4242 research outputs found
Sort by
Variations in GDGT flux and TEX86 thermometry in three distinct oceanic regimes of the Atlantic Ocean : a sediment trap study
Full text linkTEX86 (tetraether index of tetraethers consisting of 86 carbons) is based on the relative compositions of thaumarchaeotal membrane lipids, glycerol dialkyl glycerol tetraethers (GDGTs), in marine surface sediments, allowing us to estimate sea surface temperature (SST). This proxy is a promising tool for SST reconstructions worldwide. However, it has been recognized that the composition of GDGTs can be altered by non-thermal factors, leading to variable relationships between TEX86 and SST. This thesis shall contribute to a better understanding of TEX86 thermometry and the controlling environmental factors in various oceanic provinces by evaluating the GDGT flux and TEX86 related temperature estimate in sinking particles. In the first part of the thesis, the results in the eastern equatorial Guinea Basin (GBN3) show that TEXH86-derived temperatures correspond to the subsurface water depth ( 50 m), where the nutricline exists, implying the favorable habit at depth of thaumarchaeotal communities. In the coastal upwelling area off Lüderitz (LZ), the results show that the TEXH86-derived temperatures resemble the satellite-derived SSTs with a delay of 26 days during the warmer season while warm-biased estimates occur during the colder season. Relatively higher TEX86 values found imply oxygen-stress. The second part of the thesis in the eastern Fram Strait (79Adegree N; FEVI16), the TEXL86 signal corresponds to water temperature at 30-80 m depth, where nitrification might occur. In the Antarctic Polar Front (50Adegree S; PF3), the TEXL86 -derived temperatures at the shallow trap display cold and warm biases relative to the SSTs with a tendency during periods of relatively low GDGT flux, which may be more dominant in the deep trap. The warm biased TEXL86 signal ( 7 AdegreeC) compared to the SST at the deep trap and in the underlying surface sediment might be due to a contribution of GDGTs derived from Euryarchaeota or a nonliner relationship of TEXL86 with SST in the Southern Ocean. The third part of the thesis focues on the oligotrophic regions. At WAB1, which was located at the fringes of the gyre system, the TEXH86-derived temperatures of the shallow trap resemble the SSTs. The WA9 trap in the more central oligotrophic gyre shows warm biased TEXH86 temperatures due to energy stressed conditions. In the deep traps of both sites, theTEXH86-derived temperatures record subsurface temperature. It is assumed that these signals are caused by a higher relative contribution of colder signal from deep in situ production and a smaller contribution of warmer signal from shallow waters. The last part of the thesis investigates the alkenone-based temperatures. Most of Uk'37-derived temperatures display the SSTs in the tropical regions. It implies that the regional geochemical characteristics (e.g., availability of nitrogen or oxygen), which probably affect the TEX86 thermometry, do not have a profound impact on the Uk'37 thermometry. In the shallow trap of the Antarctic Polar Front, the Uk'37 record show a clear SST seasonality. The Uk'37 in the Fram Strait shows that the applicability of alkenone proxies is limited in low temperature regions that disfavor alkenone producers
Virtual Movement from Natural Language Text
Full text linkIt is a challenging task for machines to follow a textual instruction. Properly understanding and using the meaning of the textual instruction in some application areas, such as robotics, animation, etc. is very difficult for machines. The interpretation of textual instructions for the automatic generation of the corresponding motions (e.g. exercises) and the validation of these movements are difficult tasks. To achieve our initial goal of having machines properly understand textual instructions and generate some motions accordingly, we recorded five different exercises in random order with the help of seven amateur performers using a Microsoft Kinect device. During the recording, we found that the same exercise was interpreted differently by each human performer even though they were given identical textual instructions. We performed a quality assessment study based on the derived data using a crowdsourcing approach. Later, we tested the inter-rater agreement for different types of visualization, and found the RGB-based visualization showed the best agreement among the annotatorsa animation with a virtual character standing in second position. In the next phase we worked with physical exercise instructions. Physical exercise is an everyday activity domain in which textual exercise descriptions are usually focused on body movements. Body movements are considered to be a common element across a broad range of activities that are of interest for robotic automation. Our main goal is to develop a text-to-animation system which we can use in different application areas and which we can also use to develop multiple-purpose robots whose operations are based on textual instructions. This system could be also used in different text to scene and text to animation systems. To generate a text-based animation system for physical exercises the process requires the robot to have natural language understanding (NLU) including understanding non-declarative sentences. It also requires the extraction of semantic information from complex syntactic structures with a large number of potential interpretations. Despite a comparatively