The purpose of this paper is to re-investigate the estimation of multiple factor models by relaxing the convention that the number of factors is small. We first obtain the collection of all possible factors and we provide a simultaneous test, security by security, of which factors are significant. Since the collection of risk factors selected for investigation is large and highly correlated, we use dimension reduction methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) and prototype clustering, to perform the investigation. For comparison with the existing literature, we compare the multi-factor model's performance with the Fama-French 5-factor model. We find that both the Fama-French 5-factor and the multi-factor model are consistent with the behavior of "large-time scale" security returns. In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted $R^{2}$. Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns.

Stochastic optimization is key to efficient inversion in PDE-constrained optimization. Using 'simultaneous shots', or random superposition of source terms, works very well in simple acquisition geometries where all sources see all receivers, but this rarely occurs in practice. We develop an approach that interpolates data to an ideal acquisition geometry while solving the inverse problem using simultaneous shots. The approach is formulated as a joint inverse problem, combining ideas from low-rank interpolation with full-waveform inversion. Results using synthetic experiments illustrate the flexibility and efficiency of the approach.

COLUMBUS, OHIO – Ohio crews cleaning up a massive former Cold War-era uranium enrichment plant in Ohio plan this summer to deploy a high-tech helper: an autonomous, radiation-measuring robot that will roll through kilometers of large overhead pipes to spot potentially hazardous residual uranium. Officials say it's safer, more accurate and tremendously faster than having workers take external measurements to identify which pipes need to be removed and decontaminated at the Portsmouth Gaseous Diffusion Plant in Piketon. They say it could save taxpayers tens of millions of dollars on cleanups of that site and one near Paducah, Kentucky, which for decades enriched uranium for nuclear reactors and weapons. The RadPiper robot was developed at Carnegie Mellon University in Pittsburgh for the U.S. Department of Energy, which envisions using similar technology at other nuclear complexes such as the Savannah River Site in Aiken, South Carolina, and the Hanford Site in Richland, Washington. Roboticist William "Red" Whittaker, who began his career developing robots to help clean up the Three Mile Island nuclear power accident and now directs Carnegie Mellon's Field Robotics Center, said technology like RadPiper could transform key tasks in cleaning up the country's nuclear legacy.

Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.

Philippe Herve, vice president of oil and gas solutions for Austin-based SparkCognition, works with major energy producers including BP to improve efficiency in the oil patch. He spent more than 30 years at Schlumberger and other companies before joining SparkCognition two years ago. The 4-year-old firm, which employs about 250 people, develops artificial intelligence solutions for producers looking to use operations data to detect and prevent failures and cut costs, among other things. The following interview has been edited for length and clarity. In October, His Highness Sheikh Mohammed bin Rashid Al Maktoum, the Vice President and Prime Minister of the UAE, said artificial intelligence is the next major revolution of our time and selected a state minister to focus on this space.

Blend two or three metals together and you get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns. But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass that's amorphous, with its atoms arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today's best steel, plus it stands up better to corrosion and wear. Even though metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful. Now a group led by scientists at the Department of Energy's SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University has reported a shortcut for discovering and improving metallic glass – and, by extension, other elusive materials - at a fraction of the time and cost.

Drones deployed in wind turbine or power plant inspection brings great and quick result as compared to the manual ones. This sounds the perfect job for a drone. Moreover, the combination of drone inspection and Boston-based AirFusion software solution, would take ROI to next level. The AirFusion solution takes the inspection images and uses AI technology to detect and categorize blade damages, and the analysis provided saves over 90% of the time. The next step is to take the analysis to the next level, as with the damages identified and by applying algorithms the software is able to predict impact and prioritize work.

Real-world networks such as social and communication networks are too large to be observed entirely. Such networks are often partially observed such that network size, network topology, and nodes of the original network are unknown. In this paper we formalize the Adaptive Graph Exploring problem. We assume that we are given an incomplete snapshot of a large network and additional nodes can be discovered by querying nodes in the currently observed network. The goal of this problem is to maximize the number of observed nodes within a given query budget. Querying which set of nodes maximizes the size of the observed network? We formulate this problem as an exploration-exploitation problem and propose a novel nonparametric multi-arm bandit (MAB) algorithm for identifying which nodes to be queried. Our contributions include: (1) $i$KNN-UCB, a novel nonparametric MAB algorithm, applies $k$-nearest neighbor UCB to the setting when the arms are presented in a vector space, (2) provide theoretical guarantee that $i$KNN-UCB algorithm has sublinear regret, and (3) applying $i$KNN-UCB algorithm on synthetic networks and real-world networks from different domains, we show that our method discovers up to 40% more nodes compared to existing baselines.

The number of companies is showing keen interest in drone delivery, and according to the research, drone delivery of packages could reduce greenhouse gas emissions and energy use. According to the reports, around 415 million metric tons of carbon dioxide comes from medium and heavy-duty delivery trucks. Researchers decided to experiment and analyze how much energy drone delivery would consume or save, as the amount of energy used by drone depends on its weight, battery and the package it carries. Researchers used a quadcopter drone capable of delivering 1.1 pound package and octocopert drone capable of delivering 17.6 pound package, each drone had the range of 2.5 miles. Results stated that the drones did save energy, and would be environment friendly option to choose for.

The number of companies is showing keen interest in drone delivery, and according to the research, drone delivery of packages could reduce greenhouse gas emissions and energy use. According to the reports, around 415 million metric tons of carbon dioxide comes from medium and heavy-duty delivery trucks. Researchers decided to experiment and analyze how much energy drone delivery would consume or save, as the amount of energy used by drone depends on its weight, battery and the package it carries. Researchers used a quadcopter drone capable of delivering 1.1 pound package and octocopert drone capable of delivering 17.6 pound package, each drone had the range of 2.5 miles. Results stated that the drones did save energy, and would be environment friendly option to choose for.