Tesla receives SEC subpoena over Elon Musk’s tweets and faces potential racial discrimination suit
Posted: Wed Apr 06, 2022 1:17 am
Acoustic levitation allows small objects, like droplets of liquid, to float. Unless you travel into the vacuum of space, sound is all around you every day. But most of the time, you probably don't think of it as a physical presence. You hear sounds; you don't touch them. The only exceptions may be loud nightclubs, cars with window-rattling speakers and ultrasound machines that pulverize kidney stones. But even then, you most likely don't think of what you feel as sound itself, but as the vibrations that sound creates in other objects. The idea that something so intangible can lift objects can seem unbelievable, but it's a real phenomenon. Acoustic levitation takes advantage of the properties of sound to cause solids, liquids and heavy gases to float. The process can take place in normal or reduced gravity. In other words, sound can levitate objects on Earth or in gas-filled enclosures in space.|Ukraine Crisis News Feb 22 Highlights: “The escalation of tension along the Russia-Ukraine border is a matter of deep concern and the developments have the potential to undermine peace and security of the region,” India’s Permanent Representative to the UN Ambassador T S Tirumurti said at an emergency UNSC meeting on the situation in Ukraine, reported PTI. The meeting is being held, hours after Putin announced signing a decree recognizing the independence of breakaway regions Donetsk and Luhansk. Trimurti said that the immediate priority is de-escalation of tensions taking into account the legitimate security interests of all countries and aimed towards securing long term peace and stability in the region and beyond. Russian lawmakers on Tuesday authorized President Vladimir Putin to use military force outside the country - a move that could presage a broader attack on Ukraine after the U.S. Visual of the Air India special flight leaving from Ukraine to Delhi with a total of 241 passengers on-board. The flight is delayed and will arrive at 11.30 pm (tonight) at IGI airport.
Scene Background Initialization (SBI) remains an important task in the field of scene background modeling, and it has a wide range of applications, such as video surveillance, video segmentation, video compression, video inpainting, privacy protection for videos, computation photography, and so on. In real scenes, there exist challenges to achieve this aim such as sudden illumination changes, night videos, low framerate, dynamic background, camera jitter, and so on. In order to address these challenges, various background modeling methods have been proposed over the last few decades. Background modeling approaches based on deep learning have been developed in recent years. Random Access Memory (RAM) based neural network method to identify sections of the background scene in each test image. However, this approach requires the background of the scene to be correctly represented by the images, and there is no background maintenance stage. Self Organizing Background Subtraction (SOBS) which learns background motion trajectories in a self organizing manner. CNN-based approach to obtain the initial background of a scene by using just a small set of frames containing foreground objects.
This crisis, and this conversation, is no longer about Ukraine, if it ever was. As Putin would have it, this roiling calamity of his own making is about reordering the post-Cold War consensus in Europe - and perhaps evicting the U.S. Europe entirely, unwinding the gains the West made in the aftermath of the collapse of the Soviet Union. Putin, as it were, appears ready to snatch victory from the shadow of post-Cold War defeat. Nor should this be much of a surprise. For years, Putin has railed against the West’s supposed perfidy in expanding NATO following the Soviet collapse. Rather than watching independent countries make decisions about their own security arrangements, Putin and his inner circle envision one broad, U.S.-led conspiracy to supposedly encircle Moscow. Along the way, the Russian dictator has grown to see himself as not another middling, kleptocratic dictator, but as a figure of historic import, dedicated to restoring Russian greatness - and to unwinding the gains the West made when Russia was at its weakest following the Soviet collapse.
On the other hand, the ease with which you can view RSS headlines may convince you that you can subscribe to even more sites. You may end up spending just as much time reading feeds as you used to on regular Web sites. Reading feeds requires a few simple things, many of which you already have. You'll need a computer -- or compatible electronic device -- and an Internet connection. After that, you need something that can read, or aggregate, RSS files. Aggregators collect. Interpret RSS feeds in one location. That way, you can see the latest headlines from HowStuffWorks, CNN, the BBC and Reuters, all in one place. Or do you just want the headlines? Do you want everything organized by date, with the feeds mixed by most recent, or would you prefer to keep each site separate? These are options that your aggregator will give you help you enjoy your reading. Aggregators take many forms.|Scene Background Initialization (SBI) is one of the challenging problems in computer vision. Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part. However, this method needs to convert the color image into the grayscale image for processing, which leads to the neglect of the coupling information between the three channels of the color image. In this study, we propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis, so as to completely preserve the inherent color structure of the color image and the color video. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues. The results on the publicly available benchmark datasets prove that our Q-DMD outperforms the exact DMD method, and experiment results also demonstrate that the performance of our approach is comparable to that of the state-of-the-art ones.
