dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance Requirements for AOT Estimates The published literature about LandSat-8 or Sentinel-2 mission requirements (  [16, 26] ) does not provide requirements for the accuracy of surface reflectance after atmospheric correction. Defining the needs regarding surface reflectance would be a difficult task, as LandSat and Sentinel-2 data are or will be used for very different applications, such as producing land cover maps, detecting changes, monitoring bio-physical variables, such as the Leaf Area Index (LAI), the fraction of absorbed photosynthetically available radiation (fAPAR) or the plant biomass, providing crop water need estimates, estimating coastal water optical properties, etc. Vermote and Kotchenova  [27]  consider that "good" surface reflectances are known with a root mean square error (RMSE) below 0.
Multispectral Method Our version of the MS algorithm is based on the dark dense vegetation (DDV) approach (Kaufman and Sendra, 1988  [10] ). This method is used, for instance, in the MODIS data processing. It retrieves the AOT above dense vegetation pixels by means of empirical relationships between the surface reflectances in the blue, red and SWIR (around 2 µm) spectrum ranges. For vegetation, these relationships are physically justified by the correlation of the chlorophyll absorption peaks in the blue and red bands and the liquid water absorption in the SWIR  [10]. For MODIS, the operational algorithms up to Version 4  [18]  assumed that the surface reflectance in the blue (0. 47 µm, Channel 3) and the red (0. 66 µm, Channel 1) bands were one-quarter and one-half, respectively, of the surface reflectance in the SWIR (2.
Finally, the surface reflectances of the pixels contained in a zone of 20 × 20 km 2 around the sun-photometer were extracted and compared. The water pixels have been discarded from the plots. Twenty two images taken from four sites (Sevilleta, Fresno, Columbia, Konza) were used for LandSat. Only two of these sites were also used in the study of the method performance in Section 4. For FormoSat-2, due to the price of the dataset, we could not afford to use a large number of sites, and we used the same sites to compute the coefficients and to validate the AOT, which may result in somewhat optimistic values for the multi-spectral method. Eighteen images were used to obtain FormoSat-2 regression coefficients. Figure  5  shows the scatter plots obtained by comparing surface reflectances for the following pairs of bands: blue and SWIR bands, red and SWIR bands and blue and red bands.
Blue-SWIR Red-SWIR Blue-Red LandSat-5 0. 025 0. 029 0. 012 Linear regressions were computed after discarding all of the pixels which have an NDVI below 0. 2. Although LandSat-5 and 7 bands are very similar, different relationships have been calculated for each satellite. Linear regressions yielded the following results: • LandSat-5: ρ surf blue = 0. 409ρ surf SW IR -0. 007 ρ surf red = 0. 766ρ surf SW IR -0. 026 (5) ρ surf blue = 0. 568ρ surf red + 0. 002 • LandSat-7: ρ surf blue = 0. 320ρ surf SW IR + 0. 000 ρ surf red = 0. 671ρ surf SW IR -0. 016 (6) ρ surf blue = 0. 479ρ surf red + 0. 007 One part of the differences between LandSat-5 and LandSat-7 may be due to the differences between spectral bands, and another part may come from the limited amount of data used in this study to derive the regressions.
A unique continental aerosol model has been used for all of the sites and all of the dates, although better results could probably be obtained with a proper tuning of the aerosol model. Table  5  shows the RMS error between our estimated optical thickness and the sun-photometer data for different LandSat and FormoSat time series and for the three different methods. In this table, we only kept the results in stable conditions, for which less than half of the pixels of the neighborhood have been gap filled. Both methods used individually (MS and MT) provide the same magnitude of RMS errors on AOT estimates, for both satellites (around 0. 07 for FormoSat and 0. 09 for LandSat). Table  5  details the results obtained for each site and each satellite. The MT method outperforms the MS method for some sites characterized by low cloudiness and, thus, an enhanced repetitivity: Boulder and Rogers for LandSat; Ras-El-Ain for FormoSat-2.
