The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, is now accepting proposals for the ALCF Data Science Program (ADSP). Launched in 2016, the ADSP is targeted at "big data" science problems that require the scale and performance of leadership computing resources, such as the ALCF's two petascale supercomputers: Mira, an IBM Blue Gene/Q system, and Theta, an Intel-Cray system. From April 27 to June 20, 2018, the ADSP open call provides an opportunity for researchers to submit proposals for projects that will employ advanced data science and machine learning techniques to gain insights into very large datasets produced by experimental, simulation, or observational methods. The program, which currently supports eight projects, allocates computing time and supporting resources to research teams focused on using the ALCF's leadership-class systems and infrastructure to explore, demonstrate, and improve a wide range of data and learning techniques. These techniques include uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, real-time data analysis, and complex and interactive workflows.

IBM's approach to AI is probably best described as "comprehensive." Watson, the company's flagship deep learning system, has been everywhere from the Grammy Awards to the International Space Station. In its spare time the company also uses AI to save the planet. AI has quickly become an important tool for environmental scientists and researchers trying to reverse climate change, develop clean energy, and revolutionize agriculture. Across the globe, IBM's researchers are creating solutions to some of the biggest problems our planet and species face today, and machine learning is a huge part of its efforts.

Organization concepts and models are increasingly being adopted for the design and specification of multi-agent systems. Agent organizations can be seen as mechanisms of social order, created to achieve global (or organizational) objectives by more or less autonomous agents. In order to develop a theory on the relation between organizational structures, organizational objectives and the actions of agents fulfilling roles in the organization a theoretical framework is needed to describe organizational structures and actions of (groups of) agents. Current logical formalisms focus on specific aspects of organizations (e.g. power, delegation, agent actions, or normative issues) but a framework that integrates and relates different aspects is missing. Given the amount of aspects involved and the subsequent complexity of a formalism encompassing them all, it is difficult to realize. In this paper, a first step is taken to solve this problem. We present a generic formal model that enables to specify and relate the main concepts of an organization (including, activity, structure, environment and others) so that organizations can be analyzed at a high level of abstraction. However, for some aspects we use a simplified model in order to avoid the complexity of combining many different types of (modal) operators.

In the world of technology, the mantra "innovate or die" is truer for organizations than ever, and artificial intelligence (AI) is redefining industries by providing greater personalization to users, automating processes, and disrupting how we work. Like the adoption of cloud computing five years ago, the adoption of AI and the speed of its deployment varies according to industry. Here we look at some of the places where dispution from AI is already being felt. Alexey Sapozhnikov, co-dounder and CTO of Tel Aviv, Israel-based prooV points out that while virtually every industry is embracing AI, it's the sectors that are stymied by well-worn processes and regulations -- such as healthcare and government -- that are likely to lag in AI adoption. "From the Food and Drug Administration's stringent policies surrounding AI diagnosis software to developing complex proposals for government cybersecurity challenges, these processes can pose a huge stumbling block for organizations.

We could soon see more robotics and other artificial intelligence technology performing tasks and jobs in the energy sector. KPMG's recent 2018 U.S. Energy Survey, which polled 92 energy executives, revealed that the majority of them are in favor of utilizing emerging technologies like artificial intelligence and intelligent automation to improve business operations without a reduction in human workforce. Regina Mayor, KPMG's global and U.S. energy sector leader, said the survey revealed this is an exciting time in the industry as it moves toward streamlining and making production more efficient. "One of things I find really exciting and what our survey and report recently validated is our energy industry is embracing technology and what it can do for it in every facet of their business," Mayor said. "If you are in upstream, you are using it for everything from oil dynamics to drilling efficiencies to lease obligation management and all the various onshore challenges you have from an administrative perspective."

To unlock insights that deliver significant business performance improvement, applying artificial intelligence and deep learning techniques produce profound outcomes that drive asset management improvements. To learn more about how this translates into value at bottom line, get in touch with us via: enquiries@drivingfueliq.com or visit www.drivingfueliq.com

As a resource-poor nation, Japan's prosperity relies on free trade. Under worldwide protectionism, it can't survive. That's the message Economy, Trade and Industry Minister Hiroshige Seko wants to send as the emergence of protectionism across the globe becomes a worsening headache for export-reliant Japan. "If free trade collapsed, Japan would lose its base to stand on," he said in an exclusive interview with The Japan Times earlier this month. "We have to keep in mind the basic fact that what underpins resource-poor Japan's current wealth is free trade."

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (L\'evy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning machine vision. In this study, using a commercially available UAV and a publicly available package for deep learning, we constructed a machine vision system for the automatic classification of trees. In our method, we segmented a UAV photography image of forest into individual tree crowns and carried out object-based deep learning. As a result, the system was able to classify 7 tree types at 89.0% accuracy. This performance is notable because we only used basic RGB images from a standard UAV. In contrast, most of previous studies used expensive hardware such as multispectral imagers to improve the performance. This result means that our method has the potential to classify individual trees in a cost-effective manner. This can be a usable tool for many forest researchers and managements.

Deep ocean robotics is not generally an area where we expect to see much in the way of significant innovation. When we do write about submersible robots, they're usually confined to very near-surface operations. This isn't a total surprise: It seems like the only people who really worry about what's going on in the deep ocean (meaning hundreds or thousands of meters beneath the surface) are the military, the occasional scientist, and the oil and gas industry. Robots are important to these folks, even critical in some cases, but the technology has been more or less stagnant for decades, which is why we don't write about it very frequently. To be fair, there are some very good reasons why it's hard to innovate when it comes to submersible robotics.