dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Heipke and Rottensteiner 2020). Also, methods based on attention mechanisms  (Vaswani et al. 2017; Dosovitskiy et al. 2021; Voelsen et al. 2023 ) have recently made a major impact in the field. However, DL methods require a vast amount of training data in the learning phase to yield good results, and these training data are not always available. In this paper, we address this problem and suggest a method, which automatically generates labels for unlabeled samples, so called VTL, from a given amount of real training labels (RTL). We show that adding the VTL to the RTL improves crop classification using Sentinel 1 and 2 (S1 and S2) time series, i. e. fusing optical and radar imagery. The architecture proposed by  Teimouri et al. (2022)  is applied in this study to assess the impact of the VTL on the training of 3D-CNNs.
g. ,  Zhang et al. 2017; Acción et al. 2020 ); (2) Noise disturbance  (Ding et al. 2016 ); (3) Sharpness transformation  (Ledig et al. 2017) , albeit with limited success. (4) Generative adversarial networks (GANs)  (Goodfellow et al. 2014) ; since then, various extensions have been suggested. However, these networks still require a large amount of training samples and have a high computational cost. (5) Virtual labels  (Chen et al. 2016; Li et al. 2016). Labeling these samples is done before network training, thus reducing the high computing time required by GAN methods. On the other hand, network-based data augmentation methods focus on modifying the architecture or learning process of CNNs. These methods include: (1) Transfer learning  (Wurm et al. 2019; Cui et al. 2020) , taking advantage of models pre-trained on large datasets and fine-tuning them for specific tasks.
Summary of State-of-the-Art In summary, promising results have been obtained in crop classification using image time series and CNNs for the spatial domain as well as CNNs or transformers for the temporal domain. In addition, fusing various data sources, such as optical and radar time series images led to improved classification results. Notably, the decision-level fusion yielded significantly better performance compared to the featurelevel fusion approach. The lack of appropriate training data limits the success of these methods, however. To at least partly overcome this problem the generation of VTL (thus, a data-driven method) is suggested in this paper. We fuse optical and radar images and use a 3D-CNN architecture without transformers in our work.
Finally, the features extracted from each data source are concatenated and fed into the fully connected layer. More specifically, the input channels for each dataset (optical, radar, and fused) are processed separately, where the 3D convolution operators are applied to a sequence of three images with stride one in the temporal direction for each channel. Next, the features generated from each time series of a specific channel are concatenated. The network architectures for radar and optical data consist of three convolutional blocks with 32, 32, and 64 kernels, respectively. (  4 ) T (i) = 1 L L ∑ j=1 V (i) j Each block is followed by a ReLu activation function and maximum pooling with dimensions of 2 × 2 × 1. The final layers of this architecture consist of two fully connected layers with 128 and 64 neurons, respectively.
All preprocessing steps were executed using Sentinel's Application Platform (SNAP) software, the necessary parameters were taken from the available orbit files. The radar images were resampled to a spatial resolution of 10 × 10 m 2. Four spectral bands (red, green, blue, and near-infrared) of each S2 level 2A image were chosen, as these hold significant potential for crop classification  (Defourny et al. 2019; Dhau et al. 2021; You et al. 2021). All images consisted of 1593 × 2516 pixels. A ground truth map was produced by the Department of Agriculture, Livestock, Fishing and Food of the Generalitat of Catalonia. This map was resampled to 10 × 10 m 2 (Fig. 3b ) as well. For network training, 1050 training samples and 490 validation samples were used, and 3500 test samples were employed to evaluate the algorithms (where each sample is an individual pixel).
Generating VTL As described in chapter 3, there are two different inputs for VTL generation: the six first principal components derived from the optical time series images (one for each epoch), and seven VV polarization as well as seven VH polarization channels for the radar images. The 1050 pixels, all coming from the training data, were then employed as RTL to generate VTL. We generated three different sets of VTL, one each for classifying only optical and radar data separately, and one for the classification of both image types together. For the VTL for classifying the optical data, only RTL from the optical channels were used, and analogously for the radar data and the fused image set. The number of selected unlabeled pixels was chosen to be approximately three times as large as the number of RTL.
