CANBERRA – An Australian government minister on Wednesday expressed concern for three Australians arrested in Iran on suspicion of spying and separated their plight from a tense standoff in the Middle East over the weekend attack on Saudi Arabian oil facilities. Trade Minister Simon Birmingham was responding after Iran on Tuesday acknowledged for the first time that it is holding three Australian citizens, including two British dual nationals, on suspicion of espionage. "The government continues to seek information and clarity around these matters," Birmingham told Australian Broadcasting Corp. "We are concerned for the welfare of these individuals and work to make sure their treatment is as fair as possible." Iran confirmed the arrests of Melbourne University Middle East expert Kylie Moore-Gilbert in October and travel blogging couple Mark Firkin and Jolie King in July as fallout continues from Saturday's fiery missile and drone attack on the heart of Saudi Arabia's oil industry. Secretary of State Mike Pompeo was headed to Jiddah in Saudi Arabia on Tuesday to discuss possible responses to what U.S. officials believe was an attack coming from Iranian soil.

ABS and Samsung Heavy Industries (SHI) are to collaborate on the use of digital technologies to streamline designing, building, and classing assets in a joint development project (JDP) signed yesterday (Sep17) at Gastech 2019. The JDP encompasses 3D digital disclosure, data exchange, and the use of analytics to support the new construction process and pilot the survey of the future. "Data and digitalization are revolutionizing the marine and offshore industries. This JDP is further evidence of how ABS is leading the way and working with innovative partners such as SHI to shape the future of our industry," said Patrick Ryan, ABS Senior Vice President, Engineering and Technology. "ABS is committed to realizing the potential benefits of these technologies for members and clients while advancing safety at sea." "We have been leading digital innovation from design to production in order to adapt to the fourth industrial revolution and to ensure continued competitiveness," said Jong H Youn, Vice President of SHI.

Optimising this immensely complex process is critical to ensuring the efficiency and efficacy of its construction operation. Learn how Cognizant has helped Kvaerner adopt digital technologies such as AI, Machine Learning and the IoT to improve the design and construction of offshore platforms, and equip its workforce with the digital tools to enable the most demanding engineering projects in the world.

The potential use of drones to cripple as much as half of Saudi national oil production this week highlights a growing threat in modern-day conflict. The attack has shown that Saudi Arabia -- the world's third largest defence spender -- is incapable of defending arguably its most protected non-military installation in Abqaiq. It is estimated to have halted around 5 per cent of international crude output, has shocked markets and spiked prices globally. Only a decade ago, such an attack by a low-cost, remote weapon systems was largely unthinkable. And players on the world stage have seized on the shift, with groups such as Islamic State and Mexican drug cartels creating their own improvised explosive vehicles from rudimentary hobby kits purchased online and in stores.

Many in the machine learning community wish to take action on climate change, yet feel their skills are inapplicable. This workshop aims to show that in fact the opposite is true: while no silver bullet, ML can be an invaluable tool both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change. Climate change is a complex problem, for which action takes many forms - from designing smart electrical grids to tracking deforestation in satellite imagery. Many of these actions represent high-impact opportunities for real-world change, as well as being interesting problems for ML research.

Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. The companies issued a joint statement from the SIS Global Forum in Monaco announcing the launch of DELFI, a cloud-based platform loaded with software for exploration, development, production, storage tanks and pipeline projects. "Never before has our industry seen a collaboration of this kind, and of this scale," Schlumberger CEO Olivier Le Peuch said in a statement. "Working together will accelerate faster innovation with better results, marking the beginning of a new era in our industry that will enable us to elevate performance across our industry's value chain." Service Sector: Millennials now make up majority of Schlumberger's workforce Initially developed by Schlumberger for Chevron, DELFI resides entirely on Microsoft's Azure cloud computing platform.

DUBAI, UNITED ARAB EMIRATES – The assault on the beating heart of Saudi Arabia's vast oil empire follows a new and dangerous pattern that's emerged across the Persian Gulf this summer of precise attacks that leave few obvious clues as to who launched them. Beginning in May with the still-unclaimed explosions that damaged oil tankers near the Strait of Hormuz, the region has seen its energy infrastructure repeatedly targeted. Those attacks culminated with Saturday's assault on the world's biggest oil processor in eastern Saudi Arabia, which halved the oil-rich kingdom's production and caused energy prices to spike. Some strikes have been claimed by Yemen's Houthi rebels, who have been battling a Saudi-led coalition in the Arab world's poorest country since 2015. Their rapidly increasing sophistication fuels suspicion among experts and analysts however that Iran may be orchestrating them -- or perhaps even carrying them out itself as the U.S. alleges in the case of Saturday's attack.

First principles modeling of physical systems has led to significant technological advances across all branches of science. For nonlinear systems, however, small modeling errors can lead to significant deviations from the true, measured behavior. Even in mechanical systems, where the equations are assumed to be well-known, there are often model discrepancies corresponding to nonlinear friction, wind resistance, etc. Discovering models for these discrepancies remains an open challenge for many complex systems. In this work, we use the sparse identification of nonlinear dynamics (SINDy) algorithm to discover a model for the discrepancy between a simplified model and measurement data. In particular, we assume that the model mismatch can be sparsely represented in a library of candidate model terms. We demonstrate the efficacy of our approach on several examples including experimental data from a double pendulum on a cart. We further design and implement a feed-forward controller in simulations, showing improvement with a discrepancy model.

Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems from the fact that a liquid phase upgrading using mineral acid is a cost-effective chemical transformation. In this chemical upgrading process, adding a proton (positively charged atomic hydrogen) to an oxygen atom is a central step. The protonation energies of oxygen atoms in a molecule determine the thermodynamic feasibility of the reaction and likely chemical reaction pathway. A quantum chemical model based on coupled cluster theory is used to compute accurate thermochemical properties such as the protonation energies of oxygen atoms and the feasibility of protonation-based chemical transformations. However, this method is too computationally expensive to explore a large space of chemical transformations. We develop a graph neural network approach for predicting protonation energies of oxygen atoms of hundreds of bioxygenate molecules to predict the feasibility of aqueous acidic reactions. Our approach relies on an iterative local nonlinear embedding that gradually leads to global influence of distant atoms and a output layer that predicts the protonation energy. Our approach is geared to site-specific predictions for individual oxygen atoms of a molecule in comparison with commonly used graph convolutional networks that focus on a singular molecular property prediction. We demonstrate that our approach is effective in learning the location and magnitudes of protonation energies of oxygenated molecules.

This paper proposes a vision-based fire and smoke segmentation system which use spatial, temporal and motion information to extract the desired regions from the video frames. The fusion of information is done using multiple features such as optical flow, divergence and intensity values. These features extracted from the images are used to segment the pixels into different classes in an unsupervised way. A comparative analysis is done by using multiple clustering algorithms for segmentation. Here the Markov Random Field performs more accurately than other segmentation algorithms since it characterizes the spatial interactions of pixels using a finite number of parameters. It builds a probabilistic image model that selects the most likely labeling using the maximum a posteriori (MAP) estimation. This unsupervised approach is tested on various images and achieves a frame-wise fire detection rate of 95.39%. Hence this method can be used for early detection of fire in real-time and it can be incorporated into an indoor or outdoor surveillance system.