1,077 research outputs found

    Anti-Trust and Economic Theory: Some Observations from the US Experience

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    Recent developments in US anti-trust can be characterised as reflecting the uneasy interaction of two quite separate phenomena: first, the increased emphasis on economic analysis as the overriding organising principle of anti-trust policy and on economic efficiency as the primary (perhaps only) relevant goal for anti-trust; second, the long-standing reluctance of the federal judiciary to involve itself in any substantive economic analysis, and the preference, instead, for simple rules of thumb or ‘pigeon holes’ to sort out lawful from unlawful conduct. The result has been that while economics has played a major role, it has not influenced American anti-trust as thoroughly or as uniformly as might have been imagined; rather the extent and the nature of its influence have depended on the degree to which the relevant economics could be reduced to the kind of simple rules or pigeon holes that the judiciary favours. The present paper will illustrate that theme, first by reporting on the two developments separately and then by illustrating their joint influence with reference to two important areas of American anti-trust: predatory conduct and so-called vertical restraints. Finally, a contrast will be made between judicial development in those two areas and recent American merger policy which, it is argued, is carried out largely independently of the judiciary, and hence the opportunities for economics to influence the process are less inhibited by the judicial reluctance to undertake extensive economic analysis

    Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

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    The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

    LIGO detector characterization in the second and third observing runs

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    The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO–Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs

    GWpy: A Python package for gravitational-wave astrophysics

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    GWpy is a Python software package that provides an intuitive, object-oriented interface through which to access, process, and visualise data from gravitational-wave detectors. GWpy provides a number of new utilities for studying data, as well as an improved user interface for a number of existing tools. The ease-of-use, along with extensive online documentation and examples, has resulted in widespread adoption of GWpy as a basis for Python software development in the international gravitational-wave community
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