China Pledges To Stop Funding New Coal Projects Overseas

And we will not assist but be impressed on the ingenuity of a contractor named B.C. Today, we’ll fill a stadium to observe a monster truck-pulling contest. Non-Greenlanders first heard about it in 1818, nevertheless it wasn’t until 1894 that intrepid Arctic explorer Robert E. Peary truly situated the meteorite. Ahnighito, also identified because the Cape York Meteorite, is a scary-huge, 4.5 billion-yr-previous piece of an asteroid that fell out of the sky and landed in Greenland about 10,000 years ago. For some cause, he thought it could be a swell thought to take it again to the Canadian Museum of Natural History in Canada. Before Peary could get the meteorite onto a ship, he had to get it to the coast, and that required the development of Greenland’s first and solely railroad. A participant pulls a truck loaded with men during the Olde Time Coney Island Strongman Spectacular. The Canadian minister-strongman, who oversees a 120-member congregation at Canada’s Lutheran Church in Cobourg, Ont., has made a hobby out of pulling trucks with sheer brawn. Trucks generally transfer beneath their very own energy, but when the engine won’t start on your large rig, you may want to call Rev. Kevin Fast for assist. In 1996, he earned his first entry in the Guinness Book of World Records by towing a 17.6-ton (16-metric-ton) truck for 98.4 feet (30 meters). Six years after that, he improved the report by towing a 28.6-ton (26-metric ton) fire truck the identical distance, and in 2008, moved the usual even higher, by dragging a truck weighing 63.1 tons (57.2 metric tons).
Click forward to learn extra about how this enormous boulder was moved. In director Werner Herzog’s 1982 film “Fitzcarraldo,” an European ne’er-do-nicely (portrayed by Klaus Kinski) desires of turning into rubber mogul in Peru, and comes up with a bizarre solution for reaching a probably profitable, however previously inaccessible, parcel of rubber trees. Fitzcarraldo sails up a river as far as he can go in a large paddlewheel steamship, after which one way or the other convinces a crew of natives to drag the 300-ton (272.2-metric-ton) craft up a steep mountainside with ropes and pulleys in order that he can get to a different nearby river and sail on to his vacation spot. After all, if there’s a single enduring trait that humans have demonstrated all through the ages, it’s the never-ending desire to maneuver really huge, heavy objects that can’t transfer beneath their own power. In the long run, fortune still eludes Fitzcarraldo, but one thing about his willpower and sheer gall resonates with us. We nonetheless marvel at how the ancient Egyptians managed to drag 2.5-ton (2.3-metric-ton) blocks of granite for miles and then carry them into place after they built the pyramids, all with out modern machinery.
The search ranking problem is modelled as considered one of pairwise choice which is a standard strategy found in the learning To Rank literature (Cao et al., 2007). Each training instance is a pair of a booked itemizing and a non-booked listing for a given question and user. While deep studying combined with the pairwise formulation in Equation 1 proved to be a robust tool for optimizing offline NDCG and driving on-line booking positive factors, we soon realized that it had clear limitations. One of the noticeable problems was the lack of variety in search results. This problem was first dropped at our consideration when it was noticed that for many common locations, the highest ranked listings all the time seemed similar by way of visible attributes resembling worth, location, capability, and listing room sort. This concern was additional validated by looking at the information. Once we sampled pairs of high ranked listings in searches (the results of that are shown in Figure 1) we observed an excessive focus of listings with related costs and locations.
These rankers can have several functions reminiscent of imposing business logic or optimizing secondary rating objectives. T outcomes for every search. There exists a wealthy historical past of methods for managing variety in ranking. NDCG (Clarke et al., 2008) intention to solve this drawback by modifying the definition of NDCG to incorporate a penalty based mostly on subtopic relevance – basically rewarding objects with novel subtopics whereas penalizing those with redundant subtopics. This framework is also difficult to directly apply to Airbnb as listings are highly variable. Don’t easily map in a structured technique to a discrete class of subtopics. Many approaches first define setwise range metrics as within the case of Maximal Marginal Relevance (MMR) (Carbonell and Goldstein, 1998). However, pure setwise metrics don’t match effectively in e-commerce search purposes as they do not account for the significant positional bias in how results are shown to the user. Furthermore, even when listings were mapped to subtopics, it isn’t fully apparent which subtopics are relevant for a given query in contrast to in traditional Information Retrieval settings.