803 research outputs found

    Bayesian multitask inverse reinforcement learning

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    We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is to formalise the problem as statistical preference elicitation, via a number of structured priors, whose form captures our biases about the relatedness of different tasks or expert policies. In doing so, we introduce a prior on policy optimality, which is more natural to specify. We show that our framework allows us not only to learn to efficiently from multiple experts but to also effectively differentiate between the goals of each. Possible applications include analysing the intrinsic motivations of subjects in behavioural experiments and learning from multiple teachers.Comment: Corrected version. 13 pages, 8 figure

    Integrating Al with NiO nano honeycomb to realize an energetic material on silicon substrate

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    Nano energetic materials offer improved performance in energy release, ignition, and mechanical properties compared to their bulk or micro counterparts. In this study, the authors propose an approach to synthesize an Al/NiO based nano energetic material which is fully compatible with a microsystem. A two-dimensional NiO nano honeycomb is first realized by thermal oxidation of a Ni thin film deposited onto a silicon substrate by thermal evaporation. Then the NiO nano honeycomb is integrated with an Al that is deposited by thermal evaporation to realize an Al/NiO based nano energetic material. This approach has several advantages over previous investigations, such as lower ignition temperature, enhanced interfacial contact area, reduced impurities and Al oxidation, tailored dimensions, and easier integration into a microsystem to realize functional devices. The synthesized Al/NiO based nano energetic material is characterized by scanning electron microscopy, X-ray diffraction, differential thermal analysis, and differential scanning calorimetry

    Fluorescent carbon dioxide indicators

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    Over the last decade, fluorescence has become the dominant tool in biotechnology and medical imaging. These exciting advances have been underpinned by the advances in time-resolved techniques and instrumentation, probe design, chemical / biochemical sensing, coupled with our furthered knowledge in biology. Complementary volumes 9 and 10, Advanced Concepts of Fluorescence Sensing: Small Molecule Sensing and Advanced Concepts of Fluorescence Sensing: Macromolecular Sensing, aim to summarize the current state of the art in fluorescent sensing. For this reason, Drs. Geddes and Lakowicz have invited chapters, encompassing a broad range of fluorescence sensing techniques. Some chapters deal with small molecule sensors, such as for anions, cations, and CO2, while others summarize recent advances in protein-based and macromolecular sensors. The Editors have, however, not included DNA or RNA based sensing in this volume, as this were reviewed in Volume 7 and is to be the subject of a more detailed volume in the near future

    The Self Model and the Conception of Biological Identity in Immunology

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    The self/non-self model, first proposed by F.M. Burnet, has dominated immunology for sixty years now. According to this model, any foreign element will trigger an immune reaction in an organism, whereas endogenous elements will not, in normal circumstances, induce an immune reaction. In this paper we show that the self/non-self model is no longer an appropriate explanation of experimental data in immunology, and that this inadequacy may be rooted in an excessively strong metaphysical conception of biological identity. We suggest that another hypothesis, one based on the notion of continuity, gives a better account of immune phenomena. Finally, we underscore the mapping between this metaphysical deflation from self to continuity in immunology and the philosophical debate between substantialism and empiricism about identity

    Toward an internally consistent astronomical distance scale

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    Accurate astronomical distance determination is crucial for all fields in astrophysics, from Galactic to cosmological scales. Despite, or perhaps because of, significant efforts to determine accurate distances, using a wide range of methods, tracers, and techniques, an internally consistent astronomical distance framework has not yet been established. We review current efforts to homogenize the Local Group's distance framework, with particular emphasis on the potential of RR Lyrae stars as distance indicators, and attempt to extend this in an internally consistent manner to cosmological distances. Calibration based on Type Ia supernovae and distance determinations based on gravitational lensing represent particularly promising approaches. We provide a positive outlook to improvements to the status quo expected from future surveys, missions, and facilities. Astronomical distance determination has clearly reached maturity and near-consistency.Comment: Review article, 59 pages (4 figures); Space Science Reviews, in press (chapter 8 of a special collection resulting from the May 2016 ISSI-BJ workshop on Astronomical Distance Determination in the Space Age

    An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

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    For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

    The exposure of the hybrid detector of the Pierre Auger Observatory

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    The Pierre Auger Observatory is a detector for ultra-high energy cosmic rays. It consists of a surface array to measure secondary particles at ground level and a fluorescence detector to measure the development of air showers in the atmosphere above the array. The "hybrid" detection mode combines the information from the two subsystems. We describe the determination of the hybrid exposure for events observed by the fluorescence telescopes in coincidence with at least one water-Cherenkov detector of the surface array. A detailed knowledge of the time dependence of the detection operations is crucial for an accurate evaluation of the exposure. We discuss the relevance of monitoring data collected during operations, such as the status of the fluorescence detector, background light and atmospheric conditions, that are used in both simulation and reconstruction.Comment: Paper accepted by Astroparticle Physic
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