dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, baselines approaches had to be re-trained from scratch to be tested on GAADRD dataset. However, to test the proposed method on the new dataset, we had to mostly update the geometry of the semantic zones and the minimum duration of activities to match the characteristics of the dataset (e. g. , "prepare drink" and "watering the plant" events only takes a few seconds in GAADRD contrary to CHUN dataset). The structure of event models and other semantics have remained unchanged. These changes were carried out in a matter of minutes after watching a video of a person performing the target activities in the GAADRD dataset. Since GAADRD dataset introduces a new activity, "Turn on radio" we have followed the same procedure described for CHUN dataset to create a model for the new activity.
CHUN Dataset Participants aged 65 years and above were recruited by the Memory Centre of Nice Hospital to participate on a clinical study about Alzheimer's disease. The study protocol asks the participants to carry out a set of physical tasks and Instrumental Activities of Daily Living (IADL) in a Hospital observation room equipped with home appliances (Figure  4 )  [25]. Experimental recordings used a color-depth camera (Kinect ® , Microsoft © ). The activities in the experimental protocol are divided into two scenarios: guided-and semi-guided activities. Guided activities (10 min) intend to assess kinematic parameters about the participant's gait (e. g. , walking 8 m). Semi-guided activities (∼15 min) aim to evaluate the level of autonomy of the participant by organizing and carrying out a list of IADLs.
GAADRD Dataset Participants aged 65 years and above were recruited by a Greek Institute under the scope of an European project for the study of Alzheimer's disease, called Dem@care  [26]. This dataset contains recordings of seniors carrying out physical tasks and IADLs in an observation room with similar settings to those adopted in CHUN dataset. Experimental recordings have adopted a color-depth sensor (Asus Xtion Pro Live ® , ~10 frames per second). We have focused our evaluation in GAADRD subset called DS8 which contains recordings of 25 seniors. In this subset, participants are asked to perform the following IADLs: • Establish account balance (M. Payment); • Prepare drink (P. Drink, e. g. , prepare tea/coffee); • Prepare drug box (P. Pill box); • Read article; • Talk on the telephone (T.
Telephone, e. g. , calling); • Turn radio on; and • Water plant. We highlight that there are a few differences between IADLs of GAADRD and CHUN datasets. For instance, the "prepare pill box" activity in CHUN dataset consists in organizing a patient medication for a week, while in GAADRD dataset it corresponds to "taking the medicine". Moreover, GAADRD introduces the activity "Turn radio on". These differences have led to slightly different activity models between both datasets. Nevertheless, using the proposed ontological formalism we have swiftly adapted the event models' definition of CHUN dataset to GAADRD. From here on in the paper we will refer to GAADRD-DS8 subset as GAADRD dataset.
Nursing Home Dataset This dataset consists in 72 h of depth data recording about a 86 years old female, diagnosed with Alzheimer living in a nursing home apartment. Her apartment is monitored by two partially overlapping color-depth sensors. She displays agitation and aberrant motor behavior and the nursing home staff is interested in finding out more about her night time behavior, e. g. , if she wanders during the night. In this evaluation we have focused on the performance of the proposed framework at describing common events in her daily routine: entering and exiting the bed, the restroom and the apartment; and sitting on the armchair. Figure  5  illustrates an example of the monitored scene. Since the nursing home dataset only contains depth signal recordings of the scene to anonymize the participant identity, the adopted baseline method, which is designed for color images, could not be computed for this dataset.
CHUN Dataset This experiment has evaluated the performance of the proposed framework on CHUN dataset (Tables  1 2 3 ). We have observed that the proposed approach has outperformed all variants of the baseline method and it has also presented the performance with the smallest standard deviation of the mean (Table  1 ). Among baseline variants, DT-HOG has the best performance (3/6 events) followed by DT-HOF (2/6). The performance values of the proposed framework have also generalized for the recognition of physical tasks (Table  2 ) and for IADL recognition on a larger cohort of participants (Table  3 ).
