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The European CORDEX (EURO-CORDEX) initiative is a large voluntary effort that seeks to advance regional climate and Earth system science in Europe. As part of the World Climate Research Programme (WCRP) - Coordinated Regional Downscaling Experiment (CORDEX), it shares the broader goals of providing a model evaluation and climate projection framework and improving communication with both the General Circulation Model (GCM) and climate data user communities. EURO-CORDEX oversees the design and coordination of ongoing ensembles of regional climate projections of unprecedented size and resolution (0.11° EUR-11 and 0.44° EUR-44 domains). Additionally, the inclusion of empirical-statistical downscaling allows investigation of much larger multi-model ensembles. These complementary approaches provide a foundation for scientific studies within the climate research community and others. The value of the EURO-CORDEX ensemble is shown via numerous peer-reviewed studies and its use in the development of climate services. Evaluations of the EUR-44 and EUR-11 ensembles also show the benefits of higher resolution. However, significant challenges remain. To further advance scientific understanding, two flagship pilot studies (FPS) were initiated. The first investigates local-regional phenomena at convection-permitting scales over central Europe and the Mediterranean in collaboration with the Med-CORDEX community. The second investigates the impacts of land cover changes on European climate across spatial and temporal scales. Over the coming years, the EURO-CORDEX community looks forward to closer collaboration with other communities, new advances, supporting international initiatives such as the IPCC reports, and continuing to provide the basis for research on regional climate impacts and adaptation in Europe., S.S. acknowledges the support of NOTUR/NORSTORE projects NN9280K/NS9001K and the Research Council of Norway and its basic institute support of the strategic project on Climate Services. E.K. acknowledges the support of the Greek Research & Technology Network (GRNET) for provision of technical support and facilities (HPC-ARIS). L.S. and I.G. acknowledge the support of Croatian Science Foundation project CARE (2831) and Ministry of Environment and Energy project TF/HR/P3-M1-O1-0101 (www.prilagodba-klimi.hr). J. F. acknowledges support from the Spanish R+D Programme through grant INSIGNIA (CGL2016-79210-R), co-funded by ERDF/FEDER, and the Altamira Supercomputer at Instituto de Física de Cantabria (IFCA-CSIC), member of the Spanish Supercomputing Network. P.T. acknowledges support from the Belgian Science Policy (BELSPO) within the CORDEX.be (BR/143/A2) project, and the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI.M.A.G. acknowledges support from the Spanish R+D Programme through grants CGL2013-47261-R and CGL2017-89583-R, co-funded by the European Regional Development Fund. RF acknowledges support provided by ICHEC (Irish Centre for High End Computing) and the Irish Environmental Protection Agency. K.G. and S.K. gratefully acknowledge the computing time granted through JARA-HPC on the supercomputers JUROPA and JURECA at Forschungszentrum Jülich. M.B. and T.H. acknowledge support by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center – LM2015070” and the INTER-EXCELLENCE program LTT17007, and support by Charles University from the PROGRES Q16 program. We acknowledge the approval and support of the two Flagship Pilot Studies (the FPS on Convective phenomena at high resolution over Europe and the Mediterranean and the FPS on Land Use and Climate Across Scales) by WRCP CORDEX. We thank Merja Tölle for providing simulation CCLM5-0-9-JLU as contribution to the CORDEX-FPS “Convective phenomena at high resolution over Europe and the Mediterranean”. D. M., M.P., and H.T. gratefully acknowledge the support received via the projects HighEnd:Extremes, SPIRIT, and reclip:convex, funded by the Austrian Climate Research Programme (ACRP) of the Klima- und Energiefonds (nos. B368608, B960272, and B769999, respectively), as well as the Jülich Supercomputing Centre (JSC) for compute time on JURECA through the grant JJSC39 and the Vienna Scientific Cluster (VSC) through the grants 70992 and 71193.

International audience; An Ultrascale System (USS) joins parallel and distributed computing systems that will be two to three orders of magnitude larger than today's infrastructure regarding scale, performance, the number of components and their complexity. For such systems to become a reality, however, advances must be made in High Performance Computing (HPC), large-scale distributed systems, and big data solutions, also tackling challenges such as improving the energy efficiency of the IT infrastructure. Monitoring the power consumed by underlying IT resources is essential towards optimising the manner IT resources are used and hence improve the sustainability of such systems. Nevertheless, monitoring the energy consumed by USSs is a challenging endeavour as the system can comprise thousands of heterogeneous server resources spanning multiple data centres. Moreover, the amount of monitoring data, its gathering, and processing , should never become a bottleneck nor profoundly impact the energy efficiency of the overall system. This work surveys state of the art on energy monitoring of large-scale systems and methodologies for monitoring the power consumed by large systems and discusses some of the challenges to be addressed towards monitoring and improving the energy efficiency of USSs. Next, we present efforts made on designing monitoring solutions. Finally, we discuss potential gaps in existing solutions when tackling emerging large-scale monitoring scenarios and present some directions for future research on the topic.

International audience; We report the thermal transport properties of wurtzite GaN in the presence of dislocations using molecular dynamics simulations. A variety of isolated dislocations in a nanowire configuration are analyzed and found to considerably reduce the thermal conductivity while impacting its temperature dependence in a different manner. Isolated screw dislocations reduce the thermal conductivity by a factor of two, while the influence of edge dislocations is less pronounced. The relative reduction of thermal conductivity is correlated with the strain energy of each of the five studied types of dislocations and the nature of the bonds around the dislocation core. The temperature dependence of the thermal conductivity follows a physical law described by a T À1 variation in combination with an exponent factor that depends on the material's nature, type and the structural characteristics of the dislocation core. Furthermore, the impact of the dislocation density on the thermal conductivity of bulk GaN is examined. The variation and absolute values of the total thermal conductivity as a function of the dislocation density are similar for defected systems with both screw and edge dislocations. Nevertheless, we reveal that the thermal conductivity tensors along the parallel and perpendicular directions to the dislocation lines are different. The discrepancy of the anisotropy of the thermal conductivity grows with increasing density of dislocations and it is more pronounced for the systems with edge dislocations. Besides the fundamental insights of the presented results, these could also be used for the identification of the type of dislocations when one experimentally obtains the evolution of thermal conductivity with temperature since each type of dislocation has a different signature, or one could extract the density of dislocations with a simple measurement of thermal anisotropy.

