dataesr/scientific-paragraphs-categorization huggingface dataset:
Once cluster candidates were formed, we calculated a t-value for each of them, i. e. a cluster-level t-value, by summing the t-values of every data point included in the cluster candidate. We then identified the cluster candidate with the largest t-value for each hemisphere. We then needed to test whether the t-value of this winning cluster candidate is significantly different from chance. This was done using random permutations of the dataset: the data was randomly labeled as belonging to one or the other experimental condition. The same t-test statistic as before was computed for all pairs of data points in each random permutation. Cluster candidates were then formed as before. The proportion of random permutations that produce a cluster-level statistic greater than the actually observed one provides the p value of the test.
003). Testing for potential habituation effects during the time course of the experiment, the ANOVA with Time (first 2 blocks/last 2 blocks), Block Type (Standard/Deviant) and Hemisphere (LH/RH) over oxyHb (Fig. 4 ) and deoxy concentration changes yielded no significant effects or interactions, despite the numerical differences between conditions (Fig. 4 ). This is most probably due to the large variability in the dataset, as here we only average together two blocks per condition. results as the analysis with the larger ROI. We have opted to use the larger ROI as it corresponds more closely to the ROIs used in previous NIRS studies investigating speech and language processing in young infants.
Participants Twenty-one full-term, healthy French-exposed neonates, who did not participate in Experiment 1, were tested at the maternity ward of the Robert Debré Hospital in Paris. Of these, 1 was not included in the final analysis, because she didn't provide enough analyzable data due to movement artifacts and/or dense hair. Infants were not retained for analysis if more than 50% data was rejected in both conditions. The remaining 20 babies (10 girls, age range 14 days, mean age: 2. 1 day, mean weight 3394 g, weight range: 2235-4535 g Apgar at 10 min after birth: 10 and normal otoacoustic emissions test) were included in the final analysis. All parents gave informed consent prior to participation. The study was approved by the CERES ethics committee of the Université Paris Descartes.
, 2015). In sum, we have found evidence that a precursor of the ability to learn the sequential order of words is already present at birth, paving the ground for language acquisition. Investigator Grant nr. RGY-2014-073, the Labex EFL grant of the ANR progam "Investissements d'Avenir" (reference: ANR-10-LABX-0083), as well as the Marie Curie ITN grant "PredictAble" to JG. We thank Maria Clemencia Ortiz Barajas for her help with the permutation analysis. We are grateful to the personnel of the maternity of the Robert Debré Hospital, Paris, for their support and help with recruitment and testing.
Participants and study design The Whitehall II study is a prospective cohort study of British civil servants established to identify social determinants of health  [19]. The initial study population was all London-based office staff, aged 35-55 years, working in 20 departments on recruitment to the study in 1985-8. With a response proportion of 73%, the initial cohort consisted of 10308 participants who have now been followed up for over 25 years. In the present analyses we used cross-sectional data from the 2012-2013 clinical examination when the Facial Expression Recognition Task (FERT) was administered for the first time. A total of 6318 individuals participated in that phase, of whom 4505 (71. 3%) agreed to participate in testing using the FERT. After the exclusion of 43 participants with missing data on Mini-Mental State Examination (MMSE) or important covariates (n = 423)  [20] , i.
e. , educational level, depressive symptoms, or antidepressant use, the analytical sample of 4039 participants included 3016 (74. 7%) men and 1023 (25. 3%) women aged 59 to 82. After complete description of the study to the participants, all participants provided written informed consent. Ethical approval for the Whitehall II study was obtained from the University College London Medical School and the NHS London-Harrow Health Research Authority committees on the ethics of human research. Whitehall II data, protocols, and other metadata are available to bona fide researchers for research purposes (see the Whitehall II data sharing policy at http://www. ucl. ac. uk/whitehallII/data-sharing).
We performed χ 2 analyses, which confirmed this pattern of results (χ 2 = 26. 21, df = 8, p < 0. 01). The cells that most contributed to the χ 2 -value were the 3 cells occupied by crows (partial χ 2 -value based on these 3 cells: 13. 67, that is, 52% of the χ 2 -value; note that for df = 8, a χ 2 -value of 15. 51 is significant at p < 0. 05). Additional tests for the crows. We checked if the individual preferences for using the beak or a tool persisted in additional tests (see Supplementary Information). Four data points were collected: the performance in the 3 trials of the transfer task and the performance in the single trial of the novel object exploration task. Four out of the 7 crows that showed a significant preference towards one option in the test phase (Tool users: Tumulte and Tabou; Beak users: Papaye and Calypso) persisted in using this option in additional tests, so only a weak pattern was discernible.
