dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by the  National Key Research and Development Program of China  ( 2016YFA0202603 ), the  National Basic Research Program of China  ( 2013CB934103 ), the  National Natural Science Foundation of China  ( 51521001 ,  51602239 ), the  National Natural Science Fund for Distinguished Young Scholars  ( 51425204 ),  Yellow Crane Talent (Science & Technology) Program of Wuhan City  and the  Fundamental Research Funds for the Central Universities  (WUT:  2016III001 ,  2016III003 ,  2016IVA090 ) and the  Programme of Introducing Talents of Discipline to Universities  ( B17034 ). We acknowledge the funding support from the  Lorraine Region  (now part of  Grand Est Region )  Cooperation Research Lorraine/Hubei Program 2015/2017.
Study group Retrospectively, aortic root geometry was assessed and compared between patients with severe tricuspid aortic valve stenosis and controls without valvular disease in this single center study. All data were indexed to the body surface area (BSA). 25 consecutive women and 25 consecutive men with severe AS who underwent MSCT and transthoracic echocardiography (TTE) before TAVI in 2015-2018 at our Institute were included in the study. Age-matched 25 women and men without valvular pathology, who were subjected to coronary angiography CT due to suspected coronary artery regression were enrolled in a control group. Exclusion criteria included renal failure, hypersensitivity to iodinated contrast material and bicuspid aortic valve. The aspect of the aortic valve, the tricuspid or the bicuspid one were assessed by TTE and MSCT.
Multislice computed tomography All patients underwent computed tomography scanning using dual source scanner Somatom Force (Siemens Healthcare, Forchheim, Germany) with the following parameters: 2 × 192 slices with 0. 6-mm collimation, gantry rotation time 250 ms, tube voltage 70-100 kV, tube current 320-500 mAs (depending on the patient body mass). In both groups, non-enhanced prospectively ECG-gated scan (75% of R-R interval) with a slice thickness of 3 mm was performed to measure the calcium score of the aortic valve according to the modified Agatston method using dedicated calcium scoring software  [5]. After a non-enhanced scan, a retrospectively ECG-gated helical CT angiography was performed in both groups (controls and in patients with severe AS). To minimize radiation exposure, electrocardiographically-gated tube current modulation was applied in all patients.
The scan covered the heart (extending from the tracheal bifurcation to the diaphragm). In patients with AS, the ECG-gated data acquisition was followed by a non-ECG-gated CT angiographic scan of the chest, the abdomen, and the pelvis for the assessment of an access route. Iodinated contrast material (350-370 mg/ml) was injected intravenously at a flow rate of 4. 5 ml/s. CT acquisition parameters were slice collimation 192 × 0. 6 mm, gantry rotation time 250 ms, tube voltage 70-100 kV, tube current 320-500 mAs (depending on the patient body mass), pitch of 0. 16-0. 3 (depending of the heart rate). The dataset of the contrast-enhanced scan was reconstructed every 10% of the R-R interval. Detailed assessments of aortic root anatomy and the ascending aorta were performed. Measurements were taken using dedicated software syngo.
1a-d ). The sinus of Valsalva was measured on the level of the widest diameter from one sinus to another sinus. The average of three sinus-to-sinus measurements were calculated. The sinus of Valsalva height was measure from the annular plain to the highest point of the right and the left sinus of Valsalva. The area, the perimeter and the diameter of the ascending aorta were measured 4. 5 cm above the annulus on a transverse double oblique plane perpendicular to the long axis of the ascending aorta. The volume of the aortic root was calculated from the annular plane to the lowest point of the sinotubular junction using syngo. via VOI freehand Siemens software (Fig. 1e ). All diameter measurements were assessed in the systolic phase. The aortic wall and calcifications were included to all dimensions.
Echocardiography A standard comprehensive TTE was performed in each patient. The Vivid S70 and E9 (General Electric Medical Systems, Milwaukee, WI, USA) were used. Echocardiographic measurements were performed by qualified echocardiographers experienced in the quantitative assessment of valvular heart diseases. According to the latest guidelines of the European Society of Cardiology (ESC)  [2] , severe AS was determined TTE data were collected, including the left ventricular end-diastolic diameter (LVEDD), the LV end-systolic diameter (LVESD), the interventricular septum (IVS) diameter at the end-diastole, and the end-diastolic posterior wall thickness (PW), the left ventricle ejection fraction (LVEF). All measurements were made in the parasternal long axis view.