OBJECTIVE: Dental device is a very broad term that can be used to include any foreign material or product that is introduced in the host oral cavity to replace missing tissues. These devices are subjected to different environments which include dental hard tissues, tissue fluids, blood and saliva. All dental devices are continuously challenged microbiologically and a number of failures in clinical management are related to microbial colonization. Thus, the assessment of the antimicrobial properties of dental devices are extremely important. In this paper, a classification of dental devices is being proposed. This classification distinguishes the devices based on whether they are implantable or not, and also sub-classified based on their specific application and the substrate receiving the device. METHODS AND RESULTS: A literature search was conducted to identify how dental devices have been tested with relation to the microbial strains used and whether the testing has been performed in isolation or reported with other relevant tests such as material characterization and biological activity. The results of the literature review were analyzed and recommendations for antimicrobial testing of dental devices are proposed. These recommendations include the need for the setting up of pre-testing parameters such as ageing and the details of the pre-testing sterilization procedures, as these may affect the material chemistry and the specification for antimicrobial testing to be done with specific single strains or polymicrobial that are native to the region where the device is located are also suggested. Testing can be undertaken in vitro, ex vivo and in vivo. Since the antimicrobial and biological activities influence/condition one another and the material chemistry may affect both the antimicrobial and biological testing this document also makes recommendations regarding biological assessment which can be carried out in isolation or integrated with the microbiological testing and also material testing methods including chemical and physical characterization of bulk, surface, eluted and degraded materials as well as physical characterization methods. SIGNIFICANCE: The level of standardization of antimicrobial testing for the dental devices needs to be based on the device location and host interaction in order to increase the clinical applicability of the mentioned tests.

International audience; The goal of FabSpace 2.0 project is to transform Universities to Open Innovation Centers for their corresponding region and improve their societal contribution to the socioeconomic and environmental performance. For this purpose, the project provides to students, researchers, as well as to citizens or even companies, with a framework and a digital platform which gives open access to a range of geodata, i.e. Earth Observation data, but also computing and visualization tools. By this way, it is expected that the participants can work in an open co-working space, where they can interact with each other. Additionally, the participants will be assisted to assess the business aspects of any potential idea. The outcome of such an activity is the increase of possibility for participants to find a revolutionary concept, which can result to new and sustainable applications and/or services. Moreover the target group exceeds the usual Earth Observation professionals, and focuses also in professionals of other disciplines, where the use of Earth Observation data can create novel technologies.

International audience; Intangible Cultural Heritage (ICH) is a relatively recent term coined to represent living cultural expressions and practices, which are recognised by communities as distinct aspects of identity. The safeguarding of ICH has become a topic of international concern primarily through the work of UNESCO (United Nations Educational, Scientific and Cultural Organisation). However, little research has been done on the role of new technologies in the preservation and transmission of intangible heritage. The chapter examines resources, projects and technologies providing access to ICH and identifies gaps and constraints. It draws on research conducted within the scope of the collaborative research project , i-Treasures. In so doing, it covers the state of the art in technologies that could be employed for access, capture and analysis of ICH in order to highlight how specific new technologies can contribute to the transmission and safeguarding of ICH.

Part 11: New Methods and Tools for Big Data Wokshop (MT4BD); International audience; This paper presents a novel methodology for selecting the most representative features for identifying the presence of the Parkinson’s Disease (PD). The proposed methodology is based on interactive visual analytic based on multi-objective optimisation. The implemented tool processes and visualises the information extracted via performing a typical line-tracking test using a tablet device. Such output information includes several modalities, such as position, velocity, dynamics, etc. Preliminary results depict that the implemented visual analytics technique has a very high potential in discriminating the PD patients from healthy individuals and thus, it can be used for the identification of the best feature type which is representative of the disease presence.

The ability of a large ensemble of regional climate models to accurately simulate heat waves at the regional scale of Europe was evaluated. Within the EURO-CORDEX project, several state-of-the art models, including non-hydrostatic meso-scale models, were run for an extended time period (20 years) at high resolution (12 km), over a large domain allowing for the first time the simultaneous representation of atmospheric phenomena over a large range of spatial scales. Eight models were run in this configuration, and thirteen models were run at a classical resolution of 50 km. The models were driven with the same boundary conditions, the ERA-Interim re-analysis, and except for one simulation, no observations were assimilated in the inner domain. Results, which are compared with daily temperature and precipitation observations (ECA&D and E-OBS data sets) show that, even forced by the same re-analysis, the ensemble exhibits a large spread. A preliminary analysis of the sources of spread, using in particular simulations of the same model with different parameterizations, shows that the simulation of hot temperature is primarily sensitive to the convection and the microphysics schemes, which affect incoming energy and the Bowen ratio. Further, most models exhibit an overestimation of summertime temperature extremes in Mediterranean regions and an underestimation over Scandinavia. Even after bias removal, the simulated heat wave events were found to be too persistent, but a higher resolution reduced this deficiency. The amplitude of events as well as the variability beyond the 90th percentile threshold were found to be too strong in almost all simulations and increasing resolution did not generally improve this deficiency. Resolution increase was also shown to induce large-scale 90th percentile warming or cooling for some models, with beneficial or detrimental effects on the overall biases. Even though full causality cannot be established on the basis of this evaluation work, the drivers of such regional differences were shown to be linked to changes in precipitation due to resolution changes, affecting the energy partitioning. Finally, the inter-annual sequence of hot summers over central/southern Europe was found to be fairly well simulated in most experiments despite an overestimation of the number of hot days and of the variability. The accurate simulation of inter-annual variability for a few models is independent of the model bias. This indicates that internal variability of high summer temperatures should not play a major role in controlling inter-annual variability. Despite some improvements, especially along coastlines, the analyses conducted here did not allow us to generally conclude that a higher resolution is clearly beneficial for a correct representation of heat waves by regional climate models. Even though local-scale feedbacks should be better represented at high resolution, combinations of parameterizations have to be improved or adapted accordingly. © 2013 Springer-Verlag Berlin Heidelberg., The EURO-CORDEX simulations and analysis were carried out in several groups within the framework of the IMPACT2C FP7 project (Grant FP7-ENV.2011.1.1.6-1). The BTU Cottbus offered an exchange ftp site for sharing all model simulation files. The contribution from CRP-GL was funded by the Luxembourg National Research Fund (FNR) through Grant FNR C09/SR/16 (CLIMPACT). We acknowledge financial support from the Spanish R&D program through Grants CGL2010-21869 (EXTREMBLES) and CGL2010-22158-C02-01 (CORWES). Charles University runs were supported partially in framework of the Research Plan of MSMT (No. MSM 0021620860) and GACR project No. P209/11/2405. The contribution from UHOH was funded by the German Science Foundation (DFG) through project FOR 1695. The REMO simulations were supported by CSC, MPI, as well as BMBF and performed under the ‘‘Konsortial’’ share at the German Climate Computing Centre (DKRZ), as well as the CCLM simulations carried out by BTU, which we are further thankful for their various support. We are thankful to the French CCRT/TGCC supercomputing center support and the CEA and GENCI computing resource allocation agency for the WRF-IPSL-INERIS runs. The development of the modeling chain at INERIS and IPSL was carried out in part within the French national project SALUT’AIR (PRIMEQUAL research program). Part of SMHI contribution was done in the ECLISE projects that receive funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant agreement 265240 and in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). DHMZ contribution was partially supported by the Croatian Ministry of Science, Education and Sports (Grant No. 004-1193086-3035).

