92,467 research outputs found
New Lepton Family Symmetry and Neutrino Tribimaximal Mixing
The newly proposed finite symmetry Sigma(81) is applied to the problem of
neutrino tribimaximal mixing. The result is more satisfactory than those of
previous models based on A_4 in that the use of auxiliary symmetries (or
mechanisms) may be avoided. Deviations from the tribimaximal pattern are
expected, but because of its basic structure, only tan^2 (theta_12) may differ
significantly from 0.5 (say 0.45) with sin^2 (2 theta_23) remaining very close
to one, and theta_13 very nearly zero.Comment: 8 pages, no figur
A covariant entropy conjecture on cosmological dynamical horizon
We here propose a covariant entropy conjecture on cosmological dynamical
horizon. After the formulation of our conjecture, we test its validity in
adiabatically expanding universes with open, flat and closed spatial geometry,
where our conjecture can also be viewed as a cosmological version of the
generalized second law of thermodynamics in some sense.Comment: JHEP style, 9 pages, 1 figure, typos corrected, accepted for
publication in JHE
Monte-Carlo approach to calculate the proton stopping in warm dense matter within particle-in-cell simulations
A Monte-Carlo approach to proton stopping in warm dense matter is implemented
into an existing particle-in-cell code. The model is based on multiple
binary-collisions among electron-electron, electron-ion and ion-ion, taking
into account contributions from both free and bound electrons, and allows to
calculate particle stopping in much more natural manner. At low temperature
limit, when ``all'' electron are bounded at the nucleus, the stopping power
converges to the predictions of Bethe-Bloch theory, which shows good
consistency with data provided by the NIST. With the rising of temperatures,
more and more bound electron are ionized, thus giving rise to an increased
stopping power to cold matter, which is consistent with the report of a
recently experimental measurement [Phys. Rev. Lett. 114, 215002 (2015)]. When
temperature is further increased, with ionizations reaching the maximum,
lowered stopping power is observed, which is due to the suppression of
collision frequency between projected proton beam and hot plasmas in the
target.Comment: 6 pages, 4 figure
Fast network configuration in Software Defined Networking
Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%
Monte-Carlo approach to calculate the ionization of warm dense matter within particle-in-cell simulations
A physical model based on a Monte-Carlo approach is proposed to calculate the
ionization dynam- ics of warm dense matters (WDM) within particle-in-cell
simulations, and where the impact (col- lision) ionization (CI), electron-ion
recombination (RE) and ionization potential depression (IPD) by surrounding
plasmas are taken into consideration self-consistently. When compared with
other models, which are applied in the literature for plasmas near thermal
equilibrium, the temporal re- laxation of ionization dynamics can also be
simulated by the proposed model. Besides, this model is general and can be
applied for both single elements and alloys with quite different composi-
tions. The proposed model is implemented into a particle-in-cell (PIC) code,
with (final) ionization equilibriums sustained by competitions between CI and
its inverse process (i.e., RE). Comparisons between the full model and model
without IPD or RE are performed. Our results indicate that for bulk aluminium
in the WDM regime, i) the averaged ionization degree increases by including
IPD; while ii) the averaged ionization degree is significantly over estimated
when the RE is neglected. A direct comparison from the PIC code is made with
the existing models for the dependence of averaged ionization degree on thermal
equilibrium temperatures, and shows good agreements with that generated from
Saha-Boltzmann model or/and FLYCHK code.Comment: 7 pages, 4 figure
Covariant entropy conjecture and concordance cosmological models
Recently a covariant entropy conjecture has been proposed for dynamical
horizons. We apply this conjecture to concordance cosmological models, namely,
those cosmological models filled with perfect fluids, in the presence of a
positive cosmological constant. As a result, we find this conjecture has a
severe constraint power. Not only does this conjecture rule out those
cosmological models disfavored by the anthropic principle, but also it imposes
an upper bound on the cosmological constant for our own universe,
which thus provides an alternative macroscopic perspective for understanding
the long-standing cosmological constant problem.Comment: 10 pages, 1 figure, JHEP style, references added, published versio
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
In this paper, we introduce the semantic knowledge of medical images from
their diagnostic reports to provide an inspirational network training and an
interpretable prediction mechanism with our proposed novel multimodal neural
network, namely TandemNet. Inside TandemNet, a language model is used to
represent report text, which cooperates with the image model in a tandem
scheme. We propose a novel dual-attention model that facilitates high-level
interactions between visual and semantic information and effectively distills
useful features for prediction. In the testing stage, TandemNet can make
accurate image prediction with an optional report text input. It also
interprets its prediction by producing attention on the image and text
informative feature pieces, and further generating diagnostic report
paragraphs. Based on a pathological bladder cancer images and their diagnostic
reports (BCIDR) dataset, sufficient experiments demonstrate that our method
effectively learns and integrates knowledge from multimodalities and obtains
significantly improved performance than comparing baselines.Comment: MICCAI2017 Ora
Superpixel Convolutional Networks using Bilateral Inceptions
In this paper we propose a CNN architecture for semantic image segmentation.
We introduce a new 'bilateral inception' module that can be inserted in
existing CNN architectures and performs bilateral filtering, at multiple
feature-scales, between superpixels in an image. The feature spaces for
bilateral filtering and other parameters of the module are learned end-to-end
using standard backpropagation techniques. The bilateral inception module
addresses two issues that arise with general CNN segmentation architectures.
First, this module propagates information between (super) pixels while
respecting image edges, thus using the structured information of the problem
for improved results. Second, the layer recovers a full resolution segmentation
result from the lower resolution solution of a CNN. In the experiments, we
modify several existing CNN architectures by inserting our inception module
between the last CNN (1x1 convolution) layers. Empirical results on three
different datasets show reliable improvements not only in comparison to the
baseline networks, but also in comparison to several dense-pixel prediction
techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201
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