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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
The United Kingdom National Neonatal Research Database: A validation study
The National Neonatal Research Database (NNRD) is a rich repository of pre-defined clinical data extracted at regular intervals from point-of-care, clinician-entered electronic patient records on all admissions to National Health Service neonatal units in England, Wales, and Scotland. We describe population coverage for England and assess data completeness and accuracy.
We determined population coverage of the NNRD in 2008-2014 through comparison with data on live births in England from the Office for National Statistics. We determined the completeness of seven data items on the NNRD. We assessed the accuracy of 44 data items (16 patient characteristics, 17 processes, 11 clinical outcomes) for infants enrolled in the multi-centre randomised controlled trial, Probiotics in Preterm Study (PiPs). We compared NNRD to PiPs data, the gold standard, and calculated discordancy rates using predefined criteria, and sensitivity, specificity and positive predictive values (PPV) of binary outcomes.
The NNRD holds complete population data for England for infants born alive from 25+0 to 31+6 (completed weeks) of gestation; and 70% and 90% for those born at 23 and 24 weeks respectively. Completeness of patient characteristics was over 90%. Data were linked for 2257 episodes of care received by 1258 of the 1310 babies recruited to PiPs. Discordancy rates were <5% for 13/16 patient characteristics (exceptions: mode of delivery 8.7%; maternal ethnicity 10.2%, Lower layer Super Output Area 16.5%); <5% for 9/16 processes (exceptions: medical treatment for Patent ductus arteriosus 6.1%, high-dependency days 10.2%, central line days 11.2%, type of first milk 22.3%; and during first 14 days, summary of types of milk 13.8%; number of days of antibiotics 9.0%; whether antacid given 5.1%); and <5% for 10/11 clinical outcomes (exception: Bronchopulmonary dysplasia, defined as oxygen dependency at 36 weeks postmenstrual age 3.3%). The specificity of NNRD data was >85% for all outcomes; sensitivity ranged from 50-100%; PPV ranged from 58.8 (95% CI 40.8-75.4%) for porencephalic cyst to 99.7 (95% CI 99.2, 99.9%) for survival to discharge.
The completeness and quality of data held in the NNRD is high, providing assurance in relation to use for multiple purposes, including national audit, health service evaluations, quality improvement, and research.
Journal Article