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In driver 334.89 NVIDIA introduced a new proprietary rendering mode for 3D Vision that enables us to improve the 3D experience for many key DirectX 10 and 11 games. This mode is now called “3D Compatibility Mode”. We have continued to iterate on this feature in driver 344.11, increasing game support and adding some new interface elements. You can get the new driver at or via the update option in Geforce Experience. With the release of 344.11, new 3D...
We’re fortunate enough to have another fine 3D video from New Media Film Festival to share with you here on 3DVisionLive—a pop music video from Italy called “The Way,” which you can view here. Even better, New Media Film Festival has provided an interview with one of the co-directors of the video, Edoardo Ballanti, which provides insights on how the video was created and the vision behind it. Enjoy! (Alice Corsi also co-directed the video.) What was the Inspiration behind “...
The Fall Photo Contest received nearly 100 images – thanks to all that entered! The contest called for your best “nature” shots with the only other requirement being that they had to be true stereo images. Submissions ranged from shots of spiders in gardens to artistic approaches to tasteful nudes. As before, members were invited to vote for the winner by tagging images in the contest gallery as favorites. Without further ado, the winner is: Autumn Goodbye to Summer This...
In driver 334.89 NVIDIA introduced a new proprietary rendering mode for 3D Vision that enables us to improve the 3D experience for many key DirectX 10 and 11 games. This mode is now called “3D Compatibility Mode”. We have continued to iterate on this feature in beta driver 337, increasing game support and adding a toggle key to enable/disable the mode. Games with 3D Compatibility Mode will launch in this mode by default. To change the render mode back to standard 3D Vision...
3DVisionLive’s first-ever short-form 3D video contest received 14 entries that showed a great deal of diversity, ranging from video game captures to commercial-style clips to raw captures of pets or people doing cool things (such as bashing each other with swords). During judging we laughed, we cried (okay, maybe not), and we simply scratched our heads…. But seriously: thank-you to all that participated and we hope to see more of your content uploaded to the site for all to...

Recent Blog Entries

Black Friday is upon us, kicking off the holiday shopping season. While millions are scouring shopping malls for the best deals, GPU-powered technology from a promising startup, Cypheme, could be used to scrutinize the items in our carts to ensure they’re the genuine article.

Cypheme’s quest began when the mother of one of its four cofounders nearly died from taking counterfeit medicine. He and three friends — who had been working together on a different project in Silicon Valley — decided to start a crusade against counterfeit goods.

Counterfeiting is a worldwide problem, particularly affecting three major categories of products: drugs and medicines, luxury goods (including alcoholic beverages) and consumer electronics. Each can pose risks to consumer and worker safety, from dangerous side effects to toxic components. Yet the counterfeit industry generates hundreds of billions of dollars a year, according to the United Nations Office on Drugs and Crime.

“Counterfeiting is actually a big problem in society,” said Cypheme CEO Hugo Garcia-Cotte. “But since it doesn’t happen at our doorstep, we don’t talk about it.”

The Cypheme team realized that if they wanted to make a difference, they’d have to go to the source. Three-quarters of the world’s counterfeit goods from 2008 to 2010 originated in East Asia, particularly China, according to seizure data from the World Customs Organization. So they relocated to Hong Kong.

Picture perfect: Cypheme’s AI-based app can verify these pills as authentic with a smartphone camera. GPU-Powered Paper Trail

Cypheme’s appeal lies in the unobtrusive nature of its artificial intelligence-based counterfeit protection technology. It includes two components: a smartphone app and special traceable paper.

Following the paper trail: Cypheme’s AI technology recognizes the unique grain of a product’s paper label.

Factories include the traceable paper in a product’s packaging. Cypheme’s AI algorithms use a smartphone camera to recognize the individual grain of that piece of paper. Within seconds, the app can identify exactly which good is being scanned and when it was manufactured.

Speed is essential for this process, so as not to gum up the consumer’s purchasing decisions. Cypheme uses NVIDIA GPUs to accelerate its complex pattern recognition algorithm. This allows it to run at least 50 times faster than on a single CPU core.

