Using virtual microscopy for the development of sampling strategies in quantitative histology and design‐based stereology

2021 ◽  
Author(s):  
Yaroslav Kolinko ◽  
Anna Malečková ◽  
Petra Kochová ◽  
Martina Grajciarová ◽  
Tereza Blassová ◽  
...  
Keyword(s):  
Virtual Microscopy ◽  
Sampling Strategies ◽  
Quantitative Histology

Who owns a personal home page?

2003 ◽  
Vol 62 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Astrid Schütz ◽  
Franz Machilek
Keyword(s):  
Search Engine ◽  
Home Page ◽  
Sampling Strategies ◽  
Home Pages ◽  
Personal Home Pages ◽  
Age And Sex

Research on personal home pages is still rare. Many studies to date are exploratory, and the problem of drawing a sample that reflects the variety of existing home pages has not yet been solved. The present paper discusses sampling strategies and suggests a strategy based on the results retrieved by a search engine. This approach is used to draw a sample of 229 personal home pages that portray private identities. Findings on age and sex of the owners and elements characterizing the sites are reported.

Tissue microarray based classification of early and late stage colorectal cancer in optical and virtual microscopy

2007 ◽  
Vol 45 (05) ◽  
Author(s):  
F Sipos ◽  
S Spisák ◽  
T Krenács ◽  
O Galamb ◽  
B Galamb ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tissue Microarray ◽  
Late Stage ◽  
Virtual Microscopy

Investigation of Sampling Strategies for Reduced-Order Models of Rocket Combustors

2021 ◽  
Author(s):  
Christopher R. Wentland ◽  
Cheng Huang ◽  
Karthikeyan Duraisamy
Keyword(s):  
Reduced Order Models ◽  
Sampling Strategies ◽  
Reduced Order

An investigation of sampling strategies for lake benthos

1985 ◽  
Vol 19 (1) ◽  
pp. 71-77 ◽  
Author(s):  
B. V. Timms
Keyword(s):  
Sampling Strategies

EDITORIAL: PROSTATE BIOPSY QUANTITATIVE HISTOLOGY AS A STAGING AND PROGNOSTIC FACTOR

2002 ◽  
Vol 167 (2 Part 1) ◽  
pp. 526-527 ◽  
Author(s):  
Judd W. Moul
Keyword(s):  
Prognostic Factor ◽  
Prostate Biopsy ◽  
Quantitative Histology

scholarly journals How to measure uncertainty in uncertainty sampling for active learning

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Sampling Strategies ◽  
Total Uncertainty ◽  
Uncertainty Sampling ◽  
Different Types ◽  
Alternative Approaches ◽  
Active Learner ◽  
Probabilistic Nature ◽  
Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

scholarly journals Resource and dependency based test case generation for RESTful Web services

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Man Zhang ◽  
Bogdan Marculescu ◽  
Andrea Arcuri
Keyword(s):  
Web Services ◽  
Domain Knowledge ◽  
Automated System ◽  
Test Case ◽  
Test Cases ◽  
Evolutionary Search ◽  
Sampling Strategies ◽  
Mutation Operators ◽  
On Line ◽  
Restful Web Services

AbstractNowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.

Virtual Microscopy as a Learning Tool in Brazilian Medical Education

2021 ◽  
Author(s):  
Fernanda Somera dos Santos ◽  
Mariana Kiomy Osako ◽  
Gleici da Silva Castro Perdoná ◽  
Márcia Gaião Alves ◽  
Katiuchia Uzzun Sales
Keyword(s):  
Medical Education ◽  
Virtual Microscopy ◽  
Learning Tool

Designing For Diversity: Implications for Research and Practice

2020 ◽  
Vol 64 (1) ◽  
pp. 563-567
Author(s):  
Abigail R. Wooldridge ◽  
Rod D. Roscoe ◽  
Rod D. Roscoe ◽  
Shannon C. Roberts ◽  
Rupa Valdez ◽  
...  
Keyword(s):  
Interdisciplinary Teams ◽  
Team Science ◽  
Participatory Methods ◽  
Sampling Strategies ◽  
Design Problems ◽  
Sociotechnical System ◽  
The Past ◽  
Diversity Committee ◽  
The Impact ◽  
Equity And Inclusion

The Diversity Committee of HFES has led sessions at the Annual Meeting for the past three years focused on improving diversity, equity and inclusion in the society as well as providing support to human factors and ergonomics (HF/E) researchers and practitioners who aim to apply HF/E knowledge and principles to improve diversity, equity and inclusion through their work. In this panel, we bring together researchers actively engaged in designing technology and systems by considering issues of diversity, equity and inclusion to share insights and methods. Topics include the thoughtful design of sampling strategies and research approaches, alternative and participatory methods to understand the impact of automation and technology on equity, scoping design problems to be inclusive and equitable through interdisciplinary partnerships, and the application of sociotechnical system design and team science to develop interdisciplinary teams. By sharing our experiences, we hope to prepare others to successfully approach these topics.

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