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A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.
Sinkholes and depressions are typical landforms of karst regions. They pose a considerable natural hazard to infrastructure, agriculture, economy and human life in affected areas worldwide. The physio-chemical processes of sinkholes and depression formation are manifold, ranging from dissolution and material erosion in the subsurface to mechanical subsidence/failure of the overburden. This thesis addresses the mechanisms leading to the development of sinkholes and depressions by using complementary methods: remote sensing, distinct element modelling and near-surface geophysics.
In the first part, detailed information about the (hydro)-geological background, ground structures, morphologies and spatio-temporal development of sinkholes and depressions at a very active karst area at the Dead Sea are derived from satellite image analysis, photogrammetry and geologic field surveys. There, clusters of an increasing number of sinkholes have been developing since the 1980s within large-scale depressions and are distributed over different kinds of surface materials: clayey mud, sandy-gravel alluvium and lacustrine evaporites (salt). The morphology of sinkholes differs depending in which material they form: Sinkholes in sandy-gravel alluvium and salt are generally deeper and narrower than sinkholes in the interbedded evaporite and mud deposits. From repeated aerial surveys, collapse precursory features like small-scale subsidence, individual holes and cracks are identified in all materials. The analysis sheds light on the ongoing hazardous subsidence process, which is driven by the base-level fall of the Dead Sea and by the dynamic formation of subsurface water channels.
In the second part of this thesis, a novel, 2D distinct element geomechanical modelling approach with the software PFC2D-V5 to simulating individual and multiple cavity growth and sinkhole and large-scale depression development is presented. The approach involves a stepwise material removal technique in void spaces of arbitrarily shaped geometries and is benchmarked by analytical and boundary element method solutions for circular cavities. Simulated compression and tension tests are used to calibrate model parameters with bulk rock properties for the materials of the field site. The simulations show that cavity and sinkhole evolution is controlled by material strength of both overburden and cavity host material, the depth and relative speed of the cavity growth and the developed stress pattern in the subsurface. Major findings are: (1) A progressively deepening differential subrosion with variable growth speed yields a more fragmented stress pattern with stress interaction between the cavities. It favours multiple sinkhole collapses and nesting within large-scale depressions. (2) Low-strength materials do not support large cavities in the material removal zone, and subsidence is mainly characterised by gradual sagging into the material removal zone with synclinal bending. (3) High-strength materials support large cavity formation, leading to sinkhole formation by sudden collapse of the overburden. (4) Large-scale depression formation happens either by coalescence of collapsing holes, block-wise brittle failure, or gradual sagging and lateral widening.
The distinct element based approach is compared to results from remote sensing and geophysics at the field site. The numerical simulation outcomes are generally in good agreement with derived morphometrics, documented surface and subsurface structures as well as seismic velocities. Complementary findings on the subrosion process are provided from electric and seismic measurements in the area.
Based on the novel combination of methods in this thesis, a generic model of karst landform evolution with focus on sinkhole and depression formation is developed. A deepening subrosion system related to preferential flow paths evolves and creates void spaces and subsurface conduits. This subsequently leads to hazardous subsidence, and the formation of sinkholes within large-scale depressions. Finally, a monitoring system for shallow natural hazard phenomena consisting of geodetic and geophysical observations is proposed for similarly affected areas.
In the era of social networks, internet of things and location-based services, many online services produce a huge amount of data that have valuable objective information, such as geographic coordinates and date time. These characteristics (parameters) in the combination with a textual parameter bring the challenge for the discovery of geospatiotemporal knowledge. This challenge requires efficient methods for clustering and pattern mining in spatial, temporal and textual spaces.
In this thesis, we address the challenge of providing methods and frameworks for geospatiotemporal data analytics. As an initial step, we address the challenges of geospatial data processing: data gathering, normalization, geolocation, and storage. That initial step is the basement to tackle the next challenge -- geospatial clustering challenge. The first step of this challenge is to design the method for online clustering of georeferenced data. This algorithm can be used as a server-side clustering algorithm for online maps that visualize massive georeferenced data. As the second step, we develop the extension of this method that considers, additionally, the temporal aspect of data. For that, we propose the density and intensity-based geospatiotemporal clustering algorithm with fixed distance and time radius.
