dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We would like to thank the  Ebola-Tx consortium  and all those (authorities, CNTS staff, ETC staff, all  MSF staff, laboratory  staff, communities, and Ebola survivors) that have contributed to the implementation of the  Ebola-Tx. Our gratitude goes to the  Clinical Trials Unit of ITM  who supported the implementation of the trial. Financial support: The  Ebola-Tx  project was funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 666094. Additional funding and support was provided by the  Department of Economy, Science and Innovation (EWI)  of the  Flemish government and the Bill and Melinda Gates Foundation. The funders of the study had no role in study design, data collection, data analysis, interpretation of the data, or in the writing of the report.
1%) members of which are capable to also degrade petroleum hydrocarbons  29. In addition to Desulfonauticus mentioned above, we identified in moderate amounts some OTUs that are related to anaerobic mesophilic sulfate and/or sulfur reducers such as Desulfovibrio (0. 8%), order Desulfovibrionales (2. 9%), Pelobacter (0. 7%), as well as to anaerobic thermophiles (e. g. Thermovirga, 0. 4%). Members of these taxa have been already successfully isolated from petroleum hydrocarbon associated environments  [30] [31] [32]. Additionally, fermentative bacteria such as those belonging to the genus Halanaerobium (0. 6%), as already mentioned in the bacterial clone library, and the Clostridiaceae family (4. 2%) were also found to have representative OTUs in the dataset. Halanaerobium has been previously found in formation water samples from an oil reservoir  33  whereas members of the Clostridiaceae family are thought to be using hydrocarbon intermediates to generate organic acid precursors under sulfate, methanogenic or iron reducing conditions  [34] [35] [36].
As suggested in Head et al. 45  , a cryptic community using oxygen, possibly provided by water radiolysis may be present in some oil reservoirs. Despite, the in situ role of recovered aerobic microbes and especially the hyperhalophilic archaeons in the Rabi core is questionable; it is known that some of them are able to grow anaerobically using   S3  and S6 ) Alpha 1 Halobacillus trueperi/dabanensis 99 250 g. l -1 NaCl pH 7. 5 anaerobiosis (Table  S2 ) Table  3. Microbial diversity of isolates originating from the hypersaline oil reservoir and their condition of isolation. 16S rRNA gene sequences of isolates are available under Genbank accession numbers JQ690689 to JQ690697. nitrate or fumarate as terminal electron acceptors. In this respect, unknown types of anaerobic metabolism to be performed by these microorganisms cannot be excluded.
Dna extraction. A portion of the sediment was washed with the aim to remove hydrocarbons and PCR inhibitory material and improve DNA recovery and efficiency of PCR amplification  62. Washing was carried out by re-suspension in wash solution (2 mL per g of sample), vortexing for 2 min, followed by centrifugation at full speed for 5 min in a bench top microfuge (14,000 g). Three successive washes were performed in wash solution 1 (50 mM Tris-HCl pH 8. 3, 200 mM NaCl, 5 mM Na 2 EDTA, 0. 05% Triton X-100), then in wash solution 2 (50 mM Tris-HCl pH 8. 3, 200 mM NaCl, 5 mM Na 2 EDTA), and finally in wash solution 3 (50 mM Tris-HCl pH 8. 3, 0. 1 mM Na 2 EDTA). Despite there can be some potential loss of cell during these washing steps, this procedure was performed as it provided greater DNA quantities (data not shown).
Sequence analyses. Sequence manipulations, analyses and alignments were performed using the BioEdit program (http://www. mbio. ncsu. edu/BioEdit/bioedit. html). Similarities of concensus 16S rRNA gene nucleotide sequences with sequences available in the Genbank database were determined using the BLASTN program (www3. ncbi. nlm. nih. gov/BLAST). Amplicon preparation for next-generation sequencing. Amplicons of the 16S rRNA gene suitable for sequencing on a MiSeq (Illumina) v3 kit (2 × 300bp) were produced using a nested PCR approach. During the first PCR reaction the nearly full length 16S RNA gene was amplified using the bacterial primers 8F (5′-AGAGT TTGATYMTGGCTCAG-3′) and 1492R (5′-TACCTTGTTAYGACTT-3′) for 30 cycles and hybridization at 52 °C. This step was then followed by a second PCR round using the universal primer pair 515F (5′-GTGNCAGCM GCCGCGGTAA-3′) and 926R (5′-CCGYCAATTYMTTTRAGTTT-3′) for 20 cycles and hybridization at 55 °C.
