Algorithms – Understand the linear acceleration set and how strong it is

The linear acceleration set says this for every constant $ c $You can choose the language $ c $ faster It does not mean that you can make it faster by a non-constant function.

The acceleration set works by defining a new Turing machine that writes $ c $ Character of the old Turing machine in a single tape cell, and customize the transient function to simulate in a single step everything that the original machine did between entering and leaving the individual $ c $-Zeichenblock. If $ c $ Since this is a constant, it does not depend on the input. You only have to calculate the new machine once and use it for the input you want. But if you want $ c $ To have a non-constant function, you have to find out what the new machine is each time you run it. This takes an exponentially long time because exponentially many are possible $ c $Token blocks for which you need to find out the transfer function.

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Windows 7 – Disable the annoying acceleration for the volume keys

I have volume keys on my keyboard to increase or decrease the volume. This ad will appear when I change the volume: https://i.imgur.com/9EGglRq.png

Each key changes the volume by 2% per single press. If I press the raise key ten times quickly, the volume will increase by 20%. But as far as the removal of the button is concerned – I have reduced the volume by 50%! It starts slowly, at 2% per 1 press, but eventually the percentage rises above 10% per single press. I wanted to reduce the volume by about 20%, but got too low a sound or was even muted. Now it gets very annoying for me!

Does anyone know how to lower the volume when the acceleration decreases?

android studio – Emulator: Emulator: ERROR: The x86 emulation currently requires hardware acceleration! and Emulator: Operation terminated with exit code 1

I can not virtualize my mobile devices in Android Studio

Try to follow this question:
https://stackoverflow.com/questions/29136173/emulator-error-x86-emulation-currently-erquires-hardware-acceleration

The first was to see if the hardware acceleration was installed and so it was ro

2) I went to the SDK directory C: Users % USERNAME% AppData Local Android sdk extras intel Hardware_Accelerated_Execution_Manager and run the file intelhaxm-android.exe

but it tells me that virtualization is disabled
Enter the description of the picture here

Download this program and ….
Enter the description of the picture here

Virtualization appears activated

If someone can help me, I would appreciate it very much. I just bought a new computer with an Intel processor and was very excited because I thought it would be easy to virtualize

Startup acceleration Ubuntu 18.04

I also have the same issue that I have been looking for a lot, but I can find anything to fix it (I have processor = Intel Core i3, RAM = 4G, Swap = 9G) and my boot info is:

~$ systemd analysis
Startup completed in 10.299s (kernel) + 49.706s (user space) = 1min 6ms
graphics.target reached 49,694s in user space

and graphical analysis

~$ systemd-analysis critical-chain graphical.target
The time after activating or starting the device is printed after the "@" mark.
The time taken for the device to start up is printed after the "+" sign.

graphical.target @ 49.694s
Ultmulti-user.target @ 49.693s
└─mysql.service @ 39.610s + 10.082s
    Worknetwork.target @ 39.607s
└─wpa_supplicant.service @ 34.142s + 5.464s
        └─dbus.service @ 28.807s
└─Basic.target @ 28.749s
└─sockets.target @ 28.749s
└─snapd.socket @ 28,747s + 1 ms
                └─sysinit.target @ 28.719s
└─systemd-timesyncd.service @ 28.477s + 240ms
                    └─systemd-tmpfiles-setup.service @ 27.622s + 716ms
                      └─systemd-journal-flush.service @ 5,195s + 22,426s
                        └─systemd-remount-fs.service @ 4.389s + 803ms
                          └─systemd-journald.socket @ 4.354s
└─system.slice @ 4.354s
└─-.slice @ 4.334s

Time complexity – Definition of acceleration for Amdahl's law

Suppose I have an algorithm C that runs in time T, which decomposes into two "subalgorithms" A and B that run in time p * T and (1-p).T, so the algorithm C needs the time pT + (1-p) * T.

Suppose I have another algorithm C & # 39; that calculates the same thing as the algorithm C that is in time T & # 39; running, where two sub-algorithms A & # 39; and B & # 39; Similar to A and B s.t. p & # 39; * T & # 39; + (1-p & # 39;) * T & # 39; = T & # 39;

I would like to measure the "partial acceleration" of the p-part A of C as in Amdahl's law. Must I

  1. have the subalgorithm A & # 39; to compute exactly the same as A or
  2. can I have another subalgorithm A & # 39; that computes more than A.

In Case 2, can I still use the ratio of execution times from A to A? As acceleration with ratio p?

Suppose I can not do 1. because I do not have such a subalgorithm A # 2. How can I then measure the execution times of A & # 39; and A if p and p are different?

Differential Calculation – Calculate the distance at time & # 39; t & # 39; at acceleration and starting point. elastic

Ok, let's imagine we have a rubber band sling and the distance $ d = 0 $ is in the middle of this slingshot.

