323 research outputs found

    Medical Image Analysis using Deep Relational Learning

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    In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778

    Advanced gasification applications of direct carbon dioxide utilisation in integrated biomass energy cycles

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    International agreements seek to limit climate warming to no more than 2℃. For this goal to be achieved, drastic reductions in CO2 emissions from fossil fuels must be realised in very short timelines. In fact, most climate modelling predictions indicate CO2 will need to be removed from the atmosphere if the worst effects of climate change are to be avoided. Renewable biomass energy and biomass energy with carbon capture and storage (BECCS) will feature prominently in this substantial decarbonisation regime. Despite this technical forecast, BECCS technologies are unproven at scale. Innovative carbon dioxide utilisation (CDU) strategies are posited as a method for improvement of biomass energy system performance. Partially recycling CO2-rich exhaust gases from a syngas fuelled internal combustion engine to a biomass gasifier has the capability to realise a new method for direct carbon dioxide utilisation (CDU) within a bioenergy system. Simulation of an integrated, airblown biomass gasification power cycle was used to study thermodynamic aspects of this emerging CDU technology. Analysis of the thermodynamic system model at varying gasifier air ratios and exhaust recycling ratios revealed the potential for modest system improvements under limited recycling ratios. Compared to a representative base thermodynamic case with overall system efficiency of 28.14%, employing exhaust gas recycling (EGR) enhanced gasification improved system efficiency to 29.24% and reduced the specific emissions by 46.2 g-CO2/kWh. Although emissions from biomass power cycles can ultimately be considered CO2-neutral over time, this reduction in specific emissions from the cycle can minimise the “carbon debt” effect incurred during the initial deployment of biomass power sources. Further investigation of the EGR-enhanced gasification system revealed the important coupling between gasification equilibrium temperature and exhaust gas temperature through the syngas lower heating value (LHV). Major limitations to the thermodynamic conditions of EGR-enhanced gasification as a CDU strategy result from the increased dilution of the syngas fuel by N2 and CO2 at high recycling ratios, restricting equilibrium temperatures and reducing gasification efficiency. N2 dilution in the system reduces the efficiency by up to 2.5% depending on the gasifier air ratio, causing a corresponding increase in specific CO2 emissions. Thermodynamic modelling indicates pre-combustion N2 removal from an EGR gasification system could decrease specific CO2 emissions by 9.73%, emitting 118.5 g/kWh less CO2 than the basic system. A similar method for improving the efficiency of oxyfuel gasification biomass energy with carbon capture and storage (BECCS) cycles using carbon dioxide recycled from exhaust gases is described and modelled. Thermodynamic simulations show this process can increase the indicated efficiency of a representative cycle by 10.3% in part by reducing the oxygen requirements for the gasification reaction. Exhaust recycling is also shown to have a practical limit beyond which the syngas fuel becomes highly diluted. This diluted syngas results in low combustion and exhaust temperatures which, in turn, negatively influence the gasification process during exhaust recycling. For the system presented here, CO2- enhanced gasification is thermodynamically limited to equivalence ratios above λ = 0.13 and equilibrium temperatures above 576°C. This thermodynamically limited case produced an indicated system efficiency of 26.9% based on supplied biomass lower heating value (LHV). Further simulations using both ideal cycles and detailed numerical models highlight the influence of several operational settings on the thermodynamic conditions of the gasification process. Principally, the coupling between exhaust temperatures, allothermal heat, and syngas quality are shown to govern the performance of the gasification reactions. Although these simulated equilibrium calculations revealed the fundamental thermodynamic benefit of EGR-gasification cycles, variability in typical gasification processes often produces syngas compositions that differ from chemical equilibrium. An examination of the evolution of syngas from a biomass sample during gasification was needed to assess how these differences occur. Particularly, experimental confirmation that the key CO2 to CO conversion process is achievable under mild temperature conditions was required to verify the feasibility of the novel process described in this work. Results of these experimental investigations have shown the CDU conversion of CO2 into CO under process conditions similar to earlier thermodynamic modelling. Compared to pyrolysis of soda lignin as a representative biomass sample, CO2 gasification produced roughly 69% more CO while consuming 1.1 mmol CO2/g biomass. Although this conversion process performs poorly under the experimental conditions, it does illustrate the viability of the proposed technology. Significant improvement in CO2 conversion and CO production is noted as reaction temperature increases, particularly above 700℃. Additional features of lignin pyrolysis are also illustrated that suggest incomplete conversion of pyrolysis products contribute to a product syngas with higher CH4 content than expected under equilibrium conditions

    Science and Innovations for Food Systems Transformation

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    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems

