2,760 research outputs found
Contribution à la connaissance des diatomées d'eau douce de la Nouvelle-Calédonie (Océanie)
Aggregate Matching Efficiency : A Stochastic Production Frontier Approach, France 1990-1994
We evaluate the determinants of aggregate matching efficiency changes through a stochastic Cobb-Douglas production frontier model. The efficiency coefficient is represented by a stochastic function of variables meant to capture workers and firms characteristics. The model is estimated on French data covering twenty-two regions from March 1990 till February 1995. Our estimates suggest that aggregate matching efficiency has decreased steadily in the early nineties. There are also wide cross-regional differences. On average, about 30% of the variations of efficiency observed across time and regions can be related to changes in the explanatory variables used in the model. The most important explanatory variables are the proportion of youngsters, females and immigrants in the stock of job seekers. Long-term unemployment has a significant negative effect, population density a significant positive one. The huge decline in the proportion of permanent job offers has apparently little effect on matching efficiencymatching efficiency; regional unemployment; stochastic frontier
A Fast and Efficient Incremental Approach toward Dynamic Community Detection
Community detection is a discovery tool used by network scientists to analyze
the structure of real-world networks. It seeks to identify natural divisions
that may exist in the input networks that partition the vertices into coherent
modules (or communities). While this problem space is rich with efficient
algorithms and software, most of this literature caters to the static use-case
where the underlying network does not change. However, many emerging real-world
use-cases give rise to a need to incorporate dynamic graphs as inputs.
In this paper, we present a fast and efficient incremental approach toward
dynamic community detection. The key contribution is a generic technique called
, which examines the most recent batch of changes made to an
input graph and selects a subset of vertices to reevaluate for potential
community (re)assignment. This technique can be incorporated into any of the
community detection methods that use modularity as its objective function for
clustering. For demonstration purposes, we incorporated the technique into two
well-known community detection tools. Our experiments demonstrate that our new
incremental approach is able to generate performance speedups without
compromising on the output quality (despite its heuristic nature). For
instance, on a real-world network with 63M temporal edges (over 12 time steps),
our approach was able to complete in 1056 seconds, yielding a 3x speedup over a
baseline implementation. In addition to demonstrating the performance benefits,
we also show how to use our approach to delineate appropriate intervals of
temporal resolutions at which to analyze an input network
Birational Mappings and Matrix Sub-algebra from the Chiral Potts Model
We study birational transformations of the projective space originating from
lattice statistical mechanics, specifically from various chiral Potts models.
Associating these models to \emph{stable patterns} and \emph{signed-patterns},
we give general results which allow us to find \emph{all} chiral -state
spin-edge Potts models when the number of states is a prime or the square
of a prime, as well as several -dependent family of models. We also prove
the absence of monocolor stable signed-pattern with more than four states. This
demonstrates a conjecture about cyclic Hadamard matrices in a particular case.
The birational transformations associated to these lattice spin-edge models
show complexity reduction. In particular we recover a one-parameter family of
integrable transformations, for which we give a matrix representationComment: 22 pages 0 figure The paper has been reorganized, splitting the
results into two sections : results pertaining to Physics and results
pertaining to Mathematic
Estimates of the mean circulation in the deep (>2,000m) layer of the Eastern North Atlantic
A total of 131 current meter records of between 6 and 24 month duration are analysed to describe the deep flow field of the eastern North Atlantic from 19° to 54°N and from the Continental Slope to the Mid Atlantic Ridge. Mean flows are weak and may be statistically indeterminate in some records and locations, but appear to indicate cyclonic circulations around the Iberia and Porcupine abyssal plains with a generally southward flow along the Mid Atlantic ridge and a deep northward slope current (where measurements exist) along the eastern boundary. The deepest inflow to the north-eastern basin that has been identified to date takes place through the Discovery Gap of >4,700 m sill-depth at 37° 25′N 15° 45′W in the Azores-Portugal ridge. South of that ridge, observations are sparse and no systematic circulation is yet evident. These observations are discussed in relation to recent geostrophic estimates of the deep circulation
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