Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new AI-based framework that can produce X-ray images in 3D. The team, which includes members from three divisions at Argonne, has developed a method to create 3D visualizations from X-ray data. Their efforts were meant to allow them to better use the Advanced Photon Source (APS) at their lab, but potential applications of this technology range from astronomy to electron microscopy. Lab tests showed that the new approach, called 3D-CDI-NN, can create 3D visualizations from data hundreds of times faster than existing technology. "In order to make full use of what the upgraded APS will be capable of, we have to reinvent data analytics. Our current methods are not enough to keep up. Machine learning can make full use and go beyond what is currently possible," says Mathew Cherukara of the Argonne National Laboratory, corresponding author of the paper.

Computers have been able to quickly process 2D images for some time. Your cell phone can snap digital photographs and manipulate them in a number of ways. Much more difficult, however, is processing an image in three dimensions, and doing it in a timely manner. The mathematics are more complex, and crunching those numbers, even on a supercomputer, takes time. That's the challenge a group of scientists from the U.S. Department of Energy's (DOE) Argonne National Laboratory is working to overcome.

Author to whom correspondence should be addressed. Recently, methods based on Faster region-based convolutional neural network (R-CNN) have been popular in multi-class object detection in remote sensing images due to their outstanding detection performance. The methods generally propose candidate region of interests (ROIs) through a region propose network (RPN), and the regions with high enough intersection-over-union (IoU) values against ground truth are treated as positive samples for training. In this paper, we find that the detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially, detection performance of small objects is poor when choosing a normal higher threshold, while a lower threshold will result in poor location accuracy caused by a large quantity of false positives.

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The rise of TensorFlow over the past year has been amazing. It is now one of the most popular open source projects on GitHub and certainly the fastest growing deep learning library available. At the time of writing, it has amassed more GitHub stars than Linux, with 42,769 and 40,828 respectively. It is also incredibly portable, running on a multitude of platforms, ranging from Raspberry Pi, Android and Apple mobile devices through to 64-bit desktop and server systems. Furthermore, in May 2016, Google announced the creation of its tensor processing unit or TPU, which is a custom ASIC built specifically for machine learning and tailored for TensorFlow, which now operate in its data centres. So long-term investment and support is there.