146 research outputs found

    Advances in scalable learning and sampling of unnormalised models

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    We study probabilistic models that are known incompletely, up to an intractable normalising constant. To reap the full benefit of such models, two tasks must be solved: learning and sampling. These two tasks have been subject to decades of research, and yet significant challenges still persist. Traditional approaches often suffer from poor scalability with respect to dimensionality and model-complexity, generally rendering them inapplicable to models parameterised by deep neural networks. In this thesis, we contribute a new set of methods for addressing this scalability problem. We first explore the problem of learning unnormalised models. Our investigation begins with a well-known learning principle, Noise-contrastive Estimation, whose underlying mechanism is that of density-ratio estimation. By examining why existing density-ratio estimators scale poorly, we identify a new framework, telescoping density-ratio estimation (TRE), that can learn ratios between highly dissimilar densities in high-dimensional spaces. Our experiments demonstrate that TRE not only yields substantial improvements for the learning of deep unnormalised models, but can do the same for a broader set of tasks including mutual information estimation and representation learning. Subsequently, we explore the problem of sampling unnormalised models. A large literature on Markov chain Monte Carlo (MCMC) can be leveraged here, and in continuous domains, gradient-based samplers such as Metropolis-adjusted Langevin algorithm (MALA) and Hamiltonian Monte Carlo are excellent options. However, there has been substantially less progress in MCMC for discrete domains. To advance this subfield, we introduce several discrete Metropolis-Hastings samplers that are conceptually inspired by MALA, and demonstrate their strong empirical performance across a range of challenging sampling tasks

    Raising awareness of frontotemporal dementia among Nigerian immigrant communities in the UK through storytelling : an autoethnography thesis using an art-based research approach

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    Even though medical research on dementia is wide and has long roots internationally, the awareness of the condition varies among different populations. People in ethnic minority communities, for example, may view dementia issues through a traditional or cultural lens. In these communities, diagnosis is more likely to occur at an advanced stage of the disease, and there is a low take-up of mainstream dementia services. This study explores new ways of raising awareness of dementia in such groups, in this case, among Nigerian immigrants in the UK. This group is understudied, even though they represent the largest number of people of African origin in the UK. The research questions set for the research are: (1) How can the awareness of frontotemporal dementia (FTD) be raised using an art-based approach? (2) What autoethnographic process preceded the development of the play ‘My Name is Beatrice’? My research approach is art-based, and the tool I used for my data interpretation is ethnodrama, which is a written transformation and adaptation of research data into a dramatic play script. I aim to present an aesthetically sound, intellectually rich, and emotionally evocative play that can capture my audience’s attention and leave them with enduring memories. The analysis focused on both the process that preceded the writing of a play about someone with dementia in a Nigerian immigrant community and the play itself. The data comprised two sets: my previous works and desktop research. These were analysed for their contribution to the process preceding the playwriting. The art-based part of this thesis included the play ‘My Name is Beatrice’ and its critical commentary. This research explores and discusses the efficacy of using drama as an educational tool to raise awareness of a disease. Art has an instantaneous effect on an audience because it can capture their attention and leave enduring memories. In addition, my research shows evidence of the complex needs of people living with dementia in Black Minority Ethnic (BME) communities that can be highlighted through art-based research and methods in a meaningful way. This art-based research has shown how ethnodrama can facilitate engagement and action from the researcher, participant, and audience. The aim is that this research would enlighten BME communities about FTD, the importance of early diagnosis and holistic approaches to care. The research will be a microcosm for further work that will enable educators and healthcare workers to share similar information within larger BME communities in the United Kingdom, other developed countries, and Africa. It will also enable educators and medical practitioners to understand the needs of BME communities and other similar groups worldwide