I'm trying to get a distance equation so I can program the distance timed $ t $,

A bit like this slingshot, as it's narrow in the middle
Slingshot with a solid band

To my knowledge, the elastic potential energy is

$ e = Kd ^ 2 $

From where $ K $ is the elasticity and $ d $ is the distance.


So I think that my acceleration is really straight at any time
(I'm only interested in vertical acceleration):

$ A = -1 * sgn (d) * Kd ^ 2 $

On this basis, however, I have no idea how I can create a distance function or if it is even possible.


Because my last step is that I can really only determine that initial velocity 2 points given (1 possibly $ -d $ and 1 $ + d $) and their times in every place.

I would like some hints on how to solve the above problem or if I am doing it right.

Many Thanks! 🙂


TO EDIT

How can I even calculate the integral of this acceleration function if someone could show that this would be fantastic?

Performance Optimization – Acceleration of DimensionReduce

I'm interested in accelerating DimensionReduce with the "TSNE" method. I have a matrix with a size of 30000 * 10 and would like to reduce to 30000 * 8. But the calculation is very slow, all suggestions on how to speed up the calculation. Currently I do it the following way:

Data = {{0.0136704, 868., 5th, 0th, 0th, 0th, 0.0679724, 1.02337, 5th,
2391.}, {0.0327273, 65., 0., 0., 0., 0., 0.0615385, 1.02337, 5.,
2391.}, {0.0343137, 45., 2., 0., 0., 0., 0.0888889, 0.976633, 3.,
2388.}, {0.0425656, 358., 11, 0.0333333, 0, 0, 0.0642458,
0.976633, 3., 2375.}, {0.0146471, 168., 4., 0.4, 0, 0, 0.0892857,
1,02337, 5, 2378.}, {0,0255665, 362., 6, 0,0833333, 0, 0.
0.0856354, 1.02337, 5, 2368.}, {0.0215647, 443, 5, 0.0967742, 0.
0, 0.0857788, 1.02337, 5, 2377.}, {0.0242424, 435, 6,
0.0294118, 0, 0, 0.110345, 0.976633, 3., 2362.}, {0.0311943, 511.
22., 0., 0., 0., 0.129159, 1.02337, 5. 2360.}, {0.030086, 171.
4., 0., 0., 0., 0.122807, 1.02337, 5., 2361.}, {0.0324948, 222.
10., 0., 0., 0., 0.103604, 0.976633, 3. 2364.}, {0.0232975, 120.
2nd, 0th, 0th, 0th, 0.108333, 1.02337, 5th, 2380th}, {0.046616, 632th, 20th,
0, 0, 0, 0.056962, 1.02337, 5, 2354.}, {0,0187225, 381, 9.
0, 0, 0, 0.0603675, 1.02337, 5, 2342.}, {0,0234043, 421, 11.
0.133333, 0.10, 0.109264, 1.02337, 5, 2384.}, {0.0255072, 375.
8., 0., 0., 0., 0.0906667, 1.02337, 5., 2359.}, {0.0284264, 240.
15., 0.125, 0., 0., 0.129167, 1.02337, 5. 2344.}, {0.0246575, 183.
5., 0., 0., 0., 0.0819672, 1.02337, 5., 2361.}, {0.0155535, 251.
4., 0., 0., 0., 0.0876494, 0.0233665, 4., 2351.}, {0.0232186, 276.
10th, 0th, 0th, 0th, 0.0688406, 1.02337, 5th, 2346th}}


DimensionReduce[data, 8, Method -> {"TSNE", "Perplexity" -> 50}, 
    TargetDevice -> "GPU", 
    PerformanceGoal -> "Speed"]; // Absolute timing

{0.290914, zero}


DimensionReduce[data, 8, Method -> {"TSNE", "Perplexity" -> 50}, 
    PerformanceGoal -> "Speed"]; // Absolute timing

{0.307318, zero}

mp.mathematical physics – time to speed determination = & # 39; x & # 39; with changing acceleration?

A roulette ball turns around an edge.

What is the time (t) in which the velocity of the sphere = v (regardless of the number)?

The ball's delay of 4 spins was plotted on a scatter and best described with the following 3rd order polynomial:

A (t) = 0.0396t³ – 1.1035t² + 10.25t -33.898
with R² = 84.6%

I tried to find time and distance from speed and acceleration by:
(Final speed – initial speed) / (acceleration)
Unfortunately, this equation assumes a constant acceleration and is therefore nonsense to me because the acceleration of the ball is not constant.

I do not know if that will be useful, but the relationship I see is:
The higher the ball speed, the higher the deceleration rate.
So I've shown acceleration as a function of speed:

A (V) = -0.00008v3 + 0.0125v2 – 0.7688v + 14.983
R² = 0.88

Many Thanks