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.

Tue 13 Feb 2018 11.00 EST Last modified on Tue 13 Feb 2018 11.01 EST Drones invoke varying perceptions, from fun gadget to fly in the park to deadly military weapons. In the future, they may even be viewed as a handy tool in the battle to fight climate change. Greenhouse gas emissions from the tra...

A key phase in the bridge design process is the selection of the structural system. Due to budget and time constraints, engineers typically rely on engineering judgment and prior experience when selecting a structural system, often considering a limited range of design alternatives. The objective of this study was to explore the suitability of supervised machine learning as a preliminary design aid that provides guidance to engineers with regards to the statistically optimal bridge type to choose, ultimately improving the likelihood of optimized design, design standardization, and reduced maintenance costs. In order to devise this supervised learning system, data for over 600,000 bridges from the National Bridge Inventory database were analyzed. Key attributes for determining the bridge structure type were identified through three feature selection techniques. Potentially useful attributes like seismic intensity and historic data on the cost of materials (steel and concrete) were then added from the US Geological Survey (USGS) database and Engineering News Record. Decision tree, Bayes network and Support Vector Machines were used for predicting the bridge design type. Due to state-to-state variations in material availability, material costs, and design codes, supervised learning models based on the complete data set did not yield favorable results. Supervised learning models were then trained and tested using 10-fold cross validation on data for each state. Inclusion of seismic data improved the model performance noticeably. The data was then resampled to reduce the bias of the models towards more common design types, and the supervised learning models thus constructed showed further improvements in performance. The average recall and precision for the state models was 88.6% and 88.0% using Decision Trees, 84.0% and 83.7% using Bayesian Networks, and 80.8% and 75.6% using SVM.