We present a machine learning framework that blends image super-resolution technologies with passive, scalar transport in the level-set method. Here, we investigate whether we can compute on-the-fly, data-driven corrections to minimize numerical viscosity in the coarse-mesh evolution of an interface. The proposed system's starting point is the semi-Lagrangian formulation. And, to reduce numerical dissipation, we introduce an error-quantifying multilayer perceptron. The role of this neural network is to improve the numerically estimated surface trajectory. To do so, it processes localized level-set, velocity, and positional data in a single time frame for select vertices near the moving front. Our main contribution is thus a novel machine-learning-augmented transport algorithm that operates alongside selective redistancing and alternates with conventional advection to keep the adjusted interface trajectory smooth. Consequently, our procedure is more efficient than full-scan convolutional-based applications because it concentrates computational effort only around the free boundary. Also, we show through various tests that our strategy is effective at counteracting both numerical diffusion and mass loss. In simple advection problems, for example, our method can achieve the same precision as the baseline scheme at twice the resolution but at a fraction of the cost. Similarly, our hybrid technique can produce feasible solidification fronts for crystallization processes. On the other hand, tangential shear flows and highly deforming simulations can precipitate bias artifacts and inference deterioration. Likewise, stringent design velocity constraints can limit our solver's application to problems involving rapid interface changes. In the latter cases, we have identified several opportunities to enhance robustness without forgoing our approach's basic concept.

In recent years, the geospatial industry has been developing at a steady pace. This growth implies the addition of satellite constellations that produce a copious supply of satellite imagery and other Remote Sensing data on a daily basis. Sometimes, this information, even if in some cases we are referring to publicly available data, it sits unaccounted for due to the sheer size of it. Processing such large amounts of data with the help of human labour or by using traditional automation methods is not always a viable solution from the standpoint of both time and other resources. Within the present work, we propose an approach for creating a multi-modal and spatio-temporal dataset comprised of publicly available Remote Sensing data and testing for feasibility using state of the art Machine Learning (ML) techniques. Precisely, the usage of Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation that are present in the proposed dataset. Popularity and success of similar methods in the context of Geographical Information Systems (GIS) and Computer Vision (CV) more generally indicate that methods alike should be taken in consideration and further analysed and developed.

We consider the problem of learning the optimal threshold policy for control problems. Threshold policies make control decisions by evaluating whether an element of the system state exceeds a certain threshold, whose value is determined by other elements of the system state. By leveraging the monotone property of threshold policies, we prove that their policy gradients have a surprisingly simple expression. We use this simple expression to build an off-policy actor-critic algorithm for learning the optimal threshold policy. Simulation results show that our policy significantly outperforms other reinforcement learning algorithms due to its ability to exploit the monotone property. In addition, we show that the Whittle index, a powerful tool for restless multi-armed bandit problems, is equivalent to the optimal threshold policy for an alternative problem. This observation leads to a simple algorithm that finds the Whittle index by learning the optimal threshold policy in the alternative problem. Simulation results show that our algorithm learns the Whittle index much faster than several recent studies that learn the Whittle index through indirect means.

With the advances of the Internet of Things (IoT) and 5G/6G wireless communications, the paradigms of mobile computing have developed dramatically in recent years, from centralized mobile cloud computing to distributed fog computing and mobile edge computing (MEC). MEC pushes compute-intensive assignments to the edge of the network and brings resources as close to the endpoints as possible, addressing the shortcomings of mobile devices with regard to storage space, resource optimisation, computational performance and efficiency. Compared to cloud computing, as the distributed and closer infrastructure, the convergence of MEC with other emerging technologies, including the Metaverse, 6G wireless communications, artificial intelligence (AI), and blockchain, also solves the problems of network resource allocation, more network load as well as latency requirements. Accordingly, this paper investigates the computational paradigms used to meet the stringent requirements of modern applications. The application scenarios of MEC in mobile augmented reality (MAR) are provided. Furthermore, this survey presents the motivation of MEC-based Metaverse and introduces the applications of MEC to the Metaverse. Particular emphasis is given on a set of technical fusions mentioned above, e.g., 6G with MEC paradigm, MEC strengthened by blockchain, etc.