Part 6: Third Intelligent Systems for Quality of Life Information Services Workshop (ISQL 2012); International audience; Analysis of environmental information is considered of utmost importance for humans, since environmental conditions are strongly related to health issues and to a variety of everyday activities. Despite the fact that there are already many free on-line services providing environmental information, there are several cases, in which the presentation format complicates the extraction and processing of such data. A very characteristic example is the air quality forecasts, which are usually encoded in image maps of heterogeneous formats, while the initial (numerical) pollutant concentrations, calculated and predicted by a relevant model, remain unavailable. This work addresses the task of semi-automatic extraction of such information based on a template configuration tool, on methodologies for data reconstruction from images, as well as on Optical Character Recognition (OCR) techniques. The framework is tested with a number of air quality forecast heatmaps demonstrating satisfactory results.

Part 6: Third Intelligent Systems for Quality of Life Information Services Workshop (ISQL 2012); International audience; Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information platform that supports submission of user queries related to environmental conditions and orchestrates results from complementary services to generate personalized suggestions. From the technical viewpoint, the system discovers and processes reliable data in the web in order to convert them into knowledge. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. The platform is demonstrated with real world use cases in the south area of Finland showing the impact it can have on the quality of everyday life.

Full a posteriori verification of the correctness of modern software systems is practically infeasible due to the sheer complexity resulting from their intrinsic concurrent nature. An alternative approach consists of ensuring correctness by construction. We discuss the Rigorous System Design (RSD) approach, which relies on a sequence of semantics-preserving transformations to obtain an implementation of the system from a high-level model while preserving all the properties established along the way. In particular, we highlight some of the key requirements for the feasibility of such an approach, namely availability of (1) methods and tools for the design of correct-by-construction high-level models and (2) definition and proof of the validity of suitable domain-specific abstractions. We summarise the results of the extended versions of seven papers selected among those presented at the $$1\mathrm {st}$$ 1 st and the $$2\mathrm {nd}$$ 2 nd International Workshops on Methods and Tools for Rigorous System Design (MeTRiD 2018–2019), indicating how they contribute to the advancement of the RSD approach.

International audience; The main purpose of the paper is to analyze seismic site effects in alluvial basins and to discuss the influence of the knowledge of the local geology on site amplification simulations. Wave amplification is due to a combined effect of impedance ratio between soil layers and surface wave propagation due to the limited extent of the basin. In this paper, we investigate the influence of the complexity of the soil layering (simplified or detailed layering) on site effects in both time and frequency domain. The analysis is performed by the Boundary Element Method. The European test site of Volvi (Greece) is considered and 2D amplification in the basin is investigated for various soil models. Seismic signals are computed in time domain for synthetic Ricker signals as well as actual measurements. They are analyzed in terms of amplification level as well as time duration lengthening (basin effects) for both SH and SV waves. These results show that the geometry of the basin has a very strong influence on seismic wave amplification in terms of both amplification level and time duration lengthening. The combined influence of geometry/layering of alluvial basins seems to be very important for the analysis of 2D (3D) site effects but a simplified analysis could sometimes be sufficient. In the case of Volvi European test site, this influence leads to (measured and computed) 2D amplification ratios far above 1D estimations from horizontal layering descriptions.

The technique of transmitting multi-tone signals in a radiative Wireless Power Transfer (WPT) system can significantly increase its end-to-end power efficiency. The optimization problem in this system is to tune the transmission according to the receiver rectenna&rsquo, s nonlinear behavior and the Channel State Information (CSI). This is a non-convex problem that has been previously addressed by Sequential Convex Programming (SCP) algorithms. Nonetheless, SCP algorithms do not always attain globally optimal solutions. To this end, in this paper, we evaluate a set of Evolutionary Algorithms (EAs) with several characteristics. The performance of the optimized multi-tone transmission signals in a WPT system is assessed by means of numerical simulations, utilizing a simplified Single Input Single Output (SISO) model. From the model evaluation, we can deduce that EAs can be successfully applied to the waveform design optimization problem. Moreover, from the presented results, we can derive that EAs can obtain the optimal solutions in the tested cases.

This paper proposes the integration of non-orthogonal multiple access (NOMA) and broadcasting with buffer-aided (BA) full-duplex (FD) relaying to improve the spectral efficiency of a single-source two-user network. Furthermore, opportunistic relay selection is employed, activating one or two FD relays to support the end-to-end communication. The network performance is improved through buffers, allowing to switch to half-duplex (HD) transmission/reception when FD operation is not feasible. A hybrid BA FD/HD NOMA relay selection algorithm is proposed, herein called $\mathop {\mathrm{BAHyNOMA}}$ , orchestrating the three aforementioned techniques, towards a simple yet efficient and robust relay selection. $\mathop {\mathrm{BAHyNOMA}}$ is evaluated in terms of outage probability, average sum-rate and average delay, and it is observed that the average sum-rate can be doubled, while maintaining low latency.

Part 8: Modelling; International audience; We study formal modelling of architectures applied on parametric component-based systems consisting of an unknown number of instances of each component. Architecture modelling is achieved by means of logics. We introduce an extended propositional interaction logic and investigate its first-order level which serves as a formal language for the interactions of parametric systems. Our logic effectively describes the execution order of interactions which is a main feature in several important architectures. We state the decidability of equivalence, satisfiability, and validity of first-order extended interaction logic formulas, and provide several examples of formulas describing well-known architectures.

International audience; Deep Reinforcement Learning (DRL) is increasingly used to train robots to perform complex and delicate tasks, while the development of realistic simulators contributes to the acceleration of research on DRL for robotics. However, it is still not straightforward to employ such simulators in the typical DRL pipeline, since their steep learning curve and the enormous amount of development required to interface with DRL methods significantly restrict their use by researchers. To overcome these limitations, in this work we present an open-source framework that combines an established interface used by DRL researchers, the OpenAI Gym interface, with the state-of-the-art Webots robot simulator in order to provide a standardized way to employ DRL in various robotics scenarios. Deepbots aims to enable researchers to easily develop DRL methods in Webots by handling all the low-level details and reducing the required development effort. The effectiveness of the proposed framework is demonstrated through code examples, as well as using three use cases of varying difficulty.

International audience; New capabilities for measuring and monitoring are needed to prevent the shrink-swell risk caused by drought-rewetting cycles. A clayey soil in the Loire Valley at Chaingy (France) has been instrumented with two extensometers and several soil moisture sensors. Here we show by direct comparison between remote and in situ data that the vertical ground displacements due to clay expansion are well-captured by the Multi-Temporal Synthetic Aperture Radar Interferometry (MT-InSAR) technique. In addition to the one-year period, two sub-annual periods that reflect both average ground shrinking and swelling timeframes are unraveled by a wavelet-based analysis. Moreover, the relative phase difference between the vertical displacement and surface soil moisture show local variations that are interpreted in terms of depth and thickness of the clay layer, as visualized by an electrical resistivity tomography. With regard to future works, a similar treatment relying fully on remote sensing observations may be scaled up to map larger areas in order to better assess the shrink-swell risk.

