Master Index: An Illustrated Guide

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Banana seeds can vary in size, shape and color, depending the species and cultivar, and are irregular in shape, generally flat, grayish to brownish, and have a coat surrounded by a wrinkled membrane that makes it rigid Chin, These seeds consist of an embryo that can vary size but is normally small with endosperm surrounded by two integuments the outer with multiple layers and the inner thin , which protect the seed during maturation, dispersal and dormancy. The outer integument limits the germination of the embryo due to its composition Graven et al.

The high germination variation and low number or even absence of seeds in crosses to produce new hybrids are the major limitations that genetic improvement programs face, which is based on crossing triploid and tetraploid genotypes and improved or wild diploids, followed by selecting promising hybrids from the progenies Amorim et al. Due to seed mortality caused by these anomalies, even before in vivo germination, culturing embryos has become a strategy to rescue embryos before they die, making it possible to genetically improve bananas Asif et al.

In vitro cultivation of banana embryos also depends on the maturation stage of the embryo and the culture medium used Uma et al. However, it is necessary to optimize the methods used in tissue culture to rescue embryos of Musa spp. Considering there is no scale of the main anomalies observed in banana seeds, this work aims to propose an illustrated guide to classify these anomalies aiming to assist in the selection of seeds and embryos that are most suitable for embryo rescue.

The guide is subdivided into six steps. It starts with processing seeds, followed by embryo rescue, classifying seeds and embryos, embryo cultivation and seedling growth, and ends with seedling acclimatization. Step 1: Seed processing and disinfection -The banana seeds are removed from fruits in an advanced stage of maturation brown patches on the peel , which means they are completely developed Figure 1 , step 1A.

The seeds are then washed in water to remove any pulp that is adhered to the surface Figure 1 , step 1B. The seeds are placed in a container with water and those that float are discarded because they may not present an embryo or endosperm Uma et al. Finally, the seeds are suitable for embryo rescue Figure 1 , step 1C and 1D. Step 2: Embryo rescue -The seed embryos are excised and classified, with using a stereoscopic microscope in a chamber under laminar flow Figure 1 , step 2A.

A longitudinal fissure is made in each seed for excision of the embryo Figure 1 , step 2B.


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  5. Illustrated guide to the classification of banana seeds and embryos!

Step 3: Seed classification -The following scale is proposed to classify the seeds after making a longitudinal cut: embryo and endosperm present PP ; embryo absent and endosperm present AP ; embryo present and endosperm absent PA ; and embryo and endosperm absent AA Figure 1 , step 3. Step 4: Embryo classification -After extraction, the embryos are classified using the following proposed scale: 1.

Embryo normal; 2. Embryo with deformed base and apex; 7. Embryo with oxidation; and 8. I'd really like to find a way to re-word this to avoid using the term "shared secret", because it isn't really accurate. It's not a "shared secret" used to authenticate the user to the site; anyone in the world can know this value provided they don't also have Alice's unencrypted working master key.

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And as you say, Alice's SQRL app immediately throws the info away so that even if someone stole her phone that instant, they can't do a lot with it. Not even Alice knows this secret. Only Simon has it now.

This is important because SQRL does not require true shared secrets to function, unlike pretty much every other authentication system. Good point, the 'shared secret' has been renamed to 'unlock code'.

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All images have been updated. Julio I have also added your suggested changes.

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Updates are now on sqrl. Very nice. I guess you decided to use "code" instead of "secret", which I agree is good idea.


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  • The words on the text you copied from here still refer to "secret" though. Thought you might want to replace that later on. The step-by-step walkthrough makes it so much easier to understand the tech behind SQRL. I wanted an easy way to explain to less technical people what SQRL was so I made an illustrated guide. Tried to make it as easy as possible.

    Looking for some feedback, see if I need to change or add anything. Cheers, Ben. Sorry this took so long. But I decided that I would make that decision AFTER the solution was fully known and understood so that it's cost and "weight" could be known. The thing that cinched it for me, which you'll read in the "why SQRL has this" orientation text, was the idea that we're really During this morning's Security Now!

    Most of the focus will fall upon the group's past week's work on the issue of changing website SQRL identity associations. I'll be talking about the "Identity Lock" protocol I have designed to provide all of the features we want for this capability. The modeling layer is relatively simple.

    It consists of two layers of bi-LSTM. As mentioned above, the input to the modeling layer is G.

    Master Index an Illustrated Guide

    The formation of M1 and M2 from G is illustrated below. M1 and M2 are yet another matrix representation of Context words. The difference between M1 and M2 and the previous representations of Context words are that M1 and M2 have embedded in them information about the entire Context paragraph as well as the Query.

    In Minion-speak, this means that our Minions now have all the information they need to make the decision about who should be in the Answer Gang. The very last thing we need is to convert these numeric vectors to two probability values so that we can compare the Query-relevance of all Context words. And this is exactly what the output layer does.

    We then obtain p1 , the probability distribution of the start index over the entire Context, by the following steps:. Similarly, we obtain p2 , the probability distribution of the end index, by the following steps:. The steps to get p1 and p2 are depicted in the diagram below:. The best Answer span is simply a substring of the Context with the highest span score.

    The span score, in turn, is simply the product of the p1 score of the first word in that span and the p2 score of the last word in the span. We then return the span with the highest span score as our Answer.


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    • An example will make this clear. Each word in the Context is associated with one p1 value and one p2 value. Below are the the p1 values for our example:. Below are the the p2 values for our example:. If in the original Context the word with the highest p1 comes before the word with the highest p2 , then we have our best Answer span already — it will simply one that begins with the former and ends with the later. This is the case in our example. Okay, one caveat before we end this series. In the hypothetical case that the Context word with the highest p1 comes after the Context word with the highest p2 , we still have a bit of work to do.

      We then take the span with the highest span score to be our Answer. So that was it — a detailed illustration of each step in BiDAF, from start to finish sprinkled with a healthy dose of Minion-joy. I hope that this series has helped you in understanding this fascinating NLP model! Sign in. Get started.

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