Each version of the clustering algorithm has its own use case that we show in the thesis.
In the next chapter of the thesis, we look at the spatiotemporal analytics from the perspective of the sequential rule mining challenge. We design and implement the framework that transfers data into textual geospatiotemporal data - data that contain geographic coordinates, time and textual parameters. By this way, we address the challenge of applying pattern/rule mining algorithms in geospatiotemporal space. As the applicable use case study, we propose spatiotemporal crime analytics -- discovery spatiotemporal patterns of crimes in publicly available crime data.
The second part of the thesis, we dedicate to the application part and use case studies. We design and implement the application that uses the proposed clustering algorithms to discover knowledge in data. Jointly with the application, we propose the use case studies for analysis of georeferenced data in terms of situational and public safety awareness.
Data assimilation has been an active area of research in recent years, owing to its wide utility. At the core of data assimilation are filtering, prediction, and smoothing procedures. Filtering entails incorporation of measurements' information into the model to gain more insight into a given state governed by a noisy state space model. Most natural laws are governed by time-continuous nonlinear models. For the most part, the knowledge available about a model is incomplete; and hence uncertainties are approximated by means of probabilities. Time-continuous filtering, therefore, holds promise for wider usefulness, for it offers a means of combining noisy measurements with imperfect model to provide more insight on a given state.
The solution to time-continuous nonlinear Gaussian filtering problem is provided for by the Kushner-Stratonovich equation. Unfortunately, the Kushner-Stratonovich equation lacks a closed-form solution. Moreover, the numerical approximations based on Taylor expansion above third order are fraught with computational complications. For this reason, numerical methods based on Monte Carlo methods have been resorted to. Chief among these methods are sequential Monte-Carlo methods (or particle filters), for they allow for online assimilation of data. Particle filters are not without challenges: they suffer from particle degeneracy, sample impoverishment, and computational costs arising from resampling.
The goal of this thesis is to:— i) Review the derivation of Kushner-Stratonovich equation from first principles and its extant numerical approximation methods, ii) Study the feedback particle filters as a way of avoiding resampling in particle filters, iii) Study joint state and parameter estimation in time-continuous settings, iv) Apply the notions studied to linear hyperbolic stochastic differential equations.
The interconnection between Itô integrals and stochastic partial differential equations and those of Stratonovich is introduced in anticipation of feedback particle filters. With these ideas and motivated by the variants of ensemble Kalman-Bucy filters founded on the structure of the innovation process, a feedback particle filter with randomly perturbed innovation is proposed. Moreover, feedback particle filters based on coupling of prediction and analysis measures are proposed. They register a better performance than the bootstrap particle filter at lower ensemble sizes.
We study joint state and parameter estimation, both by means of extended state spaces and by use of dual filters. Feedback particle filters seem to perform well in both cases. Finally, we apply joint state and parameter estimation in the advection and wave equation, whose velocity is spatially varying. Two methods are employed: Metropolis Hastings with filter likelihood and a dual filter comprising of Kalman-Bucy filter and ensemble Kalman-Bucy filter. The former performs better than the latter.
Back pain is a problem in adolescent athletes affecting postural control which is an important requirement for physical and daily activities whether under static or dynamic conditions. One leg stance and star excursion balance postural control tests are effective in measuring static and dynamic postural control respectively. These tests have been used in individuals with back pain, athletes and non-athletes without first establishing their reliabilities. In addition to this, there is no published literature investigating dynamic posture in adolescent athletes with back pain using the star excursion balance test. Therefore, the aim of the thesis was to assess deficit in postural control in adolescent athletes with and without back pain using static (one leg stance test) and dynamic postural (SEBT) control tests.
Adolescent athletes with and without back pain participated in the study. Static and dynamic postural control tests were performed using one leg stance and SEBT respectively. The reproducibility of both tests was established. Afterwards, it was determined whether there was an association between static and dynamic posture using the measure of displacement of the centre pressure and reach distance respectively. Finally, it was investigated whether there was a difference in postural control in adolescent athletes with and without back pain using the one leg stance test and the SEBT.
