Born of Freedom and Dissent: A comparative analysis of American antiwar protest in the first 1,418 days of the Vietnam and Iraq wars

Cultural aesthetics are the latent effects of human relations informing cognitive schemas as cultural variations of social forms in specific time-space contexts. To understand what conditions produce intra-national conflict during wartime, engagement reactivity between social control mechanisms and antiwar protesters was measured. Hypothesis-1 showed high numbers of arrests were influenced by the type and duration of protest and military presence at protest events during Vietnam, whereas place and size of protest were influential during Iraq. Hypothesis-2 showed that where and how antiwar protests occur has changed. Hypothesis-3 showed that, compared to Vietnam, Iraq antiwar protest has increased initial reactivity-intensity, has more arrests and fewer injuries, and is 541.6% larger per event, with a 248.8% greater total number of protesters. This study concludes that structural flexibility and preparedness prevent intra-national conflict, the antiwar movement has become an institution, and the cultural schema for Vietnam antiwar protest has affected its present form

Rocket exhaust plume computer program improvement. Volume 1: Summary: Method of characteristics nozzle and plume programs

A summary is presented of the various documents that discuss and describe the computer programs and analysis techniques which are available for rocket nozzle and exhaust plume calculations. The basic method of characteristics program is discussed, along with such auxiliary programs as the plume impingement program, the plot program and the thermochemical properties program

Macrolide‐resistant Mycoplasma pneumoniae pneumonia in transplantation: Increasingly typical?

Mycoplasma pneumoniae is one of the most common bacterial causes of pneumonia. Macrolide‐resistant M pneumoniae (MRMP) was documented in 7.5% of isolates in the United States. Resistance portends poor outcomes to macrolide therapy, yet patients respond well to fluoroquinolones or tetracyclines such as minocycline. However, MRMP may be under‐appreciated because M pneumoniae generally causes relatively mild infections in non‐immunosuppressed adults that may resolve without effective therapy and because microbiological confirmation and susceptibility are not routinely performed. We report two cases of pneumonia due to MRMP in kidney transplant recipients. Both patients required hospital admission, worsened on macrolide therapy, and rapidly defervesced on doxycycline or levofloxacin. In one case, M pneumoniae was only identified by multiplex respiratory pathogen panel analysis of BAL fluid. Macrolide resistance was confirmed in both cases by real‐time PCR and point mutations associated with macrolide resistance were identified. M pneumoniae was isolated from both cases, and molecular genotyping revealed the same genotype. In conclusion, clinicians should be aware of the potential for macrolide resistance in M pneumoniae, and may consider non‐macrolide‐based therapy for confirmed or non‐responding infections in patients who are immunocompromised or hospitalized.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163484/2/tid13318.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163484/1/tid13318_am.pd

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours

LETTER TO THE EDITOR: Structure of the photodetachment cross section in a magnetic field: an experiment with

Photodetachment from in a magnetic field has been studied experimentally using light with energies between 14400 and . Presented here are high-resolution data which exhibit sharp magnetic field structure at thresholds and low-resolution data which show monotonically increasing cross sections. The current work is the first in any atomic or molecular system where sufficient energy resolution has been achieved to observe the shape of the cross section in a magnetic field.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48852/2/b624l4.pd

Deep imitation learning for 3D navigation tasks

Deep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: Deep-Q-networks (DQN) and Asynchronous actor critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an e�ective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples

Visible, EUV, and X-ray Spectroscopy at the NIST EBIT Facility

After a brief introduction to the NIST EBIT facility, we present the results of three different types of experiments that have been carried out there recently: EUV and visible spectroscopy in support of the microelectronics industry, laboratory astrophysics using an x-ray microcalorimeter, and charge exchange studies using extracted beams of highly charged ions.Comment: 10 page