Additional information about the software is available in the PEAT User Guide. The Photosensitive Epilepsy Analysis Tool (PEAT) was developed to provide a free resource for Web designers and computer software developers. Use of PEAT to assess material commercially produced for television broadcast, film, home entertainment, or gaming industries is prohibited. For broadcast, film, home entertainment, and gaming applications, the Harding FPA software should be employed. Contact Cambridge Research Systems HardingFPA site for more information on commercial analysis tools. The Photosensitive Epilepsy Analysis Tool (PEAT) is copyright Trace R&D Center. The PSEe engine is copyright Cambridge Research Systems Ltd. Funding for this work was provided by the National Institute on Disability and Rehabilitation Research (NIDRR), U.S. Department of Education, under grants H133E980008, H133E030012, and H133E080022. This version is old in the tooth with regard to video formats. The CAPTURE function on PEAT does not always work. We are working on a new fully-open-source version that will be updated for new technologies (the current version is open-source except for a proprietary analysis engine we purchased the rights to use). It will also be free to use. No ETA for it as yet.
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Scene Background Initialization (SBI) remains an important task in the field of scene background modeling, and it has a wide range of applications, such as video surveillance, video segmentation, video compression, video inpainting, privacy protection for videos, computation photography, and so on. In real scenes, there exist challenges to achieve this aim such as sudden illumination changes, night videos, low framerate, dynamic background, camera jitter, and so on. In order to address these challenges, various background modeling methods have been proposed over the last few decades. Background modeling approaches based on deep learning have been developed in recent years. Random Access Memory (RAM) based neural network method to identify sections of the background scene in each test image. However, this approach requires the background of the scene to be correctly represented by the images, and there is no background maintenance stage. Self Organizing Background Subtraction (SOBS) which learns background motion trajectories in a self organizing manner. CNN-based approach to obtain the initial background of a scene by using just a small set of frames containing foreground objects.
This crisis, and this conversation, is no longer about Ukraine, if it ever was. As Putin would have it, this roiling calamity of his own making is about reordering the post-Cold War consensus in Europe - and perhaps evicting the U.S. Europe entirely, unwinding the gains the West made in the aftermath of the collapse of the Soviet Union. Putin, as it were, appears ready to snatch victory from the shadow of post-Cold War defeat. Nor should this be much of a surprise. For years, Putin has railed against the West’s supposed perfidy in expanding NATO following the Soviet collapse. Rather than watching independent countries make decisions about their own security arrangements, Putin and his inner circle envision one broad, U.S.-led conspiracy to supposedly encircle Moscow. Along the way, the Russian dictator has grown to see himself as not another middling, kleptocratic dictator, but as a figure of historic import, dedicated to restoring Russian greatness - and to unwinding the gains the West made when Russia was at its weakest following the Soviet collapse.
On the other hand, the ease with which you can view RSS headlines may convince you that you can subscribe to even more sites. You may end up spending just as much time reading feeds as you used to on regular Web sites. Reading feeds requires a few simple things, many of which you already have. You'll need a computer -- or compatible electronic device -- and an Internet connection. After that, you need something that can read, or aggregate, RSS files. Aggregators collect. Interpret RSS feeds in one location. That way, you can see the latest headlines from HowStuffWorks, CNN, the BBC and Reuters, all in one place. Or do you just want the headlines? Do you want everything organized by date, with the feeds mixed by most recent, or would you prefer to keep each site separate? These are options that your aggregator will give you help you enjoy your reading. Aggregators take many forms.|Scene Background Initialization (SBI) is one of the challenging problems in computer vision. Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part. However, this method needs to convert the color image into the grayscale image for processing, which leads to the neglect of the coupling information between the three channels of the color image. In this study, we propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis, so as to completely preserve the inherent color structure of the color image and the color video. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues. The results on the publicly available benchmark datasets prove that our Q-DMD outperforms the exact DMD method, and experiment results also demonstrate that the performance of our approach is comparable to that of the state-of-the-art ones.
Additional information about the software is available in the PEAT User Guide. The Photosensitive Epilepsy Analysis Tool (PEAT) was developed to provide a free resource for Web designers and computer software developers. Use of PEAT to assess material commercially produced for television broadcast, film, home entertainment, or gaming industries is prohibited. For broadcast, film, home entertainment, and gaming applications, the Harding FPA software should be employed. Contact Cambridge Research Systems HardingFPA site for more information on commercial analysis tools. The Photosensitive Epilepsy Analysis Tool (PEAT) is copyright Trace R&D Center. The PSEe engine is copyright Cambridge Research Systems Ltd. Funding for this work was provided by the National Institute on Disability and Rehabilitation Research (NIDRR), U.S. Department of Education, under grants H133E980008, H133E030012, and H133E080022. This version is old in the tooth with regard to video formats. The CAPTURE function on PEAT does not always work. We are working on a new fully-open-source version that will be updated for new technologies (the current version is open-source except for a proprietary analysis engine we purchased the rights to use). It will also be free to use. No ETA for it as yet.
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