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Allergy and Asthma - - Bog - Springer International Publishing AG - Plusbog.dk

Testing Software and Systems - - Bog - Springer International Publishing AG - Plusbog.dk

Optimization of Automated Software Testing Using Meta-Heuristic Techniques - - Bog - Springer International Publishing AG - Plusbog.dk

Optimization of Automated Software Testing Using Meta-Heuristic Techniques - - Bog - Springer International Publishing AG - Plusbog.dk

Testing Software and Systems - - Bog - Springer International Publishing AG - Plusbog.dk

Risk Assessment and Risk-Driven Testing - - Bog - Springer International Publishing AG - Plusbog.dk

Testing Software and Systems - - Bog - Springer International Publishing AG - Plusbog.dk

Alternatives for Dermal Toxicity Testing - - Bog - Springer International Publishing AG - Plusbog.dk

Hardware and Software: Verification and Testing - - Bog - Springer International Publishing AG - Plusbog.dk

Molecular Allergy Diagnostics - - Bog - Springer International Publishing AG - Plusbog.dk

Hardware and Software: Verification and Testing - - Bog - Springer International Publishing AG - Plusbog.dk

Molecular Oncology Testing for Solid Tumors - - Bog - Springer International Publishing AG - Plusbog.dk

Molecular Oncology Testing for Solid Tumors - - Bog - Springer International Publishing AG - Plusbog.dk

Familiarity with and understanding molecular testing is becoming imperative for practicing physicians, especially pathologists and oncologists given the current explosion of molecular tests for diagnostic, prognostic and predictive indications. Molecular Oncology Testing for Solid Tumors is designed to present an up to date practical approach to molecular testing in a easy to understand format. Emphasis is placed on quality assurance (pre-analytic, analytic and post-analytic) and test interpretation, including but not limited to: the important role of pathologists in ensuring specimen adequacy for molecular testing; factors to consider in choosing platforms for molecular assays; advantages and limitations inherent to common assays/platforms that pathologists need to communicate effectively with clinicians; the importance of required quality assurance measures to ensure accurate / reproducible results; pitfalls in test interpretation (including different types of artifacts that may lead to False Positive or False Negative interpretations); test reporting using standard nomenclature; review of the current and future potential utility of next-generation sequencing in oncology. All chapters are written by pathologists and clinicians experienced in practical applications of molecular tests for solid tumors. The uniqueness of this textbook is the use of a standardized template for each of the molecular tests being discussed followed by a discussion of relevant quality assurance issues to ensure focused and efficient presentation of information. This will enable readers to easily understand the Order, Report and Evaluate (ORE) process of molecular tests. Lastly, summary tables of all the molecular assays and mutations discussed in the text are provided as an appendix for quick reference. For readers interested in more detailed information, a link to websites where additional information can be obtained is provided.

DKK 967.00
1

Mechanical Testing of Materials - Emmanuel Gdoutos - Bog - Springer International Publishing AG - Plusbog.dk

Statistical Significance Testing for Natural Language Processing - Rotem Dror - Bog - Springer International Publishing AG - Plusbog.dk

Statistical Significance Testing for Natural Language Processing - Rotem Dror - Bog - Springer International Publishing AG - Plusbog.dk

Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.

DKK 476.00
1