dataesr/scientific-paragraphs-categorization huggingface dataset:
Observations The mapping method of our ContactTrees aims to highlight various properties of social relations and interpersonal contacts. Inspired by the botanical metaphor, we incorporate key features of a tree structure, such as length, location, shape, size, and color into our design. Applying these methods to two data sets, we have been able to distinguish how ties and contacts vary by comparing main branches, small branches, leaves, and fruits. Some of the observations we made from these trees can be further verified using other empirical data sources. In the contact diaries dataset, for example, we made several inferences about a person's overall patterns of ties and contacts with others, and highlighted some trends of social relations throughout his or her life span. When we dig deeper into the original data, and even re-interview those persons whose contact diaries were used in our illustrations, some of these observations and hypotheses can be confirmed or modified.
Such changes may lead to different implications about our mapping strategies, the botanical metaphor, and substantive issues in social networks. For example, the mapping on the contact diaries dataset may be better at capturing the overall structure of gender, age, and relations, while an alternative mapping may be more sensitive to other properties. Further, as our visualizations clearly illustrate, a tree's general visual effects may lie more in the overall structure of the trunk, main branches, and small branches, than in the number of leaves. Such a tendency of visualization effects may coincide with the overall stress on interpersonal ties in the social network literature. It seems more direct to demonstrate and assess the conditions of a social network by its total size and overall structure.
Limitations of the Algorithm Highly unbalanced ContactTrees also lead to less aesthetically pleasing representations. Fig  6d  shows another example. The woman likes many of her ties very much, and these ties are positioned on the right side of the tree, as indicated by two long main branches. These long branches make the tree less realistic-looking, but it does not disrupt the readability of the data. The only concrete problem we observed in our datasets concerns branches with many leaves. Sometimes these branches intersect and the leaves overlap, as shown in Fig 7b. Even though this kind of occlusion problem can obscure potentially useful information when one focuses on a specific contact, such intersecting or overlapping is not frequent and does not mislead the user about the global shape of a tree, a main branch, or a small branch.
Without a metaphor, however, such visualization tools lack botanic features such as a trunk, main branches, small branches, leaves, and fruits. The problems that we address are very different from what previous tree-like network diagrams aim to convey. First, we deal with an individual's ties with his or her network members, but not the ties among these members. As with the case for many other egocentric networks, we do not know whether a network member who is connected to the focal person is also connected to another member. Then, a visualization better suited for complete networks is less compatible in our case. Second, the contact diary dataset includes some information about individuals' ties that cannot be readily displayed or identified using a typical egocentric network diagram. For example, it would take extraordinary efforts to readjust the tool to show gender and age distributions, as well as who is more favored by the focal person in the same graph.
It would be even more challenging to display how these properties evolve over time for a given person. While conventional network visualization tools tend to focus more on showing the whole network, our design can neatly organize and highlight the attributes of each specific tie. Third, the contact diary dataset contains information regarding various attributes about each unique contact between a focal person and the persons he or she encounters. These attributes at the contact level cannot be incorporated into conventional egocentric network diagrams. In comparison, our design takes into account the attributes at both tie and contact levels, thus disclosing the extra and rich information embedded in a sophisticated data structure. In sum, our design enables users to focus on the distribution of contacts across gender, age groups, and different periods of time, while taking into account the sequence of contacts for each individual in terms of contact duration and personal feeling towards each contact, all built into a tree.
While other network diagrams typically lack such a time dimension, ContactTrees highlights such features as the sequence of interactions that are crucial to the focal person. A similar botanical visualization tool was proposed  [46]  to reveal huge hierarchies. The main difference between this tool and our ContactTrees lies in the nature of the input data. The purpose of that tool is to visualize a hierarchy, while our tool is intended to visualize a more complex and specific data structure of social networks. Applying that other visualization to this structure would not be a simple project. We would need to find a system for extracting a hierarchy according to ties' and contacts' attributes, and we are convinced that designing a specific visualization is more efficient. Moreover, the other visualization has a 3D design and thus induces occlusion issues.
The technique used to generate that tree could also help us construct a tree following the same requirements as ours. We decided to use bundled curves instead of cones, however, for several reasons related to the selection of ties. TreeVersity  [53]  is an interactive visualization that allows users to detect node value changes and topological differences between trees. It was developed to deal with hierarchies, and thus it includes interaction features specifically for this purpose. A ContactTree does not represent a hierarchy. There are not internal nodes and the leaves are not unique for each branch. Moreover, our application dataset does not hold information such that a given leaf in a 2004 Con-tactTree corresponds to a particular leaf in a 2008 ContactTree, which makes most of the TreeVersity comparison techniques less useful for our purpose.
