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 $\mu \rightarrow 3e$ and $\tau\rightarrow3\mu$ on the LFV couplings $Z'\ell_{i}\ell_{j}$, in the contexts of the $E_{6}$ 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 $Z'$ to the decay rates of the processes $\ell_{i}\rightarrow\ell_{j}\nu_{\ell}\nu_{\ell}$, $\tau\rightarrow\mu P$ and $P\rightarrow \mu e$ with $P=\pi^{0},\eta$ and $\eta '$. Our numerical results show that the maximal values of the branching ratios for these processes are not dependent on the $Z'$ mass $M_{Z'}$ at leader order. The extra gauge boson $Z'_{X}$ predicted by the $E_{6}$ models can make the maximum value of the branching ratio $Br(\tau\rightarrow\mu\nu_{\ell}\nu_{\ell})$ reach $1.1\times10^{-7}$. All $Z'$ models considered in this paper can produce significant contributions to the process $\tau\rightarrow\mu P$. However, the value of $Br(P\rightarrow\mu e)$ 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