306,704 research outputs found
Labor Costs and the Social Dumping Debate in the European Union
This study examines the labor cost incentive for capital movement in manufacturing within the European Union, a key aspect of the social dumping debate in Western Europe. The authors find that the percentage differences in unit labor costs between the more developed and less developed countries in the Union not only were large in 1980 but actually grew between 1980 and 1986, and separate estimates of compensation and productivity growth rates do not indicate that significant convergence occurred over the remainder of the 1980s. Although these findings apparently confirm that a labor cost incentive for capital mobility does exist, analysis of foreign direct investment data indicates that during the period 1980-88 capital flows to the lower labor cost countries actually were not much larger than capital flows to the higher labor cost countries
Visualising the structure of document search results: A comparison of graph theoretic approaches
This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or âspatialisationâ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or âcluster growingâ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion
Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications
Robust Principal Component Analysis (RPCA) via rank minimization is a
powerful tool for recovering underlying low-rank structure of clean data
corrupted with sparse noise/outliers. In many low-level vision problems, not
only it is known that the underlying structure of clean data is low-rank, but
the exact rank of clean data is also known. Yet, when applying conventional
rank minimization for those problems, the objective function is formulated in a
way that does not fully utilize a priori target rank information about the
problems. This observation motivates us to investigate whether there is a
better alternative solution when using rank minimization. In this paper,
instead of minimizing the nuclear norm, we propose to minimize the partial sum
of singular values, which implicitly encourages the target rank constraint. Our
experimental analyses show that, when the number of samples is deficient, our
approach leads to a higher success rate than conventional rank minimization,
while the solutions obtained by the two approaches are almost identical when
the number of samples is more than sufficient. We apply our approach to various
low-level vision problems, e.g. high dynamic range imaging, motion edge
detection, photometric stereo, image alignment and recovery, and show that our
results outperform those obtained by the conventional nuclear norm rank
minimization method.Comment: Accepted in Transactions on Pattern Analysis and Machine Intelligence
(TPAMI). To appea
Trajectory Synthesis for Fisher Information Maximization
Estimation of model parameters in a dynamic system can be significantly
improved with the choice of experimental trajectory. For general, nonlinear
dynamic systems, finding globally "best" trajectories is typically not
feasible; however, given an initial estimate of the model parameters and an
initial trajectory, we present a continuous-time optimization method that
produces a locally optimal trajectory for parameter estimation in the presence
of measurement noise. The optimization algorithm is formulated to find system
trajectories that improve a norm on the Fisher information matrix. A
double-pendulum cart apparatus is used to numerically and experimentally
validate this technique. In simulation, the optimized trajectory increases the
minimum eigenvalue of the Fisher information matrix by three orders of
magnitude compared to the initial trajectory. Experimental results show that
this optimized trajectory translates to an order of magnitude improvement in
the parameter estimate error in practice.Comment: 12 page
Hedging Effectiveness under Conditions of Asymmetry
We examine whether hedging effectiveness is affected by asymmetry in the
return distribution by applying tail specific metrics to compare the hedging
effectiveness of short and long hedgers using crude oil futures contracts. The
metrics used include Lower Partial Moments (LPM), Value at Risk (VaR) and
Conditional Value at Risk (CVAR). Comparisons are applied to a number of
hedging strategies including OLS and both Symmetric and Asymmetric GARCH
models. Our findings show that asymmetry reduces in-sample hedging performance
and that there are significant differences in hedging performance between short
and long hedgers. Thus, tail specific performance metrics should be applied in
evaluating hedging effectiveness. We also find that the Ordinary Least Squares
(OLS) model provides consistently good performance across different measures of
hedging effectiveness and estimation methods irrespective of the
characteristics of the underlying distribution
- âŠ