Results A comparison of the results for the 3500 test samples achieved with only RTL and with a combination of RTL and VTL is presented in Tables  2  and 3. It shows that for most classes the combination leads to an increase in classification accuracy for crops in optical and radar data sources, as well as their fusion. Achieving an OA of 92. 6% and a KC of 91. 4% for the S2 images demonstrates the performance of combining RTL and VTL. The inclusion of VTL improved the OA by 4. 0% and the KC by 4. 7%. In particular, the VTL generated for corn, oat, wheat, and triticale were highly effective, with corn showing the largest improvement of 15. 6% in UA. These improvements are further supported by the F1-score analysis (Table  3 ), which confirms the enhanced performance of these classes when using RTL and VTL together.
However, it should be noted that the addition of VTL resulted in a decrease in UA for beans, although the F1-score indicates an improvement of 2. 1%. Similarly, for radar images the integration of VTL and RTL led to an increase in OA (3. 3%) and KC (3. 9%). Interestingly, for alfalfa the VTL generated using radar data showed more significant improvements compared to those generated using optical data, with an improvement of approximately 7. 2% in UA and 5. 0% in F1-score, while the impact of VTL generated from optical data was only a 1% in UA and showed a decrease in the F1-score of 0. 2%. The integration of VTL also led to improvements in UA of beans, corn, oat, triticale, and wheat; the largest improvement in F1-score was observed in corn, triticale, and wheat, with approximately 11. 5%, 2.
Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso approach on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic data from clinical trial RV144. Our method is implemented in the PPLasso R package which will be soon available from the Comprehensive R Archive Network (CRAN).
For K 1 (resp. K 2 ) in {1,. , p}, let Top K 1 (resp. Top K 2 ) be the set of indices corresponding to the K 1 (resp. K 2 ) largest values of the components of | β 10 (λ 1 , λ 2 )| (resp. | β 20 (λ 1 , λ 2 )|), then the estimator of β = ( β 1 , β 2 ) after the correction is denoted by β(λ 1 , λ 2 ) = ( β ( K 1 ) 1 (λ 1 , λ 2 ), β ( K 2 ) 2 (λ 1 , λ 2 )) where the jth component of β (K i ) i (λ 1 , λ 2 ), for i = 1 or 2, is defined by: (11) β (K i ) ij (λ 1 , λ 2 ) =    β i0j (λ 1 , λ 2 ), j ∈ Top K i K 1 th largest value of | β i0j (λ 1 , λ 2 )|, j ∈ Top K i. Note that the corrections are only performed on β 0 , the estimators α 1 and α 2 were not modified. To illustrate the interest of using a thresholding step, we generated a dataset based on Model 3 with parameters described in Section 3.
β (M 2 ) 2 -β (M 1 ) 1 ) are considered as prognostic (resp. predictive) biomarkers, where the choice of M 1 and M 2 is explained in Section 2. 4. To illustrate the benefits of using an additional thresholding step, we used the dataset described in Section 2. 2. Moreover, to simplify the graphical illustrations, we also focus on the case where λ 1 = λ 2 = λ. Figure  8  in the Supplementary material displays the number of True Positive (TP) and False Positive (FP) in prognostic and predictive biomarker identification with and without the second thresholding. We can see from this figure that the thresholding stage limits the number of false positives. Note that α 1 and α 2 are estimated by α 1 and α 2 defined in (10). 2. 4. Choice of the parameters K 1 , K 2 , M 1 and M 2. For each (λ 1 , λ 2 ) and each K 1 , we computed: (13) MSE K 1 ,K 2 (λ 1 , λ 2 ) = y -X γ (K1,K2) (λ 1 , λ 2 ) 2 2 , where γ (K1,K2) (λ 1 , λ 2 ) = ( α 1 , α 2 , β (K 1 ) 1 , β (K 2 ) 2 ) defined in (10) and in (11).
