dataesr/scientific-paragraphs-categorization huggingface dataset:
A hight resolution 1 M microarray and a 400 K custom microarray were used for each patient (Agilent Technologies, Massy, France). The 1 M microarray covered whole genome with an intragenic resolution of 1. 8 kb. The custom design had a whole genome coverage with a 5 kb resolution and was strongly enriched in 363 genes with an exonic resolution of 0. 1 kb and an intronic resolution of 0. 6 kb. These genes of interest were selected because of their certain or potential involvement in reproductive function and sexual development in both humans and mice. Hybridization was performed according to the manufacturer's protocol. Images processing and data analysis were performed with CytoGenomics software (4. 0. 3. 12) (Agilent Technologies). The ADM2 algorithm was used for statistical analysis. Copy number variations were considered significant if they were defined by tree or more oligonucleotides and were not identified in the Database of Genomic Variants as a polymorphism (http:// proje cts.
Short-read whole genome sequencing (patient 1). Genome sequencing (GS) was performed with 150-bp paired-end reads using the Illumina HiseqX Ten platform. Reads were aligned to the human genome reference (hg19) using the Burrows-Wheeler aligner. Base quality scores were recalibrated using GATK v3. 8. Candidate breakpoints were predicted using Lumpy for inversions and translocations and Control-FREEC for dup/del  48. Variants were filtered based on population frequency and their presence in public databases. Candidate breakpoints were visually inspected and selected using the Integrative Genomic Viewer (IGV) tool. GS successfully yielded 40 × mean read depth across the whole genome. SVs were analyzed using "in house" pipeline, a local pipeline combining Lumpy and Control-FREEC. The outputs of the callers, a tabulation separated file, was annotated using public databases such as DGV, GnomAD SV, Developmental Delay, and ISCA Using GS data, Lumpy predicted 1705 structural variations breakpoints, including 958 chromosomal translocations, 276 inversion and Control-FREEC predicted 471 deletions and duplications.
Whole genome sequencing (patient 2). WGS was performed for patient with DSD by the Centre National de Recherche en Génomique Humaine (Institut de biologie François Jacob, CEA). After a complete quality control, genomic DNA (1. 1 µg) has been used to prepare a library for whole genome sequencing, using the Illumina TruSeq DNA PCR-Free Library Preparation Kit, according to the manufacturer's instructions. After normalization and quality control, qualified libraries have been sequenced on a HiSeqX5 platform from Illumina (Illumina Inc. , CA, USA), as paired-end 150 bp reads. 1 lane of HiSeqX5 flow cell has been produced for each sample, in order to reach an average sequencing depth of 30 × for each sample. Sequence quality parameters have been assessed throughout the sequencing run and standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate FASTQ file for each sample.
After demultiplexing, sequences were aligned to the reference human genome hg19 using the Burrows-Wheeler Aligner  49. Downstream processing was carried out with the Genome Analysis Toolkit (GATK), SAMtools, and Picard, following documented best practices (http:// www. broad insti tute. org/ gatk/ guide/ topic? name= best-pract ices). Variant calls were made with the GATK Haplotypecallers version 3. Structural variants were assessed using Manta 50 , Canvas 51 and WisecondorX  52. The annotation process was based on the latest release of the Gencode database (v31)  53  , Gnomad (v2. 1)  54  , Clinvar (v20190815), CADD (v1. 4)  55. Variants were annotated and filtered using the Polyweb software interface designed by the Bioinformatics platform of University Paris Descartes. Patients-derived iPSCs generation and pluripotency characterization.
Finally, peroxidase activity was detected with DAB. Sections were rinsed in PBS between each step. Analyses and quantification were performed with Histolab version 11. 5. 1 Software (Microvision Instruments, https:// www. micro vision. fr) analysis software. Immunofluorescent staining and imaging analysis. For immunofluorescence staining (5hmC, D2-40), paraffin sections of EBs were submitted to antigen retrieval with tris-EDTA (121 °C, 20 min) and then blocked in 0,2% gelatine, 0. 05% Tween 20, 0. 2% BSA and PBS 1X for one hour before adding antibodies. The primary antibodies used in this study were as follows: monoclonal rabbit anti-5hmC (Active Motif ref #39769; 1:400) and monoclonal mouse anti-D2-40 (Dako, ref M3619, 1:200). Specific rabbit and mouse secondary antibodies were conjugated respectively with Alexa Fluor 594 and 488 (1:500).