Acknowledgements This work was supported by grants from  ANR (Agence Nationale pour la Recherche ; Project " Cognition and Tool-Use Economy ", ECOTOOL,  N°ANR-14-CE30-0015-01 ), and was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We would like to thank the  Province Sud of New Caledonia , the  Parc Forestier et Zoologique , in Nouméa and  Dr. Christian Rutz  for their support sourcing and exporting the crows. We also thank the  Tierpark Hellabrunn  and the  Parc Forestier et Zoologique  for housing the crows during quarantines. We thank  Dr. Gerhard Fries  and  Dr. Hermann Kempf  for veterinary support,  Irmgard Neumayr  for help with the animal caretaking and  Luitpold von Bayern  and  Ferdinand Lippe  for general support maintaining the crows.
The present paradigm involved targets located at different directions with two levels of difficulty (Easy and Hard targets), both forward and backward (Ahead and Behind targets). Performance improvements during both MI tDCS and MI sham conditions were recorded for Hard targets, whereas the time needed to complete Easy targets was reduced only under the MI tDCS condition. Different strategies might be used to maintain balance. Validating Hard targets may imply a shift of the CoP soliciting the hip joint, whereas minor deviations, such as those required to validate the Easy targets, might primarily manage through ankle involvement  52. Furthermore, one can postulate that the margin for performance improvements was higher for Hard than for Easy. Data therefore demonstrate that only MI tDCS resulted in performance gains for both types of targets.
Image Acquisition MRI data was obtained with a 3T scanner (Siemens, VERIO) with a 32-channel head coil. The MRI protocol included a sagittal T1weighted MPRAGE image (TR = 2. 3 s; TE = 4. 18 s; flip angle = 9 • ; TI = 900 ms; voxel size 1×1×1 mm 3 ; 176 slices), gradientecho echo-planar functional images sensitive to BOLD contrast (TR = 2. 1 s; TE = 25 ms; flip angle = 78 • ; voxel size 3×3×3. 15 mm 3 ; 110 volumes with anterior to posterior phase encoding + 4 volumes with posterior to anterior phase encoding, acquired in ascending order).
5 s of the target into the past (to the left). Identical models were used for the data of the 5and 10% MVC events, assuming that motor performance at these two force levels relied on the same underlying functional network. Finally, the same models constructed for the right hand were mirrored for the left hand; however, the ROI for the cerebellum was appropriately changed to the side ipsilateral to the active hand. A random effects Bayesian model selection (BMS) was performed to determine the most likely family given the observed fMRI data  (Stephan et al. , 2010)  (chosen as the family with the highest exceedance probability). An additional BMS within the winning family was performed to determine the best model (i. e. , a single model with the highest exceedance probability). Model parameters were extracted for each subject, and each connection was averaged across our group.
Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set.
Liu et al. [16]  proposed an emotion descriptors by using a novel affective probabilistic latent semantic analysis (affective-pLSA) model. Above works investigate visual representations for emotional concepts mainly by using traditional visual features  [5] , machine learning strategies  [16]  or human perceptual rules  [11]. With the popular use of social networks in recent years, the increasing literatures have exposed rich resources of semantic information conveyed by online user generated content: the images and the associated texts(captions or tags)  [9, [17] [18] [19] [20]. Sivic et al. [18]  investigated a text retrieval approach that can be successfully applied to VOC. Wang et al. [17]  built a textbased feature by using the tags of an auxiliary dataset from internet, and demonstrated that it consistently improves performance on VOC problems.
In this paper, we target on proposing an effective approach combining visual and text information for VAC. descriptors produce visual features for visual classifiers. Meanwhile, if available, the associated text is preprocessed to build the text features for text classifiers. All classifiers are then combined to predict the semantic emotion category of the input test image. In order to evaluate the effectiveness of proposed methods, we not only testify combining visual feature within two emotion models on the IAPS  [22]  and the Artistic Photos database  [5] , but also test fusing of visual and text features on the MirFlickr Affect set  [8]. As a summary, the contributions of our paper can be summarized as following three aspects: • We propose a late fusing scheme for VAC based on D-S Evidence Theory, whose interesting properties allow fusing different ambiguous sources of information.
