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CODA: Art & Ambiguity

All good art has something ambiguous about it, that gives the audience or the viewer an impetus to interpret it herself! This particular scene from the fantastic movie CODA (Oscar winner 2022) was exactly like that! In it a deaf father's daughter is singing a song for him and he is able to feel it. The second time I watched the movie, I got a totally different interpretation than the way I had interpreted it, this time from Tanuka who was watching it for the first time. She had interpreted it in a way I just did not think of, without going into specifics.  Which goes on to show the element of ambiguity in that scene which throws up a moment of aha! Or you got to use your brain to fill in the blanks and give it closure. Of course there is context and emotional depth that is necessary too, however the hallmark of good film-making are such subtly ambiguous moments and scenes without being over the top irrational or puzzling and uninterpretable! Good art, ergo the subtle ambiguity! 
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Ending Song: Meghe Dhaka Tara (2013)

Meghe Dhaka Tara (2013) directed by Kamaleshwar Mukherjee Have not seen a better or a more intensely rousing movie ending scene than this one. Absolutely electrifying! The song is also just marvelous, riveting. The landscape and the scenes of the video song  are just brilliant! Masterpiece. Every time I see or hear it, I get goosebumps. Watch the video: Aaohaan . Meghe Dhaka Tara (Cloud Capped Star) is a 2013 Indian Bengali film directed by Kamaleswar Mukherjee. The film is inspired from the life and works of Bengali film director Ritwik Ghatak. The entire film is in black and white except the last scene which has been shot in colour. In this film Saswata Chatterjee plays the character of Nilkantha Bagchi and Ananya Chatterjee plays the role of Durga, Nilkantha's wife. Besides giving an account of Ghatak's life, the film also depicts the socio-political environment of contemporary West Bengal during the Tebhaga and Naxalite movements. Kamaleshwar Mukherjee has used the metaphor
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Trial of SPARQL on GraphDB

I used the OWL ontology files from  Allen Brain Atlas (ABA) Adult Mouse Brain Ontology Then used SPARQL Query on GraphDB http://localhost:7200/import#user First you need to Import the file using Import  →  RDF  →  Server Files Then use SPARQL Query & Update - http://localhost:7200/sparql Sample SPARQL query that I used: select distinct ?o where {   ?s ?p ?o .   filter contains(?o,"Cerebral cortex")         #-- the important line } limit 100 Result: 1 Cerebral cortex, layer 5 2 Cerebral cortex 3 Cerebral cortex, layer 2-3 4 Cerebral cortex, layer 6a 5 Cerebral cortex, layers 1-6b 6 Cerebral cortex, layer 1 7 Cerebral cortex, layer 6 8 Cerebral cortex, layer 3 9 Cerebral cortex, layer 2 10 Cerebral cortex, layer 4 Schema.org - has more RDF / triples:  https://schema.org/docs/developers.html Another useful link:  Ontologies in Neo4j: Semantics and Knowledge Graphs  
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Run MySQL without Administrator privileges

Create a text file containing the password-assignment statement on a single line. Replace the password with the password that you want to use. ALTER USER 'root'@'localhost' IDENTIFIED BY 'MyNewPass'; Save the file. This example assumes that you name the file C:\mysql-init.txt Open a console window to get to the command prompt: From the Start menu, select Run, then enter cmd as the command to be run. Start the MySQL server with the special --init-file option (notice that the backslash in the option value is doubled): cd "C:\Program Files\MySQL\MySQL Server 8.0\bin" mysqld --init-file=C:\\mysql-init.txt
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Softmax function, softmax regression.

T he softmax function is also called the normalized exponential function.  It is a generalization of the  logistic function  that "squashes" a  K -dimensional vector  {\displaystyle \mathbf {z} }  of arbitrary real values to a  K -dimensional vector  {\displaystyle \sigma (\mathbf {z} )}  of real values in the range [0, 1] that add up to 1.  In  probability theory ,  t he output of the softmax function can be used to represent a  categorical distribution  – that is, a  probability distribution  over  K  different possible outcomes.  Example:  We know that every image in MNIST is of a handwritten digit between zero and nine. So there are only ten possible things that a given image can be. We want to be able to look at an image and give the probabilities for it being each digit. For example, our model might look at a picture of a nine and be 85% sure it's a nine, but give a 5% chance to it being an eight (because of the top loop) and a bit of probability to all the
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