dataesr/scientific-paragraphs-categorization huggingface dataset:
We take the largest wildfire event of the BA M data set (262 520 ha BA, 731 MW FRP). It is recorded at  [-7. 65  • E, 40 • N] and the considered weather is that of the [-7. 50 • E, 39. 75 • N] ERA-Interim grid point. Figure  6  shows the time evolution of the probability of large BA and FRP with the corresponding 90 % confidence intervals. Two black lines show the beginning and the end of the fire event. During the wildfire the probability of large BA peaks to 7 %, whereas it stays at about 3 % (BA M ) or 2 % (BA E ) the rest of the time. The probability of large FRP behaves the same way, going from 3 % to more than 6 %. The variations of these estimated probabilities are significant at the 90 % confidence level. The "background" probability refers to the background fire risk of large or intense fire events during summer.
The mean probability that a fire exceeds 200 MW is around 2. 4 % for "background" summer fire risk conditions with a standard deviation of 0. 3 % and increases to 3. 6 % in extreme weather conditions favourable to intense fires. The 90 %-level confidence interval lengths remain large for the BA data sets, with typical values of 2 and 1. 8 % for the BA M and BA E data sets respectively. For the FRP data set these lengths are smaller because of the larger number of data points, with a mean value of approximately 1 %.
In this study we use parametric distributions as they provide a simple framework to model fire risk with a limited number of coefficients, which can be of interest for the implementation of a fire risk forecast system. Non-parametric estimations of the conditional distributions of the fire variables with respect to the  meteorological covariates could be performed  (Brunel et al. , 2010). This would lead to longer computation times but probably more accurate estimations of the conditional distributions and associated probabilities. More complete data sets for BA would allow a better estimation of the conditional distribution for this particular variable and help further reduce the uncertainties. Finally, in order to improve fire risk forecasting meteorological driving factors of fire size and intensity can be used to reconstruct a conditional distribution function of either variable.
Acknowledgements. This work contributes to the  HyMeX program  (HYdrological cycle in The Mediterranean EXperiment -Drobinski et al. (2014)) through  INSU-MISTRALS  support and the  GEWEX  hydroclimate panel of the  World Climate Research Program  (WCRP). Data were provided by the  European Forest Fire Information System -EFFIS  (http://effis. jrc. ec. europa. eu) of the  European Commission Joint Research Center. The topical editor  V. Kotroni  thanks  V. Moron  and one anonymous referee for help in evaluating this paper.
Acknowledgements The authors are grateful to the artisanal fishermen of  Agia Triada ,  Pernera ,  Agia Nappa ,  Ormideia and Limassol , and to the fishmongers of Morfou fishmarket (Limassol), Skorpios fismarket (Paralimni) and  Fish House (Protaras)  for their kindness and availability for providing materials and information. The authors would also like to thank  A. Stavrinou  ( DFMR, Nicosia ) for providing a new record;  R. Causse  ( MNHN, Paris ) for his help in recording the specimens in the MNHN collection;  H. Debelius ,  S. Kimura ,  P. Louisy  and  D. Sellos  for complementary information;  A. Curd  for English language review and constructive comments; the two anonymous reviewers for their suggestions,  E. Azzurro  and  J. M. Hanson  for editorial remarks. The Cyprus-2014 expedition was partially supported by the 2014 Ifremer contract for the teaching of French fishery observers.
HGV DATABASE The relevant data from this Data Report are hosted at the Human Genome Variation Database at http://dx. doi. org/10. 6084/m9. fig share. hgv. 526, http://dx. doi. org/10. 6084/m9. figshare. hgv. 530, http://dx. doi. org/10. 6084/m9. figshare. hgv. 532, http://dx. doi. org/ 10. 6084/m9. figshare. hgv. 534, http://dx. doi. org/10. 6084/m9. fig share. hgv. 536, http://dx. doi. org/10. 6084/m9. figshare. hgv. 538, http://dx. doi. org/10. 6084/m9. figshare. hgv. 540, http://dx. doi. org/ 10. 6084/m9. figshare. hgv. 542, http://dx. doi. org/10. 6084/m9. fig share. hgv. 544, http://dx. doi. org/10. 6084/m9. figshare. hgv. 546, http://dx. doi. org/10. 6084/m9. figshare. hgv. 548, http://dx. doi. org/ 10. 6084/m9. figshare. hgv. 550, http://dx. doi. org/10. 6084/m9. fig share. hgv. 552, http://dx.