The application of deep learning in VAC task is limited as it usually requires large amounts of data and time to train a robust model. Thus, the applicability of deep learning in domains are different but related to that of the training set  [44]. In short, above approaches for VAC have largely demonstrated their effectiveness within different dataset, but the major shortcoming is still that their visual features are lack of descriptive power as regard to the high level emotional concepts. As current visual representations for high-level visual concepts (e. g. objects, events, emotions) appear to be reaching the ceiling of performance, there exists an increasing works interested in web data mining  [14, 17]  or multimodal approaches  [21, [45] [46] [47] [48] , which manage to utilize both the visual and associated text data from Internet.
eHTC: emotional Histogram of Textual Concepts In recent years, there is an strong increasement on sharing websites particularly related to photos and videos, and most of them (e. g. Flickr, Facebook, Weibo) allow users to share images and to contribute descriptions in the form of tags or captions. These texts provide valuable resources of information describing the visual data. Based on these data, Wang et al. [17]  built a text-based feature (TF) by using an auxiliary dataset of images annotated with tags, and it improves the performance of VOC particularly when the training dataset is small. Mensink et al. [47]  also employed the TF feature combing with the visual features to improve the performance of visual concept classification. In contrast to these conventional Bag-of-Words approaches, we have proposed HTC to capture the relatedness of semantic concepts through a three-step process as depicted in previously  [21] , showing in Fig  3.
Experiments and results In this section, we conduct experiments on three datasets: the IAPS set (only images and the dataset is available at: http://csea. phhp. ufl. edu/media/requestform. html), the Artistic photo set (Only images and the dataset is available at: http://www. imageemotion. org/) and the MirFlickr Affect photos (Images and texts, and the dataset is available at: http://liris. cnrs. fr/membres? idn=edelland). After describing the experimental setup, we carry out several experiments with an in-depth analysis on: (1) the performance of visual features on IAPS and Artistic photo set; (2) the performance of visual and text features on MirFlickr Affect photos, and (3) the performance of combination approach based on D-S evidence theory.
Affective image database The available datasets for VAC are rather limited, and the available public database are described as below: IAPS: According to  [5, 22] , the International Affective Picture System has being developed to provide a set of normative emotional stimuli for experimental investigations of emotion and attention. It is characterized along the dimensions of valence, arousal, and dominance. The image set contains various pictures depicting mutilations, snakes, insects, attack scenes, accidents, contamination, illness, loss, pollution, puppies, babies, and landscape scenes, among others. This data has been widely used in studies of emotion and VAC tasks  [5, 8, 16, 27, 77]. Artistic Photos: According to  [5] , the artistic photos set was downloaded from an art sharing site [78], and was built to investigate whether the conscious use of colors and textures displayed by the artists can improve VAC.
This dataset was obtained by using the emotion categories as search terms in the art sharing site, so the emotion category was determined by the artist who uploaded the photo. These photos were taken by people who attempt to evoke a certain emotion in the viewer of the photograph through the conscious manipulation of the image composition, lighting, colors, etc. MirFlickr Affect: This affective dataset  [79]  was collected of about 2000 photographs selected from MIRFLICKR25000 Collection  [80]. Compared to the IAPS and Abstract photo set, this dataset is much realistic, as the photos and tags are from the Flickr users uploaded and tagged. This collection supplies all original image tag data, which has an average number of 8. 94 words per image. The emotion model of dataset relies on a dimensional view in two primary dimensions: valence one (ranging from pleasant to unpleasant) and arousal one (ranging from calm to excited), which improve the applicability for navigation and visualization  [3].
Experimental setup The above databases are built for the studies focusing on emotional concepts, and are much professional compared with other benchmarks, e. g. PASCAL, ImageNET, ImageCLEF dataset. However, the main drawback is that these datasets are relatively small and highly unbalanced. Therefore, we need to carefully setup the experiments to get a convincing results. • To evaluate the performance of visual features on IAPS and Artistic Photo set, we followed the work from Machajdik  [5] , who employed a discrete emotion model with 8 emotions: anger, awe, disgust, fear, sadness, excitement, contentment, and amusement  [81]. Table  2  indicates that both datasets are relatively small (less than 400 images for each class) and with a quit unbalanced distributions. In order to leverage these problems, the experimental setup is done as follows: for the classifiers, we employ the Support Vector Machine framework (SVM) with Radial Basis Functions (RBFs) using one against all scheme, and choose the average true positive rate (ATPR) per class over the positive and negative classes as the evaluation measurement defined by  [5].
We carry out the experiments in 5-fold cross validation, and evaluate the validity of visual features on IAPS and Artistic Photo sets. • To evaluate the performance of visual and text features on MirFlickr Affect dataset, we firstly build six classes by equally dividing each dimension into three levels: low, neutral and high, showing in Table  3. The experiments are then set as follows: we build six SVM classifiers for each classes using one against all scheme. More specifically, LIBSVM tool  [82]  are employed, and the input features are normalized to train the RBF kernel based SVM classifiers, which produce measurement vector as the degree of input belongs to different classes. To obtain reliable results, we measure the ATPR performance of visual and text features within 5-fold cross validation.
