The Effects of War as a Basis to Consider Six Novels by Ernest Hemingway and F. Scott Fitzgerald

Curiously, the issue of war has never been considered a solid basis for examining the works of Ernest Hemingway and F. Scott Fitzgerald. Yet one common thread running through the body of their work is war. A study of the authors’ attitudes toward war and their characters’ responses to war in Hemingway’s A Farewell to Arms (1929), For Whom the Bell Tolls (1940), The Sun Also Rises (1926), and Fitzgerald’s This Side of Paradise (1920), The Great Gatsby (1925), and Tender Is the Night (1934), yields several conclusions. Hemingway’s and Fitzgerald’s works reveal two different types of human responses to the effects of war while the characters search for ways to attain quality in life. Both the Hemingway and Fitzgerald protagonists suffer from the chaotic environment wrought by war. However, contemporary readers should find Fitzgerald’s characters particularly relevant because they confront a dilemma that mirrors more realistically man’s struggle for salvation in modern, post-war society. The element of war shapes the lives of both sets of characters, and yet very little criticism exists which presents war as a basis to consider any of the works by Hemingway and Fitzgerald together. The critics traditionally see war as having a major impact on the life and works of Ernest Hemingway; in contrast, most critics see war as playing no significant role in the life and works of Fitzgerald. However, both writers’ lives were shaped immensely by war. Biographically, Hemingway volunteered to serve in Italy during World War I and was a volunteer journalist in Spain during the Spanish Civil war, experiences that helped to shape his strong sense of masculinity and superiority; though Fitzgerald never served in a war, the fact helped to create his deep sense of inferiority and failure as a man. Both men’s experiences with war helped to formulate attitudes reflected in the fiction and account, in part, for the substantial differences in their approaches to theme and setting. So, the effects of war provide a valid basis for considering the lives, and subsequently, the works of Hemingway and Fitzgerald

Orientation Program for New Employees�

Occupational and Adult Educatio

Use of a novel Förster resonance energy transfer method to identify locations of site-bound metal ions in the U2–U6 snRNA complex

U2 and U6 snRNAs pair to form a phylogenetically conserved complex at the catalytic core of the spliceosome. Interactions with divalent metal ions, particularly Mg(II), at specific sites are essential for its folding and catalytic activity. We used a novel Förster resonance energy transfer (FRET) method between site-bound luminescent lanthanide ions and a covalently attached fluorescent dye, combined with supporting stoichiometric and mutational studies, to determine locations of site-bound Tb(III) within the human U2–U6 complex. At pH 7.2, we detected three metal-ion-binding sites in: (1) the consensus ACACAGA sequence, which forms the internal loop between helices I and III; (2) the four-way junction, which contains the conserved AGC triad; and (3) the internal loop of the U6 intra-molecular stem loop (ISL). Binding at each of these sites is supported by previous phosphorothioate substitution studies and, in the case of the ISL site, by NMR. Binding of Tb(III) at the four-way junction and the ISL sites was found to be pH-dependent, with no ion binding observed below pH 6 and 7, respectively. This pH dependence of metal ion binding suggests that the local environment may play a role in the binding of metal ions, which may impact on splicing activity

Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie

Development of a Route Crossing Tool for Shared Airspace Environments

In current-day Terminal Radar Approach Control (TRACON) operations, departure and arrival controllers maintain separate and dedicated airspace for their respective traffic flows. Although this practice has obvious safety features, it also leads to inefficiencies; for example, departure aircraft may be routinely capped beneath arrival airspace. With the right decision-support and coordination tools, departures could continue to climb through arrival airspace when sufficient gaps exist. Previous studies of shared airspace have examined pre-arranged coordination procedures, as well as tools that gave feedback to the controllers on where gaps between arrivals were located and whether the departure aircraft could be scheduled to fly through those gaps [1, 2, 3, 4]. Since then, the Route Crossing Tool (RCT) has been developed to allow controllers to assess multiple pre-defined route options at points where the arrivals and departures cross, thereby increasing the possibility of climbing a departure through an arrival gap.The RCT aids in ensuring lateral separation between departure and arrival aircraft that pass through the same altitude. Since the RCT can be applied tactically, it can enable aircraft to fly through arrival flows even if these aircraft depart outside scheduled times. The RCT makes use of a set of predefined parallel departure routes crossing the arrival flow at equidistant intersecting points on the arrival route. The RCT uses the Estimated Time of Arrival (ETA) of the departure aircraft at each intersecting point to calculate the lateral separation with the neighboring arrivals when it crosses that point; this information is graphically displayed to the controller. Additionally, the RCT incorporates forecast winds in its ETA predictions.Multiple prototypes of the RCT have been iteratively developed with feedback from Subject Matter Experts (SMEs). This paper presents the final design, the design process, and lessons learned. Initial results from a simulation suggest that the tool was successful in helping controllers to safely climb more aircraft. Controller feedback on the tool was also positive

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality