Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions

On Organization of Information: Approach and Early Work

In this report we describe an approach for organizing information for presentation and display. "e approach stems from the observation that there is a stepwise progression in the way signals (from the environment and the system under consideration) are extracted and transformed into data, and then analyzed and abstracted to form representations (e.g., indications and icons) on the user interface. In physical environments such as aerospace and process control, many system components and their corresponding data and information are interrelated (e.g., an increase in a chamber s temperature results in an increase in its pressure). "ese interrelationships, when presented clearly, allow users to understand linkages among system components and how they may affect one another. Organization of these interrelationships by means of an orderly structure provides for the so-called "big picture" that pilots, astronauts, and operators strive for

The Ownership and Trading of Debt Claims in Chapter 11 Restructurings

What is the ownership structure of bankrupt debt claims? How does the ownership evolve though bankruptcy? And how does debt ownership influence Chapter 11 outcomes? To answer these questions, we construct a data set that identifies the entire capital structure for 136 companies filing for U.S. Chapter 11 bankruptcy protection between 1998 and 2009 and that covers over 71,000 different investors. We categorize the investors in the capital structure of bankrupt firms according to their institutional type and track them from the initial filing until the vote on the plan of reorganization. We document several novel facts about the role of different institutional investors, the impact of debt ownership concentration, and the role of trading in bankruptcy. We find that trading during the case leads to higher concentration of ownership, particularly among debt claims that are eligible to vote on the bankruptcy plan of reorganization. Active investors, including hedge funds, are the largest net buyers of claims in bankruptcy. While initial ownership concentration is important for coordination of a prearranged bankruptcy filing, it is consolidation of ownership during bankruptcy—and specifically consolidation of ownership of voting classes—that has an impact on the speed of restructuring, the probability of liquidation, and class-level as well as overall recovery rates

Toward Justifiable Trust in Autonomous Systems Incorporating Human Knowledge in Autonomous Systems through Machine Learning

Trust in Autonomous Systems is largely about humans trusting the decisions made by autonomous systems. This trust can be increased through learning from domain experts. In particular, autonomous systems can learn offline from past mission operations before conducting any operations of its own. Additionally, autonomous systems can learn online by obtaining human feedback during operations. We will discuss several classes of machine learning methods and our application of them to autonomous systems. The first class of methods is anomaly detection, which uses operations data to identify examples of anomalous operations. The second class of methods is inverse reinforcement learning, also known as apprenticeship learning, that takes past operations data as input and yields a controller that is able to duplicate the operations described by the data. The third class is active learning, which identifies examples on which the model is most uncertain and requests domain expert feedback

Pointing to visible and invisible targets

We investigated how the visibility of targets influenced the type of point used to provide directions. In Study 1 we asked 605 passersby in three localities for directions to well-known local landmarks. When that landmark was in plain view behind the requester, most respondents pointed with their index fingers, and few respondents pointed more than once. In contrast, when the landmark was not in view, respondents pointed initially with their index fingers, but often elaborated with a whole-hand point. In Study 2, we covertly filmed the responses from 157 passersby we approached for directions, capturing both verbal and gestural responses. As in Study 1, few respondents produced more than one gesture when the target was in plain view and initial points were most likely to be index finger points. Thus, in a Western geographical context in which pointing with the index finger is the dominant form of pointing, a slight change in circumstances elicited a preference for pointing with the whole hand when it was the second or third manual gesture in a sequence

The DEEP Groth Strip Survey VI. Spectroscopic, Variability, and X-ray Detection of AGN

We identify active galactic nuclei (AGN) in the Groth-Westphal Survey Strip (GSS) using the independent and complementary selection techniques of optical spectroscopy and photometric variability. We discuss the X-ray properties of these AGN using Chandra/XMM data for this region. From a sample of 576 galaxies with high quality spectra we identify 31 galaxies with AGN signatures. Seven of these have broad emission lines (Type 1 AGNs). We also identify 26 galaxies displaying nuclear variability in HST WFPC2 images of the GSS separated by ~7 years. The primary overlap of the two selected AGN samples is the set of broad-line AGNs, of which 80% appear as variable. Only a few narrow-line AGNs approach the variability threshold. The broad-line AGNs have an average redshift of z~1.1 while the other spectroscopic AGNs have redshifts closer to the mean of the general galaxy population (z~0.7). Eighty percent of the identified broad-line AGNs are detected in X-rays and these are among the most luminous X-ray sources in the GSS. Only one narrow-line AGN is X-ray detected. Of the variable nuclei galaxies within the X-ray survey, 27% are X-ray detected. We find that 1.9+/-0.6% of GSS galaxies to V=24 are broad-line AGNs, 1.4+/-0.5% are narrow-line AGNs, and 4.5+/-1.4% contain variable nuclei. The fraction of spectroscopically identified BLAGNs and NLAGNs at z~1 reveals a marginally significant increase of 1.3+/-0.9% when compared to the local population.Comment: 29 pages, 8 figures, accepted for publication in ApJ