• To combine the features, we employ a powerful classifier combination technique based on the D-S evidence theory, whose interesting properties allow to fuse different ambiguous sources of information. The experimental setup is carried out as follows: First, we extract features on MirFlickr Affect set and train the SVM classifiers on a training set (50% data set), which produces measurement vector as the degree of input belongs to different classes. Then, we define the belief function d n (θ k ) according to the following formulas: m n ðy k Þ ¼ d n ðy k Þ=ð X k d n ðy k Þ þ 0:5Þ ð1Þ where d n ðy k Þ ¼ exp ðÀ k w n k À y n k 2 Þ, w n k is a reference vector and will be initialized randomly. Finally, as inspired by the Al-Ani's work  [83] , the classifiers are combined by adjusting w n k so that minimizing the mean square error (MSE) of the combined classification results z k as Err ¼ jjz À tjj 2 ð2Þ z k ¼ Èm n ðy k Þ ð3Þ where t is the target output.
Meanwhile, we make a comparison with different types of combination approaches, including: meanÀ score : z k ¼ 1 N X N n¼1 y n k ð4Þ majorityÀ voting : z k ¼ argmaxðy 1 k ; y 2 k ; :::; y N k Þ ð5Þ where y n k represent the k th measurement of classifier c n. In order to testify the fusion methods for VAC, we combine the visual features for IAPS and Artistic Photo set, and fuse the visual and text features for MirFlickr Affect dataset. shows the results of the IAPS, and we can see that the texture and color features with average 55% ATPR perform better than the shape and mid-level ones for most categories. The reason is that the shape and mid-level descriptors are low dimensions and with low discriminative power to classify the high-level concepts. Also, we can see that the mid-level feature face information (mid_Face) with 56% ATPR is among one of the most powerful features for the "Amusement" category.
Performance of combining features based on D-S evidence theory In this section, we firstly show the performance of combining visual features on IAPS and Artistic Photo set, then present the performance of fusing visual and text features on MirFlickr Affect dataset. Meanwhile, we make a comparison with the state-of-arts. Fig  7  shows the results of different fusion methods on IAPS set and the comparison with the state-of-arts  [5, 29, 77]. We can see that the performance show an improvement by employing fusion methods compared to the case of best feature used, especially highlighted for "amusement", "awe", "disgust" and "sad" concepts with average 5% ATPR improvement. More specifically, the D-S evidence theory method obtains the best result with average 61% ATPR compared with other conventional methods, e.
It is clear that the performances of D-S evidence theory fusion method with average 63% ATPR outperforms the best individual feature and the other conventional methods. It also performs better than the state-of-art  [5]  in the cases of "Amusement", "Anger", "Disgust", "Excitement"and "Sad". This further demonstrate that the D-S evidence theory fusion approach has the ability to fuse different ambiguous sources of information for affective concepts, and can effectively improves the performance of VAC task. Table  4  shows the combing results on MirFlickr Affect dataset based on D-S evidence theory. It shows that the visual features perform better than textual features with average 4% ATPR, and the eHTC outperforms the eMTC except at the neutral valence class. Specifically, the fusion of mid-level group and text features perform better on the valence dimension, while the combination of color features and text features work well on the arousal dimension.
Conclusion and future work In this paper, we present a multimodal approach for the VAC task. Firstly, we proposed two text-based features to capture emotional semantics from image tags. We also evaluated various visual features, aiming at characterizing visual content related with emotional concepts. Finally, we employed a fusion method based on the D-S theory of evidence, which exploits the complementary information to resolve the conflicts between classifiers. The experiments were conducted on three databases: the IAPS, the Artistic Photo set and MirFlickr Affect set, and have shown promising results on visual affective classification. From the results, we can conclude as follows: (i) the fused method based on D-S evidence theory is proved to be useful for the VAC task in efficiently fusing different features; (ii) the proposed textual eHTC can effectively capture emotional semantics from image tags, and help to improve the performance of visual classifiers for VAC task; (iii) the classification of visual emotional concepts is still extremely challenging, and the average performance of this approach is 57%, which can be expected as the small training set.
Funding: This work was supported by the  National Natural Science Foundation of China  under Grant ( 51435011 ,  51505309 ), the  Fundamental Research Funds for the Central University in UIBE  ( 14QD21 ) and the  Sichuan Province Science and Technology Support Program Project  under Grant  2015JY0172. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
However, this analysis comprehended solely the activation space and a unique subject. In this paper, we propose to extend this analysis by extracting control strategies from a variety of subjects and by analyzing their relationships with kinematic goals. For this purpose, we present an analysis to identify generic representations of control strategies starting at the task space level, joint space level, and until finally reaching the activation (or actuation) space level, where we extract the synergies or basis control functions. First, the experimental setup used to extract the motion and muscle data is presented. Next, the control variables or features at the task, joint and activation spaces are defined. This is then followed by a detailed explanation of the methods used to extract the control representation, which encompass clustering and matrix factorization techniques.