The direction of linear polarization is reported, as a function of the phase between the two circularly polarized fields, in distributed configuration. As one can see, the direction of the linear polarization vector can be arbitrarily adjusted by changing the phase between the two fields and has a linear dependence with the phase shifter itself. If one then swaps the chirality of the two undulator groups, the same linear dependence can be obtained with opposite sign. The data were collected with open diagnostic aperture. In both cases the degree of linear polarization, ∼0. 8, was independent of the phase, within the measurement uncertainty. This result confirms the ability to finely control the polarization state using the XPU scheme. The possibility to arbitrarily modify the direction of linear polarization solely through a phase shifter change represents a powerful extension of the FEL capabilities for users interested in rapid experimental variation of the polarization vector direction, as the APPLE-II undulators installed at FERMI by themselves can produce linearly polarized light only along the horizontal or vertical planes.
People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic emotions categories, according to Ekman's emotion categories. To check the quality of performance, all recordings are evaluated by experts and volunteers. The database is available to academic community and might be useful in the study on audio-visual emotion recognition.
For this very reason, gestures might be a perfect supplement for emotion recognition methods that do not require specified sensors and may be examined from a distance. Automatic affect recognition is a pattern recognition problem. Therefore, standard pattern recognition methodology, which involves database creation, feature extraction and classification, is usually applied. The first part of this methodology is the crucial one. During the selection of samples for an emotional database one has to consider a set which would guarantee minimization of individual features, such as age and gender, as well as provide a wide range of correctly labelled complex emotional states. What is more, one should also focus on choosing the right source of affect: mimics, body language and speech seem to be the most appropriate due to lack of requirement of direct body contact with any specialized equipment during sample acquisition.
As it is presented in section 2 just several publicly accessible multimodal databases exists, which contain simultaneously recorded modalities such as face mimic, movements of full body and speech. Thus, there is clearly a space and a necessity to create such emotional databases. In this article, the authors describe an emotional database consisting of audio, video and point cloud data representing human body movements. The paper adopts the following outline. Section II presents a brief review of other relevant multimodal emotional corpora. Sections III and IV describe the process of creating the database and the process of recording. Section V presents the process of emotional recordings evaluation. Finally, section VI gives the conclusions.
Related works 3D scanned and even thermal databases of different emotions have been constructed  [24, 4]. The most well known 3D datasets are the BU-3DFE  [35] , BU-4DFE  [34] , Bosphorus  [32]  and BP4D  [37]. BU-3DFE and BU-4DFE both contain posed datasets with six expressions, the latter having higher resolution. The Bosphorus tries to address the issue of having a wider selection of facial expressions and BP4D is the only one among the four using induced expressions instead of posed ones. A sample of models from a 3D database can be seen in Fig. 1. With RGB-D databases, however, it is important to note that the data is unique to each sensor with outputs having varying density and error, so algo-Fig. 1. 3D facial expression samples from the BU-3DFE database  [35]. rithms trained on databases like the IIIT-D RGB-D  [10] , VAP RGB-D  [15]  and KinectFaceDB  [23]  would be very susceptible to hardware changes.
For comparison with the 3D databases, an RGB-D sample has been provided in Fig. 2. One of the newer databases, the iCV SASE  [22]  database, is RGB-D dataset solely dedicated to head pose with free facial expressions. Even though depth based databases are relatively new compared to other types and there are very few of them, they still manage to cover a wide range of different emotions. With the release of commercial use depth cameras like the Microsoft Kinect  [23] , they will only continue to get more popular in the future. A comprehensive survey in  [24]  showed that some wider multimodal databases such as the database created by Psaltis et al. [29]  and emoFBVP database  [30]  includes face, body gesture and voice signals. Such databases are attracting lots of attentions of researchers, however there is still a big demand for having bigger and more comprehensive multimodal databases.
Acted vs natural emotions Emotional databases can be divided into three categories, taking into account their source: spontaneous, invoked and acted or simulated emotions. The spontaneous or "natural" samples are obtained by recording in an undisturbed environment, usually people are unaware of the process or it is not their main focus. TV programs such as talk shows, reality shows or various types of live coverage are good examples of this type of acquisition. However, the quality of such material might be questionable due to factors such as background noise, artifacts, overlapping voice. These components may obscure the exact nature of recorded emotions. Such recordings do not provide position and movements of the whole body of the subject as well as cloud representing human body movements.
Moreover collections of samples must be evaluated by human decision makers to determine the gathered emotional states. Another method of sample acquisition is recording an emotional reaction provoked by staged situations or aids such as videos, images or computer simulations. Although this method is favoured by psychologists, it's main disadvantage is lack of results repeatability, as reaction to the same stimuli might differ from person to person and is highly dependant on the recorded individual. Moreover, provoking full-blown emotions might be ethically problematic. Third source are acted out emotional samples. Subjects can be both actors as well as unqualified volunteers. This type of material is usually composed of high quality recordings, with clear undisturbed emotion expression. We are fully aware that there are many disadvantages of acted emotional database.
