162 research outputs found

    Non-perturbative Renormalization of Bilinear Operators with Improved Staggered Quarks

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    We present renormalization factors for the bilinear operators obtained using the non-perturbative renormalization method (NPR) in the RI-MOM scheme with improved staggered fermions on the MILC asqtad lattices (Nf=2+1N_f = 2+1). We use the MILC coarse ensembles with 203×6420^3 \times 64 geometry and am/ams=0.01/0.05am_{\ell}/am_s = 0.01/0.05. We obtain the wave function renormalization factor ZqZ_q from the conserved vector current and the mass renormalization factor ZmZ_m from the scalar bilinear operator. We also present preliminary results of renormalization factors for other bilinear operators.Comment: 7 pages, 4 figures, Lattice 2013 Proceedin

    Non-Perturbative Renormalization for Staggered Fermions (Self-energy Analysis)

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    We present preliminary results of data analysis for the non-perturbative renormalization (NPR) on the self-energy of the quark propagators calculated using HYP improved staggered fermions on the MILC asqtad lattices. We use the momentum source to generate the quark propagators. In principle, using the vector projection operator of (γμ1ˉˉ)(\bar{\bar{\gamma_\mu \otimes 1}}) and the scalar projection operator (11ˉˉ)(\bar{\bar{1 \otimes 1}}), we should be able to obtain the wave function renormalization factor ZqZ_q' and the mass renormalization factor ZqZmZ_q \cdot Z_m. Using the MILC coarse lattice, we obtain a preliminary but reasonable estimate of ZqZ_q' and ZqZmZ_q \cdot Z_m from the data analysis on the self-energy.Comment: 7 pages, 4 figures, Contribution to proceedings of 30th International Symposium on Lattice Field Theory (Lattice 2012), June 24-29, 2012; Cairns, Australi

    Dual Formulation and Phase Diagram of Lattice QCD in the Strong Coupling Regime

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    We present the computation of invariants that arise in the strong coupling expansion of lattice QCD. These invariants are needed for Monte Carlo simulations of Lattice QCD with staggered fermions in a dual, color singlet representation. This formulation is in particular useful to tame the finite density sign problem. The gauge integrals in this limiting case β0\beta\rightarrow 0 are well known, but the gauge integrals needed to study the gauge corrections are more involved. We discuss a method to evaluate such integrals. The phase boundary of lattice QCD for staggered fermions in the μBT\mu_B-T plane has been established in the strong coupling limit. We present numerical simulations away from the strong coupling limit, taking into account the higher order gauge corrections via plaquette occupation numbers. This allows to study the nuclear and chiral transition as a function of β\beta.Comment: 16 pages, 10 figures, Proceedings of the 35th International Symposium on Lattice Field Theory, Granada, Spai

    FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference

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    The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on image-level annotations use localization maps obtained from the classifier, but these only focus on the small discriminative parts of objects and do not capture precise boundaries. FickleNet explores diverse combinations of locations on feature maps created by generic deep neural networks. It selects hidden units randomly and then uses them to obtain activation scores for image classification. FickleNet implicitly learns the coherence of each location in the feature maps, resulting in a localization map which identifies both discriminative and other parts of objects. The ensemble effects are obtained from a single network by selecting random hidden unit pairs, which means that a variety of localization maps are generated from a single image. Our approach does not require any additional training steps and only adds a simple layer to a standard convolutional neural network; nevertheless it outperforms recent comparable techniques on the Pascal VOC 2012 benchmark in both weakly and semi-supervised settings.Comment: To appear in CVPR 201
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