Overlap functions are a class of aggregation functions that measure the overlapping degree between two values. Interval-valued overlap functions were defined as an extension to express the overlapping of interval-valued data, and they have been usually applied when there is uncertainty regarding the assignment of membership degrees. The choice of a total order for intervals can be significant, which motivated the recent developments on interval-valued aggregation functions and interval-valued overlap functions that are increasing to a given admissible order, that is, a total order that refines the usual partial order for intervals. Also, width preservation has been considered on these recent works, in an intent to avoid the uncertainty increase and guarantee the information quality, but no deeper study was made regarding the relation between the widths of the input intervals and the output interval, when applying interval-valued functions, or how one can control such uncertainty propagation based on this relation. Thus, in this paper we: (i) introduce and develop the concepts of width-limited interval-valued functions and width limiting functions, presenting a theoretical approach to analyze the relation between the widths of the input and output intervals of bivariate interval-valued functions, with special attention to interval-valued aggregation functions; (ii) introduce the concept of $(a,b)$-ultramodular aggregation functions, a less restrictive extension of one-dimension convexity for bivariate aggregation functions, which have an important predictable behaviour with respect to the width when extended to the interval-valued context; (iii) define width-limited interval-valued overlap functions, taking into account a function that controls the width of the output interval; (iv) present and compare three construction methods for these width-limited interval-valued overlap functions.

Time series forecasting is a relevant task that is performed in several real-world scenarios such as product sales analysis and prediction of energy demand. Given their accuracy performance, currently, Recurrent Neural Networks (RNNs) are the models of choice for this task. Despite their success in time series forecasting, less attention has been paid to make the RNNs trustworthy. For example, RNNs can not naturally provide an uncertainty measure to their predictions. This could be extremely useful in practice in several cases e.g. to detect when a prediction might be completely wrong due to an unusual pattern in the time series. Whittle Sum-Product Networks (WSPNs), prominent deep tractable probabilistic circuits (PCs) for time series, can assist an RNN with providing meaningful probabilities as uncertainty measure. With this aim, we propose RECOWN, a novel architecture that employs RNNs and a discriminant variant of WSPNs called Conditional WSPNs (CWSPNs). We also formulate a Log-Likelihood Ratio Score as better estimation of uncertainty that is tailored to time series and Whittle likelihoods. In our experiments, we show that RECOWNs are accurate and trustworthy time series predictors, able to "know when they do not know".

Energy communities will play a key role in building the more decentralized, less carbon-intensive, and fairer energy systems of the future. Such communities enable local prosumers (consumers with own generation and storage) to generate, store and trade energy with each other--using locally owned assets, such as wind turbines, rooftop solar panels and batteries. In turn, this enables the community to use more locally generated renewable generation and shifts the market power from large utility companies to individual prosumers. Energy community projects often involve jointly-owned assets such as community-owned wind turbines or shared battery storage. Yet, this raises the question of how these assets should be controlled--often in real-time, and how the energy outputs jointly-owned assets should be shared fairly among community members, given not all members have the same size, energy needs or demand profiles.

Researchers have figured out a way to rapidly recharge ultra dense batteries capable of powering flying cars, theoretically making them suitable for everyday use. The breakthrough with electric vertical take-off and landing (eVTOL) vehicles could enable the commercialisation of next-generation transport systems in the near future, according to the researchers from Penn State university who made the discovery. "I hope that the work we have done in this paper will give people a solid idea that we don't need another 20 years to finally get these vehicles," said Chao-Yang Wang, director of the Electrochemical Engine Center, Penn State. "I believe we have demonstrated that the eVTOL is commercially viable." The research was published today, 7 June, in the scientific journal Joule.

MIT engineers have discovered a new way of generating electricity using tiny carbon particles that can create a current simply by interacting with liquid surrounding them. The liquid, an organic solvent, draws electrons out of the particles, generating a current that could be used to drive chemical reactions or to power micro- or nanoscale robots, the researchers say. "This mechanism is new, and this way of generating energy is completely new," says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT. "This technology is intriguing because all you have to do is flow a solvent through a bed of these particles. This allows you to do electrochemistry, but with no wires." In a new study describing this phenomenon, the researchers showed that they could use this electric current to drive a reaction known as alcohol oxidation -- an organic chemical reaction that is important in the chemical industry.

To mitigate local warming, cities including Milan have wrapped condominium balconies and high-rise facades in expansive vertical gardens. A dense stack of vegetation can help keep a building cool by creating natural shading and releasing moisture into the air, says Theodore Endreny, a professor of environmental resources engineering at SUNY College of Environmental Science and Forestry. The compact foliage augments the benefits trees and plants naturally provide when planted on sidewalks or roofs, including pollution removal, carbon dioxide sequestration and oxygen production. A look at how innovation and technology are transforming the way we live, work and play. Crews of urban arborists certified as tree climbers will be hired to rappel down buildings and maintain these ecosystems as they sprout on more buildings, says Dr. Endreny.

Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. Colloquially, the term "artificial intelligence" is often used to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving". Artificial intelligence (AI) can be defined in many ways: one can say it is the technology that emulates the natural intelligence that we have in machines. It is designed by humans and demonstrated by the machines.

Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. However, the options are much more limited when the prediction target is multivariate and a joint measure of uncertainty is required. For example, in predicting a 2D velocity vector a joint uncertainty would quantify the probability of any vector in the plane, which would be more expressive than two separate uncertainties on the x- and y- components. To enable joint probabilistic regression, we propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches. We demonstrate these claims in simulation and with a case study predicting two-dimensional oceanographic velocity data. An implementation of our method is available at https://github.com/stanfordmlgroup/ngboost.

A novel non-parametric quantile estimation method for continuous random variables is introduced, based on a minimal neural network architecture consisting of a single unit. Its advantage over estimations from ranking the order statistics is shown, specifically for small sample size. In a regression context, the method can be used to quantify predictive uncertainty under the split conformal prediction setting, where prediction intervals are estimated from the residuals of a pre-trained model on a held-out validation set to quantify the uncertainty in future predictions. Benchmarking experiments demonstrate that the method is competitive in quality and coverage with state-of-the-art solutions, with the added benefit of being more computationally efficient.

This paper develops probabilistic PV forecasters by taking advantage of recent breakthroughs in deep learning. It tailored forecasting tool, named encoder-decoder, is implemented to compute intraday multi-output PV quantiles forecasts to efficiently capture the time correlation. The models are trained using quantile regression, a non-parametric approach that assumes no prior knowledge of the probabilistic forecasting distribution. The case study is composed of PV production monitored on-site at the University of Li\`ege (ULi\`ege), Belgium. The weather forecasts from the regional climate model provided by the Laboratory of Climatology are used as inputs of the deep learning models. The forecast quality is quantitatively assessed by the continuous ranked probability and interval scores. The results indicate this architecture improves the forecast quality and is computationally efficient to be incorporated in an intraday decision-making tool for robust optimization.