The recordings took place in a quiet, well lit environment. The video was captured against a green background (as visible in Fig. 3 ). A medium shot was used in order to keep the actors face in the frame and compensate for any movement during the emotion expression. In case of Kinect recordings the full body was in frame, including the legs (as visible in Fig. 4 ). The samples consist of simultaneous audio, video, cloud point and skeletal data feeds. They were performed using a video camera (Sony PMW-EX1), dictaphone (Roland R-26) and a Kinect 2 device. The data was gathered in form of wav audio files (44,1kHz, 16bit, stereo), mp4 videos (1920x1080, MPEG-4) with redundant audio track, and xef files containing the 3d data. Fig. 3  shows frames from the video recordings of different emotional expressions presented by the actors.
Data Evaluation To ensure the quality of the samples a perception test was carried out with 12 subjects (6 male and 6 female). They were presented with the three modalities separately. They were allowed to watch or listen to each sample only once and then determine the presented emotional state. Each volunteer had to assess one sample of each emotion presenting by every actor -in total 96 samples. The results are presented in Fig. 6. Analyzing the chart one can observe that the highest recognition rate occurred for facial emotion expressions. Comparable, however slightly lower results were obtained in case of speech. Using gestures offered the lowest recognition rate, however it can serve as a significant, supporting information when recognition is based on all three modalities. The results obtained for three modalities simultaneously are presented in Fig.
Conclusion This paper presents the process of creation a multimodal emotional database consisting recordings of six basic emotions (fear, surprise, anger, sadness, hap-  piness, disgust) as well as natural state, performed by 16 professional actors. It contains a large collection of samples in three synchronized modalities (560 samples for each modality: face, speech and gestures) which makes this corpus interesting for researchers in different fields, from psychology to affective computing. What is more, the position of the body includes the legs, while most database focus only on hands, arms and head. Due to the size of data (especially recordings from Kinect), the corpus is not accessible via a website, however it can be made available for research upon request.
Acknowledgement The authors would like to thank  Micha l Wasażnik  (psychologist), who participated in experimental protocol creation. This work is supported  Estonian Research Council  Grant ( PUT638 ), the  Scientific and Technological Research Council of Turkey (T ÜB İTAK)  (Proje  1001 -116E097 ),  Estonian-Polish Joint Research Project , the  Estonian Centre of Excellence in IT (EXCITE)  funded by the  European Regional Development Fund. We gratefully acknowledge the support of  NVIDIA Corporation  with the donation of the Titan XP GPU used for this research.
Funding information The work was supported by the  National Research Foundation of Korea (NRF)  Grant funded by the  Ministry of Science and I C T , K o r e a { M i d -C a r e e r R e s e a r c h e r s P r o g r a m ( N R F -2017R1A2B3012961);  Individual Basic Research Program  ( NRF-2017R1D1AB03034414 );  Brain Korea 21 Plus Program  ( 21A2013800002 )}.
CO 2 solubility measurements The CO 2 solubility in the pure ILs [ImPE][Tf 2 N], [ImPEGPE] [Tf 2 N] and [ImC 12 PE][Tf 2 N ] were measured at 30 C using the isochoric saturation method. The description of the system is provided in the ESI. † Prior to measurements, the ILs were degassed for 10 h (vacuum pressure of 5 Â 10 À5 mbar) and the resulting mass of each degassed IL was determined. The pressure decrease (resulting from CO 2 absorption into the IL) was monitored over time. Depending on the type and quantity of the IL, the equilibrium was reached aer 1 to 3 h. The CO 2 solubility data measured for [emim][Tf 2 N] (used as a standard) were in good agreement with those published in the literature.
Acknowledgements P. Gaveau  ( Ingénieur de Recherche CNRS ) from the  Institute Charles Gerhardt in Montpellier, France  is sincerely acknowledged for his helpful contribution in NMR analysis. Authors also thank  L. Brun  and  T. Delage  for their assistance in experiments related to ILs graing. Franck Martin  and  Guillaume Gracy  from  SIKEMIA  are sincerely acknowledged for their advice in ILs synthesis.
First, we estimated the spatial distribution of crop losses in exclosures and in adjacent plots in oat (Avena sativa) and winter wheat (Triticum aestivum) fields on an unfenced estate in south-western Sweden managed for crop and forest production together with commercial recreational hunting. We then evaluated the relationship between this distribution and distance from human activity hotspots, forests, and topological features such as interactions of visibility and openness in this particular landscape. We utilized the data obtained to assess the economic feasibility of combined wildlife-crop-forest management at the study site and to formulate suggestions for improvements. The underlying assumption was that combined wildlife-crop-forest landscape management at the study site represents a potentially viable model for building a diverse temperate-environment landscape that is still productive and economically viable.