Acknowledgements We thank all the co-authors for their contribution, all members of the technical team of the Histology, Embryology and Cytogenetic service of  Antoine Béclère Hospital  and  Dr Claire Bouvattier  ( Paediatric Endocrinology service, Kremlin Bicêtre Hospital ) for her helpful and expert discussion. We thank  Christelle Monville  ( Istem ) for providing us with the 46,XX iPSC control. We also thank  Dr Vincent Izard  ( Urology service of Antoine Béclère Hospital ) for patient recruitment and both patients and their family. This study was supported by grants from the  French Ministry (Direction Générale de l'Organisation des Soins ), by  Institut Universitaire de France  and  DIM region Ile de France.
With the increasing number of connected devices, complex systems such as smart homes record a multitude of events of various types, magnitude and characteristics. Current systems struggle to identify which events can be considered more memorable than others. In contrast, humans are able to quickly categorize some events as being more "memorable" than others. They do so without relying on knowledge of the system's inner working or large previous datasets. Having this ability would allow the system to: (i) identify and summarize a situation to the user by presenting only memorable events; (ii) suggest the most memorable events as possible hypotheses in an abductive inference process. Our proposal is to use Algorithmic Information Theory to define a "memorability" score by retrieving events using predicative filters.
Realistic Examples We designed two different setups to test our approach. Both are inspired from smart home use cases. This choice of configuration was motivated by the challenges posed by smart homes for abductive inference: (i) as the number of connected devices increases, more events are recorded, making the detection of memorable events more important; (ii) smart homes are prone to experiencing atypical situations, highly dependent on the context, for which pre-established relations might fail to find good abduction candidates. Our choice was also motivated by the existence of previous work  [12]  involving smart home simulations capable of quickly generating data from which we could extract events and test our methods.
The "Temperature" Scenario We consider an experimental smart home setup with various sensors, which we simulate over a period of time. The smart home simulation data are then processed to identify some predefined events (such as abnormal temperatures). To perform the simulation, we used the iCasa smart home simulator platform  [12] , to which we added custom modules. iCasa allows the simulation of autonomic systems that can handle internal communications, the possible insertion of new components at runtime, or the deletion or modification of existing components. We used a basic scenario consisting of a house with four rooms, a single user, and an outdoor zone. All four rooms are equipped with a temperature control system in charge of heaters (see Figure  2 ). Based on this, we implemented a scenario spanning over 420 days, and comprising a daily cycle of outdoor weather (temperature and sunlight) fluctuations, as well as user movements.
This allows to effectively break the innermost loop in Algorithm 1; • Some of the filters, for instance, the date and rank filters, were hard-written. Events can be selected from these precomputed mappings over the memory objects, rather than by testing a predicate over all memory elements. Our code was written in Python. Examples are presented in the form of Jupyter Notebooks, which allow to quickly reproduce our results and figures. All code is available on our GitHub: https://github. com/EtienneHouze/memorability_code (accessed on 27 January 2022). Figures from the code are interactive: hovering the mouse above points displays the iD of the event, as well as the predicates used in the optimal retrieval path.
Subsequently, notions of complexity re-emerged in causal inference: in  [20]  it was found that, when a causal link exists between two random variables A → B, the decomposition of the joint probability is simpler in the direct than the inverse direction: K(P(A)) + K(P(B|A)) < K(B) + K(A|B). The relation between complexity, compression and causality was used in [21] to devise the PACK algorithm. It models a dataset by using a family of decision trees where each tree describes how one variable can be expressed given the others. By choosing the model minimizing the total description length (i. e. , description of the model and description of the errors), PACK compresses the dataset while finding relations between variables which can be further analyzed. More recently,  [22]  used Minimum Description Length to determine, given a joint probability distribution over (X, Y), whether X causes Y or Y causes X.