Historically, researchers in materials discovery have devised and tested options through some mix of hunches, informed speculation, and trial by error. But it's a difficult and time-consuming process simply given the vast array of possible substances and combinations, which can send researchers down numerous false paths. In the case of electrolyte ingredients, "you can mix and match them in billions of ways," says Venkat Viswanathan, an associate professor at Carnegie Mellon, a co-author of the Nature Communications paper, and a cofounder and chief scientist at Aionics. He collaborated with Jay Whitacre, director of the university's Wilton E. Scott Institute for Energy Innovation and the co-principal investigator on the project, along with other Carnegie researchers to explore how robotics and machine learning could help. The promise of a system like Clio and Dragonfly is that it can rapidly work through a wider array of possibilities than human researchers can, and apply what it learns in a systematic way.

With AI's help, physicists have now compressed a daunting quantum problem that until now required 100,000 equations to as few as four equations – all without sacrificing accuracy. The work'Deep Learning the Functional Renormalization Group' was published last week in Physical Review Letters revolutionizing how scientists investigate systems containing various interacting electrons. The setup is based on the Hubbard model – an idealization of several important classes of materials – enabling scientists to learn how electron behavior gives rise to sought-after phases of matter. The model would serve as a testing ground for new methods before they are utilized on more complex quantum systems. The new approach could potentially aid in the designing of materials with properties that are most sought-after, such as utility for clean energy generation and superconductivity.

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations--all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation. "We start with this huge object of all these coupled-together differential equations; then we're using machine learning to turn it into something so small you can count it on your fingers," says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy. The formidable problem concerns how electrons behave as they move on a gridlike lattice.

Ikon Science, a global provider of knowledge management solutions designed to optimize subsurface discovery, announced the release of RokDoc Version 2022.4., an industry-leading geoprediction software. As global energy demand continues to grow and drilling activities increase to meet this challenge, subsurface teams in E&P companies are challenged to deliver key reservoir insights faster and more efficiently than before. To meet this challenge, the latest version of RokDoc introduces new functionality and automation of QC and knowledge generation workflows. The quantitative analysis workflow used to characterize reservoirs has been significantly improved by adding several user enhancements to streamline and speed-up results. Additionally, we are pleased to announce the release of the Rock Physics Machine Learning (RPML) tool, a technical collaboration with Australia's national science agency, CSIRO, as an addition to our already powerful Deep QI module.

Hewlett Packard Enterprise announced that King Abdullah University of Science and Technology (KAUST) selected HPE to build its next-generation supercomputer, Shaheen III, to deliver state-of-the-art supercomputing and artificial intelligence (AI) capabilities for advancing research in fields such as food, water, energy and the environment. "Powered by AMD EPYC processors, Shaheen III will enable new discoveries that will have regional and global impacts across climate, clean energy and tectonic plate modeling, all made possible by the collaboration between KAUST scientists and HPE." Supercomputing capacity has become increasingly vital to global innovation, industry competitiveness and economic growth. From accelerating vaccine discovery to fight a pandemic, advancing clean energy systems to increase sustainability, to enabling new possibilities in AI, supercomputing is a core technology to solving the world's most challenging scientific and engineering problems. Shaheen III, set to be 20 times faster than KAUST's existing system, will be the most powerful supercomputer in the Middle East to address critical areas that have a societal and environmental impact. Built by HPE, the world's leading supercomputer provider, the new Shaheen III system will revolutionize KAUST's ability to process vast amounts of data at immense speed and scale, enabling its users to unlock discoveries that it could not have before, and realize new potentials for AI.

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations--all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation. "We start with this huge object of all these coupled-together differential equations; then we're using machine learning to turn it into something so small you can count it on your fingers," says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy. The formidable problem concerns how electrons behave as they move on a gridlike lattice.