3,412 research outputs found
Global Behavior Of Finite Energy Solutions To The -Dimensional Focusing Nonlinear Schr\"odinger Equation
We study the global behavior of finite energy solutions to the
-dimensional focusing nonlinear Schr\"odinger equation (NLS), with initial data . The
nonlinearity power and the dimension are such that the scaling index
is between 0 and 1, thus, the NLS is
mass-supercritical and energy-subcritical
For solutions with \ME[u_0]<1 (\ME[u_0] stands for an invariant and
conserved quantity in terms of the mass and energy of ), a sharp threshold
for scattering and blowup is given. Namely, if the renormalized gradient \g_u
of a solution to NLS is initially less than 1, i.e., \g_u(0)<1, then the
solution exists globally in time and scatters in (approaches some linear
Schr\"odinger evolution as ); if the renormalized gradient
\g_u(0)>1, then the solution exhibits a blowup behavior, that is, either a
finite time blowup occurs, or there is a divergence of norm in infinite
time.
This work generalizes the results for the 3d cubic NLS obtained in a series
of papers by Holmer-Roudenko and Duyckaerts-Holmer-Roudenko with the key
ingredients, the concentration compactness and localized variance, developed in
the context of the energy-critical NLS and Nonlinear Wave equations by Kenig
and Merle.Comment: 57 pages, 4 figures and updated reference
Ultra-low Q values for neutrino mass measurements
We investigate weak nuclear decays with extremely small kinetic energy
release (Q value) and thus extremely good sensitivity to the absolute neutrino
mass scale. In particular, we consider decays into excited daughter states, and
we show that partial ionization of the parent atom can help to tune Q values to
<< 1 keV. We discuss several candidate isotopes undergoing beta+, beta-, bound
state beta, or electron capture decay, and come to the conclusion that a
neutrino mass measurement using low-Q decays might only be feasible if no
ionization is required, and if future improvements in isotope production
technology, nuclear mass spectroscopy, and atomic structure calculations are
possible. Experiments using ions, however, are extremely challenging due to the
large number of ions that must be stored. New precision data on nuclear
excitation levels could help to identify further isotopes with low-Q decay
modes and possibly less challenging requirements.Comment: 7 pages, 2 figures; v2: Typos corrected, references adde
Enhancing Dark Matter Annihilation into Neutrinos
We perform a detailed and quasi model-independent analysis of direct
annihilation of Dark Matter into neutrinos. Considering different cases for
scalar and fermionic Dark Matter, we identify several settings in which this
annihilation is enhanced, contrary to some statements in the literature. They
key point is that several restrictions of, e.g., a supersymmetric framework do
not hold in general. The mass generation mechanism of the neutrinos plays an
important role, too. We illustrate our considerations by two examples that are
not (as usually) suppressed by the smallness of the neutrino mass, for which we
also present a numerical analysis. Our results can be easily used as guidelines
for model building.Comment: 33 pages, 2 figure
General Conditions for Lepton Flavour Violation at Tree- and 1-Loop Level
In this work, we compile the necessary and sufficient conditions a theory has
to fulfill in order to ensure general lepton flavour conservation, in the
spirit of the Glashow-Weinberg criteria for the absence of flavour-changing
neutral currents. At tree-level, interactions involving electrically neutral
and doubly charged bosons are investigated. We also investigate flavour changes
at 1-loop level. In all cases we find that the essential theoretical
requirements can be reduced to a few basic conditions on the particle content
and the coupling matrices. For 1-loop diagrams, we also investigate how exactly
a GIM-suppression can occur that will strongly reduce the rates of lepton
flavour violating effects even if they are in principle present in a certain
theory. In all chapters, we apply our criteria to several models which can in
general induce lepton flavour violation, e.g. LR-symmetric models or the MSSM.
In the end we give a summarizing table of the obtained results, thereby
demonstrating the applicability of our criteria to a large class of models
beyond the Standard Model.Comment: 31 pages, 2 figure
BCG vaccination and leprosy protection: review of current evidence and status of BCG in leprosy control.
The bacillus Calmette-Guérin (BCG) vaccine, initially developed to provide protection against TB, also protects against leprosy; and the magnitude of this effect varies. Previous meta-analyses did not provide a summary estimate of the efficacy due to the heterogeneity of the results. We conducted a meta-analysis of published data including recently published studies (up to June 2009) to determine the efficacy of BCG protection on leprosy and to investigate whether age at vaccination, clinical form, number of doses, type of study, the latitude of study area and year of publication influence the degree of efficacy and explain the variation. In the light of the results, we argue for more emphasis on the role of BCG vaccination in leprosy control and research
The Contributions of Harmon W. Caldwell to Higher Education in Georgia
Remarks of Merle C. Prunty, October 18, 1980, at the presentation of the Harmon W. Caldwell portrait, School of Law, University of Georgia
RBF neural net based classifier for the AIRIX accelerator fault diagnosis
The AIRIX facility is a high current linear accelerator (2-3.5kA) used for
flash-radiography at the CEA of Moronvilliers France. The general background of
this study is the diagnosis and the predictive maintenance of AIRIX. We will
present a tool for fault diagnosis and monitoring based on pattern recognition
using artificial neural network. Parameters extracted from the signals recorded
on each shot are used to define a vector to be classified. The principal
component analysis permits us to select the most pertinent information and
reduce the redundancy. A three layer Radial Basis Function (RBF) neural network
is used to classify the states of the accelerator. We initialize the network by
applying an unsupervised fuzzy technique to the training base. This allows us
to determine the number of clusters and real classes, which define the number
of cells on the hidden and output layers of the network. The weights between
the hidden and the output layers, realising the non-convex union of the
clusters, are determined by a least square method. Membership and ambiguity
rejection enable the network to learn unknown failures, and to monitor
accelerator operations to predict future failures. We will present the first
results obtained on the injector.Comment: 3 pages, 4 figures, LINAC'2000 conferenc
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