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

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 $(\bar{\bar{\gamma_\mu \otimes 1}})$ and the scalar projection operator $(\bar{\bar{1 \otimes 1}})$, we should be able to obtain the wave function renormalization factor $Z_q'$ and the mass renormalization factor $Z_q \cdot Z_m$. Using the MILC coarse lattice, we obtain a preliminary but reasonable estimate of $Z_q'$ and $Z_q \cdot Z_m$ 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

Non-perturbative Renormalization of Bilinear Operators with Improved Staggered Quarks

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 ($N_f = 2+1$). We use the MILC coarse ensembles with $20^3 \times 64$ geometry and $am_{\ell}/am_s = 0.01/0.05$. We obtain the wave function renormalization factor $Z_q$ from the conserved vector current and the mass renormalization factor $Z_m$ 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

Nonlinear Bayesian Estimation via Solution of The Fokker-Planck Equation

A general approach to optimal nonlinear filtering can be described by a recursive Bayesian approach. The key step in this approach is to determine the probability density function of the state vector conditioned on available measurements. However, an optimal solution to the Bayesian filtering problem can only be obtained exactly for a small class of problems such as linear and Gaussian cases. Therefore, in practice, approximate solutions, such as the extended Kalman filter, have been used.An optimal nonlinear filtering in a recursive Bayesian approach is a two-step process which consists of the prediction and the update process. In the update process, the priori conditional state probability density function (PDF) from the prediction process is updated through Bayes' rule using measurements from sensors. The prediction of conditional state PDF can be made by solving the Fokker-Planck equation (FPE) that governs the time-evolution the conditional state PDF. However, it is extremely difficult to obtain an analytical solution of the Fokker-Planck equation with the exception of a few special cases. So far this estimation method has not been employed much in practice because of the high computational cost needed in solving the FPE numerically. In this dissertation, methods to improve the efficiency of the numerical method in solving the FPE are investigated to enhance the efficiency of the nonlinear filtering.Two finite difference methods, namely i) the explicit forward method and ii) the alternating direction implicit (ADI) method, are used to solve the FPE numerically. Although the explicit forward method is much simpler to implement, the ADI method is preferred for its low computational cost. To reduce the computational cost further, as the first contribution of the dissertation, a moving domain scheme is developed to reduce the domain of integration required for solving the Fokker-Planck equation numerically. Simulation results show that the accuracy of the estimation is improved as compared with the Extended Kalman Filter, and at the same time the computational cost is significantly lower with the proposed moving grid scheme than the case without it.Recently a nonlinear filtering algorithm using a direct quadrature method of moments was proposed, where the associated Fokker-Planck equation is solved efficiently via discrete quadrature based on moment constraints. For some problems, however, this approach showed the phenomenon similar to the "degeneracy'' in a particle filter, which is the concentration of weight on particular particles. The possible cause of the phenomenon is that only the weights are updated through the modified Bayes' rule. Therefore, in this dissertation, as another contribution, a new hybrid filter is proposed where the measurement update equations in the extended or the unscented Kalman filter are used along with the direct quadrature method of moments to solve the FPE. In this way the "degeneracy'' problem can be mitigated.Then, new proposed filtering methods are applied to several challenging problems such as i) the bearing-only tracking problem, ii) the relative orbit position estimation problem, and iii) the orbit determination problem to demonstrate their advantages. Simulation results indicate that the performance of the proposed filters are better than existing nonlinear filtering methods, such as the Extended Kalman Filter especially with less measurement updates

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

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

The effects of reductions in public psychiatric hospital beds on crime, arrests, and jail detentions of severely mentally ill persons

This dissertation analyzed the effect of reduced psychiatric bed supply on criminal justice outcomes. Three studies were conducted. The first two studies - Study 1 and Study 2 - explored the relationships between the supply of hospital psychiatric beds and the number of crimes, arrests, and jail inmates, using state-level panel data on 50 U.S. states and the District of Columbia for the years 1982 to 1998. There was no evidence of the relationship between the total number of psychiatric beds and these criminal justice outcomes. However, hospital type was found to have differential effects on the criminal justice outcomes. A decrease in public psychiatric hospital beds was found to increase both violent and property crimes. In contrast, an increase in private psychiatric hospital beds appears to increase property crimes. Decreased public psychiatric hospital beds also negatively affected arrests for serious property crimes and drug violations as well as the number of jail inmates. Study 3 of this dissertation analyzed the impact of the supply of hospital psychiatric beds on an individual's likelihood of jail detention among persons with severe mental illness, rigorously exploring mechanisms by which reduced psychiatric bed availability would increase jail detention. The empirical analysis was based on unique longitudinal data that provide information on the use of the mental health and substance abuse treatment systems as well as the jail system in King County, Washington over the periods July 1993 through December 1998. A decrease in total psychiatric beds was found to increase the probability of jail detention among persons with mental illness - in particular black women with severe mental illness - mainly via an increase in minor offenses. Importantly, mental health service use and substance abuse were identified as the main pathways by which decreased psychiatric bed availability increases jail detention among persons with severe mental illness. A synthesis of findings reassures the importance of close, continuous communication and collaboration within and across sub-systems of a community including the inpatient mental health system, the outpatient mental health system, the substance abuse treatment system, and the criminal justice system

Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation

When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image. The temporal variations in a video allow different regions of the target object to be activated. We obtain an activated region in each frame of a video, and then aggregate the regions from successive frames into a single image, using a warping technique based on optical flow. The resulting localization maps cover more of the target object, and can then be used as proxy ground-truth to train a segmentation network. This simple approach outperforms existing methods under the same level of supervision, and even approaches relying on extra annotations. Based on VGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4, respectively, on PASCAL VOC 2012 test images, which represents a new state-of-the-art.Comment: ICCV 201

A Direct Quadrature Based Nonlinear Filtering with Extended Kalman Filter Update for Orbit Determination

Abstract-An optimal estimation of the states of a nonlinear continuous system with discrete measurements can be achieved through the solution of the Fokker-Planck equation, along with the Bayes' formula. However, solving the Fokker-Planck equation is restrictive in most cases. Recently a nonlinear filtering algorithm using a direct quadrature method of moments and the extended Kalman filter update mechanism was proposed, in which the associated Fokker-Planck equation was solved efficiently and accurately via discrete quadrature and the measurement update was done through the extended Kalman filter update mechanism. In this paper this hybrid filter based on the DQMOM and the EKF update is applied to the orbit determination problem with appropriate modification to mitigate the filter smugness. Unlike the extended Kalman filter, the hybrid filter based on the DQMOM and the EKF update does not require the burdensome evaluation of the Jacobian matrix and Gaussian assumption for system noise, and can still provide more accurate estimation of the state than those of the extended Kalman filter especially when measurements are sparse. Simulation results indicate that the advantages of the hybrid filter based on the DQMOM and the EKF update make it a promising alternative to the extended Kalman filter for orbit estimation problems