Experiments were performed on a dataset of 22 lumbar disc surgeries which is the most commonly performed spinal surgery world-wide. Sixty-thousand such interventions are performed every year in France  [2]. An interesting feature of this type of surgery is the variability of the number of phases needed during each intervention. Some phases are indeed optional (e. g. , hemostatis) and some phases have sometimes to be repeated (e. g. , disc removal/hemostatis). The number of phases can be difficult to predict from pre-operative information, as it depends upon the reaction of the patient to the surgery. The dataset contains 51,489 single surgical activities overall, classified into four phases. The contributions of this paper are: 1. A system that can predict high-level surgical phases from low-level surgical activities and its extension to consider the local context of the activities.
Note that surgeons activities can themselves be derived from sensors data in specific contexts  [10]. Other models like Hidden Markov Model (HMM) were also considered by Padoy et al. [15, 16]  for online recognition of surgical steps. In this work, surgical activities were extracted using image processing techniques on laparoscopic camera. Similarly, Bouarfa et al. [3]  used HMM with a pre-processing on the input sensor data in order to improve the detection of high-level surgical taks. SVM classifier was also considered by Lalys et al. [12]  to detect phases and low-level surgical tasks using cameras in pituitary surgery. Varadarajan et al. [22]  used HMM to recognize and segment surgical gestures for surgical assessment and training. Learning the topology of an HMM is however still challenging and improving this step continues to be investigated  [20].
Dataset We evaluate our method using clinical data composed of 22 surgeries of lumbar disc herniation which is the most commonly performed spinal surgery  [2]. The data were recorded at the Neurosurgery Department of Leipzig. The surgeries involved 9 male and 13 female patients, with a median age of 52 years. These were exclusively patients with newly diagnosed disc herniation, no patient had undergone previous lumbar spine surgery which might be supposed to increase surgical difficulties due to fibrosis. The herniated disc was approached via a posterior intermyolamar route. This procedure is composed of 4 phases: (1) approach to the spine (from skin incision to the incision of the posterior longitudinal ligament or the removal of an excluded portion of the disc), (2) disc removal (from the end of the previous step to the beginning of hemostasis or closure), (  3 ) hemostasis (this step may be not individualised if it was not performed), and (  4 ) closure (from the end of disc removal or hemostasis to the end incision closure)  [18].
Depending on patient specificities, a succession of disc removal / hemostasis phases is sometimes required. Thus, the number of phases is unknown at the beginning of the surgery. This element motivates the need for a method without assumption on the number of phases. This variability on the number of phases is actually often present due to patient specificities. Among the 22 lumbar disc herniation surgeries of the dataset, 4 (18,2%) required only 3 phases (no need for hemostasis phase), 14 (63. 6%) required 4 phases (one phase of hemostasis), 3 required 6 phases (13. 7%) (two successions of disc removal / hemostasis) and 1 (4. 5%) required 8 phases (three successions of disc removal / hemostasis). Each activity is labeled of the phase during which it is performed. We do not differentiate between disc removal / hemostasis that appear only once and disc removal / hemostasis that appears multiple times as their medical objective are identical.
For this surgery, the list of actions is: cut, coagulate, hold, dissect, install, remove, irrigate, sew, swab and drill. The list of anatomical structures is: skin, fascia, muscle, vertebra, ligament, duramater, nerveroot and disc. And the list of surgical instruments is: scalpel, scissors, dissectors, rongeurs, hooks, high-speed drill, suction tube, needle-holders, saline solution, retractors and forceps. Theoretically, 880 (10 × 8 × 11) different triplets could be created, which gives more that 1. 5M of possible different activities performed by the surgeon at a single instant (considering right and left activities and microscope use). However, as all triplets are not present (some triplets of action, instrument, anatomical structure are irrelevant), our dataset contains only 108 different triplets, leading to 23 thousands possible different activities (108 × 108 × 2).