It is displayed in the left part of Figure  2. For each λ 1 , λ 2 and a given δ ∈ (0, 1), the parameter K 2 is then chosen as follows for each K 1 : K 2 (λ 1 , λ 2 ) = Argmin K 2 ≥ 1 s. t. MSE (K 1 ,K 2 +1 )(λ 1 , λ 2 ) MSE (K 1 ,K 2 ) (λ 1 , λ 2 ) ≥ δ. The K 2 associated to each K 1 are displayed with '*' in the left part of Figure  2. Then K 1 is chosen by using a similar criterion: K 1 (λ 1 , λ 2 ) = Argmin K 1 ≥ 1 s. t. MSE (K 1 +1, K 2 )(λ 1 , λ 2 ) MSE (K 1 , K 2 ) (λ 1 , λ 2 ) ≥ δ. The values of MSE (K 1 , K 2 ) (λ 1 , λ 2 ) are displayed in the right part of Figure  2  in the particular case where λ 1 = λ 2 = λ, δ = 0. 95 and with the same dataset as the one used in Section 2. 2. K 1 is displayed with a red star. The parameters M 1 and M 2 are chosen in a similar way except that MSE K 1 ,K 2 (λ 1 , λ 2 ) is replaced by MSE M 1 ,M 2 (λ 1 , λ 2 ) where: MSE M 1 ,M 2 (λ 1 , λ 2 ) = y -X γ (M 1 ,M 2 ) (λ 1 , λ 2 ) 2 2 , with γ (M 1 ,M 2 ) (λ 1 , λ 2 ) = ( α 1 , α 2 , β (M 1 ) 1 , β (M 2 ) 2 ) defined in (  10 ) and (  12 ).
For choosing λ we used BIC (Bayesian Information Criterion) which is widely used in the variable selection field and which consists in minimizing the following criterion with respect to λ: BIC(λ) = n log(MSE(λ)/n) + k(λ) log(n), where n is the total number of samples, MSE(λ) = y -X γ(λ) 2 2 and k(λ) is the number of non null coefficients in the OLS estimator γ obtained by re-estimating only the non null components of β 1 and β 2 -β 1. The values of the BIC criterion as well as those of the MSE obtained from the dataset described in Section 2. 2 are displayed in Figure  3. Table  2  in the supplementary material provides the True Positive Rate (TPR) and False Positive Rate (FPR) when λ is chosen either by minimizing the MSE or the BIC criterion for this dataset. We can see from this table that both of them have TPR=1 (all true positives are identified).
3. 1. Simulation setting. All simulated datasets were generated from Model (3) where the n 1 (n 2 ) rows of X 1 (X 2 ) are assumed to be independent Gaussian random vectors with a covariance matrix Σ 1 = Σ 2 = Σ bm , and is a standard Gaussian random vector independent of X 1 and X 2. We defined Σ bm as: (  14 ) Σ bm = Σ 11 Σ 12 Σ T 12 Σ 22 where Σ 11 (resp. Σ 22 ) are the correlation matrix of prognostic (resp. non-prognostic) biomarkers with off-diagonal entries equal to a 1 (resp. a 3 ). Morever, Σ 12 is the correlation matrix between prognostic and non-prognostic variables with entries equal to a 2. In our simulations (a 1 , a 2 , a 3 ) = (0. 3, 0. 5, 0. 7), which is a framework proposed by  Xue and Qu (2017). We checked that the Irrepresentable Condition (IC) of  Zhao and Yu (2006)  is violated and thus the standard Lasso cannot recover the positions of the null and non null variables.
For each dataset we assumed randomized treatment allocation between standard and experimental arm with a 1:1 ratio, i. e. n 1 = n 2 = 50. We further assume a relative treatment effect of 1 (α 1 = 0 and α 2 = 1). The number of biomarkers p varies from 200 to 2000. The number of active biomarkers was set to 10 (i. e. 5 purely prognostic biomarkers with β 1j = β 2j = b 1 = 1 (j = 1,. , 5) and 5 biomarkers both prognostic and predictive with β 1j = b 1 and β 2j = b 2 = 2 (j = 6,. , 10)).
The impact of the number of true predictive biomarkers was assessed by increasing the number of predictive biomarkers from 5 to 10 in the scenario presented in Section 3. 1. When the number of predictive biomarkers increased, the impact on PPLasso is almost negligible. However, for Lasso, we can see from Figure  11  of the supplementary material that it became even harder to identify predictive biomarkers. TPR pred decreased compared to Figure  4 , especially for large p (e. g. TPR pred = 0. 124 for p = 2000). 3. 3. 4. Impact of the dimension of the dataset. In this section, we studied a different sample size: n=50 with n 1 = n 2 = 25 and a different number of biomarkers: p=5000. We can see from Figure  12  of the supplementary material that for p = 5000, the selection performance of PPLasso is not altered as compared with p = 2000 while the Lasso has more difficulties to identify both prognostic and predictive biomarkers.
