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Admission in the universities recognized by the governing bodies like WHO, UNESCO, and others. Easy Visa availability for the neighboring countries like Nigeria, Bangladesh, Kenya, India, Pakistan, Nepal, Jordan, Dubai, Saudi Arabia etc. Medical Universities in Ukraine are recognized as TOP in the world. Offer an opportunity to students to complete their Ph.D.D. 50% of the cost incurred in Universities of India, UK, USA, New Zealand and other countries. From the different medical universities of Ukraine, Uzhhorod National University, considered as the premier medical universities in Ukraine, running over the 6 decades has been helping the medical aspirants with the best of healthcare and medical condition and the world class education. Students of Uzhhorod National University can simultaneously study in a Ukrainian and a Polish university (in similar major). In this case, the student will receive two Bachelor's degrees: after the 4th year in UzhNU, and after the 3rd year in the Pomeranian Academy in Slupsk. There are also opportunities to apply for Master's degree training programs to obtain two Master's diplomas. By developing the certain skills and the different capabilities, a medical aspirant can get the best of structured life for himself/herself.|We introduce a free-viewpoint rendering method - HumanNeRF - that works on a given monocular video of a human performing complex body motions, e.g. a video from YouTube. Our method enables pausing the video at any frame and rendering the subject from arbitrary new camera viewpoints or even a full 360-degree camera path for that particular frame and body pose. This task is particularly challenging, as it requires synthesizing photorealistic details of the body, as seen from various camera angles that may not exist in the input video, as well as synthesizing fine details such as cloth folds and facial appearance. Our method optimizes for a volumetric representation of the person in a canonical T-pose, in concert with a motion field that maps the estimated canonical representation to every frame of the video via backward warps. The motion field is decomposed into skeletal rigid and non-rigid motions, produced by deep networks. We show significant performance improvements over prior work, and compelling examples of free-viewpoint renderings from monocular video of moving humans in challenging uncontrolled capture scenarios. We also evaluate its generalization power across different classifiers, sampled frames, and search spaces. Especially, we achieve 76.9% mAP and 21.7 GFLOPs on ActivityNet with an impressive throughput: 123.9 Video/s on a single TITAN Xp GPU. With the explosive popularity of social media platforms as well as bountiful online video content, there comes wider attention on effective and scalable approaches that can deal with actions or events recognition in the face of the video data deluge. Although these models achieve superior accuracy performance, their computational expense limits their application in real-world scenarios where the model deployment is resource-constrained and requires to process high data volumes with stringent latency and throughput requirements. Being unaware of the complexity of video contents and instance-specific difficulties for video recognition, these models treat all the videos equally and adopt naive sampling strategies. These approaches all model the frame selection problem as a sequential decision task and prefer these decisions to be made individually per frame, leaving out the subsequent parts of the video.  yolo meaning

















































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