Data Availability Statement: All code and data used for the experiments can be found at https: //github. com/EtienneHouze/memorability_code (accessed on 27 January 2022). The iCasa smart home simulator from the Adele research Group, which was used to generate sensor data, can be found at: http://adeleresearchgroup. github. io/iCasa/snapshot/index. html (accessed on the 27 January 2022).
Supervised Classification Models The pipeline summarized in Figure  1  requires a machine learning algorithm for performing the step of jointly segmenting the signal and classifying the time points into walking or non-walking activities. As part of the experimental design, each time point of the collected data was carefully labelled as being part of a walking or non-walking activity. In addition, we designed a feature space composed of two variables that are the values at each time point of the LM-QDTS and the LSD-QDTS, properly normalized (see Figure  8 ). We therefore aim to train an appropriate classifier that uses this feature space to predict time point labels into walking or non-walking activities. We used the tidymodels (https://www. tidymodels. org (accessed on 17 March 2022)) consistent ecosystem of R packages to train, tune, test and compare models.
0 algorithm  [46]  of the C50 package and an in-house algorithm from Spark (https: //spark. apache. org (accessed on 17 March 2022)) in the sparklyr package. We opted for the CART algorithm, which is the most flexible. In particular, it offers control over the size of the tree, which provides more interpretable trees. We tuned all three hyper-parameters. Support Vector Machine (SVM). This model divides the feature space in three parts: (i) an area in which non-walking points are more likely, (ii) an area in which walking points are more likely and (iii) a margin that separates the first two areas  [47]. In the tidymodels ecosystem, there are three types of SVM models, namely linear, polynomial and radial basis function (RBF) SVMs. Figure  8  suggests that we should aim at a non-linear border and that walking points are surrounded by non-walking points.
This type of topology is usually well captured by radial basis function support vector machines (https://scikit-learn. org/stable/auto_ examples/classification/plot_classifier_comparison. html (accessed on 17 March 2022)), (https://gist. github. com/WittmannF/60680723ed8dd0cb993051a7448f7805 (accessed on 17 March 2022)). Classification-based RBF SVMs rely on a total of two hyper-parameters: (i) a real positive number representing the cost of predicting a sample within or on the wrong side of the margin and (ii) a real positive number which controls the RBF Gaussian kernel standard deviation. In the tidymodels (https://parsnip. tidymodels. org/reference/svm_ rbf. html (accessed on 17 March 2022)) ecosystem, RBF SVMs can be estimated by only one algorithm, namely ksvm (https://parsnip. tidymodels.
In the tidymodels (https://parsnip. tidymodels. org/reference/logistic_reg. html (accessed on 17 March 2022)) ecosystem, logistic regression models can be estimated by six different algorithms. We opted for the simplest one, carried out by the glm (https://parsnip. tidymodels. org/reference/details_logistic_reg_glm. html (accessed on 17 March 2022)) function of the stats package  [51]. In effect, all the other engines aim to include the penalty hyperparameters which we do not need since the feature space is only two-dimensional. Logistic regression models predict class probabilities. We therefore tune a single hyper-parameter which is a threshold that turns such probability into a hard membership. Table  3  summarizes for each model the hyper-parameters on which they rely and that need to be set along with the tuning grid that was used for each of them.
e. , around 17 trials), used for model estimation; • an assessment set, with 20% of the trials from the training set (i. e. , around four trials), used for evaluating model performance. This cross-validation strategy provides five independent estimates of each performance metric from which we could obtain a mean value and a standard error estimate. Figure  9  summarizes the full data splitting scheme. 1. Model Tuning: For each split (Section 3. 1), each model is fitted using the blue folds for a number of combinations of its hyper-parameters (Section 2. 3). Performance metrics (Section 3. 2) are evaluated using the leftout fold in each split to choose the best set of hyper-parameters (Section 3. 4).