Application to gene expression data in breast cancer We applied the previously described methods to publicly available transcriptomic data from the RV144 vaccine trial  (Rerks-Ngarm et al. (2009) ). This trial showed reduced risk of HIV-1 acquisition by 31. 2% with vaccination with ALVAC and AIDSVAX as compared to placebo. Transcriptomic profiles of in vitro HIV-1 Env-stimulated peripheral blood mononuclear cells (PBMCs) obtained pre-immunization and 15 days after the immunization (D15) from both 40 vaccinees and 10 placebo recipients were generated to better understand underlying biological mechanisms  (Fourati et al. (2019) , Gene Expression Omnibus accession code: GSE103671). For illustration purpose, the absolute change at D15 in gene mTOR was considered as the continuous endpoint (response).
mTOR plays a key role in mTORC1 signaling pathway which has been shown to be associated with risk of HIV-1 acquisition  (Fourati et al. (2019) ,  Akbay et al. (2020) ). The gene expression has been normalized as in the original publication of  Fourati et al. (2019). After removing non-annotated genes (LOCxxxx and HS. xxxx), the top 2000 genes with the highest empirical variances were included as candidate biomarkers for prognostic and predictive identification from PPLasso and Lasso. The penalty parameter λ for the Lasso was selected through the classical cross-validation approach. For PPLasso, λ was selected based on the criterion described in Section 2. 6. The estimation of Σ was obtained by comparing several candidate estimators from the cvCovEst R package and by selecting the estimator having the smallest estimation error.
Motivation: Studies of sets of proteins are a central point in biology. In particular, the application of omics in the last decades has generated lists of several hundreds or thousands of proteins or genes. However, these lists are often not inspected globally, possibly due to the lack of tools capable of simultaneously visualizing the feature architectures of a large number of proteins. Results: Here, we present ProFeatMap, an intuitive Python-based website. For a given set of proteins, it allows to display features such as domains, repeats, disorder or post-translational modifications and their organization along the sequences, into a highly customizable 2D map. Starting from a user-defined protein list of UniProt accession codes, ProFeatMap extracts the most important annotated features available for each protein from one of the wellestablished databases such as Uniprot or InterPro, allocates shapes and colors, potentially depending on quantitative or qualitative data and sorts the protein list based on homologous feature content.
Introduction Most omics studies produce datasets involving substantial lists of proteins. A useful approach for examining such lists is to analyze either their protein-associated biological properties as by Gene Ontology (The Gene Ontology Consortium, 2019) or their sequences. While Gene Ontology might be able to find over-represented features in the protein list, it loses information of their relative location, size and organization. Alternatively, sequence examination implies complex analyses as multiple alignments and requires similar proteins to be informative. An intermediate scale of analysis is to focus on features such as domains, amino acid or domain repeats, post-translational modifications, sequence variants, secondary structures, low-complexity regions and their organization along the sequence.
Several online tools are already available to depict domain organization in a manual or semi-automatic manner (Table  1 ). However, even the most versatile programs, such as MyDomains on Prosite  (Hulo et al. , 2008) , can only scrutinize one single protein at the same time, representing a tedious and time-consuming approach even for a list containing a limited number of proteins. The interactive Tree of Life (iTOL) tool bypasses this limitation, but specifically needs a phylogenetic tree as input  (Letunic and Bork, 2021). The recent web interface CFVisual allows to plot domain organization for large protein datasets, but is still fed with DNA sequences  (Chen, 2022 ). To our knowledge, a versatile tool allowing to visualize annotated features for large protein datasets in both a global and customizable way is still awaited.
The use of lists of unique and simple identifiers such as the Uniprot entries rather than tedious protein or DNA sequences would be highly beneficial for this purpose. Here, we introduce ProFeatMap, a freely available, user-friendly and interactive web interface. Gathering annotated features directly from well-established databases such as Uniprot (The UniProt Consortium, 2021), the program represents the overall domain organization along the sequence and many other features in a 2D map in which one dimension corresponds to the different proteins contained in the list, and the second dimension to their size. ProFeatMap proposes default parameters for a 1-click run, or advanced options allowing users to produce specific plots tailored to their needs. ProFeatMap is a powerful tool for overviewing at a glance a large protein set and gaining further insights into their common or distinctive properties.