Microscopic and SEM observations of the core did not show any physical evidence (disruption, mineralogical contamination) of penetration of the drilling fluid into the core. As the clays from core samples were green and not oxidized, it seems that coring processes did not affect physico-chemical conditions of the reservoir. Only the inner part (6 cm) of the core (10 cm) was inoculated into culture media or submitted to DNA extraction. Community structures (molecular CE-SSCP fingerprints of 16S rRNA genes, data not shown) of inner and outer (2-3 cm) compartments were highly similar, supporting the absence of detectable exogenous microbe contamination by the drilling fluid. Furthermore, microbes retrieved from the molecular surveys were related to organisms showing physiological features compatible to in situ conditions.
Characterization Electron Microscopy TEM was performed by depositing 10 µL of the colloidal suspension on a carbon coated copper grid which is left to to dry in air for a few hours. The nanoparticles were imaged with a Tecnai G2 FEI microscope operated at 120kV. AgNWs, as well as the oriented monolayer thin films were imaged by SEM using a JEOL 6700F microscope operating at 1 kV and 10 μA. The orientation analysis was done on SEM pictures using OrientationJ, 54 a plugin developed for ImageJ,  55  which is based on the computation of the structure tensor in the local neighbourhood of each pixel. The original SEM picture is first binarized, and the OrientationJ plugin is applied with a Gaussian gradient on a window of 3 pixels. As a result, an orientation angle is given for each pixel, and a color-code which represents each angle by a color is used to build the images given in Fig.
Acknowledgements The authors thank  Marc Schmutz  and  Christian Blanck  ( ICS, France ) for TEM measurements,  Cedric Leuvrey  ( IPCMS, France ) for SEM measurements. The sheet resistance measurements were done in the STnano cleanroom facility in Strasbourg, France. S. S. thanks the  IDEX program  of the  Strasbourg University  for funding. V. L. thanks the  Labex CSC "Chemistry of Complex Systems"  for financial support. M. P. thanks the " Chaire d'Excellence" program  of  CNRS  and the  Université de Strasbourg  for partial funding. G. D. thanks the  Institut Universitaire de France  for financial support.
However, the SIRENE file did not supply numbers of employees but only workforces by size category, meaning on the one hand that no non-salaried employees were included in the file (it should be noted that 47% of establishments had no employee in 1994), and, on the other, that the number of salaried employees was known only approximately in the categories with high numbers in particular (by group of 100 employees). Using these partial data, our work consisted in estimating the number of employees by relying on the results of the previous surveys and on comparisons with the employment figures supplied by the INSEE aggregated on the scale of the area of study. For 1994, we calculated employment on the basis of the SIRENE file with the FRETURB software which used the results of the first campaign of surveys to assign a number of employees according to their ST115 and their size category.
Size effects due to differences in BSA were removed. Results were visualised by deforming the computed mean LV shape along the shape vector from low values for the control group towards high values for the ASO group. Differences were assessed using non-parametric Mann-Whitney-U test. Correlations between shape features and CMR volumetric data (LVEF, iEDV and zEDV = EDV z-scores) were assessed using Pearson's correlation. Significance was assumed at p <. 05.
These studies contain useful information about response time. Another challenge is the multi-objective optimization of mechatronic systems. Two techniques are currently used, the first is by aggregating all the criteria in one multi-criteria indicator  [8]. The second method is to keep the criteria independent and try to find a Pareto Front. In this paper, the failsafe technology and the backlash effect of the gear stop are addressed by using Modelica language. The ET model is then connected to ModelCenter to perform a multi-objective optimization separately from the controller. The paper is therefore organized as following; in section 2 we present globally the ET. In section 3, we consider the system modeling based on the cited methodology with a focus on using the technology early in the preliminary design.