GAADRD Dataset The second experiment has compared the performance of the proposed framework to baseline methods on GAADRD dataset (Table  4 ). The proposed framework has outperformed the baseline approaches in all event categories (Table  4 ). It has also presented the smallest standard deviation of the mean in performance. Results have also showed that HOF and MBH variants of the baseline method have completely failed to recognize the activities: prepare drug box, talk on the telephone and water the plant.
Nursing Home Dataset Finally, the last experiment has evaluated the performance of the proposed method in the nursing home dataset. We have divided the results of this evaluation per point of views of the scene (bed or living room) and per day of recording (Table  5 ). In this experiment, event models make use of the physical object type "scene". The scene object carries global information about the monitored scene and tracks its dynamics, like how the number of people in the scene varies over time. This type of concept is particularly useful to model the semantics of events related to entering/exiting the scene.
Overall People Tracking To evaluate the quality of people tracking we have carried out an qualitative evaluation on the entire CHUN dataset and a subset of GAADRD dataset. The visualization of results has shown that in the short-term scenarios, such as the recognition of physical tasks, the tracking quality was nearly 100%. In mid-and long-term scenarios, like daily living activities and nursing home events, the tracking quality has dropped in cases of poor detection due to partial occlusion of a person's body (e. g. , person close to image borders or to scene furniture) or to the person be spending several minutes outside of the field of view of the sensor.
CHUN Dataset The proposed approach has outperformed all variants of the baseline method and it has also presented the performance with the smallest standard deviation of the mean. Among baseline approaches, DT-HOG has the best performance (3/6 events) followed by DT-HOF (2/6). DT-HOG and DT-HOF variants of the baseline method have outperformed the proposed method on the activity "Search bus line". This happens because "Search bus line" activity is carried out at the edge of the image and generally behind scene furniture. The resulting occlusion causes the step of people detection of the proposed framework to fail, which in consequence, causes event recognition to fail. However, since the adopted baseline method relies on a local representation of optical flow motion, and most of the person's upper body motion is still visible, the performance of the baseline method is higher.
The superior performance of the proposed method is mostly due to its capability to handle variable frame rate (here 4-15 frames per second) and to model the temporal dependencies between activity components. Since baseline methods rely on a temporal sliding window to capture temporal dependencies in test time, the information about short-duration activities is generally shadowed by the information of longer ones (e. g. , the short "water the plant" versus the long "prepare drink"). The performance of the proposed framework (Tables  2  and 3 ) can be also favorably compared to state-of-the-art approaches in a dataset with similar activities but different participants and camera setting  [3]. Our framework has achieved a performance similar to prior work at the recognition of physical tasks (average recall: +1.
GAADRD Dataset The proposed framework has also outperformed baseline approaches in this dataset and, as in CHUN dataset, it has presented the smallest standard deviation of the mean in recognition performance. Baseline methods have particularly failed to recognize the activities of "prepare drug box", "water the plant" and "talk on the telephone". These activities are particularly challenging because they have taken place either at the back of the scene or the edge of the image and their most discriminative feature is their localization. Since the amount of visible motion is very low, the decision of the classifier using HOF and MBH descriptors tends to be random. On the other hand, by using HOG descriptor, which describe edges, the corresponding classifier has achieved a higher performance than the other two descriptors for "Talk on telephone" activity.
Moreover, baseline methods were strongly affected by the low and variable frame rate of the dataset recordings (4 to 10 frames per seconds), a characteristic that the proposed framework can handle by focusing on relative temporal relations between sub-events and timestamp information instead of frames. In summary, the ontology-driven formalism has a recognition performance that is superior to baselines with the great advantage of facilitating the transfer of event knowledge between different scenes, a important feature for real-world applications that baselines lack.
