The platform uses geospatial data and satellite imagery to provide data-based applications for mineral exploration and discovery and promises to increase hypothesis testing and the speed of the exploration lifecycle. "Traditionally, remote sensing is carried out by specialists (remote sensing geologists) on behalf of the mineral exploration team. Although they still have a role in supporting the process, the Descartes Labs platform puts the technology into the hands of the exploration geologists who know the project areas the best. By leveraging the data obtained from satellite and airborne imagery, they can accelerate their hypothesis formulation and exploration strategies to find new deposits," James Orsulak, senior director of business and sales at Descartes Labs, told MINING.COM. MDC: Your platform puts emphasis on the data refinery.

We study the problem of diffeomorphometric geodesic landmark matching where the objective is to find a diffeomorphism that via its group action maps between two sets of landmarks. It is well-known that the motion of the landmarks, and thereby the diffeomorphism, can be encoded by an initial momentum leading to a formulation where the landmark matching problem can be solved as an optimisation problem over such momenta. The novelty of our work lies in the application of a derivative-free Bayesian inverse method for learning the optimal momentum encoding the diffeomorphic mapping between the template and the target. The method we apply is the ensemble Kalman filter, an extension of the Kalman filter to nonlinear observation operators. We describe an efficient implementation of the algorithm and show several numerical results for various target shapes.

With high forward gain, a negative feedback system has the ability to perform the inverse of a linear or non linear function that is in the feedback path. This property of negative feedback systems has been widely used in analog circuits to construct precise closed-loop functions. This paper describes how the property of a negative feedback system to perform inverse of a function can be used for training neural networks. This method does not require that the cost or activation functions be differentiable. Hence, it is able to learn a class of non-differentiable functions as well where a gradient descent-based method fails. We also show that gradient descent emerges as a special case of the proposed method. We have applied this method to the MNIST dataset and obtained results that shows the method is viable for neural network training. This method, to the best of our knowledge, is novel in machine learning.

For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers. This critical challenge has recently been somewhat addressed in [1]. This paper significantly extends this existing work. First, we reduce the potential leakage information by proposing a fundamentally different post-processing method, using public information of grid losses rather than power dispatch, which achieve a higher level of privacy protection. Second, we protect more sensitive parameters, i.e., branch shunt susceptance in addition to series impedance (complete pi-model). This protects power flow data for the transmission high-voltage networks, using differentially private transformations that maintain the optimal power flow consistent with, and faithful to, expected model behaviour. Third, we tested our approach at a larger scale than previous work, using the PGLib-OPF test cases [10]. This resulted in the successful obfuscation of up to a 4700-bus system, which can be successfully solved with faithfulness of parameters and good utility to data analysts. Our approach addresses a more feasible and realistic scenario, and provides higher than state-of-the-art privacy guarantees, while maintaining solvability, fidelity and feasibility of the system.

Osaka, Japan - Scientists from the Division of Sustainable Energy and Environmental Engineering at Osaka University employed deep learning artificial intelligence to improve mobile mixed reality generation. They found that occluding objects recognized by the algorithm could be dynamically removed using a video game engine. This work may lead to a revolution in green architecture and city revitalization. Mixed reality (MR) is a type of visual augmentation in which real-time images of existing objects or landscapes can be digitally altered. As anyone who has played Pokémon Go! or similar games knows, looking at a smartphone screen can feel almost like magic when characters appear alongside real landmarks.

Today was a fun and exciting day at the Esri Federal GIS Conference 2021 highlighted by great user presentations, inspiring talks, and a powerful technology showcase. The imagery and remote sensing demonstration showed how AI was effectively put to use in a SAAS environment. Driving the AI was a pre-trained model that is downloadable for all users from ArcGIS Living Atlas. This is just one of the many models that have been released on ArcGIS Living Atlas of the World. Ever since the pre-trained geospatial deep learning models were released on ArcGIS Living Atlas, they have been well received.

David Keyes is a professor of applied mathematics and computational science and director of the Extreme Computing Research Center at the King Abdullah University of Science and Technology, Saudi Arabia.

The Arab region, composed of 22 countries spanning Asia and Africa, opens ample room for communications and networking innovations and services and contributes to the critical mass of the global networking innovation. While the Arab world is considered an emerging market for communications and networking services, the rate of adoption is outpacing the global average. In fact, as of 2019, the mobile Internet penetration stands at 67.2% in the Arab world, as opposed to a global average of 56.5%.12 Furthermore, multiple countries in the region are either building new infrastructure or developing existing infrastructure at an unprecedented pace. Examples include, Neom city in Saudi Arabia, the new administrative capital in Egypt, as well as the Smart Dubai 2021 project in the United Arab Emirates (UAE), among others. This provides a unique opportunity to fuse multiple advanced networking technologies as an integral part of the infrastructure design phase and not just as an afterthought.

Oil and gas (O&G) sources will still supply around 50% of the global energy demand by 2040.a In this article, we make the case for why the Arab region is well positioned for building world-class data science teams to fill the supply shortage of data professionals,5 especially in the O&G field critical to region's economy. This article presents challenges facing O&G industry players, such as governments, regulatory bodies, operators, and investors, and shows how Raisa Energy (with its Egypt-based data science team) is efficiently and effectively solving these challenges. Such challenges aim at assessing the economic viability of an O&G asset that depends on several factors (as shown in the accompanying figure) such as estimating well production, O&G prices, and risks associated with inputs uncertainty. It is worth emphasizing that the challenges presented here are global in nature and yet are tackled with a team fully formed from the region working at a world-class research and development level.

Qatar is a small peninsular nation on the northeastern coast of the Arabian Peninsula. Qatar is endowed with abundant hydrocarbon resources and is the world's largest producer of liquified natural gas (LNG), which accounts for over 80% of its export earnings. Like many of its wealthy neighbors, Qatar faces a unique dilemma with the onset of artificial intelligence (AI) technologies. Despite having one of the world's highest per-capita income and a highly educated local population, the majority of Qataris are under-employed and working in government white collar jobs where they are unable to fully realize the potential of their level of education. These are precisely the occupations that are likely to be made redundant by AI.1 The bulk of the workforce in Qatar consists of expatriates drawn primarily from South Asia and the Middle East and North Africa (MENA) region.