10,471 research outputs found

    The first stars: formation, properties, and impact

    Full text link
    The first generation of stars, often called Population III (or Pop III), form from metal-free primordial gas at redshifts 30 and below. They dominate the cosmic star formation history until redshifts 15 to 20, at which point the formation of metal-enriched Pop II stars takes over. We review current theoretical models for the formation, properties and impact of Pop III stars, and discuss existing and future observational constraints. Key takeaways from this review include the following: (1) Primordial gas is highly susceptible to fragmentation and Pop III stars form as members of small clusters with a logarithmically flat mass function. (2) Feedback from massive Pop III stars plays a central role in regulating subsequent star formation, but major uncertainties remain regarding its immediate impact. (3) In extreme conditions, supermassive Pop III stars can form, reaching masses of several 10^5 Msun. Their remnants may be the seeds of the supermassive black holes observed in high-redshift quasars. (4) Direct observations of Pop III stars in the early Universe remain extremely challenging. Indirect constraints from the global 21cm signal or gravitational waves are more promising. (5) Stellar archeological surveys allow us to constrain both the low-mass and the high-mass ends of the Pop III mass distribution. Observations suggest that most massive Pop III stars end their lives as core-collapse supernovae rather than as pair-instability supernovae.Comment: To appear in Annual Reviews of Astronomy and Astrophysics (75 pages, 14 figures, 600 references

    Colour technologies for content production and distribution of broadcast content

    Get PDF
    The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model

    Neuroanatomical and gene expression features of the rabbit accessory olfactory system. Implications of pheromone communication in reproductive behaviour and animal physiology

    Get PDF
    Mainly driven by the vomeronasal system (VNS), pheromone communication is involved in many species-specific fundamental innate socio-sexual behaviors such as mating and fighting, which are essential for animal reproduction and survival. Rabbits are a unique model for studying chemocommunication due to the discovery of the rabbit mammary pheromone, but paradoxically there has been a lack of knowledge regarding its VNS pathway. In this work, we aim at filling this gap by approaching the system from an integrative point of view, providing extensive anatomical and genomic data of the rabbit VNS, as well as pheromone-mediated reproductive and behavioural studies. Our results build strong foundation for further translational studies which aim at implementing the use of pheromones to improve animal production and welfare

    Learning disentangled speech representations

    Get PDF
    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Statistical-dynamical analyses and modelling of multi-scale ocean variability

    Get PDF
    This thesis aims to provide a comprehensive analysis of multi-scale oceanic variabilities using various statistical and dynamical tools and explore the data-driven methods for correct statistical emulation of the oceans. We considered the classical, wind-driven, double-gyre ocean circulation model in quasi-geostrophic approximation and obtained its eddy-resolving solutions in terms of potential vorticity anomaly and geostrophic streamfunctions. The reference solutions possess two asymmetric gyres of opposite circulations and a strong meandering eastward jet separating them with rich eddy activities around it, such as the Gulf Stream in the North Atlantic and Kuroshio in the North Pacific. This thesis is divided into two parts. The first part discusses a novel scale-separation method based on the local spatial correlations, called correlation-based decomposition (CBD), and provides a comprehensive analysis of mesoscale eddy forcing. In particular, we analyse the instantaneous and time-lagged interactions between the diagnosed eddy forcing and the evolving large-scale PVA using the novel `product integral' characteristics. The product integral time series uncover robust causality between two drastically different yet interacting flow quantities, termed `eddy backscatter'. We also show data-driven augmentation of non-eddy-resolving ocean models by feeding them the eddy fields to restore the missing eddy-driven features, such as the merging western boundary currents, their eastward extension and low-frequency variabilities of gyres. In the second part, we present a systematic inter-comparison of Linear Regression (LR), stochastic and deep-learning methods to build low-cost reduced-order statistical emulators of the oceans. We obtain the forecasts on seasonal and centennial timescales and assess them for their skill, cost and complexity. We found that the multi-level linear stochastic model performs the best, followed by the ``hybrid stochastically-augmented deep learning models''. The superiority of these methods underscores the importance of incorporating core dynamics, memory effects and model errors for robust emulation of multi-scale dynamical systems, such as the oceans.Open Acces

    Review of Methodologies to Assess Bridge Safety During and After Floods

    Get PDF
    This report summarizes a review of technologies used to monitor bridge scour with an emphasis on techniques appropriate for testing during and immediately after design flood conditions. The goal of this study is to identify potential technologies and strategies for Illinois Department of Transportation that may be used to enhance the reliability of bridge safety monitoring during floods from local to state levels. The research team conducted a literature review of technologies that have been explored by state departments of transportation (DOTs) and national agencies as well as state-of-the-art technologies that have not been extensively employed by DOTs. This review included informational interviews with representatives from DOTs and relevant industry organizations. Recommendations include considering (1) acquisition of tethered kneeboard or surf ski-mounted single-beam sonars for rapid deployment by local agencies, (2) acquisition of remote-controlled vessels mounted with single-beam and side-scan sonars for statewide deployment, (3) development of large-scale particle image velocimetry systems using remote-controlled drones for stream velocity and direction measurement during floods, (4) physical modeling to develop Illinois-specific hydrodynamic loading coefficients for Illinois bridges during flood conditions, and (5) development of holistic risk-based bridge assessment tools that incorporate structural, geotechnical, hydraulic, and scour measurements to provide rapid feedback for bridge closure decisions.IDOT-R27-SP50Ope