Fair to excellent reliabilities was recorded for the static (one leg stance) and dynamic (star excursion balance) postural control tests in the subjects of interest. No association was found between variables of the static and dynamic tests for the adolescent athletes with and without back pain. Also, no statistically significant difference was obtained between adolescent athletics with and without back pain using the static and dynamic postural control test.
One leg stance test and SEBT can be used as measures of postural control in adolescent athletes with and without back pain. Although static and dynamic postural control might be related, adolescent athletes with and without back pain might be using different mechanisms in controlling their static and dynamic posture. Consequently, static and dynamic postural control in adolescent athletes with back pain was not different from those without back pain. These outcome measures might not be challenging enough to detect deficit in postural control in our study group of interest.
Since 1980 Iraq passed through various wars and conflicts including Iraq-Iran war, Saddam Hussein’s the Anfals and Halabja campaigns against the Kurds and the killing campaigns against Shiite in 1986, Saddam Hussein’s invasion of Kuwait in August 1990, the Gulf war in 1990, Iraq war in 2003 and the fall of Saddam, the conflicts and chaos in the transmission of power after the death of Saddam, and the war against ISIS . All these wars left severe impacts in most households in Iraq; on women and children in particular.
The consequences of such long wars could be observed in all sectors including economic, social, cultural and religious sectors. The social structure, norms and attitudes are intensely affected. Many women specifically divorced women found them-selves in challenging different difficulties such as social as well as economic situations. Thus the divorced women in Iraqi Kurdistan are the focus of this research.
Considering the fact that there is very few empirical researches on this topic, a constructivist grounded theory methodology (CGT) is viewed as reliable in order to come up with a comprehensive picture about the everyday life of divorced women in Iraqi Kurdistan. Data collected in Sulaimani city in Iraqi Kurdistan. The work of Kathy Charmaz was chosen to be the main methodological context of the research and the main data collection method was individual intensive narrative interviews with divorced women.
Women generally and divorced women specifically in Iraqi Kurdistan are living in a patriarchal society that passing through many changes due to the above mentioned wars among many other factors. This research is trying to study the everyday life of divorced women in such situations and the forms of social insecurity they are experiencing. The social institutions starting from the family as a very significant institution for women to the governmental and non-governmental institutions that are working to support women, and the copying strategies, are in focus in this research. The main research argument is that the family is playing ambivalent roles in divorced women’s life. For instance, on one side families are revealed to be an essential source of security to most respondents, on the other side families posed also many threats and restrictions on those women. This argument supported by what called by Suad joseph "the paradox of support and suppression" . Another important finding is that the stat institution(laws , constitutions ,Offices of combating violence against woman and family) are supporting women somehow and offering them protection from the insecurities but it is clear that the existence of the laws does not stop the violence against women in Iraqi Kurdistan, As explained by Pateman because the laws /the contract is a sexual-social contract that upholds the sex rights of males and grants them more privileges than females. The political instability, Tribal social norms also play a major role in influencing the rule of law.
It is noteworthy to refer that analyzing the interviews in this research showed that in spite that divorced women living in insecurities and facing difficulties but most of the respondents try to find a coping strategies to tackle difficult situations and to deal with the violence they face; these strategies are bargaining, sometimes compromising or resisting …etc. Different theories used to explain these coping strategies such as bargaining with patriarchy. Kandiyoti who stated that women living under certain restraints struggle to find way and strategies to enhance their situations. The research finding also revealed that the western liberal feminist view of agency is limited this is agree with Saba Mahmood and what she explained about Muslim women agency. For my respondents, who are divorced women, their agency reveals itself in different ways, in resisting or compromising with or even obeying the power of male relatives, and the normative system in the society. Agency is also explained the behavior of women contacting formal state institutions in cases of violence like the police or Offices of combating violence against woman and family.
Business process management is an established technique for business organizations to manage and support their processes. Those processes are typically represented by graphical models designed with modeling languages, such as the Business Process Model and Notation (BPMN).
