In macOS, I can test if the deeplink is working by simply executing in the terminal open "[deeplink]", for instance, open "my_app_name:import_file=454d1618-fb9b-45ea-b4c3-f32e6200cce4". This redirects me to my app and does (or don't) the apropiate action so I can later check the logs, etc.

I have been using DeepSkyStacker to get the most out of my astrophotography images since I began shooting through a telescope in 2011. This useful and easy-to-use freeware tool simplifies the pre-processing steps of creating a beautiful deep sky image.


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I have used DeepSkyStacker to align, calibrate and integrate every deep-sky astrophotography image I have ever taken. It is well worth your time to learn how to use this free software successfully, as you will enjoy it for years to come.

Integration is the key to great astrophotography image. This is the reason why amateur astrophotographers spend multiple nights collecting pictures on a single deep sky target. Calibration is another vitally important component of the process, as this removes unwanted elements from your image that would otherwise spoil the picture.

For many amateur astrophotographers, DeepSkyStacker (DSS) is an integral part of their image-processing workflow. For myself, I find that DeepSkyStacker does an exceptional job of registering astrophotography images taken using a variety of methods. This includes everything from untracked DSLR and camera lens shots to deep sky astrophotography through a telescope.

DSS can register images of everything from a wide-angle Milky Way panorama to a deep sky emission nebula. Most of my experience with this software has been on a Windows 10 PC, stacking Canon RAW files from a DSLR. To run Deep Sky Stacker on a Mac computer, a workaround such as using a virtual machine is necessary.

I regularly capture images on the same deep-sky object over multiple nights to increase the signal-to-noise ratio. I shoot through heavy light pollution in my backyard, which means I need to capture up to 4x or more the amount of exposure time someone living under dark skies would (see this article for a better understanding of this calculation).

Although I mostly use DSS for deep sky images, it is also very useful to stack wide-angle astrophotos through a camera lens as well. The same signal-to-noise benefits can be achieved by stacking multiple images together.

I am a multitasker. Usually, I have 5-6 windows open at a time from my Google Chrome browser to Adobe Photoshop. This all uses RAM on your machine, which DSS uses to process your image. Give DeepSkyStacker your full RAW capacity to use during its process.

My top bet would be a windows update thats either pending or in progress. Have you rebooted the PC to see if that clears it? There have been strange issues with Windows updates recently, for example after one update the sliders in EQMOD mysteriously vanished, only to be restored after another update.

I'm renovating the windows of my 1936 house. Doing the first window and I notice there's a big gap between the window frame and the wall and I was wondering the best way to fill it in so I can caulk it.

I bought a 1/2 inch backer rod, and that seems the right size, HOWEVER, the hole is really deep and gets a lot wider about 1/2 inch in. This means the backer rod keeps falling through the gap when I try to install it.

Also, a backer rod is required, you can't just caulk back to the foam. A backer rod is actually a releasing agent so the caulking will stick to the two materials on the sides (in your case, brick and wood frame) and not to the material behind the caulking. DO NOT OVER FILL. It's not how much caulking you can get in the joint, but rather the proper depth. Most people fill too deep. Most manufacturers recommend a ratio of 3:1. That is to say, 3 wide and 1 deep. This allows the caulking to expand and contract and still stay adhered to the sides. So, if the joint is 3/4" wide, do not fill any deeper than 1/4".

While not as advanced as other stackers, it nonetheless allows you to calibrate your light frames with dark and flat calibration frames. It also allows you to remove light pollution, reduce noise, and perform other simple tasks on the stacked image.

Several weeks ago, the .NET Blog featured a post What is .NET, and why should you choose it?. It provided a high-level overview of the platform, summarizing various components and design decisions, and promising more in-depth posts on the covered areas. This post is the first such follow-up, deep-diving into the history leading to, the design decisions behind, and implementation details of async/await in C# and .NET.

Virtual network elements such as Hyper-V Virtual Switch, Hyper-V Network Virtualization, Software Load Balancing, and RAS Gateway are designed to be integral elements of your SDN infrastructure. You can also use your existing SDN-compatible devices to achieve deeper integration between your workloads running in virtual networks and the physical network.

One of my tutorials is linked to above - it doesn't discuss the blending mode but as I reveal above its "screen blend" or "add". ...... A tip here - make sure that after you have done the background extraction with the stars-only image that you a ) offset the background so that the average "deep space" pixel values are (15,15,15) and b ) add some noise back to the image - in Photoshop I use the add noise filter set to 0.1 to 0.3 (background extraction leaves the image unnaturally smooth). For the comet-only image you can use horizontal / vertical de-banding filters to get the final star trail artefacts removed (rotate the image to make the faint trails horizontal or vertical - and when done reverse the rotation) and set the background "deep space" pixel values to (10,10,10) - (15,15,15). After the screen blend when combining the two images the background values add together to give (25,25,25) to (30,30,30) which is an ideal background brightness (for a 0 - 255 RGB brightness scale)


Also Kerry-Ann Lecky Hepburn has written up the standard technique (in not too much detail regarding settings and tool options I'm afraid - but she has at least got screen shots of the steps so you can at least see what is expected) - -processing-for-non-trailing-stars-and-comet

One of my tutorials is linked to above - it doesn't discuss the blending mode but as I reveal above its "screen blend" or "add". ...... A tip here - make sure that after you have done the background extraction with the stars-only image that you a ) offset the background so that the average "deep space" pixel values are (15,15,15) and b ) add some noise back to the image - in Photoshop I use the add noise filter set to 0.1 to 0.3 (background extraction leaves the image unnaturally smooth). For the comet-only image you can use horizontal / vertical de-banding filters to get the final star trail artefacts removed (rotate the image to make the faint trails horizontal or vertical - and when done reverse the rotation) and set the background "deep space" pixel values to (5,5,5) - (10,10,10). After the screen bland when combining the two images the background values add together to give (20,20,20) to (25,25,25) which is an ideal background brightness (for a 0 - 255 RGB brightness scale)


Also Kerry-Ann Lecky Hepburn has written up the standard technique (in not too much detail regarding settings and tool options I'm afraid - but she has at least got screen shots of the steps so you can at least see what is expected) - -processing-for-non-trailing-stars-and-comet

NVIDIA CUDA-X AI is a complete deep learning software stack for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision. CUDA-X AI libraries deliver world leading performance for both training and inference across industry benchmarks such as MLPerf.

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Deep learning frameworks offer building blocks for designing, training and validating deep neural networks, through a high level programming interface. Widely used deep learning frameworks such as PyTorch, TensorFlow, and JAX rely on GPU-accelerated libraries such as cuDNN and TensorRT to deliver high-performance GPU accelerated training and inference.

CUDA-X AI libraries accelerate deep learning training in every framework with high-performance optimizations delivering world leading performance on GPUs across applications such as conversational AI, natural language understanding, recommenders, and computer vision. The latest GPU performance is always available in the Deep Learning Training Performance page.

As deep learning is being applied to complex tasks such as language understanding and conversational AI, there has been an explosion in the size of models and compute resources required to train them. A common approach is to start from a model pre-trained on a generic dataset, and fine tune it for a specific industry, domain, and use case. NVIDIA AI toolkit provides libraries and tools to start from pre-trained models to perform transfer learning and fine tuning so you can maximize the performance and accuracy of your AI application.

CUDA Deep Neural Network (cuDNN) is a high-performance library with building blocks for deep neural network applications including deep learning primitives for convolutions, activation functions, and tensor transformations.

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