Using triangulation to identify word senses

Word sense disambiguation is the task of determining which sense of a word is intended from its context. Previous methods have found the lack of training data and the restrictiveness of dictionaries' choices of senses to be major stumbling blocks. A robust novel algorithm is presented that uses multiple dictionaries, the Internet, clustering and triangulation to attempt to discern the most useful senses of a given word and learn how they can be disambiguated. The algorithm is explained, and some promising sample results are given

Structure and elasticity of model disordered, polydisperse and defect-free polymer networks

The elasticity of disordered and polydisperse polymer networks is a fundamental problem of soft matter physics that is still open. Here, we report a simulation study of a model for such systems, prepared with either trivalent or tetravalent crosslinks. The networks are self-assembled via equilibrium simulations that result in an exponential strand length distribution, similar to that of experimental randomly crosslinked systems. We find that the fractal structure of the network depends on the initial density $\rho_\text{init}$, but that systems with the same mean valence and same $\rho_\text{init}$ have the same structural properties. Moreover, we compute the long-time limit of the mean-squared displacement, also known as the (squared) localization length, of the crosslinks and of the middle monomers of the strands, showing that the dynamics of long strands is well described by the tube model. Finally, we find a relation connecting these two localization lengths at high density, and connect the crosslink localization length to the shear modulus of the system.Comment: 33 pages, 11 figure

Boron doped diamond electrooxidation of 6:2 fluorotelomers and perfluorocarboxylic acids. Application to industrial wastewaters treatment

The aim of this study was to determine the viability of electrochemical oxidation to degrade and mineralize poly- and perfluoroalkyl substances (PFASs) in wastewaters from an industrial facility dedicated to the production of side-chain-fluorinated polymers and fluorotelomer-based products for fire-fighting foams. 6:2 fluorotelomer sulfonamide alkylbetaine (6:2 FTAB, 1111 μg/L), 6:2 fluorotelomer sulfonic acid (6:2 FTSA, 242.5 μg/L) and 6:2 fluorotelomer sulfonamide propyl N,N dimethylamine (M4, 34.4 μg/L) were the most abundant PFASs in the industrial wastewater, that also contained perfluorocarboxylic acids (ΣPFCAs, 12.2 μg/L), high TOC and chloride as main anion. 2 L samples were treated in bench scale experiments performed at a current density of 50 mA/cm2, in a commercial cell equipped with a boron doped diamond (BDD) anode (70 cm2). 97.1% of the initial PFASs content was removed after 8 h of electrochemical treatment. Furthermore, the TOC removal (82.5%) and the fluoride release confirmed the PFASs mineralization. Based on the evolution of the different PFASs, electrochemical degradation pathways were proposed. Fluorotelomers sulfonamides 6:2 FTAB and M4 would be degraded into 6:2 FTSA, which conversely would give rise to PFHpA and preferentially PFHxA. The latter PFCAs were transformed into shorter-chain PFCAs, and eventually into CO2 and fluoride. The reported results support the technical viability of BDD electrooxidation for the treatment of PFASs in industrial wastewater.This work was supported by the Spanish Ministry of Economy and Competitiveness (CTM2013-44081-R and CTM2016-75509-R). B. Gomez also thanks the FPI postgraduate research grant (BES-2014-071045)

Efficient electrochemical degradation of poly- and perfluoroalkyl substances (PFASs) from the effluents of an industrial wastewater treatment plant

This paper reports the electrochemical treatment of poly- and perfluoroalkyl substances (PFASs) in the effluent from an industrial wastewater treatment plant (WWTP). While most of the previous research focused on the electrochemical degradation of perfluorooctanoic acid and perfluorooctane sulfonate in model solutions, this work studies the simultaneous removal of 8 PFASs at environmentally relevant concentrations in real industrial emissions, which also contained organic matter and inorganic anions. The overall PFASs content in the WWTP effluent was 1652 µg/L, which emphasized the need to develop innovative technologies for the management of PFASs emissions. 6:2 fluorotelomer sulfonamide alkylbetaine (6:2 FTAB) and 6:2 fluorotelomer sulfonate (6:2 FTSA) were the major contributors (92% w/w) to the overall PFASs content, that also contained significant amounts of short-chain perfluorocarboxylic acids (PFCAs). Using a boron doped diamond (BDD) anode of 0.0070 m2, the effluent (2 L) was treated by applying a current density of 50 mA/cm2 for 10 h, that resulted in 99.7% PFASs removal. The operation at lower current densities (5 and 10 mA/cm2) evidenced the initial degradation of 6:2 fluorotelomers into perfluoroheptanoic and perfluorohexanoic acids, that were later degraded into shorter chain PFCAs. The high TOC removal, >90%, and the fluoride release revealed that PFASs mineralization was effective. These results highlight the potential of the electrochemical technology for the treatment of PFASs contained in industrial wastewaters, which nowadays stands as the main source of this group of persistent pollutants into the environment.Financial support of project CTM2013-44081-R (MINECO, SPAIN-FEDER 2014–2020) is acknowledged. B. Gomez also thanks the FPI grant (BES-2014-071045)

