2,299 research outputs found
Capital Accumulation and Annuities in an Adverse Selection Economy
This paper suggests that adverse selection problems in competitive annuity markets can generate quantity constrained equilibria in which some agents whose length of lifetime is uncertain find it advantageous to accumulate capital privately. This occurs despite the higher rates of return on annuities. The welfare properties of these allocations are analyzed. It is shown that the level of capital accumulation is excessive in a Paretian sense. Policies which eliminate this inefficiency are discussed.
Fault-tolerant additive weighted geometric spanners
Let S be a set of n points and let w be a function that assigns non-negative
weights to points in S. The additive weighted distance d_w(p, q) between two
points p,q belonging to S is defined as w(p) + d(p, q) + w(q) if p \ne q and it
is zero if p = q. Here, d(p, q) denotes the (geodesic) Euclidean distance
between p and q. A graph G(S, E) is called a t-spanner for the additive
weighted set S of points if for any two points p and q in S the distance
between p and q in graph G is at most t.d_w(p, q) for a real number t > 1.
Here, d_w(p,q) is the additive weighted distance between p and q. For some
integer k \geq 1, a t-spanner G for the set S is a (k, t)-vertex fault-tolerant
additive weighted spanner, denoted with (k, t)-VFTAWS, if for any set S'
\subset S with cardinality at most k, the graph G \ S' is a t-spanner for the
points in S \ S'. For any given real number \epsilon > 0, we obtain the
following results:
- When the points in S belong to Euclidean space R^d, an algorithm to compute
a (k,(2 + \epsilon))-VFTAWS with O(kn) edges for the metric space (S, d_w).
Here, for any two points p, q \in S, d(p, q) is the Euclidean distance between
p and q in R^d.
- When the points in S belong to a simple polygon P, for the metric space (S,
d_w), one algorithm to compute a geodesic (k, (2 + \epsilon))-VFTAWS with
O(\frac{k n}{\epsilon^{2}}\lg{n}) edges and another algorithm to compute a
geodesic (k, (\sqrt{10} + \epsilon))-VFTAWS with O(kn(\lg{n})^2) edges. Here,
for any two points p, q \in S, d(p, q) is the geodesic Euclidean distance along
the shortest path between p and q in P.
- When the points in lie on a terrain T, an algorithm to compute a
geodesic (k, (2 + \epsilon))-VFTAWS with O(\frac{k n}{\epsilon^{2}}\lg{n})
edges.Comment: a few update
Anatomic and Compression Topography of the Lesser Occipital Nerve.
BACKGROUND: The surgical treatment of occipital headaches focuses on the greater, lesser, and third occipital nerves. The lesser occipital nerve (LON) is usually transected with relatively limited available information regarding the compression topography thereof and how such knowledge may impact surgical treatment.
METHODS: Eight fresh frozen cadavers were dissected focusing on the LON in relation to 3 clinically relevant compression zones. The x axis was a line drawn through the occipital protuberance (OP) and the y axis, the posterior midline (PM). In addition, a prospectively collected cohort of 36 patients who underwent decompression of the LON is presented with their clinical results, including migraine headache index scores.
RESULTS: The LON was found in compression zone 1, with a mean of 7.8 cm caudal to the OP and 6.3 cm lateral to the PM. The LON was found at the midpoint of compression zone 2, with an average of 5.5 cm caudal to the OP and 6.2 cm lateral to the PM. At compression zone 3, the medial-most LON branch was located approximately 1 cm caudal to the OP and 5.35 cm lateral to the PM, whereas the lateral-most branch was identified 1 cm caudal to the OP and 6.5 cm lateral to the PM. Of the 36 decompression patients analyzed, only 5 (14%) required neurectomy as the remainder achieved statistically significant improvements in migraine headache index scores postoperatively.
CONCLUSION: The knowledge of LON anatomy can aid in nerve dissection and preservation, thereby leading to successful outcomes without requiring neurectomy
A reduced semantics for deciding trace equivalence using constraint systems
Many privacy-type properties of security protocols can be modelled using
trace equivalence properties in suitable process algebras. It has been shown
that such properties can be decided for interesting classes of finite processes
(i.e., without replication) by means of symbolic execution and constraint
solving. However, this does not suffice to obtain practical tools. Current
prototypes suffer from a classical combinatorial explosion problem caused by
the exploration of many interleavings in the behaviour of processes.
M\"odersheim et al. have tackled this problem for reachability properties using
partial order reduction techniques. We revisit their work, generalize it and
adapt it for equivalence checking. We obtain an optimization in the form of a
reduced symbolic semantics that eliminates redundant interleavings on the fly.Comment: Accepted for publication at POST'1
Approximate Minimum Diameter
We study the minimum diameter problem for a set of inexact points. By
inexact, we mean that the precise location of the points is not known. Instead,
the location of each point is restricted to a contineus region (\impre model)
or a finite set of points (\indec model). Given a set of inexact points in
one of \impre or \indec models, we wish to provide a lower-bound on the
diameter of the real points.
In the first part of the paper, we focus on \indec model. We present an
time
approximation algorithm of factor for finding minimum diameter
of a set of points in dimensions. This improves the previously proposed
algorithms for this problem substantially.
Next, we consider the problem in \impre model. In -dimensional space, we
propose a polynomial time -approximation algorithm. In addition, for
, we define the notion of -separability and use our algorithm for
\indec model to obtain -approximation algorithm for a set of
-separable regions in time
Approximating Nearest Neighbor Distances
Several researchers proposed using non-Euclidean metrics on point sets in
Euclidean space for clustering noisy data. Almost always, a distance function
is desired that recognizes the closeness of the points in the same cluster,
even if the Euclidean cluster diameter is large. Therefore, it is preferred to
assign smaller costs to the paths that stay close to the input points.
In this paper, we consider the most natural metric with this property, which
we call the nearest neighbor metric. Given a point set P and a path ,
our metric charges each point of with its distance to P. The total
charge along determines its nearest neighbor length, which is formally
defined as the integral of the distance to the input points along the curve. We
describe a -approximation algorithm and a
-approximation algorithm to compute the nearest neighbor
metric. Both approximation algorithms work in near-linear time. The former uses
shortest paths on a sparse graph using only the input points. The latter uses a
sparse sample of the ambient space, to find good approximate geodesic paths.Comment: corrected author nam
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