dataesr/scientific-paragraphs-categorization huggingface dataset:
When signifi- cant differences were detected, differences among means were tested using Tukey's comparison test with FDR correction (false discovery rate, emmeans package of R). For all variables, the Gaussian distribution of residuals and the heteroscedasticity of the model were checked. Results are presented in Table  2. Variable v 1 0 for 1 = 0. 20 was not affected by the fixed effect of "days" ( χ 2 (1) = 38. 7, p = 0. 11 ), but was sig- nificantly affected by the fixed effect of "week" ( χ 2 (1) = 2. 51, p < 0. 001). The synthetic variable A 2 for 2 = 0. 28 was not affected by the effect of "days" ( χ 2 (1) = 2. 52, p = 0. 11) , but was significantly affected by the fixed effect of "week" ( χ 2 (1) = 28. 4, p < 0. 001). The anova test showed that the effects of "days" and "week" were not sig- nificant (p > 0.
This is of major interest in refining selection strategies for an improved adaptive capacity in the face of pathogens. In this approach, the phase plane of pathogen load versus an indicator of performance such as body weight, feed intake or other health indicators was plotted. A score was then assigned to each time step depending on the health status of the animal. Using this method, the authors concluded on the dynamic interrelationship between resistance and tolerance in the animal's response. In our study, the phase plane was presented for two indicators of a goat's performance, i. e. , the phase plane represented a response-response plot, which differed slightly from the strategy of presenting a cause-response plot (e. g. pathogen load versus performance). Other models have been developed to quantify animals' response to perturbations, using time series data  [13] [14] [15] [16].
7  to analyse the post-prandial pH evolution during the 8 h after delivery of the diet. In their study, two main types of synthetic variables were proposed, the first calculated directly from the initial data and the second corresponding to the parameters of a cubic model ( pH t = at 3 + bt 2 + ct + d) , or derived therefrom. They finally used a PCA analysis to study the relevant descriptors of the post-prandial evolution of pH. In our approach, given the five post-prandial measures of pH, a quadratic function with three parameters was used to avoid over-parametrisation of the model  44. The re-parametrisation of the model allowed coefficients a, b and c to be replaced by a set of variables with biological meaningful parameters. The synthetic variable v 0 corresponds to the rumen status of the animal just before the morning feed allowance.
Inspired by the field of animation synthesis, we propose to automatically create a neutral motion in order to extract body expression from any kind of FIGURE 1. Our method automatically recognizes the expression of a human 3D skeleton animation. In the databases used to evaluate our method, the skeletal movement was captured by Kinect or any other motion capture systems that are now more and more accessible. input motion. This method allows to separate motion from expression, making proposed expression recognition method invariant to motion. Thus, main challenge we have tackled in this research work is to produce automatically neutral motion. We have derived and aggregated different features from the state of the art in motion analysis, from psychology and computer vision domains in order to precisely characterize neutral motion.
Following the same paradigm as our previous work  [11] , the body expression is obtained by providing to a classifier the difference between the expressive motion and the corresponding neutral motion in the frequency domain using the Fast Fourier Transform  [12]. As improvement to our previous work, the synthesis of neutral motion is automatically computed by a new method providing more realistic motions, and more efficient for the classification task. To the best of our knowledge, state-of-the-art approaches focus mostly on databases containing similar movements. We extended the evaluation phase by testing four different databases containing heterogeneous motions and expressions. This article is organized as follows. Section II presents the state of the art on emotion analysis. Section III describes databases that are used to evaluate proposed algorithm.
Nevertheless, for body expression recognition, there is a lack of large datasets, which are required for deep learning. Secondly, actions and expressions are blended and most of the current literature is specialized in one specific type of motion. Lastly, model-based approaches that propose efficient analysis on the skeleton motion are still preferred  [23] , in comparison to brute force approaches letting a network explores a very large movement dataset. Thus, many articles focus on the most common motions such as walking  [25] ,  [25] -  [30] , action of knocking  [31] ,  [32] , talking persons  [33] , or other artistic performance  [34]. These methods are adapted on specific motions, but they fail when the challenge is to recognize body expressions from different scenarios. Few articles tackled challenge of analysis of heterogeneous body movement.
