Methodologies for building robust schedules

COMPASS is the name of a Computer Aided Scheduling System designed and built for NASA. COMPASS can be used to develop schedule of activities based upon the temporal relationships of the activities and their resource requirements. COMPASS uses this information, and guided by the user, develops precise start and stop times for the activities. In actual practice however, it is impossible to know with complete certainty what the actual durations of the scheduled activities will really be. The best that one can hope for is knowledge of the probability distribution for the durations. This paper investigates methodologies for using a scheduling tool like COMPASS that is based upon definite values for the resource requirements, while building schedules that remain valid in the face of the schedule execution perturbations. Representations for the schedules developed by these methodologies are presented, along with a discussion of the algorithm that could be used by a computer onboard a spacecraft to efficiently monitor and execute these schedules

Efficient Parallel Translating Embedding For Knowledge Graphs

Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that ParTrans-X can speed up the training process by more than an order of magnitude.Comment: WI 2017: 460-46

The HR 4796A Debris System: Discovery of Extensive Exo-Ring Dust Material

The optically and IR bright, and starlight-scattering, HR 4796A ring-like debris disk is one of the most (and best) studied exoplanetary debris systems. The presence of a yet-undetected planet has been inferred (or suggested) from the narrow width and inner/outer truncation radii of its r = 1.05" (77 au) debris ring. We present new, highly sensitive, Hubble Space Telescope (HST) visible-light images of the HR 4796A circumstellar debris system and its environment over a very wide range of stellocentric angles from 0.32" (23 au) to ~ 15" (1100 au). These very high contrast images were obtained with the Space Telescope Imaging Spectrograph (STIS) using 6-roll PSF-template subtracted coronagraphy suppressing the primary light of HR 4796A and using three image plane occulters and simultaneously subtracting the background light from its close angular proximity M2.5V companion. The resulting images unambiguously reveal the debris ring embedded within a much larger, morphologically complex, and bi-axially asymmetric exoring scattering structure. These images at visible wavelengths are sensitive to, and map, the spatial distribution, brightness, and radial surface density of micron size particles over 5 dex in surface brightness. These particles in the exo-ring environment may be unbound from the system and interacting with the local ISM. Herein we present a new morphological and photometric view of the larger than prior seen HR 4796A exoplanetary debris system with sensitivity to small particles at stellocentric distances an order of magnitude greater than has previously been observed.Comment: 28 pages, 17 figures, accepted for publication in the Astronomical Journal 21 December 201

System model development for nuclear thermal propulsion

A critical enabling technology in the evolutionary development of nuclear thermal propulsion (NTP) is the ability to predict the system performance under a variety of operating conditions. This is crucial for mission analysis and for control subsystem testing as well as for the modeling of various failure modes. Performance must be accurately predicted during steady-state and transient operation, including startup, shutdown, and post operation cooling. The development and application of verified and validated system models has the potential to reduce the design, testing, and cost and time required for the technology to reach flight-ready status. Since Oct. 1991, the U.S. Department of Energy (DOE), Department of Defense (DOD), and NASA have initiated critical technology development efforts for NTP systems to be used on Space Exploration Initiative (SEI) missions to the Moon and Mars. This paper presents the strategy and progress of an interagency NASA/DOE/DOD team for NTP system modeling. It is the intent of the interagency team to develop several levels of computer programs to simulate various NTP systems. The first level will provide rapid, parameterized calculations of overall system performance. Succeeding computer programs will provide analysis of each component in sufficient detail to guide the design teams and experimental efforts. The computer programs will allow simulation of the entire system to allow prediction of the integrated performance. An interagency team was formed for this task to use the best capabilities available and to assure appropriate peer review

Sensory Prediction or Motor Control? Application of Marr–Albus Type Models of Cerebellar Function to Classical Conditioning

Marr–Albus adaptive filter models of the cerebellum have been applied successfully to a range of sensory and motor control problems. Here we analyze their properties when applied to classical conditioning of the nictitating membrane response in rabbits. We consider a system-level model of eyeblink conditioning based on the anatomy of the eyeblink circuitry, comprising an adaptive filter model of the cerebellum, a comparator model of the inferior olive and a linear dynamic model of the nictitating membrane plant. To our knowledge, this is the first model that explicitly includes all these principal components, in particular the motor plant that is vital for shaping and timing the behavioral response. Model assumptions and parameters were systematically investigated to disambiguate basic computational capacities of the model from features requiring tuning of properties and parameter values. Without such tuning, the model robustly reproduced a range of behaviors related to sensory prediction, by displaying appropriate trial-level associative learning effects for both single and multiple stimuli, including blocking and conditioned inhibition. In contrast, successful reproduction of the real-time motor behavior depended on appropriate specification of the plant, cerebellum and comparator models. Although some of these properties appear consistent with the system biology, fundamental questions remain about how the biological parameters are chosen if the cerebellar microcircuit applies a common computation to many distinct behavioral tasks. It is possible that the response profiles in classical conditioning of the eyeblink depend upon operant contingencies that have previously prevailed, for example in naturally occurring avoidance movements