The reported precision and accuracy are to be thought of as lower bounds. When using more powerful wearable sensors, one could also think of enriching the feature space, which might further improve the performance of the WARM. In the future, it could be interesting to add other comparison metrics especially designed for classification problems involving time series, such as the ones proposed by Gensler and Sick  [52]. Additionally, we are currently in the process of significantly increasing the database. We will therefore update all the models once it is finalized at a later point in time. In addition, we plan to investigate other transformations of the unit quaternion time series that could complement well the current feature space. The k-NN model also deserves particular attention because it stands out on the test set and could be straightforwardly generalized to directly use the original unit quaternion time series without transformation into real-valued ones.
org/reference/logistic_reg. html (accessed on 17 March 2022)) ecosystem, logistic regression models can be estimated by six different algorithms. We opted for the simplest one, carried out by the glm (https://parsnip. tidymodels. org/reference/details_logistic_reg_glm. html (accessed on 17 March 2022)) function of the stats package [51]. In effect, all the other engines aim to include the penalty hyperparameters which we do not need since the feature space is only two-dimensional. Logistic regression models predict class probabilities. We therefore tune a single hyper-parameter which is a threshold that turns such probability into a hard membership.
The satellite image time series are used for several applications such as predictive analysis. New techniques such as deep learning (DL) algorithms generally require long sequences of data to perform well; however, the complexity of satellite image preprocessing tasks leads to a lack of preprocessed datasets. Moreover, using conventional collection and preprocessing methods is timeand storage-consuming. In this paper, a workflow for collecting, preprocessing, and preparing Sentinel-1 images to use with DL algorithms is proposed. The process mainly consists of using scripts for collecting and preprocessing operations. The goal of this work is not only to provide the community with easily modifiable programs for image collection and batch preprocessing but also to publish a database with prepared images.
The experimental results allowed the researchers to build three time series of Sentinel-1 images corresponding to three study areas, namely the Bouba Ndjida National Park, the Dja Biosphere Reserve, and the Wildlife Reserve of Togodo. A total of 628 images were processed using scripts based on the SNAP graph processing tool (GPT). In order to test the effectiveness of the proposed methodology, three DL models were trained with the Bouba Ndjida and Togodo images for the prediction of the next occurrence in a sequence.
LSTM networks are a particular type of recurrent neural network (RNN) introduced by Hochreiter and Schmidhuber in  [9]. The LSTM architectures are generally used with data organized in sequences and have cells that store the state from previous layers  [2]. DL algorithms for time series prediction typically require datasets made of long image sequences  [10] [11] [12] ; however, the complexity of SITS preprocessing tasks and the lack of training datasets are some limitations related to the use of DL techniques, as mentioned in  [2]. Indeed, for works on the prediction of land cover classes using DL algorithms, for example, it is not easy to find datasets with SITS already preprocessed. Starting from scratch to build a long series can also be difficult for non-experts in the remote sensing domain.
To resolve this, it has been suggested to reveal methods for automatic image batch processing and increase the number of available training datasets. In this way, the number of works using DL algorithms for SITS prediction could considerably increase. An interesting application in this field is for example the prediction of deforestation from SITS with DL approaches. There are several publicly available remote sensing databases to use with DL algorithms  [13] [14] [15] [16] ; however, these datasets are mostly used for image classification tasks  [17, 18]. For example, the authors in  [16]  proposed a georeferenced dataset for training and validation of deep learning algorithms for flood detection for Sentinel-1 images. In  [13] , an image dataset for ecological investigations of birds in wind farms was constructed.
The authors in  [14]  also proposed OpenSARUrban, a Sentinel-1 dataset dedicated to the content-related interpretation of urban SAR images. Moreover, although some studies have explored the problem of a lack of datasets  [19] [20] [21] [22] [23] [24] [25] , there are few studies that have addressed the constitution of datasets suitable for SITS forecasting problems. For ship detection, for example, the authors in  [25]  carried out a study in which ship positions have been automatically extracted in batch from Sentinel-1A images, and in  [15] , the Large-Scale SAR Ship Detection Dataset-v1. 0 from Sentinel-1 have been proposed. In  [20] , a workflow allowing the production of a set of preprocessed Sentinel-1 GRD data was presented. However, the proposed scripts allow the preprocessing of the images one by one.