In October 2017, the Sentinel-5 Precursor (S5P) mission was launched, carrying the TROPOspheric Monitoring Instrument (TROPOMI), which provides a daily global coverage at a spatial resolution as high as 7 km × 3.5 km and is expected to extend the European atmospheric composition record initiated with GOME/ERS-2 in 1995, enhancing our scientific knowledge of atmospheric processes with its unprecedented spatial resolution. Due to the ongoing need to understand and monitor the recovery of the ozone layer, as well as the evolution of tropospheric pollution, total ozone remains one of the leading species of interest during this mission. In this work, the TROPOMI near real time (NRTI) and offline (OFFL) total ozone column (TOC) products are presented and compared to daily ground-based quality-assured Brewer and Dobson TOC measurements deposited in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Additional comparisons to individual Brewer measurements from the Canadian Brewer Network and the European Brewer Network (Eubrewnet) are performed. Furthermore, twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, which form part of the SAOZ network (Système d'Analyse par Observation Zénitale), are used for the validation. The quality of the TROPOMI TOC data is evaluated in terms of the influence of location, solar zenith angle, viewing angle, season, effective temperature, surface albedo and clouds. For this purpose, globally distributed ground-based measurements have been utilized as the background truth. The overall statistical analysis of the global comparison shows that the mean bias and the mean standard deviation of the percentage difference between TROPOMI and ground-based TOC is within 0 –1.5 % and 2.5 %–4.5 %, respectively. The mean bias that results from the comparisons is well within the S5P product requirements, while the mean standard deviation is very close to those limits, especially considering that the statistics shown here originate both from the satellite and the ground-based measurements. Additionally, the TROPOMI OFFL and NRTI products are evaluated against already known spaceborne sensors, namely, the Ozone Mapping Profiler Suite, on board the Suomi National Polar-orbiting Partnership (OMPS/Suomi-NPP), NASA v2 TOCs, and the Global Ozone Monitoring Experiment 2 (GOME-2), on board the Metop-A (GOME-2/Metop-A) and Metop-B (GOME-2/Metop-B) satellites. This analysis shows a very good agreement for both TROPOMI products with well-established instruments, with the absolute differences in mean bias and mean standard deviation being below +0.7 % and 1 %, respectively. These results assure the scientific community of the good quality of the TROPOMI TOC products during its first year of operation and enhance the already prevalent expectation that TROPOMI/S5P will play a very significant role in the continuity of ozone monitoring from space.

Non-orthogonal multiple access (NOMA) aims to increase the spectral efficiency of fifth generation networks by relaxing the orthogonal use of radio-resources. In this paper, a network with multiple half-duplex buffer-aided (BA) relays is considered, where the source transmits with a fixed rate toward two users. The users might demand the same rate by the source (e.g., two cellular users requiring the same service), or they could have different rate requirements (e.g., a cellular user coexisting with an Internet of Things device). By deploying multiple BA relays, increased reliability and additional degrees of freedom are provided. Leveraging the spectral efficiency of NOMA and the increased diversity gain of BA relaying, two relay selection algorithms with broadcasting are proposed for power-domain NOMA and hybrid NOMA/OMA, namely BA-NOMA and BA-NOMA/OMA, respectively. BA-NOMA can improve the performance in terms of the outage probability when the power allocation factor α is selected such that robustness against channel uncertainties due to, e.g., outdated channel state information is provided. Moreover, BA-NOMA/OMA further improves the sum-rate by switching to OMA when the relays cannot serve the users through NOMA. For both cases, a theoretical analysis for the outage probability is conducted and the asymptotic performance is studied. Finally, numerical results and comparisons with other state-of-the-art algorithms are provided for the outage probability, average throughput, and average delay.

Temporal feature integration refers to a set of strategies attempting to capture the information conveyed in the temporal evolution of the signal. It has been extensively applied in the context of semantic audio showing performance improvements against the standard frame-based audio classification methods. This paper investigates the potential of an enhanced temporal feature integration method to classify environmental sounds. The proposed method utilizes newly introduced integration functions that capture the texture window shape in combination with standard functions like mean and standard deviation in a classification scheme of 10 environmental sound classes. The results obtained from three classification algorithms exhibit an increase in recognition accuracy against a standard temporal integration with simple statistics, which reveals the discriminative ability of the new metrics.

Greece hosts a variety of magmatic-hydrothermal ore deposits/prospects with porphyry- and epithermal styles playing a major role in its total gold endowment. These deposit types are mainly clustered in two areas, the Rhodope- and Attico-Cycladic massifs, and formed from about 33 Ma to the Pleistocene, as a result of back-arc extension in the Aegean Sea, metamorphic core complex formation, and contemporaneous post-subduction and arc magmatism. In the Serbo-Macedonian massif, porphyry Cu-Au deposits include Skouries, Fisoka, Tsikara, Vathi and Gerakario. Causative intrusives are Oligocene to Miocene granodiorites to monzonites. Gold and PGE mineralization is associated with potassic alteration of the intrusives. In the eastern Rhodope massif and the NE Aegean islands, porphyry prospects occur at Pagoni Rachi, Konos Hill, Myli, Melitena (west Thrace), Fakos, Sardes, and Kaspakas (Limnos island) and Stypsi (Lesvos island). Mineralization is associated with Oligocene to Miocene subvolcanics of calc-alkaline to shoshonitic affinity. Feature of these prospects, which they share in common with several porphyry Au-only systems, is their shallow depth of emplacement, the presence of potassic/sodic-calcic and/or phyllic alteration, a strong epithermal overprint, their low Cu content, an extreme Re-enrichment, the multistage introduction of Au, the presence of banded quartz veinlets, and the local presence of tourmaline. New discoveries of porphyry-style mineralization at King Arthur, St. Philippos and Aisymi, increases the gold potential in west Thrace. High-intermediate sulfidation epithermal Au-Ag polymetallic deposits/prospects overprint and/or occur laterally from porphyry-style mineralization, where they are spatially associated with lithocaps of advanced argillic alteration. High-intermediate sulfidation Au-Ag epithermal mineralization at Perama Hill, Mavrokoryfi and Pefka in west Thrace, and at Pterounda, Mesotopos and Megala Therma on Lesvos island is controlled by steeply-dipping extensional faults within volcanic rocks, without any obvious genetic relationship to spatially-related porphyry-style mineralization. Polymetallic epithermal deposits and prospects contain critical and energy critical metals (e.g., Te, Se, Bi, Sb, In, Ge and Ga), which may be considered as by-products. In the Attico-Cycladic area, porphyry Mo-W mineralization occurs as sheeted quartz veins and stockworks cutting a potassic- to sericitic-altered Miocene granodiorite stock in the Lavrion district. Bonanza grade Au- and/or Ag-rich veins with epithermal affinities crosscut metamorphic rocks at Lavrion, and on Syros, Tinos, Antiparos and Anafi islands. Milos island is characterized by shallow submarine volcanic-hosted IS-HS epithermal Au-Ag-Te and base metal deposits. Antimony-As-Ag-Au deposits/prospects on Chios, Samos and Kos islands in the eastern Aegean Sea, indicate the potential for Carlin-style mineralization in Greece. Several factors played a role to the metal endowment of the Aegean porphyry-epithermal systems: magma fertility in the source regions, depth of emplacement of causative intrusives, relative contribution of mantle versus crustal material, redox state of subduction-related magmas, and physico-chemical fluid conditions at the site of ore deposition.

