10,477 research outputs found

    On information captured by neural networks: connections with memorization and generalization

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    Despite the popularity and success of deep learning, there is limited understanding of when, how, and why neural networks generalize to unseen examples. Since learning can be seen as extracting information from data, we formally study information captured by neural networks during training. Specifically, we start with viewing learning in presence of noisy labels from an information-theoretic perspective and derive a learning algorithm that limits label noise information in weights. We then define a notion of unique information that an individual sample provides to the training of a deep network, shedding some light on the behavior of neural networks on examples that are atypical, ambiguous, or belong to underrepresented subpopulations. We relate example informativeness to generalization by deriving nonvacuous generalization gap bounds. Finally, by studying knowledge distillation, we highlight the important role of data and label complexity in generalization. Overall, our findings contribute to a deeper understanding of the mechanisms underlying neural network generalization.Comment: PhD thesi

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Urinary biomarkers of biofortified beef in healthy women explored by untargeted metabolomics

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    Background: The prevalence of overweight and non-communicable chronic diseases is rising all over the globe. The high consumption of energy dense foods on behalf of high nutrient-dense food leads to lower intake of essential vitamins and minerals, such as vitamins D, E, K, and selenium. These micronutrients are related with numerous human vital functions and their deficiency is positively associated with higher risk of chronic diseases and mortality. Bovine meat is an important source of several micronutrients, with higher bioavailability compared to other plant-based foods. Meat consumption is expected to increase worldwide, therefore the biofortification of bull’s feeds can be an innovative strategy to increase population’s exposure to nutrients. Metabolomics techniques are capable to explore if the supplementation will ultimately lead to a higher micronutrient’s uptake in the body. Objective: The aim of the present study was to explore the differences on urinary metabolic fingerprint of women ingesting 300g of beef a day from bulls fed concentrate supplemented with extra vitamin D, E, K, and selenium compared to the regular composite feed. Methodology: A 32 days double-blind randomized cross-over human intervention study with two intervention periods, each for 6 days, was conducted in 35 healthy women. The participants were instructed to eat 300g of grinded beef meat as raw weight per day, either from bulls fed with regular control feed or meat supplemented with vitamin D, E, K and selenium, combined with their habitual diet. Fasting urine samples were collected in the morning before and after each intervention period and were analyzed by LC-MS untargeted metabolomics. Multivariate and univariate analysis were applied do identify discriminative features between the two interventions. Results: A total of 7 and 6 metabolites for positive and negative mode, respectively, were selected as discriminative of the two interventions. Among these, markers of overall meat intake, as well as markers of animal feed, markers related with the participants diet and inflammation-related markers were identified as upregulated or downregulated for the supplemented intervention. No markers specifically related to the biofortification were observed. Conclusions: Based on our methodology, the ingestion of biofortified beef did not results in a higher level of related metabolites when comparing the two interventions. Minor changes indicate that consequences of biofortification were very small. Further research is needed to understand if a higher increase of vitamin D, E, K, and selenium on animal´s feed composite can lead to different outcomes

    Towards ultrasound full-waveform inversion in medical imaging

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    Ultrasound imaging is a front-line clinical modality with a wide range of applications. However, there are limitations to conventional methods for some medical imaging problems, including the imaging of the intact brain. The goal of this thesis is to explore and build on recent technological advances in ultrasonics and related areas such as geophysics, including the ultrasound data parallel acquisition hardware, advanced computational techniques for field modelling and for inverse problem solving. With the significant increase in the computational power now available, a particular focus will be put on exploring the potential of full-waveform inversion (FWI), a high-resolution image reconstruction technique which has shown significant success in seismic exploration, for medical imaging applications. In this thesis a range of technologies and systems have been developed in order to improve ultrasound imaging by taking advantage of these recent advances. In the first part of this thesis the application of dual frequency ultrasound for contrast enhanced imaging of neurovasculature in the mouse brain is investigated. Here we demonstrated a significant improvement in the contrast-to-tissue ratio that could be achieved by using a multi-probe, dual frequency imaging system when compared to a conventional approach using a single high frequency probe. However, without a sufficiently accurate calibration method to determine the positioning of these probes the image resolution was found to be significantly reduced. To mitigate the impact of these positioning errors, a second study was carried out to develop a sophisticated dual probe ultrasound tomography acquisition system with a robust methodology for the calibration of transducer positions. This led to a greater focus on the development of ultrasound tomography applications in medical imaging using FWI. A 2.5D brain phantom was designed that consisted of a soft tissue brain model surrounded by a hard skull mimicking material to simulate a transcranial imaging problem. This was used to demonstrate for the first time, as far as we are aware, the experimental feasibility of imaging the brain through skull using FWI. Furthermore, to address the lack of broadband sensors available for medical FWI reconstruction applications, a deep learning neural network was proposed for the bandwidth extension of observed narrowband data. A demonstration of this proposed technique was then carried out by improving the FWI image reconstruction of experimentally acquired breast phantom imaging data. Finally, the FWI imaging method was expanded for3D neuroimaging applications and an in silico feasibility of reconstructing the mouse brain with commercial transducers is demonstrated.Open Acces

    CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

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    This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship

    Modeling, Simulation and Data Processing for Additive Manufacturing

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    Additive manufacturing (AM) or, more commonly, 3D printing is one of the fundamental elements of Industry 4.0. and the fourth industrial revolution. It has shown its potential example in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing and local on-demand manufacturing are some of the current benefits. Businesses based on AM have experienced double-digit growth in recent years. Accordingly, we have witnessed considerable efforts in developing processes and materials in terms of speed, costs, and availability. These open up new applications and business case possibilities all the time, which were not previously in existence. Most research has focused on material and AM process development or effort to utilize existing materials and processes for industrial applications. However, improving the understanding and simulation of materials and AM process and understanding the effect of different steps in the AM workflow can increase the performance even more. The best way of benefit of AM is to understand all the steps related to that—from the design and simulation to additive manufacturing and post-processing ending the actual application.The objective of this Special Issue was to provide a forum for researchers and practitioners to exchange their latest achievements and identify critical issues and challenges for future investigations on “Modeling, Simulation and Data Processing for Additive Manufacturing”. The Special Issue consists of 10 original full-length articles on the topic
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