Phase prediction using local-context In this experiment, we used the augmented version using the local-context. In order to use this evolution of the method, the w parameter corresponding to the size of the time window has to be fixed. To set-up this parameter on our dataset, we tested a range of time window sizes within [0; 200] with a step of 5. We used an identical leave-out-approach as presented in the previous experiment. The Figure  4  illustrates the evolution of the f-measure according to the window size. We used the f-measure as it combines the information from the precision and the recall. From this experiment, we identified that the optimal value of w for our application was 70. The Table  3  presents the results for the different phases with the parameter w = 70. In this experiment, the f-measure is reaching 0.
Phase prediction under noisy data Noise is an important parameter when processing surgical activity data. It is often present in activities inferred from sensors data  [10]  and even from data captured by an observer  [6]. Noise can deeply influence the quality of phases prediction. In this experiment, we evaluate how our phase prediction system is influenced by noisy data. We artificially added noise to the available dataset by randomly switching the value of a given feature of an activity under a certain probability. This noise introduction simulated errors in manual labelling or detec-Approach to the disc Disectomy Hemostasis Closure prediction : ground truth :  tion errors of systems using sensors data. An example of noise introduction could be to switch the instrument of an activity from scalpel to forceps.
Phase prediction among clusters of surgeries In the previous experiments, we used the entire dataset of 22 surgeries. In this experiment, we first perform a clustering of this dataset to create groups of similar surgeries. We then apply the phase prediction systems to the surgeries present in each cluster. The influence of reducing a training set using clustering techniques before learning a decision tree has already been investigated in the past  [19, 23]. As surgeries can exhibit important differences, we are expecting to improve the prediction results by creating clusters of highly similar surgeries. To create the clusters, we used the methodology proposed by Forestier et al. [5, 6]  which relies on Dynamic Time Warping (DTW) and ascendant hierarchical clustering. This methodology has proven its efficiency in creating clusters of similar surgeries  [7].
The Figure  6  presents the dendrogram obtained from the hierarchical clustering process. From the analysis of this dendrogram, we identified two main clusters, named Cluster 1 and Cluster 2 and containing respectively 15 and 7 surgeries. We then applied the system for phase prediction proposed in this paper individually to each cluster. The Table  4  presents the results of phases prediction within the two clusters for the methods with and without local-context usage. The results within Cluster 1 (15 surgeries) are quite interesting as they are better than the ones obtained on the entire dataset. Without the use of the local-context, we obtained a f-measure of 0. 804 compared to 0. 780 using the entire dataset. When using the local-context, the results further improved, reaching 0. 845 compared to 0.
828 when using the entire datasets. The results within Cluster 2 (7 surgeries) are however lower than using the entire dataset with a f-measure of 0. 693 without the local-context and 0. 720 while using it. The results of the application of the tree learned from the data of Cluster 1 to the entire dataset (Cluster 1 + Cluster 2) is presented in Table  6. In this Table, the f-measure is reaching 0. 864 compared to 0. 828 when using all the dataset to learn the tree.
e. , it only represents 8% of the activities of the dataset). The approach and disectomy phases have respectively a precision of 0. 790 and 0. 784 which is acceptable as they are the most present phases of the dataset (respectively 48% and 30% of whole the activities). The overall recall is of 0. 785 which is also a good result. When considering the local-context of the activities (Table  3 ), the overall f-measure increases from 0. 780 to 0. 828. Almost all the results increase, especially the precisions. The precision of the hemostasis phase increased from 0. 386 to 0. 777 but its recall fell from 0. 299 to 0. 197. This result means that the number of activity affected to the hemostasis phase reduced but the precision in the prediction increased. Thus, by considering what the surgeon did in his/her previous activities, the level of prediction errors can be reduced.
