598 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Methods for large-scale genome-wide association studies

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    Genome-wide association studies (GWAS) have led to the identification of thousands of associations between genetic polymorphisms and complex traits or diseases, facilitating several downstream applications such as genetic risk prediction and drug target prioritisation. Biobanks containing extensive genetic and phenotypic data continue to grow, creating new opportunities for the study of complex traits, such as the analysis of rare genomic variation across multiple populations. These opportunities are coupled with computational challenges, creating the need for the development of novel methodology. This thesis develops computational tools to facilitate large-scale association studies of rare and common variation. First, we develop methods to improve the analysis of ultra-rare variants, leveraging the sharing of identical-by-descent (IBD) genomic regions within large biobanks. We compare ∼ 400k genotyped UK Biobank (UKBB) samples with 50k exome-sequenced samples and devise a score that quantifies the extent to which a genotyped individual shares IBD segments with carriers of rare loss-of-function mutations. Our approach detects several associations and replicates 11/14 loci of a pilot exome sequencing study. Second, we develop a linear mixed model framework, FMA, that builds on previous techniques and is suitable for scalable and robust association testing. We benchmark FMA and several state-of-the-art approaches using synthetic and UKBB data, evaluating computational performance, statistical power, and robustness to known confounders, such as cryptic relatedness and population stratification. Finally, we integrate FMA with recently developed methods for genealogical analysis of complex traits, enabling it to perform scalable genealogy-based estimation of narrow-sense heritability and association

    Statistical Methods in the Era of Large Astronomical Surveys

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    Statistical methods play a crucial role in modern astronomical research. The development and understanding of these methods will be of fundamental importance to future work on large astronomical surveys. In this thesis I showcase three different statistical approaches to survey data. I first apply a semi-supervised dimensionality reduction technique to cluster similar high resolution spectra from the GALAH survey to identify 54 candidate extremely metal-poor stars. The approach shows promising potential for implementation in future large-scale stellar spectroscopic surveys. Next, I employ a method to classify sources in the Gaia survey as stars, galaxies or quasars, making use of additional infrared photometry from CatWISE2020 and discussing the importance of applying adjusted priors to probabilistic classification. Lastly, I utilise a method to estimate the rotational parameters of star clusters in Gaia, with an application to open clusters. This is done by considering the rotation of a cluster as a 3D solid body, and finding the best fitting parameters by sampling constructed likelihood functions. The methods developed in this thesis underscore the significant contributions statistical methodologies make to astronomy, and illustrate how the development and application of statistical methods will be essential for extracting meaningful insights from future large scale astronomical surveys

    Functional connectivity and dendritic integration of feedback in visual cortex

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    A fundamental question in neuroscience is how different brain regions communicate with each other. Sensory processing engages distributed circuits across many brain areas and involves information flow in the feedforward and feedback direction. While feedforward processing is conceptually well understood, feedback processing has remained mysterious. Cortico-cortical feedback axons are enriched in layer 1, where they form synapses with the apical dendrites of pyramidal neurons. The organization and dendritic integration of information conveyed by these axons, however, are unknown. This thesis describes my efforts to link the circuit-level and dendritic-level organization of cortico-cortical feedback in the mouse visual system. First, using cellular resolution all-optical interrogation across cortical areas, I characterized the functional connectivity between the lateromedial higher visual area (LM) and primary visual cortex (V1). Feedback influence had both facilitating and suppressive effects on visually-evoked activity in V1 neurons, and was spatially organized: retinotopically aligned feedback was relatively more suppressive, while retinotopically offset feedback was relatively more facilitating. Second, to examine how feedback inputs are integrated in apical dendrites, I optogenetically stimulated presynaptic neurons in LM while using 2-photon calcium imaging to map feedback-recipient spines in the apical tufts of layer 5 neurons in V1. Activation of a single feedback-providing input was sufficient to boost calcium signals and recruit branch-specific local events in the recipient dendrite, suggesting that feedback can engage dendritic nonlinearities directly. Finally, I measured the recruitment of apical dendrites during visual stimulus processing. Surround visual stimuli, which should recruit relatively more facilitating feedback, drove local calcium events in apical tuft branches. Moreover, global dendritic event size was not purely determined by somatic activity but modulated by visual stimuli and behavioural state, in a manner consistent with the spatial organization of feedback. In summary, these results point toward a possible involvement of active dendritic processing in the integration of feedback signals. Active dendrites could thus provide a biophysical substrate for the integration of essential top-down information streams, including contextual or predictive processing

