Experimental determination of the complete spin structure for anti-proton + proton -> anti-\Lambda + \Lambda at anti-proton beam momentum of 1.637 GeV/c

The reaction anti-proton + proton -> anti-\Lambda + \Lambda -> anti-proton + \pi^+ + proton + \pi^- has been measured with high statistics at anti-proton beam momentum of 1.637 GeV/c. The use of a transversely-polarized frozen-spin target combined with the self-analyzing property of \Lambda/anti-\Lambda decay allows access to unprecedented information on the spin structure of the interaction. The most general spin-scattering matrix can be written in terms of eleven real parameters for each bin of scattering angle, each of these parameters is determined with reasonable precision. From these results all conceivable spin-correlations are determined with inherent self-consistency. Good agreement is found with the few previously existing measurements of spin observables in anti-proton + proton -> anti-\Lambda + \Lambda near this energy. Existing theoretical models do not give good predictions for those spin-observables that had not been previously measured.Comment: To be published in Phys. Rev. C. Tables of results (i.e. Ref. 24) are available at http://www-meg.phys.cmu.edu/~bquinn/ps185_pub/results.tab 24 pages, 16 figure

Holistic biomimicry: a biologically inspired approach to environmentally benign engineering

Humanity's activities increasingly threaten Earth's richness of life, of which mankind is a part. As part of the response, the environmentally conscious attempt to engineer products, processes and systems that interact harmoniously with the living world. Current environmental design guidance draws upon a wealth of experiences with the products of engineering that damaged humanity's environment. Efforts to create such guidelines inductively attempt to tease right action from examination of past mistakes. Unfortunately, avoidance of past errors cannot guarantee environmentally sustainable designs in the future. One needs to examine and understand an example of an environmentally sustainable, complex, multi-scale system to engineer designs with similar characteristics. This dissertation benchmarks and evaluates the efficacy of guidance from one such environmentally sustainable system resting at humanity's doorstep - the biosphere. Taking a holistic view of biomimicry, emulation of and inspiration by life, this work extracts overarching principles of life from academic life science literature using a sociological technique known as constant comparative method. It translates these principles into bio-inspired sustainable engineering guidelines. During this process, it identifies physically rooted measures and metrics that link guidelines to engineering applications. Qualitative validation for principles and guidelines takes the form of review by biology experts and comparison with existing environmentally benign design and manufacturing guidelines. Three select bio-inspired guidelines at three different organizational scales of engineering interest are quantitatively validated. Physical experiments with self-cleaning surfaces quantify the potential environmental benefits generated by applying the first, sub-product scale guideline. An interpretation of a metabolically rooted guideline applied at the product / organism organizational scale is shown to correlate with existing environmental metrics and predict a sustainability threshold. Finally, design of a carpet recycling network illustrates the quantitative environmental benefits one reaps by applying the third, multi-facility scale bio-inspired sustainability guideline. Taken as a whole, this work contributes (1) a set of biologically inspired sustainability principles for engineering, (2) a translation of these principles into measures applicable to design, (3) examples demonstrating a new, holistic form of biomimicry and (4) a deductive, novel approach to environmentally benign engineering. Life, the collection of processes that tamed and maintained themselves on planet Earth's once hostile surface, long ago confronted and solved the fundamental problems facing all organisms. Through this work, it is hoped that humanity has taken one small step toward self-mastery, thus drawing closer to a solution to the latest problem facing all organisms.Ph.D.Committee Chair: Bert Bras; Committee Member: David Rosen; Committee Member: Dayna Baumeister; Committee Member: Janet Allen; Committee Member: Jeannette Yen; Committee Member: Matthew Realf

Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey

This study investigates the level of service quality of domestic airlines in Turkey travelling between Istanbul and London and compares those airline companies according to a set of predetermined criteria. A practical multi-criteria decision making approach combining hesitant and interval type 2 fuzzy sets is adopted and proposed for assessing the service quality of airline companies. The main finding of this study is that passengers care for service prioritization and personalization for a better flight experience and important differences occur in the service quality among the airline companies. Hence, handling of customer complaints, flight problems and individual attention could provide better insights for improving the service quality

