What’s the significance of different Laser Types in Starfinder

As the title says, What is up with all the different types of lasers, there’s Zenith, Parallax, Corona and others. But I can’t seem to find the significance of these subtypes of laser? I am assuming there are maybe certain armors or something that resist say a zenith laser, but not a corona laser?

using crtl+f doesn’t seem to be picking up any relevant info in my PDF. The index is of equal uselessness.

Any help on determining what all these types mean and what i should be looking for in arming myself with a laser weapon would be highly appreciated.

What is the significance of ML in Information Security?

This is not a technical question rather

  • I am seeking an advice about how ML and AI is going to take over the our information security tasks that are currently done manually.
  • How should I prepare myself future ready against such digital labour that is going to be cheap and more efficient.
  • Which other areas should I start exploring apart from just IS which would be beneficial in securing job.

How to deal with loss of significance in the case $f(x) = \sqrt{x+2} -\sqrt{x}$?

I would like to evaluate the expression $ f(x) = \sqrt{x+2} -\sqrt{x}$ with cases when $ x = 3.2 \times 10^{30}$ and $ x= 3.2 \times 10^{16}$ . I tried using N[Sqrt[x+2] - Sqrt[x], 100] and

ScientificForm[Sqrt[x+2] - Sqrt[x], 100], both yielding 0 as an output. How can I obtain the desired output?

I also tried the method in Making a calculation with high precision by applying .`100 as a suffix but my mathematica 12 doesn’t seem to be recognizing it.

what time complexity is more significance

my question: a certian algorithm executes n operations of 3 types, inesert delete and find. we know that n/10 of the operations are insert and the rest are delete and find. you are given 2 options of implementation to the algorithm. The first implementation implements insert at the worst case of theta(n) time complexity and the cost of every operation (including insert) is theta(1) amortized cost. The second implementation implemets insert at the worst case of theta(logn) worst case and every operation(including insert) at theta(logn) amortized cost. which implementation whould you recommend? why? thanks:)

Significance of integrally closed in an affinoid algebra

A Tate affinoid k-algebra is defined as a pair $ (R,R^+)$ where $ R$ is a Tate algebra and $ R^+$ is an open and integrally closed subring of $ R$ contained in the ring of powerbounded elements.

See for instance Defn 2.6 here.

What is the (geometric) significance of requiring $ R^+ $ to be integrally closed?

On a related note, what is the difference (geometrically) between total integral closure and integrally closed. It is surprisingly hard to find anything about total integral closures…

Intercept and dummy variables regression significance

I am trying to find the joint significance of the alpha (the intercept) and the dummy variables, $ \gamma_1$ and $ \gamma_2$ . I know the significance of $ \alpha$ from the regression intercept t-stat and I can find the significance of the dummy variables using a partial F-stat.

$ Y=\alpha +\beta_1X_1 +\beta_2X_2 + \gamma_1X_1^*+\gamma_2X_2^*$

Is there anyway to get the joint significance of the dummy variables and alpha (this quantity represents the total “alpha” of the regression)?

What is the significance of the three dots “…” on menus and buttons and how to use them right?

Adding three dots after the title of items in a dropdown menu seems to be a common practice (as you can see on the picture of a drop down menu in Google Chrome). They generaly mean that there is “something” after clicking on it.

Google chrome dropdown menu (french version)

These dots are also sometimes presents in the text of action links and buttons.

I am wondering about their utility and relevancy…

In your opinion :

  • What kind of information should be conveyed by these dots ?
  • How and when to use them ?
  • Is it realy relevant to the user, and easily understood by them ?

Where can I publish this new math paper whose main significance is its applicability to a field other than mathematics?

I have come up with a new function fitting method, where we fit a function to a given data. I have described the mathematics of it, and proved relevant theorems to show how it works. The sole importance of this method is its applications to machine learning. If I go to mathematicians, they are saying that the theorems are right, but the mathematics of it is nothing unusual but expected. They don’t readily know much about Machine Learning, and have no inclination to know. So its difficult to impress a math journal editor for a publication acceptance. So the mathematicians are advising to go to machine learning experts. On the other hand, If I go to machine learning experts, they are reluctant to comment, as they don’t readily understand the relevant math (unless they take some time and refer to a few books). Moreover the concept is a bit counter intuitive to the latest beliefs in machine learning world, where almost everyone believes that the ML problems are to be solved in very high dimensions, and most of the successful tools like deep convolutional neural networks or the traditional kernel methods or the graph based methods, are designed, keeping high dimensions in mind and over the belief that the ML problems can only be solved in very high dimensions. My methods calls for solving in as low dimensions as possible, requiring traditional domain knowledge based feature representation combined with dimensionality reduction tools, as pre-processors to reduce dimensions. So if I talk about my method to ML people, they might say that ML problems are best solved in very high dimensions, so my method being virtually impractical for very high dimensions, they deem it useless for machine learning.

I am able to apply my method and and demonstrate, for solving a few ML datasets of the likes of IRIS. I am also able to show the inner workings of my method through visualizations on simulated datasets in 2 dimensions, just for sake of illustrations.

I need better workstations and some time and funding to apply and solve harder ML problems, for which I need some support and funding, which is possible only if someone buys my idea and sponsor, as a form of startup. My strategy is to first publish this mathematical method of function fitting, in a math journal, so that it gets some authenticity and help me get some serious attention from ML experts for providing labs/infrastructure or attract venture capitalists for a startup.

Appreciate some suggestions whether my strategy is good idea. If so, what are some math journals I can target for this purpose. I don’t expect to go to mathematicians and say that I have done something incredible, but I just want to garner enough interest to get published in a descent journal, so that it will be easy for me to gather attention from ML world.

what is the significance of alias in python?

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I am looking at the unit test infrastructure code (Ref. https://svn.python.org/projects/stackless/branches/release30-maint/Lib/unittest.py) and I see lot of aliases like the following:

assertEqual = assertEquals = failUnlessEqual

assertNotEqual = assertNotEquals = failIfEqual

assertAlmostEqual = assertAlmostEquals = failUnlessAlmostEqual

assertNotAlmostEqual = assertNotAlmostEquals = failIfAlmostEqual

assertRaises = failUnlessRaises

assert_ = assertTrue = failUnless

assertFalse = failIf

I am wondering what could be the useful feature in using aliases?

I typically avoid aliases as they can be hard to debug and reduces code readability.