164,783 research outputs found
Physiology-Aware Rural Ambulance Routing
In emergency patient transport from rural medical facility to center tertiary
hospital, real-time monitoring of the patient in the ambulance by a physician
expert at the tertiary center is crucial. While telemetry healthcare services
using mobile networks may enable remote real-time monitoring of transported
patients, physiologic measures and tracking are at least as important and
requires the existence of high-fidelity communication coverage. However, the
wireless networks along the roads especially in rural areas can range from 4G
to low-speed 2G, some parts with communication breakage. From a patient care
perspective, transport during critical illness can make route selection patient
state dependent. Prompt decisions with the relative advantage of a longer more
secure bandwidth route versus a shorter, more rapid transport route but with
less secure bandwidth must be made. The trade-off between route selection and
the quality of wireless communication is an important optimization problem
which unfortunately has remained unaddressed by prior work.
In this paper, we propose a novel physiology-aware route scheduling approach
for emergency ambulance transport of rural patients with acute, high risk
diseases in need of continuous remote monitoring. We mathematically model the
problem into an NP-hard graph theory problem, and approximate a solution based
on a trade-off between communication coverage and shortest path. We profile
communication along two major routes in a large rural hospital settings in
Illinois, and use the traces to manifest the concept. Further, we design our
algorithms and run preliminary experiments for scalability analysis. We believe
that our scheduling techniques can become a compelling aid that enables an
always-connected remote monitoring system in emergency patient transfer
scenarios aimed to prevent morbidity and mortality with early diagnosis
treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare
Informatics (ICHI 2017), Park City, Utah, 201
Strong Duality for a Multiple-Good Monopolist
We characterize optimal mechanisms for the multiple-good monopoly problem and
provide a framework to find them. We show that a mechanism is optimal if and
only if a measure derived from the buyer's type distribution satisfies
certain stochastic dominance conditions. This measure expresses the marginal
change in the seller's revenue under marginal changes in the rent paid to
subsets of buyer types. As a corollary, we characterize the optimality of
grand-bundling mechanisms, strengthening several results in the literature,
where only sufficient optimality conditions have been derived. As an
application, we show that the optimal mechanism for independent uniform
items each supported on is a grand-bundling mechanism, as long as
is sufficiently large, extending Pavlov's result for items [Pavlov'11]. At
the same time, our characterization also implies that, for all and for all
sufficiently large , the optimal mechanism for independent uniform items
supported on is not a grand bundling mechanism
A numerical algorithm for semi-discrete optimal transport in 3D
This paper introduces a numerical algorithm to compute the optimal
transport map between two measures and , where derives from a
density defined as a piecewise linear function (supported by a
tetrahedral mesh), and where is a sum of Dirac masses.
I first give an elementary presentation of some known results on optimal
transport and then observe a relation with another problem (optimal sampling).
This relation gives simple arguments to study the objective functions that
characterize both problems.
I then propose a practical algorithm to compute the optimal transport map
between a piecewise linear density and a sum of Dirac masses in 3D. In this
semi-discrete setting, Aurenhammer et.al [\emph{8th Symposium on Computational
Geometry conf. proc.}, ACM (1992)] showed that the optimal transport map is
determined by the weights of a power diagram. The optimal weights are computed
by minimizing a convex objective function with a quasi-Newton method. To
evaluate the value and gradient of this objective function, I propose an
efficient and robust algorithm, that computes at each iteration the
intersection between a power diagram and the tetrahedral mesh that defines the
measure .
The numerical algorithm is experimented and evaluated on several datasets,
with up to hundred thousands tetrahedra and one million Dirac masses.Comment: 23 pages, 14 figure
An economic theory of church strictness
This paper makes several contributions to the growing literature on the economics of religion. First, we explicitly introduce spatial- location models into the economics of religion. Second, we offer a new explanation for the observed tendency of state (monopoly) churches to locate toward the "low-tension" end of the "strictness continuum" (in a one-dimensional product space): This result is obtained through the conjunction of "benevolent preferences" (denominations care about the aggregate utility of members) and asymmetric costs of going to a more or less strict church than one prefers. We also derive implications regarding the relationship between religious strictness and membership. The driving forces of our analysis, religious market interactions and asymmetric costs of membership, high-light new explanations for some well-established stylized facts. The analysis opens the way to new empirical tests, aimed at confronting the implications of our model against more traditional explanations.Location theory, economics of religion
Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization
Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this paper we propose a measurement-based classification framework that exploits unsupervised learning to accurately categorise network anomalies to specific classes. We introduce the combinatorial use of two-class and multi-class unsupervised Support Vector Machines (SVM)s to first distinguish normal from anomalous traffic and to further classify the latter category to individual groups depending on the nature of the anomaly
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