Randomize MUJ

PrepBytes in association with ACM Student Chapter and Randomize(); Manipal University, Jaipur is organizing an Online *FREE WORKSHOP* on "How to prepare for COMPETITIVE CODING and PLACEMENTS in IT/Software companies"

This includes a LIVE CODING SESSION with industry expert from top global companies like Amazon and Samsung followed by Q&A session

Date : 2 May
Time : 12 noon

Register Now : https://us02web.zoom.us/webinar/register/7015875608330/WN_tfDwxWMCTy-Hnme35xJ9dA

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Greetings Everyone_📢

Cyber Security 👨‍💻 continues to be an increasingly hot topic – and it’s no surprise why. We are all more connected than ever before, and although that is a great thing, it is not without risks.

If you are the one who wish to venture into this ever-increasing, demanding and challenging 💪industry, then you are at the right place. ☑️

We at Randomize(); in association with 180 Degrees Consulting Manipal University Jaipur would be organizing - A session on Cyber Security and Ethical Hacking to provide our community with industry relevant knowledge 👔. The session would be very interesting with a Q&A program at the end of the session.

The session would be taken by Mr. Ajinkya Lohakare, CEO Ditto Security. He is a great innovator in the field of technology. For his excellence work he was awarded with Vajra World Record , Assam World Record and Business Series Entrepreneur award.And the list continues...

Date : 27th February 2020
Time : 4pm-6pm
Venue : Smt. Sharda Pai Auditorium
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Visual recognition is deep learning’s strongest skill. Computer vision algorithms are analyzing medical images, enabling self-driving cars, and powering face recognition. But training models to recognize actions in videos has grown increasingly expensive. This has fueled concerns about the technology’s carbon footprint and its increasing inaccessibility in low-resource environments
Researchers at the MIT-IBM Watson AI Lab have now developed a new technique for training video recognition models on a phone or other device with very limited processing capacity. Typically, an algorithm will process video by splitting it up into image frames and running recognition algorithms on each of them. It then pieces together the actions shown in the video by seeing how the objects change over subsequent frames. The method requires the algorithm to “remember” what it has seen in each frame and the order in which it has seen it. This is unnecessarily inefficient.
In the new approach, the algorithm instead extracts basic sketches of the objects in each frame, and overlays them on top of one another. Rather than remember what happened when, the algorithm can get an impression of the passing of time by looking at how the objects shift through space in the sketches. In testing, the researchers found that the new approach trained video recognition models three times faster than the state of the art. It was also able to quickly classify hand gestures with a small computer and camera running only on enough energy to power a bike light.
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An abundant yet mysterious part of the universe, dark matter has eluded scientific searches. Though gravitational pull signals dark matter’s existence, its invisible particles remain unidentified.

Scientists at PNNL are now bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data. Their model, a neural network that emulates a human brain, “learns” about the characteristics of different subatomic particles and can distinguish among them.

The goal is to pinpoint instances of weakly interacting massive particles (WIMPs), which are on the shortlist of candidates for explaining dark matter. PNNL scientists Tom Grimes, Alex Hagen, and Chris Jackson are leading the work, which PNNL funded with Laboratory-Directed Research and Development funds as part of the Deep Learning for Scientific Discovery Agile investment.
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