Deep learning-based schemes for singularly perturbed convection-diffusion problems

Deep learning-based numerical schemes such as Physically Informed Neural Networks (PINNs) have recently emerged as an alternative to classical numerical schemes for solving Partial Differential Equations (PDEs). They are very appealing at first sight because implementing vanilla versions of PINNs based on strong residual forms is easy, and neural networks offer very high approximation capabilities. However, when the PDE solutions are low regular, an expert insight is required to build deep learning formulations that do not incur in variational crimes. Optimization solvers are also significantly challenged, and can potentially spoil the final quality of the approximated solution due to the convergence to bad local minima, and bad generalization capabilities. In this paper, we present an exhaustive numerical study of the merits and limitations of these schemes when solutions exhibit low-regularity, and compare performance with respect to more benign cases when solutions are very smooth. As a support for our study, we consider singularly perturbed convection-diffusion problems where the regularity of solutions typically degrades as certain multiscale parameters go to zero

Cellular distribution of the histamine H3 receptor in the basal ganglia : functional modulation of dopamine and glutamate neurotransmission

This is the author's version of a work that was accepted for publication in Basal ganglia. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Vol. 3 Núm. 2 (Jul. 2013)Altres ajuts: Red_de_Trastornos_Adictivos/RD06/0001/0015Histamine H3 receptors (H3R) are widely expressed in the brain where they participate in sleep-wake cycle and cognition among other functions. Despite their high expression in some regions of the basal ganglia, their functional role in this forebrain neural network remains unclear. The present findings provide in situ hybridization and immunohistochemical evidence for H3R expression in several neuronal populations of the rat basal ganglia but not in astrocytes (glial fibrillary acidic protein immunoreactive cells). We demonstrate the presence of H3R mRNA and protein in dopaminergic neurons (tyrosine hydroxylase positive) of the ventral tegmental area and substantia nigra. In the dorsal and ventral (nucleus accumbens) striatal complex we show H3R immunoreactivity in cholinergic (choline acetyltransferase immunoreactive) and GABAergic neurons (substance P, proenkephalin or dopamine D1 receptor positive) as well as in corticostriatal terminals (VGLUT1-immunoreactive). Double-labelling experiments in the medial prefrontal cortex show that H3R is expressed in D1R-positive interneurons and VGLUT1-positive corticostriatal output neurons. Our functional experiments confirm that H3R ligands modulate dopamine synthesis and the probability of glutamate release in the striatum from cortico-striatal afferents. The presence of H3R in such different neuronal populations and its involvement in the control of striatal dopaminergic and glutamatergic transmission ascribes a complex role to H3R in the function of the basal ganglia neural network

The use of selected neutron absorption resonance filters to suppress spurious events on hot neutron spectrometers

Resonant absorption can be used as a filter for high energy neutron spectroscopy. Here we report the transmission of eight thin foil filters: erbium, indium, iridium, dysprosium, hafnium, gadolinium, cadmium and samarium, measured using neutron time-of-flight techniques over a range of energies (1 meV to 10 eV). Measured transmission is converted into energy-dependent absorption cross-section which compares closely to tabulated values. Each resonance is characterized from 91 meV (samarium) to 2815 meV (gadolinium) by Lorentzian fits. Possibilities for the use of neutron filters depending on the type of spurious background are discussed and the performance is simulated for a specific example of a hot neutron triple axis spectrometer experiment. (C) 2015 Elsevier B.V. All rights reserved

The Mechanism of High-Temperature Superconductivity with a Pinch of Iron

It has long been conjectured that the unusual magnetic excitation spectrum of the cuprate high-temperature superconductors play an important role in the mechanism for superconductivity. I argue that the remarkable number of similarities in the excitation spectra of novel iron based high-temperature superconductors must mean that the same mechanism is at play. Through neutron spectroscopy measurements on iron tuned Fe1+yTe0.7Se0.3 we discover development of an hourglass dispersion above Tc and opening of a spin gap below Tc [1]. We conclude the hourglass is a necessary – but not sufficient – requisite for superconductivity. Based on this insight we propose a guide line for discovering new families of high-temperature superconductors. References [1] N. Tsyrulin et al., Magnetic hourglass dispersion and its relation to high-temperature superconductivity in iron-tuned Fe1+yTe0.7Se0.3, New Journal of Physics 14, 073025 (2012