ATAQ, Yemen – Again and again over the past two years, a military coalition led by Saudi Arabia and backed by the United States has claimed it won decisive victories that drove al-Qaida militants from their strongholds across Yemen and shattered their ability to attack the West. Here's what the victors did not disclose: many of their conquests came without firing a shot. That's because the coalition cut secret deals with al-Qaida fighters, paying some to leave key cities and towns and letting others retreat with weapons, equipment and wads of looted cash, an investigation by The Associated Press has found. Hundreds more were recruited to join the coalition itself. These compromises and alliances have allowed al-Qaida militants to survive to fight another day -- and risk strengthening the most dangerous branch of the terror network that carried out the 9/11 attacks. Key participants in the pacts said the U.S. was aware of the arrangements and held off on any drone strikes.

A Saudi-led coalition has launched air raids on Yemen's Hodeidah, in an apparent resumption of military operations on the strategic Red Sea city after Houthi rebels attacked two Saudi oil tankers and one of the United Arab Emirates' (UAE) main airports. The Houthi-run al-Masirah TV said in a series of tweets on Friday that coalition air strikes had targeted a radio station inside the city and a fishing pier. There were no immediate reports of casualties. The latest offensive on the port city of Hodeidah came a day after Houthi rebels claimed responsibility for a drone attack on Abu Dhabi's international airport. According to the Al-Masirah television channel, the Sammad-3 drone launched three attacks on the airport.

Yemen's Houthi rebels say they attacked Abu Dhabi's international airport in the United Arab Emirates with a drone. According to the Houthi-run Al-Masirah television channel, the Sammad-3 drone launched three attacks on the airport on Thursday. It was not immediately clear if there was any damage or casualties. Abu Dhabi airport tweeted earlier in the day there had been an incident involving a supply vehicle that had not affected operations. It was unclear if it was related to the reported drone attack.

In April 2018, the Nobel Prize Inspiration Initiative and 3M hosted the lecture, Climate Change: Science and Policy with Dr. Mario Molina. Molina won the Nobel Prize for Chemistry for his scientific discovery of the chemistry of the stratospheric ozone layer and its susceptibility to human-made activities. He co-authored research in 1974 in Nature magazine on the threat to the ozone layer from chlorofluorocarbon (CFC) gasses being used in spray cans. Molina has also served on the United States President's Council of Advisors on Science and Technology from 1994 to 2000 and again in 2010-2016. "Science doesn't tell you what to do. Science isn't either good or bad so you can not give Nobel prizes in science to good people, you do that in principle for the science," said Molina.

On March 29, 2018, a Toyota Land Cruiser carrying five members of the Al Manthari family was travelling through the Yemeni province of Al Bayda, inland from the Gulf of Aden. The family were heading to the city of al-Sawma'ah to pick up a local elder to witness the sale of a plot of land. At two in the afternoon, a rocket from a US Predator drone hit the vehicle, killing three of its passengers. One of the four men killed, Mohamed Saleh al Manthari, had three children aged between one and six. His father, Saleh al Manthari, says Mohamed was the family's only breadwinner.

Alex Honnold has his own verb. "To honnold"--usually written as "honnolding"--is to stand in some high, precarious place with your back to the wall, looking straight into the abyss. The verb was inspired by photographs of Honnold in precisely that position on Thank God Ledge, located 1,800 feet off the deck in Yosemite National Park. Honnold side-shuffled across this narrow sill of stone, heels to the wall, toes touching the void, when, in 2008, he became the first rock climber ever to scale the sheer granite face of Half Dome alone and without a rope. Had he lost his balance, he would have fallen for 10 long seconds to his death on the ground far below. Honnold is history's greatest ever climber in the free solo style, meaning he ascends without a rope or protective equipment of any kind. Above about 50 feet, any fall would likely be lethal, which means that, on epic days of soloing, he might spend 12 or more hours in the Death Zone. On the hardest parts of some climbing routes, his fingers will have no more contact with the rock than most people have with the touchscreens of their phones, while his toes press down on edges as thin as sticks of gum. Just watching a video of Honnold climbing will trigger some degree of vertigo, heart palpitations, or nausea in most people, and that's if they can watch them at all. Even Honnold has said that his palms sweat when he watches himself on film. All of this has made Honnold the most famous climber in the world.

SYDNEY – Australia will invest 7 billion Australian dollars ($5.2 billion) to develop and buy high-tech U.S. drones for joint military operations and to monitor waters including the South China Sea, it said Tuesday. Canberra has been embarking on its largest peacetime naval investment through a massive shipbuilding strategy that includes new submarines, offshore patrol vessels and frigates to shore up its defense capabilities. As part of this, the government will spend AU$1.4 billion to buy the first of six MQ-4C Triton maritime surveillance drones, with the aircraft to enter service from mid-2023, complementing seven P-8A Poseidon planes currently in use. "Together these aircraft will significantly enhance our anti-submarine warfare and maritime strike capability, as well as our search and rescue capability," Prime Minister Malcolm Turnbull said in a statement. "This investment will protect our borders and make our region more secure."

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.

This document gives a knowledge-oriented analysis of about 20 interesting Recognizing Textual Entailment (RTE) examples, drawn from the 2005 RTE5 competition test set. The analysis ignores shallow statistical matching techniques between T and H, and rather asks: What would it take to reasonably infer that T implies H? What world knowledge would be needed for this task? Although such knowledge-intensive techniques have not had much success in RTE evaluations, ultimately an intelligent system should be expected to know and deploy this kind of world knowledge required to perform this kind of reasoning. The selected examples are typically ones which our RTE system (called BLUE) got wrong and ones which require world knowledge to answer. In particular, the analysis covers cases where there was near-perfect lexical overlap between T and H, yet the entailment was NO, i.e., examples that most likely all current RTE systems will have got wrong. A nice example is #341 (page 26), that requires inferring from "a river floods" that "a river overflows its banks". Seems it should be easy, right? Enjoy!

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.