Construct Validity, Test-Retest Reliability, and Internal Consistency of the Photo Elicitation Semantic Differential Scale (PESD) in Disability Studies

The Photo Elicitation Semantic Differential scale (PESD), developed to examine the social perception of disability and attitudes towards people with a disability (PwD), comprises six dimensions: communicativeness, competence, attractiveness, intelligence, industriousness, and popularity. This paper aims to assess the construct validity, test-retest reliability, and internal consistency of the PESD. A longitudinal study with 40 participants of the Swiss general population and 2 (test-retest) * 8 (different photographs) measurements per subject was performed. Construct validity was examined via Principal Component Analysis (PCA), test-retest reliability via the Intraclass Correlation Coefficient (ICC) and a frequency analysis of deviations among test-retest scores, and internal consistency via Cronbach's alpha. PCA extracted two factors corresponding to hard and soft skills for the test and a single factor for the retest. ICCs ranged from 0.44 (industriousness) to 0.60 (intelligence). Deviations between tests exceeding +/-1 were rather rare ranging from 6% (intelligence) to 14% (competence). Cronbach's alpha equalled 0.814 and 0.858 for test and retest, respectively. Summarising, in our study the PESD appears to be a valid and reliable tool for the examination of the social perception of disability and attitudes towards Pw

Funktionalisierung von Silikonoberflächen

Poly(dimethylsiloxan) (PDMS) ist ein wichtiges Polymer, das zunehmend in der Mikroelektronik aufgrund seiner hervorragenden Elastizität und thermischen Stabilität Verwendung findet. Ein limitierender Faktor für den Einsatz von PDMS ist aufgrund des Fehlens von reaktiven Gruppen und der niedrigen freien Oberflächenenergie seine geringe Adhäsion zu anderen Materialien. Zur Erhöhung der Adhäsion ist deshalb die Einführung von polaren, funktionellen Gruppen notwendig. Hier lag die Motivation der vorliegenden Arbeit, die sich eine gezielte Funktionalisierung von PDMS-Oberflächen als Aufgabe gesetzt hatte. Im ersten Teil der Arbeit wurde eine Verbesserung der Adhäsion zu einem fotostrukturierbaren Epoxidharz mittels der Sauerstoff- und Ammoniakplasmabehandlung angestrebt. In beiden Fällen führte die Plasmabehandlung zu der Einführung von unterschiedlichsten funktionellen Gruppen auf die Oberfläche und zu einer Verbesserung des Benetzungsverhaltens gegenüber Wasser. Zudem wurden Haftfestigkeiten erzielt, die um ein Vielfaches höher waren als jene zwischen Epoxidharz und einer unbehandelten PDMS-Oberfläche. Jedoch waren die hydrophilen Eigenschaften nach der Plasmabehandlung während der Lagerung an Luft zeitlich begrenzt, die PDMS-Oberfläche kehrt innerhalb kurzer Zeit in den einst hydrophoben Ausgangszustand zurück. Der Alterungsvorgang wird als „Hydrophobic Recovery“ bezeichnet und ist bei PDMS-Oberflächen, die höheren Plasmaleistungen und Behandlungszeiten ausgesetzt wurden, besonders auffällig. Die Vermeidung dieser Problematik war der Ausgangspunkt für den zweiten Teil der Arbeit. Auf der Grundlage der über die Plasmabehandlungen erzeugten funktionellen Gruppen wurden neue Konzepte für eine kovalente Anbindung von verschiedenen funktionellen Homo- und Copolymeren über die „Grafting to“-Technik entwickelt. Neben der Erhöhung der Adhäsion zu dem Epoxidharz war es möglich, das Benetzungsverhalten gegenüber Wasser durch die Unterbindung der „Hydrophobic Recovery“ zu stabilisieren. Des Weiteren gelang es, durch die Wahl der funktionellen Polymere, die PDMS-Oberfläche gezielt mit gewünschten Eigenschaften auszustatten. Somit ist der Einsatz der polymermodifizierten Oberflächen, außer in der Mikroelektronik, auch auf andere Anwendungen, wie der Biomedizin, der Mikrofluidik oder der Softlithografie übertragbar, in denen eine beständige, definierte Oberflächenfunktionalisierung ein wichtiges Kriterium darstellt

Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images.Comment: 20 page

Code Generator Composition for Model-Driven Engineering of Robotics Component & Connector Systems

Engineering software for robotics applications requires multidomain and application-specific solutions. Model-driven engineering and modeling language integration provide means for developing specialized, yet reusable models of robotics software architectures. Code generators transform these platform independent models into executable code specific to robotic platforms. Generative software engineering for multidomain applications requires not only the integration of modeling languages but also the integration of validation mechanisms and code generators. In this paper we sketch a conceptual model for code generator composition and show an instantiation of this model in the MontiArc- Automaton framework. MontiArcAutomaton allows modeling software architectures as component and connector models with different component behavior modeling languages. Effective means for code generator integration are a necessity for the post hoc integration of applicationspecific languages in model-based robotics software engineering.Comment: 12 pages, 4 figures, In: Proceedings of the 1st International Workshop on Model-Driven Robot Software Engineering (MORSE 2014), York, Great Britain, Volume 1319 of CEUR Workshop Proceedings, 201

Aggregated End-to-end Visibility and its Application on Rapid and Automatic Outage Triage in Monitoring Microservices

In a microservice architecture, a user request can go through a large number of servers owned by several different teams before a response is returned. The request can fail due to failure in any of the servers. Troubleshooting an outage that affects the end user experience in microservice architecture can involve multiple teams and can take a substantial amount of time. This disclosure describes techniques to rapidly locate the root cause entity of a customer-facing failure to node(s) deep within the infrastructure of the service. Per the techniques, end user product teams mark requests with metadata known as critical user interactions (CUI). The metadata is propagated along with the request. Performance metrics are gathered from servers that the requests go through. The performance metric is keyed by CUI, server node, and peer node for every adjacent pair of nodes. These piecemeal metrics keyed by CUI together offer end-to-end visibility for a set of requests grouped by the CUI of the end product, enabling the rapid and automatic triage of an outage to an interior server without requiring domain expertise on the product or the server

A Note on Ex-Ante Stable Lotteries

We study ex-ante priority respecting (ex-ante stable) lotteries in the context of object allocation under thick priorities. We show that ex-ante stability as a fairness condition is very demanding: Only few agent-object pairs have a positive probability of being matched in an ex-ante stable assignment. We interpret our result as an impossibility result. With ex-ante stability one cannot go much beyond randomly breaking ties and implementing a (deterministically) stable matching with respect to the broken ties