    Modelling Perception of Large-Scale Thematic Structure in Music

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    Large-scale thematic structure—the organisation of material within a musical composition—holds an important position in the Western classical music tradition and has subsequently been incorporated into many influential models of music cognition. Whether, and if so, how, these structures may be perceived provides an interesting psychological problem, combining many aspects of memory, pattern recognition, and similarity judgement. However, strong experimental evidence supporting the perception of large-scale thematic structures remains limited, often arising from difficulties in measuring and disrupting their perception. To provide a basis for experimental research, this thesis develops a probabilistic computational model that characterises the possible cognitive processes underlying the perception of thematic structure. This modelling is founded on the hypothesis that thematic structures are perceptible through the statistical regularities they form, arising from the repetition and learning of material. Through the formalisation of this hypothesis, features were generated characterising compositions’ intra-opus predictability, stylistic predictability, and the amounts of repetition and variation of identified thematic material in both pitch and rhythmic domains. A series of behavioural experiments examined the ability of these modelled features to predict participant responses to important indicators of thematic structure. Namely, similarity between thematic elements, identification of large-scale repetitions, perceived structural unity, sensitivity to thematic continuation, and large-scale ordering. Taken together, the results of these experiments provide converging evidence that the perception of large-scale thematic structures can be accounted for by the dynamic learning of statistical regularities within musical compositions

    Cosmology with the Laser Interferometer Space Antenna

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    The Laser Interferometer Space Antenna (LISA) has two scientific objectives of cosmological focus: to probe the expansion rate of the universe, and to understand stochastic gravitational-wave backgrounds and their implications for early universe and particle physics, from the MeV to the Planck scale. However, the range of potential cosmological applications of gravitational-wave observations extends well beyond these two objectives. This publication presents a summary of the state of the art in LISA cosmology, theory and methods, and identifies new opportunities to use gravitational-wave observations by LISA to probe the universe

    Optimization for Deep Learning Systems Applied to Computer Vision

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    149 p.Since the DL revolution and especially over the last years (2010-2022), DNNs have become an essentialpart of the CV field, and they are present in all its sub-fields (video-surveillance, industrialmanufacturing, autonomous driving, ...) and in almost every new state-of-the-art application that isdeveloped. However, DNNs are very complex and the architecture needs to be carefully selected andadapted in order to maximize its efficiency. In many cases, networks are not specifically designed for theconsidered use case, they are simply recycled from other applications and slightly adapted, without takinginto account the particularities of the use case or the interaction with the rest of the system components,which usually results in a performance drop.This research work aims at providing knowledge and tools for the optimization of systems based on DeepLearning applied to different real use cases within the field of Computer Vision, in order to maximizetheir effectiveness and efficiency

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing

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    Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST

    AN ADAPTIVE MULTIPLE-OBJECT TRACKING ARCHITECTURE FOR LONG-DURATION VIDEOS WITH VARIABLE TARGET DENSITY

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    Multiple-Object Tracking (MOT) methods are used to detect targets in individual video frames, e.g., vehicles, people, and other objects, and then record each unique target’s path over time. Current state-of-the-art approaches are extremely complex because most rely on extracting and comparing visual features at every frame to track each object. These approaches are geared toward high-difficulty-tracking scenarios, e.g., crowded airports, and require expensive dedicated hardware, e.g., Graphics Processing Units. In hardware-constrained applications, researchers are turning to older, less complex MOT methods, which reveals a serious scalability issue within the state-of-the-art. Crowded environments are a niche application for MOT, i.e., there are far more residential areas than there are airports. Given complex approaches are not required for low-difficulty-tracking scenarios, i.e., video showing mainly isolated targets, there is an opportunity to utilize more efficient MOT methods for these environments. Nevertheless, little recent research has focused on developing more efficient MOT methods. This thesis describes a novel MOT method, ClusterTracker, that is built to handle variable-difficulty-tracking environments an order of magnitude faster than the state-of-the-art. It achieves this by avoiding visual features and using quadratic-complexity algorithms instead of the cubic-complexity algorithms found in other trackers. ClusterTracker performs spatial clustering on object detections from short frame sequences, treats clusters as tracklets, and then connects successive tracklets with high bounding-box overlap to form tracks. With recorded video, parallel processing can be applied to several steps of ClusterTracker. This thesis evaluates ClusterTracker’s baseline performance on several benchmark datasets, describes its intended operating environments, and identifies its weaknesses. Subsequent modifications patch these weaknesses while also addressing the scalability concerns of more complex MOT methods. The modified architecture uses clustering feedback to separate isolated targets from non-isolated targets, re-processing the latter with a more complex MOT method. Results show ClusterTracker is uniquely suited for such an approach and allows complex MOT methods to be applied to the challenging tracking situations for which they are intended
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