The added value of the local-context adjustment is even more visible when processing noisy data. In the Figure  5 , one can see that the use of local-context al- lows the method to be less impacted by noisy data. The prediction system using only the current activity sees its performance decrease linearly with the increasing level of noise, while the method using the local-context is more robust. This is explained by the fact that the method relies on the predictions performed from previous activities. Thus, the influence of noise in one activity among the set of considered activities (A w ) is reduced. The Figure  6  illustrates the clustering of the surgeries used in the experiment. We created two clusters out of the dendrogram obtained by hierarchical clustering. The results of the Cluster 1 (see Table  4 ) are better than the results on the entire dataset (Table  2  and 3 ).
These results show that using an important number of surgeries does not necessarily improve the results. By reducing the number of surgeries (15 instead of 22), we substantially improved the results. This can be explained by the fact that the surgeries present in Cluster 1 are the most similar surgeries of the dataset. Consequently, it is easier for the decision tree algorithm to create decision rules that are valid for these 15 similar surgeries than a model for the entire dataset. However, the Cluster 2 shows lower results than when using the entire dataset. This can be explained by the higher dissimilarity within the clusters, which is visible from the height of the dendrogram links in Figure  6. Furthermore, Cluster 2 only contains 7 surgeries and with the leave-one-out cross-validation, only 6 surgeries were used at each fold to learn a predictive model.
This limited number of instances and the higher variability can explain these lower results. Table  5  shows the result of the application of the tree learned from Cluster 1 to the data of Cluster 2 (Cluster 2*). In this configuration, the f-measure increased from 0. 693 to 0. 735 without the local-context and from 0. 745 to 0. 788 with the local-context. This result shows that using a model learned from one cluster can actually improves the results when applied to other clusters. Table  6  shows the result of the appli-cation of the tree learned on Cluster 1 to the entire dataset (Cluster 1 + Cluster 2). In that case, the fmeasure is reaching 0. 864 which is better than using the entire dataset (0. 828). Thus, these results highlight that in building a CAI system for phase prediction, the data used for the learning step are really important and can substantially affect the results.
Moreover, due to patient abnormality or intervention specific context, the sequence of the phases can also be totally original. In this situation, not relying on a temporal model allows our system to be used anyway as no assumption is made on the phase sequencing. Thus, the system will work even if no data in the training set exhibits the phase sequencing of the surgery being processed. Furthermore, the system can be applied regardless of the number of phases in the surgery. The low computational complexity of decision tree allows the system to be easily embedded on low powered devices present the OR (e. g. , Google Glass). Furthermore, decision tree can be visualized as a set of classification rules that could be used alone to take actions in the organization of the OR. Note that the source code of the methods proposed in this paper is available for download 1  so that they can easily be integrated and compared with other systems.
Conclusion In this paper, we used decision trees to automatically predict high-level surgical phases from low-level activities. We proposed a method which uses the local context of an activity to draw a better prediction than a method using a single activity. Experiments highlighted that using the local-context improves the quality of the prediction. We also performed an evaluation to assess the robustness of the method towards noisy data. The use of the local context made the method more robust to noise in the data. Furthermore, we showed that creating clusters of similar surgeries could be used as a pre-processing step to improve the results of CAI phase prediction systems. Indeed, we showed that using a subset of 15 surgeries instead of the 22 of the entire dataset allowed to improve the results of the proposed methods.
The individual photomultiplier signals were discriminated and combined in a logical AND to reduce noise effects and the signals were sent to a multichannel scaler module. These scaler data were time-stamped using a 10 kHz precision clock. The scaler module is free running and is operated independently of the trigger used for the data acquisition. This module has been tested to function without losing events with input frequencies of up to 100 MHz, although in practice the count rate in experiments is limited to significantly less (≈ 10 kHz). With this method, the raw data are directly used for analysis since no dead time or pile up corrections are required. This method was used recently at GANIL to deduce the half-life of 19 Ne to high precision (0. 03%)  [6]. Beam intensities were controlled using a series of attenuators in the beam line located 10 m upstream from the experimental station.
