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With the macroeconomic input-output model of the Planning Commission taken as a basis for spatial planning, this paper discusses methodology for regional allocation of national sectoral outputs and demands and construction and calibration of a transport model for India. Although national outputs are determined in the framework of an input-output model, regional supplies are estimated by shift and share technique and regional demands by regional input-output equations. Generation and distribution of traffic are determined by the doubly constrained gravity model of Wilson and modal splits are carried out on the basis of comparative advantages of different modes in terms of generalised cost. The paper also discusses data base and calibration techniques for the model and provides empirical results.

Low current applications, like neuromorphic circuits, where operating currents can be as low as a few nanoamperes or less, suffer from huge transistor mismatches, resulting in around or less than 1-bit precisions. Recently, a neuromorphic programmable- kernel 2-D convolution chip has been reported where each pixel included two compact calibrated digital-to-analog converters (DACs) of 5-bit resolution, for currents down to picoamperes. Those DACs were based on MOS ladder structures, which although compact require unit transistors ( is the number of calibration bits). Here, we present a new calibration approach not based on ladders, but on individually calibratable current sources made with MOS transistors of digitally adjustable length, which require only -sized transistors. The scheme includes a translinear circuit-based tuning scheme, which allows us to expand the operating range of the calibrated circuits with graceful precision degradation, over four decades of operating currents. Experimental results are provided for 5-bit resolution DACs operating at 20 nA using two different translinear tuning schemes. Maximum measured precision is 5.05 and 7.15 b, respectively, for the two DAC schemes. ; Gobierno de España TEC2006-11730-C03-01, TEC-417 ; European Union IST-2001-34124

This paper presents a mathematical model for the propagation of errors in body segment kinematics to the location of the center of rotation. Three functional calibration techniques, usually employed for the gleno-humeral joint, are studied: the methods based on the pivot of the instantaneous helical axis (PIHA) or the finite helical axis (PFHA), and the ¿symmetrical center of rotation estimation¿ (SCoRE). A procedure for correcting the effect of soft tissue artifacts is also proposed, based on the equations of those techniques and a model of the artifact, like the one that can be obtained by double calibration. An experiment with a mechanical analog was performed to validate the procedure and compare the performance of each technique. The raw error (between 57 and 68 mm) was reduced by a proportion of between 1:6 and less than 1:15, depending on the artifact model and the mathematical method. The best corrections were obtained by the SCoRE method. Some recommendations about the experimental setup for functional calibration techniques and the choice of a mathematical method are derived from theoretical considerations about the formulas and the results of the experiment. ; This work has been funded by the Spanish Government (Grants DPI2009-13830-C02-01, DPI2009-13830-C02-02, DPI2010-20814-C02-01, and DPI2010-20814-C02-02). ; De Rosario Martínez, H.; Page Del Pozo, AF.; Besa Gonzálvez, AJ.; Valera Fernández, Á. (2013). Propagation of soft tissue artifacts to the center of rotation: A model for the correction of functional calibration techniques. Journal of Biomechanics. 46(15):2619-2625. doi:10.1016/j.jbiomech.2013.08.006 ; S ; 2619 ; 2625 ; 46 ; 15

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

In this paper, I juxtapose three periods in the history of x-rays: the 1890s, 1900-18 and 1918-28. During these three periods, x-ray work went from cacophony to symphony: to begin with, x-ray workers each `did their own thing', and comparison of results was difficult, if not impossible; but by the 1920s there was great coordination, culminating in an international standard of x-ray intensity. I show this development by characterizing calibration techniques, the organization of x-ray work and the x-ray economy, in each of these three periods. The aim of the paper is to show that standards (a topic of central concern in science studies) can be applied with great utility in fields that are usually compartmentalized and isolated: history of science, labour history and economic history.

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R(2)), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better ...

A balanced sampling design is a design in which Horvitz-Thompson estimators of population totals for a set of auxiliary variables equal the known totals of these variables. On the other hand, calibration is a technique where the modification of design weights occurs in such a way that the new weights, when applied to auxiliary variables, reproduce, i.e. estimate withouterror, the known totals for these variables. The general idea behind balanced sampling and calibration is thus the same — both techniques tend to reproduce known totals of the auxiliary variables. The purpose of the paper is to describe and compare both techniques, considering them as alternatives in achieving the same goal. More attention was devoted to balanced sampling. The algorithm for selecting a sample was illustrated with two numerical examples. The comparison between balanced sampling and calibration, as alternatives, indicates calibration, but the best strategy is to use both methods simultaneously.

