Abstract: Methods of manufacturing multi-material fibers having one or more electrically-connectable devices disposed therein are described. In certain instances, the methods include the steps of: positioning the electrically-connectable device(s) within a corresponding pocket provided in a preform material; positioning a first electrical conductor longitudinally within a first conduit provided in the preform material; and drawing the multi-material fiber by causing the preform material to flow, such that the first electrical conductor extends within the multi-material fiber along a longitudinal axis thereof and makes an electrical contact with a first electrode located on each electrically-connectable device. A metallurgical bond may be formed between the first electrical conductor and the first electrode while drawing the multi-material fiber and/or, after drawing the multi-material fiber, the first electrical conductor may be located substantially along a neutral axis of the multi-material fiber.

Abstract: Methods of manufacturing multi-material fibers having one or more electrically-connectable devices disposed therein are described. In certain instances, the methods include the steps of: positioning the electrically-connectable device(s) within a corresponding pocket provided in a preform material; positioning a first electrical conductor longitudinally within a first conduit provided in the preform material; and drawing the multi-material fiber by causing the preform material to flow, such that the first electrical conductor extends within the multi-material fiber along a longitudinal axis thereof and makes an electrical contact with a first electrode located on each electrically-connectable device. A metallurgical bond may be formed between the first electrical conductor and the first electrode while drawing the multi-material fiber and/or, after drawing the multi-material fiber, the first electrical conductor may be located substantially along a neutral axis of the multi-material fiber.

Abstract: One embodiment of the present invention sets forth a technique for performing deinterlacing. The technique includes separating a first interlaced video frame into a first sequence of fields ordered by time, the first sequence of fields including a first field. The technique also includes generating, by applying a deinterlacing network to a first field in the first sequence, a second field that is missing from the first sequence of fields and is complementary to the first field. The technique further includes constructing a progressive video frame based on the first field and the second field.

Abstract: According to one implementation, an image enhancement system includes a computing platform including a hardware processor and a system memory storing a software code configured to provide a normalizing flow based generative model trained using an objective function. The hardware processor executes the software code to receive an input image, transform the input image to a latent space representation of the input image using the normalizing flow based generative model, and perform an optimization of the latent space representation of the input image to identify an enhanced latent space representation of the input image. The software code then uses the normalizing flow based generative model to reverse transform the enhanced latent space representation of the input image to an enhanced image corresponding to the input image.

Abstract: One embodiment of the present invention sets forth a technique for performing deinterlacing. The technique includes separating a first interlaced video frame into a first sequence of fields ordered by time, the first sequence of fields including a first field. The technique also includes generating, by applying a deinterlacing network to a first field in the first sequence, a second field that is missing from the first sequence of fields and is complementary to the first field. The technique further includes constructing a progressive video frame based on the first field and the second field.

Abstract: According to one implementation, an image enhancement system includes a computing platform including a hardware processor and a system memory storing a software code configured to provide a normalizing flow based generative model trained using an objective function. The hardware processor executes the software code to receive an input image, transform the input image to a latent space representation of the input image using the normalizing flow based generative model, and perform an optimization of the latent space representation of the input image to identify an enhanced latent space representation of the input image. The software code then uses the normalizing flow based generative model to reverse transform the enhanced latent space representation of the input image to an enhanced image corresponding to the input image.