The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States

We synthesize insights from current understanding of drought impacts at stand‐to‐biogeographic scales, including management options, and we identify challenges to be addressed with new research. Large stand‐level shifts underway in western forests already are showing the importance of interactions involving drought, insects, and fire. Diebacks, changes in composition and structure, and shifting range limits are widely observed. In the eastern US, the effects of increasing drought are becoming better understood at the level of individual trees, but this knowledge cannot yet be confidently translated to predictions of changing structure and diversity of forest stands. While eastern forests have not experienced the types of changes seen in western forests in recent decades, they too are vulnerable to drought and could experience significant changes with increased severity, frequency, or duration in drought. Throughout the continental United States, the combination of projected large climate‐induced shifts in suitable habitat from modeling studies and limited potential for the rapid migration of tree populations suggests that changing tree and forest biogeography could substantially lag habitat shifts already underway. Forest management practices can partially ameliorate drought impacts through reductions in stand density, selection of drought‐tolerant species and genotypes, artificial regeneration, and the development of multistructured stands. However, silvicultural treatments also could exacerbate drought impacts unless implemented with careful attention to site and stand characteristics. Gaps in our understanding should motivate new research on the effects of interactions involving climate and other species at the stand scale and how interactions and multiple responses are represented in models. This assessment indicates that, without a stronger empirical basis for drought impacts at the stand scale, more complex models may provide limited guidance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134257/1/gcb13160_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134257/2/gcb13160.pd

Antitumour activity of a potent MEK inhibitor RDEA119/BAY 869766 combined with rapamycin in human orthotopic primary pancreatic cancer xenografts

<p>Abstract</p> <p>Background</p> <p>Combining MEK inhibitors with other signalling pathway inhibitors or conventional cytotoxic drugs represents a promising new strategy against cancer. RDEA119/BAY 869766 is a highly potent and selective MEK1/2 inhibitor undergoing phase I human clinical trials. The effects of RDEA119/BAY 869766 as a single agent and in combination with rapamycin were studied in 3 early passage primary pancreatic cancer xenografts, OCIP19, 21, and 23, grown orthotopically.</p> <p>Methods</p> <p>Anti-cancer effects were determined in separate groups following chronic drug exposure. Effects on cell cycle and downstream signalling were examined by flow cytometry and western blot, respectively. Plasma RDEA119 concentrations were measured to monitor the drug accumulation <it>in vivo</it>.</p> <p>Results</p> <p>RDEA119/BAY 869766 alone or in combination with rapamycin showed significant growth inhibition in all the 3 models, with a significant decrease in the percentage of cells in S-phase, accompanied by a large decrease in bromodeoxyuridine labelling and cell cycle arrest predominantly in G1. The S6 ribosomal protein was inhibited to a greater extent with combination treatment in all the three models. Blood plasma pharmacokinetic analyses indicated that RDEA119 levels achieved <it>in vivo </it>are similar to those that produce target inhibition and cell cycle arrest <it>in vitro</it>.</p> <p>Conclusions</p> <p>Agents targeting the ERK and mTOR pathway have anticancer activity in primary xenografts, and these results support testing this combination in pancreatic cancer patients.</p

Consensus statement on concussion in sport—the 5 th international conference on concussion in sport held in Berlin, October 2016

The 2017 Concussion in Sport Group (CISG) consensus statement is designed to build on the principles outlined in the previous statements1–4 and to develop further conceptual understanding of sport-related concussion (SRC) using an expert consensus-based approach. This document is developed for physicians and healthcare providers who are involved in athlete care, whether at a recreational, elite or professional level. While agreement exists on the principal messages conveyed by this document, the authors acknowledge that the science of SRC is evolving and therefore individual management and return-to-play decisions remain in the realm of clinical judgement. This consensus document reflects the current state of knowledge and will need to be modified as new knowledge develops. It provides an overview of issues that may be of importance to healthcare providers involved in the management of SRC. This paper should be read in conjunction with the systematic reviews and methodology paper that accompany it. First and foremost, this document is intended to guide clinical practice; however, the authors feel that it can also help form the agenda for future research relevant to SRC by identifying knowledge gaps