openaire: EC/H2020/736876/EU//VISORSURF We present the design, fabrication, and characterization of a proof-of-concept reconfigurable multifunctional metasurface. Our implementation relies on a printed circuit board loaded with commercial-off-the-shelf resistors and capacitors (surface mount devices) to shape the metasurface response in the microwave regime spanning 4-12 GHz. In a broader vision, these static loads are to be replaced by computer-controlled chips, thus realizing a software-defined metamaterial vision. In the current implementation, the same type of board is loaded with different combinations of resistive and reactive loads, to model different configurations and realize the corresponding functionalities, such as absorption, steering, and polarizing. Our anechoic chamber measurements indicate good agreement between simulation and experiment.

International audience; Active vision approaches hold the credentials for improving the accuracy of Deep Learning (DL) models for many challenging visual analysis tasks and varying environmental conditions. However, active vision approaches are typically closely tied to the underlying hardware, slowing down their adoption, while they typically increase the latency of perception systems, since sensory data must be recaptured. In this work, we propose a pseudo-active data refinement method that works by appropriately refining the sensory input, without having to reacquire the sensor data through traditional camera control approaches. The proposed method is fully differentiable and can be trained for the task at hand in an end-to-end fashion, while it can be directly deployed in a wide variety of systems, tasks and conditions. The effectiveness and robustness of the proposed method is demonstrated across a variety of tasks using two challenging datasets.

International audience; Deep Learning (DL) models that support adaptive computational graphs allow for easily adapting the computations to the available resources by selecting the most appropriate computational path. However, such models are typically used in classification settings, e.g., using early exits, despite that DL models often aim at extracting representations, e.g., for face recognition. In this work, we provide a metric-learning oriented early exit methodology for DL models. As we demonstrate, employing early exits in metric learning scenarios pose unique challenges compared to existing methodologies for classificationoriented early exits. To this end, we employ the Bag-of-Features model to efficiently extract compact representations from any layer of a DL model that is then combined with an efficient linear regressor to match the final representation of the model (without having to feedforward the whole computational graph). The proposed method is agile and can be directly used with any pretrained DL model, while it is end-to-end differentiable, allowing for further fine-tuning the models towards having multiple early exits. The effectiveness of the proposed method is demonstrated using five face verification/recognition datasets.

Part 16: Recommendation Systems; International audience; In the clothing industry, design, development, and procurement teams have been affected more than any other industry and are constantly under pressure to present more products with fewer resources in a shorter time. The diversity of garment designs created as new products is not found in any other industry and is almost independent of the size of the business. Science4Fashion is a semi-autonomous intelligent personal assistant for fashion product designers. Our system consists of an interactive environment where a user utilizes different modules responsible for a) data collection from online sources, b) knowledge extraction, c) clustering, and d) trend/product recommendation. This paper is focusing on two core modules of the implemented system. The Clustering Module combines various clustering algorithms and offers a consensus that arranges data in clusters. At the same time, the Product Recommender and Feedback module receives the designer’s input on different fashion products and recommends more relevant items based on their preferences. The experimental results highlight the usefulness and the efficiency of the proposed subsystems in aiding the creative fashion process.

Part 17: Sentiment Analysis; International audience; Even though Deep Learning (DL) models are increasingly used in recent years to develop trading agents, most of them solely rely on a restricted set of input information, e.g., price time-series. However, this is in contrast with the information that is usually available to human traders that, apart from relying on price information, also take into account their prior knowledge, sentiment that is expressed regarding various markets and assets, as well as general news and forecasts. In this paper, we examine whether the use of sentiment information, as extracted by various online sources, including news articles, is beneficial when training DL agents for trading. More specifically, we provide an extensive evaluation that includes several different configurations and models, ranging from Multi-layer Perceptrons (MLPs) to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), examining the impact of using sentiment information when developing DL models for trading applications. Apart from demonstrating that sentiment can indeed lead to improved trading efficiency, we also provide further insight on the use of sentiment-enriched data sources for cryptocurriences, such as Bitcoin, where its seems that sentiment information might actually be a stronger predictor compared to the information provided by the actual price time-series.

Part 2: AI in Biomedical Applications; International audience; Biomedical Named Entity Recognition is a difficult task, aimed to identify all named entities in medical literature. The importance of the task becomes apparent as these entities are used to identify key features, enable better search results and can accelerate the process of reviewing related evidence to a medical case. This practice is known as Evidence-Based Medicine (EBM) and is globally used by medical practitioners who do not have the time to read all the latest developments in their respective fields. In this paper we propose a methodology which achieves state-of-the-art results in a plethora of Biomedical Named Entity Recognition datasets, with a lightweight approach that requires minimal training. Our model is end-to-end and capable of efficiently modeling significantly longer sequences than previous models, benefiting from inter-sentence dependencies.

International audience; The increasing complexity of deep learning models led to the development of Knowledge Distillation (KD) approaches that enable us to transfer the knowledge between a very large network, called teacher and a smaller and faster one, called student. However, as recent evidence suggests, using powerful teachers often negatively impacts the effectiveness of the distillation process. In this paper, the reasons behind this apparent limitation are studied and an approach that transfers the knowledge to smaller models more efficiently is proposed. To this end, multiple highly specialized teachers are employed, each one for a small set of skills, overcoming the aforementioned limitation, while also achieving high distillation efficiency by diversifying the ensemble. At the same time, the employed ensemble is formulated in a unified structure, making it possible to simultaneously train multiple models. The effectiveness of the proposed method is demonstrated using three different image datasets, leading to improved distillation performance, even when compared with powerful state-of-the-art ensemble-based distillation methods.

Publisher Copyright: © 2002-2012 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Full duplex (FD) systems are able to transmit and receive signals over the same frequency band simultaneously with the potential of even doubling the spectral efficiency in comparison with traditional half duplex (HD) systems. When combined with relaying, FD systems are expected to dramatically increase the throughput of future wireless networks. However, the degrading effect of self-interference (SI) due to the simultaneous transmission and reception at the relay threatens their efficient rollout in real-world topologies. At the same time, non-orthogonal multiple access (NOMA) can further increase the spectral efficiency of the network. In this article, we present how buffering at the relays can be integrated with FD and NOMA in order to improve the performance of relay networks. More specifically, the key points for the successful integration of buffer-aided relays with spatial sharing paradigms are presented, and details on buffer-state-information-based relay selection and its fully distributed implementation are discussed. Furthermore, a mathematical framework for the analysis of FD and HD buffer-aided relay networks is rigorously discussed. Performance evaluation shows the importance of data buffering toward mitigating SI and improving the sum-rate of the network. Finally, the goal of this work is both to summarize the current state of the art and, by calling attention to open problems, to spark interest toward targeting these and related problems in relay networks.

