Each passing day, Museum and Cultural Places visitors' lives are subtly shaped by AI-driven technologies. In this smartphone glutted world, there lies a huge challenge for both the Museums and Cultural places to attract visitors. The question arises, What is the role of AI in a Museum? To learn more about visitors, manage visitor experience and collect relevant data for boosting the traffic and developing future growth strategies, Museums and Cultural Places across the globe are using artificial intelligence in several ways. The most commonly used modes of AI are - Robots & Chatboxes, Computer Visions and Natural language processing amongst others. Unlike traditional methods of managing generic data once a year, Museums rely on structured data to benefit both the visitors and the employees.

In estimations of ocean heat content – important when assessing and predicting the effects of climate change – calculations have often presented the rate of warming as a gradual rise from the mid 20th century to today. However, new research from UC Santa Barbara scientists Timothy DeVries and Aaron Bagnell could overturn that assumption, suggesting the ocean maintained a relatively steady temperature throughout most of the 20th century, before embarking on a steep rise. The newly discovered dynamics may have significant implications for what we might expect in the future. "There wasn't an onset of an imbalance until about 1990, which is later than most estimates," said DeVries, an associate professor in the Department of Geography, and a co-author on a paper that appears in the journal Nature Communications. According to the study, the period from 1950 to1990 saw temperature fluctuations in the water column but no net warming.

This study proposes multivariate kernel density estimation by stagewise minimization algorithm based on $U$-divergence and a simple dictionary. The dictionary consists of an appropriate scalar bandwidth matrix and a part of the original data. The resulting estimator brings us data-adaptive weighting parameters and bandwidth matrices, and realizes a sparse representation of kernel density estimation. We develop the non-asymptotic error bound of estimator obtained via the proposed stagewise minimization algorithm. It is confirmed from simulation studies that the proposed estimator performs competitive to or sometime better than other well-known density estimators.

Israel's defense minister warned Thursday that his country is prepared to strike Iran, issuing the threat against the Islamic Republic after a fatal drone strike on a oil tanker at sea that his nation blamed on Tehran. The comments by Benny Gantz come as Israel lobbies countries for action at the United Nations over last week's attack on the oil tanker Mercer Street that killed two people. The tanker, struck off Oman in the Arabian Sea, is managed by a firm owned by an Israeli billionaire. The U.S. and the United Kingdom also blamed Iran for the attack, but no country has offered evidence or intelligence to support the claim. Iran, which along with its regional militia allies has launched similar drone attacks, has denied being involved.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. DUBAI, United Arab Emirates (AP) – The United States has joined the United Kingdom and Israel in accusing Iran of carrying out a deadly drone strike that killed two aboard a tanker off Oman. U.S. Secretary of State Antony Blinken made the announcement in a statement Sunday. Blinken said: "Upon review of the available information, we are confident that Iran conducted this attack, which killed two innocent people, using one-way explosive (drones), a lethal capability it is increasingly employing throughout the region." He added that there was "no justification for this attack, which follows a pattern of attacks and other belligerent behavior."

For over hundreds of millions of years, sea turtles and their ancestors have swum in the vast expanses of the ocean. They have undergone a number of evolutionary changes, leading to speciation and sub-speciation. However, in the past few decades, some of the most notable forces driving the genetic variance and population decline have been global warming and anthropogenic impact ranging from large-scale poaching, collecting turtle eggs for food, besides dumping trash including plastic waste into the ocean. This leads to severe detrimental effects in the sea turtle population, driving them to extinction. This research focusses on the forces causing the decline in sea turtle population, the necessity for the global conservation efforts along with its successes and failures, followed by an in-depth analysis of the modern advances in detection and recognition of sea turtles, involving Machine Learning and Computer Vision systems, aiding the conservation efforts.

The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., data are not observed over equally-spaced time intervals. In multi-dimensional time series analysis, a task is to conduct evolutionary subspace clustering, aiming at clustering temporal data according to their evolving low-dimensional subspace structures. Many existing methods can only process time series with regular time steps while time series are unevenly sampled in many situations such as missing data. In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced. We demonstrate that this method can not only interpolate data at any time step for the evolutionary subspace clustering task, but also achieve higher accuracy than other state-of-the-art evolutionary subspace clustering methods. Both synthetic and real-world data are used to illustrate the efficacy of our proposed method.

Computational intelligence-based ocean characteristics forecasting applications, such as Significant Wave Height (SWH) prediction, are crucial for avoiding social and economic loss in coastal cities. Compared to the traditional empirical-based or numerical-based forecasting models, "soft computing" approaches, including machine learning and deep learning models, have shown numerous success in recent years. In this paper, we focus on enabling the deep learning model to learn both short-term and long-term spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural Network (WGNN) approach is proposed to integrate the advantages of wavelet transform and graph neural network. Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction. Experimental results show that the proposed WGNN approach outperforms other models, including the numerical models, the machine learning models, and several deep learning models.

There is a natural synergy (yes, we're using that word) among the many subcategories that make up the AI world. It would be impossible to talk about synthetic data without talking about machine learning, computer vision, software, ethics, privacy (and neural rendering, and GANs, and our Marketing and Sales director Michael's daughter's book Neural Networks for Babies) -- so that's why we don't do that. But synthetic data remains the apple of our eye. So we were thrilled to discover that Gartner Inc.'s June Report predicts that by 2030, the most used type of data in AI will be synthetic. Modernization can be a tricky thing, especially when it requires industry-wide adjustments.

Tropical cyclones can be of varied intensity and cause a huge loss of lives and property if the intensity is high enough. Therefore, the prediction of the intensity of tropical cyclones advance in time is of utmost importance. We propose a novel stacked bidirectional long short-term memory network (BiLSTM) based model architecture to predict the intensity of a tropical cyclone in terms of Maximum surface sustained wind speed (MSWS). The proposed model can predict MSWS well advance in time (up to 72 h) with very high accuracy. We have applied the model on tropical cyclones in the North Indian Ocean from 1982 to 2018 and checked its performance on two recent tropical cyclones, namely, Fani and Vayu. The model predicts MSWS (in knots) for the next 3, 12, 24, 36, 48, 60, and 72 hours with a mean absolute error of 1.52, 3.66, 5.88, 7.42, 8.96, 10.15, and 11.92, respectively.