    Numerical modelling for the hydrothermal activity & habitability of Mars

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    Modern space and planetary explorations are enthusiastically searching for extraterrestrial biosignatures, and even intelligence in our cosmic neighbourhood. Mars is the epicentre of planetary research and astrobiology, as during ancient geological periods, the Red Planet should have had a thicker atmosphere, and exhibits evidence for ancient aqueous, volcanic and hydrothermal activity. Such physical processes that persist on a planetary body through geological time increase the probability of the emergence and evolution of antediluvian microbial species. However, present-day Mars is a cold and arid desert. So, could the Red Planet host evidence of extinct or/and even extant microbial life? To contribute towards deciphering this mystery, this PhD research focuses on determining the thermodynamic and hydrological evolution, and subsequent habitability of ancient hydrous environments on Mars. Martian habitability, especially during the planet’s ancient geological history, has not been decisively established yet. Moreover, quantitative analyses and models for the ancient or present bioenergetic potential on Mars are scarce. Water – rock interactions enduring in longlived hydrothermal settings on Earth yield appreciable quantities of chemical nutrients that support microbial species under hydrothermal conditions. Through this perspective, the habitability of simulated Martian hydrothermal systems deserves to be computed and analysed. This PhD research explores simulated volcanogenic and impact-induced hydrodynamics on Mars, and the astrobiological potential of such ancient or more recent Martian aqueous environments via computational scenarios. High-resolution numerical simulations for the aqueous circulation and thermodynamics in a variety of putative Martian hydrothermal systems have been constructed and interpreted. Rock permeability, porosity, temperature, pressure, enthalpy, heat capacity, and thermal conductivity comprise governing physical parameters for the duration and mechanics of the hydrothermal cycle in each simulation. Therefore, the presented thermodynamic simulations explore thoroughly the evolution and duration of putative impact-induced or magmatic-induced hydrological systems on Mars from the pre-Noachian to the late Amazonian. The thermodynamic results of these models are then used as input conditions in further computations for Martian water – basaltic rock reaction pathways and their subsequent bioenergetic yield (habitability). Eventually, quantitative habitability assessments are conducted based on the energy – chemical nutrient availability and on the thermal constraints that cumulatively render these environments habitable or uninhabitable for hypothetical lithotrophic microbial species in the Martian subsurface. In parallel, NWA 8159 (shergottite) and Lafayette (nakhlite) Martian meteorite samples were examined through Scanning Electron Microscopy (SEM) analysis to identify their Martian mineralogies, and detect alteration phases – fluid compositions that have affected these basaltic rocks on Mars, or on Earth due to weathering processes after their fall. Petrological analyses provided additional insights into the geochemical composition and evolution of these Martian rocks. Furthermore, image processing on acquired SEM-BSE montage maps of the NWA 8159 and Lafayette samples revealed the porosity of these Martian rocks, and subsequently constrained and enhanced the hydromechanic and habitability models of this PhD research. The hydrothermal and habitability simulations indicate that the Martian basaltic subsurface could have supported hydrogenotrophic microbial life for periods ranging from 0.1 Myr to 3 Myr under preserved hydrothermal conditions. The modelling results additionally suggest that deeper basaltic domains (subsurface depth ≥ 1.5 km) in large impact craters (100-, 200-km diameters) or intrusive volcanic rock settings, could comprise the most promising sites for astrobiological research. The ideal habitable thermal range in which nutrients, and specifically H2, are released in appreciable amounts through ongoing water – rock reactions is from 50 °C to 121 °C. Under such hydrothermal conditions, the Martian subsurface is modelled able to support the survival and growth criteria of hydrogenotrophic life. However, aqueous circulation and geochemical reactions should endure for an average minimum period of 120 Kyr to support microbial growth, and conceivably, the microbial colonization of the Martian subsurface. The numerical simulations of this research support that cold aqueous flows and short-induration hydrological systems on Mars are unable to support the survival of potential microbial species for a period ≥ 2 Kyr. Finally, even in the most optimistic thermodynamic scenarios for Martian habitability, microbial species in the deep Martian subsurface cannot be supported for a period longer than 1 – 2 Myr, after hydrothermal activity has halted. This indicates that any potentially inhabited environments on Mars could have supported microbial life only for an average maximum period of 3 – 4 Myr. Conclusively, planetary environments beyond Earth that may have been hosting hydrothermal or aqueous activity continuously for Myr or even Gyr (i.e.: the Jovian and Kronian moons, beneath their icy crusts) comprise the most habitable extraterrestrial niches of the Solar System, and promising sites for astrobiological findings

    2016 GREAT Day Program

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    SUNY Geneseo’s Tenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1010/thumbnail.jp

    Trends and Prospects in Geotechnics

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    The Special Issue book presents some works considered innovative in the field of geotechnics and whose practical application may occur in the near future. This collection of twelve papers, in addition to their scientific merit, addresses some of the current and future challenges in geotechnics. The published papers cover a wide range of emerging topics with a specific focus on the research, design, construction, and performance of geotechnical works. These works are expected to inspire the development of geotechnics, contributing to the future construction of more resilient and sustainable geotechnical structures

    Convolutional Neural Network in Pattern Recognition

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    Since convolutional neural network (CNN) was first implemented by Yann LeCun et al. in 1989, CNN and its variants have been widely implemented to numerous topics of pattern recognition, and have been considered as the most crucial techniques in the field of artificial intelligence and computer vision. This dissertation not only demonstrates the implementation aspect of CNN, but also lays emphasis on the methodology of neural network (NN) based classifier. As known to many, one general pipeline of NN-based classifier can be recognized as three stages: pre-processing, inference by models, and post-processing. To demonstrate the importance of pre-processing techniques, this dissertation presents how to model actual problems in medical pattern recognition and image processing by introducing conceptual abstraction and fuzzification. In particular, a transformer on the basis of self-attention mechanism, namely beat-rhythm transformer, greatly benefits from correct R-peak detection results and conceptual fuzzification. Recently proposed self-attention mechanism has been proven to be the top performer in the fields of computer vision and natural language processing. In spite of the pleasant accuracy and precision it has gained, it usually consumes huge computational resources to perform self-attention. Therefore, realtime global attention network is proposed to make a better trade-off between efficiency and performance for the task of image segmentation. To illustrate more on the stage of inference, we also propose models to detect polyps via Faster R-CNN - one of the most popular CNN-based 2D detectors, as well as a 3D object detection pipeline for regressing 3D bounding boxes from LiDAR points and stereo image pairs powered by CNN. The goal for post-processing stage is to refine artifacts inferred by models. For the semantic segmentation task, the dilated continuous random field is proposed to be better fitted to CNN-based models than the widely implemented fully-connected continuous random field. Proposed approaches can be further integrated into a reinforcement learning architecture for robotics

    Memorial Issue Dedicated to Dr. Howard D. Flack: The Man behind the Flack Parameter

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    The book is dedicated to the work and achievements of Howard Flack. It combines articles which describe his own work and the advances he made in the field of crystallography, with original research articles which focus on aspects related to Howard Flack's interests
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