Hossein Salami, the commander-in-chief of the Islamic Revolutionary Guard Corps, addressing the issue at a military ceremony in Sanandaj, Iran, said the drone had been shot down in Iranian airspace. "We are not going to get engaged in a war with any country, but we are fully prepared for war," Mr. Salami said, according to a translation from Press TV. "Today's incident was a clear sign of this precise message so we are continuing our resistance." The Revolutionary Guards said in a separate statement that the aircraft was an American-made Global Hawk surveillance drone, according to Press TV. American officials said last week that Iran had fired a surface-to-air missile at a drone over the Gulf of Oman, on the same day that two oil tankers were attacked.

TEHRAN - Iran's Revolutionary Guard said Thursday it shot down a U.S. drone amid heightened tensions between Tehran and Washington over its collapsing nuclear deal. The U.S. military declined to immediately comment. The reported shootdown of the RQ-4 Global Hawk comes after the U.S. military previously alleged Iran fired a missile at another drone last week that responded to the attack on two oil tankers near the Gulf of Oman. The U.S. blames Iran for the attack on the ships, which Tehran denies. The attacks come against the backdrop of heightened tensions between the U.S. and Iran following President Donald Trump's decision to withdraw from Tehran's nuclear deal with world powers a year ago. The White House separately said it was aware of reports of a missile strike on Saudi Arabia amid a campaign targeting the kingdom by Yemen's Iranian-allied Houthi rebels.

ABU DHABI - President Donald Trump's national security adviser warned Iran on Wednesday that any attacks in the Persian Gulf will draw a "very strong response" from the U.S., taking a hard-line approach with Tehran after his boss only two days earlier said America wasn't "looking to hurt Iran at all." John Bolton's comments are the latest amid heightened tensions between Washington and Tehran that have been playing out in the Middle East. Bolton spoke to journalists in Abu Dhabi, the capital of the United Arab Emirates, which only days earlier saw former Defense Secretary Jim Mattis warn there that "unilateralism will not work" in confronting the Islamic Republic. The dueling approaches highlight the divide over Iran within American politics. The U.S. has accused Tehran of being behind a string of incidents this month, including the alleged sabotage of oil tankers off the Emirati coast, a rocket strike near the U.S. Embassy in Baghdad and a coordinated drone attack on Saudi Arabia by Yemen's Iran-allied Houthi rebels. On Wednesday, Bolton told journalists that there had been a previously unknown attempt to attack the Saudi oil port of Yanbu as well, which he also blamed on Iran.

Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. In January, a group of high-level military commanders gathered at an air base in Yemen.

SANAA - The Saudi-led military coalition in Yemen carried out several airstrikes on the Houthi-held capital Sanaa on Thursday after the Iranian-aligned movement claimed responsibility for drone attacks on Saudi oil installations. The Sanaa strikes targeted nine military sites in and around the city, residents said, with humanitarian agencies reporting a number of casualties. Rubble filled a populated street lined by mud-brick houses, a Reuters journalist on the scene said. A crowd of men lifted the body of a women, wrapped in a white shroud, into an ambulance. Houthi-run Masirah television quoted the Houthi health ministry as saying six civilians, including four children, had been killed and 60 wounded, including two Russian women working in the health sector.

Vikram Mahidhar reminds us all that AI is only as good as the humans supervising it and programming it. The biases and artefacts that come out of the processing are reflective of the biases programmed in at the beginning. A program trained to recognise totalled car bodies for insurance purposes, for example, will need close supervision of its decision-making outputs, for regulatory and consumer confidence and acceptance of the decision. There is a call and a growth in a new class of AI--one that is explainable, and that builds trust by providing evidence. Vikram also reminds us that a governance strategy is key to engendering trust in our organisation, processes and people.

Carbon sequestration Cutting greenhouse-gas emissions alone won't be enough to prevent sharp increases in global temperatures. We'll also need to remove vast amounts of carbon dioxide from the atmosphere, which not only would be incredibly expensive but would present us with the thorny problem of what to do with all that CO2. A growing number of startups are exploring ways of recycling carbon dioxide into products, including synthetic fuels, polymers, carbon fiber, and concrete. That's promising, but what we'll really need is a cheap way to permanently store the billions of tons of carbon dioxide that we might have to pull out of the atmosphere. Grid-scale energy storage Renewable energy sources like wind and solar are becoming cheap and more widely deployed, but they don't generate electricity when the sun's not shining or wind isn't blowing. That limits how much power these sources can supply, and how quickly we can move away from steady sources like coal and natural gas.

Modern computing and communication technologies can make data collection procedures very efficient. However, our ability to analyze large data sets and/or to extract information out from them is hard-pressed to keep up with our capacities for data collection. Among these huge data sets, some of them are not collected for any particular research purpose. For a classification problem, this means that the essential label information may not be readily obtainable, in the data set in hands, and an extra labeling procedure is required such that we can have enough label information to be used for constructing a classification model. When the size of a data set is huge, to label each subject in it will cost a lot in both capital and time. Thus, it is an important issue to decide which subjects should be labeled first in order to efficiently reduce the training cost/time. Active learning method is a promising outlet for this situation, because with the active learning ideas, we can select the unlabeled subjects sequentially without knowing their label information. In addition, there will be no confirmed information about the essential variables for constructing an efficient classification rule. Thus, how to merge a variable selection scheme with an active learning procedure is of interest. In this paper, we propose a procedure for building binary classification models when the complete label information is not available in the beginning of the training stage. We study an model-based active learning procedure with sequential variable selection schemes, and discuss the results of the proposed procedure from both theoretical and numerical aspects.

The high seas are probably the most lawless place left on Earth. They're a portal back in time to the way the world looked for most of our history: fierce and open competition for resources and contested territories. Pirating continues to be a way to make a living. It's not a complete free-for-all--most countries require registration of fishing vessels and enforce environmental protocols. Cooperative agreements between countries oversee fisheries in international waters.

Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our method performs better than all published results on these datasets.