2,477 research outputs found
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
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