Decision trees usefully represent sparse, high dimensional and noisy data. Having learned a function from this data, we may want to thereafter integrate the function into a larger decision-making problem, e.g., for picking the best chemical process catalyst. We study a large-scale, industrially-relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk. This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pre-trained regression tree models. Decision makers may wish to optimize discrete models to repurpose legacy predictive models, or they may wish to optimize a discrete model that particularly well-represents a data set. We develop several heuristic methods to find feasible solutions, and an exact, branch-and-bound algorithm leveraging structural properties of the gradient-boosted trees and penalty functions. We computationally test our methods on concrete mixture design instance and a chemical catalysis industrial instance.

The history of smart motorcycle helmets is a mixed bag, from clip-on heads-up displays to the Skully debacle that ended with a great piece of hardware being cratered by financially irresponsible founders. But technology moves on, and next year a new smart helmet from Jarvish will be vying for the heads of nerdy motorcyclists. The Taiwanese company will be introducing two helmets in the coming year. The first is the $799 Jarvish X, with voice activation and support for Siri, Google Assistant and Alexa. Riders can ask for directions and weather reports, and they can control media playing on their smartphone.

It's tempting to assume that Tesla's Autopilot represents the gold standard for semi-autonomous driving features, but Consumer Reports would beg to differ. The publication has released the results of its first rankings for automated driving systems, and Cadillac's Super Cruise edged out Autopilot to receive the top rating. Both rivals fared well in terms of abilities -- Cadillac's advantage was in safety. Autopilot currently checks for driver attention solely through the steering wheel, sending a warning (and if necessary, stopping the car) if you take your hands away for 24 seconds. Super Cruise, however, uses a camera to track your gaze and will deliver a warning if you look away for just four seconds.

Abstract--The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data. IGITAL satellite data with different spatial and spectral resolution are available for almost every locality on the Earth's land surface [1]-[5]. This enables the procurement of detailed information from surficial features and processes at different scales. Linear features are considered as one of the most important surficial features in different fields of study [6]-[8]. R. Scalzo is with the Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia (email: richard.scalzo@sydney.edu.au). Linear features represent the expression of some degree of linearity of a single or diverse grouping of both natural and cultural features [9], [10].

Environmental author Wendell Berry might shudder at this comparison, but farmers are like data scientists. To make decisions, they ferret out meaning from a sea of data. That data just happens to be related to environmental conditions like temperature, rainfall, salinity, nitrogen, pests, commodity prices, and other variables. What that data often shows is trouble: increasingly costly or scarce water supplies, new and more voracious pests, herbicide-resistant weeds, and extreme weather. All of this can result in lower farm yields and higher costs.

A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The primary purpose of the data is thought to be building a classification model for early prediction of the rare event. However, it can also be used for multivariate time series data exploration and building other supervised and unsupervised models.

Eric Ward is the co-founder and CEO of both Odyne Space and Aten Engineering, two space tech startups with a ton of promise for the future. Eric is an experienced systems architect who sees growing the space industry as the next step to progressing humanity beyond the planet. At Odyne, Eric and the team are working on phase one: launch, and run a large scale nano and micro satellite launch program to allow more satellite tech companies easier access to space. Aten Engineering on the other hand deals with what to do once we get there and is an asteroid mining company to provide humanity with inexpensive access to the materials we need to become a space-faring civilization. Eric recently received a Master's degree in Systems Design and Management from MIT, has published multiple technical documents on Systems Architecture and the Space Industry, has been featured in Fast Company, and co-founded the MIT New Space Age Conference.

One of the intriguing aspects of the popular 1960s television show "Mission Impossible" was the opening sequence of every episode, which featured a secret agent listening to a recorded message about an upcoming mission. At the end of the recording each week, the tape would sizzle, crackle, and disintegrate into a heap of smoke and debris, ensuring no one else could access the top-secret information it contained. Until recently, self-destructing electronic systems remained within the realm of science fiction, but advances in chemistry, engineering, and materials science are finally allowing researchers to construct circuits that break down on their own timetable. This includes systems that rely on conventional complementary oxide semiconductor (CMOS) technology. "The goal is to develop functional circuits that can operate for a period of time and then vaporize," says Amit Lal, Robert M. Scharf 1977 Professor of Engineering in the Electrical and Computer Engineering Department at Cornell University in Ithaca, NY, and director of the university's SonicMEMs lab.

For scale, consider the Statue of Liberty, standing 305 feet tall. At 466 feet, the average wind turbine in the U.S. dwarfs Lady Liberty by more than half. And when GE's next-generation monster wind turbine, the Haliade-X, hits the market in 2021, it will nearly double that size to 877 feet, just shy of the Eiffel Tower. A single Haliade-X rotor blade will stretch 315 feet, longer than a football field. As a general rule of thumb, when it comes to energy and energy exploration, bigger is better: the larger the machinery, the deeper the dig, the greater the production yield.

We employ a set of standard basis functions, e.g., polynomials, to approximate the governing equation with high accuracy. Upon recasting the problem into a function approximation problem, we discuss several important aspects for accurate approximation. Most notably, we discuss the importance of using a large number of short bursts of trajectory data, rather than using data from a single long trajectory. Several options for the numerical algorithms to perform accurate approximation are then presented, along with an error estimate of the final equation approximation. We then present an extensive set of numerical examples of both linear and nonlinear systems to demonstrate the properties and effectiveness of our equation recovery algorithms.