Before committing to a company, investors want to know if there are any potential controversies brewing, or if the company shows particular leadership in an area of ESG, such as diversity in the workforce. Refinitiv is a global provider of financial market data and infrastructure, and this article describes how their Labs team is exploring the use of NLP to give their clients a competitive edge in global financial markets. Currently, Refinitiv analysts search for news stories about a specific company using a set of ESG-related keywords, and if there's a positive match, the story is subject to further scrutiny. CHICAGO (Reuters) -- The agricultural unit of German chemicals company Bayer AG will halt future U.S. sales of an insecticide that can be used on more than 200 crops after losing a fight with the U.S. Environment Protection Agency, the company said on Friday. This can take the analyst some considerable time.

Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems. These optimization problems are nonconvex and global optimization is desired. However, previous literature observed computational burdens limiting deterministic global optimization to Gaussian processes trained on few data points. We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound solver branches only on the degrees of freedom and McCormick relaxations are propagated through explicit Gaussian process models. The approach also leads to significantly smaller and computationally cheaper subproblems for lower and upper bounding. To further accelerate convergence, we derive envelopes of common covariance functions for GPs and tight relaxations of acquisition functions used in Bayesian optimization including expected improvement, probability of improvement, and lower confidence bound. In total, we reduce computational time by orders of magnitude compared to state-of-the-art methods, thus overcoming previous computational burdens. We demonstrate the performance and scaling of the proposed method and apply it to Bayesian optimization with global optimization of the acquisition function and chance-constrained programming. The Gaussian process models, acquisition functions, and training scripts are available open-source within the "MeLOn - Machine Learning Models for Optimization" toolbox~(https://git.rwth-aachen.de/avt.svt/public/MeLOn).

Moog Inc. has automated the evaluation of copper (Cu) alloy grain size using a deep-learning convolutional neural network (CNN). The proof-of-concept automated image acquisition and batch-wise image processing offers the potential for significantly reduced labor, improved accuracy of grain evaluation, and decreased overall turnaround times for approving Cu alloy bar stock for use in flight critical aircraft hardware. A classification accuracy of 91.1% on individual sub-images of the Cu alloy coupons was achieved. Process development included minimizing the variation in acquired image color, brightness, and resolution to create a dataset with 12300 sub-images, and then optimizing the CNN hyperparameters on this dataset using statistical design of experiments (DoE). Over the development of the automated Cu alloy grain size evaluation, a degree of "explainability" in the artificial intelligence (XAI) output was realized, based on the decomposition of the large raw images into many smaller dataset sub-images, through the ability to explain the CNN ensemble image output via inspection of the classification results from the individual smaller sub-images.

The moon is a treasure trove of valuable resources. Gold, platinum, and many rare Earth metals await extraction to be used in next-generation electronics. But there's one resource in particular that has excited scientists, rocket engineers, space agency officials, industry entrepreneurs--virtually anyone with a vested interest in making spaceflight to distant worlds more affordable. If you split water into hydrogen and oxygen, and then liquefy those constituents, you have rocket fuel. If you can stop at the moon's orbit or a lunar base to refuel, you no longer need to bring all your propellant with you as you take off, making your spacecraft significantly lighter and cheaper to launch.

Dimensionality reduction is the process of expressing high-dimensional data in a reduced number of dimensions such that each one contains the most amount of information. Dimensionality reduction may be used for visualization of high-dimensional data or to speed up machine learning models by removing low-information or correlated features. Principal Component Analysis, or PCA, is a popular method of reducing the dimensionality of data by drawing several orthogonal (perpendicular) vectors in the feature space to represent the reduced number of dimensions. The variable number represents the number of dimensions the reduced data will have. In the case of visualization, for example, it would be two dimensions.

Machine learning (ML) is the field of training machines to achieve high level of cognition and perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our daily lives and operations as well as complex and interdisciplinary fields. With the rise of commercial, open-source and user-catered ML tools, a key question often arises whenever ML is applied to explore a phenomenon or a scenario: what constitutes a good ML model? Keeping in mind that a proper answer to this question depends on a variety of factors, this work presumes that a good ML model is one that optimally performs and best describes the phenomenon on hand. From this perspective, identifying proper assessment metrics to evaluate performance of ML models is not only necessary but is also warranted. As such, this paper examines a number of the most commonly-used performance fitness and error metrics for regression and classification algorithms, with emphasis on engineering applications.

Their study, published today in Science Advances, is the first to apply an experimentally validated machine learning method to rapidly design and develop advanced gas separation membranes. "Our work points to a new way of materials design and we expect it to revolutionize the field," says the study's PI Sanat Kumar, Bykhovsky Professor of Chemical Engineering and a pioneer in developing polymer nanocomposites with improved properties. Plastic films or membranes are often used to separate mixtures of simple gases, like carbon dioxide (CO2), nitrogen (N2), and methane (CH4). Scientists have proposed using membrane technology to separate CO2 from other gases for natural gas purification and carbon capture, but there are potentially hundreds of thousands of plastics that can be produced with our current synthetic toolbox, all of which vary in their chemical structure. Manufacturing and testing all of these materials is an expensive and time-consuming process, and to date, only about 1,000 have been evaluated as gas separation membranes.

IMAGE: Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part... view more Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources.

Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene--a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources. "Using clean electricity to convert CO2 into ethylene, which has a $60 billion global market, can improve the economics of both carbon capture and clean energy storage," says Professor Ted Sargent, one of the senior authors on a new paper published today in Nature.

In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the ASNet architecture to make it more expressive, while still remaining invariant to a range of symmetries that exist in PPDDL problems. We also present a thorough experimental evaluation of ASNets, including a comparison with heuristic search planners on seven probabilistic and deterministic domains, an extended evaluation on over 18,000 Blocksworld instances, and an ablation study. Finally, we show that sparsity-inducing regularisation can produce ASNets that are compact enough for humans to understand, yielding insights into how the structure of ASNets allows them to generalise across a domain.