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I would like to transform xml files in below structure to another one pretty similar to this, let's say each element name changes (ie A..F -> ANew..FNew). I would like to do it in streamable mode introduced by XSLT 3.0 using Saxon-EE v9.7 since I need to process very big files. Although I can partially succeed it (Root/Head section), I could not come up with a solution totally address the whole document since I cannot meet streamability rules Saxon-EE applies and I am lost among them... I appreciate any xslt suggestion that would work in streaming mode with Saxon-EE v9.7.

We present the first end-to-end solution to create high-quality free-viewpoint video encoded as a compact data stream. Our system records performances using a dense set of RGB and IR video cameras, generates dynamic textured surfaces, and compresses these to a streamable 3D video format. Four technical advances contribute to high fidelity and robustness: multimodal multi-view stereo fusing RGB, IR, and silhouette information; adaptive meshing guided by automatic detection of perceptually salient areas; mesh tracking to create temporally coherent subsequences; and encoding of tracked textured meshes as an MPEG video stream. Quantitative experiments demonstrate geometric accuracy, texture fidelity, and encoding efficiency. We release several datasets with calibrated inputs and processed results to foster future research.

Is there no way to embed a streamable video through the url? The original video on my local hard disk turns out to be corrupted. So i'm looking for any way to use the streamable video which still works.

As this datasets needs 134GB of disk space to load I thought the streaming feature of load_dataset would come handy here but apparently streaming is not possible on Natural Questions. It was my assumption that all datasets were streamable on huggingface. Was my assumption incorrect?

Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network parameters, the data transfer by sending and receiving the entire model parameters prevents this emerging technology from being used in many practical scenarios. We propose streamable neural fields, a single model that consists of executable sub-networks of various widths. The proposed architectural and training techniques enable a single network to be streamable over time and reconstruct different qualities and parts of signals. For example, a smaller sub-network produces smooth and low-frequency signals, while a larger sub-network can represent fine details. Experimental results have shown the effectiveness of our method in various domains, such as 2D images, videos, and 3D signed distance functions. Finally, we demonstrate that our proposed method improves training stability, by exploiting parameter sharing. 17dc91bb1f