The overall RMS error is 0. 06, which is in line with the needs expressed in Section 3. 1. For LandSat, the estimates are a little less accurate, but show the same trend with a much greater number of estimates obtained with the hybrid method. The increased noise might be explained by three reasons: first, in our LandSat dataset, the proportion of arid sites is higher and the observed accuracy is lower for these sites; second, given that, for LandSat, the time series areacquired by two different sensors, LandSat 5 and LandSat 7, additional noise due to the differences in spectral bands (see Figure  4 ) from both LandSat sensors is present (this should not be the case with Sentinel-2); and third, the repetitivity of the LandSat time series is reduced compared to that of FormoSat-2. In terms of bias, a very small bias is observed with FormoSat-2, while the bias is a little greater for LandSat.
For the MS method, we decided to implement a simple method to avoid processing a huge amount of data to estimate the relations between spectral bands. During our preliminary analysis, we showed that the correlation of the surface reflectance of the blue and red bands is much higher than the correlation of blue and SWIR bands or red and short wave infrared (SWIR) bands. The "blue-red" relation is also applicable to FormoSat-2, while the blue-SWIR one is not. However, compared to the method implemented with MODIS that uses the blue-SWIR and red-SWIR relations, our MS method is not able to estimate the aerosol model and uses a constant model for a given site, which is a drawback for regions where the aerosol model is subject to large variations. To assess the performances, we processed hundreds of images from six LandSat and four FormoSat-2 sites.
Acknowledgments The authors would like to thank  Carol J. Bruegge ,  Eric Ceschia ,  Philippe Goloub ,  Brent Holben ,  David Meyer ,  Doug Moore ,  Bernard Mougenot ,  Didier Tanré ,  Steve Tate  and their staff for establishing and maintaining the AERONET sites used in this investigation. We are also grateful to the  U. S. Geological Survey  for the free distribution of LandSat images. Part of the activity was funded by  CNES/TOSCA  ( Terre Océan Surfaces Continentales Atmosphère ) program or by a  CNES grant. We would like to thank the anonymous reviewers whose constructive comments helped to improve the quality of the article.
Tool-centric apps Most companies develop their apps as tools to provide users with utility. Tool-centric apps can be extremely diverse. The main goals of tool-centric apps are to identify the motivations and requirements of consumers in using/buying products and to develop services to assist consumers in these processes. For example, L'Oreal's The Color Genius app (Figure  3 ) allows users to take a photo of their outfits and identify a look as for day or night; the app then proposes an on-trend combination of make-up products that suits a user's outfits. The Colgate Dental Advisor app includes a toothbrush timer to teach recommended brushing techniques and allows users to set reminders for their next dental appointment. Heinz's Food In A Minute app provides a database of 700 recipes that can be searched by ingredients or names.
Social-centric apps Social media has become a buzzword in the web 2. 0 decade. From the user perspective, social-centric apps are designed to target socializing for fun. One successful example is the UNIQLOOKS app designed by Japanese apparel retailer UNIQLO (Figure  5 ). This app affords customers, fans, and Other examples of social-centric apps include the Avon Send A Kiss app, which allows users to take a picture of their lips, create personalized messages using their lip prints, and share kisses with friends and family via e-mail, Facebook, and Twitter. Similarly, Dove's Body Language Messenger app allows users to type a message, watch the Dove girls use their bodies to create each letter, and share this animated message with friends via e-mail, SMS, and social networks. These two social-centric apps focus From the business perspective, social-centric apps aim to increase the sense of intimacy with customers, foster brand engagement by building a community of loyal customers, or allow customers to communicate positive brand images with their social circles.
In relation to UGC, research has shown that mobile apps tend to focus on sharing content in the form of photos and videos rather than text. For example, both the UNIQLOOKS app (Figure  5 ) and Avon's Send A Kiss app allow users to take photos of themselves--either their outfits or lips--and share them with social networks. Dove's Body Language Messenger app uses user-generated text, but the text is highly innovative due to the fact that the letters provided by Dove are shaped by bodies. In relation to content personalization, a content tagging feature is rarely incorporated into current branded app designs. In relation to content rating, commenting, and sharing, most branded apps allow themselves or their contents to be liked, rated, commented on by users, and shared with external social networks.
