this study presents a novel photonic crystal (PC) pressure sensor design and three-dimensional (3D) modeling and simulation for three different structures. A 2D PC slab based on silicon is used to implement the device...
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An active laminar flow mixer for chemical and biological reagents is developed using acoustic microcavitation streaming. the mixing is induced by the resonance frequency of the cavitative air bubble and the acoustic w...
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Development in the field of Machine Learning and Artificial Intelligence are greatly simplifying the critical bio-medical engineering applications. the first outbreak of Covid’19 pandemic was observed in Mainland Chi...
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Data science is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment, and prevention of diseases. However, the unprecedented scale and complexity of large-scale...
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ISBN:
(纸本)9781450366663
Data science is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment, and prevention of diseases. However, the unprecedented scale and complexity of large-scale biomedical data have presented critical computational bottlenecks requiring new concepts and enabling tools. To address the challenging problems in current biomedical data science, we proposed several novel large-scale machine learning models for multi-dimensional data integration, heterogeneous multi-task learning, longitudinal feature learning, etc. Meanwhile, to deal withthe big data computations, we proposed new asynchronous distributed stochastic gradient and coordinate descent methods for efficiently solving convex and non-convex problems, and also parallelized the deep learning optimization algorithms with layer-wise model parallelism. We applied our new large-scale machine learning models to analyze the multi-modal and longitudinal Electronic Medical Records (EMR) for predicting the heart failure patients' readmission and drug side effects, integrate the neuroimaging and genome-wide array data to recognize the phenotypic and genotypic biomarkers, and detect the histopathological image markers and the multi-dimensional cancer genomic biomarkers in precision medicine studies.
Causal questions are being answered every day in the biomedical domain, and have significant impact on biomedical experimentation design, data analysis, and healthcare decision making. In this tutorial, we focus on id...
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ISBN:
(纸本)9781450366663
Causal questions are being answered every day in the biomedical domain, and have significant impact on biomedical experimentation design, data analysis, and healthcare decision making. In this tutorial, we focus on identifying the (quantitative) causal effect of interventions. We will introduce a) introduction of causal inference, and popular frameworks for formulating causal inference;b) basics of causal effect identification algorithms;c) state-of-the-art methods by incorporating deep learning and machine learning;d) applications in biomedical data analytics, as well as challenges and opportunities moving forward.
this book – in conjunction withthe volumes LNCS 8588 and LNAI 8589 – constitutes the refereed proceedings of the 10thinternationalconference on Intelligent Computing, ICIC 2014, held in Taiyuan, China, in August ...
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ISBN:
(数字)9783319093307
ISBN:
(纸本)9783319093291
this book – in conjunction withthe volumes LNCS 8588 and LNAI 8589 – constitutes the refereed proceedings of the 10thinternationalconference on Intelligent Computing, ICIC 2014, held in Taiyuan, China, in August 2014. the 58 papers of this volume were carefully reviewed and selected from numerous submissions. the papers are organized in topical sections such as machine learning; neural networks; image processing; computational systems biology and medical informatics; biomedical informatics theory and methods; advances on bio-inspired computing; protein and gene bioinformatics: analysis, algorithms, applications.
the morphological size distribution is introduced as a general concept for analyzing structures in binary as well as in grey-value images. the morphological transformation can be used to treat light and dark structure...
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ISBN:
(纸本)0818620625
the morphological size distribution is introduced as a general concept for analyzing structures in binary as well as in grey-value images. the morphological transformation can be used to treat light and dark structures separately. thus, it is possible to describe the objects in a configuration and the spaces between or convex and concave parts in analyzing the shape. Examples are given which demonstrate that, using the size distribution, it is possible to characterize texture, shape, and configuration.
When people are engaged in an immersive task or experience, they can become so absorbed in it that they lose track of time and place. Narrative transportation, has similar effects, producing meaningful psychological r...
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the proceedings contain 21 papers. the topics discussed include: a formal soundness proof of region-based memory management for object-oriented paradigm;program models for compositional verification;a unified model ch...
ISBN:
(纸本)354088193X
the proceedings contain 21 papers. the topics discussed include: a formal soundness proof of region-based memory management for object-oriented paradigm;program models for compositional verification;a unified model checking approach with projection temporal logic;formal analysis of the bakery protocol with consideration of nonatomic reads and writes;towards abstraction for dynalloy specifications;partial translation verification for untrusted code-generators;a practical approach to partiality - a proof based approach;a representative function approach to symmetry exploitation for CSP refinement checking;practical automated partial verification of multi-paradigm real-time models;specifying and verifying sensor networks: experiment of formal methods;and modeling and proof of a tree-structured file system in event-B and Rodin.
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