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Cleaning Up Radioactive Waste from World War II With Supercomputing

Cleaning Up Radioactive Waste from World War II With Supercomputing

Cleaning Up Radioactive Waste from World War II With Supercomputing

The Handford site in southeastern Washington is the largest radioactive waste site in the United Sates and is still awaiting cleanup after more than 70 years. Cleaning up radioactive waste is extremely complicated since some elements stay radioactive for thousands of years.

Scientists from Lawrence Berkeley National Laboratory and six universities: The State University of New York at Buffalo, University of Alabama, University of Minnesota, Washington State University and Rice University are using the NVIDIA Tesla GPU-accelerated Titan supercomputer at Oak Ridge National Laboratory to study the chemistry of radioactive elements called actinides — uranium, plutonium and other metals that release huge amounts of energy when their atoms are split.

Cleaning Up Radioactive Waste from World War II With Supercomputing

Cleaning Up Radioactive Waste from World War II With Supercomputing

The supercomputer is providing the scientists with simulations of the chemical reactions which will help them develop new methods of decontaminating the waste.

Cleaning Up Radioactive Waste from World War II With Supercomputing

Cleaning Up Radioactive Waste from World War II With Supercomputing

AI-Powered Flying Camera to Replace Your Selfie Stick

AI-Powered Flying Camera to Replace Your Selfie Stick

AI-Powered Flying Camera to Replace Your Selfie Stick

After raising $25 million in funding, Beijing-based ZeroZero Robotics came out of stealth mode and launched their Hover Camera just days before the GMIC Beijing 2016 trade show, the ‘CES of China.’

Co-founder MQ Wang, a Stanford PhD alum focused on machine learning and natural language processing, was inspired to create the autonomous Hover Camera after watching a documentary about a man who walked 1600 miles solo across Australia.

Using a machine learning model trained on an NVIDIA Tesla K40 GPU, the camera automatically tracks your face to take photos and capture videos wherever you go.

AI-Powered Flying Camera to Replace Your Selfie Stick

AI-Powered Flying Camera to Replace Your Selfie Stick

“We wanted the user experience to be very natural — easy to use,” MQ told Mashable. “We wanted to make sure the learning curve for users is minimum. So there is no remote control or anything. There’s no calibration process.”

The final version of the product will be released this summer.

GPU-Accelerated Model Reveals Details of Nuclear Fission

GPU-Accelerated Model Reveals Details of Nuclear Fission

GPU-Accelerated Model Reveals Details of Nuclear Fission

Scientists from University of Washington, Warsaw University of Technology in Poland, Pacific Northwest National Laboratory, and Los Alamos National Laboratory, have developed a model that provides a detailed look at what happens during the last stages of the fission process.

According to their research paper, nuclear fission has almost reached the venerable age of 80 years and yet we still lack an understanding in terms of a fully quantum microscopic approach.

Using the new model, the scientists determined that fission fragments remain connected far longer than expected before the daughter nuclei split apart and the predicted kinetic energy agrees with results from experimental observations. This discovery indicates that complex calculations of real-time fission dynamics without physical restrictions are feasible and opens a pathway to a theoretical microscopic framework with abundant predictive power.

Snapshots of the total density profile of the 240Pu fission process.

Snapshots of the total density profile of the 240Pu fission process.

Evaluating the theory amounted to solving about 56,000 complex coupled nonlinear, time-dependent, three-dimensional partial differential equations for a 240Pu nucleus using a highly efficient parallelized GPU code. The calculations required nearly 2,000 NVIDIA GPUs on the Titan supercomputer at Oak Ridge National Lab.

By accurately modeling fission dynamics, the work will impact research areas such as future reactor fuel compositions, nuclear forensics, and studies of nuclear reactions.

Algorithm Achieves Better Accuracy Than Humans at Reading Lips

Algorithm Achieves Better Accuracy Than Humans at Reading Lips

Algorithm Achieves Better Accuracy Than Humans at Reading Lips

Researchers at the University of East Anglia in the UK developed an algorithm that is able to interpret mouthed words with a greater degree of accuracy than human lip readers.

Using Tesla K80 GPUs, the researchers trained a deep learning model to recognize mouth shapes corresponding to certain sounds as they are spoken, without any audio input cues at all.