The reduction factors were chosen so that the initial count rate in the scintillator did not exceed ≈ 10 kHz. A full description of the SPIRAL identification station, associated electronics, and further details about the SPIRAL low-energy facility can be found in Ref. [17]. Data were collected in cycles. A typical cycle consisted of a background collection time of 1 s, followed by a beam-on period between 2 and 14 s to implant ions on the tape, 3 s of tape movement were then required to move the sample from the implantation site to the measurement site in front of the scintillator, a 450 s decay counting period (approximately 20 half-lives of 21 Na), and 5 s of tape movement to remove long-lived contaminant activity away from the scintillator and into a shielded tape storage box before the start of the next cycle.
This article presents an effective solution for the localization of a vehicle in dense urban areas where GNSS-based methods fail because of poor satellite visibility. It advocates the use of a visual-based method processing georeferenced landmarks obtained after a learning path and stored in a new layer of the geographical information system (GIS) used for navigation. Real-time localization gives, with few failures, accurate results in the areas covered by the GIS. The integrity of the localization is obtained by running another algorithm in parallel, processing odometric data combined with the geometric model of the drivable area and, when available, GNSS data in tight coupling. An ellipsoidal confidence domain is updated by using both extended Kalman filtering (EKF) and set-membership estimation.
The first one is essentially a map-aided odometric algorithm using, when available, satellite data in tight coupling. Due to the simplicity of the odometry together with the generalized use of the GIS, the reliability of the results is almost 100%, but even though all of the available data at each instant and in any place are used, the accuracy is not sufficient for navigation at the level of the lane. The second one is vision-based and makes use of georeferenced visual landmarks contained in a new dedicated layer of the GIS. This technique is accurate but may fail due to some defects or in some areas not covered by the dedicated layer of the GIS. Both methods are iterative and share a common ellipsoidal confidence domain in the 6D configuration space (position + orientation). The size of the domain quantifies the accuracy of the localization estimate.
However, the 3D model needs to be accurate and realistic enough to allow for a reliable matching of visual features. Such models are not widely available. Therefore, it is preferable to build a visual landmark database associated with a set of streets by driving a pioneer vehicle through the area in an initial learning phase. This database can be built using visual SLAM (simultaneous localization and mapping) or SFM (structure from motion). Vision only SLAM has been developed and even made possible for real-time situations in small environments  [6]. However, SLAM's complexity is too high for city-sized environments. For vehicle localization, visual odometry is preferred  [7]  but the drawback of this method is the drift of the localization. Several approaches have been proposed to reduce or completely suppress this drift.
Scaramuzza et al. [8]  use the non-holonomic constraint of the vehicle to reduce drift. Alternatively, an IMU can also be used to compute the scale of monocular SLAM and reduce drift  [9]. In order to obtain a georeferenced localization, it is necessary to integrate additional data in the visual SLAM process. Lothe et al. [10]  use a coarse 3D city model to register the visual reconstruction and suppress drift. The 3D model can be replaced by an aerial view  [11]. The problem which must be addressed when constructing large-scale visual reconstructions of cities with the integration of either low-cost GPS measurements or GIS databases is the possible lack of accuracy or error of these data. Some authors have proposed methods to integrate this information in a bundle adjustment framework. For example, Lhuillier  [12]  includes GPS measurements as constraints in the minimization process.
Stretcha et al. [13]  use a skeleton graph model to reduce the complexity of the optimization process in large-scale reconstructions based on images, low-cost GPS and GIS databases. It is also possible to record high precision GPS data along with images in an initial mapping step. Lategahn et al. [14]  use this method and they combine visual odometry and GPS data in an optimization process to produce a reference trajectory and a point cloud of 3D visual landmarks. We do nearly the same in the vision part of our algorithm, but we register the GPS and SFM trajectories differently to further reduce the re-projection error. The approach that is proposed here uses two concurrent localization processes that continuously update an ellipsoidal confidence domain. The shape of this domain is compatible with statements of accuracy in both Bayesian  [15, 16]  and set-membership  [17, 18, 19]  estimation.