Accelerated | Java Collision Prediction library

I model a swing that has its own drive. This drive consists of a housing and a weight inside it. The weight is accelerated with an electromagnetic field, so that it hits the wall of the housing at high speed and thus sets the swing in motion. I am farely new to unity but i thought i did everything correct. At the push of a button the weight was accelerated, hit the housing and the swing startet moving. It worked very well until i started to increase the force which is accelerating the weight (The weight is pretty small, so it needs a lot of speed to move the swing). Now the weight is flying out of the housing. I checked all collision boxes. They are correct and i even made them overlapping to ensure this is not the mistake. I have no idea how to fix this problem and would be grateful for any help. Here is the code that accelerates the weight, in case you need it:

Using PuTTY command line, I connect with unix host which is under PAM context.

The connection string is

I'm creating a scene with a bunch of scrolling layers (foreground, midground, background etc...) but annoyingly I get a flicker on Safari (14.0.3) when the animation restarts. This doesn't occur on Chrome or Firefox.

I've created a minimum reproducible example here:

https://brendon.github.io/safari_flicker/index.html

Here's the code:

I have huge dynamically created tables/matrices in MATLAB of varying first dimension, whose rows represent (sorted) combinations of integers in the range 1-50 of order 6.

I would like to assign to each combination a unique value (hash, ranking), so that I can check if the same combinations appear in different tables. Different combinations are not allowed to have same value assigned, i.e. no collisions. I have to make a lot of such comparisons between a lot of such tables. So, for performance reasons, I would like to accomplish this by vectorization of uint32 operations to make it suitable for GPU acceleration in MATLAB.

Things I have thought of so far:

1. Lexicographic ranking: no idea how to vectorize the standard fast recursive algorithms well, and the only option seems to be to parfor it through the rows, which is slower than other options. IIRC, the direct explicit formula, though vectorizable, requires computation of binomials, which in turn requires log Gamma function in order to avoid huge factorials + double type to avoid collision if I am not mistaken, i.e. is slower because it's 'very numerical'.
2. Cantor pairing function: one can successively apply Cantor's pairing, which is nice because it's a polynomial expression, but it produces huge numbers well beyond uint32 and is definitely slower than other options.
3. Base 51 (no pun intended) integers: sends a combination/row vector (x_1,...,x_6) to x_1 + x_2 * 51 + ... + x_6 * 51^5. This is the fastest I currently have. It's easily vectorizable, but unfortunately still requires uint64 or double for rank-6 combinations of 50 elements, which is slower than uint32 or single type operations would be.

So, I guess, I am looking for a 'clever' injective function on these combinations that computes within the uint32 range and is also well vectorizable (in MATLAB).

Any help would be much appreciated!

EDIT: Here is a routine that benchmarks both ranking and searching in uint32, single, and double. I have used MATLAB's gputimeit to produce accurate results.

Let's say I have a texture that I need to mipmap but I want the mipmapping done hardware accelerated. I decided the best route to take would be something like this:

I'm trying to install tensorflow on an M1 Mac. I've been trying to follow this tutorial to install the pre release version of tensorflow for Mac. From the pre-release repo:

This pre-release delivers hardware-accelerated TensorFlow and TensorFlow Addons for macOS 11.0+. Native hardware acceleration is supported on M1 Macs and Intel-based Macs through Apple’s ML Compute framework.

The pre-release requires Python 3.8, so that's what I set up my conda environment with.

The tutorial above tries to install a previous version of tensorflow for M1 Mac, so I adjusted the commands to use the version of tensorflow for M1 Mac that I've downloaded.

I've been trying to do this install in a virtual environment using Miniforge, as it is what multiple tutorials have recommended for M1 Mac installs. From the Miniforge repo:

This repository holds a minimal installer for Conda specific to conda-forge. It is comparable to Miniconda

When I get to the step to install tensorboard, pip install tensorboard, the install gets as far as:

Im reading a sine wave from a wave generator with a Labview FPGA code, when the frequency is around 1Hz, this is what I read (as expected)

However when I increase the frequency, this happens.

I see that the ticking speed does not change, so I think it would be solved if it accelerated just as much as I needed to match my sine wave. Also, I passed the data through a FIFO and to a .txt file that I then plotted and I cant see the sine wave either, so its not only a problem of the chart display.

How can I control this?

PS. I checked my hardware and it supports 50 MS/s, so it should not be a problem to nicely read a 10 Hz sine wave. In particular, Im using the NI 5751 ADC, FPGA 7951R, PXI 1071 chassis.

This is the code

Using ffmpeg I add a video overlay successfully over an origin video (origin has audio, overlay doesn't). However the audio of the origin video doesn't appear in the result video.

I'm uploading a video to S3 using aws-sdk in a reaction environment. And Use an accelerated endpoint for faster data transfers.

the endpoint is bucket-name.s3-accelerate.amazonaws.com

And I changed the option to 'Enabled' for accelerating transmission in bucket properties.

Below is my code for uploading file objects to s3.