Kleinsmith et al. [2]  proposed UCLIC database featuring 13 participants from different cultural regions, portraying four emotions (anger, fear, happiness and sadness). Wang et al. [35]  proposed a real-time system that recognizes emotions from body movements. They used a combination of 3D postural features, kinematic and geometrical features. Truong et al. [36]  proposed a new set of 3D gesture descriptors based on a generalization of the Laban descriptor model proposed by Rudolf Laban for gestures expressiveness. They evaluated their classification approach on their own database which contains 882 gestures. Dewan et al. [37]  extended the Laban movement analysis by combining it with a temporal window, which can be seen as a more conceptually elaborate formulation of Laban theory. They evaluated their algorithm on the UCLIC database  [2].
III. MATERIALS The proposed approach is evaluated on four databases that are listed in Table  1. A brief description of the databases is presented below. The first three databases are real actors recorded by motion capture, while SIGGRAPH database (referred with same title in the remainder of this work) consists of synthetic animations generated by human animator extended by the method of Xia et al. [38]. Brief description of these four databases is given below. 1) Emilya Database  [39] : this recorded database contains 10001 motions of 7 actions: walking, sitting down, knocking at a door, lifting and throwing an object with one hand and moving objects with two hands. It includes 8 expressions (joy, anger, panic fear, anxiety, sadness, shame, pride and neutral). The expressive motions are performed by 11 actors (6 females and 5 males) with an average age of 26 years.
2) UCLIC Affective Body Posture and Motion  [2] : this database consists of 183 real motions with 4 expressions (fear, sad, happy, angry). The movements are carried out by 13 individuals from different cultural backgrounds. The subjects were asked to perform the emotion postures in their own way. The movements are acquired by a motion capture system which delivers a set of 32 body joints (3D positions). 3) MPI Emotional Body Expressions Database for Narrative Scenarios  [33] : this database gathers 1447 motions. The scenarios were performed by 8 actors, 4 females and 4 males with an average age of 25 years. Actors were asked to imagine that they were narrating several stories to children. It contains 11 expressions (amusement, anger, disgust, fear, joy, neutral, pride, relief, sadness, shame, surprise).
Database is recorded using motion capture system, which collects 3D postures leading to a set of 22 joints. Unfortunately, it is important to highlight that this database is highly imbalanced in terms of expressions. Joy is the most represented expression with 227 instances while shame is the less represented one with only 58 instances. 4) SIGGRAPH database  [38] : this database contains synthetic sequences. It contains 572 animations with 8 body expressions or style (angry, childlike, depressed, neutral, old, proud, sexy, strutting). The motivation of using the SIGGRAPH database is that it includes a large range of movements: jump, run, kick, walk, punching and transitions between these motions.
CMA-ES presents the advantage to be derivative-free methods for numerical optimization of non-linear or non-convex continuous optimization problems, which is our case as well. CMA-ES is mainly used in animation with great success. We have carried out experiments/simulations with different values of λ, γ, β and we have empirically found that λ = 0. 8, γ = 0. 6 and β = 0. 5 provides good default setting for all databases.
For these two PCAs and the weight optimization presented in Section IV-A1, we isolated a subset of all animations of the databases in order to limit the risk of overfitting. The first PCA is computed on all the postures of this subset. We have re-targeted different skeletons topologies of the databases in one skeleton in a similar way as proposed by Holden et al. [42]. The unified skeleton is composed of 30 joints. Before the PCA, using 3D position for joints, a posture is described by 90 float values. By applying PCA to all the postures of the different databases, we evaluated that for our purpose a posture can be represented by 7 values, the 7 first components of the PCA. These 7 values preserve 95% of the original variance. We have tried to keep more components of the PCA without significant improvement in the recognition rates whereas less values decrease the rate.