ProFeatMap implementation ProFeatMap is entirely coded in Python (3. 9. 6). It is freely accessible as a web interface developed using the Dash library (2. 0. 0), making it compatible with most common web browsers. Maps and corresponding legends are drawn with the pillow library (7. 2. 0). The program can be run through a website without any account or registration. Alternatively, all scripts are freely accessible on GitHub and can be run locally. ProFeatMap follows four main steps: protein list uploading, features extraction, addition of user numerical values (optional) and map creation and customization. A user guide for each step is found in the help sections of the web interface. A detailed user guide is available within the Supplementary Information. According to a user-defined protein list, ProFeatMap automatically downloads the UniProt file for each protein (https://uniprot.
The ProFeatMap usage The user provides a protein list as a table file (xlsx, xls, ods, csv, tsv and txt) containing the UniProt Accession codes and the optional unique protein identifiers. Alternatively, a compatible list can be obtained from UniProt by downloading a protein selection through a query or the basket option. Entry lists may also be composed of the UniProt accession codes of various proteins obtained by experimental omics approaches. After extracting feature occurrences, ProFeatMap generates a highly customizable 2D map. By default, ProFeatMap sorts the proteins based on the occurrences of domain, repeat, region and motif features. The number of similar features leads to a similarity value, while the number of differences gives a dissimilarity value. A first sorting by ascending order of the dissimilarities is done, followed by a second sorting by descending order of the similarities.
One-click run In a one-click run, according to a user-defined protein list, ProFeatMap automatically downloads, extracts and maps feature data using all default parameters. It randomly assigns a combination of color and shape to each of the most frequent features displayed on the 2D map (Fig. 1A ). The resulting map helps provide a global view of the ensemble of features. Such a one-click run is preferentially used for ab initio lists obtained for instance in the context of omics data. To even better appreciate the advantages of ProFeatMap, an example generated for human kinases (with 489 proteins) is visible on Supplementary File S1. Supplementary File S2 allows the user to compare it with the map generated by a one-click run for the PDZ list provided as an example in Step #1.
Conclusions ProFeatMap is a powerful and highly customizable tool to quickly create publication-quality maps displaying domains and other key features along the protein sequences for a set of proteins of interest, and potentially including quantitative or qualitative data. Compared with some conventional motif visualization tools, ProFeatMap does not need nor use any sequence-based predictions. It merely uses feature information accessible in potentially several protein databases. The general overview provided by the representation helps gaining valuable insights into feature organization. The advantages and versatility of ProFeatMap can be even better appreciated when considering either a set of proteins with common features in a unique species, or a set of a particular protein originating from several species.
Funding This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  INSERM , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR-20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program. G. B. is a recipient of the Ph. D. grant by the  Ligue contre le Cancer. This work was also supported by the  Ligue contre le Cancer  (E ´quipe Labellisee 2015 to G. T. ) and the  Canceropole Grand-Est  (Project  Emergent 2018  to Y. N. ).
This is possible thanks to the manual annotation of feedbacks performed previously  (Boudin et al. , 2021). We present in this paper a new annotated resource for studying engagement in interaction, together with tools and methods offering a way to address this question in the more general case: unrestricted conversations. Engagement being not really well defined in the literature, we propose in the second section a large survey of this question both in human-machine and human-human environments. Section 3 presents dataset and features selection. Results are described and discussed in Sections 4 and 5.
Dataset The dataset is built from the corpus Paco-Cheese  (Amoyal et al. , 2020; Priego-Valverde et al. , 2020) , containing audiovideo recordings of natural dyadic face-to-face interactions between native French speakers. The corpus is made of 31 conversations, lasting between 15 and 20 mns. Participants were instructed to read a short humorous story before engaging in an unconstrained conversation. The corpus is fully transcribed and enriched with various annotations. Interpausal units (IPUs), tokens, laughter and pauses were semi-automatically transcribed and aligned onto the audio signal thanks to the SPPAS software  (Bigi, 2012). Our annotation procedure involves segmenting the audio-visual recordings according to the conversation status (speaker/interlocutor) of each participant. As explained below, the segmentation is based on IPUs and feedbacks time boundaries.