dataesr/scientific-paragraphs-categorization huggingface dataset:
This highlights the sensitivity to uncertainty in the observational dataset used as 'truth'. Above all, Figure  11  shows that the use of an ensemble results in an improvement over each of the members, with the k-means clustered average performing the best of the three ensemble methods. Similar ensemble scores are obtained against the NAVO K10 SST (not shown). RMS scores seem dependent on the number of clusters: preliminary tests (not shown) from one to 10 clusters indicate significant improvements. This assessment is ongoing, with further analysis of the ensemble mean computation for forecasts and for other ocean parameters. One of the key aspects of this community effort is the real-time provision of these OOFS outputs.
2013; Le Borgne et al. 2012). A third consequence relates to the overall quality of reference product in near-real time. If some biases exist between these products and information of the same kind that is assimilated, then validation scores can be impacted or wrong. Assessment using Class 4 metrics can be distinguished from assimilation diagnostics in several ways. By assimilation diagnostics, we refer to statistics in observation space derived from the 'background minus observation' (i. e. the so-called misfits); from the 'increments' (i. e. the correction applied to the background to obtain the analysis); or from the 'analysis residual' (i. e. analysis minus observations). For most assimilation systems, there is a pre-processing of Global DAC (GDAC) data through editing, filtering or thinning, in order to limit the number of assimilation observations.
Summary Significant progress has been made in ocean-model skill assessment during the last 5-10 years. Under the constraints of real-time operation, many forecasting centres have implemented more mature validation and performanceassessment procedures. The most advanced examples are operationally integrated, modular and able to use any available reference dataset. Based on a large number of metrics, they permit a diverse validation strategy: (1) comparing old and new systems to measure potential improvements and degradations; (2) comparing coarse resolution 'father' and nested high-resolution 'son' systems to quantify the added value of downscaling; (3) comparing adjacent or overlapping systems to verify the consistency of adjacent forecasts; (4) multi-model comparison to better characterize model error growth using different systems running in parallel; and (5) ensemble approaches to assess the benefit of ensemble versus individual system estimates.
Real-time assessments suffer from limitations imposed owing to observation availability and quality, as many high-quality reference datasets can only be used off-linemeaning that the routine monitoring skill evaluation is less efficient. To avoid spurious effects from erroneous real-time data (for assimilation or validation), quality checking and control of input information (observations, forcing fields) is performed by most OOFS. Moreover, the systematic feedback of quality control information and observation 'blacklists' to providers is starting to be integrated into OOFSs. More complex metrics that are better suited to assessing physical, ecosystem and biogeochemical forecast processes are being progressively adopted in operational centres. Multivariate metrics now complement univariate techniques in order to enhance the parameters-oriented assessments to full ocean process evaluations.
Acknowledgements During the final preparation of this article, our co-author Nicolas Ferry passed away. He was an active member of GOV and MyOcean, and was a significant innovator in the validation activity at Mercator Océan, mentioned in the present work. We will greatly miss the scientific expert, the gentle colleague and the good friend. We thank the reviewers for their helpful comments and assistance in improving the clarity of this synthesis article. This work was supported by the  European Commission funded projects MyOcean  ( FP7-SPACE-2007-1 ) and  MyOcean2  ( FP7-SPACE-2011-1 ).
We collect numerous data over a broad region where the earthquake has been felt, but very little within one specific locality. Two kinds of forms exist: one for individual person and one for a whole city. Therefore, analysts at the observatory works on a resulting data set, consisting either on a sum of individual answers or on an statistical answer at the scale of one city. Using fast Internet communications, macroseismic maps can be produced over entire affected zones, either as preliminary maps through an automatic procedure or as consolidated maps after a subsequent analysis. At the same time, remote sensing techniques have revolutionised data access to damages to buildings. Several services are now able to provide a map of damages in a few hours or days after the earthquake. It is therefore legitimate to address the following questions: Why do specialists go to the field, spend time and money, sometimes running the risk of injuries from exposure of aftershocks? Could Internet reports and remote sensing observations entirely replace the field observations? Why is the fieldwork essential for improving the quality of macroseismic observations?.
The Necessity of Field Observations In France, two types of informations have been systematically processed by BCSF to evaluate the EMS-98 intensity (Gru ¨nthal 1998). The first one comes from individuals spontaneously reporting to the BCSF web site, 1  within a few minutes after the shock. These individual reports correspond to the answers of 43 questions. In order to estimate in real time the shake levels and the intensity, we use the pictures provided by the person filling in the report (Fig. 16. 1). Doing so, we get an individual value of the intensity (Single Query Intensity -SQI). The average of a number of SQI over each locality gives the preliminary Internet Intensity, available few minutes after the schock on our Internet web site. We archived 50,000 testimonies in our database since 2000.