A. Problem description We are searching for the system configuration that respects the following specifications:  The rise time (0° to 90°) should be lower than 150 ms (12V supply voltage)  The return time with the failsafe system (90° to 10°) should be lower than 300ms (0V supply voltage and open circuit)  The maximum torque of the throttle should be between 2N. m and 3. 5N. m The response time specifications may appear very fast for the application but in reality the performances would be lower because of the aerodynamic torque of the airflow. The problem presented contains two contradictory objectives because if we want to decrease the return time we should use a spring with high stiffness and this will delay the rise time. Therefore a multi-objective optimization is applied by combining Modelica with ModelCenter software.
B. Results To carry out the optimization, Modelica was combined with ModelCenter software. And from the algorithms available in its library, we have chosen the NSGA II algorithm (Nondominating Sorting Genetic Algorithm)  [11]. Since the problem is multi-objective, the solution is not unique but we obtain a set of Pareto solutions. The Pareto Front is presented in the Figure  7. For validation the solution 2 is compared with the initial set as shown in Figure  8.
V. CONCLUSION This article has presented a novel approach of preliminary design and optimization of a mechatronic system. This approach was applied successfully to an Electronic Throttle. We first integrated the technology of the system in the preliminary design thanks to Modelica language. Then, a multiobjective optimization was performed to obtain good configurations that respect the response time requirements and the torque constraint with a low time cost. In our further work, we will try to make experimental tests and identifications to improve the model and make more validations.
For example, Wang and Yu  [15]  used the semantic description of colours to associate an emotional semantic to an image. Concerning textures, the orientation of the different lines contained in the images is sometimes considered. According to Liu et al. [1] , oblique lines could be associated with dynamism and action; horizontal and vertical ones with calm and relaxation. Our work is part of this last family of approaches. We evaluated some low level features well adapted for object recognition and image retrieval  [16, 17, 18, 19, 20, 21, 22]  and conducted our work on two databases: • A set of natural images that was assessed during subjective evaluations: Study of Emotion on Natural image databaSE (SENSE)  [23] ; • A database considered as a reference on psychological studies of emotions: International Affective Picture System (IAPS)  [24].
Image databases In the domain of emotion extraction, the choice of the database is not trivial since there is no reference for all emotion studies and applications, some authors even built their own dataset without spreading it. We choose in this study to considered two databases: the first one is composed of low semantic images and the second of more semantic ones. In this paper, "low-semantic" means, that the images do not shock and do not force a strong emotional response. We think that even low semantic images, including abstract representation, may produce emotions according to the viewer sensitivity and the viewing time. We define this "low-semantic" criteria in response to some high semantic images on IAPS  [24] , a reference in psychological studies on emotions. In this database, which will be described in subsection 2.
The International Affective Picture System (IAPS) This dataset is developed since the late 1980s at NIMH Center for Emotion and Attention (CSEA) at the University of Florida  [24] , which is composed of photographs used in emotion research. It is considered as a reference in psychological studies and many papers present results on this base [1,  13, 14]. The images of IAPS are scored according to the affective ratings: pleasure, arousal and dominance. It corresponds to a dimensional representation of emotions. The affective norms for the pictures in the IAPS were obtained in 18 separate studies involving approximately 60 pictures for each session. Each of the 1182 images from their dataset was evaluated by about 100 participants. 1 The emotion of the images we have chosen is based of those defined in different papers  [25, 26, 27]  using three class classification: "Pleasant", "Neutral" and "Unpleasant".
