(1) Since some people laugh at Cardano project and it's peer review aspect, let me share a story from work for all of you
(2) There is company called Databricks and they open sourced recently a technology called: https://t.co/gpZyKKiMVD . This technology is super important

(3) In BigData delta allows one to perform DELETE and MERGE operations contrary to Hadoop + Hive where this is not possible, seeking through many partitions finding customer data to remove and then rewriting it is not only expensive as an operation but also very dangerous
(4) This promising technology is something that many companies, which have big data turn into as they need to be GDPR compliant.
(5) I was always reading and accepting all details from their website as given and the "truth"
(6) There is however an amazing Principal Developer at our company that found some of the claims from their website as dubious, e.g. ACID claims on such a distributed file system
(7) It turns out this is not the first time this company advertises something that is simply not true
(8) on another occasion this company made untrue claims about Cassandra database and this then became source of disputes e.g by dr. @martinkl - my fave expert in distributed systems
(9) All professional developers also know of course famous Jepsen tests -> https://t.co/Hsu9IJhxS0
(10) Jepsen tests are now used for many many years to validate databases - claims that vendor make against harsh reality. In many cases claims on paper never met with hard reality of distributed systems, sometimes it was a basis for vendors to fix those databases, e.g. mongoDB
(11)Why am I writing all this. IT IS TRULY an advantage that Cardano's Ouroboros went through a rigorous process of being reviewed by academics on major conferences. It is a slow and methodical process but correct one. Cardano critics have often no experience in claims vs reality
(12) Wait, does it mean it for sure will succeed and become world's financial operating system or part of it? Of course not - this post is to show you that this claim of peer reviewed approach is something to brag about - albeit of course at the end of the day - adoption is key.
#Cardano

Ouroboros Praos: An adaptively-secure, semi-synchronous
proof-of-stake blockchain: https://t.co/jCletKZCN9

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THREAD: How is it possible to train a well-performing, advanced Computer Vision model 𝗼𝗻 𝘁𝗵𝗲 𝗖𝗣𝗨? 🤔

At the heart of this lies the most important technique in modern deep learning - transfer learning.

Let's analyze how it


2/ For starters, let's look at what a neural network (NN for short) does.

An NN is like a stack of pancakes, with computation flowing up when we make predictions.

How does it all work?


3/ We show an image to our model.

An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.

Here is what it might look like for a black and white image


4/ The picture goes into the layer at the bottom.

Each layer performs computation on the image, transforming it and passing it upwards.


5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.

The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!

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Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.


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Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.