We have to know how many building are affected in each vulnerability class (Fig. 16. 2) and to what degree of damage they suffer (Fig. 16. 3). However, this description is very difficult for municipal officials or inhabitants using collective or individual forms. This work is much more complicated than simply answering the questions: inhabitants may have been worried, frightened or panicked, for instanced, or the objects may have moved or fallen, or many people may have gone out in the street for the first level of intensities. In fact, the vulnerability of the buildings depends on the type of structure, and people do not to know exactly how buildings are constructed. We have observed widely varied estimates for the same municipality in our database since 2000. In addition, in France intensity is an important criterion for the refund of damages by insurance companies.
Acknowledgments My thanks are sent to the various institutions, both French and foreign, involved in the GIM and which supported this project of field enquiries, all the members of the group, as well as  Michel Cara ,  Michel Granet , Frederic Masson, successive directors of the BCSF who have supported the development of the group. Special thanks goes to  Antoine Schlupp , who is my first management partner for the GIM. Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
The framework was tested with 12 datasets from the Hawaii, Azores, and Canaries. The explanatory power of directional network models for explaining species composition patterns, assessed by the Jaccard dissimilarity index, was compared with simpler time-isolation models. The amount of variation explained by the network models ranged from 5. 5% (for Coleoptera in Hawaii) to 60. 2% (for Pteridophytes in Canary Islands). In relation to the four studied taxa, the variation explained by network models was higher for Pteridophytes in the three archipelagos. By the contrary, small fractions of explained variation were observed for Coleoptera (5. 5%) and Araneae (8. 6%) in Hawaii. Time-isolation models were, in general, not statistical significant and explained less variation than the equivalent directional network models for all the datasets.
Case studies A crucial challenge in insular biogeography is to understand the processes responsible for the assembly of communities on islands. We hypothesize that directional effects have an important role in shaping community composition variation among islands. To test this hypothesis, we applied the directional network modeling approach to 12 datasets consisting of exhaustive checklists of the distribution of ferns (Pteridophytes), seed plants (Spermatophytes), spiders (Araneae), and beetles (Insecta, Coleoptera) across the Hawaii, Azores, and Canary Islands archipelagos (Fig. 2 ). Introduced species, subspecies, and varieties were excluded from the analysis. The principal data sources and a summary of biological and geographical data are provided on Appendix S1 in Supporting Information.
Modeling the spatio-temporal variation in species composition From the first nearest neighbor networks, two eigenfunctions were retained for Hawaii, while four eigenfunctions were selected for Canaries and Azores. For each dataset, we tested different combinations of eigenfunctions and selected the model, with partial significant terms (a = 0. 1), that explained the largest amount of variation in species composition in terms of R 2 a values, by dbRDA (Table  1 ). The amount of variation explained by the network models ranged from 5. 5% (for Coleoptera in Hawaii) to 60. 2% (for Pteridophytes in Canary Islands). In relation to the four studied taxa, the variation explained by network models was higher for Pteridophytes for the three archipelagos. In contrast, small fractions of explained variation were observed for Coleoptera and Araneae for Hawaii.
Comparing the performance of network models with simpler time-isolation models The construction of directional network models was based on two basic variables: the age of islands and the distance of a given island to its nearest older neighbor. Models including these raw variables were, in general, not statistical significant and explained less variation than the equivalent directional network models for all the datasets (Table  2 ). Because both models used the same variables, these results testify the importance of considering directionality in model building for oceanic archipelagos.
The cases of Hawaii, Azores, and Canaries archipelagos The framework here provided allowed to improve our knowledge on how community assembly of native invertebrate flora and fauna was shaped for Hawaiian, Azorean, and Canarian archipelagos. We have shown that directional spatio-temporal effects can explain a significant proportion of variation of community composition for most of the studied datasets. Comparatively to simpler time-isolation models, directional networks performed much better. This provides evidence that the chronological sequence of appearance of islands and their spatial location exerted an important role in shaping species distributions. According to our hypothesis, the patterns exhibited by the corresponding eigenfunctions differed among archipelagos as expected. For Hawaii, the selected eigenfunctions suggest the influence of a linear spatial structure on community composition variation, consistent with a predominant stepping-stone mode of dispersal from older to younger islands in the chain  (Funk and Wagner 1995; Cowie and Holland 2006, 2008).
Acknowledgments The authors express their gratitude to  RJ Whittaker  for comments on a draft of this manuscript and to  FG Blanchet  for helpful guidance with the AEM package. The authors are most grateful to  A Beckerman  and three anonymous reviewers for the helpful comments on the original version of the manuscript. This research was funded by the project  FCT -PTDC/BIA-BIC/119255/2010  -"Biodiversity on oceanic islands: toward a unified theory". JCC received a small grant from  Azorean Biodiversity Group  to work in Azores in the summer of 2011.
