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

Impact of the number of prior chemotherapy regimens on outcomes for patients with metastatic breast cancer treated with eribulin: A post hoc pooled analysis

In a pivotal phase 3 study (Study 305), eribulin mesylate improved overall survival (OS) in patients with previously treated metastatic breast cancer (MBC) compared with treatment of physician's choice (TPC). This post hoc, pooled subgroup analysis of two phase 3 studies (Study 305 and Study 301) reports the influence of the number of prior chemotherapy regimens (0‐6) on OS in patients with locally advanced/MBC randomized to eribulin versus TPC/capecitabine. Patients with ≤ 3 prior chemotherapies for locally advanced/MBC had longer median OS with eribulin (15.3 months) versus control (13.2 months; hazard ratio, 0.858; P = .01)

Algorithms for identification and categorization

The main features of a family of efficient algorithms for recognition and classification of complex patterns are briefly reviewed. They are inspired in the observation that fast synaptic noise is essential for some of the processing of information in the brain.Comment: 6 pages, 5 figure

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 $\rho_{|r|}<1$

The effect of neural adaptation of population coding accuracy

Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.Comment: 35 pages, 8 figure

Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_discovery and https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision (ACCV) 201

Role of the environment in the stability of anisotropic gold particles

International audienceDespite the long-lasting interest in the synthesis control of nanoparticles (NPs) in both fundamental and applied nanosciences, the driving mechanisms responsible for their size and shape selectivity in an environment (solution) are not completely understood, and a clear assessment of the respective roles of equilibrium thermodynamics and growth kinetics is still missing. In this study, relying on an efficient atomistic computational approach, we decipher the dependence of energetics, shapes and morphologies of gold NPs on the strength and nature of the metal–environment interaction. We highlight the conditions under which the energy difference between isotropic and elongated gold NPs is reduced, thus prompting their thermodynamic coexistence. The study encompasses both monocrystalline and multi-twinned particles and extends over size ranges particularly representative of the nucleation and early growth stages. Computational results are further rationalized with arguments involving the dependence of facet and edge energies on the metal–environment interactions. We argue that by determining the abundance and diversity of particles nucleated in solution, thermodynamics may constitute an important bias influencing their final shape. The present results provide firm grounds for kinetic simulations of particle growth