ordinates are almost same in one axis while the other axis increments) having some arbitrary degree turns.
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