dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank the following individuals:  Veronique Corbière ,  Françoise Mascart  and  Audrey Godefroid of the Laboratory of Vaccinology and Mucosal Immunity, Université Libre de Bruxelles (Brussels, Belgium)  for their substantial help in laboratory analysis of syndecan-1;  Anita Verleije ,  Kimberly Gonsette ,  Shalini Raj-Lawrence  and all post-graduate students of the  Department of Anaesthesiology of Erasme Hospital  for providing care to the study patients and help in data collection;  Luc Barvais  and  Alexandre Joosten  for their critical discussion of this study's methods and results.
The face image of an individual is important for most biometrics systems. The face picture gives loads of helpful informations, including the individual's personal identity, gender, ethnicity, age, emotional expression, and so forth. As of late, a few applications that endeavor age estimation have risen. This paper was aimed to address the problem of image-based human age estimation. It has the following main contributions. We used the advantages of the recent method and algorithms named Gcforest, which proved in several classification tasks, this novel approach includes the power of the decision trees and the advantages of a Cascades structure which allows the interaction between trees. We provide a comparison between two feature types handcrafted and deep feature, we used three databases FG NET, PAL and MORPH II.
There has been a developing enthusiasm for the human age estimation process from facial pictures because of an assortment of potential applications. As to assessments, assessing the age of a person from the numerical examination of his face picture is a carefully present topic since it remains a troublesome issue that is impacted by innate and superfluous components. Age estimation by numerical examination of the face picture has diverse potential applications. It can possibly be powerful and helpful for cutting edge video reconnaissance, segment measurement assortment, business knowledge, and client profiling, and inquiry improvement in huge databases. Also, receiving elective approaches to appraise t the human age is found out from different face pictures dependent on the connection between the age data and the facial picture.
Based on LBP and LPQ, the idea behind the BSIF consists in automatically learning a fixed unit from filters starting from a small whole of natural images, instead of using filters manufactured-with-the-hand like LBP or LPQ. BSIF implies a training, instead of a manual adjustment, to obtain a statistically significant representation of the image, which allows encoder effective information by using the quantification by simple element. The training also provides an easy and flexible manner to adjust the length of the descriptor and to adapt it to the applications presenting of the characteristics of unusual images. 4) Visual Geometry Group (VGG16) Face features: VGG16 is a model of convolutional neural network suggested  [7]. The model reaches an accuracy of 92,7% on the basis of data ImageNet [AKR12] which contains more than 14 million images belonging to 1000 classes.
5) DEX-IMDB-WIKI and DEX-ChaLearn-ICCV2015 features: The Deep EXpectation (DEX) is a model based in its networks on the VGG-16 architecture, this model pre-trained on ImageNet. What's more, the creators investigated the advantage of fine-tuning over slithered Internet face pictures with available age. In total, they gathered in excess of 500,000 pictures of famous people from IMDb and Wikipedia. The DEX networks were fine-tuned on the crawled pictures and afterward on the gave pictures with apparent age comments from the ChaLearn LAP 2015 challenge on apparent age estimation. We extracted the features by two networks: DEX-IMDBWIKI and DEX-ChaLearn-ICCV2015. The first was prepared on real age prediction utilizing the trimmed and adjusted faces of the IMDB-WIKI dataset, while the subsequent one is a calibrated rendition of the past model, pre-trained on obvious age utilizing the challenge images.
C. databases 1) MORPH II: This database contains pictures of 13,618 people (guys and females). It contains in excess of 55000 unique of a kind pictures. Every facial picture is commented on with a sequential age. Ages are somewhere in the range of 16 and 77 years. Transform can be separated into more than ethnicity: African, European and other. We utilize the five overlap cross-approval. furthermore, the folds are chosen in such an approach to prevent algorithms from learning the character of the people in the training set by ensuring that all pictures of individual subjects are just in the same fold. 2) FG-NET: This is a widely known database in age estimation. This database has a large variation in lighting conditions, pose and expression. FG-NET contains 1002 facial images associated with 82 individuals.
Each individual has more than 10 photos taken in different ages. FG-NET age range is from zero to 69. As in  [8] ,  [9] , we use the Leave One Person Out cross validation on FG-NET. We leave one individual images out for testing and the other 81 individuals images for training. 3) PAL: The Predictive Aging Lab face is another database from Texas university. It contains 1046 frontal face images (430 males, 616 females). PAL contains faces with different expressions. We perform the random partition as in  [10] ,  [11] , where we randomly partition images in 80% training and the other 20% for testing. It is repeated five times. The average of the five different splits will be the final performance.
