CAPE CANAVERAL, FLORIDA – Spacewalking astronauts carried out a high-flying, high-tech version of musical chairs Wednesday, rearranging pumps outside the International Space Station. Popping out early, NASA astronauts Drew Feustel and Ricky Arnold quickly swapped the positions of two spare ammonia pumps that are part of the space station's critical cooling system. One pump got too cold because of a power shutdown 17 years ago and is called Frosty; flight controllers plan to test it in the coming days to see if it still works. The other, a failed unit dubbed Leaky, spewed out ammonia five years ago. Frosty took Leaky's spot on a robot-arm mechanism, while Leaky was moved to a long-term storage platform. Ammonia coolant is toxic, and Mission Control repeatedly warned the spacewalkers to be careful of any leaks.

Machine learning algorithms are everywhere. It is not just Facebook and Google. Companies are using them to provide personalized education services and advanced business intelligence services, to fight cancer and to detect counterfeit goods. The technology will make us collectively wealthier and more capable of providing for human welfare, human rights, human justice and the fostering of the virtues we need to live well in communities. We should welcome it and do all that we can to promote it. As with any new technology, there are ethical challenges.

The first panel depicts a collection of talks by Doug Pontious of Amerisure titled Cultivating an Analytics-Driven Culture to Ensure Successful Insight Generation, and Jacob Ablowitz and William Hickson at dmi.io titled "What's My Data Worth?" Pontious' session discussed some best practices learned at Amerisure as they unified many different data sources into an enterprise repository. Ablowitz and Hickson's presentation covered the fundamentals of commercializing data. Bradley A. Rhine of Fulton Financial Corporation and Kristin M. Love of GSK – Not the Return on Investment: Alternatives to Measuring Your Data Integration Strategy; Peter Haynes Aiken at Data Blueprint, Ed Kelly at the State of Texas, Jeffrey Kriseman at the State of Tennessee, and Michael Leahy at the State of Maryland – Challenges Facing the "First" State CDO (Not Initially Different from the Private Sector); JG Cowper of Healthbridge – How Prescribing "Data Glasses" to Eye Surgeons Is Transforming How They See Their Industry; Michael Scofield of Loma Linda University – Good Data, Bad Information – Why the Disconnect; and, Ian Rowlands of ASG Technologies – Data for Everyone: A Changing Data World. Cathy S Normand of ExxonMobil – Making Metadata Valuable – ExxonMobil's Journey Collecting and Cataloging Metadata; Lori Hurley and Denise Janci at Allstate – Divergent Approaches to Metadata Management: Lessons Learned; Ron Klein at Klein Admonition – Deriving New Business Terms from Technical Metadata; Liju Fan of OFR – Semantic Metadata Management: Leveraging Intuitive Ontologies Developed with Best Practices; David N Plotkin of MUFG – Metadata Quality: Ignore at Your Own Risk!; and, Susan Swanson at HCSC – Leveraging the Enterprise Metadata Repository for Data Governance Oversight and Data Quality Monitoring.

North Korea has been secretly selling facial recognition software, a new report states. This photo shows a German official identified by a computer with an automatic facial recognition system that was not mentioned in the report. North Korea has been secretly selling facial recognition software, a new report states. This photo shows a German official identified by a computer with an automatic facial recognition system that was not mentioned in the report. North Korea has been secretly selling facial recognition technology, fingerprint scanning and other products overseas. That's what researchers at the James Martin Center for Nonproliferation Studies found by investigating the country's information technology networks.

The U.S. Naval Research Laboratory (NRL) will use Alta Devices' "highly efficient, flexible, and light-weight" solar technology to help power the "breakthrough" Hybrid Tiger UAV. The Hybrid Tiger is a project designed to create a Group-2 UAV that will stay aloft for at least three and a half days, and Alta Devices says that technologies developed for the project will be applicable to other unmanned vehicles. "Widespread use of small UAVs in both the military and industry has been limited to-date by endurance. The Hybrid Tiger will demonstrate that very long endurance flights, with sophisticated telemetry and capabilities, can be achieved with the inclusion of solar arrays," says Jian Ding, Alta Devices CEO. "This project will open the door for many new solar powered UAV applications, and we look forward to achieving next generation breakthroughs via this cooperative effort."