“We’re looking at visual cues and saying how do they vary? We know they vary for different people. How are they using them? What’s the differences? And can we actually use that knowledge in this particular training method for our model? And we can,” says Dr. Helen Bear who created the visual speech recognition system as part of her PhD, along with Prof Richard Harvey of UEA’s School of Computing Sciences.

Examples of features captured for improving lip reading accuracy. The green marks relate to key points used in Active Appearance Models when tracking a speaker’s face.

Examples of features captured for improving lip reading accuracy. The green marks relate to key points used in Active Appearance Models when tracking a speaker’s face.

According to Dr. Bear, the core challenge is that humans make more sounds than distinct visual cues. For example, there are several sounds with confusingly similar lip shapes such as ‘/p/,’ ‘/b/,’ and ‘/m/’ — all of which typically cause difficulties for human lip readers. UEA’s visual speech model is able to more accurately distinguish between these visually similar lip shapes.

This technology may one day help people who have hearing and speech impairments, generate audio for video-only-security video footage or enhance poor audio quality on mobile for video calls.

Detecting and Labeling Diseases in Chest X-Rays with Deep Learning

Detecting and Labeling Diseases in Chest X-Rays with Deep Learning

Detecting and Labeling Diseases in Chest X-Rays with Deep Learning

Researchers from the National Institutes of Health in Bethesda, Maryland are using NVIDIA GPUs and deep learning to automatically annotate diseases from chest x-rays.

Accelerated by Tesla GPUs, the team trained their convolutional neural networks on a publicly available radiology dataset of chest x-rays and reports to describe the characteristics of a disease, such as location, severity and the affected organs.

Examples of annotation generations (light green box) compared to true annotations (yellow box) for input images in the test set.

Examples of annotation generations (light green box) compared to true annotations (yellow box) for input images in the test set.

The researchers mention this is the first study (to the best of their knowledge) that mines from a publicly available radiology image and report dataset, not only to classify and detect disease in images, but also to describe their context similar to how a human observer would read.

Moodbox First Emotionally Intelligent Speaker Trained on GPUs

 Moodbox First Emotionally Intelligent Speaker Trained on GPUs

Moodbox First Emotionally Intelligent Speaker Trained on GPUs

Created by researchers at the Hong Kong University of Science and Technology, the MoodBox speaker is billed as the first ever high quality wireless speaker that senses human emotions.

Using NVIDIA Tesla GPUs and deep learning, the speaker operates with cutting edge sensory recognition technology named “Emi”. Emi collects and analyzes audio signals and music lyrics to provide efficient retrieval of millions of songs by genres, styles, mood, and artist. Emi not only converses, but also suggests appropriate music, adjusts the lighting to music, reports on weather conditions, and offers wake-up calls.

“We are bringing the latest R&D in speech, music and emotion recognition technology to people’s lives,” explains creator and emotional intelligence pioneer Pascale Fung, PhD. “When you speak to MoodBox, the predictive engine delineates emotional state from tone of voice and content of speech.”

With less than two weeks remaining of their Indiegogo campaign, the team already surpassed their $40,000 funding goal.

Monash University Upgrades MASSIVE GPU-Accelerated Supercomputer

Monash University Upgrades MASSIVE GPU-Accelerated Supercomputer

Monash University Upgrades MASSIVE GPU-Accelerated Supercomputer

To accelerate biomedical research, Australia’s Monash University boosted its research infrastructure with a third GPU-accelerated supercomputer called MASSIVE-3.

MASSIVE-3 is equipped with both Tesla K80 GPUs and GRID K1 GPUs for data processing and visualization, driving the new system nearly four times faster than MASSIVE-2.

Over the past five years, MASSIVE has played a key role in driving discoveries across many disciplines including biomedical sciences, materials research, engineering, and geosciences.

Alongside the MASSIVE supercomputers at Monash, the university also hosts the CAVE2 immersive visualization platform. This 21st Century Microscope empowers researchers to interactively explore data from electron microscopes and medical imaging instruments.

In this brief video, Dr. David Barnes, senior research fellow at Monash provides an inside-look into their visualization environment.

“Our collaboration with NVIDIA will take Monash research to new heights. By coupling some of Australia’s best researchers with NVIDIA’s accelerated computing technology we’re going to see some incredible impact. Our scientists will produce code that runs faster, but more significantly, their focus on deep learning algorithms will produce outcomes that are smarter,” said Professor Ian Smith, Vice Provost (Research and Research Infrastructure), Monash University.

