filters | Collection of community-authored custom display filters for PixiJS | Graphics library

Ok, basically by reading the ssm session manager plugin source code I came up with the following simplified reimplementation that is capable of just grabbing the command output: (you need to pip install websocket-client construct)

[temp.names]/5 says that a name prefixed by template must be a template-id, meaning that it must have a template argument list. (Or it can refer to a class/alias template without template argument list, but this is deprecated in the current draft as a result of P1787R6 authored by @DavisHerring.)

There is even an example almost identical to yours under it, identifying your use of template as ill-formed.

The requirement and example comes from CWG defect report 96, in which the possible ambiguity without the requirement is considered.

Open GCC bug report for this is here. I was not able to find a Clang bug report, but searching for it isn't that easy. Its implementation status page for defect reports however does list the defect report as unimplemented.

Old x86-64 CPUs don't support lzcnt, so rustc/llvm won't emit it by default. (They would execute it as bsr but the behavior is not identical.)

Use -C target-feature=+lzcnt to enable it. Try.

More generally, you may wish to use -C target-cpu=XXX to enable all the features of a specific CPU model. Use rustc --print target-cpus for a list.

In particular, -C target-cpu=native will generate code for the CPU that rustc itself is running on, e.g. if you will run the code on the same machine where you are compiling it.

if you want append #filter to #move-filters-here you can do it like this:

I have followed a lazy, not-best-efficient, yet useful, strategy. Since dealing with docx is less flexible than html, I converted the markdown md to html first, then moved from html to docx like this:

I think the main problem with this code was that the AVAudioConverter was being created before calling engine.prepare() which can and will change the mainMixerNode output format. Aside from that, there was a redundant connection of mainMixerNode to outputNode, along with a probably incorrect format - mainMixerNode is documented to be automatically created and connected to the output node "on demand". The tap also did not need a format.

If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

Some further ideas:

• If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
• If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

Your title says you are using Java 9. With Activiti 6 you will have to use JDK 1.8 (Java 8).

This might have a performance impact if your list is really big, but since .filter() returns back an array, why not chain 5 filters together.

For example,