As egocentric networks may differ a great deal by political, economic, and cultural institutions, cross-cultural or cross-national comparisons would further reveal variations in interpersonal ties and contacts  [50, 55]. When compatible samples and data become available from other societies or cultures, the design could be applied and further tested for more widespread usefulness. The use of ContactTrees can supplement other formats or sources of network data. Among the abundant resources about social interactions, the booming online social media represent one of the most challenging yet promising sources upon which ContactTrees could be applied with ease for both researchers and other end-users. Our design could also be extended to other domains to help identify patterns and trends of social interactions.
9 and a mean bias of 14 ± 26 % (see Table  1 ). Biases between IASI-SOFRID and UTLS columns from ozonesondes were estimated to be 17. 5 ± 19. 3 %  (Dufour et al. , 2012)  and 10 ± 10 %  (Barret et al. , 2011)  once the ozonesondes profiles were smoothed by IASI AvK. The mean value of the GC UTLS columns over our study region is therefore most likely to be in good agreement with ozonesondes. The good behavior of GC UTLS O 3 is corroborated by comparisons between GC and MOZAIC profiles at Hyderabad which show a very good agreement between the surface and 200 hPa during the May-August period (see Fig. 4 ). Unfortunately, no O 3 data are available from MOZAIC Hyderabad-Frankfurt flights in September and October 2009. When smoothing is applied to GC profiles, the features of the O 3 distribution remain similar but some corrections are introduced.
Second, in order to construct a compact, robust, and pertinent subset of the descriptor's features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. In Section 4, an evaluation of the proposed head pose estimator on the public Pointing'04 database is performed to validate our approach and to compare it with the most representative and the best state-of-the-art methods. After that, we have acquired and annotated a driver video sequence simulating attention and inattention states in order to validate the proposed estimator in a real environment. Finally, we present a conclusion and discussion in Section 5.
Then, the model is learned using a structural SVM for joint object localization and continuous state estimation and a new training approach which reduces the processing time. Among the experiments, the authors perform the head pose estimation over the Pointing'04 database without considering the detection task, since they note that the images contain clean backgrounds. Before applying their method, the heads are cropped manually and the HOG descriptor is applied on three scales. Based on the relationship between the symmetry of the face image and the head pose, the authors in  [28]  propose a face representation method for head yaw estimation which is robust against rotations and illumination variations. First, they extract the multi-scale and multi-orientation Gabor representations of the face image, and then they use covariance descriptors to compute the symmetry between two regions in terms of Gabor representations under the same scale and orientation.
This team has equipped a prototype car with many sensors allowing to look in and look out of a vehicle. Such equipment is too expensive to be widely used in car industry. Unfortunately, we cannot compare with these approaches since their database is not accessible and the systems are not detailed enough to allow reproduction. The goal of our global work is to propose a system for monitoring driver vigilance level based on low cost equipment. In this paper, we focus our attention on estimating driver head pose respecting the requirements (R1), (R2), and (R3). In Table  1 , we summarize the properties of some methods presented above and we precise with the signs " * " and "+" the approaches that will be used for comparison in Section 4. The sign " * " is associated to the most used references for benchmarks in literature, and the sign "+" corresponds to the recent works providing the best results.
Experimental results Since there is no public database containing various driver head poses, we have acquired video sequences representing a driver in different head poses to perform our experiment. However, this is not enough to prove the robustness of our system which requires to be compared with the state-of-the-art approaches. To guarantee unbiased comparison, we perform experiments on the public Pointing'04 database  [34] , which is the most used database in literature for head pose estimation  [14]. Moreover, this database could represent the driving environment since the distance between the subjects and the camera is comparable to the one between the driver and the dashboard, where the camera is mounted.
Experiments on public database 4. 1. 1 The Pointing'04 database The Pointing'04 database contains head poses labeled according to pitch and yaw angles, and it is composed from 15 sets of near-field images. Each set contains two series of 93 images of the same person at 93 discrete head poses  [34]. These ones span both pitch and yaw included in the set {0; ±90; ±60; ±30; ±15} and the interval [-90 • ; +90 • ] with a displacement of 15 • , respectively. The subjects range in age from 20 to 40 years old, five possessing facial hair and seven wearing glasses. Each subject was photographed against a uniform background, and head pose ground truth was obtained by directional suggestion. In Fig. 3 , we show the frontal pose (pitch = yaw = 0 • ) of the thirty Pointing'04 folds. We perform several series of experiments on the Pointing'04 database using 80 % as training set (2232 images), 10 % as validation set (279 images), and 10 % as test set (279 images).
System optimization In our paper, we deal with the problem of estimating driver head pose according to two degrees of freedom (pitch and yaw angles) in order to identify three classes (cl) for each angle. However, the Pointing'04 database is composed of 9 poses for pitch and 13 poses for yaw. We propose to cluster the poses into three classes for pitch and three classes for yaw to match our problem formulation. For SVMs, we find that the RBF kernel with γ = 0. 15 provides the best classification results. The optimal values of descriptor parameters are presented in the Section 3. In Table  2 , we show the results of each descriptor evaluated separately in addition to all possible combinations of these descriptors (two, three, and four elements), in terms of accuracy and kappa statistic for both pitch and yaw angles.