Observations of ozonesonde measurements of the NDACC/SHADOZ program and humidity profiles from the daily Météo-France radiosondes at Réunion Island (21. 1°S, 55. 5°E) from November 2013 to April 2016 are analyzed to identify the origin of wet upper tropospheric air masses with low ozone mixing ratio observed above the island, located in the South West Indian Ocean (SWIO). A seasonal variability in hydration events in the upper troposphere was found and linked to the convective activity within the SWIO basin. In the upper troposphere, ozone mixing ratios were lower (mean of 57 ppbv) in humid air masses (RH>50%) compared to the background mean ozone mixing ratio (73. 8 ppbv). A convective signature was identified in the ozone profile dataset by studying the probability of occurrence of different ozone thresholds.
During austral summer (November to April), the Inter Tropical Convergence Zone (ITCZ) moves closer to Réunion Island and convective activity in the SWIO is more pronounced with tropical cyclones forming in the region. Blamey and Reason (2012)  estimated that the east of Mozambique Channel is the most convective zone of the region. In this paper, we analyze ozonesonde measurements of the NDACC/SHADOZ program and humidity profiles from daily Météo-France radiosondes from Reunion Island between November 2013 and April 2016 to identify the origin of wet upper tropospheric air masses with low ozone mixing ratio observed above the island and understand the role of transport, detrainment, and mixing processes on the composition of the tropical upper-troposphere over Réunion Island. We use infrared brightness temperature data from the METEOSAT 7 geostationary satellite to identify deep convective clouds over the SWIO region.
In addition, we use data from operational daily meteorological Meteomodem M10 radiosondes launches performed by Météo-France (MF) at 12 UTC since 2013. The MF dataset provides RH measurements at a higher frequency than the SHADOZ data and this is important to study the day to day variability of the impact of convection on the upper troposphere. The Meteomodem M10 radiosondes provide measurements of temperature, pressure, relative humidity (RH) and zonal/meridional winds. For both MF and SHADOZ sondes, the average ascent speed of the balloon is 5m/s and measurements are recorded every second so the mean native vertical resolution is around 5 m for both datasets. Vertical gaps as high as 500 m can occur in the two datasets and the native vertical resolution varies with altitude. Thus NDACC/SHADOZ ozonesonde and MF radiosonde data are interpolated to a regular vertical grid with a 200-m grid spacing.
METEOSAT 7 geostationary satellite data METEOSAT 7 is a geostationary satellite positioned at the longitude 58°E that provides images for the Indian Ocean since December 2005. The Thermal Infrared channel (wavelength 10. 5-12. 5 µm) of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard METEOSAT 7, has a temporal resolution of 30 minutes and a horizontal resolution of 5 km at nadir. Here we use METEOSAT 7 hourly infrared brightness temperature product available from the ICARE data archive (ftp://ftp. icare. univ-lille1. fr). https://doi. org/10. 5194/acp-2020-2 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4. 0 License. We assume black body radiation  (Slingo, 2004; Tissier et al. , 2016)  to estimate the brightness temperature. Young et al.
(2013)  have classified clouds in the tropics from the CloudSat and MODIS database for one year of observations (  2007 ) over the 30°S-30°N latitude band. They established that cirriform clouds have, on average, higher brightness temperatures than deep convective clouds (respectively 268. 5K against 228. 5K, Figure  5  of  Young et al. , 2013). Therefore, we identify deep convective clouds by selecting METEOSAT 7 pixels with brightness temperature lower than 230K. In order to fold FLEXPART weekly products with METEOSAT 7 infrared brightness temperature data, the latter dataset is interpolated to a regular latitude-longitude grid with a 1° resolution. In addition, for every day between November 2013 and April 2016, we create a map of the deepest convective clouds valid for the previous 7 days by accumulating their positions over the prior week.
Convective influence on the upper troposphere In this part of the study, we use the NDACC/SHADOZ dataset to analyze the convective influence on air masses observed above Réunion Island. The 2013-2016 NDACC/SHADOZ ozone dataset has a mean background value of 81 ppbv in the upper troposphere (average ozone mixing ratio between 10 and 13 km). Figure  6  shows the ozone distributions for the lower troposphere (below 5km, green bars on Figure  6 ) and the upper troposphere (10-13 km, grey bars on Figure  6 ). 76. 8% of the lower tropospheric ozone data have values ranging from 15 to 40 ppbv. These values agree with ozone mixing ratios typically observed for air masses in the marine boundary layer (20 ppbv), we note that the values larger than 20 ppbv can be explained by mixing with air masses of the tropical troposphere with climatological higher ozone content.
