Tabla de Contenidos: “…Viewing the ESS reports -- 9.1 Viewing hardware asset management reports -- 9.1.1 Viewing an ESS hardware asset summary report -- 9.1.2 Viewing a detail report for an ESS -- 9.1.3 Viewing an LIC summary report -- 9.2 Viewing the ESS capacity management reports -- 9.2.1 Viewing an ESS summary report -- 9.2.2 Viewing a SCSI/FC host summary report -- 9.2.3 Viewing a historical capacity report -- 9.2.4 Capacity management report considerations -- 9.3 Viewing performance management reports -- 9.3.1 Viewing Ranked Performance reports -- 9.3.2 Viewing Storage Server Performance and Threshold Summary reports -- 9.3.3 Viewing Logical Volume reports -- 9.3.4 Viewing disk utilization summary report -- 9.3.5 Viewing disk information and cache summary report -- 9.3.6 Viewing Cache Summary report -- 9.3.7 Considerations on performance summary reports -- 9.3.8 Viewing performance detail reports -- 9.4 Viewing host volume data, LUN to disk host mapping -- 9.4.1 Interface for viewing host volume data - host data collected -- 9.4.2 Interface for viewing host volume data: no host data collected -- 9.4.3 View data for a single volume by the LUN serial number -- 9.4.4 View data for a single volume by the device name on a host -- 9.4.5 Neither Asset/Capacity or Host Data collected -- Chapter 10. …”

Tabla de Contenidos: “…10.2.3 Variant Calling From RNA Sequencing Data -- 10.2.4 Variant Call Format (VCF) -- 10.2.5 Evaluating VCF Results -- 10.3 Structural Variant (SV) Calling -- 10.3.1 Short-Read-Based SV Calling -- 10.3.2 Long-Read-Based SV Calling -- 10.3.3 CNV Detection -- 10.3.4 Integrated SV Analysis -- 10.4 Annotation of Called Variants -- References -- 11 Clinical Sequencing and Detection of Actionable Variants -- 11.1 Clinical Sequencing Data Generation -- 11.1.1 Patient Sample Collection -- 11.1.2 Library Preparation and Sequencing Approaches -- 11.2 Read Mapping and Variant Calling -- 11.3 Variant Filtering -- 11.3.1 Frequency of Occurrence -- 11.3.2 Functional Consequence -- 11.3.3 Existing Evidence of Relationship to Human Disease -- 11.3.4 Clinical Phenotype Match -- 11.3.5 Mode of Inheritance -- 11.4 Variant Ranking and Prioritization -- 11.5 Classification of Variants Based On Pathogenicity -- 11.5.1 Classification of Germline Variants -- 11.5.2 Classification of Somatic Variants -- 11.6 Clinical Review and Reporting -- 11.6.1 Use of Artificial Intelligence in Variant Reporting -- 11.6.2 Expert Review -- 11.6.3 Generation of Testing Report -- 11.6.4 Variant Validation -- 11.6.5 Incorporation Into a Patient's Electronic Health Record -- 11.6.6 Reporting of Secondary Findings -- 11.6.7 Patient Counseling and Periodic Report Updates -- 11.7 Bioinformatics Pipeline Validation -- References -- 12 De Novo Genome Assembly With Long And/or Short Reads -- 12.1 Genomic Factors and Sequencing Strategies for De Novo Assembly -- 12.1.1 Genomic Factors That Affect De Novo Assembly -- 12.1.2 Sequencing Strategies for De Novo Assembly -- 12.2 Assembly of Contigs -- 12.2.1 Sequence Data Preprocessing, Error Correction, and Assessment of Genome Characteristics -- 12.2.2 Contig Assembly Algorithms -- 12.2.3 Polishing -- 12.3 Scaffolding and Gap Closure…”

Tabla de Contenidos: “…2.11 RetinaNet -- 2.11.1 Focal loss -- 2.12 Detection performances -- 2.13 Conclusion -- References -- Biographies -- 3 Learning with limited supervision -- 3.1 Introduction -- 3.2 Context-aware active learning -- 3.2.1 Active learning -- 3.2.2 Context in active learning -- 3.2.3 Framework for context-aware active learning -- 3.2.4 Applications -- 3.3 Weakly supervised event localization -- 3.3.1 Network architecture -- 3.3.2 k-max multiple instance learning -- 3.3.3 Coactivity similarity -- 3.3.4 Applications -- 3.4 Domain adaptation of semantic segmentation using weak labels -- 3.4.1 Weak labels for category classification -- 3.4.2 Weak labels for feature alignment -- 3.4.3 Network optimization -- 3.4.4 Acquiring weak labels -- 3.4.5 Applications -- 3.4.6 Output space visualization -- 3.5 Weakly-supervised reinforcement learning for dynamical tasks -- 3.5.1 Learning subgoal prediction -- 3.5.2 Supervised pretraining -- 3.5.3 Applications -- 3.6 Conclusions -- Acknowledgments -- References -- Biographies -- 4 Efficient methods for deep learning -- 4.1 Model compression -- 4.1.1 Parameter pruning -- 4.1.2 Low-rank factorization -- 4.1.3 Quantization -- 4.1.4 Knowledge distillation -- 4.1.5 Automated model compression -- 4.2 Efficient neural network architectures -- 4.2.1 Standard convolution layer -- 4.2.2 Efficient convolution layers -- 4.2.3 Manually designed efficient CNN models -- 4.2.4 Neural architecture search -- 4.2.5 Hardware-aware neural architecture search -- 4.3 Conclusion -- References -- 5 Deep conditional image generation -- 5.1 Introduction -- 5.2 Visual pattern learning: a brief review -- 5.3 Classical generative models -- 5.4 Deep generative models -- 5.5 Deep conditional image generation -- 5.6 Disentanglement for controllable synthesis -- 5.6.1 Disentangle visual content and style -- 5.6.2 Disentangle structure and style…”