Nursing Home Dataset In the nursing home dataset the proposed approach has also presented a high recognition performance (mean recall and precision are, respectively, 89. 27% and 85. 78% for living area events, and 91. 66% and 86. 35% for bed area events). We have observed that this performance generalizes across the monitored days, a fact which highlights the robustness of the proposed approach for more unconstrained scenarios. However, even though it has achieved a reasonable recognition performance in this unconstrained setting, a few challenges remain for future work. For instance, the low performance in "exit restroom", "enter and leaving room" and "bed exit" events. This problem happens due to strong occlusion of the person's body by walls, door frames (Figure  5a ) and scene furniture (Figure  5b ), like the bed.
Summary The proposed framework outperforms baselines approaches on a variety of activities of daily living and on different datasets. Results also demonstrate that the performance of the proposed framework scales both for a larger number of participants and for unconstrained scenarios, like nursing home apartments. The demonstrated improvements come from addressing previously described limitations of event recognition using color-depth sensing  [3] , like the short-range field of view of the depth sensor; the underestimation of people's body size-due to noisy depth signal and occlusion of body parts, and event reasoning on recordings with variable and low frame-rate. By handling these limitations, the proposed framework enables the modeling of longer temporal relations between events which are natural of real-life scenarios.
It excels at applications where one can make use of common sense knowledge and prior information to pinpoint relevant characteristic of events, and hence avoid acquiring several examples of target activities to generalize the same information from training data. The proposed framework is currently being used at a partner medical institute to support the daily evaluation of symptoms of Alzheimer's disease; as a mean of interaction between a senior and serious games, and at the study of daily activities of seniors living at nursing home's apartments and at their own residences. Further work will investigate how to extend the proposed framework to handle uncertainty at low-level data and at semantic level, how to efficiently fuse multiple sensor data, and how to support the automatic diagnosis of cognitive disorders from behavioral data.
Fermi-LAT The Large Area Telescope (LAT), on board the Fermi Gamma-ray Space Telescope, is a pairconversion detector that covers the energy range from 20 MeV to more than 500 GeV with a field of view of approximately 2. 4 sr  [17]. The main observation mode of the Fermi-LAT is survey mode during which the LAT scans the entire sky every 3 hrs. We analyzed the Fermi-LAT data for 1ES 1215+303 from 2008-08-04, the start of the mission, up until to 2017-09-04. The data analysis was performed using the Fermi Science Tools package, version v10r0p5, and the latest P8R2_SOURCE_V6 instrument response functions. We have considered a maximum zenith angle of 90 • in order to reduce contributions from the Earth limb; and photons with energies greater than 100 MeV. For each data sample, we considered photons in a 10 • region of interest centered at the position of 1ES 1215+303; and contributions from sources from the 3FGL within a 16.
Preliminary results Fig. 2  shows the long-term light curves with VERITAS 1-day data points on the top and 3-day LAT data on the bottom. Zoomed-in image of 1-day light curves are shown for the brightest flares of 1ES 1215+303 for the LAT data, and 5 min bins for the brightest VERITAS flare. The LAT upper limits are shown as green downward triangles. The VERITAS light curve includes archival data up to 2014; the 2015-2017 data were not previously published. An additional interesting feature can be observed in Fig. 3 , where a possible increase in the average flux of 1ES 1215+303 is shown in 30-day and 10-day-binned light curves. A detailed analysis of the quiescent state will be necessary in order to understand this possible trend. Moreover, we report the detection of a coincidental GeV-TeV flare on 2017-04-01.
Discussion The LAT data have demonstrated a preference for a log-parabola fit to the GeV spectrum (a power-law fit was rejected at the 5. 26σ level with the first 8 years of LAT data). This indicates that the γ-ray spectrum of this blazar begins to turn over in the LAT energy range, i. e. , at energies below those that can be attributed purely to absorption by the EBL thus indicating curvature intrinsic to the source itself. The EBL-absorbed LAT log-parabola spectrum is consistent with the spectrum measured by VERITAS at higher energies, and therefore already subjected to the effects of EBL absorption. Unlike what has been observed for other blazars at TeV  [19] [20] and GeV energies  [21] , the spectral index of this blazar does not show significant evidence for hardening when it enters a higher flux state (Fig.