8 ppbv). A convective signature was identified in the ozone profile dataset by studying the probability of occurrence of different ozone thresholds. It was found that ozone mixing ratios lower than 45 to 50 ppbv had a local maximum of occurrence near the surface and between 10 and 13km in altitude, indicative of the mean level of convective outflow, in agreement with  Solomon et al. (2005)  and  Avery et al. (2010). Combining FLEXPART Lagrangian backtrajectories with METEOSAT 7 infrared brightness temperature products, we established the origin of convective influence on the upper troposphere above Réunion island. We found that the ozone chemical signature of convective outflow above Réunion Island is associated with air masses detrained from the ITCZ located north of the island and tropical cyclones in the vicinity of the island (2100km around the island).
Acknowledgments OPAR ( Observatoire de Physique de l'Atmosphère à La Réunion , including Maïdo Observatory) is part of  OSU-R (Observatoire des Sciences de l'Univers à La Réunion ) which is being funded by  Université de la Réunion ,  CNRS-INSU , 720  Météo-France , and the french research infrastructure  ACTRIS-France (Aerosols, Clouds and Trace gases Research Infrastructure). This work was supported by the  French LEFE CNRS-INSU Program (VAPEURDO).
In this paper, a new knowledge-based framework is proposed to support the design and production activities of industrial PSS. This work is conducted within the European project "ICP4Life" that aims at developing a collaborative framework for the management of the whole PSS lifecycle  (Matsas et al. , 2016). The paper focuses on two main phases of the PSS development: design and production. Theses phases are supported in the ICP4Life by the Designer and Planner modules. The Designer assists engineers from different disciplines to find optimal solutions for PSS components and their integration in a unique system. The Planner module helps to assist the planning and decisionmaking activities for the PSS production. It includes supplier network design functionality through a smart assistance for supplier identification, matching, evaluation and optimized selection.
The literature is reviewed to describe the main characteristics of the PSS concept and the related development support frameworks. Section 2. 3 introduces the ICP4Life framework as a context of this research work. PSS design and plaining activities within the Designer and Planner components are then detailed in sections 3 and 4 respectively. Software implementation issues are given in section 5. Finally, a conclusion about the advantages/limits of the proposed approach and further research perspectives are discussed in the last section.
This collaborative platform consists of five modules namely Procurement, Designer, Customizer, Planner, and Services (Figure  1 ). Every module is dedicated to specific category of business stakeholders to cover the whole PSS development and customization process. Procurement module supports the data entry to the system from different users. Particularly, Procurement Module contributes to the PSS Design through providing the data entry input to the Designer module. Designer module supports the creation, management, and sharing of product and service data by engineers and designers of multiple disciplines, with different backgrounds through the use of a common product-service data model and a set of knowledge-based and collaboration facilities. Customizer supports the configuration of different types of products and services by customers of different profiles.
Planner supports the stage of process planning and production of PSS for the realization of the product-service considering critical sustainability aspects of the PSS. Finally, Services modules are dedicated to the management of the usage phase of a specific PSS providing useful feedback both to the end user and the provider of the PSS. The provided services are sensor-based and could be among others the machine health monitoring or the machine performance optimization  (Alexopoulos et al. 2017). A common knowledge repository is used and supports the interactions between the modules.
Slight adaptations can be achieved on the machine structure to take in consideration these constraints. Meanwhile, the PSS pattern and instances design evolve constantly with the evolution of markets demands. Through the co-evolution of PSS pattern and PSS instance, important cost and time reductions are expected for the whole PSS development. It can be foreseen that as the accumulating and updating of new PSS solutions, the knowledge base will be more powerful to support PSS design. (Belkadi et al. , 2017)  In the ICP4Life platform, the definition of the integration solution is achieved by both manual operations and semiautomatic facilities supporting the decision making process. These facilities include PSS components selection and compatibility checking software modules as it is shown in the Designer architecture presented in the next section.
The Pattern-based design process with the Designer In practice, when a new PSS is requested, the ICP4LIFE platform analyzes similar PSS and proposes the most suitable solutions according to previous PSS patterns or instances. The similarity can be based on common products components, common service units, common customer constraints, etc. Figure  6  shows a simplified nominal scenario of the creation of PSS pattern. The scenario is achieved through multiple interactions between the user and the Designer tool, between the Designer software modules and between the Designer and the Planner tools. This process starts with a creation of a new PSS pattern as a request from the engineer to the "PSS pattern and instance management" module. The user fulfills the key characteristics of product and service in the PSS data editor.
g. legacy CAX tools, would be required to conduct the technical study. After verification, technical data, e. g. parameters, performance, cost, time, will be documented and attached to fill in the PSS conceptual solutions. Once the resource configuration and technical integration solution, usually mean a couple of alternatives, are verified at the technical level, there is another step to validate the feasibility of PSS implementation alternatives as bill of processes, manufacturing resources, best suppliers, etc. This is similar to "manufacturability analysis" as done in classical product development process. This last step is fulfilled in the Planner module as it is explained in the following section.