high density of semantic references to body movements, exercise instructions still contain large amounts of underspecified information. Detecting, and bridging and/or filling such underspecified elements is extremely challenging when relying on methods from NLU alone. However, humans can often add such implicit information with ease due to its embodied nature. We present a process that contains the combination of a semantic parser and a Bayesian network. In the semantic parser, the system extracts all the information present in the instruction to generate the animation. The Bayesian network adds some brain to the system to extract the information that is implicit in the instruction. This information is very important for correctly generating the animation and is very easy for a human to extract but very difficult for machines. Using crowdsourcing, with the help of human brains, we updated the Bayesian network. The combination of the semantic parser and the Bayesian network explicates the information that is contained in textual movement instructions so that an animation execution of the motion sequences performed by a virtual humanoid character can be rendered. To generate the animation from the information we basically used two different types of Markup languages. Behaviour Markup Language is used for 2D animation. Humanoid Animation uses Virtual Reality Markup Language for 3D animation
Biomarker selection and cutoff estimation in drug development
Full text linkIn this cumulative thesis we discuss topics in the area of biomarker selection and cutoff estimation, where both subjects are related to the usability and applicability of biomarkers in drug development. The growing role of targeted medicine has led to an increased focus on the development of actionable biomarkers. Current penalized selection methods that are used to identify biomarker panels for classification in high-dimensional data, however, often result in highly complex panels that need careful pruning for practical use. In the framework of regularization methods, a penalty that is a weighted sum of the L1 and L0 norm has been proposed to account for the complexity of the resulting model. In practice, the limitation of this penalty is that the objective function is non-convex, non-smooth, the optimization is computationally intensive and the application to high-dimensional settings is challenging. In the first part of the thesis, we propose a stepwise forward variable selection method which combines the L0 with L1 or L2 norms. The penalized likelihood criterion that is used in the stepwise selection procedure results in more parsimonious models, keeping only the most relevant features. Moreover in this thesis, we introduce a new approach to derive the distribution of the cutoff and predictive values of a biomarker assay. To enable targeted therapies and enhance medical decision-making, biomarkers are increasingly used as screening and diagnostic tests. When using quantitative biomarkers for classification purposes, this often implies that an appropriate cutoff for the biomarker has to be determined and its clinical utility must be assessed. In the context of drug development, it is of interest how the probability of response changes with increasing values of the biomarker. Unlike sensitivity and specificity, predictive values are functions of the accuracy of the test, depend on the prevalence of the disease and therefore are a useful tool in this setting. We propose a Bayesian method to not only estimate the cutoff value using the negative and positive predictive values, but also estimate the uncertainty around this estimate. We use a step function, which serves as an approximate model facilitating classification into two groups that have different response rates. The advantage of using the step function is that both the cutoff and the predictive values are parameters of the model. Using Bayesian inference allows us to incorporate prior information and obtain posterior estimates and credible intervals for the cut-off and associated predictive values. Lastly, we further discuss the simultaneous variable selection and cutoff estimation (of the selected variables) by controlling the clinical utility, which is expressed in terms of negative and positive predictive values. A Bayesian variable selection method is introduced, which incorporates information about the predictive values into the biomarker selection process and simultaneously estimates the cutoff value on the risk score of the selected markers. The selection of the predictors in the final model is done under the constraint that the predictive values can take values in prespecified interval. The choice of different prior distributions is discussed. We conclude with discussions at each chapter of the dissertation
How video games changed my life : Life-Changing Testimonies and The Last of Us
Full text linkIn the following article, I explore YouTube videos and forum discussions on Reddit with content related to the theme or titled How video games changed my life , focusing especially on the mainstream video game The Last of Us (Naughty Dog 2013/2014). My aim is to understand how players use and follow an emerging and shared narrative describing a positive life-change. Through communal sharing online, the narratives afford a testimonial format or model. I see that the life-change narratives - or, in other words, transformational speeches - serve both as individual identity reflections, affirmations, and testimonies. Furthermore, through the act of public sharing on video platforms or through forum discussion, they can bring together an emerging community. Following Tuija Hovi s (2007, 2016) conceptualisations of religious narrative, the article shows how the argued testimonial tone underlines a unified and newly formed The Last of Us fan community. In addition, it presents a case study of how meaningful connections are built through shared narratives in today s online spaces. The article joins the scholarly conversations examining active meaning-making in popular culture