4 ). This relationship will be investigated further in our ongoing more-detailed analysis  [22]  as this relationship has been shown to be complex when shorter-term spectra were derived -e. g. for the case of Mrk 421  [23]. The fact that we were able to measure the GeV flux of this blazar in such unprecedented detail over a ten-year span has allowed us to evaluate the evolution of the flux with time. An interesting feature that we have already found is an apparent increasing trend in its flux over the duration of the observations (Fig. 3 ). We find that, for the GeV γ-ray flux, an increasing-flux model is preferred to a constant flux at more than 15σ level. Additionally, a preliminary yearly study of the complete LAT data set has shown some indication of a spectral-hardening trend of 1ES 1215+303 as a function of time.
Image-based techniques In the previous section, we have presented an overview of the documentation available from the past project  (2000). Most of the techniques used in 1999 have changed to so-called imagebased techniques  (Stylianidis et al. , 2016). In this section, we investigate about such solutions of 3D reconstruction of the Mosque based on the processing of the photogrammetric archives. We explore the possibilities of using these archives to generate dense point clouds using Photogrammetry and Structure from Motion methods  (Murtiyoso & Grussenmeyer, 2017). Several control points, based on visible details of the monument, and recorded by total station during the 1999 campaign, have been used for the exterior orientation of the images. Agisoft PhotoScan and PhotoModeler software packages have been used to orient the selected images and finally to generate dense point clouds of the inner part of the Mosque and the minaret (Figure  14 ).
Exterior orientation Although Agisoft PhotoScan does not manipulate metric cameras (i. e. the fiducial marks are not measurable in PhotoScan) and therefore no rigorous internal orientation can be achieved, pre-calibrated interior orientation parameters can be fixed. To achieve the automatic interior orientation in PhotoScan, the focal length, pixel size and sensor size must be defined. In our project, three cameras models were taken into account according to these parameters. Relative and absolute orientations of the images that cover the inner part and the minaret were performed successfully (Figure  14 ). Tie points automatically extracted by the software were used to achieve the relative orientation (Table  2 ). On the other hand, the absolute orientation was calculated using all available control points.
The bad results for the minaret are probably due to a poor configuration (the stereo-pairs were initially planned for stereo-restitution only). We can see in figure 14 a lack of oblique images for the minaret. Hence, the efficiency of the SfM approach is reduced for that part of the object. These results should be improved by revising the camera calibration parameters and the interior orientation process, as PhotoScan is not able to manage fiducial poins. We plan to recompute the project in PhotoModeler, as this software package is able to manage fiducial points.
Processing of dense point clouds Dense point clouds, for the inner walls and the minaret were automatically generated from oriented images with PhotoScan. There are some holes in the resulted point clouds (Figure  15 ), due to the poor quality of the black and white images (film scanned as described in §3. 2) and insufficient overlap between some images. To improve the final model and especially fill the holes, we applied the following method to complete the point clouds: • We first transformed existing CAD drawings from the initial documentation into shapefiles, and export the resulted shapefiles into point clouds (LAS format, Figure  16 ). This step was achieved with the ArcGIS software; • Finally, we merged all the point clouds in a unique point cloud (Figure  17 ) with CloudCompare.
DISCUSSION AND FUTURE WORKS This project is a contribution to the CIPA Database for saving the Heritage of Syria  (Silver et al. , 2016). Taking into account two decades of cultural heritage documentation projects managed by the first author, the photogrammetric archives of the great Omayyad Mosque of Aleppo (Syria) can be considered as a reference. The aim of this documentation collected between 1999 and 2001 by the Engineering Unit of the University of Aleppo was to provide topographical maps and elevations, planimetric and altimetric details of the walls and entrances, of the dome and the minaret of the Great Mosque. The archives used the Aleppo project can be considered as a valuable data source required for 3D reconstruction of destroyed or damaged historical monuments. There are other interesting archives of the Aleppo Mosque recorded by Prof.
