2,165 research outputs found

    On the outer boundary of the sunspot penumbra

    Get PDF
    Comparison of photographic observations and vector-magnetograph measurements demonstrate, that the outer boundary of the sunspot penumbra --even in complex sunspot groups-- closely follows the 0.075T isogauss line of the total value of the magnetic field, corresponding approximately to the equipartition value in the photosphere. Radio observations also show this feature. The thick penumbra model with interchange convection (Jahn and Schmidt, 1994) gives the best explanation of the penumbral structure.Comment: accepted to Solar Physic

    Spectroscopic studies of fluorescent perylene dyes

    Get PDF
    The lowest electronic transition of the fluorescent perylene dye bis-(3,5-di-tertbutylphenyl)-perylene-3, 4:9,10-biscarboximide has been investigated

    Driven transverse shear waves in a strongly coupled dusty plasma

    Get PDF
    The linear dispersion properties of transverse shear waves in a strongly coupled dusty plasma are experimentally studied by exciting them in a controlled manner with a variable frequency external source. The dusty plasma is maintained in the strongly coupled fluid regime with (1 < Gamma << Gamma_c) where Gamma is the Coulomb coupling parameter and Gamma_c is the crystallization limit. A dispersion relation for the transverse waves is experimentally obtained over a frequency range of 0.1 Hz to 2 Hz and found to show good agreement with viscoelastic theoretical results.Comment: The manuscripts contains five pages and 6 figure

    I Still Can Dream

    Get PDF
    https://digitalcommons.library.umaine.edu/mmb-vp/4228/thumbnail.jp

    My Bajadere

    Get PDF
    https://digitalcommons.library.umaine.edu/mmb-vp/4798/thumbnail.jp

    A Novel Generic Framework for Track Fitting in Complex Detector Systems

    Full text link
    This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement. This goal is achieved through a completely modular design. Fitting algorithms are implemented as interchangeable modules. At present, the framework contains a validated Kalman filter. Track parameterizations and the routines required to extrapolate the track parameters and their covariance matrices through the experiment are also implemented as interchangeable modules. Different track parameterizations and extrapolation routines can be used simultaneously for fitting of the same physical track. Representations of detector hits are the third modular ingredient to the framework. The hit dimensionality and orientation of planar tracking detectors are not restricted. Tracking information from detectors which do not measure the passage of particles in a fixed physical detector plane, e.g. drift chambers or TPCs, is used without any simplifications. The concept is implemented in a light-weight C++ library called GENFIT, which is available as free software

    Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

    Full text link
    Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC

    Magnetometry via a double-pass continuous quantum measurement of atomic spin

    Full text link
    We argue that it is possible in principle to reduce the uncertainty of an atomic magnetometer by double-passing a far-detuned laser field through the atomic sample as it undergoes Larmor precession. Numerical simulations of the quantum Fisher information suggest that, despite the lack of explicit multi-body coupling terms in the system's magnetic Hamiltonian, the parameter estimation uncertainty in such a physical setup scales better than the conventional Heisenberg uncertainty limit over a specified but arbitrary range of particle number N. Using the methods of quantum stochastic calculus and filtering theory, we demonstrate numerically an explicit parameter estimator (called a quantum particle filter) whose observed scaling follows that of our calculated quantum Fisher information. Moreover, the quantum particle filter quantitatively surpasses the uncertainty limit calculated from the quantum Cramer-Rao inequality based on a magnetic coupling Hamiltonian with only single-body operators. We also show that a quantum Kalman filter is insufficient to obtain super-Heisenberg scaling, and present evidence that such scaling necessitates going beyond the manifold of Gaussian atomic states.Comment: 17 pages, updated to match print versio

    A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment

    Full text link
    The singular value decomposition (SVD) is a widely used matrix factorization tool which underlies plenty of useful applications, e.g. recommendation system, abnormal detection and data compression. Under the environment of emerging Internet of Things (IoT), there would be an increasing demand for data analysis to better human's lives and create new economic growth points. Moreover, due to the large scope of IoT, most of the data analysis work should be done in the network edge, i.e. handled by fog computing. However, the devices which provide fog computing may not be trustable while the data privacy is often the significant concern of the IoT application users. Thus, when performing SVD for data analysis purpose, the privacy of user data should be preserved. Based on the above reasons, in this paper, we propose a privacy-preserving fog computing framework for SVD computation. The security and performance analysis shows the practicability of the proposed framework. Furthermore, since different applications may utilize the result of SVD operation in different ways, three applications with different objectives are introduced to show how the framework could flexibly achieve the purposes of different applications, which indicates the flexibility of the design.Comment: 24 pages, 4 figure
    corecore