A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. II. Quantifying morphological k-correction in the COSMOS field at 1<z<2: Ks band vs. I band

We quantify the effects of \emph{morphological k-correction} at $1<z<2$ by comparing morphologies measured in the K and I-bands in the COSMOS area. Ks-band data have indeed the advantage of probing old stellar populations for $z<2$, enabling a determination of galaxy morphological types unaffected by recent star formation. In paper I we presented a new non-parametric method to quantify morphologies of galaxies on seeing limited images based on support vector machines. Here we use this method to classify $\sim$$50 000$ $Ks$ selected galaxies in the COSMOS area observed with WIRCam at CFHT. The obtained classification is used to investigate the redshift distributions and number counts per morphological type up to $z\sim2$ and to compare to the results obtained with HST/ACS in the I-band on the same objects from other works. We associate to every galaxy with $Ks<21.5$ and $z<2$ a probability between 0 and 1 of being late-type or early-type. The classification is found to be reliable up to $z\sim2$. The mean probability is $p\sim0.8$. It decreases with redshift and with size, especially for the early-type population but remains above $p\sim0.7$. The classification is globally in good agreement with the one obtained using HST/ACS for $z<1$. Above $z\sim1$, the I-band classification tends to find less early-type galaxies than the Ks-band one by a factor $\sim$1.5 which might be a consequence of morphological k-correction effects. We argue therefore that studies based on I-band HST/ACS classifications at $z>1$ could be underestimating the elliptical population. [abridged]Comment: accepted for publication in A&A, updated with referee comments, 12 pages, 10 figure

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A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description

We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS classification but with an unlimited number of dimensions and non-linear boundaries between decision regions, is fully automated and thus particularly well adapted to large cosmological surveys. The source code is available for download at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method, we use a seeing limited near-infrared ($K_s$ band, $2,16\mu m$) sample observed with WIRCam at CFHT at a median redshift of $z\sim0.8$. The machine is trained with a simulated sample built from a local visually classified sample from the SDSS chosen in the high-redshift sample's rest-frame (i band, $0.77\mu m$) and artificially redshifted to match the observing conditions. We use a 12-dimensional volume, including 5 morphological parameters and other caracteristics of galaxies such as luminosity and redshift. We show that a qualitative separation in two main morphological types (late type and early type) can be obtained with an error lower than 20% up to the completeness limit of the sample ($KAB\sim 22$) which is more than 2 times better that what would be obtained with a classical C/A classification on the same sample and indeed comparable to space data. The method is optimized to solve a specific problem, offering an objective and automated estimate of errors that enables a straightforward comparison with other surveys.Comment: 11 pages, 7 figures, 3 tables. Submitted to A&A. High resolution images are available on reques

Parametric CAD modeling: An analysis of strategies for design reusability

CAD model quality in parametric design scenarios largely determines the level of flexibility and adaptability of a 3D model (how easy it is to alter the geometry) as well as its reusability (the ability to use existing geometry in other contexts and applications). In the context of mechanical CAD systems, the nature of the feature-based parametric modeling paradigm, which is based on parent-child interdependencies between features, allows a wide selection of approaches for creating a specific model. Despite the virtually unlimited range of possible strategies for modeling a part, only a small number of them can guarantee an appropriate internal structure which results in a truly reusable CAD model. In this paper, we present an analysis of formal CAD modeling strategies and best practices for history-based parametric design: Delphi's horizontal modeling, explicit reference modeling, and resilient modeling. Aspects considered in our study include the rationale to avoid the creation of unnecessary feature interdependencies, the sequence and selection criteria for those features, and the effects of parent/child relations on model alteration. We provide a comparative evaluation of these strategies in the form of a series of experiments using three industrial CAD models with different levels of complexity. We analyze the internal structure of the models and compare their robustness and flexibility when the geometry is modified. The results reveal significant advantages of formal modeling methodologies, particularly resilient techniques, over non-structured approaches as well as the unexpected problems of the horizontal strategy in numerous modeling situations. (C)2016 Elsevier Ltd. All rights reserved.Camba, JD.; Contero, M.; Company, P. (2016). Parametric CAD modeling: An analysis of strategies for design reusability. Computer-Aided Design. 74:18-31. doi:10.1016/j.cad.2016.01.003S18317

