Taxonomic and functional plant diversity of the Santorini-Christiana island group (Aegean Sea, Greece)

This is the first attempt to analyse vascular plant diversity patterns regarding the seven vegetated islands of the Santorini archipelago (Aegean Sea, Greece) as a whole. Hitherto unpublished floristic records, combined with critical use of taxonomic and chorological information from previous and most recent literature, resulted in a total of 696 infrageneric taxa (species and subspecies) occurring in the area. Detailed qualitative and quantitative phytodiversity spectra per individual island are presented, and floristic dissimilarity (beta-diversity) between islands is considered. Spatial distribution of 28 chorological, ecological, vegetative and reproductive traits for each recorded taxon have been calculated in order to detect regional and fundamental patterns in functional biogeography beyond traditional species-based approaches, based on both taxonomic and functional components of diversity. Mediterranean species constitute the most abundant chorological element and therophytes the most abundant life-form element in the region. Surface area is the most influential variable contributing to species richness; very strong relationships in (1) species per area, (2) functional richness per area and (3) functional richness per species richness are revealed for the Santorini archipelago. Floristic cross-correlations revealed an overall high floristic heterogeneity among the individual islands. The phytodiversity assessment presented is undoubtedly of documentary value in consideration of expected future eruptive events in the area which may damage the plant cover at least on some of the involved islands to a yet unpredictable extent

New solar axion search in CAST with 4^4He filling

The CERN Axion Solar Telescope (CAST) searches for $a\to\gamma$ conversion in the 9 T magnetic field of a refurbished LHC test magnet that can be directed toward the Sun. Two parallel magnet bores can be filled with helium of adjustable pressure to match the X-ray refractive mass $m_\gamma$ to the axion search mass $m_a$. After the vacuum phase (2003--2004), which is optimal for $m_a\lesssim0.02$ eV, we used $^4$He in 2005--2007 to cover the mass range of 0.02--0.39 eV and $^3$He in 2009--2011 to scan from 0.39--1.17 eV. After improving the detectors and shielding, we returned to $^4$He in 2012 to investigate a narrow $m_a$ range around 0.2 eV ("candidate setting" of our earlier search) and 0.39--0.42 eV, the upper axion mass range reachable with $^4$He, to "cross the axion line" for the KSVZ model. We have improved the limit on the axion-photon coupling to $g_{a\gamma}< 1.47\times10^{-10} {\rm GeV}^{-1}$ (95% C.L.), depending on the pressure settings. Since 2013, we have returned to vacuum and aim for a significant increase in sensitivity.Comment: CAST Collaboration 6 pages 3 figure

A high-resolution, integrated system for rice yield forecasting at district level

To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project

A Learnable Model with Calibrated Uncertainty Quantification for Estimating Canopy Height from Spaceborne Sequential Imagery

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/.Global-scale canopy height mapping is an important tool for ecosystem monitoring and sustainable forest management. Various studies have demonstrated the ability to estimate canopy height from a single spaceborne multispectral image using end-to-end learning techniques. In addition to texture information of a single-shot image, our study exploits multi temporal information of image sequences to improve estimation accuracy. We adopt a convolutional variant of a long short-term memory (LSTM) model for canopy height estimation from multitemporal instances of Sentinel-2 products. Furthermore, we utilize the deep ensembles technique for meaningful uncertainty estimation on the predictions and postprocessing isotonic regression model for calibrating them. Our lightweight model (∼320k trainable parameters) achieves the mean absolute error (MAE) of 1.29 m in a European test area of 79 km2. It outperforms the state-of-the-art methods based on single-shot spaceborne images as well as costly airborne images while providing additional confidence maps that are shown to be well calibrated. Moreover, the trained model is shown to be transferable in a different country of Europe using a fine-tuning area of as low as ∼2 km2 with MAE = 1.94 m.publishedVersio

Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation

This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of $\langle$subject, predicate, object$\rangle$ triplets. Recognizing the predicate between subject and object pairs is imbalanced and multi-label in nature, ranging from ubiquitous interactions such as spatial relationships (\eg \emph{in front of}) to rare interactions such as \emph{twisting}. In widely-used benchmarks such as Action Genome and VidOR, the imbalance ratio between the most and least frequent predicates reaches 3,218 and 3,408, respectively, surpassing even benchmarks specifically designed for long-tailed recognition. Due to the long-tailed distributions and label co-occurrences, recent state-of-the-art methods predominantly focus on the most frequently occurring predicate classes, ignoring those in the long tail. In this paper, we analyze the limitations of current approaches for scene graph generation in videos and identify a one-to-one correspondence between predicate frequency and recall performance. To make the step towards unbiased scene graph generation in videos, we introduce a multi-label meta-learning framework to deal with the biased predicate distribution. Our meta-learning framework learns a meta-weight network for each training sample over all possible label losses. We evaluate our approach on the Action Genome and VidOR benchmarks by building upon two current state-of-the-art methods for each benchmark. The experiments demonstrate that the multi-label meta-weight network improves the performance for predicates in the long tail without compromising performance for head classes, resulting in better overall performance and favorable generalizability. Code: \url{https://github.com/shanshuo/ML-MWN}.Comment: ICMR 202

A Novel Piecewise Linear Recursive Convolution Approach for Dispersive Media Using the Finite-Difference Time-Domain Method

Peer reviewedPublisher PD

Investigating Metropolitan Hierarchies through a Spatially Explicit (Local) Approach

Assuming a non-neutral impact of space, an explicit assessment of metropolitan hierarchies based on local regression models produces a refined description of population settlement patterns and processes over time. We used Geographically Weighted Regressions (GWR) to provide an enriched interpretation of the density gradient in Greece, estimating a spatially explicit rank–size relationship inspired by Zipf’s law. The empirical results of the GWR models quantified the adherence of real data (municipal population density as a predictor of metropolitan hierarchy) to the operational assumptions of the rank–size relationship. Local deviations from its prediction were explained considering the peculiarity of the metropolitan cycle (1961–2011) in the country. Although preliminary and exploratory, these findings decomposed representative population dynamics in two stages of the cycle (namely urbanization, 1961–1991, and suburbanization, 1991–2011). Being in line with earlier studies, this timing allowed a geographical interpretation of the evolution of a particularly complex metropolitan system with intense (urban) primacy and a weak level of rural development over a sufficiently long time interval. Introducing a spatially explicit estimation of the rank–size relationship at detailed territorial resolutions provided an original contribution to regional science, covering broad geographical scales