SENSE Study of Emotion on Natural image databaSE (SENSE) is the database used in  [23, 28]. It is a low semantic, natural and diversified database containing 350 images free to use for research and publication. It is composed of animals, food and drink, landscapes, historic and tourist monuments as shown on Fig. 2  with some examples. Some transformations were applied on some images of the database: geometric modifications and changes on colour balance, so some images are repeated twice or more. In this database, only 17 images (4. 86%) contain human faces to ensure that the facial expression does not induce bias. It is composed of low semantic images since they minimize the potential interactions between emotions on following images during subjective evaluations. This aspect is important to ensure that the emotion indicated for an image is really related to its content and not to the emotional impact of the previous one.
Classification process Traditionally, for CBIR task, an image is represented by a visual signature, which concentrates the usefull information of the content of the image into a smaller descriptor. For a recognition or retrieval task, two images are considered as visually close if their visual signatures are close too. There are different ways to obtain this visual signature  [17, 37, 38] , but the large majority of them depends on a visual codebook and visual words. We evaluate two different visual signatures methods: Bag of Visual Words (BoVW)  [37]  and VLAD  [17]. Here, the visual codebook is a set a visual words (a set of features) obtained by a K-Means algorithm. The idea is to resume an image database with K visual words (features). Then, after obtaining the signature, the decision process on emotion recognition uses a classification algorithm such as SVM for example.
This characterizes the distribution of the vectors with respect to the center. Assuming the local descriptor to be d-dimensional, the dimension D of VLAD representation is D = K * d. Except for GIST, on all our descriptors the visual codebooks are obtained with a K-Means algorithm. The size of the K-Means codebook is obtained with the equation (  6 ): K = 4 √ N * d, (6) where K is the number of visual words and N is the number of descriptors and d the size of vector of characteristics. For VLAD we only compute K=64 visual words based on the results obtained by Jégou et al  [17]. In the particular case of GIST, we use PCA as advised by Oliva and Torralba  [36]. To perform PCA we compute the principal components on the GIST descriptors from the full database considered. We select the K axes that preserve 99% of the initial information.
Classification with SVM For emotion recognition, Liu et al. [1] among others, use an SVM classifier with a linear kernel in its multiclass extension "One against one" for classification. They showed that this method gives the best results using their descriptors and databases, so we naturally choose to test our descriptors and databases with this classifier. The inputs of the classifier are the visual signatures according to the considered descriptor. We performed a two class classification: positive and negative emotions. We do not consider the "Neutal" class since, during our database assessment on SENSE, this class was given by observers which whether cannot decide about the nature of the emotion or really feel neutral when looking at the images. Moreover, the neutral images are rarely considerd in the literature  [4, 13]  due to the same restriction.
On IAPS, some authors  [25, 26, 27]  list in their articles the images according to their hedonic valence that induces the three groups: Pleasant, Neutral and Unpleasant images, which respectively correspond to positive, neutral and negative. However, to perform a fair comparison between our results on the two databases in the rest of this paper we only keep positive and negative images. After classification for each descriptor we combined the results for the different descriptors using "Majority Voting" algorithm as shown on the Fig. 3.
• WA4 and WA5 respectively means Wave Atoms Scale 4 and Wave Atoms Scale 5; • CM denotes Colour Moments and CMI Colour Moment Invariants; • OpSIFT means OpponentSIFT; • We use the notation Dataset Visual codebook to resume the different configurations we have tested. Then in SENSE I configuration, the visual signatures of the images of SENSE are computed using the visual vocabulary from IAPS. The different configurations allow us to determine whether or not the results are dependent on the image database used to create the visual dictionary. In  [39] , we have studied the impact of the visual signature according to the nature of the features. We noticed that VLAD representation matches better for the local descriptors and BoVW for global ones. In general, on local features, VLAD outperformed BoVW by 10% and on global features underperformed them by 20%, interested reader may refer to  [39]  for more details.
Then for the following results we use BOVW for global feature descriptors and VLAD for local feature descriptors. One can easily see on Table  1  that the selected descriptors have a different behaviour whether the nature of emotion is positive or negative. However the average of correct classification for each nature of emotion remains higher than the random selection (50%). In general, when considering IAPS as test database or as visual codebook, the negative images are more easily classified. It comes from the nature of images in IAPS since many of them are voluntarily modified to ensure a strong emotional response. For SENSE database, the average results are more consistent for negative and positive emotions with 55. 55% and 54. 16%. We can also conclude that the visual dictionary has little impact on the behaviour of descriptors for SENSE and IAPS.
