Transforming triangulations on non planar-surfaces

We consider whether any two triangulations of a polygon or a point set on a non-planar surface with a given metric can be transformed into each other by a sequence of edge flips. The answer is negative in general with some remarkable exceptions, such as polygons on the cylinder, and on the flat torus, and certain configurations of points on the cylinder.Comment: 19 pages, 17 figures. This version has been accepted in the SIAM Journal on Discrete Mathematics. Keywords: Graph of triangulations, triangulations on surfaces, triangulations of polygons, edge fli

Efficient AUC Optimization for Information Ranking Applications

Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC using additive regression trees, with a special emphasis on the use of multi-class AUC (MAUC) because multiple relevance levels are widely used in many ranking applications. Compared to a conventional linear approach, the performance of the non-linear approach is comparable on binary-relevance benchmark datasets and is better on multi-relevance benchmark datasets.Comment: 12 page

Domain Adaptive Neural Networks for Object Recognition

We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks

Interferometric Mapping of Magnetic fields: NGC2071IR

We present polarization maps of NGC2071IR from thermal dust emission at 1.3 mm and from CO J=$2 \to 1$ line emission. The observations were obtained using the Berkeley-Illinois-Maryland Association array in the period 2002-2004. We detected dust and line polarized emission from NGC2071IR that we used to constrain the morphology of the magnetic field. From CO J=$2 \to 1$ polarized emission we found evidence for a magnetic field in the powerful bipolar outflow present in this region. We calculated a visual extinction $A_{\rm{v}} \approx 26$ mag from our dust observations. This result, when compared with early single dish work, seems to show that dust grains emit polarized radiation efficiently at higher densities than previously thought. Mechanical alignment by the outflow is proposed to explain the polarization pattern observed in NGC2071IR, which is consistent with the observed flattening in this source.Comment: 17 pages, 4 Figures, Accepted for publication in Ap

Absolute Convergence of Rational Series is Semi-decidable

International audienceWe study \emph{real-valued absolutely convergent rational series}, i.e. functions $r: \Sigma^* \rightarrow {\mathbb R}$, defined over a free monoid $\Sigma^*$, that can be computed by a multiplicity automaton $A$ and such that $\sum_{w\in \Sigma^*}|r(w)|<\infty$. We prove that any absolutely convergent rational series $r$ can be computed by a multiplicity automaton $A$ which has the property that $r_{|A|}$ is simply convergent, where $r_{|A|}$ is the series computed by the automaton $|A|$ derived from $A$ by taking the absolute values of all its parameters. Then, we prove that the set ${\cal A}^{rat}(\Sigma)$ composed of all absolutely convergent rational series is semi-decidable and we show that the sum $\sum_{w\in \Sigma^*}|r(w)|$ can be estimated to any accuracy rate for any $r\in {\cal A}^{rat}(\Sigma)$. We also introduce a spectral radius-like parameter $\rho_{|r|}$ which satisfies the following property: $r$ is absolutely convergent iff $\rho_{|r|}<1$