Distribution of Buxbaumia viridis (Moug. ex Lam. et DC.) Brid. ex Moug. et Nestl. (Bryophyta) in Montenegro

The present paper is a contribution to the knowledge of the distribution of the moss species Buxbaumia viridis in Montenegro. The records are from 14 known sites at elevations over 1300 m a.s.l. in the northern and north-eastern parts of the country. Population size is remarkable in Durmitor National Park at Crno jezero lake, where sporophytes can be found on ca 50 tree trunks

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Aquatic and Wetland Vegetation Along the Sava River

Diverse hydrological, climate, and soil conditions along the Sava River caused significant diversification of vegetation. Therefore, the objective of this chapter is to integrate and present all the available data on variability of the aquatic and riparian plant communities along the Sava River and its main tributaries as well as to identify the environmental factors, which are related to the distribution of different vegetation types. Special attention has been also paid on the detection of threats for rare and endangered plant species and fragile wetland ecosystems along the Sava River. Based on data review, syntaxonomic revision of aquatic and riparian vegetation based on common, pan-European databank is required. Ecological studies that involve inventory, monitoring, modeling, and prediction of changes in populations, ecological communities, and ecosystems require both georeferenced databases and computational tools for application of statistical methods.Milačič R, Ščančar J, Paunović M, editors. The Sava River. Berlin, Heidelberg: Springer-Verlag; 2015. p. 249-316