Fault diagnosis of sequential batch reactor (SBR), a widely applied wastewater treatment technology in papermaking industry with huge discharge, has been a significant challenge due to the inherent multi-period characteristics of the process. In this paper, based on the conventional multi-way principal component analysis (MPCA) method (Scenario 0), two sub-period division strategies based on the processing phases of SBR process (Scenario 1) and the similarities of the loading matrices between the adjacent time slices (Scenario 2) are proposed for the detection of faults. Combined with Scenario 0, using the field data of blower current, level of SBR reactor, dissolved oxygen of wastewater and the blower valve opening in the SBR process of paper mill, two different fault diagnosis models with Scenarios 1 and 2 are developed and evaluated, respectively. The study results revealed that, both the calculated statistics of T2 and sum of prediction errors (SPE) of the fault diagnosis models with Scenarios 1 and 2 could detect the faults and identify the fault locations and sources. Compared to Scenario 0 which neglects the correlations between the different stages of SBR process, the fault diagnosis model by Scenario 2 demonstrated a superiority ability in fault identification in terms of the fault’s time onset and fault’s sources with adequate accuracy. The results enable the feasible and reliable implementation of the developed sub-MPCA diagnosis model with Scenario 2 in the actual SBR plants.

International audience; Performing fast few-shot learning is increasingly important in a number of embedded applications. Among them, a form of gradient-descent free learning known as Weight Imprinting was recently established as an efficient way to perform few-shot learning on Deep Learning (DL) accelerators that do no support back-propagation, such as Edge Tensor Processing Units (Edge TPUs). Despite its efficiency, WI comes with a number of critical limitations. For example, WI cannot effectively handle multimodal novel categories, while it is especially prone to overfitting that can have devastating effects on the accuracy of the models on novel categorizes. To overcome these limitations, in this paper we propose a robust hypersphere-based WI approach that allows for regularizing the training process in an imprintingaware way. At the same time, the proposed formulation provides a natural way to handle multimodal novel categories. Indeed, as demonstrated through the conducted experiments, the proposed method leads to significant improvements over the baseline WI approach.

International audience; Several deep supervised hashing techniques have been proposed to allow for querying large image databases. However, it is often overlooked that the process of information retrieval can be modeled using information-theoretic metrics, leading to optimizing various proxies for the problem at hand instead. Contrary to this, we propose a deep supervised hashing algorithm that optimizes the learned codes using an informationtheoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of large-scale hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI), that is inspired by QMI, but leads to significant better retrieval precision. Indeed, the effectiveness of the proposed method is demonstrated under several different scenarios, using different datasets and network architectures, outperforming existing deep supervised image hashing techniques.

International audience; We experimentally present a 10-Gb/s Fiber Wireless IFoF/V-band uplink of four625Mbaud 16QAM signals with a linear, high-power monolithically integrated reflective electroabsorptionmodulator with semiconductor optical amplifier for 5G mmWave fronthaul networks.

International audience; Visual Question Answering (VQA) is one of the most challenging emerging applications of deep learning. Providing powerful attention mechanisms is crucial for VQA, since the model must correctly identify the region of an image that is relevant to the question at hand. However, existing models analyze the input images at a fixed and typically small resolution, often leading to discarding valuable fine-grained details. To overcome this limitation, in this work we propose a reinforcement learningbased active perception approach that works by applying a series of transformation operations on the images (translation, zoom) in order to facilitate answering the question at hand. This allows for performing fine-grained analysis, effectively increasing the resolution at which the models process information. The proposed method is orthogonal to existing attention mechanisms and it can be combined with most existing VQA methods. The effectiveness of the proposed method is experimentally demonstrated on a challenging VQA dataset.

International audience; We report the synthesis of nanocomposites made of silica nanoparticles whose six surface dimples are decorated with magnetic maghemite nanoparticles and their use for detection and recovery of arsenic in aqueous media. Precursor silica nanoparticles have aminated polystyrene chains at the bottom of their dimples and the maghemite nanoparticles are surface functionalized with carboxylic acid groups in two steps: amination with 3-aminopropyltrimethoxysilane, then derivatization with succinic anhydride in the presence of triethylamine. In the end, the colloidal assembly consists of the regioselective grafting of the carboxylic acid-modified iron oxide nanoparticles onto the 6-dimple silica nanoparticles. Several characterization techniques such as transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), dynamic light scattering (DLS) are employed to assess the grafting process and study the influence of the maghemite functional groups on the quality of the composites formed. The resulting magnetic nanocomposites are used for the environmentally benign detection and removal of arsenic from aqueous medium, being readily extracted through means of magnetic separation.

International audience; Bag-of-Features (BoF)-based models have been traditionally used for various computer vision tasks, due to their ability to provide compact semantic representations of complex objects, e.g., images, videos, etc. Indeed, BoF has been successfully combined with various feature extractions methods, ranging from handcrafted feature extractors to powerful deep learning models. However, BoF, along with most of the pooling approaches employed in deep learning, fails to capture the temporal dynamics of the input sequences. This leads to significant information loss, especially when the informative content of the data is sequentially distributed over the temporal dimension, e.g., videos. In this paper we propose a novel stateful recurrent quantization and aggregation approach in order to overcome the aforementioned limitation. The proposed method is inspired by the well-known Bag-of-Features (BoF) model, but employs a stateful trainable recurrent quantizer, instead of plain static quantization, allowing for effectively encoding the temporal dimension of the data. The effectiveness of the proposed approach is demonstrated using three video action recognition datasets.

International audience; The chemical bath deposition (CBD) of ZnO nanowires (NWs) is of high interest, but their formation occurs in a growth medium containing many impurities including carbon, nitrogen, and hydrogen, rendering the accurate determination of predominant crystal defects as highly debated. In addition to the typical interstitial hydrogen in bond-centered sites (HBC) and zinc vacancy-hydrogen (VZn−nH) complexes, we reveal that nitrogen-related defects play a significant role on the physical properties of unintentionally doped ZnO NWs. We show by density functional theory that the VZn−NO−H defect complex acts as a deep acceptor with a relatively low formation energy and exhibits a prominent Raman line at 3078cm–1 along with a red-orange emission energy of ∼1.82 eV in cathodoluminescence spectroscopy. The nature and concentration of the nitrogen- and hydrogen-related defects are found to be tunable using thermal annealing under oxygen atmosphere, but a rather complex, fine evolution including successive formation and dissociation processes is highlighted as a function of annealing temperature. ZnO NWs annealed at the moderate temperature of 300 °C specifically exhibit one of the smallest free charge carrier densities of 5.6×1017cm–3 along with a high mobility of ∼60 cm2/Vs following the analysis of longitudinal optical phonon-plasmon coupling. These findings report a comprehensive diagram showing the complex interplay of each nitrogen- and hydrogen-related defect during thermal annealing and its dependence on annealing temperature. They further reveal that the engineering of the nitrogen- and hydrogen-related defects as the major source of crystal defects in ZnO NWs grown by CBD is capital to precisely control their electronic structure properties governing their electrical and optical properties in any devices.

