Medicare Advantage Reforms: Comparing House and Senate Bills

Compares House and Senate approaches to reforming the Medicare Advantage payment system to reduce costs. Discusses geographic unit of payment, risk adjustments, quality management bonuses, and beneficiary protections and enrollment simplification

Institutional Collective Action in Ontario’s Fire Service: Conducive and Inhibiting Factors of Local Collaboration of Fire Safety Inspections and Enforcement

The economic, political, and social environment in Ontario is placing increased pressure on the delivery of local fire prevention services and fire chiefs and elected officials are expected to do more with fewer resources. Subsequently, exploring mechanisms to work collaboratively with neighbouring fire departments in order to improve the efficacy of fire prevention programs should be a priority. This paper examines the conducive and inhibiting factors of voluntary collaboration for fire prevention activities within Ontario’s fire service. A cross-sectional study and analysis was undertaken to collect data of relevant variables at a specific point in time using a triangulation approach. The findings reveal that the conducive and inhibiting factors of voluntary collaboration of fire prevention activities were consistent with the literature. This leads to the conclusion that local municipalities considering alternative service delivery options might explore opportunities to voluntarily collaborate with one or more of their neighbours to meet their local needs and circumstances

Matching Image Sets via Adaptive Multi Convex Hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

Bs Mixing and Electric Dipole Moments in MFV

We analyze the general structure of four-fermion operators capable of introducing CP-violation preferentially in Bs mixing within the framework of Minimal Flavor Violation. The effect requires a minimum of O(Yu^4 Yd^4) Yukawa insertions, and at this order we find a total of six operators with different Lorentz, color, and flavor contractions that lead to enhanced Bs mixing. We then estimate the impact of these operators and of their close relatives on the possible sizes of electric dipole moments (EDMs) of neutrons and heavy atoms. We identify two broad classes of such operators: those that give EDMs in the limit of vanishing CKM angles, and those that require quark mixing for the existence of non-zero EDMs. The natural value for EDMs from the operators in the first category is up to an order of magnitude above the experimental upper bounds, while the second group predicts EDMs well below the current sensitivity level. Finally, we discuss plausible UV-completions for each type of operator.Comment: 11 pages; v2: references adde

TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN

Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach

Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a higher dimensional space. However, since these manifolds belong to non-Euclidean topological spaces, exploiting their structures is computationally expensive, especially when one considers the clustering analysis of massive amounts of data. To this end, we propose an efficient framework to address the clustering problem on Riemannian manifolds. This framework implements random projections for manifold points via kernel space, which can preserve the geometric structure of the original space, but is computationally efficient. Here, we introduce three methods that follow our framework. We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds. Experimental results demonstrate that our framework maintains the performance of the clustering whilst massively reducing computational complexity by over two orders of magnitude in some cases

Medicare Advantage Payment Provisions: Health Care and Education Affordability Reconciliation Act of 2010 H.R. 4872

The Health Care and Education Affordability Reconciliation Act of 2010 would make major changes to Medicare Advantage (MA) payment policies. Overall, payments to MA plans would be reduced from the current national average of 113 percent of local fee-for-service (FFS) costs to a new average of 101 percent of FFS costs. The Congressional Budget Office (CBO) has estimated that the new polices would reduce Medicare spending by $132 billion over 10 years. The new policies would set county payment benchmarks for MA plans at 115 percent, 107.5 percent, 100 percent, and 95 percent of local FFS costs depending of the relative level of FFS costs in the county. The MA plan rebate policy would be reduced from the current level of 75 percent. A new program of plan performance-based payments would increase benchmarks and rebates to plans with high performance scores. This issue brief presents analysis, using data from 2009, of the impact of these new policies on payments to private plans across the nation