Concept A challenge for the establishment of PSS is the appropriate planning of the resources for production, deployment, and installation into the customers' site. Companies that provide PSS solutions are lacking the proper tools for resources' planning in a dynamic environment. Following the aforementioned gaps, ICP4Life Planner introduces a resource planning method and tool for optimizing production, delivery, and installation of IPSS  (Alexopoulos et al. 2017). Planner module generates alternative PSS's production and installation plans and evaluates them on performance measures for production and installation such as time and cost. The planning tool has been designed using the Softwareas-a-Service (SaaS) approach. Planner module is responsible for the Semi-automatic configuration of the suppliers' network and production plan for the realization of the product-service considering critical sustainability aspects of the PSS, supporting in that way the PSS production process.
Planner Functional architecture Planner consists of the subcomponents presented in the Figure  8. Entering the Planner module, the user can have a summary of all the useful data. The Process Plan Request Manager is responsible for providing the functionality of managing the incoming process planning requests from the Designer component. Creating a response to a specific process plan request, all the available data for it appear in the tab. Here the user is enabled to select the appropriate tasks together with their sequence in order to build the BOP. After saving the Bill of Processes, the Planner Module triggers the planning software that takes as input the bill of processes along with the available resources of the company and the suitability properties of each resource, it plans the tasks and presents them in a Gantt chart.
When the PSS is developed and deployed, the critical stage is then the planning and the management of the use stage as well as the involved resources. Indeed, this stage has many more interactions including operations optimization and partial asset renewal. So, it is important to anticipate in the design stage the definition of operational scenarios alternatives and related organizational capabilities to cover different potential situations within the usage stage. Even if the planning modules of the ICP4Life Framework are mainly dedicated to the production stage, the algorithms can be used as well for the organization of operational activities at the usage stage. Supplier network design is a generic tool that helps the identification of best organizational resources for the outsourcing of some product parts or services.
In particular, the workflow of this implementation scenario is presented in the Figure  9. The PSS Framework has been designed and implemented with scalability and performance in mind in order to be able to address the challenges of an IoT environment. In particular, the raw data coming from the edge tier is collected and stored in a database built using the Apache Cassandra. This database is optimized for operation with timestamped data, such as the measurements of a sensor. The Integration Framework and PSS Framework share a java modeling based on the ontology used in the Knowledge Repository that enables this functionality. The Knowledge repository itself can be accessed using SPARQL queries and has been developed using the Apach Jena Framework, which is a free and open source Java framework for building Semantic Web and Linked Data applications.
The first step for the implementation of such framework is to set-up the structure of the common repository that includes both Designer and Planner data among others. Figure  10  shows an overview of main data connected to the Designer and the Planer respectively. The logic is that the integration solution covers the design and the production solutions. The first one is to identify the PSS Bill of Material and the second is to manage the Bill of processes with related resources and suppliers. From this meta-model, an extended model is developed and translated as an RDF ontology file to serve the PSS Framework to get access and organize the large set of information, which is linked to time-series instances and knowledge shared between ICP4Life components. More details about this ontology are given in  (Maleki et al.
, 2017b). The first step after receiving a demand from the sales or marketing representative departments is the design of new PSS pattern that meet with the specificities of the target market/customers. Figure  11  shows the main GUI of the Designer component. The web portal Liferay usage arose as an integration requirement, to maintain a common and single web page between all the ICP4Life tools. With that basis the first Designer and Planner Prototypes have been created. The main view of the Designer module is divided into several sections depicted in red in the previous figure:  According to the Designer global architecture presented in Figure  5 , three main categories of data are managed within the Designer interfaces: Standard PSS components like sensors, machine, etc. this allows analyzing their technical features as specified by the providers.
With the aim to address the incoming request, the production engineer is going to the Process Plan Response Creator. Here he could view the main information of the incoming PSS request the available suppliers and deactivate if necessary some of them and accordingly to create the process plan as it is presented in the Figure  15. After saving the Bill of Processes, the Planner Module triggers the planning software that takes as input the bill of processes along with the available resources of the company and the suitability properties of each resource; it plans the tasks and presents them in a Gantt chart. In the Figure  16  the process plan is presented with the grey zone days to be the days off. The option of rescheduling is available. In that way, Planner will give a response to the incoming requests from the Designer component.
Using these facilities is well applied in the industrial domain for the support of product development. However, fewer initiatives are dedicated to the emerging paradigm of PSS. Furthermore, the current frameworks are generally developed to cover the specific scope of the product lifecycle. One of the originality of the proposed framework is its capacity to cover wide panel of the PSS lifecycle, including part of its usage stage. The idea is to propose a set of interconnected software modules able to separately address one of several needs. Moreover, the integration of all these components provides a common development process from the customer need to the usage management. In this context, planning of the usage stage is made by the extension of Planner functions to define the necessary operational processes and related resources at this stage.