Spatio-temporal variations of observed and modelled stratospheric trace gases
Full text linkThe satellite instrument SCIAMACHY was operational for almost 10 years during the period 2002-2012 aboard the Envisat of the ESA, measuring a number of important atmospheric trace gases in three different modes. SCIAMACHY measured the spectra of the solar light scattered by the atmosphere (or transmitted through the atmosphere in the occultation mode). These spectra were used to retrieve vertical profiles or total columns of the atmospheric trace gases as well as aerosol and cloud parameters. The purpose of this study was to investigate the spatio-temporal changes of stratospheric species such as ozone (O3) and nitrogen dioxide (NO2) and reveal possible drivers of the observed variations. Taking into account the importance of understanding the changes in the atmospheric composition it was crucial to 1) find an atmospheric model, which adequately describes chemical-dynamical processes in the stratosphere and 2) have an accurate knowledge of trace gases distribution
Top-Managers of Foreign Multinational Enterprises in Mexico : Socialization, Leadership Style and Impact
Full text linkThis study focuses on the top-managers who run the subsidiaries of foreign multinational enterprises (MNEs) in Mexico. While some of them are Mexican, others are foreigners who have been sent from the countries of origin of their enterprises. The thesis explores and compares the socialization, worldviews, values, identities and social distinction practices of these top-managers and investigates the intercultural interactions, identity, struggles and communication problems between Mexican and expatriate managers. In addition, the relationship and misunderstandings between foreign managers and local workers are taken into account. Furthermore, the impact of foreign multinational enterprises and foreign business elites on their local employees, their families and communities, and on Mexican society as a whole is examined. The question Are foreign multinational enterprises and elites agents of cultural and institutional change and, if so, which impact do they have on Mexican society? is addressed
Model Selection in Approximate and Dynamic Factor Models
No full textThe variety in factor modelling for multivariate time series implies the necessity to develop the model selection methodology as the 'optimally' chosen model is not only important for understanding the underlying nature of a certain data generating process, but can also be useful in constructing more efficient forecasts. The majority of the methods developed in the literature on factor models consider their time domain representation, meanwhile the frequency domain representation of factor models for multivariate time series offers a number of attractive Features which can be exploited in developing more efficient estimation and/or model selection methods. The present dissertation presents two novel approaches for model selection for dynamic and/or approximate factor models, DFMs and AFMs, respectively, formulated and estimated in the frequency domain. The first approach combines theoretical findings in simultaneous statistical inference with testing common and idiosyncratic factors for autocorrelation. The second approach is based on the recent theoretical findings in the random matrix theory and presents a cross-validatory method of selecting the a optimala number of common factors
Autonomous Operation of a Reconfigurable Multi-Robot System for Planetary Space Missions
Full text linkReconfigurable robots can physically merge and form new types of composite systems. This ability leads to additional degrees of freedom for robot operations especially when dynamically composed robotic systems offer capabilities that none of the individual systems have. Research in the area of reconfigurable multi-robot systems has mainly been focused on swarm-based robots and thereby to systems with a high degree of modularity but a heavily restricted set of capabilities. In contrast, this thesis deals with heterogeneous robot teams comprising individually capable robots which are also modular and reconfigurable. In particular, the autonomous application of such reconfigurable multi-robot systems to enhance robotic space exploration missions is investigated. Exploiting the flexibility of a reconfigurable multi-robot system requires an appropriate system model and reasoner. Hence, this thesis introduces a special organisation model. This model accounts for the key characteristics of reconfigurable robots which are constrained by the availability and compatibility of hardware interfaces. A newly introduced mapping function between resource structures and functional properties permits to characterise dynamically created agent compositions. Since a combinatorial challenge lies in the identification of feasible and functionally suitable agents, this thesis further suggests bounding strategies to reason efficiently with composite robotic systems. This thesis proposes a mission planning algorithm