Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Semantic annotation of high resolution TerraSAR-X images using Information Mining

This paper addresses the problem of High Resolution Synthetic Aperture Radar (SAR) image semantic annotation using a Knowledge Based Information Mining (KIM) System. The authors propose the assessment of the capabilities of KIM to perform an automatic urban classification on TerraSAR-X data. Four test sites have been used in the experiment to prove that the system is generic and data independent

Latent knowledge discovery in satellite images

The paper describes research that seeks to supersede human inductive learning and reasoning in high-level scene understanding and content extraction

Results of in vitro fertilization after diagnosis and treatment of chronic endometritis

OBJECTIVE: A significant cause of infertility is the inability of the embryo to implant. Endometritis is one of the major causes affecting embryo implantation. The present study addressed the diagnosis and effects of chronic endometritis (CE) treatment on pregnancy rates after in vitro fertilization (IVF). PATIENTS AND METHODS: We conducted this retrospective study on 578 infertile couples treated with IVF. In 446 couples, we performed a control hysteroscopy with biopsy before IVF. In addition, we examined the visual aspects of the hysteroscopy and the results of the endometrial biopsies, followed by antibiotic therapy if necessary. Finally, the results of IVF were compared. RESULTS: Of the 446 cases studied, we diagnosed 192 (43%) with chronic endometritis, either by direct observation or based on the histopathological result. In addition, the cases diagnosed with CE we treated with a combination of antibiotics. The group diagnosed at CE and subsequently treated with antibiotic therapy had a significantly higher pregnancy rate after IVF (43.2%) than the group without treatment (27.3%). CONCLUSIONS: Hysteroscopic examination of the uterine cavity was particularly important for the success of IVF. The initial CE diagnosis and treatment were an advantage for the cases in which we performed the IVF procedures

Classification of dynamic evolutions from satellite image time series based on similarity measures

With a continuous increase in the number of Earth Observation satellites, leading to the development of satellitar image time series (SITS), the number of algorithms for land cover analysis and monitoring has greatly expanded. This paper offers a new perspective in dynamic classification for SITS

Knowledge based information mining for urban classification using multispectral high resolution images

Space imagery offers great support in various types of applications. The huge amount of information provided in a remote sensed manner facilitates the analysis of Earth surface. The image content classification is one of the first steps to follow in the data mining process

Time evolution analysis and change detection for data mining systems

The paper proposes two complementary methods for change analysis and feature extraction: A time evolution analysis of satellite images and using the correlation coefficient together with conditional information