“There is a point where CPUs are just out of the game,” said Garcia-Cotte.

Cypheme’s offering doesn’t mar the consumer product or require a relatively expensive, embedded RFID chip, yet it traces products with precision. The only requirement is an eight megapixel camera, which makes the solution available to nearly all smartphone users.

Thus far, Cypheme has been working with a major French luxury brand to protect 3 million products a year, and plans to expand into other counterfeit plagued sectors soon.

The post The Real Deal: How GPU-Accelerated Computing Is Putting a Crimp on Counterfeiting appeared first on The Official NVIDIA Blog.

Genetic interpretation. Giant datasets. Deep learning. This is cancer research, beyond the microscope.

A team at the University of Toronto, led by Dr. Brendan Frey, is advancing computational cancer research by developing a “genetic interpretation engine” – a GPU-powered, deep learning method for identifying cancer-causing mutations.

Today the NVIDIA Foundation, our employee-driven philanthropy arm, awarded Frey and his team a US$200,000 grant to further that work — and help them usher in an era of personal and effective cancer care.

Compute the Cure

Cancer kills almost 600,000 people each year in the U.S. alone. It can be caused by any one of an endless variety of mutations, across many different genes. This can make it hard to identify quickly and treat in a highly targeted way.

As computers grow more powerful, scientists are delving into giant datasets and deploying computer simulations to research how cancer develops.

Part of our “Compute the Cure” initiative, the NVIDIA Foundation’s grant will help Frey’s team scale up their GPU-powered methods so they can be applied to a large number of personal genomes in clinical settings, ultimately involving hundreds of thousands of genomes.

“To make a big difference in genomic medicine, we’ve developed GPU-accelerated technologies for the computationally intensive work,” Frey said. “Now, we’re focused on the next step — to change the lives of patients stricken with cancer — by experimentally validating our technologies using data from these patients.”

Frey’s team, including Leo J. Lee (far left) and Brendan J. Frey (second from right) plan the software architecture of a computational splicing predictor.

Leo Lee, a senior research associate in Frey’s lab, will manage the deployment of GPU-accelerated computational tools and the development of clinical experiments to validate them. Andrew Delong, who co-developed some of tools as a postdoctoral fellow in Frey’s lab, will advise the team on tool deployment and clinical validation.

In addition to demonstrating the utility of the tools in cancer biology, the team will ensure that libraries are freely available for use by other biomedical researchers working on cancer and other genetic diseases, according to Delong.

The university team used servers with eight each of NVIDIA Tesla K80, K40 and K20 GPU accelerators, plus several desktop machines with NVIDIA GeForce GTX TITAN X graphics cards.

Overcoming Roadblocks Depiction of an RNA molecule being transcribed from DNA, juxtaposed with an artificial neural network interpreting the genomic sequence.The computations performed by the neural network run inside NVIDIA GPUs.

The team’s computational approach aims to overcome some of the roadblocks in personalized medicine.

At present a patient’s cancer-causing “driver” mutations have to be identified and separated from their many benign cancer-caused “passenger” mutations.

Today, a highly trained genome diagnostician can spend hours trying to understand the impact of a single mutation, pouring through databases and research papers, often coming up empty-handed.

Deep learning can help identify driver mutations more quickly, consistently and accurately than ever before. In other words, it allows human diagnosticians to scale.

The team’s approach to predicting cancer “hot spots” can learn from new genomes going forward, and, by exposing it to new data, can be trained to find causal mutations for other diseases.

With genomics data now being collected in unprecedented quantities, the University of Toronto’s project represents a groundbreaking way to analyze and gain insights from it.

“The crucial next step is validating that it works and building the clinical bridge,” Frey said. “Then the technology can be used widely and help change the lives of people with cancer.”

For more on the team’s work, watch the TedX talk by Frey.

The post University of Toronto Receives $200K NVIDIA Foundation Award for Cancer Research appeared first on The Official NVIDIA Blog.