The Gaia-ESO survey (GES) is now in its fifth and last year of observations and has produced tens of thousands of high-quality spectra of stars in all Milky Way components. This paper presents the strategy behind the selection of astrophysical calibration targets, ensuring that all GES results on radial velocities, atmospheric parameters, and chemical abundance ratios will be both internally consistent and easily comparable with other literature results, especially from other large spectroscopic surveys and from Gaia. The calibration of GES is particularly delicate because of (i) the large space of parameters covered by its targets, ranging from dwarfs to giants, from O to M stars; these targets have a large wide of metallicities and also include fast rotators, emission line objects, and stars affected by veiling; (ii) the variety of observing setups, with different wavelength ranges and resolution; and (iii) the choice of analyzing the data with many different state-of-the-art methods, each stronger in a different region of the parameter space, which ensures a better understanding of systematic uncertainties. An overview of the GES calibration and homogenization strategy is also given, along with some examples of the usage and results of calibrators in GES iDR4, which is the fourth internal GES data release and will form the basis of the next GES public data release. The agreement between GES iDR4 recommended values and reference values for the calibrating objects are very satisfactory. The average offsets and spreads are generally compatible with the GES measurement errors, which in iDR4 data already meet the requirements set by the main GES scientific goals.© ESO, 2017. ; This work was partly supported by the European Union FP7 program through ERC grant number 320360 and by the Leverhulme Trust through grant RPG-2012-541. We acknowledge the support from INAF and Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR) in the form of the grant >Premiale VLT 2012>. The results presented here benefit from discussions held during the Gaia-ESO workshops and conferences supported by the ESF (European Science Foundation) through the GREAT Research Network Programme. S.F. and T.B. acknowledge the support from the New Milky Way project funded by a grant from the Knut and Alice Wallenberg foundation. C.L. gratefully acknowledges financial support from the European Research Council (ERC-CoG-646928, Multi-Pop, PI: N. Bastian). U.H. and A.J.K acknowledge support from the Swedish National Space Board (Rymdstyrelsen). The research of A.L. has been subsidized by the Belgian Federal Science Policy Office under contract No. BR/143/A2/BRASS. R.S. acknowledges support by the National Science Center of Poland through grant 2014/15/B/ST9/03981. C.A.P. is thankful for support from the Spanish Ministry of Economy and Competitiveness (MINECO) through grant AYA2014-56359-P.J.M. acknowledges support from the ERC Consolidator Grant funding scheme (project STARKEY, G.A. No. 615604). T.M. acknowledges financial support from Belspo for contract PRODEX Gaia-DPAC. S.G.S acknowledges the support by Fundacao para a Ciencia e Tecnologia (FCT) through national funds and a research grant (project ref. UID/FIS/04434/2013, and PTDC/FIS-AST/7073/2014). S.G.S. also acknowledge the support from FCT through Investigador FCT contract of reference IF/00028/2014 and POPH/FSE (EC) by FEDER funding through the program >Programa Operacional de Factores de Competitividade - COMPETE>. L.S. acknowledges support by the Ministry of Economy, Development, and Tourism's Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics (MAS). M.Z. acknowledges support by the Ministry of Economy, Development, and Tourism's Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics (MAS), by Fondecyt Regular 1150345 and by the BASAL CATA PFB-06. E.J.A. and M.T.C acknowledge the financial support from the Spanish Ministerio de Economia y Competitividad, through grant AYA2013-40611-P.S.Z. acknowledge the support from the INAF grant >PRIN INAF 2014>, >Star won't tell their ages to Gaia, Galactic Archaelogy with wide-area asterosismic>. ; Peer Reviewed