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Assessment of parametric assembly models based on CAD quality dimensions

[EN] An approach to convey CAD quality-oriented strategies to beginning users to create bottom-up assemblies is described. The work builds on previous efforts in the area of single part history-based, feature-based parametric modeling evaluation by defining, testing, and validating a set of quality dimensions that can be applied to MCAD assembly assessment. The process of redefining and adapting dimension descriptors and achievement levels of parts rubrics to make them applicable to assemblies is addressed, then the results of two experimental studies designed to analyze the inter-rater reliability of this approach to assembly evaluation are reported. Results suggest the mechanism is reliable to provide an objective assessment of assembly models. Limitations for the formative selfevaluation of CAD assembly skills are also identified.This work was partially supported by the Spanish grant DPI2017-84526-R (MINECO/AEI/FEDER, UE), project CAL-MBE, Implementation and validation of a theoretical CAD quality model in a Model-Based Enterprise (MBE) context. , and the ANNOTA2 project funded by Universitat Politècnica de València.Otey, J.; Company, P.; Contero, M.; Camba, JD. (2019). Assessment of parametric assembly models based on CAD quality dimensions. Computer-Aided Design and Applications. 16(4):628-653. https://doi.org/10.14733/cadaps.2019.628-653S62865316

Beyond shareholder primacy? Reflections on the trajectory of UK corporate governance.

Core institutions of UK corporate governance, in particular the City Code on Takeovers and Mergers, the Combined Code on Corporate Governance and the law on directors’ duties, are strongly orientated towards the norm of shareholder primacy. Beyond the core, however, stakeholder interests are better represented, in particular at the intersection of insolvency and employment law. This reflects the influence of European Community laws on information and consultation of employees. In addition, there are signs that some institutional shareholders are redirecting their investment strategies, under government encouragement, away from a focus on short-term returns, in such a way as to favour stakeholder-inclusive practices by firms. On this basis we suggest that the UK system is currently in a state of flux and that the debate over shareholder primacy has not been concluded

Computing option pricing models under transaction costs

AbstractThis paper deals with the Barles–Soner model arising in the hedging of portfolios for option pricing with transaction costs. This model is based on a correction volatility function Ψ solution of a nonlinear ordinary differential equation. In this paper we obtain relevant properties of the function Ψ which are crucial in the numerical analysis and computing of the underlying nonlinear Black–Scholes equation. Consistency and stability of the proposed numerical method are detailed and illustrative examples are given

A quantitative analysis of parametric CAD model complexity and its relationship to perceived modeling complexity

Digital product data quality and reusability has been proven a critical aspect of the Model-Based Enterprise to enable the efficient design and redesign of products. The extent to which a history-based parametric CAD model can be edited or reused depends on the geometric complexity of the part and the procedure employed to build it. As a prerequisite for defining metrics that can quantify the quality of the modeling process, it is necessary to have CAD datasets that are sorted and ranked according to the complexity of the modeling process. In this paper, we examine the concept of perceived CAD modeling complexity, defined as the degree to which a parametric CAD model is perceived as difficult to create, use, and/or modify by expert CAD designers. We present a novel method to integrate pair-wise comparisons of CAD modeling complexity made by experts into a single metric that can be used as ground truth. Next, we discuss a comprehensive study of quantitative metrics which are derived primarily from the geometric characteristics of the models and the graph structure that represents the parent/child relationships between features. Our results show that the perceived CAD modeling complexity metric derived from experts’ assessment correlates particularly strongly with graph-based metrics. The Spearman coefficients for five of these metrics suggest that they can be effectively used to study the parameters that influence the reusability of models and as a basis to implement effective personalized learning strategies in online CAD training scenarios