859 research outputs found
Influence of clamp-widening on the quality factor of nanomechanical silicon nitride resonators
Nanomechanical resonators based on strained silicon nitride (SiN)
have received a large amount of attention in fields such as sensing and quantum
optomechanics due to their exceptionally high quality factors (s).
Room-temperature s approaching 1 billion are now in reach by means of
phononic crystals (soft-clamping) and strain engineering. Despite great
progress in enhancing s, difficulties in fabrication of soft-clamped samples
limits their implementation into actual devices. An alternative means of
achieving ultra-high s was shown using trampoline resonators with engineered
clamps, which serves to localize the stress to the center of the resonator,
while minimizing stress at the clamping. The effectiveness of this approach has
since come into question from recent studies employing string resonators with
clamp-tapering. Here, we investigate this idea using nanomechanical string
resonators with engineered clampings similar to those presented for
trampolines. Importantly, the effect of orienting the strings diagonally or
perpendicularly with respect to the silicon frame is investigated. It is found
that increasing the clamp width for diagonal strings slightly increases the
s of the fundamental out-of-plane mode at small radii, while perpendicular
strings only deteriorate with increasing clamp width. Measured s agree well
with finite element method simulations even for higher-order resonances. The
small increase cannot account for previously reported s of trampoline
resonators. Instead, we propose the effect to be intrinsic and related to
surface and radiation losses.Comment: 7 pages, 4 figure
The effect of changing climate and vegetation on the topographic evolution of catchments in the Chilean Coastal Cordillera over millennial timescales
The complex interplay between climate change, the composition and density of surface vegetation cover and physical surface processes has been a focus of scientific research in the field of geomorphology for the last few decades. The classical approach for explaining differences in topography only considers the influence of tectonic processes and lithological material constants as endogenic forcings and the effect of precipitation as an exogenic forcing, where the stabilizing effect of vegetation cover has mostly been neglected. To develop a more complete view of the complex topographic system and incorporate the dynamics between climate and vegetation, a numerical model framework consisting of a dynamic vegetation model (LPJ-GUESS) and a landscape evolution model (Landlab) was developed and tested against four different study areas situated in the Chilean Coastal Cordillera on the basis of‚ available paleoclimate data. These study areas were chosen based on homogeneous endogenic forcings, with a comparable tectonic uplift rate and the same granodioritic lithology, all of which allow for better parameterization of surface processes. Three main simulation experiments were conducted: 1. LPJ-GUESS simulations, which gave insight about large-scale dynamic adjustments of vegetation cover and composition for the respective climate zones. 2. Landlab simulations which were designed to reproduce steady-state topographic metrics observed in the focus areas and to determine the reaction of the topographic system to transient, external changes in precipitation or vegetation cover 3. Coupled simulations with direct feedback during model-runtime between LPJ-GUESS and Landlab for the timeperiod since the last glacial maximum (21ka before present) to present day. The experiments show a complex reaction of both vegetation and topography to climatic forcings, with absolute changes in vegetation cover not exceeding 10%, but large-magnitude adjustments of plant composition due to changes in climate. Simulations show magnitudes and time-scales of adjustment are highly dependent on initial catchment vegetation cover and amount of annual precipitation received. Coupled simulations show large short-term variations in catchment-mean erosion rates for very arid and very humid focus areas, while areas with mediterranean climate show lesser magnitude fluctuations but a more pronounced long-term decrease in mean erosion rates. In summary, this thesis helps in understanding the complex climate-erosion interactions by showing the nature of the threshold-controlled system which governs the reactions of topography to natural changes in climate or vegetation cover
A physics-based approach to flow control using system identification
Control of amplifier flows poses a great challenge, since the influence of environmental noise sources and measurement contamination is a crucial component in the design of models and the subsequent performance of the controller. A modelbased approach that makes a priori assumptions on the noise characteristics often yields unsatisfactory results when the true noise environment is different from the assumed one. An alternative approach is proposed that consists of a data-based systemidentification technique for modelling the flow; it avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design. This technique is applied to flow over a backward-facing step, a typical example of a noise-amplifier flow. Physical insight into the specifics of the flow is used to interpret and tailor the various terms of the auto-regressive model. The designed compensator shows an impressive performance as well as a remarkable robustness to increased noise levels and to off-design operating conditions. Owing to its reliance on only timesequences of observable data, the proposed technique should be attractive in the design of control strategies directly from experimental data and should result in effective compensators that maintain performance in a realistic disturbance environment
End-to-end Incremental Learning
Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from (catastrophic forgetting), a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model---a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees.This work has been funded by project TIC-1692 (Junta de Andalucía), TIN2016-80920R (Spanish Ministry of Science and Technology) and Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
The importance of an ethical framework for trust calibration in AI
The transformative power of AI raises serious concerns about ethical issues within organizations and implicates the need for trust. To cope with that, numerous ethical frameworks are generally published, but only on a theoretical level. Furthermore, proper trust calibration in AI is of high relevance for the workers. Up to now, only limited studies have been carried out to investigate how an ethical framework can foster proper trust calibration of workers in practice. To close this gap, an ethical framework is investigated that ensures trust calibration by targeting AI reliability and AI safety. Finally, the effectiveness of the applied framework is evaluated based on 17 interviews within an international automotive supplier. As a result, this ethical framework led to a major increase in trust. This is a groundbreaking outcome since workers are willing to accept a lower level of AI safety and AI reliability at the same time
How Engineers perform Data Science Work: Designing Hybrid Roles
As of today, organizations are still struggling to derive consistent value from data science projects. The basic relevance of domain knowledge for data science work can be considered as common sense. Engineers, in particular, offer a unique view on emerging data science work, based on their critical role within traditional industries. As a constraint, current studies on data science work consider domain experts as rather passive and engineering-related studies are rare. To further explore these challenges, the present study analyses data science work of 30 engineers at an international automotive supplier. Investigating three cases, the evolvement of hybrid data science work can be derived, which combines two perspectives: engineering and data science. Thus, engineers actively incorporate the data science perspective, particularly when development activities involve minimal participation of data scientists. This contribution significantly enhances existing knowledge by demonstrating how engineers embrace the data scientists’ perspective and perform hybrid data work
High performance and small footprint spot size converters based on SWG metamaterial lenses
Spot size converters with high expansion ratio are required in a variety of situations. This is the case of non-focusing Silicon on Insulator (SOI) fiber-to-chip grating couplers, which typically require long adiabatic tapers (Ltaper >100μm) from the narrow single-mode waveguides (WSi-wire ~ 500nm) to the wide grating region (Wgrating ~ 15μm). Here, we explore the potential of subwavelength grating (SWG) dielectric metamaterials to implement integrated GRaded INdex (GRIN) lenses to expand the mode field. Our designs achieve the desired Beam Expansion (BE) with insertion losses below 1dB over a distance of only LBE ~ 17μm.Ministerio de Economía y Competitividad, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad (cofinanciado FEDER), (TEC2016-80718-R);
Universidad de Málaga.