Decision maker's preferences are often captured by some choice functions which are used to rank prospects. In this paper, we consider ambiguity in choice functions over a multi-attribute prospect space. Our main result is a robust preference model where the optimal decision is based on the worst-case choice function from an ambiguity set constructed through preference elicitation with pairwise comparisons of prospects. Differing from existing works in the area, our focus is on quasi-concave choice functions rather than concave functions and this enables us to cover a wide range of utility/risk preference problems including multi-attribute expected utility and $S$-shaped aspirational risk preferences. The robust choice function is increasing and quasi-concave but not necessarily translation invariant, a key property of monetary risk measures. We propose two approaches based respectively on the support functions and level functions of quasi-concave functions to develop tractable formulations of the maximin preference robust optimization model. The former gives rise to a mixed integer linear programming problem whereas the latter is equivalent to solving a sequence of convex risk minimization problems. To assess the effectiveness of the proposed robust preference optimization model and numerical schemes, we apply them to a security budget allocation problem and report some preliminary results from experiments.

Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities, leveraging AM's strengths in fabricating complex geometries and SM's precision and quality to produce finished parts. We present a systematic approach to automated computer-aided process planning (CAPP) for HM that can identify nontrivial, qualitatively distinct, and cost-optimal combinations of AM/SM modalities. A multimodal HM process plan is represented by a finite Boolean expression of AM and SM manufacturing primitives, such that the expression evaluates to an'as-manufactured' artifact. We show that primitives that respect spatial constraints such as accessibility and collision avoidance may be constructed by solving inverse configuration space problems on the'as-designed' artifact and manufacturing instruments. The primitives generate a finite Boolean algebra (FBA) that enumerates the entire search space for planning. The FBA's canonical intersection terms (i.e., 'atoms') provide the complete domain decomposition to reframe manufacturability analysis and process planning into purely symbolic reasoning, once a subcollection of atoms is found to be interchangeable with the design target. We demonstrate the practical potency of our framework and its computational efficiency when applied to process planning of complex 3D parts with dramatically different AM and SM instruments. Keywords: 1. Introduction Hybrid Manufacturing, Process Planning, Spatial Reasoning, Additive Manufacturing, Machining Hybrid manufacturing (HM), combining the capabilities of additive and subtractive manufacturing, is the new frontier of part fabrication. While additive manufacturing (AM) continues to enable unprecedented levels of structural complexity and customization, subtractive manufacturing (SM) remains indispensable for producing highprecision, mission-critical, and reliable mechanical components with functional interfaces. Versatile'multitasking' machines with simultaneous high-axis computer numerical control (CNC) of multiple AM and SM instruments (e.g., deposition heads and cutting tools) keep emerging on the market, enabling efficient use-cases for fabrication and repair (reviewed in Section 1.1).

With the overall increase in the elderly population comes additional, necessary medical needs and costs. Medicare is a U.S. healthcare program that provides insurance, primarily to individuals 65 years or older, to offload some of the financial burden associated with medical care. Even so, healthcare costs are high and continue to increase. Fraud is a major contributor to these inflating healthcare expenses. Our paper provides a comprehensive study leveraging machine learning methods to detect fraudulent Medicare providers. We use publicly available Medicare data and provider exclusions for fraud labels to build and assess three different learners. In order to lessen the impact of class imbalance, given so few actual fraud labels, we employ random undersampling creating four class distributions. Our results show that the C4.5 decision tree and logistic regression learners have the best fraud detection performance, particularly for the 80:20 class distribution with average AUC scores of 0.883 and 0.882, respectively, and low false negative rates. We successfully demonstrate the efficacy of employing machine learning with random undersampling to detect Medicare fraud.

Medical claims fraud is a major contributor to increased healthcare costs, but the negative impact can be lessened through effective fraud detection. In this paper, we combine Medicare provider utilization and payment data from 2012 to 2015 with corresponding fraud labels from the List of Excluded Individuals/Entities (LEIE) database. We demonstrate the effectiveness of detecting Medicare fraud with a limited number of known perpetrators, leading to severe class imbalance. For each of the three selected specialties, we use random undersampling to create four class distributions. Random Forest and Logistic Regression learners are built and evaluated based on fraud detection performance. Good fraud detection is demonstrated through the use of random undersampling, across three selected medical specialties. Statistically significant results are seen across the class distributions, with the 80:20 distribution having the best results. Overall, Random Forest (with either 100 or 500 trees), for each class distribution across all specialties, significantly outperforms Logistic Regression, with average AUC scores of 0.881 and 0.88, respectively.

Healthcare informatics focuses on the efficient and effective acquisition, management, and use of information in healthcare. Advancing health informatics has been declared a grand challenge by the National Academy of Engineering and is a major area of emphasis for agencies such as the Centers for Medicare and Medicaid Services. As such, it has been identified as an area of national need. Sample uses of AI in health informatics includes expert systems for decision support, machine learning and data mining to discover patterns across patients, image analysis to assist in diagnosis, and natural language processing to extract information from free text medical documents. The areas of interest for this track include healthcare decision support, medical image processing, machine learning and data mining in healthcare, processing and managing patient records, syndromic surveillance, drug discovery, and personalization of clinical care.