Drones are finding increasing use in Japanese agriculture as farmers start to use the unmanned aerial vehicles for crop inspection and other purposes. Drones "are effective in promoting data-based agriculture and reducing agricultural work" at a time when many aged farmers are struggling to find successors, says an official at the farm ministry's Technology Policy Office. In Japan, it is necessary for unmanned helicopters that spray pesticide, fertilizer or seeds to be registered with a special organization. Registration became necessary for drones in 2015. As of January, 673 drones had been registered, up about three times since last March.

From the BBC World Service … The world's biggest mining companies are seeing a boost to their bottom lines thanks to rising global commodity prices. We'll tell you how the battery revolution is helping shape the overall market. Then, India is opening up its coal industry, allowing foreign companies to bid for coal mines in the country. But will more investment from some of the world's biggest companies translate into better quality of life for residents there? Afterward, a conversation about whether growing use of artificial intelligence presents a looming danger.

According to the report, Apple is seeking to lock down a long-term deal, securing several thousand metric tons a year, for a last five years. The move puts Apple in direct competition with other big players who are also looking for a similar agreement, and advantage. BMW, Volkswagen and Samsung's own battery division are thought to be engaged in similar negotiations for their own EV projects. It's clear from the piece that Apple is only seeking to secure material for batteries that go inside its consumer hardware. CEO Tim Cook has been open about his company's interest in the "autonomous systems" market, but wouldn't be drawn on what exactly was being worked on.

Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that instance. For low class-label noise levels, especially when simple base learners are used, emphasis should be made on instances for which the disagreement rate is high. When more flexible classifiers are used and as the noise level increases, the emphasis on these uncertain instances should be reduced. In fact, at sufficiently high levels of class-label noise, the focus should be on instances on which the ensemble classifiers agree. The optimal type of emphasis can be automatically determined using cross-validation. An extensive empirical analysis using the beta distribution as emphasis function illustrates that vote-boosting is an effective method to generate ensembles that are both accurate and robust.

Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items. This work exposes a serious problem with the state of learning-to-rank algorithms, which is that they are based on convex proxies that lead to poor approximations. We then discuss the possibility of "exact" reranking algorithms based on mathematical programming. We prove that a relaxed version of the "exact" problem has the same optimal solution, and provide an empirical analysis.

To manufacture medicines, chemists must find the right combinations of chemicals to make the necessary chemical structures. This is more complicated than it sounds, as typical chemical reactions employ several different components, and each chemical involved in a reaction adds another dimension to the calculations. In an ideal world, chemists would like to predict which combination of chemicals would deliver the highest yield of product and avoid unintended by-products or other losses, but predicting the outcome of these multi-dimensional reactions has proven challenging. A group of researchers led by Abigail Doyle, the A. Barton Hepburn Professor of Chemistry at Princeton University, and Dr. Spencer Dreher of Merck Research Laboratories, has found a way to accurately predict reaction yields while varying up to four reaction components, using an application of artificial intelligence known as machine learning. They have turned their method into software that they have made available to other chemists.

In order to design and operate the process it is important to know which variables have the strongest influence on performance. To estimate this we compared each state with the length and diameter of the resulting fibers, performing second order polynomial fit using samples over all 9 experiments, and noting the value of the resulting correlation coefficients R. The correlations between process parameters and the Length and Diameter is contained in Supplementary Table S.1. The angle and position have very little influence on the characteristics of the produced fibers. Polymer flow has a moderate influence (0.37) on length, but only a weak influence on diameter. Thus the most significant influence on overall performance is solvent speed followed by channel width.

The mining industry has traditionally been a laggard when it comes to innovation. The 21st-century economy, however, dominated by emerging technologies like electric vehicles, green energy sources and ever-more advanced mobile devices, is demanding creative approaches to efficiently delivering the raw materials that will fuel modern economies. Canadian mining is seeing a real drive to innovate that is bolstering our ability to get materials to global markets as efficiently and cost-effectively as possible. And it's changing what it means to work in mining. New technologies are enabling workers to make quicker, more informed decisions at the front lines of operations.

Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario.

Those who dream of getting an Amazon package, a prescription drug, or even a beer delivered to their doorsteps via drone might have their wishes fulfilled sooner than expected.