Simultaneously, a research program focused on the development of a numerical strategy  [7]  for the simulation of such interactions. The goal of the research program was to develop a numerical strategy accurate enough to predict experimental results and that could be integrated within Snecma's industrial environment as a first step toward the inclusion of structural contacts within the design process of compressor and turbine blades. The need for such numerical strategy called for the development of a dedicated in-house code as commercial softwares could not provide satisfying results due to several limitations such as the impossibility to combine the use of efficient component mode synthesis methods with a precise contact treatment procedure (respect of non-penetration conditions between structures) and accounting for both centrifugal and gyroscopic effects.
In addition, strategies for the modeling of the abradable removal are currently non-existent in commercial softwares. This paper aims at describing the strategy adopted by Snecma for acquiring knowledge on rotor/stator interactions initiated by blade-tip/casing or abradable coating contacts. First of all, the experimental test benches developped for the simulation of rotor/stator interactions are introduced. During the last ten years two series of tests were performed and their main results are presented herein. Secondly the original numerical approach, namely COROS, used at Snecma to study the contact between rotating part and casing is briefly detailed. Then, applications of COROS for the simulation of industrial cases are reviewed. Finally, the paper deals with on-going Research and Development projects following two guidelines: (1) the characterization of abradable materials and (2) the numerical improvement of COROS.
Industrial integration In order to ensure a full integration of the numerical strategy within Snecma industrial environment, the input finite element modes are industrial 3D finite element models obtained from Samcef. The output result files are written in such way they can be read by Samcef post-processing modules: Samres and Samfac. A dedicated graphic user interface is created and advanced postprocessing tools (interaction maps, automated Fourier analyses, wear maps. ) are created with Python codes.
Social interactions are enacted by intelligent agents that interface the users participating in the curation workflow with the EI infrastructure. The multiagent system supports collective decision making, representing the actions of the users within the EI, where the agents advocate and support the desires of their users e. g. aggregating opinions for deciding which images are interesting enough to be discussed, and proposing interactions and resolutions between disagreeing group members. Throughout the paper, we describe the enabling technologies of WeCurate, the peer-to-peer EI infrastructure, the agent collective decision making capabilities and the multi-modal interface. We present a system evaluation based on data collected from cultural exhibitions in which WeCurate was used as supporting multiuser interactive.
, 2012; Yee-King et al. , 2013; Hazelden et al. , 2013)  by describing the underlying peer-to-peer EI infrastructure and presenting an analysis of the decision making models employed by the agents. The evaluation is based on data collected from cultural exhibitions in which WeCurate was used as a supporting multiuser museum interactive. The rest of the paper is organised as follows. Section 2 provides an overview of the system, whereas Section 3, 4, 5, and 6 respectively describe the EI infrastructure and workflow, the personal assistant agents, the interface and the adopted technologies. Section 7 presents the evaluation of our system. After discussing the evaluation's results (Section 8), Section 9 presents several works that relate to ours from different perspectives. Finally, in Section 10 we draw some conclusions and we envision some of the ideas we have in mind to improve the current system.
System overview WeCurate is a museum interactive which provides a multiuser curation workflow where the aim is for the users to synchronously view and discuss a selection of images, finally choosing a subset of these images that the group would like to add to their group collection. In the process of curating this collection, the users are encouraged to develop a discourse about the images in the form of weighted tags and comments, as well as a process of bilateral argumentation. Further insight into user preferences and behaviours is gained from data about specific user actions such as image zooming and general activity levels. A multiuser interactive is a typical example of a system in which human and software agents can enter and leave the system and behave according to the norms that are appropriate for that specific society.
The EI specification is then interpreted by a workflow engine which controls the workflow execution and the messages sent over the EI. We omit the details about the EI specification language and the EI workflow engine; the reader can find a more extensive description in de  Jonge et al. (2013) ,  Arcos et al. (2005)  and de  Jonge et al. (2014). In what follows, we present the workflow we used for modelling the activity of community curation carried out by the users in the WeCurate system, and how we implement scene transitions as decision making models of the agents.