We chose to retain 400 relevant variables using ReliefF as the best configuration, since it provides a good compromise between processing time, accuracy, and kappa coefficient. In the last test of this subsection, we show the result of the best configuration when using the k-fold cross validation (CV) process with k = 10. The cross-validation reorders the database and divided it into 10 equal parts. Then, for each iteration, one part is used for the test and the other nine parts for learning the classifier. All results are collected and averaged at the end of the cross-validation. From the last line of Table  3 , we note that the result obtained by cross-validation (CV) improves the conventional test, which proves that the proposed approach allows a good classification of poses even when varying samples.
Comparison with existing approaches The major part of approaches using the Pointing'04 database for evaluation uses its standard representation of poses which corresponds to 9 angles for pitch and 13 angles for yaw. To provide a fair comparison, we increase the number of classes considered by our system in order to respect the standard representation. Therefore, in this experiment, the pitch-SVM and yaw-SVM must classify 9 and 13 head angles, respectively. Moreover, we present the results in terms of angular mean absolute errors (MAE) between the estimated and ground-truth angles for botch pitch and yaw, since all considered approaches for comparison use them. In Table  5 , we present the result of our approach compared to the best approaches in literature and also to the most referenced ones (see Table  1 ).
In  [26] , Gourier et al. measure the human performance for estimating head poses on the Pointing'04 database and find that the angular MAE correspond to 11 • for pitch and 11. 9 • for yaw. From Table  5 , we can conclude that our head pose estimator is more precise than the human performance. As can be seen, it provides the best results among all studied approaches. In the next experiment, we can show the result obtained when using our head pose estimation technique on real video sequence representing driver with various head poses.
Experiment on driver video sequence We have acquired a video sequence, as shown in Fig. 4 , with a cheap visible spectrum phone camera representing a driver in various head poses and composed from 2636 video frames. Each frame has a resolution of 1280 × 720 pixels. Since we deal with estimating driver head pose, we have annotated our sequence using three classes for pitch and three classes for yaw. Figure  5  represents an example of frames corresponding to each class according to the pitch and yaw angles. The result obtained when applying our head pose estimator on driver video sequence using the best parameters determined in Section 4. 1 is given by the second row in Table  6. The first result in this table reports the same experiment applied on Pointing'04 database, previously presented in line 5, Table  3.
Since no public database is available for estimating driver head pose, we perform several experiments on the public database Pointing'04 to validate our approach and compare it with the recent and the most cited state-of-the-art techniques. We have also acquired a video sequence using a cheap visible spectrum camera representing a driver in various attention levels and we find that our head pose estimator can achieve an accuracy of 97. 5 % for pitch and 98. 2 % for yaw. As future work, we can improve our global system for monitoring driver vigilance level by adding a gaze estimation approach in order to determine driver focus of attention. Since we use a visible spectrum camera, the acquisition can be perturbed at night and the usage of IR light could be considered to resolve this problem.
Associative memory √ √ √ [32] ( * ,4) 2008 Cl+Rg SVM + SVR √ × √ [35] ( * ,4) 2007 DR+Cl LDA + linear learning √ √ √ [6] (0) 2008 Tr Tracking using particle filters √ √ √Best result approaches (plus sign), most used references for benchmarks (asterisk). Databases: "0": Own; "1": FacePix [48] ; "2": BU [49] ; "3": MIT [50] ; "4": Pointing'04 [34]  GM geometric model, FM flexible model, AT appearance template, Cl classification, Rg regression, Tr tracking, DR dimensionality reduction.
The Highly Pathogenic Avian Influenza H5N1 (HPAI) virus is now considered endemic in several Asian countries. In Cambodia, the virus has been circulating in the poultry population since 2004, with a dramatic effect on farmers' livelihoods and public health. In Thailand, surveillance and control are still important to prevent any new H5N1 incursion. Risk mapping can contribute effectively to disease surveillance and control systems, but is a very challenging task in the absence of reliable disease data. In this work, we used spatial multicriteria decision analysis (MCDA) to produce risk maps for HPAI H5N1 in poultry. We aimed to i) evaluate the performance of the MCDA approach to predict areas suitable for H5N1 based on a dataset from Thailand, comparing the predictive capacities of two sources of a priori knowledge (literature and experts), and ii) apply the best method to produce a risk map for H5N1 in poultry in Cambodia.