Data and tomography algorithm To construct the upper mantle velocity structure beneath the central Mediterranean, we use P and S travel limes from the International Seismological Centre (ISC) catalogue in a time period from 1964 to 2012. The calculations were performed using generally same computing schemes as in  Koulakov et al. (2009)  but with the use of a large amount of data accumu-  lated in the ISC catalogue since 2007 (the last year of data in the previous study). Here we use more than 11 million travel times (∼ 8. 9 × 10 6 of P and 2. 3 × 10 6 of S picks) from more than 150 000 events (72 picks per event). In the previous study by  Koulakov et al. (2009) , the number of data was several times smaller. Prior to using the ISC data for tomography, they were reprocessed using the location tools and outlier analysis developed by  Koulakov and Sobolev (2006) , which resulted in rejection of almost 30 % of the data.
Acknowledgements. Ivan Koulakov is supported by  Russian Science Foundation  Grant no. 14-17-00430 ;  Andrey Jakovlev  is supported by  Integration Project of SB RAS  no. 76 , and  Irina Zabelina  is supported by  RFBR  Grant no. 14-05-31186. The authors extend their appreciation to the  Deanship of Scientific Research at King Saud University  for funding the study through the international research group project No  IRG14-21. This study is based on freely available data catalogue provided by the  International Seismological Center. We are grateful to reviewers  Lapo Boschi  and  Maximiliano Bezada  for their constructive and friendly comments that helped us to improve the manuscript. Edited by:  V. Sallares.
AC K N OW L E D G E M E N T S This research was supported by the CNRS national program 'Physique et Chimie du Milieu Interstellaire'. MHV and FL acknowledge the french embassy of Cuba and Campus France for financial support. We also thank the CPER Haute-Normandie/CNRT/Energie, Electronique, Matériaux. JK is grateful for support from the US National Science Foundation (Grant No. CHE-1213332 to professor M. H. Alexander). JK acknowledges financial support from the CNRS for the visiting position at University of Le Havre.
After a human operator selects the person to search for from one of the frames, our system does the retrieval in two steps. Firstly, we extract the contour of persons by using a motion detection approach. For each contour that contains a specific person we compute a set of keypoints. Then, we train a Gaussian Mixture Model (GMM) with these features, and determine the Fisher Kernel representation with respect to this new GMM. Finally, a SVM is trained using the initial human feedback, yielding a specialized classifier in the new feature space. This paper extends our previous work in  [1]  by introducing a new Fisher Kernel representation framework for video surveillance, including evaluation on new datasets and considering more feature extraction schemes. In  [1]  we propose a new relevance feedback algorithm based on Fisher Kernel representation in the context of multimodal video classification (using the visual, audio, motion and textual information).
The algorithm is developed specifically for capturing in particular video temporal variation for video scenes/sequences classification. In contrast, the novel contributions of this work can be synthesized with the following: -We propose a novel, frame-based, method for automated content-based retrieval of regions of interest in video surveillance that exploits a combination of Fisher Kernels and SVMs; -We demonstrate the generality of our approach by evaluating it on a broad range of keypoint descriptors. We achieve better performance than other state-of-the-art approaches whereas evaluation is carried out on two standard datasets  [2, 3]. This makes the results both relevant and reproducible. The reminder of the paper is organized as follows. Section II discusses several relevant video surveillance approaches and situates our work accordingly.
The approaches focused on the understanding of video contents using the visual and spatio-temporal information  [10]. For instance, Muller-Schneiders et al. [11]  proposed a real-time video surveillance system which was specifically designed for a low volume of false positives because surveillance guards might get deviated by too many alarms caused by, e. g. , rain, trees, varying illumination conditions or small camera motion. This system uses the temporal information of the video, e. g. , Cuboid detector, Hessian 3D detector or SURF 3D  [12]. In spite of their good performance, feature descriptors are limited by their computational complexity (e. g. , processing a large-scale video database may take days or weeks) that makes them unsuitable for real-time scenarios. In this respect, current research addresses the development of low complexity algorithms to combine global with local strategies.
III. THE PROPOSED SYSTEM The proposed system works as in the following. The operator selects from few frames a region of interest of the object / human that needs to be searched in the database. Then, the system uses these frames to create a model for the searched object. This step defines the query to the system. Based on the user's interrogation, on the next layer, the system automatically searches in the entire database all the instances of the object / human to be found. The architecture of the proposed system is presented in Figure  1 , and it consists of four different layers, namely: (1) Firstly, the cameras collect the video information, which is transmitted to the motion detection layer. This module targets the extraction of moving objects, such as persons or cars. Motion analysis is very important because it optimizes the next stages performance by selecting relevant information, removing the irrelevant frame sections and so reducing the computational load.