From this representation, we can represent a full motion with a temporal sequence by vector of 7 components. Then, we projected each posture of the motion on the first learned PCA, thus, reducing the dimension of a motion from 90 x n frames to 7 x n frames where n is the number of frames of the motion. In a second step, another PCA is applied on the motion representation described above. This second PCA is applied in order to reduce the 7 x n dimension. By applying this second PCA on all animations of the subset of the databases, we have evaluated that a motion can be represented by only 13 components while preserving 95% of the original variance. As for the posture, we have tested multiple values and found that 13 provides the best accuracy on the results. Figure  4  shows the projection of the PCA applied on all the postures of the SIGGRAPH database.
N eutral(M otion) = n j=1 α j f j n j=1 α j (2) where α j corresponds to the weight of the j th feature f j and n is the number of features. Weights α j are computed by an optimization on a set of animations. To avoid overfitting it is necessary to extract a subset of animations from the different databases described in Section V. The same optimal weights are then used for all experiment on the rest of animations. This optimization seeks to produce a score that tends towards zero for all neutral animations extracted from the datasets. All posture and temporal features f j formalized in this study are described below. • Posture features 1) The Body openness f bodyOpenness measures whether arms are far from the body and/or feets are farm from each other. 2) The Sagittal body leaning f bodyLeaning measures forward/backward leaning.
V. EXPERIMENTS AND RESULTS To evaluate proposed approach, several experiments were carried out on several databases presented in Section III. To generate neutral animations, our algorithm optimizes a neutral function computed by a weighted average of different terms as explained in the Section IV. This optimization is done in a space reduced by two steps of PCA. The weights are the results of a preliminary optimization. To compute the PCA and to determine the adapted weights reducing overfitting as much as possible, 25% of animations are extracted from all databases. These 25% of animations are used only for the weights computation and the PCA, they are not used in the recognition process. Table  2  presents recognition rate achieved by proposed approach and compares performance of proposed method with different classifiers in Figure  5  : • Support Vector Machine (SVM) with χ 2 kernel;  Figure  5  shows the influence of the size of the training set on the performance of the three classifiers used in evaluation, precisely achieved on Emilya database.
An oversampling method is used, balancing the dataset by increasing the size of rare samples. New rare samples are generated by using repetition on data. this table, it can be observed that the recognition rate is better for "Sad" expression as the dataset is now balanced. Above presented results prove ability and robustness of proposed method in recognizing body expressions. In summary, the main conclusions that can be drawn from these experiences are: 1) Table  2    for MPI database which outperforms the state-of-theart. The state-of-the-art on this database was our previous work  [11]  with a non-optimal synthesized neutral motion, i. e. robotic and with different artifacts. With this result, the idea that the more realistic the neutral motion is, the the recognition rate is, is verified.
3) To the best of our knowledge, no method in the literature on body expression recognition has tested complete Emilya database. Proposed method achieved body expression recognition rate of 82% on it. This is an encouraging result as Emilya database is relatively large and contains lot of different motions and expressions. This database presents a challenging problem as variety and number of motions in this database are heterogeneous. 4) Finally, for the UCLIC database, we compared proposed generic method to a specific approach  [37]  which uses a more conceptually elaborate formulation of the Laban theory combined with a temporal window. [37]  achieved good results for UCLIC database 87. 30% compared to our result 74%. This could be explained as  [37]  is well specifically suited for UCLIC database but with no genericity by nature.
VI. DISCUSSIONS AND CONCLUSION In this paper a novel approach for automatic generic recognition of body expressions through 3D skeleton provided by motion capture data is presented. A novel method for synthesizing realistic neutral motion is introduced, which is used to detect body expression from an expressive input motion. Quantization of neutrality in a motion is formalized by a function. Novel cost function is proposed which is optimized for synthesizing the neutral motion from a given expressive motion. This cost function allows to extract the body expression by computing the difference, in the spectral domain, from the input motion and the synthesized neutral motion. The results obtained by proposed method show its robustness as the evaluation is done on heterogeneous databases with different motions and expressions.
