673 research outputs found
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
Penning traps with unitary architecture for storage of highly charged ions
Penning traps are made extremely compact by embedding rare-earth permanent
magnets in the electrode structure. Axially-oriented NdFeB magnets are used in
unitary architectures that couple the electric and magnetic components into an
integrated structure. We have constructed a two- magnet Penning trap with
radial access to enable the use of laser or atomic beams, as well as the
collection of light. An experimental apparatus equipped with ion optics is
installed at the NIST electron beam ion trap (EBIT) facility, constrained to
fit within 1 meter at the end of a horizontal beamline for transporting highly
charged ions. Highly charged ions of neon and argon, extracted with initial
energies up to 4000 eV per unit charge, are captured and stored to study the
confinement properties of a one-magnet trap and a two-magnet trap. Design
considerations and some test results are discussed
Optimization And Learning For Rough Terrain Legged Locomotion
We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and \u27certificates\u27 that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of types of rough terrain. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behavior
Macrolideâresistant Mycoplasma pneumoniae pneumonia in transplantation: Increasingly typical?
Mycoplasma pneumoniae is one of the most common bacterial causes of pneumonia. Macrolideâresistant MÂ 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 underâappreciated because MÂ pneumoniae generally causes relatively mild infections in nonâimmunosuppressed 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, MÂ pneumoniae was only identified by multiplex respiratory pathogen panel analysis of BAL fluid. Macrolide resistance was confirmed in both cases by realâtime PCR and point mutations associated with macrolide resistance were identified. MÂ 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 MÂ pneumoniae, and may consider nonâmacrolideâbased therapy for confirmed or nonâresponding 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 eïżœective 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
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