dataesr/scientific-paragraphs-categorization huggingface dataset:
It is made available The copyright holder for this preprint (which was this version posted December 23, 2019. ; https://doi. org/10. 1101/867218 doi: bioRxiv preprint To investigate the dose-effect of tunicamycin we performed the regression analysis using generalized estimating equation (GEE) approach. Here we also used Poisson distribution to model the count outcome -red cells count per 1000 cells -with autoregressive covariance structure. 3480 observations were used grouped into 145 clusters (according to group, tunicamycin dose and a field of view; cluster sizes were equal to 24 -time points). DMSO treated negative controls were excluded from GEE analysis. The model was built with statsmodels package for Python. The following factors were considered: Tu -tunicamycin concentration, Intron -intron is present in experimental group of cells and is lacking in positive controls, Time -is a factor of observation time.
The model demonstrated that all the factors had significant effect and that interactions of Tu and Intron, and Intron and Time were also significant (Table  5 ). Thus, the regression models also support our conclusions from Mann-Whitney tests about opposite effects of tunicamycin dosage in experimental group and in positive control. The original data can be found in supplementary table  ST2. The source code used to produce the plots and to perform the analysis can be found https://github. com/denatns/CellCountCV.
This leads to significant time costs and generates the influence of the human factor on the result of the study. We also tried to process these images with either ImageJ  (10)  or with classical computer vision techniques such as watershed algorithm etc. , realized in OpenCV library, but the obtained solutions were found to be inaccurate. That is why we decided to make a dedicated neural network-based model for processing microimages. To develop a prototype of software for automation of endoplasmic reticulum stress assessment we used biosensor XBP1-TagRFP cell line produced from 293A HEK cells. The developed FCNN model was integrated into a simple web service named CellCountCV. It was found to effectively deal with high variance of cell shapes and sizes and their tendency to form dense clusters.
; https://doi. org/10. 1101/867218 doi: bioRxiv preprint of 5208 images was processed in several hours. If image processing with counting model runs in parallel with image acquisition, then all the results will be ready to the end of the experiment without any significant time delays. The CellContCV application can be used either through simple web user interface at http://cellcounter. nprog. ru or programmatically through JSON-RPC calls to http://cellcounter. nprog. ru/api. Some additional examples and the source code used for plotting and to perform the analysis can be found at https://github. com/denatns/CellCountCV. The FCNN approach used here also makes it possible to adapt the models for counting any homogenous small objects using the user labelled data. In near future we plan to extend CellCountCV functionality with additional colour filters for fluorescence detection and to create a module for model adaptation to allow researchers to count objects of interest on their images.
Conclusions and Way Forward The present method has been implemented as a Python library with a simple user interface which enables the user to generate wing and fuselage geometries, read 2D input polars from XFOIL, MSES and Tau2D, and compute complete 3-dimensional lift curves. Typical computation times for an isolated wing are ~4 (~25 for a wing-fuselage configuration) on a single core. Given this low computational cost, the present method has great potential for use in the preliminary design process. The comparison cases shown in this report suggest that the method is able to predict the onset of stall with reasonable accuracy for a wide design space of isolated wings and wing-body configurations with proper sensitivity to the design parameters. Notably, the method is predominantly physically-based, scarcely relying on empirical data.
Related work Over the last years, several approaches have been proposed to learn semantic representations for sentences. This includes supervised and task-specific techniques with recursive networks  (Socher et al. , 2013) , convolutional networks  (Kalchbrenner et al. , 2014)  or self-attentive networks  (Lin et al. , 2017; Conneau et al. , 2017) , but also unsupervised methods producing universal representations given large text corpora. Examples of the latter include models such as FastSent  (Hill et al. , 2016) , QuickThought  (Logeswaran and Lee, 2018) , Word Information Series  (Arroyo-Fernández et al. , 2019) , Universal Sentence Encoder  (Cer et al. , 2018) , or SkipThought  (Kiros et al. , 2015) , where a sentence is encoded with a Gated Recurrent Unit (GRU), and two GRU decoders are trained to reconstruct the adjacent sentences in a dataset of ordered sentences.
, 2013)  and enforcing word vectors to be close to their corresponding visual features  (Lazaridou et al. , 2015b)  or their visual context  (Zablocki et al. , 2018)  in a multimodal representation space. These approaches learn word representations while we specifically target sentences. This task is more challenging since sentences are inherently different than words due to their sequential and compositional nature. Moreover, a great number of different sentences can be generated for the same image. Finally, some works learn sentence representations by aligning visual data to sentences within captioning datasets. Chrupala et al. (2015)  propose the IMAGINET model where two sentence encoders share word embeddings: a first GRU encoder learns a language model objective and the other one is trained to predict the visual features associated to a sentence.
