Researchers at MIT are employing machine learning techniques to better understand turbulent plasma phenomena in fusion devices. According to MIT News, a new deep learning framework was developed that leverages artificial neural networks to represent a reduced turbulence theory. The research is described in two papers, published in Physical Review E and Physics of Plasmas. If researchers hope to control fusion for energy production, they need a better understanding of the turbulent motion of ions and electrons in plasmas moving through fusion reactors. The field lines of toroidal structures known as tokamaks force the plasma particles; the intent is to confine them long enough to produce significant net energy gains, but that's a challenge with extraordinarily high temperatures but also small spaces.

In recent years, machine learning and pattern recognition methods have become common in Earth and space sciences. This is especially true for remote sensing applications, which often rely on massive archives of noisy data and so are well suited to such artificial intelligence (AI) techniques. As the data science revolution matures, we can assess its impact on specific research disciplines. We focus here on imaging spectroscopy, also known as hyperspectral imaging, as a data-centric remote sensing discipline expected to benefit from machine learning. Imaging spectroscopy involves collecting spectral data from airborne and satellite sensors at hundreds of electromagnetic wavelengths for each pixel in the sensors' viewing area.

Florian Ellsaesser Frankfurt School of Economics and Finance Guido Fioretti University of Bologna Gail E. James Gail James contributed a unique series of cognitive maps with her PhD thesis at University of Colorado, Boulder, 1996. We would like to have her as a co-author. If anyone knows where she is, please contact us. Abstract Sometimes unexpected, novel, unconceivable events enter our lives. The cause-effect mappings that usually guide our behaviour are destroyed. Surprised and shocked by possibilities that we had never imagined, we are unable to make any decision beyond mere routine. Among them there are decisions, such as making investments, that are essential for the long-term survival of businesses as well as the economy at large. We submit that the standard machinery of utility maximization does not apply, but we propose measures inspired by scenario planning and graph analysis, pointing to solutions being explored in machine learning. We wish to thank Jochen Runde and Jean Czerlinki for helpful comments and remarks on previous versions of this manuscript. Introduction Sometimes, unexpected events destroy certain causal relations that used to provide a few firm signposts in spite of all uncertainty involved in managing a business.

We employ machine learning techniques to estimate household carbon footprints (HCFs) for the average household in each Census tract-geographic areas that represent roughly 4,000 people. We find that there is significant variation in carbon footprints across income and geography; income effects are driven by higher footprints related to transportation and consumer products and services, while geographic effects are primarily a result of the variable carbon intensity of the electricity grid. Using these footprints, we assess the net effects of various climate policies on households in the United States paying particular attention to the distribution across geography, urbanity, and income groups. Our objective is to improve the understanding of the potential for regressivity, geographic transfers, and rural-urban transfers among climate policy options and test for ways to control for transfers-preserving transfers from high-income households to low-income households, but mitigating transfers from rural areas to urban areas and from the Midwest and South to the Coasts. Our focus is on the net increase or decrease of annual household expenses under 12 different policy scenarios, which included both carbon pricing schemes and regulatory standards.

In the last decade, several studies have proposed the use of automated techniques to estimate the effort of agile software development. In this paper we perform a close replication and extension of a seminal work proposing the use of Deep Learning for agile effort estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baseline techniques (i.e., Random, Mean and Median effort prediction) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SE), as done in the original study. To this end, we use both the data from the original study and a new larger dataset of 31,960 issues, which we mined from 29 open-source projects. Using more data allows us to strengthen our confidence in the results and further mitigate the threat to the external validity of the study. We also extend the original study by investigating two additional research questions. One evaluates the accuracy of Deep-SE when the training set is augmented with issues from all other projects available in the repository at the time of estimation, and the other examines whether an expensive pre-training step used by the original Deep-SE, has any beneficial effect on its accuracy and convergence speed. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SE in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play a role in improving its accuracy and convergence speed. ...

This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set Programming (ASP). These include problems related to the dependency or conflicts between concerns, how to mitigate an issue, and what the most suitable mitigation strategy for a given issue would be. It then shows how ASP can be used to develop an implementation that addresses the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.

With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture product- and process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for context-specific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project.

After just seven months, a huge team of scientists who work with the Dark Energy Spectroscopic Instrument have already mapped a larger swath of the cosmos than all other 3D surveys combined. And since they're only 10 percent of the way through their five-year mission, there's much more to come. DESI, pronounced like Desi Arnaz's name, has revealed a spectacular cosmic web of more than 7.5 million galaxies, and it will scan up to 40 million. The instrument is funded by the US Department of Energy and installed at the Nicholas U. Mayall 4-meter Telescope at Kitt Peak National Observatory near Tucson, Arizona. It measures the precise distances of galaxies from Earth and their emitted light at a range of wavelengths, achieving quantity and quality at the same time.

Smart cities use a mix of low-power sensors, cameras, and AI algorithms to continuously monitor the city's efficiency. Governments benefit greatly from the use of computer vision and other related technologies. These technologies allow city administrators to easily integrate and manage assets. As the'eyes' of the city, computer vision plays an important role in smart city management. Greater urban density usually means more automobiles, which means more traffic congestion, longer travel times, accidents, local air pollution, and carbon emissions – not to mention a general sensation of exhaustion, tension, and anxiety.

Between presentations launching new PC processors and candy-colored refrigerators at last week's CES, companies at the annual tech industry jamboree made a lot of big, flashy proclamations about climate change, some more serious than others, and most seeming to include at least one stock video clip of trees, solar panels and children frolicking in grassy meadows or on pristine beaches. General Motors unveiled a new zero-emission pickup truck and dropped hints about new EV models to come, while Panasonic, which calculated that it released 110 million tons of CO2 per year and accounted for 1% of global electricity consumption, reiterated a pledge to decarbonize its operations by 2030 and promised to make its products more efficient. LG--which has pledged carbon neutrality by 2030, and to use fully renewable power by 2050--rolled out glass-fronted refrigerators (to avoid wasting energy while you look inside) and washing machines that use AI to shorten wash cycles. Samsung, whose CO2 emissions actually rose in 2020, and which has faced controversy over its reliance on coal energy, offered promises like devices that would use less standby power, which some environmentalists criticized as greenwashing. A version of this story first appeared in the Climate is Everything newsletter. To sign up, click here.