Hubble telescope detects most distant star ever seen, near cosmic dawn
Posted: Sat Apr 02, 2022 2:48 am
[b]Hubble telescope detects most distant star ever seen, near cosmic dawn [/b] Jenn Abelson
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Moreover, we study the safety of multi-stage transactions via two proposals: multi-stage serializability (MS-SR) and multi-stage invariant confluence with Apologies (MS-IA). This is infeasible for edge applications that require real-time processing but cannot afford to place expensive hardware at the edge. This prohibits the use of faraway cloud resources. In these approaches, we notice a trade-off between accuracy and performance. The accuracy of a compressed model is typically lower compared to the full model while performance is improved dramatically. An important aspect that is overlooked in many video analytics solutions is that they are not integrated with the system’s data processing and management. Video analytics generates insights from videos that would typically be used in a data management application. For example, detecting objects in V/AR might feed into a mobile game, immersive social network, or other application. We propose Croesus, a multi-stage edge-cloud video processing framework that aims to manage the performance-accuracy trade-off in DL models.
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We further demonstrate our method’s effectiveness on a range of fakes not seen in training including those without video manipulation, that were not addressed in prior work. Humans tend to trust what they see, especially when it comes to video. Historically, video has been the best proof that an event has indeed occurred. However, in the rapidly evolving misinformation landscape of the present digital era, this may not be true for long. Video manipulation techniques are more accessible than ever, while the reach of Internet and social media enables rapid spread of falsified content. Not only is detection increasingly challenging, most methods are ineffective against cheapfakes falsified through conventional techniques (e.g., speeding up or slowing down a video), or with no video manipulation at all (a lookalike, audio dubbing). In this work, we aim to detect video falsifications related to a person’s identity. Specifically, our goal is to recognize whether the purported person “seen” in a video is indeed themselves. This is distinct from the deepfake detection problem, where the goal is to distinguish between pristine (non-manipulated) and generated/altered videos.
Moreover, we study the safety of multi-stage transactions via two proposals: multi-stage serializability (MS-SR) and multi-stage invariant confluence with Apologies (MS-IA). This is infeasible for edge applications that require real-time processing but cannot afford to place expensive hardware at the edge. This prohibits the use of faraway cloud resources. In these approaches, we notice a trade-off between accuracy and performance. The accuracy of a compressed model is typically lower compared to the full model while performance is improved dramatically. An important aspect that is overlooked in many video analytics solutions is that they are not integrated with the system’s data processing and management. Video analytics generates insights from videos that would typically be used in a data management application. For example, detecting objects in V/AR might feed into a mobile game, immersive social network, or other application. We propose Croesus, a multi-stage edge-cloud video processing framework that aims to manage the performance-accuracy trade-off in DL models.
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