Special Report, Tech, Americas

[ms-marvelnen]

Unless it should fall through, Zelenskiy will be tested fairly soon at a Normandy Four meeting with the leaders of Russia, Germany, and France. At a briefing with Slovak President Zuzana Caputova in Kyiv on September 16, Zelensky said he hoped France and Germany will support the position of Ukraine at a future Normandy format meeting. A date has not yet been set, although Prystayko said that Ukraine, Germany, and France had agreed to hold talks in Paris at the initiative of French President Emmanuel Macron, on September 16. He said that Russia, however, declined that offer. Putin's plans are not yet clear, and Prystayko said a meeting was unlikely before October. Macron spoke with Putin by phone on September 8, and the French president's office said they agreed that the prisoner swap between Kyiv and Moscow had given momentum to talks on resolving the war in eastern Ukraine. With Putin's approval ratings falling since his 2018 reelection and his political party taking a beating in a Moscow election on September 8, a senior Western diplomat in Kyiv also noted that it might be a good time for the Russian president to turn the country's attention elsewhere. Putin, adding that there were also economic incentives. Kremlin aide Yury Ushakov said last week that Russia wanted to hold the meeting, but that three preconditions should be met beforehand. They include forces in eastern Ukraine disengaging in three key areas along the front line, Kyiv and Moscow agreeing on the terms of autonomy for the areas not under government control, and a preliminary agreement on what the summit's conclusions should be. Suggesting a possible roadblock, Prystayko said that the sides remained at odds over the level of autonomy for the eastern regions and that the third precondition, which he learned about from the media, came as a surprise.|Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. However, when inferring on long video sequences presenting low motion (i.e. in which some parts of the scene barely move), recurrent models diverge through recurrent processing, generating high frequency artifacts. To the best of our knowledge, no study about VSR pointed out this instability problem, which can be critical for some real-world applications. Video surveillance is a typical example where such artifacts would occur, as both the camera and the scene stay static for a long time. In this work, we expose instabilities of existing recurrent VSR networks on long sequences with low motion. We demonstrate it on a new long sequence dataset Quasi-Static Video Set, that we have created. Finally, we introduce a new framework of recurrent VSR networks that is both stable and competitive, based on Lipschitz stability theory.




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