Part 13: Machine Learning; International audience; Nowadays, businesses are highly competitive as most markets are extremely saturated. As a result, customer management is of critical importance to avoid dissatisfaction that leads to customer loss. Thus, predicting customer loss is crucial to efficiently target potential churners and attempt to retain them. By classifying customers as churners and non-churners, customer loss is equated to a binary classification problem. In this paper, a new real-world dataset is used, originating from a popular web-based drug information platform, in order to predict subscriber churn. A number of methods that belong to different machine learning categories (linear, nonlinear, ensemble, neural networks) are constructed, optimized and trained on the subscription data and the results are presented and compared. This study provides a guide for solving churn prediction problems as well as a comparison of various models within the churn prediction context. The findings co-align with the notion that ensemble methods are, in principle, superior whilst every model maintains satisfying results.

N. Ban et al., Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of ∼3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from ∼ −40% at 12 km to ∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales., Open access funding provided by University of Innsbruck and Medical University of Innsbruck., The ETH team, MZ, CNRM IPSL, ICTP, SMHI, Met-Office, DMI, CMCC, HZG, KNMI acknowledge funding from the HORIZON 2020 EUCP (European Climate Prediction System) project (https://www.eucp-project.eu, grant agreement No. 776613). AL-G acknowledges support by the Spanish government through grant BES-2016-078158 and MINECO/FEDER co-funded project MULTI-SDM (CGL2015-66583-R).

The PICOSEC Micromegas detector can time the arrival of Minimum Ionizing Particles with a sub-25 ps precision. A very good timing resolution in detecting single photons is also demonstrated in laser beams. The PICOSEC timing resolution is determined mainly by the drift field. The arrival time of the signal and the timing resolution vary with the size of the pulse amplitude. Detailed simulations based on GARFIELD++ reproduce the experimental PICOSEC timing characteristics. This agreement is exploited to identify the microscopic physical variables, which determine the observed timing properties. In these studies, several counter-intuitive observations are made for the behavior of such microscopic variables. In order to gain insight on the main physical mechanisms causing the observed behavior, a phenomenological model is constructed and presented. The model is based on a simple mechanism of "time-gain per interaction" and it employs a statistical description of the avalanche evolution. It describes quantitatively the dynamical and statistical properties of the microscopic quantities, which determine the PICOSEC timing characteristics, in excellent agreement with the simulations. In parallel, it offers phenomenological explanations for the behavior of these microscopic variables. The formulae expressing this model can be used as a tool for fast and reliable predictions, provided that the input parameter values (e.g. drift velocities) are known for the considered operating conditions., Comment: Corresponding author S. E. Tzamarias, 47 pages, 19 figures, 2 appendices, 8 tables

During the last few years, there is a growing concern about climate change and its negative effects on water availability. This study aims to evaluate the performance of regional climate models (RCMs) in simulating seasonal precipitation over the mountainous range of Central Pindus (Greece). To this end, observed precipitation data from ground-based rain gauge stations were compared with RCMs grid point&rsquo, s simulations for the baseline period 1974&ndash, 2000. Statistical indexes such as root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficient, and standard deviation (SD) were used in order to evaluate the model&rsquo, s performance. The results demonstrated that RCMs fail to represent the temporal variability of precipitation time series with exception of REMO. Although, concerning the model&rsquo, s prediction accuracy, it was found that better performance was achieved by the RegCM3 model in the study area. In addition, regarding a future projection (2074&ndash, 2100), it was highlighted that precipitation will significantly decrease by the end of the 21st century, especially in spring (&minus, 30%). Therefore, adaption of mountainous catchment management to climate change is crucial to avoid water scarcity.

International audience; Early exits provide an effective way of implementing adaptive computational graphs over deep learning models. In this way it is possible to adapt them on-the-fly to the available computational resources or even to the difficulty of each input sample, reducing the energy and computational power requirements in many embedded and mobile applications. However, performing this kind of adaptive inference also comes with several challenges, since the difficulty of each sample must be estimated and the most appropriate early exit must be selected. It is worth noting that existing approaches often lead to highly unbalanced distributions over the selected early exits, reducing the efficiency of the adaptive inference process. At the same time, only a few resources can be devoted to the aforementioned process, in order to ensure that an adequate speedup will be obtained. The main contribution of this work is to provide an easy to use and tune adaptive inference approach for early exits that can overcome some of these limitations. In this way, the proposed method allows for a) obtaining a more balanced inference distribution among the early exits, b) relying on a single and interpretable hyperparameter for tuning its behavior (ranging from faster inference to higher accuracy), and c) improving the performance of the networks (increasing the accuracy and reducing the time needed for inference). Indeed, the effectiveness of the proposed method over existing approaches is demonstrated using four different image datasets.

One of the possible ways to increase the end-to-end power transfer efficiency in a radiative Wireless Power Transfer (WPT) system is by transmitting multi-tone signals optimized according to the receiver rectenna's nonlinear behavior and the Channel State Information (CSI). This optimization problem is a non-convex problem that has been tackled in the past with Sequential Convex Programming (SCP) algorithms. Since SCP algorithms do not guarantee to track the globally optimal solutions, there is interest in applying some other optimization methods to this problem. Here we apply various Evolutionary Algorithms (EAs) with different characteristics. The performance of the designed waveforms is evaluated in Matlab, using a simplified Single Input Single Output (SISO) system model. EAs are successfully applied to waveform design for WPT and seem to track the optimal solutions in the tested cases. Moreover, the effectiveness of the SCP-QCLP method is verified.

International audience; Early exits are capable of providing deep learning models with adaptive computational graphs that can readily adapt on-the-fly to the available resources. Despite their advantages, existing early exit methods suffer from many limitations which limit their performance, e.g., they ignore the information extracted from previous exit layers, they are unable to efficiently handle feature maps with large sizes, etc. To overcome these limitations we propose a Bag-of-Features (BoF)-based method that is capable of constructing efficient hierarchical early exit layers with minimal computational overhead, while also providing an adaptive inference method that allows for early stopping the inference process when the network is confident enough for its output, leading to significant performance benefits. To this end, the BoF model is extended and adapted to the needs of early exits by constructing additive shared histogram spaces that gradually refine the information extracted from the various layers of a network, in a hierarchical manner, while also employing a classification layer reuse strategy to further reduce the number of parameters needed per exit layer. Note that the proposed method is generic and can be readily combined with any neural network architecture. The effectiveness of the proposed method is demonstrated using five different image datasets, proving that early exits can be readily transformed into a practical tool, which can be effectively used in various real-world embedded applications.