One of them, denoted C, controls the tradeoff between margin maximization and error minimization. Also, additional parameters may appear for non-linear mapping into feature space, namely the kernel parameters. In most of the experiments these parameters are globally tuned for the dataset  [29]. However, a better strategy is to approximate the optimal value of these parameters at query level. In line with this, we divide the feedback samples in two parts: one for training, and one for the evaluation of the SVM parameters performance. We change the values of these parameters until the optimal parameters are obtained. This approach is not computational expensive mainly because the training and evaluation steps are done on a reduced set of results. We use two types of SVM kernels: a fast linear kernel and the RBF nonlinear kernel.
A. Datasets The validation of the proposed approach was carried out on two standard video datasets, namely: Scouter  [2]  and PEVID-HD  [3]  (see Figure  2 ). Scouter: represents an indexed video collection that contains several complex automated surveillance scenarios. It is composed by videos documents, acquired with several video surveillance cameras installed in the convention hall of UTI Grup company. The dataset consists of 30 video documents (3 different days x 10 cameras). The videos are recorded at 6 to 10 fps, with a resolution of 704 x 675 pixels. In total, the collection contains (3 days) x (10 cameras) x (average 120 seconds clip) x (10 frames per second) = approximately 36,000 annotated frames; PEVID-HD: consists of 21 video clips (16 seconds each, full HD 1,920 x 1,080 pixels, 25 fps).
Video clips show people performing various actions in indoor and outdoor environments during day and night times. The people shown in the videos are of different gender and ethnicity. These datasets are in particular challenging due to the diversity of video footage, and specifically the variability of videos within the same categories. Also, the video footage contains variable lighting conditions as well as different levels of difficulty and includes several challenges such as noise, low quality image or blurring, increasing the difficulty of its analysis. Figure  2  illustrates some image examples in this respect.
B. Evaluation To assess retrieval performance, we use a global measure of performance, the Mean Average Precision (MAP), which is computed as the mean of the average precision scores for each query:    Q q Q q AP MAP 1 ) ( (4) where Q represents the number of queries, and AP() is:    n k k c k v f m AP 1 ) ( 1  (5)  where n is the number of frames, m is the number of frames Also, we compute the classical precision and recall. Precision represents the proportion of the true positives against all the positive results (measure of false positives) and recall is the ratio of the number of relevant records retrieved to the total number of relevant records in the database (measure of false negatives). We also compute the  F -Score  [42]  that combines the precision and the recall: recall precision recall precision F       2 2 ) 1 (    where b represent the parameter that allows us to weigh recall more than precision or vice versa.
D. Parameter tuning In this experiment we study the influence of Fisher Kernel parameters on the system's performance. First of all, to make the Fisher Kernel computationally feasible, we apply Principal Component Analysis (PCA) on these features and reduce the dimensions by around 40%. This step represents a common practice for many object recognition algorithms  [17, 26, 30, 31]. We also applied the 2 L and power normalization, that was demonstrated to improve the performance of Fisher kernel  [17]. The first parameter used in our approach is represented by the local descriptor algorithms, used for the description of the keypoints. We tested three different algorithms: SIFT, SURF and PHOW (which represent common features for image retrieval tasks and gives good results on Pascal VOC datasets  [31] ).
The results are presented in Table  II. For both datasets, the PHOW local descriptors have the highest stability and robustness: 55. 69% MAP for the Scouter dataset and 43. 21% MAP for the PEVID-HD dataset. SIFT also proves high stability in many situations, but it provide high sensitivity at illumination changes. Overall, the performance of SIFT is with 2-4% lower than the PHOW features. SURF provides faster speed but also it has many drawbacks, e. g. , it is not stable to rotation and illumination changes. Therefore, it provides the lowest percentage of MAP values: 52. 12% for the Scouter dataset and 38. 22% for the PEVID-HD dataset. In the second experiment we analyze the influence of the number of centroids. The results are presented in Figure  3. One can observe that the performance increases with increasing the number of centroids.
The best performance is obtained with 250 centroids. After this value, the performance decreases with 1 percent. A big improvement can be noticed compared to version with only one centroid: for the Scouter dataset the MAP parameter goes from 42% to 51% and from 45% to 55. 69% for the SVM with RBF and Linear kernels. Also, for the PEVID-HD dataset the increase of number of centroids significantly improves the results: from 29% to 38% and from 31% to 43. 21% for the SVM with RBF and Linear kernels. The last parameter that has to be taken into consideration is the SVM kernel. The second experiment shows that we obtain better results with linear kernel.