Since process models do not only serve the purpose of documentation but are also a basis for implementation and automation of the processes, they have to satisfy certain correctness requirements. In this regard, the notion of soundness of workflow nets was developed, that can be applied to BPMN process models in order to verify their correctness. Because the original soundness criteria are very restrictive regarding the behavior of the model, different variants of the soundness notion have been developed for situations in which certain violations are not even harmful.
All of those notions do only consider the control-flow structure of a process model, however. This poses a problem, taking into account the fact that with the recent release and the ongoing development of the Decision Model and Notation (DMN) standard, an increasing number of process models are complemented by respective decision models. DMN is a dedicated modeling language for decision logic and separates the concerns of process and decision logic into two different models, process and decision models respectively.
Hence, this thesis is concerned with the development of decisionaware soundness notions, i.e., notions of soundness that build upon the original soundness ideas for process models, but additionally take into account complementary decision models. Similar to the various notions of workflow net soundness, this thesis investigates different notions of decision soundness that can be applied depending on the desired degree of restrictiveness. Since decision tables are a standardized means of DMN to represent decision logic, this thesis also puts special focus on decision tables, discussing how they can be translated into an unambiguous format and how their possible output values can be efficiently determined.
Moreover, a prototypical implementation is described that supports checking a basic version of decision soundness. The decision soundness notions were also empirically evaluated on models from participants of an online course on process and decision modeling as well as from a process management project of a large insurance company. The evaluation demonstrates that violations of decision soundness indeed occur and can be detected with our approach.
Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation
(2019)
Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed:
• Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases?
• How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations?
• How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization?
To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained.
Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum.
Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments.
Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale.
Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales.
Light-switchable proteins are being used increasingly to understand and manipulate complex molecular systems. The success of this approach has fueled the development of tailored photo-switchable proteins, to enable targeted molecular events to be studied using light. The development of novel photo-switchable tools has to date largely relied on rational design. Complementing this approach with directed evolution would be expected to facilitate these efforts. Directed evolution, however, has been relatively infrequently used to develop photo-switchable proteins due to the challenge presented by high-throughput evaluation of switchable protein activity. This thesis describes the development of two genetic circuits that can be used to evaluate libraries of switchable proteins, enabling optimization of both the on- and off-states. A screening system is described, which permits detection of DNA-binding activity based on conditional expression of a fluorescent protein. In addition, a tunable selection system is presented, which allows for the targeted selection of protein-protein interactions of a desired affinity range. This thesis additionally describes the development and characterization of a synthetic protein that was designed to investigate chromophore reconstitution in photoactive yellow protein (PYP), a promising scaffold for engineering photo-controlled protein tools.
Interlocutors typically link their utterances to the discourse environment and enrich communication by linguistic (e.g., information packaging) and extra-linguistic (e.g., eye gaze, gestures) means to optimize information transfer. Psycholinguistic studies underline that ‒for meaning computation‒ listeners profit from linguistic and visual cues that draw their focus of attention to salient information. This dissertation is the first work that examines how linguistic compared to visual salience cues influence sentence comprehension using the very same experimental paradigms and materials, that is, German subject-before-object (SO) and object-before-subject (OS) sentences, across the two cue modalities. Linguistic salience was induced by indicating a referent as the aboutness topic. Visual salience was induced by implicit (i.e., unconscious) or explicit (i.e., shared) manipulations of listeners’ attention to a depicted referent.
In Study 1, a selective, facilitative impact of linguistic salience on the context-sensitive OS word order was found using offline comprehensibility judgments. More precisely, during online sentence processing, this impact was characterized by a reduced sentence-initial Late positivity which reflects reduced processing costs for updating the current mental representation of discourse. This facilitative impact of linguistic salience was not replicated by means of an implicit visual cue (Study 2) shown to modulate word order preferences during sentence production. However, a gaze shift to a depicted referent as an indicator of shared attention eased sentence-initial processing similar to linguistic salience as revealed by reduced reading times (Study 3). Yet, this cue did not modulate the strong subject-antecedent preference during later pronoun resolution like linguistic salience. Taken together, these findings suggest a significant impact of linguistic and visual salience cues on sentence comprehension, which substantiates that both the information delivered via language and via the visual environment is integrated into the mental representation of the discourse; but, the way how salience is induced is crucial to its impact.