A user may add new tags for an image. When the disagreement persists (MCDðimÞ ¼ ¼ 0), the users will decide by voting. In what follows, U ¼ fu 1 ; …; u n g is a set of users, and Args t ðu i Þ is the set of arguments of user u i at step t. At the beginning of a session, the sets of arguments of all users are assumed to be empty (i. e. , Args 0 ðu i Þ ¼ ∅). Moreover, the set of images contains all the available images in the database of the museum, that is I 0 ¼ I. We assume also that a user u i is interested in having a joint experience with other users. The protocol uses a communication language based on four locutions: Invite: it is used by a user to invite a set of users for engaging in a dialogue. Send is used by agents for sending information to other agents. Accept is used mainly by users for accepting requests made to them by other users.
Interaction protocol: 1. Send(EIManager, U, SelectScene) (the EIManager starts a select scene). 2. Send(MediaAgent, U, RandðI t Þ) (the Media Agent select an image from the museum database and sends it to all the UserAssistant agents). 3. Each UserAssistant agent displays the image RandðI t Þ and each user u j A U: (a) Expresses an image preference r j ðRandðI t ÞÞ A S. (b) When a user u j is sure about his preference, he clicks on the "Go To Discuss" button in the WeCurate interface. (c) Send(UserAssistant j , EIManager, f agg ð r ! Þ) (the UserAssistant agent of u j computes the group preference f agg ð r ! Þ and sends it to the EIManager). 4. If (intðRandðI t ÞÞ ¼ ¼0), then I t þ 1 ¼ I t ⧹fRandðI t ÞÞg and go to Step 1. 5. If (intðRandðI t ÞÞ¼ ¼1), then Send(EIManager, U, ForumScene) (the EIManager starts a forum scene).
14. If (voteðRandðI t ÞÞ ¼ ¼0), then I t þ 1 ¼ I t ⧹fRandðI t ÞÞg and go to Step 1. It is worth mentioning that when a user does not express opinion about a given tag, then he is assumed to be indifferent w. r. t. that tag. Consequently, the value 0 is assigned to the tag. Note also that the step 8 is not mandatory. Indeed, the invitation to a bilateral argumentation is initiated by users who really want to persuade their friends. The previous protocol generates dialogues that terminate either when all the images in the database of the museum are displayed or when users exit. The outcome of each iteration of the protocol may be either an image on which all users agree or disagree to be added to the group collection.
The social layer is implemented as a multi-agent system whose institutional agents are responsible for guaranteeing the correct execution of an EI according to the specification of its rules. User Assistant agents are implemented as Java programs extending an existing Java agent that abstracts away all the underlying communication protocols. More details on the p2p EI implementation can be found in de  Jonge et al. , 2013. The user interface is implemented using Javascript drawing to an HTML5 canvas element, which is a cross platform and plug-in free solution. The Interface does not communicate directly with the institutional agents since it is not a part of the FreePastry network. Instead, the interface sends events formatted as JSON to the User Assistant agent which hosts an HTTP server.
The User Assistant agents pick up the event queue, then in turn generate scene snapshots in JSON format which are sent to the interface. Scene snapshots are used to define the state of the interface. One advantage of this queued event and snapshot model with regard to evaluation is that all interface events and interface state snapshots are stored in the system for later inspection. This allows a complete, interactive reconstruction of activity of the users and the agents for qualitative analysis as well as providing a lot of data for quantitative analysis. In the next section we describe how this data was analysed.
Method and data The WeCurate system was set up as an interactive exhibit in a major London museum, supported by the research team. 4 The museum provided 150 images from their collection; the task for the social groups interacting with the system was to decide which of these images they would like to have as a postcard, via the curation process. Multiple sources of qualitative and quantitative data were collected. Participants were filmed during the activities and their interactions with the system was recorded in timestamped logs. Data was gathered and cross referenced from adhoc observation of the trials themselves, inspection of the video footage, transcription of the interviews and the system log files. The ages of participants ranged from 4 years (with assistance) to 45 years. The average time each group used the WeCurate system was 5 min 38 s, the longest session logged was 21 min 16 s.