This study aimed at producing risk maps for HPAI H5N1 in Asia  14. Although it provided strong evidence of the validity of MCDA approaches to establish risk maps at a continental level, the impreciseness of the source of outbreak data (EMPRES-i) used for validation was an important limitation to this study. In the present work, we aimed to i) evaluate the performance of an MCDA approach to predict suitable areas for HPAI H5N1 based on a dataset from Thailand, comparing the predictive capacities of two sources of a priori knowledge (literature and experts), and ii) apply the best method to produce a risk map for HPAI H5N1 in poultry in Cambodia.
Data collection and geoprocessing of HPAI H5N1 risk factor layers. The density of free-grazing ducks, farm ducks, and backyard chickens was calculated at a subdistrict level (third-level administrative division, mean area 70 km 2 ) in Thailand, using a dataset obtained from the 2005 Census made by the Department of Livestock Development (DLD). In Cambodia, similar factors were computed at a commune level (also a third-level administrative division, mean area 112 km 2 ) using a census made by the National Veterinary Research Institute in 2010. The proportion of surface covered with rice fields and the mean number of rice crops were calculated in a 2-km radius around each pixel for the two countries from a time series of MODIS images collected in 2005 using methods described elsewhere  29, 30.
The proportion of surface covered with open water for the same radius was calculated from a vector map obtained on DIVA-GIS (http://www. diva-gis. org/), as well as the density of human population (from the LandScan ™ High Resolution Global Population raster dataset, http:// web. ornl. gov/sci/landscan/, 1-km resolution) and the mean altitude (SRTM, Shuttle Radar Topography Mission, http://srtm. csi. cgiar. org/SELECTION/inputCoord. asp, 90-m resolution). A 'proximity to main cities' layer was obtained from a raster map generated from locational point data corresponding to cities of > 100,000 inhabitants in Thailand  18  and provincial capitals in Cambodia. All predictors were transformed into raster layers with a pixel size of 90 m × 90 m.
Map validation. The predictive ability of the MCDA models was quantitatively evaluated by calculating the Area Under the Curve (AUC) from ROC analysis (Supplementary Information, section S1). Predicted values were obtained from the suitability maps by calculating the average risk in each subdistrict (Thailand) and in each district and commune (Cambodia). Observed values corresponded to presence/absence data for HPAI H5N1 at the same aggregation level. In Thailand, outbreak data were obtained from the DLD. Out of 7366 subdistricts, 767 had been infected by HPAI H5N1 from July 2004 to May 2005  18. It has been shown that the surveillance system had a good sensitivity at that time  34. In Cambodia, poultry outbreaks of H5N1 are under-detected by the national surveillance system 2. Validation of MCDA maps was thus carried out instead using fine-scale data obtained from an outbreak investigation study  35, 36  described in Supplementary Information, section S2.
Although poultry data was collected at a similar (3 rd ) administrative level, the resolution was coarser in Cambodia than in Thailand (the average area was 112 km 2 for communes in Cambodia vs 70 km 2 for Thailand subdistricts). Second, it is also possible that experts' weighting in Cambodia was finally less relevant regarding HPAI outbreak location than in Thailand. This could be explained by the fact that experts in Cambodia had to attribute weights without being able to rely on previous statistical models (as no spatial statistical models for HPAI H5N1 have yet to be developed in Cambodia), and in the absence of precise knowledge of the spatial location of previous HPAI outbreaks (as outbreaks reported in OIE/WHO databases are too sparse to have a clear picture of the actual spatial pattern of H5N1).
Risk maps which were produced at a regional level in Southeast Asia  14, 23  provide interesting insights for Cambodia, but their spatial resolution remain relatively coarse (500-m to 1-km). In the present work, a spatial MCDA approach applied to an original dataset of explanatory factors made it possible to produce a risk map for HPAI H5N1 in poultry in Cambodia at the very fine resolution of 90-m. In Cambodia, a comprehensive and quantitative map validation was not possible due to the incompleteness of HPAI H5N1 data in this country. However, validation of the map with outbreak data collected in the two provinces gave fair results at the district level (AUC = 0. 72, CI95: 0. 59-0. 85). Moreover, the SA demonstrated that that the predicted risk areas for HPAI H5N1 occurrence according to the suitability index are robust.
Conclusion The results of this study, which used a Thailand dataset to compare the performance of literature and experts as sources of a priori knowledge for spatial MCDA, showed that the expert-based model had a very high predictive capacity. This suggests that expert opinion can be used to produce risk maps in situations where disease data are scarce, i. e. under-reporting of outbreaks for epidemic/endemic diseases or quasi-absence of outbreaks in the case of emerging infectious diseases. Applied in Cambodia, expert-based MCDA risk mapping made it possible to identify hotspots suitable for HPAI H5N1 in poultry. The Tonlé Sap watershed was identified as an area exposed to high HPAI H5N1 risk, with localized hotspots around the cities of Battambang and Kampong Cham, as well as in provinces located in the lower part of the basin bordering Vietnam.