For instance, the baseball and tennis images in Figure  1  may be different, but they are both sports images, and thus their corresponding sentences should be somehow close in the grounded space. Finally, it supposes that we have a dataset of images associated with several captions. To cope with these limitations, we consider the structure of the visual space and use the content of images. The intuition is that the structure of the textual space should be modeled on the structure of the visual one to extract visual semantics. We choose to preserve similarities between related elements across spaces (cf. green elements in Figure  1 ). We thus assume that the similarity between two sentences in the grounded space should be correlated with the similarity between their corresponding images in the visual space.
e. , θ i g ). 4 Evaluation protocol 4. 1 Datasets Textual dataset. Following  (Kiros et al. , 2015; Hill et al. , 2016) , we use the Toronto BookCorpus dataset as the textual corpus. This corpus consists of 11K books, and 74M ordered sentences, with an average of 13 words per sentence. Visual dataset. We use the MS COCO  (Lin et al. , 2014).
Baselines and Scenarios In the experiments, we focus on one of the most established sentence models: SkipThought (noted T) as the textual baseline: the parameters of the sentence embedding model are obtained by minimizing L T. Then, we derive several baselines and scenarios based on T, each representing a different approach of grounding. Since our focus is to study the impact of grounding on sentence representations, all baselines and scenarios share the same representation dimension d t = 2048 and are trained on the same datasets (cf. sect. 4. 1). We also report a textual model of dimension dt 2 that we call T 1024 , to compare with the GroundSent model of  (Kiela et al. , 2018). Model Scenarios. We test variants of our grounding model presented in sect. 3, all based on T: T + C g , T + P g , T + C g + P g , where C g (resp.
Classification benchmarks. All extrinsic evaluations are carried out using the SentEval pipeline  (Conneau and Kiela, 2018). The tasks are the following: opinion polarity (MPQA)  (Wiebe and Cardie, 2005) , movie review sentiment (MR)  (Pang and Lee, 2005) , subjectivity/objectivity classification (SUBJ)  (Pang and Lee, 2004) , customer reviews (CR)  (Hu and Liu, 2004) , binary sentiment analysis on SST  (Socher et al. , 2013) , paraphrase identification (MSRP)  (Dolan et al. , 2004)  as well as two entailment classification benchmarks: SNLI  (Bowman et al. , 2015)  and SICK  (Marelli et al. , 2014b). For each dataset, a logistic regression classifier is learned from the extracted sentence embeddings, and we report the classification accuracy. Structural measures. To probe the learned grounded space, we define structural measures, and report their values on the validation set of MS COCO (5K images, 25K captions).
Influence of concreteness To understand in which cases grounding is useful, we compute the average visual concreteness c of the STS benchmark, which is divided in three categories  (Captions, News, Forum). This is done by using a concreteness dataset built by  Brysbaert et al. (2013)  consisting of human ratings of concreteness (between 0 and 5) for 40,000 English words; for a given benchmark, we compute the sum of these scores and average over all words that are in the concreteness dataset. The performance gain ∆ between C g +P g and T are observed when the visual concreteness c is high: for Captions (c = 3. 10), the improvement is substantial: (∆ = +43); for benchmarks with a lower concreteness (News with c = 2. 61 and Forum with c = 2. 39), the improvement is smaller (∆ = +12). Thus, grounding brings useful complementary information, especially for concrete sentences.
Nearest neighbors search Furthermore, we show in Table  2  that concrete knowledge acquired via our grounded model can also be transferred to abstract sentences. To do so, we manually build sentences using words with low concreteness (between 2. 5 and 3. 5) from the USF dataset  (Nelson et al. , 2004). Then, nearest neighbors are retrieved from the set of sentences of Flickr30K  (Plummer et al. , 2015). In this sample, we see that our grounded model is more accurate than the purely textual model to capture visual meaning. The observation that visual information propagates from stands for the average accuracies reported in the other columns. ' †': the model has been re-implemented (we obtained higher scores than the one given in the original papers). ' ‡': the baseline is an adaptation of the model to the case of sentences.
dataset as the visual corpus. This image captioning dataset consists of 118K/5K/41K (train/val/test) images, each with five English descriptions. Note that the number of sentences in the training set of COCO (590K sentences) only represents 0. 8% of the sentence data in BookCorpus, which is negligible, and the additional textual training data cannot account for performance discrepancies between textual and grounded models.
Acknowledgments This work is partially supported by the  CHIST-ERA   EU  project  MUSTER  ( ANR-15-CHR2-0005 ) and the  Labex SMART  ( ANR-11-LABX-65 ) supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir program  under reference  ANR-11-IDEX-0004-02.
Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km).