The Role of Loss Functions in Regression Problems

In regression analysis, the goal is to capture the influence of one or more explanatory variables X1, . . . ,Xm on a response variable Y in terms of a regression function g : Rm -> R. An estimate ĝ of g is then found or evaluated in terms of its ability to predict a prespecified statistical functional T of the conditional distribution L(Y |X1, . . . ,Xm). This is done with the help of a loss function that penalizes estimates that perform poorly in predicting T(L(Y |X1, . . . ,Xm)). More precisely, it is done by using loss functions that are consistent for T. Clearly, the outcome of the evaluation or estimation strongly depends on the functional T. However, when we focus on a specific functional T a vast collection of suitable loss functions may be available and the result can still be sensitive to the choice of loss function. There are several viable solution strategies to approach this issue. We can, for instance, impose additional properties on the loss function or the resulting estimate so that only one of the possible loss functions remains reasonable. In this doctoral thesis we adopt another approach. The underlying idea is that we would naturally prefer an estimate ĝ that is optimal with respect to several consistent loss functions for T, as then the choice of loss function seems to impact the outcome less severely. In Chapter 1, we consider the nonparametric isotonic regression problem. We show that this regression problem is special in that for identifiable functionals T, solutions which are simultaneously optimal with respect to an entire class of consistent losses exist and can be characterized. There are, however, several functionals of interest that are not identifiable. The expected shortfall is just one prominent example. However, some of those functionals can be obtained as a function of a vector-valued elicitable functional. In the second Chapter, we investigate when simultaneous optimality with respect to a class of consistent losses holds for these functionals and introduce the solution to the isotonic regression problem for a specific loss in the case where simultaneous optimality is not fulfilled. In parametric regression, on the other hand, different consistent loss functions often yield different parameter estimates under misspecification. This motivates to consider the set of these parameters as a way to measure misspecification. We introduce this approach in Chapter 3 and show how the set of these model parameters can be calculated on the population and on the sample level for an isotonic regression function g

Improving the matching of registered unemployed to job offers through machine learning algorithms

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDue to the existence of a double-sided asymmetric information problem on the labour market characterized by a mutual lack of trust by employers and unemployed people, not enough job matches are facilitated by public employment services (PES), which seem to be caught in a low-end equilibrium. In order to act as a reliable third party, PES need to build a good and solid reputation among their main clients by offering better and less time consuming pre-selection services. The use of machine-learning, data-driven relevancy algorithms that calculate the viability of a specific candidate for a particular job opening is becoming increasingly popular in this field. Based on the Portuguese PES databases (CVs, vacancies, pre-selection and matching results), complemented by relevant external data published by Statistics Portugal and the European Classification of Skills/Competences, Qualifications and Occupations (ESCO), the current thesis evaluates the potential application of models such as Random Forests, Gradient Boosting, Support Vector Machines, Neural Networks Ensembles and other tree-based ensembles to the job matching activities that are carried out by the Portuguese PES, in order to understand the extent to which the latter can be improved through the adoption of automated processes. The obtained results seem promising and point to the possible use of robust algorithms such as Random Forests within the pre-selection of suitable candidates, due to their advantages at various levels, namely in terms of accuracy, capacity to handle large datasets with thousands of variables, including badly unbalanced ones, as well as extensive missing values and many-valued categorical variables

A Bayesian Approach for Quantile Optimization Problems with High-Dimensional Uncertainty Sources

International audienceRobust optimization strategies typically aim at minimizing some statistics of the uncertain objective function and can be expensive to solve when the statistic is costly to estimate at each design point. Surrogate models of the uncertain objective function can be used to reduce this computational cost. However, such surrogate approaches classically require a low-dimensional parametrization of the uncertainties, limiting their applicability. This work concentrates on the minimization of the quantile and the direct construction of a quantile regression model over the design space, from a limited number of training samples. A Bayesian quantile regression procedure is employed to construct the full posterior distribution of the quantile model. Sampling this distribution, we can assess the estimation error and adjust the complexity of the regression model to the available data. The Bayesian regression is embedded in a Bayesian optimization procedure, which generates sequentially new samples to improve the determination of the minimum of the quantile. Specifically, the sample infill strategy uses optimal points of a sample set of the quantile estimator. The optimization method is tested on simple analytical functions to demonstrate its convergence to the global optimum. The robust design of an airfoil’s shock control bump under high-dimensional geometrical and operational uncertainties serves to demonstrate the capability of the method to handle problems with industrial relevance. Finally, we provide recommendations for future developments and improvements of the method