Within this scope, the goal of this paper is to propose a complete workflow with scripts for collecting and batch processing S1 images. Then, a method to prepare the datasets to use with the DL algorithms is presented. The purpose of this experiment is to build public datasets of SITS that can be used for multiple other purposes. The code used to collect images in bulk was generated from NASA's Vertex platform. For preprocessing, the scripts were based on the graph processing tool (GPT), proposed by the engine part of the Sentinel application platform (SNAP) software, Version 8. 0  [26]. This special command based on the SNAP graph processing framework (GPF) is used to process raster data in batches, through extensible markup language (XML) files containing graph operators. To test the effectiveness of the proposed methodology and demonstrate the practicality of the built datasets, the CNN-LSTM, ConvLSTM, and Stack-LSTM architectures were used to design DL models for the next frame forecasting in S1 image sequences.
Experiments have shown that longer time series provide better performance than shorter ones. Compared to similar works in the literature, the methodology proposed in this study has the following particularities: (1) the process described in this paper allows us to easily build datasets of SITS suitable for prediction tasks with DL algorithms. The images of each series allow us to represent the different states of the land cover classes thanks to the data acquired over time. Contrary to the dataset used mainly for classification tasks, the data collected here are more adequate for forecasting problems; (2) the procedure is complete, going from data collection to data preparation; (3) the proposed preprocessing chains allow us to execute operations in batch and not individually; (4) the VV and VH bands of images are separated during the process to allow a better analysis; (5) the preprocessed datasets and all the scripts used in this study are available to users.
The experiments conducted on three study areas, namely the Bouba Ndjida National Park (BNP), the Dja Biosphere Reserve (DBR), and the Wildlife Reserve of Togodo (WRT) provided the preprocessed datasets for time series applications. The land cover classes on the selected AOI are diverse. This will allow us to create several types of DL models for many applications. In sum, the contributions of this paper are as follows: • Proposal of a complete workflow for S1 image time series collection and preprocessing; • Development of a GPT-based script for S1 images batch processing that can be easily modified by users; • Construction of a public database made of three SITS; • Presentation of a method for preparing image time series to use with DL algorithms. The rest of this paper is organized as follows: Section 2 describes the data used for this study.
Main Characteristics The S1 satellites are the first in the family of six EO satellite missions (Sentinel-1 to Sentinel-6). The S1 mission consists of a constellation of two satellites namely Sentinel-1A (S1A), launched on 3 April 2014, and Sentinel-1B (S1B) launched on 25 April 2016. The two satellites S1A and S1B share the same orbital plane. Together they provide images every six days, day and night, regardless of the weather conditions. Each of the two satellites flies over the same area every 12 days  [8]. The S1 sensors are equipped with synthetic aperture radar (SAR)-type instruments that operate in the C-band. These sensors, called C-SAR, can acquire data covering large areas (up to 400 km) and can reach five meters resolution in four exclusive modes: stripmap mode (SM), interferometric wide swath (IW) mode, extra wide swath (EW) mode and wave mode (WV).
Thanks to a transmission chain and two parallel reception chains for horizontal (H) and vertical (V) polarizations, the S1 C-band SAR instruments operate in single polarization (HH or VV) and dual polarization (HH + HV or VV + VH). The products acquired in SM, IW, and EW modes are available in single or dual polarization while the products acquired in WV are only available in single polarization. The Sentinel images are free of charge to all users, including the general public and commercial users. All the products are distributed in the Sentinel standard archive format for Europe (SAFE). For each mode, images are available in three levels corresponding to the processing level: Level-0, Level-1, and Level-2. The Level-0 products correspond to unfocused raw SAR data. These data must be decompressed and preprocessed in order to be usable.