However, the extent and distribution of irrigated areas remain indefinite in spite of the significant impact of irrigation on food security and water resources. At a global scale, several irrigation products are available at different spatial resolutions. Existing irrigation extent maps such as the FAO (Food and Agriculture Organization of the United Nations) database  [6] , the Global Map of Irrigated Areas version 5 (GMIA 5. 0)  [7, 8]  and the MIRCA2000 product  [9]  have been primary derived from national statistical data. The Global Rain-Fed Irrigated and Paddy Croplands (GRIPC) with spatial resolution of 500 m was produced using MODIS (Moderate resolution imaging spectroradiometer) satellite data  [10]. The low spatial resolution of these products is an obstacle for using them in irrigation management especially in regards to small to medium agricultural areas.
Chen et al. [18]  proposed a new approach to map irrigation extent, time and frequency in an arid region located in Hexi Corridor of northwest China by merging the 30 m spatial resolution Landsat images with 250 m MODIS data and ancillary data. In their study, they used the GI to detect irrigation events during the first half of the growing season. The overall accuracy for detecting irrigation water supplements reached 87%. Recently, Xiang et al. [19]  mapped irrigated areas of northeast China by comparing the MODIS derived LSWI (Land Surface Water Index) of the agricultural areas to the surrounding natural vegetation such as forests. They assumed that the canopy moisture indicated by the LSWI is higher for irrigated crops than the adjacent forest. Thus, they mapped irrigated areas by fixing a threshold for the difference between LSWI of agricultural plots and that of the forest.
Their results showed that the radar signal in X-band increased by approximately 1. 4 dB due to irrigation events occurring one day before the radar acquisition. They concluded that the radar signal could be used to identify three-day-old irrigated plots. The recently launched Sentinel-1 (S1) SAR satellite offers a powerful tool for agricultural area classification and monitoring under various weather conditions. The S1 satellites (S1A and S1B) provide an exceptional combination of high spatial and high temporal resolutions for dual polarization SAR data (six days of temporal resolution and a 10 m × 10 m pixel spacing) available in free open access mode. S1 time series has been newly exploited by several studies for land cover classification  [20, [31] [32] [33]. High quality classification mapping has been produced by applying either classical machine learning, such as random forest and the support vector machine, or complex deep learning methods on S1 time series.
SIGPAC Data SIGPAC data provides referenced geographic boundaries of plot land in Catalonia attributed with alphanumeric information. The data are produced and maintained by the General Direction of Rural Development of the Department of Agriculture, Livestock, Fishing and Food of the Generalitat of Catalunya. The plots are digitized using orthophoto images of 25-cm spatial resolution at scale 1:2000. Group of fields are associated in the database of the provided geographical data describing each plot by a unique identification code, surface area, land use, irrigation coefficient (0 for non-irrigated and 100 for irrigated), average slope and others. The SIGPAC data can be freely downloaded for the whole region in vector format  [40]. For each plot, the crop type could be obtained from the agricultural crop map of Catalonia (DUN).
This map is produced based on the SIGPAC geographic boundaries and the annual declaration of the agricultural exploitation. The agricultural map of Catalonia is also available in free and open access mode  [40]. In this study, SIGPAC data was used as a reference and validation of our proposed classification approaches. In our study, only agricultural crops (summer and winter crops) were considered for the irrigation classification. Forests, urban and orchards plots were removed from the SIGPAC database using the DUN crop map. Finally, a total of 193,327 plots of different crop types were used in the study forming a total area of 3795 km 2. The pie chart of Figure  3  summarizes the distribution of dominant crop types present in the studied zone. It shows the number of plots per crop type present in the study area.
In general, winter cereals such as wheat, oat and barley are rarely irrigated. However, it may happen that these crops are exceptionally irrigated very few times. On the other hand, irrigated plots mainly include alfalfa, maize, grassland, beans, rapeseed and rice. SIGPAC data provides referenced geographic boundaries of plot land in Catalonia attributed with alphanumeric information. The data are produced and maintained by the General Direction of Rural Development of the Department of Agriculture, Livestock, Fishing and Food of the Generalitat of Catalunya. The plots are digitized using orthophoto images of 25-cm spatial resolution at scale 1:2000. Group of fields are associated in the database of the provided geographical data describing each plot by a unique identification code, surface area, land use, irrigation coefficient (0 for non-irrigated and 100 for irrigated), average slope and others.
The SIGPAC data can be freely downloaded for the whole region in vector format  [40]. For each plot, the crop type could be obtained from the agricultural crop map of Catalonia (DUN). This map is produced based on the SIGPAC geographic boundaries and the annual declaration of the agricultural exploitation. The agricultural map of Catalonia is also available in free and open access mode  [40]. In this study, SIGPAC data was used as a reference and validation of our proposed classification approaches. In our study, only agricultural crops (summer and winter crops) were considered for the irrigation classification. Forests, urban and orchards plots were removed from the SIGPAC database using the DUN crop map. Finally, a total of 193,327 plots of different crop types were used in the study forming a total area of 3795 km 2.