Those global average results prove that our choice to consider classic CBIR features for emotion evaluation gives interesting results. When differentiating the descriptors, the results are less consistent. For example, SIFT have approximately the same results for negative and positive emotions with yet a higher classification rate for negative ones (63. 28%). It is also the only descriptor which allows a classification significantly higher than random one for both positive and negative emotions whether the dataset and the visual dictionary. CSIFT and OpSIFT compete with SIFT for global recognition of emotions, but OpSIFT is less efficient for negative images with SENSE or using SENSE visual codebook. In spite of this less efficiency, it is the best descriptor for IAPS using IAPS codebook with 60.
However, it appears rather clearly that negative images will be more easy to extract than positive ones. To verify this hypothesis we performed a fusion based on Majority Voting. The results are presented in Table  2. In Table  2  we compare the classification rates before and after fusion with Majoriting Voting. As we think considering the previous results, there is a significant improvement after the fusion. For example, regardless the codebook used, the recognition of negative images is increased by 15% on average for the two datasets. Moreover, the best classification rates are obtained after merging using the dictionary built from IAPS. Before the fusion, 54. 86% and 45. 02% of positive images were respectively recognized on SENSE and IAPS against 64. 58% and 46. 55% after. If we generally consider these results after fusion, we see that they have especially been improved on our image database (SENSE), independently of visual dictionaries and emotions: • ∼ +15% for negative images and ∼ +6% for positive ones; • ∼ +17% with the codebook from IAPS and ∼ +3.
7% with the codebook from SENSE. Note that for IAPS, positive image average results (47. 13% and 45. 02%) are lower than a simple random selection (50%). There are two hypotheses for this: the construction of the database itself or the fact that negative images are easier to recognize. The proposed algorithm highlights the complementarity of the chosen features. In fact, we have also tested different fusion combinations and the better configuration is the fusion of all of the features to obtain the best classification of positive and negative images. We also prove that CBIR methods are effective for the emotional impact recognition.
Comparison with literature on IAPS As stated in the introduction, we want to point out the difficulty of comparison of results on emotion recognition since the datasets vary and, even on IAPS, there is no consensus on the training set. Regarding the divergences induced by this disparity, the goal of this comparison is only to present the interval of the classification rates. We chose three results on IAPS to make the comparison: • Wei et al. [4]  using a semantic description of the images for emotional classification of images. The authors chose a discrete modeling of emotions in 8 classes: "Anger", "Despair", "Interest", "Irritation", "Joy", "Fun", "Pride" and "Sadness". The classification rates they get are between 33. 25% for the class "Pleasure" and 50. 25% for "Joy. " • Liu et al. [1] proposing a system based on colour, texture, shape features and a set of semantic descriptors based on colours.
Conclusions and future works In this paper, we propose an evaluation of different standard CBIR features for emotion prediction on low and high semantic images. The emotional impact of images is the result of many parameters both intrinsic and extrinsic. On the one hand, colours, textures or shapes are intrinsic parameters. On the other hand, culture, state of mind and experience of the viewer can be considered as extrinsic. Our goal in this work is to model the intrinsic ones using local and global features, and to deal with the large variability of images. So we have tested our algorithm on two databases: one low semantic (SENSE) and the other more semantic (IAPS). Whether they are local or global, the different descriptors have proven their efficiency for emotional impact recognition on both databases.
Acknowledgments LF acknowledges support by Grant  MTM2011-24766  of the MICINN, Spain. LF and VP acknowledge support from  EU  project  FP7-295217-HPC-GA  and also by the  Basque Government  through the  BERC 2014-2017 program  and by  Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa  excellence accreditation SEV-2013-0323. MRSF acknowledges financial support from  CNPq, Brazil.