For centuries, the Rub' al-Khali desert near Saudi Arabia and Dubai -- known as the Empty Quarter -- was dismissed as a lifeless sea of sand. In 2002, Sheikh Mohammed bin Rashid Al Maktoum, ruler of Dubai, spotted unusual dune formations and a large black deposit while flying over the desert. That led to the discovery of Saruq Al-Hadid, an archaeological site rich in remnants of copper and iron smelting, which is now believed to be part of a 5,000-year-old civilization buried beneath the sands. Researchers have now found traces of this ancient society approximately 10 feet beneath the desert surface, hidden in plain sight and long overlooked due to the harsh environment and shifting dunes of the Empty Quarter. This discovery brings fresh life to the legend of a mythical city known as'Atlantis of the Sands.'

In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of oil to a reliance on renewable energy, and, in particular, wind. Modeling wind for assessing potential energy output in a country as large, geographically diverse and understudied as Saudi Arabia is a challenge which implies highly non-linear dynamic structures in both space and time. To address this, we propose a spatio-temporal model whose spatial information is first reduced via an energy distance-based approach and then its dynamical behavior is informed by a sparse and stochastic recurrent neural network (Echo State Network). Finally, the full spatial data is reconstructed by means of a non-stationary stochastic partial differential equation-based approach. Our model can capture the fine scale wind structure and produce more accurate forecasts of both wind speed and energy in lead times of interest for energy grid management and save annually as much as one million dollar against the closest competitive model.

We investigate the CLIP image encoder by analyzing how individual model components affect the final representation. We decompose the image representation as a sum across individual image patches, model layers, and attention heads, and use CLIP's text representation to interpret the summands. Interpreting the attention heads, we characterize each head's role by automatically finding text representations that span its output space, which reveals property-specific roles for many heads (e.g. location or shape). Next, interpreting the image patches, we uncover an emergent spatial localization within CLIP. Finally, we use this understanding to remove spurious features from CLIP and to create a strong zero-shot image segmenter. Our results indicate that a scalable understanding of transformer models is attainable and can be used to repair and improve models.

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that encode censoring information in the neural network architecture. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, $r$-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2.5) concentration over the whole of Saudi Arabia.

DUBAI, UNITED ARAB EMIRATES – Yemen's Houthi movement launched drone attacks on oil facilities in a remote area of Saudi Arabia, the group's Al Masirah TV said Saturday, but there was no immediate confirmation from Saudi authorities or state oil giant Aramco. A Saudi-led coalition is battling the Iran-aligned Houthis to try to restore Yemen's government, which was ousted from power in the capital, Sanaa, by the group in late 2014. The war has been in military stalemate for years. The Houthis have stepped up cross-border missile and drone attacks on Saudi Arabia in recent months. "Ten drones targeted Aramco's Shaybah oilfield and refinery in the first Operation: Balance of Deterrence in the east of the kingdom," the Al Masirah channel reported, citing a Houthi military spokesman.

DUBAI, UNITED ARAB EMIRATES – High above Yemen's rebel-held city of Hodeida, a drone controlled by Emirati forces hovered as an SUV carrying a top Shiite Houthi rebel official turned onto a small street and stopped, waiting for another vehicle in its convoy to catch up. Seconds later, the SUV exploded in flames, killing Saleh al-Samad, a top political figure. The drone that fired that missile in April was not one of the many American aircraft that have been buzzing across the skies of Yemen, Iraq and Afghanistan since Sept. 11, 2001. Across the Middle East, countries locked out of purchasing U.S.-made drones due to rules over excessive civilian casualties are being wooed by Chinese arms dealers, the world's main distributor of armed drones. "The Chinese product now doesn't lack technology, it only lacks market share," said Song Zhongping, a Chinese military analyst and former lecturer at the People's Liberation Army Rocket Force University of Engineering.

ABU DHABI – Walking through a trade show all about military drones, Emirati officials made a point on Sunday to stop first at a stand run by Chinese officials with a mock armed drone hanging above them. Defense analysts believe that drone, the Wing Loong II, is now being used by the Emirati military while the United Arab Emirates remains barred from purchasing weaponized drones from the United States. That purchase, as well as Abu Dhabi hosting the Unmanned Systems Exhibition & Conference this week in the Emirati capital, shows the power these weapons now hold across the Middle East. Top UAE officials, including Abu Dhabi's powerful crown prince, Mohammed bin Zayed Al Nahyan, were on hand for the drone conference, which opened on Sunday. The UAE, home to skyscraper-studded Dubai, already has embraced drones.