Unsupervised Adversarial Depth Estimation using Cycled Generative Networks

While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps and show that the depth estimation task can be effectively tackled within an adversarial learning framework. Specifically, we propose a deep generative network that learns to predict the correspondence field i.e. the disparity map between two image views in a calibrated stereo camera setting. The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other. Extensive experiments on the publicly available datasets KITTI and Cityscapes demonstrate the effectiveness of the proposed model and competitive results with state of the art methods. The code and trained model are available on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.Comment: To appear in 3DV 2018. Code is available on GitHu

Ultrastructure and Development of Anthracoidea Elynae Ustilospores

The aim of the study was to examine the ultrastructure of Anthracoidea elynae ustilospores isolated from Kobresia myosuroides (Vill.) Fiori plant ovaries, harvested in the Bucegi Mountains, Romania. Samples examination was performed using scanning (SEM) and transmission (TEM) electron microscopy. The results showed that A. elynae ustilospores had a dynamic ultrastructure, because their three-layered cell wall, nucleus shape, lipid and glycogen accumulations in the cytoplasm changed at each developmental stage. In conclusion, according to the ultrastructural changes, A. elynae ustilospores development may be divided into three stages

Directional turnover towards larger-ranged plants over time and across habitats

Species turnover is ubiquitous. However, it remains unknown whether certain types of species are consistently gained or lost across different habitats. Here, we analysed the trajectories of 1827 plant species over time intervals of up to 78 years at 141 sites across mountain summits, forests, and lowland grasslands in Europe. We found, albeit with relatively small effect sizes, displacements of smaller- by larger-ranged species across habitats. Communities shifted in parallel towards more nutrient-demanding species, with species from nutrient-rich habitats having larger ranges. Because these species are typically strong competitors, declines of smaller-ranged species could reflect not only abiotic drivers of global change, but also biotic pressure from increased competition. The ubiquitous component of turnover based on species range size we found here may partially reconcile findings of no net loss in local diversity with global species loss, and link community-scale turnover to macroecological processes such as biotic homogenisation

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Learning in Low Data Regimes for Image and Video Understanding

The use of Deep Neural Networks with their increased representational power has allowed for great progress in core areas of computer vision, and in their applications to our day-to-day life. Unfortunately the performance of these systems rests on the "big data" assumption, where large quantities of annotated data are freely and legally available for use. This assumption may not hold due to a variety of factors: legal restrictions, difficulty in gathering samples, expense of annotations, hindering the broad applicability of deep learning methods. This thesis studies and provides solutions for different types of data scarcity: (i) the annotation task is prohibitively expensive, (ii) the gathered data is in a long tail distribution, (iii) data storage is restricted. For the first case, specifically for use in video understanding tasks, we have developed a class agnostic, unsupervised spatio-temporal proposal system learned in a transductive manner, and a more precise pixel-level unsupervised graph based video segmentation method. At the same time, we have developed a cycled, generative, unsupervised depth estimation system that can be further used in image understanding tasks, avoiding the use of expensive depth map annotations. Further, for use in cases where the gathered data is scarce we have developed two few-shot image classification systems: a method that makes use of category-specific 3D models to generate novel samples, and one that increases novel sample diversity by making use of textual data. Finally, data collection and annotation can be legally restricted, significantly impacting the function of lifelong learning systems. To overcome catastrophic forgetting exacerbated by data storage limitations, we have developed a deep generative memory network that functions in a strictly class incremental setup

Distribution and Phytocoenotic Context of Kobresia simpliciuscula (Wahlenb.) Mack. in South-Eastern Carpathians

This study proposes a critical analysis of the distribution and habitat requirements of the rare arctic-alpine plant species Kobresia simpliciuscula (Wahlenb.) Mack. in the South-Eastern Carpathians. The species was recorded in this part of Carpathians only from Romania, in Bucegi Mountains. The mention of K. simpliciuscula in Rodna Mountains (Eastern Carpathians) is considered to be erroneous. K. simpliciuscula was found in the Southern Carpathians in a different habitat type compared to the one characteristic for populations in the Arctic and the Alps. The species does not grow in the pioneer phytocoenoses of the Caricion bicoloris-atrofuscae alliance but, on the contrary, in dry calciphilous alpine vegetation included in Oxytropido-Elynion. The plant communities where K. simpliciuscula was found in Bucegi Mountains belong to Achilleo schurii-Dryadetum (Beldie 1967) Coldea 1984. These phytocoenoses are very similar to those described for the species in Belianske Tatra Mountains (Western Carpathians, Slovakia)