Proposed generic method achieves better recognition rate for body expression on three databases compared to state-of-the-art methods which focused on one specific database. This performance highlights that proposed novel approach is invariant to the input motion as it is based on computing difference between the input motion and the neutral motion produced by proposed method. In a more general objective of expression recognition, future work would be to use our approach as a brick in a multi-modal scheme. Body expression recognition could be used in conjunction with facial expression recognition, by synthesizing neutral facial expression as well. Mixing body and face approaches must clearly be more investigated by researchers  [45] , and moreover, approaches of data fusion with speech and physiological signals  [14]  could allow to formalize a more adapted framework for in the wild applications.
shows the comparison of the recognition rate of proposed framework with the state-of-the-art methods. Angry Anxious Joy N PF Proud Sad Angry 0. 90 0. 01 0. 02 0. 02 0. 03 0. 02 0 Anxious 0. 02 0. 90 0. 03 0 0. 04 0. 01 0 Joy 0. 02 0 0. 90 0. 01 0. 03 0. 03 0 N 0. 01 0. 01 0. 01 0. 95 0. 01 0. 01 0 PF 0. 01 0. 02 0. 02 0. 01 0. 92 0. 02 0 Proud 0. 02 0. 02 0. 04 0. 02 0. 01 0. 89 0 Sad 0. 02 0. 03 0. 06 0. 02 0. 03 0. 04 0. 80 It is noticeable that proposed method achieves body expression recognition accuracy that globally exceeds results obtained by state-of-the-art methods. Moreover, it is important to highlight the fact that we are comparing proposed method with specific methods developed for one specific database containing often only.
All IRIS cases observed in the study were classified as paradoxical, tuberculosis-associated IRIS, described as an worsening of TB signs and symptoms starting after cART initiation during TB-treatment, mainly presenting enlargement of lymph nodes and inflammatory signs, not explained by any other diseases or by an adverse effect of drug therapy  [50, 51] , as recently detailed/reviewed by our group  [52]. In general, the IRIS cases included in the present study were self-resolving, or, if necessary, the patients were treated with corticoidbased therapy, such as Prednisone. Demographic and clinical data throughout the followup period, as well as blood samples from the baseline visit, were available for the present study. Skin color was self-declared.
HLA typing High-resolution HLA typing of HLA-B and HLA-C genes was determined by sequencing-based typing (SBT) according to the manufacturer's instructions on an ABI platform using commercial kits (SeCore® Sequencing Kit -Invitrogen by Life Technologies, Brown Deer, Wisconsin, USA). HLA-B and HLA-C genes were assigned using a four-digit designation using uTYPE® v6. 0 SBT software (Invitrogen by Life Technologies, Brown Deer, Wisconsin, USA), which also solves the ambiguous results. Due to technical issues, HLA-C typing was performed for 21 individuals by next-generation sequencing (NGS) -MiSeq platform. A pre-validation assay showed that the alleles assigned by SBT and by NGS are the same, which means that the results are comparable (data not shown). The grouping of HLA-B genes in HLA Bw4 and/or Bw6 epitope-associated specificities followed the Immuno Polymorphism Database (IPD)-international ImMunoGeneTics project (IMGT)/HLA nomenclature guidelines  [53].
KIR genotyping The presence or absence of KIR genes was determined using a commercial kit based on a sequence-specific primer amplification method -SSP (SSP KIR Genotyping kit -Invitrogen, Brown Deer, Wisconsin, USA). A total of 14 KIR genes (2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, and 3DS1) and 2 KIR pseudogenes (2DP1 and 3DP1) were screened using this approach. KIR group genotype nomenclature was designated according to the current working definition, which characterizes genotypes AA and Bx based on the combinations of haplotype A (absence of all the activating genes, except KIR2DS4) and haplotype B (presence of one or more of the activating genes)  [12, 56]. We also classified KIR genotypes by the ID assigned by the Allele Frequency Net Database (http://www.
