29,306 research outputs found
A simple model for the complex lag structure of microquasars
The phase lag structure between the hard and soft X-ray photons observed in
GRS 1915+105 and XTE J1550+564 has been said to be ``complex'' because the
phase of the Quasi-Periodic Oscillation fundamental Fourier mode changes with
time and because the even and odd harmonics signs behave differentely.
From simultaneous X-ray and radio observations this seems to be related to
the presence of a jet (level of radio emission). We propose a simple idea where
a partial absorption of the signal can shift the phases of the Fourier modes
and account for the phase lag reversal. We also briefly discuss a possible
physical mechanism that could lead to such an absorption of the quasi-periodic
oscillation modulation.Comment: accepted by A&A Letter
LFV couplings of the extra gauge boson Z' and leptonic decay and production of pseudoscalar mesons
Considering the constraints of the lepton flavor violating (LFV) processes
and on the LFV couplings
, in the contexts of the models, the left-right
(LR) models, the "alternative" left-right (ALR) models and the 331 models, we
investigate the contributions of the extra gauge boson to the decay rates
of the processes ,
and with and
. Our numerical results show that the maximal values of the branching
ratios for these processes are not dependent on the mass at
leader order. The extra gauge boson predicted by the models
can make the maximum value of the branching ratio
reach . All
models considered in this paper can produce significant contributions to the
process . However, the value of
is far below its corresponding experimental upper bound.Comment: 14 pages, 2 figures; matches published versio
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that handles these three challenges. Using deep metric learning with humans in
the loop, we learn a low dimensional feature embedding with anchor points on
manifolds for each category. These anchor points capture intra-class variances
and remain discriminative between classes. In each round, images with high
confidence scores from our model are sent to humans for labeling. By comparing
with exemplar images, labelers mark each candidate image as either a "true
positive" or a "false positive". True positives are added into our current
dataset and false positives are regarded as "hard negatives" for our metric
learning model. Then the model is retrained with an expanded dataset and hard
negatives for the next round. To demonstrate the effectiveness of the proposed
framework, we bootstrap a fine-grained flower dataset with 620 categories from
Instagram images. The proposed deep metric learning scheme is evaluated on both
our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show
significant performance gain using dataset bootstrapping and demonstrate
state-of-the-art results achieved by the proposed deep metric learning methods.Comment: 10 pages, 9 figures, CVPR 201
The source-lens clustering effect in the context of lensing tomography and its self-calibration
Cosmic shear can only be measured where there are galaxies. This source-lens
clustering (SLC) effect has two sources, intrinsic source clustering and cosmic
magnification (magnification/size bias). Lensing tomography can suppress the
former. However, this reduction is limited by the existence of photo-z error
and nonzero redshift bin width. Furthermore, SLC induced by cosmic
magnification cannot be reduced by lensing tomography. Through N-body
simulations, we quantify the impact of SLC on the lensing power spectrum in the
context of lensing tomography. We consider both the standard estimator and the
pixel-based estimator. We find that none of them can satisfactorily handle both
sources of SLC. (1) For the standard estimator, SLC induced by both sources can
bias the lensing power spectrum by O(1)-O(10)%. Intrinsic source clustering
also increases statistical uncertainties in the measured lensing power
spectrum. However, the standard estimator suppresses intrinsic source
clustering in the cross-spectrum. (2) In contrast, the pixel-based estimator
suppresses SLC through cosmic magnification. However, it fails to suppress SLC
through intrinsic source clustering and the measured lensing power spectrum can
be biased low by O(1)-O(10)%. In short, for typical photo-z errors
(sigma_z/(1+z)=0.05) and photo-z bin sizes (Delta_z^P=0.2), SLC alters the
lensing E-mode power spectrum by 1-10%, with ell~10^3$ and z_s~1 being of
particular interest to weak lensing cosmology. Therefore the SLC is a severe
systematic for cosmology in Stage-IV lensing surveys. We present useful scaling
relations to self-calibrate the SLC effect.Comment: 13 pages, 10 figures, Accepted by AP
Experimental Evaluation of SDN-Controlled, Joint Consolidation of Policies and Virtual Machines
Middleboxes (MBs) are ubiquitous in modern data centre (DC) due to their crucial role in implementing network security, management and optimisation. In order to meet network policy's requirement on correct traversal of an ordered sequence of MBs, network administrators rely on static policy based routing or VLAN stitching to steer traffic flows. However, dynamic virtual server migration in virtual environment has greatly challenged such static traffic steering. In this paper, we design and implement Sync, an efficient and synergistic scheme to jointly consolidate network policies and virtual machines (VMs), in a readily deployable Mininet environment. We present the architecture of Sync framework and open source its code. We also extensively evaluate Sync over diverse workload and policies. Our results show that in an emulated DC of 686 servers, 10k VMs, 8k policies, and 100k flows, Sync processes a group of 900 VMs and 10 VMs in 634 seconds and 4 seconds respectively
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