    Computing Interpretable Representations of Cell Morphodynamics

    Get PDF
    Shape changes (morphodynamics) are one of the principal ways cells interact with their environments and perform key intrinsic behaviours like division. These dynamics arise from a myriad of complex signalling pathways that often organise with emergent simplicity to carry out critical functions including predation, collaboration and migration. A powerful method for analysis can therefore be to quantify this emergent structure, bypassing the low-level complexity. Enormous image datasets are now available to mine. However, it can be difficult to uncover interpretable representations of the global organisation of these heterogeneous dynamic processes. Here, such representations were developed for interpreting morphodynamics in two key areas: mode of action (MoA) comparison for drug discovery (developed using the economically devastating Asian soybean rust crop pathogen) and 3D migration of immune system T cells through extracellular matrices (ECMs). For MoA comparison, population development over a 2D space of shapes (morphospace) was described using two models with condition-dependent parameters: a top-down model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. A variety of landscapes were discovered, describing phenotype transitions during growth, and possible perturbations in the tip growth machinery that cause this variation were identified. For interpreting T cell migration, a new 3D shape descriptor that incorporates key polarisation information was developed, revealing low-dimensionality of shape, and the distinct morphodynamics of run-and-stop modes that emerge at minute timescales were mapped. Periodically oscillating morphodynamics that include retrograde deformation flows were found to underlie active translocation (run mode). Overall, it was found that highly interpretable representations could be uncovered while still leveraging the enormous discovery power of deep learning algorithms. The results show that whole-cell morphodynamics can be a convenient and powerful place to search for structure, with potentially life-saving applications in medicine and biocide discovery as well as immunotherapeutics.Open Acces

    COVID-19 Outbreak and Beyond

    Get PDF
    The COVID-19 pandemic drastically changed our lifestyle when, on 30 January 2020, the World Health Organization declared the coronavirus disease outbreak a public health emergency of international concern. Since then, many governments have introduced unprecedented containment measures, hoping to slow the spread of the virus. International research suggests that both the pandemic and the related protective measures, such as lockdown, curfews, and social distancing, are having a profound impact on the mental health of the population. Among the most commonly observed psychological effects, there are high levels of stress, anxiety, depression, and post-traumatic symptoms, along with boredom and frustration. At the same time, the behavioral response of the population is of paramount importance to successfully contain the outbreak, creating a vicious circle in which the psychological distress impacts the willingness to comply with the protective measures, which, in turn, if prolonged, could exacerbate the population’s distress. This book includes: i) original studies on the worldwide psychological and behavioral impact of COVID-19 on targeted individuals (e.g., parents, social workers, patients affected by physical and mental disorders); ii) studies exploring the effect of COVID-19 using advanced statistical and methodological techniques (e.g., machine learning technologies); iii) research on practical applications that could help identify persons at risk, mitigate the negative effects of this situation, and offer insights to policymakers to manage the pandemic are also highly welcomed

    Using Genetic Approaches to Study Local Adaptation and Reproductive Success in Snow Buntings (Plectrophenax nivalis)

    Get PDF
    Genetic analyses can facilitate effective and timely conservation and management actions. Arctic-migratory species in particular are in need of conservation genetic insights as they are experiencing substantial population pressures due to the impact of climate change (and other anthropogenic effects) on processes that affect their survival and reproduction. Therefore, identification of genetic mechanisms driving population divergence and variation in reproductive fitness in such species is critical. The goal of this thesis is to examine reproductive isolation among breeding populations of an Arctic-migratory passerine, the snow bunting (Plectrophenax nivalis) and determine factors that drive local adaptation and variation in reproductive success in this species. Using neutral and functional genetic markers, I show substantial population isolation among six globally distributed snow bunting breeding populations that is primarily driven by high levels of genetic drift and stabilizing selection, but with divergent selection acting at key functional loci. While there were no significant predictors of within-pair reproductive success, I identify key male quality traits such as body mass, testosterone levels, and breast plumage as important drivers of extra-pair reproductive success, which ultimately contribute to realized fitness in snow buntings. My work highlights the population-specific responses that reinforce the importance of genetic variability of individuals and their subsequent reproductive outcomes. The information contained in this thesis, combined with the methodological approaches, will help direct conservation efforts at the among- and within-population levels to maintain genetic diversity and adaptive potential as rapid environmental change continues to threaten Arctic-migratory species
    corecore