Comparing the frequencies of all HLA-B alleles found in this study with those reported for the general Brazilian population (REDOME dataset), we observed a significant difference in the frequencies of HLA-B*15, HLA-B*53, HLA-B*81 alleles (Additional file 1: Table  S4 ). One hundred sixty out of the 162 individuals included in the study had their HLA-C genotypes determined, and 14 allelic groups were identified. The DNA samples from the remaining two individuals were submitted repeatedly times to the sequencing protocol, but the typing was unsuccessful. A large variety of specific HLA-C alleles was also detected, and, as well as was done for HLA-B alleles, and we opted to present the results using the two-digit designation. The most frequent HLA-C alleles were HLA-C*07, HLA-C*04, HLA-C*06, and HLA-C*03, with prevalence rates of 40.
0, 37. 5, 19. 4, and 18. 8%, respectively (Additional file 1: Table  S2 ). Fortythree individuals (26. 9%) were HLA-C1 homozygous, and 41 individuals (25. 6%) were HLA-C2 homozygous, while seventy-six individuals (47. 5%) were HLA-C1/C2 heterozygous (Additional file 1: Table  S2 ). Unfortunately, HLA-C frequencies are not available at the REDOME dataset for comparisons of our data with the general Brazilian population. All 16 KIR genes (14 genes and 2 pseudogenes) were detected in the study population. One hundred   S5 ). The inhibitory KIR genes were more frequent than the activating genes (58. 7% vs. 24. 5%) (Additional file 1: Tables  S2  and S5 ). Considering the AA and Bx genotype classification, 112 individuals (69. 1%) had KIR genes arranged as Bx haplotypes, and in the 50 remaining individuals (30.
HLA-B*41 allotypes have already been associated with susceptibility to TB in patients with AIDS from the northeast region of the state of São Paulo  [20] , but no association with IRIS has been described for this allele yet. The frequency of the HLA-B*41 allele is low in different populations (Allele Frequency Net Database), differing from the frequency found in the IRIS cases included in the present study. The KIR2DS2 gene was also associated with IRIS onset among TB-HIV co-infected individuals in the present study. The high frequency of this gene described across all studied groups (51. 2%) was similar to those observed in several other populations, such as on the African continent (> 54%) and in the Cambodian population (49. 9%)  [90] , where the occurrence of IRIS is higher than that observed in this study  [31].
Our results further indicate that the uppermost permafrost degradation has the potential to release a larger amount of DOC and TDN (mostly as DON and NH 4 + ) than the active layer currently releases to high-latitude terrestrial and aquatic ecosystems. These findings are in agreement with observations of thawing permafrost leading to shifts in the supply of carbon and nutrients to surface waters  46, 47. Using the NCSCDv2 dataset  3  for the pan-Canadian permafrost area, we estimated the DOC pool available right upon thaw increasing from 0. 2 [0. 2-0. 5] Pg C in 0-1 m, to 1. 1 [0. 7-2. 1] Pg C in 1-2 m and decreasing to 0. 9 [0. 7-1. 5] Pg C in 2-3 m depth interval. Although DOC is a relatively small component of the total permafrost carbon pool, its depletion through hydrological mobilization will likely contribute relatively fast to soil carbon loss in Arctic landscapes  48.
Methods This study initiated from work conducted as part of the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Frontiers project Arctic Development and Adaptation to Permafrost in Transition (ADAPT), which brought together 15 laboratories from across Canada to develop an integrated Earth systems science framework on diverse aspects related to thawing permafrost conditions in the Canadian Arctic  22. Data produced with the standard protocols for cryostratigraphy and soil analyses from the ADAPT project are published through the Nordicana D database  51  and briefly described below. Permafrost cores. We investigated 25 soil cores that were collected by members of the ADAPT project teams  22  in nine distinct regions across the Canadian Arctic (Table  1 ). The cored areas represent a broad range of Canadian permafrost conditions, soil types, and landscapes covered by diverse ecosystems such as boreal forest, palsa peatlands, and various tundra vegetation, including dry heath, mesic shrub tundra, and wet sedge.