Agent decision models analysis The observations provided a dataset for assessing the different types of agent decision models. To this end, we compare the decision criteria of the agents (Section 4) w. r. t. the final decision of the users in the vote scene. The dataset analysed consists of 224 observations about image evaluations in the different WeCurate scenes. The images evaluated were selected from a finite set of 150 images, browsed during 165 sessions in which groups up to 4 users participated. 130 images were chosen from the original set and the 73. 1% was finally added to the group collection. Each image was seen from 1 to 4 times during the different sessions. Among the 224 observations, 176 corresponded to image evaluations in which an image was added to the group collection and 48 in which an image was rejected by voting.
g. self-organisation, resilience to faults and attacks, low barrier to deployment, and privacy management). The p2p EI infrastructure used in WeCurate has been developed with the ambition that EIs become a pervasive mechanism to coordinate very large networks of humans and devices in the next years. Our current efforts in improving the infrastructure and a roadmap on EIs development in the last 20 years are reported in de  Jonge et al. (2015). Several systems exist that enable realtime personalised experience and multiuser collaboration in virtual workspaces, both in industry and in academia. In industry, web conferencing software such as Adobe Connect allows complex media and text driven interactions; shared document editors such as Google Drive enable co-editing of office-type documents.
Acknowledgements The authors would like to thank the reviewers for their valuable comments. This work is supported by the  Collective Mind project (Spanish Ministry of Economy and Competitiveness , under Grant number  TEC2013-49430-EXP ), the  PRAISE  project (funded by the  European Commission , under Grant number  388770 ), and the  ACE  project (funded by the  European framework ERA-Net CHIST-ERA , under contract  CHRI-001-03 ). Roberto Confalonieri  would like to thank  Andrea Mattivi  for his suggestions on the data analysis.
In this paper, we present a new automated quantitative approach based on a convex envelope (AQoCE). This approach can be considered as a simulation of manual fitting, which can ensure efficiency and robustness, as well as give reasonably accurate results. AQoCE is a blackbox method, so lacking physical meaning is still inevitable. To evaluate the performance of this new quantitative technique in grading the diagnosis of brain gliomas, AQoCE was applied to analyze the MRS data from 78 glioma cases acquired before brain tumor surgery, then the results were compared with their pathology reports. When compared to the MRS data processing software from Siemens (Trio Tim 3. 0 T MRI's workstation), our AQoCE approach provides results with higher specificity and sensitivity on these samples.
Materials and Methods Materials This research was approved by the medical ethics committee of Beijing Tian Tan Hospital, Capital Medical University. Written informed consent of each adult patient (participants) was obtained, and the written informed consents of the three minor patients' guardian completed on behalf of the minor participants were also obtained. Data were obtained from 78 cases, including 48 males (10-73 years: average age, 44. 71 years); 30 females (22-68 years: average age, 43. 37 years), male/female, 1. 6:1; all of the cases have been confirmed by pathology. All cases (n = 78) were classified into two groups: 1) 36 cases of low-grade gliomas (LGG, WHO I-II), including 18 astrocytoma, 5 oligodendroglioma, 12 oligdendro-astrocytoma, and 1 mixed neuro-glioma; and 2) 42 cases of high-grade gliomas (HGG, WHO III-IV), including 10 anaplastic oligdendro-astrocytoma, 7 astrocytoma, 24 glioblastoma, and 1 gliomatosis cerebri.
Method of analysis of metabolite components A) ROI selection. For each case, two neuroimaging experts with years of experience made the following selections: Determine the location of the CSI slice. Normally, it is the one slice of an axial image which has a maximum volume of tumor. Put three regions of interest (ROI) in the enhanced tumoral area, peritumoral area which is about 1 cm from tumor margins, and contralateral areas distant to the tumor. In each ROI, choose 1~3 MRS voxels to measure according to the tumor size. Use the average to reduce the error. Finally, normalize the results of the two experts and also obtain the average. C) Statistical analysis. Our design for statistical tests is presented here. All statistical tests are two-sided, performed with SPSS19. 0 (SPSS Inc. , Chicago, IL).