5. errors and accuracy
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The document defines different types of errors that can occur when taking measurements in an experiment: personal errors due to mistakes by the observer, systematic errors caused by defects in measurement instruments, and random errors from sudden changes in experimental conditions. It explains that the difference between the measured value and the actual value is called error, and discusses how to classify errors into personal, systematic, and random categories. Accuracy is defined as how close a measurement is to the true or standard value.
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The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
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2:同时对留学生所学专业登记给予评定
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Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
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5. errors and accuracy
- 2. The difference between the measured value and the actual value is called Error. Error
- 3. In an experiment certain errors may arise due to experimenter, or to the instrument used or due to both. we divide these errors into three categories, named: 1. Personal Errors 2. Systematic Errors 3. Random Errors Errors and Accuracy
- 4. 1. Personal Error: An error comes into play because of faulty procedure adopted by the observer is called "PERSONAL ERROR". Personal error comes into existence due to making an error in reading a scale. It is due to faulty procedure adopted by the person making measurement. Errors and Accuracy
- 5. 1. Systematic Error: The type of error arises due to defect in the measuring device is known as "SYSTEMATIC ERROR" Generally it is called "ZERO ERROR". it may be positive or negative error. Systematic error can be removed by correcting measurement device. Errors and Accuracy
- 6. 1. Random Error The error produced due to sudden change in experimental conditions is called "RANDOM ERROR". Errors and Accuracy
- 7. The degree to which the result of a measurement, calculation, or specification conforms to the correct value or a standard is accuracy. Accuracy