Tabla de Contenidos: “…3.6.4 Key frame selection and ranking -- 3.7 Time-complexity analysis -- 3.7.1 Complexity analysis of an exocentric video summarization algorithm -- 3.7.2 Complexity analysis of the scalable exocentric video summarization algorithm -- 3.8 Experimental test bed -- 3.8.1 Dataset(s) -- 3.8.2 Performance measures -- 3.8.2.1 Objective measures -- 3.8.2.2 Subjective measures -- 3.9 Results of Delaunay graph based exocentric video summarization methods -- 3.9.1 Results of constrained Delaunay graph clustering based summary -- 3.9.1.1 Performance analysis with information theoretic presampling -- 3.9.1.2 Performance analysis with deviation ratio constraint -- 3.9.1.3 Performance comparison with state-of-the-art methods -- 3.9.1.4 Performance comparison with K-means clustering -- 3.9.1.5 Clustering performance analysis -- 3.9.1.6 Tuning of the parameters -- 3.9.1.7 Key frame visualization -- 3.9.2 Results of Delaunay graph based summary with user customization -- 3.9.3 Results of Delaunay graph based summary in enhanced feature space -- 3.9.3.1 Performance analysis with semantic features -- 3.9.3.2 Performance analysis with CCA -- 3.10 Results of graph modularity based solution -- 3.11 Results of scalable exocentric video summarization -- 3.11.1 Objective evaluations -- 3.11.2 Subjective evaluations -- 3.11.3 Comparison of execution times -- 3.12 Summary -- 3.12.1 Summary of exocentric video summarization -- 3.12.2 Summary of scalable exocentric video summarization -- References -- 4 Multi-view exocentric video summarization -- 4.1 Motivation -- 4.2 Chapter organization -- 4.3 Related work -- 4.4 Proposed method -- 4.4.1 Video preprocessing -- 4.4.1.1 Shot detection and representation -- 4.4.1.2 Feature extraction -- 4.4.2 Unimportant shot elimination using Gaussian entropy -- 4.4.3 Multi-view correlation using bipartite matching…”

“…If, for example, a corundum surface, which is very well-established in the tire industry, is used on the tire test bench instead of a real track surface, for wet track conditions ranking inversions in the tested compounds occur…”

“…Scotland (UK) is a strong innovator, ranking among the top 20% of economies among European regions, with strengths in university-firm collaborations and skills for innovation. …”

“…The Rust programming language has held the most-loved technology ranking on Stack Overflow for 6 years running, while JavaScript has been the most-used programming language for 9 years straight as it runs on every web browser. …”

“…In a recent 2017 Medscape publication, burn-out rates in Critical Care physicians ranked in 9th place and Pediatricians ranked 13th among 27 subspecialties. …”

“…They can also be ranked along an indicator of institutional coherence which measures the extent to which building blocks “fit together”. …”

“…Using colorful illustrations and examples, the authors bring modern information retrieval to life, covering such diverse topics as relevance ranking, faceted navigation, multi-touch, and augmented reality together with a practical guide to help us make search better today…”

“…Beyond anecdotes: run the numbers with one of the world's leading investment researchers and top-ranked business school professors Guidance on understanding the real opportunities and not-so-obvious risks associated with each strategy--and instruction on how to mitigate the risks…”

“…These comprise maintaining and monitoring a stabilised and controlled state of the damaged plant; implementing provisions against further failures; evaluating the plant damaged state from a physical and radiological standpoint and ranking related risks; preparing and achieving fuel retrieval (either fuel assemblies stored in spent fuel pools or fuel debris from damaged reactors); and managing safely plant recovery and accident waste. …”

“…The framework has three components: an interpersonally comparable measure of well-being, which functions to translate outcomes into lists ("vectors") of well-being numbers, one for each person in the population; a rule (the SWF) for ranking well-being vectors, such as the utilitarian SWF (which simply adds up well-being numbers), a continuous-prioritarian SWF (which gives greater weight to the worse off), or some other; and a procedure for ranking policies, understood as probability distributions across outcomes. …”

“…Part II is focused on certain areas of policy with a strong multilateral dimension: Europeanisation, institutional rankings and typologies and cross-border mobility…”

“…In 1996, The Netherlands was the fourth-ranking DAC Member in terms of ODA/GNP ratio and the fifth-ranking in absolute terms. …”

“…Search engine optimization helps improve Web site rankings, and it is often complex and confusing. This task-based, hands-on guide covers the concepts and trends and then lays out a day-by-day strategy for developing, managing, and measuring a successful SEO plan. …”

“…Power is intimately involved in cultural reproduction, and Weiner describes the location of power in each society, showing how the degree of control over the production and distribution of cloth wealth coincides with women's rank and the development of hierarchy in the community. …”