2,318 research outputs found
Localization of an experimental ecological unit in the Maradi region of Nigeria
A detailed topographical and geomorphological description of a specific ecological unit in the Maradi region of the Sahel in the Niger Republic is presented. Sandy structures are classified into active dunes and covered dunes and an extensives vocabulary is developed to describe sub-categories. The descriptions are based on meteorological data (anemometric and rainfall) from local weather stations, ground observations, aerial photographs and LANDSAT pictures. The problem of dune reactivation and desertification is discussed both from the standpoint of causes and possible counter measures
Lessons From The Conference: “Highlighting Massage Therapy In Complementary And Integrative Medicine”
A landmark conference, Highlighting Massage Therapy in Complimentary and Integrative Medicine, was held in Seattle, Washington, on May 13th–15th, 2010. The conference was designed to address the status of research related to massage therapy, as well as to have an open discussion regarding attitudes towards research and professional issues. Leaders from diverse manual therapy professions presented interesting and important data. The itinerary and summaries of the meeting can be found at http://www.massagetherapyfoundation.org/researchconference2010.html. In this brief report, rather than summarizing the presentations, we will share a combination of our observations and impressions, as well as suggestions for the direction of massage therapy research
Rapid method for determination of antimicrobial susceptibilities pattern of urinary bacteria
Method determines bacterial sensitivity to antimicrobial agents by measuring level of adenosine triphosphate remaining in the bacteria. Light emitted during reaction of sample with a mixture of luciferase and luciferin is measured
Backbone of complex networks of corporations: The flow of control
We present a methodology to extract the backbone of complex networks based on
the weight and direction of links, as well as on nontopological properties of
nodes. We show how the methodology can be applied in general to networks in
which mass or energy is flowing along the links. In particular, the procedure
enables us to address important questions in economics, namely, how control and
wealth are structured and concentrated across national markets. We report on
the first cross-country investigation of ownership networks, focusing on the
stock markets of 48 countries around the world. On the one hand, our analysis
confirms results expected on the basis of the literature on corporate control,
namely, that in Anglo-Saxon countries control tends to be dispersed among
numerous shareholders. On the other hand, it also reveals that in the same
countries, control is found to be highly concentrated at the global level,
namely, lying in the hands of very few important shareholders. Interestingly,
the exact opposite is observed for European countries. These results have
previously not been reported as they are not observable without the kind of
network analysis developed here.Comment: 24 pages, 12 figures, 2nd version (text made more concise and
readable, results unchanged
Local Algorithms for Block Models with Side Information
There has been a recent interest in understanding the power of local
algorithms for optimization and inference problems on sparse graphs. Gamarnik
and Sudan (2014) showed that local algorithms are weaker than global algorithms
for finding large independent sets in sparse random regular graphs. Montanari
(2015) showed that local algorithms are suboptimal for finding a community with
high connectivity in the sparse Erd\H{o}s-R\'enyi random graphs. For the
symmetric planted partition problem (also named community detection for the
block models) on sparse graphs, a simple observation is that local algorithms
cannot have non-trivial performance.
In this work we consider the effect of side information on local algorithms
for community detection under the binary symmetric stochastic block model. In
the block model with side information each of the vertices is labeled
or independently and uniformly at random; each pair of vertices is
connected independently with probability if both of them have the same
label or otherwise. The goal is to estimate the underlying vertex
labeling given 1) the graph structure and 2) side information in the form of a
vertex labeling positively correlated with the true one. Assuming that the
ratio between in and out degree is and the average degree , we characterize three different regimes under which a
local algorithm, namely, belief propagation run on the local neighborhoods,
maximizes the expected fraction of vertices labeled correctly. Thus, in
contrast to the case of symmetric block models without side information, we
show that local algorithms can achieve optimal performance for the block model
with side information.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract here is shorter than that in the PDF fil
Implicitly Constrained Semi-Supervised Least Squares Classification
We introduce a novel semi-supervised version of the least squares classifier.
This implicitly constrained least squares (ICLS) classifier minimizes the
squared loss on the labeled data among the set of parameters implied by all
possible labelings of the unlabeled data. Unlike other discriminative
semi-supervised methods, our approach does not introduce explicit additional
assumptions into the objective function, but leverages implicit assumptions
already present in the choice of the supervised least squares classifier. We
show this approach can be formulated as a quadratic programming problem and its
solution can be found using a simple gradient descent procedure. We prove that,
in a certain way, our method never leads to performance worse than the
supervised classifier. Experimental results corroborate this theoretical result
in the multidimensional case on benchmark datasets, also in terms of the error
rate.Comment: 12 pages, 2 figures, 1 table. The Fourteenth International Symposium
on Intelligent Data Analysis (2015), Saint-Etienne, Franc
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease
A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America
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