1,591 research outputs found
Highlights of the SLD Physics Program at the SLAC Linear Collider
Starting in 1989, and continuing through the 1990s, high-energy physics
witnessed a flowering of precision measurements in general and tests of the
standard model in particular, led by e+e- collider experiments operating at the
Z0 resonance. Key contributions to this work came from the SLD collaboration at
the SLAC Linear Collider. By exploiting the unique capabilities of this
pioneering accelerator and the SLD detector, including a polarized electron
beam, exceptionally small beam dimensions, and a CCD pixel vertex detector, SLD
produced a broad array of electroweak, heavy-flavor, and QCD measurements. Many
of these results are one of a kind or represent the world's standard in
precision. This article reviews the highlights of the SLD physics program, with
an eye toward associated advances in experimental technique, and the
contribution of these measurements to our dramatically improved present
understanding of the standard model and its possible extensions.Comment: To appear in 2001 Annual Review of Nuclear and Particle Science; 78
pages, 31 figures; A version with higher resolution figures can be seen at
http://www.slac.stanford.edu/pubs/slacpubs/8000/slac-pub-8985.html; Second
version incorporates minor changes to the tex
A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment
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
Quantum measurement of a mesoscopic spin ensemble
We describe a method for precise estimation of the polarization of a
mesoscopic spin ensemble by using its coupling to a single two-level system.
Our approach requires a minimal number of measurements on the two-level system
for a given measurement precision. We consider the application of this method
to the case of nuclear spin ensemble defined by a single electron-charged
quantum dot: we show that decreasing the electron spin dephasing due to nuclei
and increasing the fidelity of nuclear-spin-based quantum memory could be
within the reach of present day experiments.Comment: 8 pages, 2 figures; minor changes, published versio
Arrhythmic Genotypes in Familial Dilated Cardiomyopathy: Implications for Genetic Testing and Clinical Management
Cardiac arrhythmias are frequently seen in patients with dilated cardiomyopathy (DCM) and can precipitate heart failure and death. In patients with non-ischaemic DCM, evidence for the benefit of an implantable cardioverter-defibrillator (ICD) for primary prevention of sudden cardiac death has recently been questioned. Algorithms devised to identify high-risk individuals who might benefit most from ICD implantation have focussed on clinical criteria with little attention paid to the underlying aetiology of DCM. Malignant ventricular arrhythmias often occur as a nonspecific consequence of DCM but can also be a primary manifestation of disease in heritable forms of DCM and may precede DCM onset. We undertook a literature search and identified 11 genes that have been associated with DCM and ventricular arrhythmias in multiple kindreds. Many of these genes fall into a diagnostic grey zone between left-dominant arrhythmogenic right ventricular cardiomyopathy and arrhythmic DCM. Genes associated predominantly with arrhythmic DCM included LMNA and SCN5A, as well as the more recently-reported DCM disease genes, RBM20, FLNC, and TTN. Recognition of arrhythmic DCM genotypes is important, as this may impact on clinical management. In particular, prophylactic ICD implantation and early referral for heart transplantation may be indicated in genotype-positive individuals. Collectively, these findings argue in favour of including genetic testing in standard-of-care management of familial DCM. Further studies in genotyped patient cohorts are required to establish the long-term health and economic benefits of this strategy
Magnetometry via a double-pass continuous quantum measurement of atomic spin
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
Detection and prevention of financial abuse against elders
This article is made available through the Brunel Open Access Publishing Fund. Copyright @ The Authors. This article is published under the Creative Commons Attribution (CC BY 3.0) licence. Anyone
may reproduce, distribute, translate and create derivative works of this article (for both
commercial and non-commercial purposes), subject to full attribution to the original publication
and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/
by/3.0/legalcode.Purpose â This paper reports on banking and finance professionals' decision making in the context of elder financial abuse. The aim was to identify the case features that influence when abuse is identified and when action is taken.
Design/methodology/approach â Banking and finance professionals (n=70) were shown 35 financial abuse case scenarios and were asked to judge how certain they were that the older person was being abused and the likelihood of taking action.
Findings â Three case features significantly influenced certainty of financial abuse: the nature of the financial problem presented, the older person's level of mental capacity and who was in charge of the client's money. In cases where the older person was more confused and forgetful, there was increased suspicion that financial abuse was taking place. Finance professionals were less certain that financial abuse was occurring if the older person was in charge of his or her own finances.
Originality/value â The research findings have been used to develop freely available online training resources to promote professionals' decision making capacity (www.elderfinancialabuse.co.uk). The resources have been advocated for use by Building Societies Association as well as CIFAS, the UK's Fraud Prevention Service.The research reported here was funded by the UK cross council New Dynamicsof Ageing Programme, ESRC Reference No. RES-352-25-0026, with Mary L.M. Gilhooly asPrincipal Investigator. Web-based training tools, developed from the research findings, weresubsequently funded by the ESRC follow-on fund ES/J001155/1 with Priscilla A. Harries asPrincipal Investigator
Management of recent-onset sustained atrial fibrillation: pharmacologic and nonpharmacologic strategies
Abstract not availableDennis H.Lau, Jonathan Kalman, Prashanthan Sander
Morphing Ensemble Kalman Filters
A new type of ensemble filter is proposed, which combines an ensemble Kalman
filter (EnKF) with the ideas of morphing and registration from image
processing. This results in filters suitable for nonlinear problems whose
solutions exhibit moving coherent features, such as thin interfaces in wildfire
modeling. The ensemble members are represented as the composition of one common
state with a spatial transformation, called registration mapping, plus a
residual. A fully automatic registration method is used that requires only
gridded data, so the features in the model state do not need to be identified
by the user. The morphing EnKF operates on a transformed state consisting of
the registration mapping and the residual. Essentially, the morphing EnKF uses
intermediate states obtained by morphing instead of linear combinations of the
states.Comment: 17 pages, 7 figures. Added DDDAS references to the introductio
CAR-Net: Clairvoyant Attentive Recurrent Network
We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.Comment: The 2nd and 3rd authors contributed equall
Phase Mixing of Nonlinear Plasma Oscillations in an Arbitrary Mass Ratio Cold Plasma
Nonlinear plasma oscillations in an arbitrary mass ratio cold plasma have
been studied using 1-D particle-in-cell simulation. In contrast to earlier work
for infinitely massive ion plasmas it has been found that the oscillations
phase mix away at any amplitude and that the rate at which phase mixing occurs,
depends on the mass ratio () and the amplitude. A
perturbation theoretic calculation carried upto third order predicts that the
normalized phase mixing time depends on the amplitude
and the mass ratio as . We have confirmed this scaling in our simulations and
conclude that stable non-linear oscillations which never phase mix, exist only
for the ideal case with and . These cold plasma results
may have direct relevance to recent experiments on superintense laser beam
plasma interactions with applications to particle acceleration, fast ignitor
concept etc.Comment: pp 10 and two figures in PS forma
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