1,476 research outputs found

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Neuromodulatory effects on early visual signal processing

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    Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brain’s ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells. In summary, I first present several experimental and computational methods that allow to study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide

    Multivariate Modeling of Quasar Variability with an Attention-based Variational Autoencoder

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    This thesis applied HeTVAE, an attention-based VAE neural network capable of multivariate modeling of time series, to a dataset of several thousand multi-band AGN light curves from ZTF and was one of the first attempts to use a neural network to harness the stochastic light curves in their multivariate form. Whereas standard models of AGN variability make prior assumptions, HeTVAE uses no prior knowledge and is able to learn the data distribution in a regularized latent space, reading semantic information via its up-to-date self-supervised training regimen. We have successfully created a dataset class for preprocessing the irregular multivariate time series and in order to interface with the quasi-off-the-shelf network more conveniently. Also, we have trained several different model iterations using one, two or all three of the filter dimensions from ZTF on Durham’s NCC compute cluster, while configuring useful hyper parameter choices to work robustly for the astronomical dataset. In the network's training, we employed the Adam optimizer with a reduce-on-plateau learning rate schedule and a KL-annealing schedule optimize the VAE’s performance. In experimenting, we show how the VAE has learned the data distribution of the light curves by generating simulated light curves and its interpretability by visualizing attention scores and by visualizing the way the light curves are distributed along the continuous latent space using PCA. We show it orders the light curves across a smooth gradient from those those that have both low amplitude short-term variation and high amplitude long-term variation, to those with little variability, to those with both short-term and long-term high-amplitude variation in the condensed space. We also use PCA to display a potential filtering algorithm that enables parsing through large datasets in an intuitive way and present some of the pitfalls of algorithmic bias in anomaly detection. Finally, we fine-tuned the structurally correct but imprecise multivariate interpolations output by HeTVAE to three objects to show how they could improve constraints on time-delay estimates in the context of reverberation mapping for the relatively poor-cadenced ZTF data. In short, HeTVAE's use cases are ranged and it is a step in the right direction as far as being able to help organize and process the millions of AGN light curves incoming from Vera C. Rubin Observatory’s Legacy Survey of Space and Time in their full 6 optical broadband filter multivariate form

    Addiction in context

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    The dissertation provides a comprehensive exploration of the interplay between social and cultural factors in substance use, specifically focusing on alcohol use disorder (AUD) and cannabis use disorder (CUD). It begins by introducing the concept of social plasticity, which posits that adolescents' susceptibility to AUD is influenced by their heightened sensitivity to their social environment, but this sensitivity increases the potential for recovery in the transition to adulthood.A series of studies delves into how social cues impact alcohol craving and consumption. One study using functional magnetic resonance imaging (fMRI) investigated social alcohol cue reactivity and its relationship to social drinking behavior, revealing increased craving but no significant change in brain activity in response to alcohol cues. Another fMRI study compared social processes in alcohol cue reactivity between adults and adolescents, showing age-related differences in how social attunement affects drinking behavior. Shifting focus to cannabis, this dissertation discusses how cultural factors, including norms, legal policies, and attitudes, influence cannabis use and processes underlying CUD. The research presented examined various facets of cannabis use, including how cannabinoid concentrations in hair correlate with self-reported use, the effects of cannabis and cigarette co-use on brain reactivity, and cross-cultural differences in CUD between Amsterdam and Texas. Furthermore, the evidence for the relationship between cannabis use, CUD, and mood disorders is reviewed, suggesting a bidirectional relationship, with cannabis use potentially preceding the onset of bipolar disorder and contributing to the development and worse prognosis of mood disorders and mood disorders leading to more cannabis use

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation

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    The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision. In this paper, we develop an Uncertainty-Guided Spatial Pruning (UGSP) architecture to skip redundant computation for efficient frame interpolation dynamically. Specifically, pixels with low uncertainty indicate easy regions, where the calculation can be reduced without bringing undesirable visual results. Therefore, we utilize uncertainty-generated mask labels to guide our UGSP in properly locating the easy region. Furthermore, we propose a self-contrast training strategy that leverages an auxiliary non-pruning branch to improve the performance of our UGSP. Extensive experiments show that UGSP maintains performance but reduces FLOPs by 34%/52%/30% compared to baseline without pruning on Vimeo90K/UCF101/MiddleBury datasets. In addition, our method achieves state-of-the-art performance with lower FLOPs on multiple benchmarks.Comment: ACM Multimedia 202

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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