Reliable statistical modeling of weakly structured information

The statistical analysis of "real-world" data is often confronted with the fact that most standard statistical methods were developed under some kind of idealization of the data that is often not adequate in practical situations. This concerns among others i) the potentially deficient quality of the data that can arise for example due to measurement error, non-response in surveys or data processing errors and ii) the scale quality of the data, that is idealized as "the data have some clear scale of measurement that can be uniquely located within the scale hierarchy of Stevens (or that of Narens and Luce or Orth)". Modern statistical methods like, e.g., correction techniques for measurement error or robust methods cope with issue i). In the context of missing or coarsened data, imputation techniques and methods that explicitly model the missing/coarsening process are nowadays wellestablished tools of refined data analysis. Concerning ii) the typical statistical viewpoint is a more pragmatical one, in case of doubt one simply presumes the strongest scale of measurement that is clearly "justified". In more complex situations, like for example in the context of the analysis of ranking data, statisticians often simply do not worry about purely measurement theoretic reservations too much, but instead embed the data structure in an appropriate, easy to handle space, like e.g. a metric space and then use all statistical tools available for this space. Against this background, the present cumulative dissertation tries to contribute from different perspectives to the appropriate handling of data that challenge the above-mentioned idealizations. A focus here is on the one hand on analysis of interval-valued and set-valued data within the methodology of partial identification, and on the other hand on the analysis of data with values in a partially ordered set (poset-valued data). Further tools of statistical modeling treated in the dissertation are necessity measures in the context of possibility theory and concepts of stochastic dominance for poset-valued data. The present dissertation consists of 8 contributions, which will be detailedly discussed in the following sections: Contribution 1 analyzes different identification regions for partially identified linear models under interval-valued responses and develops a further kind of identification region (as well as a corresponding estimator). Estimates for the identifcation regions are compared to each other and also to classical statistical approaches for a data set on wine quality. Contribution 2 deals with logistic regression under coarsened responses, analyzes point-identifying assumptions and develops likelihood-based estimators for the identified set. The methods are illustrated with data of a wave of the panel study "Labor Market and Social Security" (PASS). Contribution 3 analyzes the combinatorial structure of the extreme points and the edges of a polytope (called credal set or core in the literature) that plays a crucial role in imprecise probability theory. Furthermore, an efficient algorithm for enumerating all extreme points is given and compared to existing standard methods. Contribution 4 develops a quantile concept for data or random variables with values in a complete lattice, which is applied in Contribution 5 to the case of ranking data in the context of a data set on the wisdom of the crowd phenomena. In Contribution 6 a framework for evaluating the quality of different aggregation functions of Social Choice Theory is developed, which enables analysis of quality in dependence of group specific homogeneity. In a simulation study, selected aggregation functions, including an aggregation function based on the concepts of Contribution 4 and Contribution 5, are analyzed. Contribution 7 supplies a linear program that allows for detecting stochastic dominance for poset-valued random variables, gives proposals for inference and regularization, and generalizes the approach to the general task of optimizing a linear function on a closure system. The generality of the developed methods is illustrated with data examples in the context of multivariate inequality analysis, item impact and differential item functioning in the context of item response theory, analyzing distributional differences in spatial statistics and guided regularization in the context of cognitive diagnosis models. Contribution 8 uses concepts of stochastic dominance to establish a descriptive approach for a relational analysis of person ability and item difficulty in the context of multidimensional item response theory. All developed methods have been implemented in the language R ([R Development Core Team, 2014]) and are available from the author upon request. The application examples corroborate the usefulness of weak types of statistical modeling examined in this thesis, which, beyond their flexibility to deal with many kinds of data deficiency, can still lead to informative substance matter conclusions that are then more reliable due to the weak modeling.Die statistische Analyse real erhobener Daten sieht sich oft mit der Tatsache konfrontiert, dass übliche statistische Standardmethoden unter einer starken Idealisierung der Datensituation entwickelt wurden, die in der Praxis jedoch oft nicht angemessen ist. Dies betrifft i) die möglicherweise defizitäre