Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of ensemble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Currently, the diagnosis of Alzheimer's disease is a complex and error-prone process. Improving this diagnosis could allow earlier detection of the disease and improve the quality of life of patients and their families. For this work, we will use 249 brain images from two modalities: PET and MRI, taken from the ADNI database, and labelled into three classes according to the degree of development of Alzheimer's disease. We propose the development of a convolutional neural network to perform the classification of these images, during which, we will study the appropriate depth of the networks for this problem, the importance of pre-processing medical images, the use of transfer learning and data augmentation techniques as tools to reduce the effects of the problem of having too little data, and the simultaneous use of multiple medical imaging modalities. We also propose the application of an evaluation method that guarantees a good degree of repeatability of the results even when using a small dataset. Following this evaluation method, our best final model, which makes use of transfer learning with COVID-19 data, achieves an accuracy d 68\%. In addition, in an independent test set, this same model achieves 70\% accuracy, a promising result given the small size of our dataset. We further conclude that augmenting the depth of the networks helps with this problem, that image pre-processing is a fundamental process to address this type of medical problem, and that the use of data augmentation and the use of pre-trained networks with images of other diseases can provide significant improvements.

Monitoring biodiversity is paramount to manage and protect natural resources. Collecting images of organisms over large temporal or spatial scales is a promising practice to monitor the biodiversity of natural ecosystems, providing large amounts of data with minimal interference with the environment. Deep learning models are currently used to automate classification of organisms into taxonomic units. However, imprecision in these classifiers introduces a measurement noise that is difficult to control and can significantly hinder the analysis and interpretation of data. {We overcome this limitation through ensembles of Data-efficient image Transformers (DeiTs), which not only are easy to train and implement, but also significantly outperform} the previous state of the art (SOTA). We validate our results on ten ecological imaging datasets of diverse origin, ranging from plankton to birds. On all the datasets, we achieve a new SOTA, with a reduction of the error with respect to the previous SOTA ranging from 29.35% to 100.00%, and often achieving performances very close to perfect classification. Ensembles of DeiTs perform better not because of superior single-model performances but rather due to smaller overlaps in the predictions by independent models and lower top-1 probabilities. This increases the benefit of ensembling, especially when using geometric averages to combine individual learners. While we only test our approach on biodiversity image datasets, our approach is generic and can be applied to any kind of images.

In Ukraine's port city of Odessa, residents have recently found themselves hiding not from the thunder of rocket attacks but from the whir of buzzing Iranian drones in the sky. The machines have been playing an important role since Russia invaded seven months ago -- forming part of reconnaissance operations, missile firings or bomb drops. Awakened with a start on Saturday morning by a roar from the sky, Maryna Kondratieva ran to hide in the cellar with her two young children, fearing the worst. "I understand now that everything can change in five minutes," Kondratieva, who lives in a well-to-do part of the city and whose terrace overlooks the Black Sea, told AFP. Odessa -- the'capital' of the southwest and Ukraine's main port -- had seemed largely safe from Moscow, whose troops failed to take it at the beginning of the war.

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.

In modern geo-politics the role of Indian Navy is going to be more challenging and its active participation could decide the place of India in global power play. The seminar "Swavlamban" chaired by the PM Modi on SPRINT Challenges on July 18th 2022, is showcasing the seriousness of New Delhi towards the strengthening the Indian Navy through the modern indigenous technologies. The presence of Chinses third generation research and survey ship "Yuan Wang 5" in Hambantota, Sri Lanka, is sufficient to explain that the Indo-Pacific is going to be future coliseum of geo-politics. It is provoking India to adopt modern cutting-edge naval technologies to protect the country's interest and control the foreign powers. Technology is always an important agent, which decides or redefines the war parameters with some distinctive outputs.

Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users' needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.

Hannover Messe (or Hannover Fair), the #1 global industrial tradeshow, was back in action earlier this month. The event that took place from 30 May–02 June 2022, in Hannover, Germany, showcased once again the latest developments and industrial technology trends. Despite a much smaller crowd (75,000 visitors--roughly 40% of pre-pandemic levels), the fairgrounds were buzzing and filled with senior executives from many of the leading industrial hardware, software, and service providers. The conference remains one of those rare fairs where you randomly walk into senior executives, like a Head of Engineering for a major industrial conglomerate, and not only into the pre-sales representatives giving you the usual pitch. "In the face of disrupted supply chains, rising energy prices, inflation, and climate change, it was all the more important to meet face-to-face again in the exhibition halls after two years marked by a pandemic, to take in the latest technology trends and get a window to the future."

We introduce a method for long-distance maritime route planning in polar regions, taking into account complex changing environmental conditions. The method allows the construction of optimised routes, describing the three main stages of the process: discrete modelling of the environmental conditions using a non-uniform mesh, the construction of mesh-optimal paths, and path smoothing. In order to account for different vehicle properties we construct a series of data driven functions that can be applied to the environmental mesh to determine the speed limitations and fuel requirements for a given vessel and mesh cell, representing these quantities graphically and geospatially. In describing our results, we demonstrate an example use case for route planning for the polar research ship the RRS Sir David Attenborough (SDA), accounting for ice-performance characteristics and validating the spatial-temporal route construction in the region of the Weddell Sea, Antarctica. We demonstrate the versatility of this route construction method by demonstrating that routes change depending on the seasonal sea ice variability, differences in the route-planning objective functions used, and the presence of other environmental conditions such as currents. To demonstrate the generality of our approach, we present examples in the Arctic Ocean and the Baltic Sea. The techniques outlined in this manuscript are generic and can therefore be applied to vessels with different characteristics. Our approach can have considerable utility beyond just a single vessel planning procedure, and we outline how this workflow is applicable to a wider community, e.g. commercial and passenger shipping.

Iran's recent seizure of unmanned US Navy boats shined a light on a pioneering Pentagon program to develop networks of air, surface, and underwater drones for patrolling large regions, meshing their surveillance with artificial intelligence. The year-old program operates numerous unmanned surface vessels, or USVs, in the waters around the Arabian peninsula, gathering data and images to be beamed back to collection centers in the Gulf. The program operated without incident until Iranian forces tried to grab three seven-meter Saildrone Explorer USVs in two incidents, on August 29-30 and September 1. In the first, a ship of Iran's Islamic Revolutionary Guard Corps hooked a line to a Saildrone in the Gulf and began towing it away, only releasing it when a US Navy Patrol boat and helicopter sped to the scene. In the second, an Iranian destroyer picked up two Saildrones in the Red Sea, hoisting them aboard.