– See more at: https://news.developer.nvidia.com/monash-university-upgrades-massive-gpu-accelerated-supercomputer/#sthash.RekJmhDy.dpuf

GPUs Help Measure Rising Sea Levels in Real-Time

GPUs Help Measure Rising Sea Levels in Real-Time-GPUs Help Measure Rising Sea Levels in Real-Time

GPUs Help Measure Rising Sea Levels in Real-Time

Sea levels have traditionally been measured by marks on land – but the problem with this approach is that parts of the earth’s crust move too.

A group of researchers from Chalmers University of Technology in Sweden are using GPS receivers along the coastline in combination with reflections of GPS signals that bounce off the water’s surface. NVIDIA GPUs then crunch those data signals to compute the water level in real-time.

The researchers are using the cuFFT library, alongside NVIDIA Tesla and GeForce GPUs to process the nearly 800 megabits per second of data that come from the reflectometry stream systems.

Schematics of the data flow for a software-defined radio GNSS-R solution. Direct (RHCP) and reflected (LHCP) signals are received, A/D converted and sent to a host PC, where a Tesla K40 GPU handles signal processing.

Schematics of the data flow for a software-defined radio GNSS-R solution. Direct (RHCP) and reflected (LHCP) signals are received, A/D converted and sent to a host PC, where a Tesla K40 GPU handles signal processing.

“Without the use of GPUs, we would not have been able to process all our signals in real-time,” said Thomas Hobiger, a researcher on the project.

This work has placed the team among the top five finalists for NVIDIA’s 2016 Global Impact Award which awards a $150,000 grant to researchers doing groundbreaking work that addresses social, humanitarian and environmental problems.

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Google Research presents a new real-time approach to object detection that exploits the efficiency of cascade classifiers with the accuracy of deep neural networks. Pedestrian detectors is very important as it relates to a variety of applications including advanced driver assistance systems, or surveillance systems. The need for very high-accurate and real-time speed is crucial that can be relied on and are fast enough to run on systems with limited compute power.

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

The research team combined a fast cascade with a cascade of deep neural networks which is both very fast, running in real-time at 67 milliseconds on GPU per image or 15 frames per second. Their approach was trained using the publicly available ‘cuda-convnet2’ code  running on an NVIDIA Tesla K20 GPU.

How GPUs are Revolutionizing Machine Learning

How GPUs are Revolutionizing Machine Learning

How GPUs are Revolutionizing Machine Learning

NVIDIA announced that Facebook will accelerate its next-generation computing system with the NVIDIA Tesla Accelerated Computing Platform which will enable them to drive a broad range of machine learning applications.

Facebook is the first company to train deep neural networks on the new Tesla M40 GPUs – introduced last month – this will play a large role in their new open source “Big Sur” computing platform, Facebook AI Research’s (FAIR) purpose-built system designed specifically for neural network training.

How GPUs are Revolutionizing Machine Learning---Open Rack V2 compatible 8-GPU server. Big Sur is two times faster than Facebook’s existing system and will enable the company to train twice as many neural networks which in return will help develop more accurate neural network models and new classes of advanced applications.

How GPUs are Revolutionizing Machine Learning—Open Rack V2 compatible 8-GPU server. Big Sur is two times faster than Facebook’s existing system and will enable the company to train twice as many neural networks which in return will help develop more accurate neural network models and new classes of advanced applications.

Training the sophisticated deep neural networks that power applications such as speech translation and autonomous vehicles requires a massive amount of computing performance.

With GPUs accelerating the training times from weeks to hours, it’s not surprising that nearly every leading machine learning researcher and developer is turning to the Tesla Accelerated Computing Platform and the NVIDIA Deep Learning software development kit.

A recent article on WIRED explains how GPUs have proven to be remarkably adept at deep learning and how large web companies like Facebook, Google and Baidu are shifting their computationally intensive applications to GPUs.

The artificial intelligence is on and it’s powered by GPU-accelerated machine learning.



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My name is Sayed Ahmadreza Razian and I am a graduate of the master degree in Artificial intelligence .
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Related topics such as image processing, machine vision, virtual reality, machine learning, data mining, and monitoring systems are my research interests, and I intend to pursue a PhD in one of these fields.

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  • Data mining - Big Data
  • CUDA Programming
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