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Edge learning for distributed big data analytics : theory, algorithms, and system design
\"\"Traditionally, to develop these intelligent services and applications, big data are stored and processed in a centralized model. However, with the proliferation of edge devices and edge data, traditional centralized learning frameworks are required to upload all training data from different sources to a remote data server, which incurs significant communication overhead, service latency, as well as security and privacy issues. Therefore, it is urgent to shift model training and inference from the cloud to the edge, which is the essential idea of edge learning. Edge Learning is a fusion of big data, edge computing, and machine learning, and it is an enabling technology for edge intelligence. This book presents the basic knowledge of training machine learning models, key challenges and issues in edge learning, and comprehensive techniques from three aspects, i.e., fundamental theory, edge learning algorithms, and system design issues in edge learning. We believe that this book will stimulate fruitful discussions, inspire further research ideas, and attract researchers and developers from both academia and industry in this field\"-- Provided by publisher.
Book
Data Science Strategy For Dummies
All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the \"what\" and the \"why\" of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you'll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. * Learn exactly what data science is and why it's important * Adopt a data-driven mindset as the foundation to success * Understand the processes and common roadblocks behind data science * Keep your data science program focused on generating business value * Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.
eBook
Reciprocity, evolution, and decision games in network and data science
\"Learn how to analyze and manage evolutionary and sequential user behaviors in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision-making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behavior and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing\"-- Provided by publisher.
Book
Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1
A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios—such as in monitoring disease progression of coronavirus disease 2019—to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.
Journal Article
EMPIAR: a public archive for raw electron microscopy image data
Raw 2D image data sets are often considerably larger than the final 3D reconstructions (gigabytes to terabytes versus megabytes to gigabytes), and their incorporation into EMDB is not currently feasible. In an effort to address the needs of the EM community and assess the challenges involved in data transfer and archiving, PDBe has developed EMPIAR, which is designed to handle data sets with sizes in the terabyte range. The EMPIAR website is the main portal to EMPIAR data. It includes a web-based deposition system and functionality to search, browse, view and download EMPIAR data sets (Fig. 1). Data can be transferred using Aspera (http://asperasoft.com
Journal Article
A checklist for identifying determinants of practice: A systematic review and synthesis of frameworks and taxonomies of factors that prevent or enable improvements in healthcare professional practice
Background
Determinants of practice are factors that might prevent or enable improvements. Several checklists, frameworks, taxonomies, and classifications of determinants of healthcare professional practice have been published. In this paper, we describe the development of a comprehensive, integrated checklist of determinants of practice (the TICD checklist).
Methods
We performed a systematic review of frameworks of determinants of practice followed by a consensus process. We searched electronic databases and screened the reference lists of key background documents. Two authors independently assessed titles and abstracts, and potentially relevant full text articles. We compiled a list of attributes that a checklist should have: comprehensiveness, relevance, applicability, simplicity, logic, clarity, usability, suitability, and usefulness. We assessed included articles using these criteria and collected information about the theory, model, or logic underlying how the factors (determinants) were selected, described, and grouped, the strengths and weaknesses of the checklist, and the determinants and the domains in each checklist. We drafted a preliminary checklist based on an aggregated list of determinants from the included checklists, and finalized the checklist by a consensus process among implementation researchers.
Results
We screened 5,778 titles and abstracts and retrieved 87 potentially relevant papers in full text. Several of these papers had references to papers that we also retrieved in full text. We also checked potentially relevant papers we had on file that were not retrieved by the searches. We included 12 checklists. None of these were completely comprehensive when compared to the aggregated list of determinants and domains. We developed a checklist with 57 potential determinants of practice grouped in seven domains: guideline factors, individual health professional factors, patient factors, professional interactions, incentives and resources, capacity for organisational change, and social, political, and legal factors. We also developed five worksheets to facilitate the use of the checklist.
Conclusions
Based on a systematic review and a consensus process we developed a checklist that aims to be comprehensive and to build on the strengths of each of the 12 included checklists. The checklist is accompanied with five worksheets to facilitate its use in implementation research and quality improvement projects.
Journal Article
SQL all-in-one
Your one-stop guide to SQL. This relational database coding language is one of the most used languages in professional software development. And, as it becomes ever more important to take control of data, there's no end in sight to the need for SQL know-how. You can take your career to the next level with this guide to creating databases, accessing and editing data, protecting data from corruption, and integrating SQL with other languages in a programming environment. Become a SQL guru and turn the page on the next chapter of your coding career.
Book
Crowdsourcing biomedical research: leveraging communities as innovation engines
Key Points
Crowdsourcing is emerging as a novel framework to tackle scientific problems.
A variant of crowdsourcing, scientific competitions known as 'Challenges', enables a rigorous validation of methods, promotes reproducibility and fosters community building.
Challenges also accelerate scientific discovery by allowing large numbers of groups to work jointly on a problem.
Integrating predictions from different methods submitted by participants to solve a Challenge provides a robust solution that is often better than the best individual solution, a phenomenon known as the 'wisdom of crowds'.
The patterns of similar findings that emerge from several independent Challenges can provide useful insight into various key questions in genetics and genomics.
Considerable resources are required to gain maximal insights into the diverse big data sets in biomedicine. In this Review, the authors discuss how crowdsourcing, in the form of collaborative competitions (known as Challenges), can engage the scientific community to provide the diverse expertise and methodological approaches that can robustly address some of the most pressing questions in genetics, genomics and biomedical sciences.
The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.
Journal Article