Magnetic field effects on the density of states of orthorhombic superconductors

The quasiparticle density of states in a two-dimensional d-wave superconductor depends on the orientation of the in-plane external magnetic field H. This is because. in the region of the gap nodes, the Doppler shift due to the circulating supercurrents around a vortex depend on the direction of H. For a tetragonal system the induced pattern is four-fold symmetric and, at zero energy, the density of states exhibits minima along the node directions. But YBa_2C_3O_{6.95} is orthorhombic because of the chains and the pattern becomes two-fold symmetric with the position of the minima occuring when H is oriented along the Fermi velocity at a node on the Fermi surface. The effect of impurity scattering in the Born and unitary limit is discussed.Comment: 24 pages, 11 Figure

Local density of states induced by anisotropic impurity scattering in a d-wave superconductor

We study a single impurity effect on the local density of states in a d-wave superconductor accounting for the momentum-dependent impurity potential. We show that the anisotropy of the scattering potential can alter significantly the spatial dependence of the quasiparticle density of states in the vicinity of the impurity.Comment: 8 pages, revtex4, 14 figure

Orthorhombicity mixing of s- and d- gap components in YBa2Cu3O7YBa_2Cu_3O_7 without involving the chains

Momentum decoupling develops when forward scattering dominates the pairing interaction and implies tendency for decorrelation between the physical behavior in the various regions of the Fermi surface. In this regime it is possible to obtain anisotropic s- or d-wave superconductivity even with isotropic pairing scattering. We show that in the momentum decoupling regime the distortion of the $CuO_2$ planes is enough to explain the experimental reports for s- mixing in the dominantly d-wave gap of $YBa_2Cu_3O_7$. In the case of spin fluctuations mediated pairing instead, a large part of the condensate must be located in the chains in order to understand the experiments.Comment: LATEX file and 3 Postscript figure

Impact of thixotropy on flow patterns induced in a stirred tank : numerical and experimental studies

Agitation of a thixotropic shear-thinning fluid exhibiting a yield stress is investigated both experimentally and via simulations. Steady-state experiments are conducted at three impeller rotation rates (1, 2 and 8 s−1) for a tank stirred with an axial-impeller and flow-field measurements are made using particle image velocimetry (PIV) measurements. Threedimensional numerical simulations are also performed using the commercial CFD code ANSYS CFX10.0. The viscosity of the suspension is determined experimentally and is modelled using two shear-dependant laws, one of which takes into account the flow instabilities of such fluids at low shear rates. At the highest impeller speed, the flow exhibits the familiar outward pumping action associated with axial-flow impellers. However, as the impeller speed decreases, a cavern is formed around the impeller, the flow generated in the vicinity of the agitator reorganizes and its pumping capacity vanishes. An unusual flow pattern, where the radial velocity dominates, is observed experimentally at the lowest stirring speed. It is found to result from wall slip effects. Using blades with rough surfaces prevents this peculiar behaviour and mainly resolves the discrepancies between the experimental and computational results

Fermi-Liquid Interactions in d-Wave Superconductor

This article develops a quantitative quasiparticle model of the low-temperature properties of d-wave superconductors which incorporates both Fermi-liquid effects and band-structure effects. The Fermi-liquid interaction effects are found to be classifiable into strong and negligible renormalizaton effects, for symmetric and antisymmetric combinations of the energies of $k\uparrow$ and $-k\downarrow$ quasiparticles, respectively. A particularly important conclusion is that the leading clean-limit temperature-dependent correction to the superfluid density is not renormalized by Fermi-liquid interactions, but is subject to a Fermi velocity (or mass) renormalization effect. This leads to difficulties in accounting for the penetration depth measurements with physically acceptable parameters, and hence reopens the question of the quantitative validity of the quasiparticle picture.Comment: 4 page

Effect of pseudogap formation on the penetration depth of underdoped high TcT_c cuprates

The penetration depth is calculated over the entire doping range of the cuprate phase diagram with emphasis on the underdoped regime. Pseudogap formation on approaching the Mott transition, for doping below a quantum critical point, is described within a model based on the resonating valence bond spin liquid which provides an ansatz for the coherent piece of the Green's function. Fermi surface reconstruction, which is an essential element of the model, has a strong effect on the superfluid density at T=0 producing a sharp drop in magnitude, but does not change the slope of the linear low temperature variation. Comparison with recent data on Bi-based cuprates provides validation of the theory and shows that the effects of correlations, captured by Gutzwiller factors, are essential for a qualitative understanding of the data. We find that the Ferrell-Glover-Tinkham sum rule still holds and we compare our results with those for the Fermi arc and the nodal liquid models.Comment: 14 pages, 9 figures, submitted to PR

Evaluating automatic LFG f-structure annotation for the Penn-II treebank

Lexical-Functional Grammar (LFG: Kaplan and Bresnan, 1982; Bresnan, 2001; Dalrymple, 2001) f-structures represent abstract syntactic information approximating to basic predicate-argument-modifier (dependency) structure or simple logical form (van Genabith and Crouch, 1996; Cahill et al., 2003a) . A number of methods have been developed (van Genabith et al., 1999a,b, 2001; Frank, 2000; Sadler et al., 2000; Frank et al., 2003) for automatically annotating treebank resources with LFG f-structure information. Until recently, however, most of this work on automatic f-structure annotation has been applied only to limited data sets, so while it may have shown lsquoproof of conceptrsquo, it has not yet demonstrated that the techniques developed scale up to much larger data sets. More recent work (Cahill et al., 2002a,b) has presented efforts in evolving and scaling techniques established in these previous papers to the full Penn-II Treebank (Marcus et al., 1994). In this paper, we present a number of quantitative and qualitative evaluation experiments which provide insights into the effectiveness of the techniques developed to automatically derive a set of f-structures for the more than 1,000,000 words and 49,000 sentences of Penn-II. Currently we obtain 94.85% Precision, 95.4% Recall and 95.09% F-Score for preds-only f-structures against a manually encoded gold standard

Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce

Homepage is the first touch point in the customer's journey and is one of the prominent channels of revenue for many e-commerce companies. A user's attention is mostly captured by homepage banner images (also called Ads/Creatives). The set of banners shown and their design, influence the customer's interest and plays a key role in optimizing the click through rates of the banners. Presently, massive and repetitive effort is put in, to manually create aesthetically pleasing banner images. Due to the large amount of time and effort involved in this process, only a small set of banners are made live at any point. This reduces the number of banners created as well as the degree of personalization that can be achieved. This paper thus presents a method to generate creatives automatically on a large scale in a short duration. The availability of diverse banners generated helps in improving personalization as they can cater to the taste of larger audience. The focus of our paper is on generating wide variety of homepage banners that can be made as an input for user level personalization engine. Following are the main contributions of this paper: 1) We introduce and explain the need for large scale banner generation for e-commerce 2) We present on how we utilize existing deep learning based detectors which can automatically annotate the required objects/tags from the image. 3) We also propose a Genetic Algorithm based method to generate an optimal banner layout for the given image content, input components and other design constraints. 4) Further, to aid the process of picking the right set of banners, we designed a ranking method and evaluated multiple models. All our experiments have been performed on data from Myntra (http://www.myntra.com), one of the top fashion e-commerce players in India.Comment: Workshop on Recommender Systems in Fashion, 13th ACM Conference on Recommender Systems, 201