498 research outputs found

    Comparing Apollo and Mars Exploration Rover (MER) Operations Paradigms for Human Exploration During NASA Desert-Rats Science Operations

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    NASA's Desert Research and Technology Studies (D-RATS) field test is one of several analog tests that NASA conducts each year to combine operations development, technology advances and science under planetary surface conditions. The D-RATS focus is testing preliminary operational concepts for extravehicular activity (EVA) systems in the field using simulated surface operations and EVA hardware and procedures. For 2010 hardware included the Space Exploration Vehicles, Habitat Demonstration Units, Tri-ATHLETE, and a suite of new geology sample collection tools, including a self-contained GeoLab glove box for conducting in-field analysis of various collected rock samples. The D-RATS activities develop technical skills and experience for the mission planners, engineers, scientists, technicians, and astronauts responsible for realizing the goals of exploring planetary surfaces

    Towards a Lagrange-Newton approach for PDE constrained shape optimization

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    The novel Riemannian view on shape optimization developed in [Schulz, FoCM, 2014] is extended to a Lagrange-Newton approach for PDE constrained shape optimization problems. The extension is based on optimization on Riemannian vector space bundles and exemplified for a simple numerical example.Comment: 16 pages, 4 figures, 1 tabl

    Differential Increase in Taurine Levels by Low-Dose Ethanol in the Dorsal and Ventral Striatum Revealed by Microdialysis With On-Line Capillary Electrophoresis

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66164/1/01.ALC.0000131979.78003.34.pd

    A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

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    In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

    NASA's Desert RATS Science Backroom: Remotely Supporting Planetary Exploration

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    NASA's Desert Research and Technology Studies (Desert RATS) is a multi-year series of tests designed to exercise planetary surface hardware and operations in conditions where long-distance, multi-day roving is achievable. In recent years, a D-RATS science backroom has conducted science operations and tested specific operational approaches. Approaches from the Apollo, Mars Exploration Rovers and Phoenix missions were merged to become the baseline for these tests. In 2010, six days of lunar-analog traverse operations were conducted during each week of the 2-week test, with three traverse days each week conducted with voice and data communications continuously available, and three traverse days conducted with only two 1-hour communications periods per day. In 2011, a variety of exploration science scenarios that tested operations for a near-earth asteroid using several small exploration vehicles and a single habitat. Communications between the ground and the crew in the field used a 50-second one-way delay, while communications between crewmembers in the exploration vehicles and the habitat were instantaneous. Within these frameworks, the team evaluated integrated science operations management using real-time science operations to oversee daily crew activities, and strategic level evaluations of science data and daily traverse results. Exploration scenarios for Mars may include architectural similarities such as crew in a habitat communicating with crew in a vehicle, but significantly more autonomy will have to be given to the crew rather than step-by-step interaction with a science backroom on Earth

    Assumptions behind grammatical approaches to code-switching: when the blueprint is a red herring

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    Many of the so-called ‘grammars’ of code-switching are based on various underlying assumptions, e.g. that informal speech can be adequately or appropriately described in terms of ‘‘grammar’’; that deep, rather than surface, structures are involved in code-switching; that one ‘language’ is the ‘base’ or ‘matrix’; and that constraints derived from existing data are universal and predictive. We question these assumptions on several grounds. First, ‘grammar’ is arguably distinct from the processes driving speech production. Second, the role of grammar is mediated by the variable, poly-idiolectal repertoires of bilingual speakers. Third, in many instances of CS the notion of a ‘base’ system is either irrelevant, or fails to explain the facts. Fourth, sociolinguistic factors frequently override ‘grammatical’ factors, as evidence from the same language pairs in different settings has shown. No principles proposed to date account for all the facts, and it seems unlikely that ‘grammar’, as conventionally conceived, can provide definitive answers. We conclude that rather than seeking universal, predictive grammatical rules, research on CS should focus on the variability of bilingual grammars

    Science Operations for the 2008 NASA Lunar Analog Field Test at Black Point Lava Flow, Arizona

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    Surface science operations on the Moon will require merging lessons from Apollo with new operation concepts that exploit the Constellation Lunar Architecture. Prototypes of lunar vehicles and robots are already under development and will change the way we conduct science operations compared to Apollo. To prepare for future surface operations on the Moon, NASA, along with several supporting agencies and institutions, conducted a high-fidelity lunar mission simulation with prototypes of the small pressurized rover (SPR) and unpressurized rover (UPR) (Fig. 1) at Black Point lava flow (Fig. 2), 40 km north of Flagstaff, Arizona from Oct. 19-31, 2008. This field test was primarily intended to evaluate and compare the surface mobility afforded by unpressurized and pressurized rovers, the latter critically depending on the innovative suit-port concept for efficient egress and ingress. The UPR vehicle transports two astronauts who remain in their EVA suits at all times, whereas the SPR concept enables astronauts to remain in a pressurized shirt-sleeve environment during long translations and while making contextual observations and enables rapid (less than or equal to 10 minutes) transfer to and from the surface via suit-ports. A team of field geologists provided realistic science scenarios for the simulations and served as crew members, field observers, and operators of a science backroom. Here, we present a description of the science team s operations and lessons learned
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