ABSTRACT A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images. We adopt an alternating algorithm to optimize the PWLS-ST cost function that alternates between a CT image update step and a sparse coding step. We adopt a relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed OS-LALM) to accelerate the CT image update step by reducing the number of forward and backward projections. Numerical experiments on the XCAT phantom show that for low dose levels, the proposed PWLS-ST method dramatically improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer (PWLS-EP).

Every year at the United Nations, member states deliver statements during the General Debate discussing major issues in world politics. These speeches provide invaluable information on governments' perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international politics. This paper introduces a new dataset consisting of over 7,701 English-language country statements from 1970-2016. We demonstrate how the UN General Debate Corpus (UNGDC) can be used to derive country positions on different policy dimensions using text analytic methods. The paper provides applications of these estimates, demonstrating the contribution the UNGDC can make to the study of international politics.

If I got a dollar every time artificial intelligence (AI) came up in a conversation around jobs, I would be very rich by now. I want to spend a few minutes on the potential of AI--the way I see it. And let me tell you, it's not in the future, it's here and now. There is no point being an ostrich and burying our heads in the sand. Automation has been part of our fabric since 1771, with the advent of the first fully automated spinning mill, and continues to be an integral part of every manufacturing process.

Please invite your friends and colleagues to join the conversation and let me know what you think, and what we're missing. Just reply to this email, or email steve@axios.com. Let's dive right in with a question: 1 big idea: Could robots make us even more polarized? Sam Jayne / Axios Over the last decade or so, we've seen ordinarily apolitical topics polarize us into angry opposing mobs, among them vaccines, atmospheric gases and Russia. When there has been a super-strong view one way or another, it's been sucked into the hothouse and associated with an ideology.

BENGALURU: Intel and NVIDIA battle are locked in new battle for turf, the booming data centre market and at the heart of this skirmish the technology that's changing the world: Artificial Interlligence (AI). In the recent quarter ended April 30, NVIDIA's revenue increased by 48% reaching $1.94 billion compared to previous year. A big revenue bump came from its Data centre business which recorded $409 million revenue in the first quarter of this fiscal, up 186% year-on-year. The reason for the exponential increase is the spike in demand for a specific kind of microprocessor called Graphic Processing Unit (GPU) made by NVIDIA. Large technology companies like Google, Amazon, Microsoft, Facebook, IBM, and Alibaba have all installed NVIDIA's elite Tesla GPUs to power their data centres to perform machine learning to analyse data gathered from the cloud and derive insights. "We have seen the PC era, which was followed by the mobile era, and now we see the emergence of the AI era," said Vishal Dhupar, MD, NVIDIA, adding, "Once viewed just as a gaming technology, GPUs are now making inroads into data centres driving initiatives around machine learning (ML) and artificial intelligence (AI)".

There's a hall of champions at the University of Alberta that only computer science students know where to find -- more of a hallway, really, one office after the next, the achievements archived on hard drives and written in code. It's there you'll find the professors who solved the game of checkers, beat a top human player in the game of Go and used cutting-edge artificial intelligence to outsmart a handful of professional poker players for the very first time. But lately it's Richard Sutton who is catching people's attention on the Edmonton campus. He's a pioneer in a branch of artificial intelligence research known as reinforcement learning -- the computer science equivalent of treat-training a dog, except in this case the dog is an algorithm that's been incentivized to behave in a certain way. U of A computing science professors and artificial intelligence researchers (left to right) Richard Sutton, Michael Bowling and Patrick Pilarski are working with Google's DeepMind to open the AI company's first research lab outside the U.K., in Edmonton.

In the race to develop self-driving technology, Chinese Internet giant Baidu unveiled its 50 partners in an open source development program, revised its timeline for introducing autonomous driving capabilities on open city roads, described the Project Apollo consortium and its goals, and declared Apollo to be the'Android of the autonomous driving industry'. At a developer's conference last week in Beijing, Baidu described its plans and timetable for its self-driving car technology. It will start test-driving in restricted environments immediately, before gradually introducing fully autonomous driving capabilities on highways and open city roads by 2020. Baidu's goal is to get those vehicles on the roads in China, the world's biggest auto market, with the hope that the same technology, embedded in exported Chinese vehicles, can then conquer the United States. To do so, Baidu has compiled a list of cooperative partners, a consortium of 50 public and private entities, and named it Apollo, after NASA's massive Apollo moon-landing program. The program is making its autonomous car software open source in the same way that Google released its Android operating system for smartphones.

We'll look at some of the most important papers that have been published over the last 5 years and discuss why they're so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas. The one that started it all (Though some may say that Yann LeCun's paper in 1998 was the real pioneering publication). This paper, titled "ImageNet Classification with Deep Convolutional Networks", has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a "large, deep convolutional neural network" that was used to win the 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge). For those that aren't familiar, this competition can be thought of as the annual Olympics of computer vision, where teams from across the world compete to see who has the best computer vision model for tasks such as classification, localization, detection, and more. The next best entry achieved an error of 26.2%, which was an astounding improvement that pretty much shocked the computer vision community. Safe to say, CNNs became household names in the competition from then on out. In the paper, the group discussed the architecture of the network (which was called AlexNet).

BingoBox, a Chinese chain of artificial intelligence-powered, self-service convenience stores, has raised over 100 million yuan ($14.7 million) in its first round of funding, led by technology investment firm GGV Capital, according to a report by Sixth Tone. Powered by AI and other innovative technologies, the self-service convenience stores aim to reduce costs on staff. The retail stores are slowly expanding in big cities across China, drawing the attention of both customers and investors in the process. BingBox partnered with French retail firm Auchan to launch trial operations in Zhongshan, Guangdong province in August 2016, before opening its first store in Shanghai in June. "We are optimistic about opportunities in the retail industry, and especially about the BingoBox team," said Eric Xu, a managing partner at GGV Capital.

Even though heat and sound are both forms of energy, when you were a kid, you probably didn't need to be told not to speak in thermal convection. And each time your children come across a stray animal, they likely don't have to self-consciously rehearse a subroutine of zoological attributes to decide whether it's a cat or a dog. Human beings come pre-loaded with the cognitive gear to simply perceive these distinctions. The differences appear so obvious, and knowing the differences comes so naturally to us, that we refer to it as common sense. Computers, in contrast, need step-by-step handholding--in the form of deterministic algorithms--to render even the most basic of judgments. Despite decades of unbroken gains in speed and processing capacity, machines can't do what the average toddler does without even trying.