which permits to exploit the flexibility of reconfigurable multi-robot systems. The implemented planner builds upon the developed organisation model so that multi-robot missions can be specified by high-level functionality constraints which are resolved to suitable combinations of robots. Furthermore, the planner synchronises robot activities over time and characterises plans according to three objectives: efficacy, efficiency and safety. The plannera s evaluation demonstrates an optimization of an exemplary space mission. This research is based on the parallel development of theoretical concepts and practical solutions while working with three reconfigurable multi-robot teams. The operation of a reconfigurable robotic team comes with practical constraints. Therefore, this thesis composes and evaluates an operational infrastructure which can serve as reference implementation. The identification and combination of applicable state-of-the-art technologies result in a distributed and dynamically extensible communication infrastructure which can maintain the properties of reconfigurable multi-robot systems. Field tests covering semi-autonomous and autonomous operation have been performed to characterise multi-robot missions and validate the autonomous control approach for reconfigurable multi-robot systems. The practical evaluation identified critical constraints and design elements for a successful application of reconfigurable multi-robot systems. Furthermore, the experiments point to improvements for the organisation model. This thesis is a wholistic approach to automate reconfigurable multi-robot systems. It identifies theoretical as well as practical challenges and it suggests effective solutions which permit an exploitation of an increased level of flexibility in future robotics missions
Fungus Detection Using Computer Vision and Machine Learning Techniques
Full text linkFungus is extremely disreputable and dangerous for food, human health and archives because it causes food loss, various life threatening diseases to human and destroy important documents. Thousands of different fungus species exist in the world and their spores always present indoor and outdoor environments. Its sign and symptom are non-specific in medical science for extremely large areas and containers, which poses severe threat to human health, food and archives. Numerous traditional techniques were applied to meet the challenge of early detection of fungus but all are costly, laborious, time-consuming and required skilled staff. This revolutionary era emphasizes the need for novel, simple, automatic fungal detection system to control the devastation caused by fungal species. In this research, we develop a fungus detection system and algorithms to automatically detect fungus. Three computer vision based techniques were developed for the detection of fungus spores. One of them used HOG based features and achieved convincing results. Other technique consisted of fusion of Fourier transform and SIFT features to achieve promising results. Third method based on superpixel and handcrafted features. The results of all these techniques encourage for the possibility of early detection of fungus spores from dirt particles. The other main objective of this research was to develop a CNN based approach for the detection and classification of different types of fungus spores. However, a large amount of data is an essential prerequisite for its effective application. In pursuing this idea, we developed a new novel fungus dataset of its kind, with the goal of advancing the state-of-the-art in fungus classification by placing the question of fungus detection. This is achieved by gathering various images of complex fungal spores by extracting samples from contaminated fruits, archives and lab incubated fungus colonies. These images primarily consisted of five different types of fungus spores and dirt. The fungus detection system was utilized to obtain these images. Which were further annotated to mark fungal spores as a region of interest using specially designed graphical user interface. As a result, 40,800 labeled images were used to develop fungus dataset to aid in precise fungus detection and classification. A CNN architecture was designed and it showed the promising result with an accuracy of 94.8%. The obtained results proved the possibility of early detection and classification of several types of fungus spores using CNN and could estimate all possible threats due to fungus
Strategic design of a hydrogen infrastructure under uncertainty
Full text linkDue to an increasing demand for electric energy and a decreasing amount of fossil fuel sources, renewable and clean energy systems are positioned as alternative energy supply options. A transformation of the supply of energy from fossil to renewable sources of energy is accompanied by substantial challenges such as their intermittent behavior and integrating high shares in transportation sector, which require smart transition strategies. In this thesis, special attention is given to the concept for integrating renewable energy into transportation sector through applying hydrogen-based system, given the high penetration rate of fuel cell electric vehicles into passenger transport. Mathematical modeling and optimization tools are used at the network level. A hydrogen production network defined as a supply chain is modeled and Germany is chosen as a case study due to its progressive policies towards increasing the use of renewable energy sources and lowering greenhouse gas emissions