Ministerio de Educación, Cultura y Deporte (MECD) (FPU16/06121);
Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Organisationaler Wandel in soziokulturellen Vereinen
Im weitgefächerten Feld freiwilliger Vereinigungen bzw. im Sektor von Nonprofit Organisationen sehen sich Organisationen mit sehr speziellen Herausforderungen konfrontiert um ihre Existenz zu sichern. In diesem Zusammenhang zeigt sich gerade bei der Beobachtung von organisationalem Wandel, im Spannungsfeld zwischen Identität und Wandel, wo Probleme geortet und Entwicklungspotentiale aufgedeckt werden können. Im präsentierten Fallbeispiel, eines soziokulturellen Vereins im Umfeld der Universität Wien, wurde zu diesem Zweck eine Organisationsdiagnose durchgeführt. Im Fokus standen in Verbindung mit dem organisationalem Wandel vor allem Fragen der Kommunikation, der Mitgliedschaft, der Umwelt und dem Wissenstransfer der Organisation. Als theoretische Basis hierfür diente die systemische Organisationstheorie, welche in Kontext zu Erkenntnissen der Nonprofit Organisationsforschung gesetzt wurde. Im Fallbeispiel des Erasmus Student Networks (ESN) kamen vor allem qualitative-interpretative Forschungsmethoden zum Einsatz. Ergänzende Erkenntnisse brachten die Analysen von Artefakten. Im Zuge der Rekonstruktion der Organisationsgeschichte konnte somit unter anderem bestätigt werden, dass organisationaler Wandel dann passiert, wenn starke Umweltveränderungen auftreten oder Probleme der Kommunikation zu Krisen innerhalb der Organisation führen. Zudem wurde die Bedeutung von Wissen als Steuerungsmedium des speziellen Organisationstypus identifiziert und anhand des Fallbeispiels gezeigt, wie der Wissenstransfer in soziokulturellen Vereinen Anwendung finden kann.Organisations in the wide range of charitable societies face many specific challenges to ensure their existence. Therefore it seems to be useful to analyse organisational change between the conflicting demands of keeping and changing organisational structures, while searching for organisational problems and unfolding opportunities for the organisation. In this particular case, of a charitable student organisation at the University of Vienna, a qualitative organisational analysis was conducted. Its focus lied on aspects concerning the topic of organisational change and contained mainly questions regarding organisational communication, membership, the environment and knowledge transfer of the observed organisation. For that purpose the organisational theory based systems-theory and findings of non-profit organisations research served as the basis of this research. In case of the Erasmus Student Network (ESN) it was mainly qualitative-interpretative methods that were used. In addition, analyses of artefacts were conducted and provided additional insight. In the course of the reconstruction of the organisation and its history it was possible to confirm that organisational change occurs when either the environment changes dramatically or the organisation faces major communication issues, which could also lead to organisational crises. Furthermore knowledge has been identified as the major control instrument of the specific type of organisation, which was analysed in the case study as well
Entwicklung einer Web Scraping Plattform für mobile Anwendungen
Smartphones sind für viele Menschen zu alltäglichen Begleitern geworden. Damit einher geht auch eine breite Auswahl an Apps für diese. Für Forschungszwecke, Erstellung von Statistiken, Archivierung oder ähnliche Szenarien ist es nützlich, eine einfache Möglichkeit zu haben, um möglichst systematisch und automatisiert Metadaten über solche Apps zu erhalten. Da es für die großen App Stores keine oder nur unzulängliche öffentliche API's oder anderweitige Schnittstellen zur Datenabfrage gibt, ist es notwendig, auf Web Scraping zurückzugreifen.
In dieser Arbeit wird ein solcher Web Scraper für den Google Play Store und den Apple App Store entwickelt, welcher basierend auf den Webseiten der Stores die relevanten Daten extrahiert. Dabei wird zunächst darauf eingegangen, wie die Daten übertragen und repräsentiert werden und anschließend eine Implementierung entwickelt, die es möglichst einfach machen soll, neue Stores zu ergänzen und Änderungen seitens der Stores umzusetzen. Zuletzt wird die Funktionalität dieser Scrapers in Form einer REST-API zur Verfügung gestellt, um einen Ressourcen-zentrierten Zugriff zu erlauben und Programmiersprachen-Unabhängigkeit zu erlangen
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