Geographic data sources Data on the location of roads and buildings and a digital elevation model (DEM) of the terrain were obtained from the Swedish National Land Survey. Laser Imaging Detection and Ranging (LIDAR) data for 2011 and 2012 were also obtained from the Swedish National Land Survey, together with ortophotographs from the same sources taken in 2014 for the lower part of the study area (mostly the wheat field) and in 2012 for the upper part (mostly the oat field). There was substantial agreement between all sources, in particular tree height maps calculated from the LIDAR data and the ortophotographs (Fig  1 ). Two local features (a wild boar enclosure and a slaughterhouse) were mapped during field surveys.
Data analysis Data analysis was performed in ArcGIS (version 10. 4. 1, ESRI, Redlands, California, USA) and R  [33]. Tree height was estimated by subtracting the DEM layer data from the LIDAR layer data. The main geographic analyses performed were proximity and topological analyses. We calculated the distance of each sampling point from forest boundaries (determined by applying a threshold of 1. 5 m height on the tree height raster previously calculated) and from features that could be expected to affect animal behavior, such as houses, roads, the wild boar enclosure, and the slaughterhouse. Using the three-dimensional LIDAR data, we also ran topological analyses (3D Analyst toolbox in ArcGIS). The inclusion of visibility when analyzing the effect of roads on wildlife is suggested to be important for understanding landscape-wildlife interactions  [26].
The relative importance of the components and the associated uncertainty were evaluated by bootstrapping (R package relaimpo; Gro ¨mping, 2006). The metric chosen was lmg, as recommended by the package author, which is equivalent to the hierarchical partitioning approach  [36]  and decomposes the overall R 2 of the regression into relative components that add up to 1. These were multiplied by the R 2 itself to obtain an absolute measure of overall variance. Values calculated per hectare were calculated by area average (geometric mean) over the whole cultivated surface. All data derived from the sampling points were interpolated into a raster with inverse distance weighting (IDW). Values were considered statistically significant at p<0. 05 unless otherwise specified.
The economic sustainability of game wildlife in a landscape characterized by cropland mixed with forest So far, the only available source of estimated wildlife damage to crops in Sweden is a study from 2014 based on a farm survey. Fallow deer were reported by farmers in Va ¨stergo ¨tland county, where our study area was situated, to be responsible for 9. 1% of total harvest losses in oat and 7. 5% in wheat  [51]. At a national scale, the reported losses due to wildlife are on average rather small, in particular compared with those observed in our study, but with very high variation. These data are based on farmer reports and expected revenues, so they present a high uncertainty compared to the results of an exclosure experiment. The crop harvest losses we recorded relative to the production level inside the exclosure cages was substantially larger, and might suggest that the national estimates underestimate the damage.
For this, they used probes designed to detect the neutrophilic SOBs found in the clone libraries. Contrary to clone libraries, FISH is a quantitative technique for counting specific bacterial populations in environmental samples. It is not based on PCR, which is biased by the fact that rRNA operons have different copy numbers in different bacterial species. For example, according to the National Center for Biotechnology Information (NCBI) genome database [94], Acidithiobacillus thiooxidans and Thiomonas intermedia have one copy of the rRNA operon, Thiothrix sp. , Thiobacillus denitrificans and Halothiobacillus neapolitanus have two copies and Thiomonas intermedia four copies. Additionally, in traditional PCR (end-point PCR), the proportion of dominant amplicons does not necessarily reflect the abundance of specific sequences, due to preferential amplification of certain sequences and accumulation of amplicons in the plateau phase of amplification, irrespective of their original abundance.
Threats with many interactions aligned roughly with the threats with high VOI, suggesting a possible link between the number of threat interactions and the impact of uncertainty on management effectiveness. Some results of this study may be place-specific or expertspecific. Our study elicited data from a particular set of experts, and it is possible that a different set of experts would assign different benefits during elicitation. Indeed there is evidence that the importance of threats vary in different parts of the world  26  , and this may make different expert rankings valid in different places. Our findings nonetheless represent the largest survey of threat importance and the VOI to date and are likely to be useful for areas that face similar threats to New South Wales, and our methods pave the way for similar surveys to be conducted in other jurisdictions.
Already these have been applied to prioritise management of almost 450 site-managed species using a cost-effectiveness approach  33. This process of prioritising threatened species projects led to the creation of a database of expert-derived data that is critical for VOI. For example, the SoS database contains estimates of the expected persistence of threatened species in the absence of management to mitigate threats. Key threatening processes (KTPs) are drivers of extinction for species and ecological communities. Managing KTPs that affect many species can be more cost-effective than managing species individually  1. Although there are threats other than KTPs that are important factors affecting listed species (such as grazing), KTPs are a legislative focus of the SoS programme. NSW currently has 38 listed KTPs, which are processes that adversely affect threatened species or ecological communities or could cause species or ecological communities to become threatened.