Qualität der Daten, die beispielsweise durch Vorhandensein von Messfehlern, durch systematischen Antwortausfall im Kontext sozialwissenschaftlicher Erhebungen oder auch durch Fehler während der Datenverarbeitung bedingt ist und ii) die Skalenqualität der Daten an sich: Viele Datensituationen lassen sich nicht in die einfachen Skalenhierarchien von Stevens (oder die von Narens und Luce oder Orth) einordnen. Modernere statistische Verfahren wie beispielsweise Messfehlerkorrekturverfahren oder robuste Methoden versuchen, der Idealisierung der Datenqualität im Nachhinein Rechnung zu tragen. Im Zusammenhang mit fehlenden bzw. intervallzensierten Daten haben sich Imputationsverfahren zur Vervollständigung fehlender Werte bzw. Verfahren, die den Entstehungprozess der vergröberten Daten explizit modellieren, durchgesetzt. In Bezug auf die Skalenqualität geht die Statistik meist eher pragmatisch vor, im Zweifelsfall wird das niedrigste Skalenniveau gewählt, das klar gerechtfertigt ist. In komplexeren multivariaten Situationen, wie beispielsweise der Analyse von Ranking-Daten, die kaum noch in das Stevensche "Korsett" gezwungen werden können, bedient man sich oft der einfachen Idee der Einbettung der Daten in einen geeigneten metrischen Raum, um dann anschließend alle Werkzeuge metrischer Modellierung nutzen zu können. Vor diesem Hintergrund hat die hier vorgelegte kumulative Dissertation deshalb zum Ziel, aus verschiedenen Blickwinkeln Beiträge zum adäquaten Umgang mit Daten, die jene Idealisierungen herausfordern, zu leisten. Dabei steht hier vor allem die Analyse intervallwertiger bzw. mengenwertiger Daten mittels partieller Identifikation auf der Seite defzitärer Datenqualität im Vordergrund, während bezüglich Skalenqualität der Fall von verbandswertigen Daten behandelt wird. Als weitere Werkzeuge statistischer Modellierung werden hier insbesondere Necessity-Maße im Rahmen der Imprecise Probabilities und Konzepte stochastischer Dominanz für Zufallsvariablen mit Werten in einer partiell geordneten Menge betrachtet. Die vorliegende Dissertation umfasst 8 Beiträge, die in den folgenden Kapiteln näher diskutiert werden: Beitrag 1 analysiert verschiedene Identifikationsregionen für partiell identifizierte lineare Modelle unter intervallwertig beobachteter Responsevariable und schlägt eine neue Identifikationsregion (inklusive Schätzer) vor. Für einen Datensatz, der die Qualität von verschiedenen Rotweinen, gegeben durch ExpertInnenurteile, in Abhängigkeit von verschiedenen physikochemischen Eigenschaften beschreibt, werden Schätzungen für die Identifikationsregionen analysiert. Die Ergebnisse werden ebenfalls mit den Ergebissen klassischer Methoden für Intervalldaten verglichen. Beitrag 2 behandelt logistische Regression unter vergröberter Responsevariable, analysiert punktidentifizierende Annahmen und entwickelt likelihoodbasierte Schätzer für die entsprechenden Identifikationsregionen. Die Methode wird mit Daten einer Welle der Panelstudie "Arbeitsmarkt und Soziale Sicherung" (PASS) illustriert. Beitrag 3 analysiert die kombinatorische Struktur der Extrempunkte und der Kanten eines Polytops (sogenannte Struktur bzw. Kern einer Intervallwahrscheinlichkeit bzw. einer nicht-additiven Mengenfunktion), das von wesentlicher Bedeutung in vielen Gebieten der Imprecise Probability Theory ist. Ein effizienter Algorithmus zur Enumeration aller Extrempunkte wird ebenfalls gegeben und mit existierenden Standardenumerationsmethoden verglichen. In Beitrag 4 wird ein Quantilkonzept für verbandswertige Daten bzw. Zufallsvariablen vorgestellt. Dieses Quantilkonzept wird in Beitrag 5 auf Ranking-Daten im Zusammenhang mit einem Datensatz, der das "Weisheit der Vielen"-Phänomen untersucht, angewendet. Beitrag 6 entwickelt eine Methode zur probabilistischen Analyse der "Qualität" verschiedener Aggregationsfunktionen der Social Choice Theory. Die Analyse wird hier in Abhäangigkeit der Homogenität der betrachteten Gruppen durchgeführt. In einer simulationsbasierten Studie werden exemplarisch verschiedene klassische Aggregationsfunktionen, sowie eine neue Aggregationsfunktion basierend auf den Beiträgen 4 und 5, verglichen. Beitrag 7 stellt einen Ansatz vor, um das Vorliegen stochastischer Dominanz zwischen zwei Zufallsvariablen zu überprüfen. Der Anstaz nutzt Techniken linearer Programmierung. Weiterhin werden Vorschläge für statistische Inferenz und Regularisierung gemacht. Die Methode wird anschließend auch auf den allgemeineren Fall des Optimierens einer linearen Funktion auf einem Hüllensystem ausgeweitet. Die flexible Anwendbarkeit wird durch verschiedene Anwendungsbeispiele illustriert. Beitrag 8 nutzt Ideen stochastischer Dominanz, um Datensätze der multidimensionalen Item Response Theory relational zu analysieren, indem Paare von sich gegenseitig empirisch stützenden Fähigkeitsrelationen der Personen und Schwierigkeitsrelationen der Aufgaben entwickelt werden. Alle entwickelten Methoden wurden in R ([R Development Core Team, 2014]) implementiert. Die Anwendungsbeispiele zeigen die Flexibilität der hier betrachteten Methoden relationaler bzw. "schwacher" Modellierung insbesondere zur Behandlung defizitärer Daten und unterstreichen die Tatsache, dass auch mit Methoden schwacher Modellierung oft immer noch nichttriviale substanzwissenschaftliche Rückschlüsse möglich sind, die aufgrund der inhaltlich vorsichtigeren Modellierung dann auch sehr viel stärker belastbar sind