Remote Sensing Data In this study, a database of radar (SAR) and optical data was prepared over the study site described in Section 2. 1. While the S1 satellite constellation provides SAR images, the S2 satellite provides optical images. Table  1  summarizes the characteristics of remote sensing data used in the study. More details on each data type are further provided in the following subsection. A time series of S1 images collected over our study site was used to generate SAR temporal profiles at both VV and VH polarizations. Eighty-two S1 images obtained from the S1A and S1B satellite constellation operating at the C-band (frequency = 5. 406 GHz, wavelength ~6 cm) were collected for the period between 28 August 2017 and 27 December 2018. Four S1 (two S1A and two S1B) footprints cover the western part of Catalonia with a six days temporal resolution as shown in Figure  1b.
Sentinel-2 Optical Data Currently, S2 offers fine spectral and spatial resolution optical images with 13 bands ranging from 10 m to 60 m spatial resolution with 5 days revisit time. Seventeen free optical images were obtained over our study site during the period between August 2017 and December 2018 at a frequency of one image per month. The S2 images were downloaded from the Theia (French land data center) website (https://www. theia-land. fr/). The Theia website provides ortho-rectified S2 images at level-2A corrected for atmospheric effect. However, starting from April 2018, Theia website started providing a monthly synthesis of cloud free images (level-3A). These images were used for the period between April 2018 and December 2018.
Overview An overview of the methodology is presented in Figure  4. First, the average S1 SAR backscattering coefficients (σ • ) were calculated at both plot scale (SIGPAC plots) and grid scale (10 km × 10 km). The mean signal at grid scale will be used to discriminate between rainfall and irrigation events. In general, we suppose that if the mean signal increases at the base of 10 km × 10 km then a rainfall event had occurred. Next, the PCA and the WT were performed on the obtained S1 time series. Moreover, the PCA was applied on the temporal series of the NDVI established at plot scale using the S2 optical data. Finally, two classification approaches including the RF classifier and the CNN were investigated. In each approach, three scenarios have been tested. The first scenario is based on the use of SAR data only, the second scenario considers the use of optical data only, and the third scenario includes the combined use of both optical and SAR data.
Principal Component Analysis (PCA) To reduce the volume of the data prior to the application of any classification method, a data compression is necessary to be applied especially when a huge amount of data is involved in the study. This data compression reduces the dimensionality of the classifier workspace and eliminates redundancies and nonessential information in raw data that may lead to inaccurate classification. Among data compression techniques, the principal component analysis is a statistical compression technique which converts high dimensional data to low dimensional data by selecting the most important features capturing maximum information about the dataset  [49, 50]. Data compression by means of PCA is accomplished by projecting each sample block of data along the directions of the individual orthonormal eigenvectors of the covariance matrix of the data.
Random Forest Classifier Random forest (RF) is an ensemble of machine learning algorithms consisting of large number of decision tree classifiers where each tree is constructed using a different subset of the training set. In its architecture, the RF classifier fits a number of decision tree classifiers on various subsamples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting  [51]. When compared to other classical machine learning algorithms, the random forest classifier has demonstrated its ability to yield high quality classification mapping with fast computation time  [52, 53]. The tree depth and minimum sample size are the two most important parameters in the RF classifier. Three different scenarios were tested using the RF classifier. First, the RF classifier was modeled using SAR data only (one for PC values and another for the WT coefficients).
Second, NDVI optical data was tested in another RF classifier. Finally, we combined SAR and optical data in a third RF classifier. Using SAR data only, the RF classifier was applied for the PC variables and the WT coefficients separately in two different classifiers denoted later by PC-RF and WT-RF respectively. First, each RF classifier (PC-RF and WT-RF) was trained using all variables of each method (328 values for the PC-RF and 256 coefficients for the WT-RF). This step gives the importance of each variable which is a number between 0 and 1 evaluating the contribution of the variable in the classification. The importance of the variables was then analyzed and a threshold value of the importance was determined to select the significant variables only. This step allows reducing the number of variables for each classifier while retaining good accuracy.
Then, both RF classifiers were regenerated using only important variables for each method. In all tested RF classifiers, the number of trees was set to 500. This number is fixed by increasing the number of trees until the accuracy of the classification converges. The tree depth was kept at its default value which is the square root of the number of variables available. In order to train and validate the model on two independent datasets, we randomly split the database into 50% for training and 50% for validation  [23, 54]. Using optical data only, the 17 PC values obtained from the NDVI data are implemented in a random forest classifier later denoted by NDVI-RF. As done for SAR data, important variables were then retained based on a threshold value of a variable's importance, and the RF classifier was reestablished using these variables only.