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[AAinnonna]

As shown below, SmartMedia cards are extremely simple. A plane electrode is connected to the flash-memory chip by bonding wires. The flash-memory chip, plane electrode and bonding wires are embedded in a resin using a technique called over-molded thin package (OMTP). This allows everything to be integrated into a single package without the need for soldering. The OMTP module is glued to a base card to create the actual card. Power and data is carried by the electrode to the Flash-memory chip when the card is inserted into a device. A notched corner indicates the power requirements of the SmartMedia card. Looking at the card with the electrode facing up, if the notch is on the left side, the card needs 5 volts. If the notch is on the right side, it requires 3.3 volts. SmartMedia cards erase, write and read memory in small blocks (256- or 512-byte increments). This approach means that they are capable of fast, reliable performance while allowing you to specify which data you wish to keep.They are less rugged than other forms of removable solid-state storage, so you should be very careful when handling and storing them.|The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their success with videos has proven challenging. Sets of high-quality facial videos are lacking, and working with videos introduces a fundamental barrier to overcome - temporal coherency. We propose that this barrier is largely artificial. The source video is already temporally coherent, and deviations from this state arise in part due to careless treatment of individual components in the editing pipeline. We leverage the natural alignment of StyleGAN and the tendency of neural networks to learn low frequency functions, and demonstrate that they provide a strongly consistent prior. We draw on these insights and propose a framework for semantic editing of faces in videos, demonstrating significant improvements over the current state-of-the-art. Our method produces meaningful face manipulations, maintains a higher degree of temporal consistency, and can be applied to challenging, high quality, talking head videos which current methods struggle with.




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