    LOOKING INTO ACTORS, OBJECTS AND THEIR INTERACTIONS FOR VIDEO UNDERSTANDING

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    Automatic video understanding is critical for enabling new applications in video surveillance, augmented reality, and beyond. Powered by deep networks that learn holistic representations of video clips, and large-scale annotated datasets, modern systems are capable of accurately recognizing hundreds of human activity classes. However, their performance significantly degrades as the number of actors in the scene or the complexity of the activities increases. Therefore, most of the research thus far has focused on videos that are short and/or contain a few activities performed only by adults. Furthermore, most current systems require expensive, spatio-temporal annotations for training. These limitations prevent the deployment of such systems in real-life applications, such as detecting activities of people and vehicles in an extended surveillance videos. To address these limitations, this thesis focuses on developing data-driven, compositional, region-based video understanding models motivated by the observation that actors, objects and their spatio-temporal interactions are the building blocks of activities and the main content of video descriptions provided by humans. This thesis makes three main contributions. First, we propose a novel Graph Neural Network for representation learning on heterogeneous graphs that encode spatio-temporal interactions between actor and object regions in videos. This model can learn context-aware representations for detected actors and objects, which we leverage for detecting complex activities. Second, we propose an attention-based deep conditional generative model of sentences, whose latent variables correspond to alignments between words in textual descriptions of videos and object regions. Building upon the framework of Conditional Variational Autoencoders, we train this model using only textual descriptions without bounding box annotations, and leverage its latent variables for localizing the actors and objects that are mentioned in generated or ground-truth descriptions of videos. Finally, we propose an actor-centric framework for real-time activity detection in videos that are extended both in space and time. Our framework leverages object detections and tracking to generate actor-centric tubelets, capturing all relevant spatio-temporal context for a single actor, and detects activities per tubelet based on contextual region embeddings. The models described have demonstrably improved the ability to temporally detect activities, as well as ground words in visual inputs

    Deep Neural Networks and Tabular Data: Inference, Generation, and Explainability

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    Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods

    Neural Techniques for German Dependency Parsing

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    Syntactic parsing is the task of analyzing the structure of a sentence based on some predefined formal assumption. It is a key component in many natural language processing (NLP) pipelines and is of great benefit for natural language understanding (NLU) tasks such as information retrieval or sentiment analysis. Despite achieving very high results with neural network techniques, most syntactic parsing research pays attention to only a few prominent languages (such as English or Chinese) or language-agnostic settings. Thus, we still lack studies that focus on just one language and design specific parsing strategies for that language with regards to its linguistic properties. In this thesis, we take German as the language of interest and develop more accurate methods for German dependency parsing by combining state-of-the-art neural network methods with techniques that address the specific challenges posed by the language-specific properties of German. Compared to English, German has richer morphology, semi-free word order, and case syncretism. It is the combination of those characteristics that makes parsing German an interesting and challenging task. Because syntactic parsing is a task that requires many levels of language understanding, we propose to study and improve the knowledge of parsing models at each level in order to improve syntactic parsing for German. These levels are: (sub)word level, syntactic level, semantic level, and sentence level. At the (sub)word level, we look into a surge in out-of-vocabulary words in German data caused by compounding. We propose a new type of embeddings for compounds that is a compositional model of the embeddings of individual components. Our experiments show that character-based embeddings are superior to word and compound embeddings in dependency parsing, and compound embeddings only outperform word embeddings when the part-of-speech (POS) information is unavailable. Thus, we conclude that it is the morpho-syntactic information of unknown compounds, not the semantic one, that is crucial for parsing German. At the syntax level, we investigate challenges for local grammatical function labeler that are caused by case syncretism. In detail, we augment the grammatical function labeling component in a neural dependency parser that labels each head-dependent pair independently with a new labeler that includes a decision history, using Long Short-Term Memory networks (LSTMs). All our proposed models significantly outperformed the baseline on three languages: English, German and Czech. However, the impact of the new models is not the same for all languages: the improvement for English is smaller than for the non-configurational languages (German and Czech). Our analysis suggests that the success of the history-based models is not due to better handling of long dependencies but that they are better in dealing with the uncertainty in head direction. We study the interaction of syntactic parsing with the semantic level via the problem of PP attachment disambiguation. Our motivation is to provide a realistic evaluation of the task where gold information is not available and compare the results of disambiguation systems against the output of a strong neural parser. To our best knowledge, this is the first time that PP attachment disambiguation is evaluated and compared against neural dependency parsing on predicted information. In addition, we present a novel approach for PP attachment disambiguation that uses biaffine attention and utilizes pre-trained contextualized word embeddings as semantic knowledge. Our end-to-end system outperformed the previous pipeline approach on German by a large margin simply by avoiding error propagation caused by predicted information. In the end, we show that parsing systems (with the same semantic knowledge) are in general superior to systems specialized for PP attachment disambiguation. Lastly, we improve dependency parsing at the sentence level using reranking techniques. So far, previous work on neural reranking has been evaluated on English and Chinese only, both languages with a configurational word order and poor morphology. We re-assess the potential of successful neural reranking models from the literature on English and on two morphologically rich(er) languages, German and Czech. In addition, we introduce a new variation of a discriminative reranker based on graph convolutional networks (GCNs). Our proposed reranker not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech. Our analysis points out that the failure is due to the lower quality of the k-best lists, where the gold tree ratio and the diversity of the list play an important role
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