The reconstruction work of the Aleppo Great Mosque from the archives images is still in progress but the first results are very promising. Most of the images from past projects have been recorded with "metric" cameras with fiducial marks. But only a few recent software packages (such as PhotoModeler) are still able to take the fiducial marks into account in the photogrammetric orientation process. We plan to reprocess the project with this tool in order to improve the accuracy and include more images, especially for the minaret.
ACKNOWLEDGEMENTS The authors would like to thank  Prof. Marwan Hamze  (director of the  Engineering Unit of the University of Aleppo ) who managed the documentation project in 1999, and offered us the possibility to contribute. We thank also  Dr. Marwan Al Heib  ( Ineris, France ) who introduced the Photogrammetry and  Geomatics Group  from  INSA Strasbourg  to the  Colleagues of the University of Aleppo  twenty years ago.
We show on both simulated and real data sets that our method produces gene trees whose quality is similar to those constructed by competitive methods, often in a smaller amount of time. The novelties of this method compared to the previous ones are: (1) a lower running time, allowing it to run on larger datasets; (2) guaranteed optimal solutions; and (3) a simpler input -the input data consists of a gene tree with supports and a species tree, without the need to supply a gene alignment or a sample of gene trees that other methods rely on. Moreover, our method is integrated into the user-friendly ecceTERA package so that anyone having a gene tree with supports -output by standard ML software such as PhyML or RAxML -and a species tree (with or without dates), can quickly obtain a better-quality gene tree, or even correct a whole database in reasonable time.
Practical issues In order to turn the algorithmic principle described in the previous section into a workable method for biological datasets, we have to handle three issues: one is that the position of the root in the gene tree is usually unknown; a second is that species trees are usually undated; the last and most difficult one is the choice between multiple solutions. Indeed, in some cases the solution space of the problems defined in the previous sections is huge and two different solutions can be far apart. But some information from branch supports -encoded in the tripartitions to be used in the amalgamation algorithm -can be used to efficiently find a good solution. We address this issue first.
Application on simulated and biological data In this section, we use three different data sets, two simulated and one from microbial genomes, to compare the performance of our algorithm with seven different gene tree reconstruction methods: TERA  (Scornavacca et al. , 2015) , ALE  (Szöllősi et al. , 2013) , TreeFix-DTL  (Bansal et al. , 2014) , MowgliNNI  (Nguyen et al. , 2013) , JPrIME-DLTRS  (Sjöstrand et al. , 2014) , RAxML  (Stamatakis, 2014)  and PhyML  (Guindon et al. , 2010). The first five use information from the sequences and the species tree, while the last two use only information from the sequences. The method described in this paper has been integrated to the ecceTERA package and thus it is called ecceTERA in the comparisons. TERA has also been implemented in the same package, but it is a different method, described in  (Scornavacca et al.
(2015). Fig. 2. Accuracy of several methods on the simulated proteobacteria data set: we measure the normalized Robinson-Foulds distance of the reconstructed tree to the true gene tree for all 1000 gene trees, for 7 methods. Computing times -except for the computation of the RAxML trees -are given in the boxes (s=seconds, m=minutes). In terms of quality, TreeFix-DTL achieves the best accuracy but is relatively slow. ecceTERA is comparable with the second best methods, both in accuracy and computing time. We ran TreeFix-DTL with default parameters, GTR with a gamma distribution as models of evolution, and as a starting tree the RAxML tree. The RAxML tree (with bootstrap values) was also the input given to MowgliNNI and ecceTERA that were run with a threshold for weak edges equal to 70. As in  Scornavacca et al.