Die Kombination von qualitativen Daten mit der Qualitative Comparative Analysis (QCA) verspricht großes analytisches Potenzial, da sie sowohl detaillierte Untersuchungen sozialer Prozesse als auch systematische Fallvergleiche ermöglicht. Viele QCA-Anwendungen greifen auf qualitative Daten zurück. Dennoch bleibt eine zentrale methodologische Frage für QCA-Anwendungen mit qualitativen Daten weitgehend unbeantwortet: Wie können die Informationen qualitativer Daten "kalibriert", das heißt, in formalisierte Fuzzy Sets übersetzt werden? Die sogenannte "Kalibrierung" beeinflusst QCA-Ergebnisse in entscheidender Weise, sodass die Reliabilität des Kalibrierungsverfahrens enormen Einfluss auf die Qualität und Glaubwürdigkeit einer Studie hat. Die fehlende Diskussion der Kalibrierung qualitativer Daten in der methodologischen QCA-Literatur überrascht umso mehr, da QCA in anderen Bereichen stetige methodologische Weiterentwicklungen erfährt und die fehlende Transparenz von Messverfahren in QCA vermehrt kritisiert wird. Im vorliegenden Artikel entwickle ich Anchored Calibration als einen Ansatz zur Kalibrierung qualitativer Daten, der wichtige Lücken in bisherigen Ansätzen schließt und dabei hilft, die Reliabilität von Kalibrierungen zu erhöhen. Anchored Calibration besteht aus drei Arbeitsschritten: 1. der Formulierung eines konzeptuellen Rahmens für Bedingungen und das Outcome, 2. der Verankerung dieses Rahmens in den empirischen Daten und 3. der Anwendung dieses verankerten konzeptuellen Rahmens zur Vergabe von Mitgliedswerten in Fuzzy Sets. Ich diskutiere diese drei Arbeitsschritte sowie die dazu notwendigen Teilschritte im Detail und illustriere das Vorgehen am Beispiel von qualitativen Daten aus Leitfadeninterviews zum Thema Bildungsaufstieg.

Die Quantilsregression untersucht die Quantilfunktion QY |X (τ ), sodass ∀τ ∈ (0, 1), FY |X [QY |X (τ )] = τ erfu ̈llt ist, wobei FY |X die bedingte Verteilungsfunktion von Y gegeben X ist. Die Quantilsregression ermo ̈glicht eine genauere Betrachtung der bedingten Verteilung u ̈ber die bedingten Momente hinaus. Diese Technik ist in vielerlei Hinsicht nu ̈tzlich: beispielsweise fu ̈r das Risikomaß Value-at-Risk (VaR), welches nach dem Basler Akkord (2011) von allen Banken angegeben werden muss, fu ̈r "Quantil treatment-effects" und die "bedingte stochastische Dominanz (CSD)", welches wirtschaftliche Konzepte zur Messung der Effektivit ̈at einer Regierungspoli- tik oder einer medizinischen Behandlung sind. Die Entwicklung eines Verfahrens zur Quantilsregression stellt jedoch eine gro ̈ßere Herausforderung dar, als die Regression zur Mitte. Allgemeine Regressionsprobleme und M-Scha ̈tzer erfordern einen versierten Umgang und es muss sich mit nicht- glatten Verlustfunktionen besch ̈aftigt werden. Kapitel 2 behandelt den Einsatz der Quantilsregression im empirischen Risikomanagement w ̈ahrend einer Finanzkrise. Kapitel 3 und 4 befassen sich mit dem Problem der h ̈oheren Dimensionalit ̈at und nichtparametrischen Techniken der Quantilsregression. ; Quantile regression studies the conditional quantile function QY|X(τ) on X at level τ which satisfies FY |X QY |X (τ ) = τ , where FY |X is the conditional CDF of Y given X, ∀τ ∈ (0,1). Quantile regression allows for a closer inspection of the conditional distribution beyond the conditional moments. This technique is par- ticularly useful in, for example, the Value-at-Risk (VaR) which the Basel accords (2011) require all banks to report, or the "quantile treatment effect" and "condi- tional stochastic dominance (CSD)" which are economic concepts in measuring the effectiveness of a government policy or a medical treatment. Given its value of applicability, to develop the technique of quantile regression is, however, more challenging than mean regression. It is necessary to be adept with general regression problems and M-estimators; additionally one needs to deal with non-smooth loss functions. In this dissertation, chapter 2 is devoted to empirical risk management during financial crises using quantile regression. Chapter 3 and 4 address the issue of high-dimensionality and the nonparametric technique of quantile regression.