Mr. Putin’s announcement came just hours after the Ukrainian president rejected Moscow’s claims that his country poses a threat to Russia and made a passionate, last-minute plea for peace. “The people of Ukraine and the government of Ukraine want peace,” President Volodymyr Zelenskyy said in an emotional overnight address, speaking in Russian in a direct appeal to Russian citizens. “But if we come under attack, if we face an attempt to take away our country, our freedom, our lives and lives of our children, we will defend ourselves. Mr. Zelenskyy said he asked to arrange a call with Mr. Putin late Wednesday, but the Kremlin did not respond. “Any provocation, any spark could trigger a blaze that will destroy everything,” he said. At an emergency meeting of the U.N. Security Council called by Ukraine because of the imminent threat of a Russian invasion, members still unaware of Mr. Putin’s announcement appealed to him to stop an attack. U.N. Secretary-General Antonio Guterres opened the meeting, just before the announcement, telling Mr. Putin: “Stop your troops from attacking Ukraine. Next, since videos are perceived as infinite continuous signals, one needs to develop an appropriate sampling scheme to use them in a practical framework. Finally, one needs to accordingly redesign the discriminator to operate in the new sampling pipeline. To solve the first issue, we develop positional embeddings with time-varying wave parameters which depend on motion information, sampled uniquely for different videos. This motion information is represented as a sequence of motion codes produced by a padding-less conv1d-based model. RNN’s instability when unrolled to large depths. To produce frames non-autoregressively. Next, we investigate the question of how many samples are needed to learn a meaningful video generator. Finally, since our model sees only 2-4 randomly sampled frames per video, it is highly redundant to use expensive conv3d-blocks in the discriminator, which are designed to operate on long sequences of equidistant frames. That’s why we replace it with a conv2d-based model, which aggregates information temporarily via simple concatenation and is conditioned on the time distances between its input frames.
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