Recently, drought is recognized as a crucial factor on controlling inter-annual variations of the terrestrial carbon cycle. Remote sensing of hydrological and ecological land surface variables offers a very important aid for identifying the impact of droughts on terrestrial ecosystems. GLEAM soil moisture (SM), reconstructed GRACE terrestrial total terrestrial water storage change (TWS), GIMMS normalized difference vegetation index (NDVI), reconstructed continuous solar-induced fluorescence (SIF) data from OCO-2, and SIF data from SCIACHAMY and GOME-2, in conjunction with multiple carbon flux data and CRUNCEP meteorological data, were employed to study the drought impacts on the terrestrial ecosystems in Europe during 2001-2015. We found GLEAM SM and GRACE TWS can effectively detect hydrological anomalies for four reported strongest drought years on the continental scale, in line with evident reductions of terrestrial carbon uptake indicated by models. Most vegetation products agree well on indicating drought impacts, but with some unexpected inconsistencies. Also, these land products are able to reasonably indicate the spatial patterns of drought impacts on ecosystems. We highlight the importance of ensemble satellite land products in deepening the interpretation of drought impacts on terrestrial ecosystems, and also on examining carbon cycle models and identifying potential gaps in satellite data products within the context of drought-carbon cycle studies.

Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications.Prior work often studies entity relatedness in static settings and an unsupervised manner.However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time.In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision.Our model is capable of learning rich and different entity representations in a joint framework.Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.

In this paper, experimental measurements of the received signal strength in a smart home test bed are presented. The measurements are used to develop a topology-independent probabilistic indoor path loss model. The proposed path loss model adds a random loss component to the log-distance path loss model to account for wall penetration, reflection, scattering, and diffraction effects. The proposed model is incorporated into the network simulator ns-3 to simulate IEEE 802.11 and IEEE 802.15.4 networks. Our results indicate that the throughput of both networks degrades as the density of the nodes increases due to increasing the connectivity of the interference links.

Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, web archives are emerging as gold-mines for content analytics of many sorts. However, supporting search, which goes beyond navigational search via URLs, is a very challenging task in these unique structures with huge, redundant and noisy temporal content. In this paper, we address the search needs of expert users such as journalists, economists or historians for discovering a topic in time: Given a query, the top-k returned results should give the best representative documents that cover most interesting time-periods for the topic. For this purpose, we propose a novel random walk-based model that integrates relevance, temporal authority, diversity and time in a unified framework. Our preliminary experimental results on the large-scale real-world web archival collection shows that our method significantly improves the state-of-the-art algorithms (i.e., PageRank) in ranking temporal web pages.

Many complex maneuvers involving aircraft, vehicles and persons are carried out at airport aprons. Manual video surveillance used for safety and security purposes is inefficient and privacy protection must be guaranteed. In this paper, we propose a system named ASEV that automatically assesses situations for airport surveillance. It combines four main components: a low-level image processing unit based on a new hardware implementation to extract features in real time, a high-level image processing unit for scene analysis, a real-time inference engine for scene understanding, and a data protection stage for log encryption. In addition, four often neglected aspects are successfully addressed: two-way communication between system and operator, power consumption, monitored people privacy and operator activity control. Extensive evaluation at a real airport shows that the proposed system improves the operator performance with sound and visual alerts based on the automatic assessment of various events.