A 3D forest monitoring system, called FORSAT (a satellite very high resolution image processing platform for forest assessment), was developed for the extraction of 3D geometric forest information from very high resolution (VHR) satellite imagery and the automatic 3D change detection. FORSAT is composed of two complementary tasks: (1) the geometric and radiometric processing of satellite optical imagery and digital surface model (DSM) reconstruction by using a precise and robust image matching approach specially designed for VHR satellite imagery, (2) 3D surface comparison for change detection. It allows the users to import DSMs, align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes (together with precision values) between epochs. FORSAT is a single source and flexible forest information solution, allowing expert and non-expert remote sensing users to monitor forests in three and four (time) dimensions. The geometric resolution and thematic content of VHR optical imagery are sufficient for many forest information needs such as deforestation, clear-cut and fire severity mapping. The capacity and benefits of FORSAT, as a forest information system contributing to the sustainable forest management, have been tested and validated in case studies located in Austria, Switzerland and Spain. This research received funding from the EUROSTARS [grant number E!7358] funding scheme, co-funded by the European Commission and the participating countries. We thank the anonymous reviewers for their valuable comments. Their contribution improved the quality of the manuscript Publisher's Version

This paper investigates the local variability in nanoscale triple-gate junctionless FinFETs utilizing an analytical symmetric and continuous compact model combined with Monte Carlo simulations. Initially, the device parameters are extracted from the experimental transfer characteristics, such as threshold voltage, ideality factor, low-field mobility, source-drain series resistance, channel length modulation factor and mobility degradation factor. Then, statistical analysis is performed to calculate the mean values and standard deviations of the differences of the aforementioned parameters for the local pairs and large number of devices with reference to the mean value of all dies. Monte Carlo simulations allow the compact model to reproduce successfully the drain current local variability. Most important process parameters have been considered and assessed as variability sources, by using the error propagation formula and exploiting the proposed drain current compact model. Successful application to experimental data, revealed the extent of the variabilities in most important process parameters of the devices (sources of variability).

International audience; Recent advances in Deep Reinforcement Learning (DRL) led to the development of powerful agents that can learn how to perform complicated tasks in an end-to-end fashion operating directly on raw unstructured data, e.g., images. However, the real world performance of such methods critically relies on the quality of the simulation environments used for training them. The main contribution of this paper is the development of a realistic simulation environment, by employing a state-of-the-art graphics engine, for training DRL agents that are able to control a drone for performing active shooting. In contrast with previous approaches, that solely relied on simplistic constrained datasets, the environment employed in this work supports a challenging open-world setting, providing a solid step towards developing effective RL methods for various drone control tasks. An appropriate reward shaping approach is also introduced in this work, ensuring that the agent will behave as expected, avoiding erratic movements, as demonstrated through the conducted experiments.

International audience; Crystal defects in unintentionally doped ZnO nanowires grown by chemical bath deposition (CBD) play a capital role in their optical and electrical properties, governing the performances of many nanoscale engineering devices. However, the nature of these crystal defects is still highly debated. In particular, the hydrogen-related defects have not been explored in detail yet although the growth medium operates in an aqueous solution. Using four-point probe resistivity measurements, we show that ZnO nanowires grown by CBD using zinc nitrate and hexamethylenetetramine exhibit a high electrical conductivity with electron densities ranging from 2.7 × 1018 to 3.1 × 1019 cm−3. Most of them have metallic electrical conduction. By combining density-functional theory calculations with cathodoluminescence and Raman spectroscopy, we reveal that the high electrical conductivity mostly originates from the formation of interstitial hydrogen in bond-centered sites (HBC) and of zinc vacancy−hydrogen (VZn-nH) complexes. In particular, the HBC and (VZn-3H) complex are found to act as two shallow donors with very low formation energy, for which the most stable configurations are reported. Additionally, this combined theoretical and experimental approach allows us to revisit the highly debated origin of the visible and ultraviolet emission bands in the luminescence spectra. They are found to be mostly related to VZn and (VZn-nH) complexes located in the bulk and on the surfaces of ZnO nanowires. These findings represent an important step forward in the identification of the predominant native and extrinsic defects driving the electronic structure properties of ZnO nanowires grown by CBD. They further reveal the significance of hydrogen engineering to tune the source of crystal defects for optimizing the physical properties of ZnO nanowires.

International audience; The efficient design of compact antennas operating over multiple bands suitable for the Internet of Things (IoT) is addressed by means of an instance of the system-by-design (SbD) paradigm. More specifically, an iterative strategy that combines different software modules for the search spaceexploration, the fast physicalmodeling of the radiators, and the quality evaluation of the guess solutions is proposed. To enable such a SbD instance, an innovative strategy that exploits an orthogonal array (OA) scheme to determine the training set of a Learning-by-Example (LBE) algorithm based on a support vector regressor (SVR) is introduced for the efficient physicalmodeling of the layout to be optimized. The features and the potentialities of the proposed methodological approach are assessed in different applicative scenarios by considering representative numerical and experimental validation examples.

Herein, two fully renewable copolyester series, namely poly(pentylene 2,5-furandicarboxylate-co-caprolactone) (PPeCFs) and poly(hexamethylene 2,5-furandicarboxylate-co-caprolactone) (PHeCFs) were successfully synthesized with combining e-caprolactone (CL) with poly(pentylene 2,5-furandicarboxylate) (PPeF) and poly(hexamethylene 2,5-furan dicarboxylate) (PHeF) with different molar ratios. These materials, with a CL content ranging from 10 to 50 mol%, were synthesized for first time using stannous octoate as catalyst via ring opening polymerization (ROP). Their chemical structures and molar composition were evaluated by 1H NMR, 13C NMR and FTIR spectroscopies, while their thermal properties were investigated in detail using Fast Scanning Calorimetry (FSC), Differential scanning calorimetry (DSC) and Thermogravimetric analysis (TGA). The obtained results, in combination with Wide-Angle-X-ray diffractometry (WAXD), showed that copolymerization of CL with PHeF and PPeF led to semi-crystalline and partially amorphous copolyesters respectively, providing the basis for significant thermal properties enhancement with respect to the polycaprolactone (PCL) homopolymer, and therefore a much wider range of melting points (Tm) and glass transition temperatures (Tg) were obtained. TGA of the new copolymers showed excellent thermal stability, exceeding 310 °C and 360 °C for PHeCFs and PPeCFs respectively, while their decomposition mechanism was evaluated by pyrolysis-gas chromatography/mass spectroscopy (Py-GC/MS). Almost all copolyesters and mainly the ones with 40 and 50 mol% CL content showed accelerated enzymatic hydrolysis rate.