Materialized views with fast refresh in Oracle 11g SE

I am kinda lost in available features of Standard Edition Oracle Database 11g. I want to know if I am able to use materialized views with fast refresh in SE without violating any kind of licenses. I’m aware of list if features on Oracle site but I don’t know which feature MV fast refresh is a part of. Could anyone please explain?

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How fast is telepathic communication (with a familiar)?

The spell description of the spell find familiar states:

While your familiar is within 100 feet of you, you can communicate with it telepathically.

Which to me raises a question: how fast is this communication?

Speed is often defined in latency – delay until the first bit of information arrives – and throughput – amount of information per second.

I believe I can be quite sure that the latency will be no less than that of regular speech which at 100 feet is next to nothing1

But when it comes to the throughput I seem to be in the dark – how quicly can concepts be explained through telepathic comunication?

This becomes important especially in the case of familiars when looking at surprise rules.

A member of a group can be surprised even if the other members aren’t.

So, the familiar’s awareness doesn’t directly benefit the wizard unless it is able to timely communicate the presence of foes to the wizard.

How quickly can a familiar warn its wizard of the oncomming danger? Is that instantaneous, is it as fast as regular speech, or something in between?

1 100 feet divided by 1124 feet/s = 0.08 seconds with the speed of sound. Telepathy doesn’t require air so probably goes with the speed of light resulting in 0.0000001 seconds.

This question is different to How much can you communicate with your familiar in that that question asks what kind of concepts can be conveyed whereas this question asks how fast those concepts can be explained.

Fast query on the number of elements in a quarter plane

I have a scatter of 2D elements on a 2D plane.

I would like an efficient algorithm to prepare and query the number of points in a quarter plane (inclusive of boundary).

The quarter plane is defined by a point $ (x,y)$ . All elements $ (x’, y’)$ where $ x<=x’$ and $ y<=y’$ are in the quarter plane.

For example

  • I have elements [(1,1), (2,2), (1,2), (3,2)],
  • My queries are [(1,1), (2,2), (3,3)]

The program should return [1,3,4].

This is for a past competitive programming challenge (WiFi Network problem in this competition)

Fast approach to do losummation in Compile[]?

My code does massive Summation and Matrix multiplication

Compile[] has boosted it distinctly. But I read some literatures related to my program, It seems there are approches to make it even faster. Maybe it can be improved from optimizing MMA language or algorithm itself.

My first piece code is below.

MomentComputing =   Compile[{{Mmax, _Integer}, {k, _Integer}, {image, _Real,      2}, {xLegendreP, _Real, 2}, {yLegendreP, _Real, 2}},    Block[{m, n, width, momentMatrix},    width = Length[image];    momentMatrix = Table[0., {Mmax + 1}, {Mmax + 1}];    Do[ momentMatrix[[m + 1, n + 1]] = ((2. (m - n) + 1.) (2. n + 1.)/((k k)*width width)) xLegendreP[[         m - n + 1]].image.yLegendreP[[n + 1]], {m, 0, Mmax}, {n, 0,       m}];        momentMatrix], CompilationTarget -> "C",    RuntimeAttributes -> {Listable}, Parallelization -> True,     RuntimeOptions -> "Speed"] 

It should be better if I don’t use any loop operations. But I can not figure out any other approaches. Probably matrix vector multiplication should be time-consuming as well.

Second piece.

Reconstruction =    Compile[{{lambdaMatrix, _Real, 2}, {lPoly, _Real, 2}},     Block[{Mmax, width, x, y, m, n, reconstructedImage},     {Mmax, width} = Dimensions[lPoly];     reconstructedImage = Table[0., {width}, {width}];     Do[      reconstructedImage[[x, y]] =        Sum[lambdaMatrix[[m + 1, n + 1]]*lPoly[[m - n + 1, x]]*         lPoly[[n + 1, y]], {m, 0, Mmax - 1}, {n, 0, m}]      {x, 1, width}, {y, 1, width}];     reconstructedImage], CompilationTarget -> "C",     RuntimeAttributes -> {Listable}, Parallelization -> True,     RuntimeOptions -> "Speed"]; 

Likewise, I don’t want Do[] loop here. In addition, I think Sum[] is a very slow function.

I can give all my code if necessary.

Edit 1:

According to Micheal’s suggestion, the first part is fast enough. It does not need acceleration anymore. The second part is the main time-consuming part, I believe it can speed up anyway.

How to fast play Traps/Loot/Hidden treasure

As new players, we ran our first dongeon and the session left me a bitter taste. Some part lasted a long time without being usefull, those parts were when we were searching for treasures or traps.

Indeed, before entering EVERY room the group was : "we look at the door/every tile we step on/wall to determine if there is a hidden trap", and the DM to reply "OK roll for an investigation/perception". And actually there were only one trap for the full dongeon…

Then when we entered a new rom, EVERY player were asking the DM : "I am searching for a hidden treasure/loots/secret door". And the DM, one player at a time : "ok roll", just in order to loot a few pieces from the dead bodies or nothing most of the time…

My questions is : how to manage Traps/Loots/Hidden Treasures without rolling everytime for everyone and avoiding (as much as possible) to miss a secret door/treasure/hidden trap ?

Looking for fast LP solver algorithm for my Special case

I am interested to know what is the fastest algorithm (complexity wise) known to us to solve the following linear program. Due to its simplicity, I hope for a very fast algorithm. Your help is greatly appreciated and I would appreciate you providing me with relevant papers as well.

Context: I am trying to give an algorithm related to graph theory. Unfortunately, my knowledge of LP is very limited that is why I need your guidance. In the following algorithm $ 2k$ is the degree of the investigated vertex. Ideally, I am looking for $ O(k)$ or $ O(k \log k)$ solution, or in total $ O(n)/O(n\log n)$ when this applies to all the vertices. Note that $ |E|= O(n)$ . However, any fast algorithm would be appreciated.

Minimize: $ T$
Subject to:
$ \forall i \in [2k] \qquad 1 \le t_i \le 2k$

Comment: Ensuring that $ \forall i, j \in [2k],\ i \neq j \qquad |t_i – t_j| \ge 1$
Comment: I want $ t_i$ ‘s to be distinct positive integers in [2k]
$ \forall i, j \in [2k],\ i \neq j \qquad 1 \ge t_i – t_j$
$ \forall i, j \in [2k],\ i \neq j \qquad 1 \ge t_j – t_i$

$ \forall i \in [2k],\ i \text{ is an odd number} \qquad t_i \le t_{i+1}$

$ \forall i \in [k] \qquad 0 \le x_i \le 1$
$ \forall i \in [k] \qquad \text{A linear constraint}\ F(x_i,\ t_{2i},\ t_{2i-1},\ T)$

Assuming the Exponential time hypothesis is true, what’s the fast possible algrotimh’s that can be produced for NP-complete problems [duplicate]

Assuming the Exponential time hypothesis is true, what’s the fast possible algrotimh’s that can be produced for NP-complete problems?

If 3-Sat takes exponential time, then could it be possible that some NP-complete problems can be solved in $ n^{log^k(n)}$ time? $ 2^{n^{1/log(log(n))}}$ time? $ 2^{n^{0.5}}$ time?

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