Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing, that can be divided in three groups: State of the art postprocessing techniques from statistics (ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression), established machine learning methods (gradient-boosting extended EMOS, quantile regression forests) and neural network-based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using six years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.

Greater direct electrification of end-use sectors with a higher share of renewables is one of the pillars to power a carbon-neutral society by 2050. This study uses a recent deep learning technique, the normalizing flows, to produce accurate probabilistic forecasts that are crucial for decision-makers to face the new challenges in power systems applications. Through comprehensive empirical evaluations using the open data of the Global Energy Forecasting Competition 2014, we demonstrate that our methodology is competitive with other state-of-the-art deep learning generative models: generative adversarial networks and variational autoencoders. The models producing weather-based wind, solar power, and load scenarios are properly compared both in terms of forecast value, by considering the case study of an energy retailer, and quality using several complementary metrics.

A stealthy autonomous underwater robot that can track elusive underwater creatures without disturbing them could help us better understand the largest daily migration of life on Earth. Mesobot, a 250-kilogram robot that operates either unconnected to a power source or tethered with a lightweight fibre-optic cable, is able to move around below the surface unobtrusively. The ocean's twilight zone – known more formally as the mesopelagic zone – lies between about 200 metres and 1 kilometre in depth. It is the site of the diel vertical migration (DVM), a daily phenomenon during which deep-dwelling animals come closer to the surface to feed on the more plentiful food supplies found there, while dodging predators. The DVM is seen by biologists as a very important way in which nutrients – and carbon dioxide captured via photosynthesis – can be rapidly transported to depth, where carbon can be stored for the long term.

Sparse linear regression is a fundamental problem in high-dimensional statistics, but strikingly little is known about how to efficiently solve it without restrictive conditions on the design matrix. We consider the (correlated) random design setting, where the covariates are independently drawn from a multivariate Gaussian $N(0,\Sigma)$ with $\Sigma : n \times n$, and seek estimators $\hat{w}$ minimizing $(\hat{w}-w^*)^T\Sigma(\hat{w}-w^*)$, where $w^*$ is the $k$-sparse ground truth. Information theoretically, one can achieve strong error bounds with $O(k \log n)$ samples for arbitrary $\Sigma$ and $w^*$; however, no efficient algorithms are known to match these guarantees even with $o(n)$ samples, without further assumptions on $\Sigma$ or $w^*$. As far as hardness, computational lower bounds are only known with worst-case design matrices. Random-design instances are known which are hard for the Lasso, but these instances can generally be solved by Lasso after a simple change-of-basis (i.e. preconditioning). In this work, we give upper and lower bounds clarifying the power of preconditioning in sparse linear regression. First, we show that the preconditioned Lasso can solve a large class of sparse linear regression problems nearly optimally: it succeeds whenever the dependency structure of the covariates, in the sense of the Markov property, has low treewidth -- even if $\Sigma$ is highly ill-conditioned. Second, we construct (for the first time) random-design instances which are provably hard for an optimally preconditioned Lasso. In fact, we complete our treewidth classification by proving that for any treewidth-$t$ graph, there exists a Gaussian Markov Random Field on this graph such that the preconditioned Lasso, with any choice of preconditioner, requires $\Omega(t^{1/20})$ samples to recover $O(\log n)$-sparse signals when covariates are drawn from this model.

The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation based on the natural language processing (NLP) method, to construct graph data query statements via knowledge reasoning, and to complete the accurate knowledge search and analysis to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop knowledge correlation reasoning analysis in tremendous knowledge. The proposed work can also provide a basis for the context-aware intelligent question and answer.

A new take on artificial intelligence may open many doors for 3D printing and designing advanced nuclear reactors. The future of clean energy is hot. Temperatures hit 800 Celsius in parts of solar energy plants and advanced nuclear reactors. Finding materials that can stand that type of heat is tough. So experts look to Mark Messner for answers.

All others have a large and varying degree of missing values. Within the missingno library, there are four types of plots for visualising data completeness: the barplot, the matrix plot, the heatmap, and the dendrogram plot. Each has its own advantages for identifying missing data. Let's take a look at each of these in turn. The barplot provides a simple plot where each bar represents a column within the dataframe. The height of the bar indicates how complete that column is, i.e, how many non-null values are present.

Artificial intelligence (AI) has quickened its progress in 2021. A new administration is in place in the US and the talk is about a major push for Green Technology and the need to stimulate next generation infrastructure including AI and 5G to generate economic recovery with David Knight forecasting that 5G has the potential - the potential - to drive GDP growth of 40% or more by 2030. The Biden administration has stated that it will boost spending in emerging technologies that includes AI and 5G to $300Bn over a four year period. On the other side of the Atlantic Ocean, the EU have announced a Green Deal and also need to consider the European AI policy to develop next generation companies that will drive economic growth and employment. It may well be that the EU and US (alongside Canada and other allies) will seek ways to work together on issues such as 5G policy and infrastructure development. The UK will be hosting COP 26 and has also made noises about AI and 5G development.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Robot housemaids, Jet packs and flying cars are all promises for the 21st century. Flying cars have the potential to save a lot of time and improve productivity and open the sky corridors to future transportation. The requirements for electric vertical takeoff and landing vehicles are very challenging for the potential battery power sources. The automotive e-vehicle transformation is setting up the platform for urban air mobility. However, people must not be naive in believing that e-vehicle batteries will serve for electric flight.

Showcasing America's most promising young scientists and engineers, the Blavatnik Family Foundation and the New York Academy of Sciences today named 31 finalists for the world's largest unrestricted prize honoring early-career scientists and engineers. Three winners of the Blavatnik National Awards for Young Scientists – in life sciences, chemistry, and physical sciences and engineering – will be announced on July 20, each receiving $250,000 as a Blavatnik National Awards Laureate. The finalists, culled from 298 nominations by 157 United States research institutions across 38 states, have made trailblazing discoveries in wide-ranging fields, from the neuroscience of addiction to the development of gene-editing technologies, from designing next-generation battery storage to understanding the origins of photosynthesis, from making improvements in computer vision to pioneering new frontiers in polymer chemistry. Descriptions of the honorees' research are listed below. "Each day, young scientists tirelessly seek solutions to humanity's greatest challenges," said Len Blavatnik, founder and chairman of Access Industries, and head of the Blavatnik Family Foundation. "The Blavatnik Awards recognize this scientific brilliance and tenacity as we honor these 31 finalists. We congratulate them on their accomplishments and look forward to their continued, future discoveries and success." President and CEO of the New York Academy of Sciences Nicholas B. Dirks said: "Each year, it is a complete joy to see the very'best of the best' of American science represented by the Blavatnik National Awards Finalists."