dataesr/scientific-paragraphs-categorization huggingface dataset:
ucsusa. org/resources/satellite-database (accessed on 25 August 2021). Orbiting satellites carry calibrated sensors to detect various wavelengths along the electromagnetic spectrum, often including visible light. Systems are either active, where the source of energy is part of the sensor (Radar imaging), or passive, where the source of energy is the sun or Earth/atmosphere (optical imaging)  [2, 36]. Optical remote sensing generally covers the visible (VIS), near infrared (NIR), middle infrared (MIR), and thermal infrared (TIR) range of the electromagnetic spectrum. In near and middle infrared spectra, radiations are reflected like visible light, while thermal radiation is the heat energy emitted by the surface. As opposed to optical systems, radar systems emit microwaves and measure the back scattered energy.
Description of the SITS Prediction Problem Let X t be a satellite image representing a given area acquired at time t. A satellite image time series (SITS) is a sequence of images at different consecutive times  [37]. Images may come from different sources but always represent the same scene. Thus, a SITS can be formulated by the series described in Equation (  1 ): SITS = X 1 , X 2 ,. , X t-1 , X t ,. , X t+1 ,. , X t+n (1) where X t represents the image X at time t. When talking about Earth observation SITS, the term Earth observation data cube is widely used by scientists  [38]. Prediction in SITS not only consists of the forecast future or unseen event. In some cases, it is related to data fusion or missing data reconstruction in sequences. When the predicted data concern future events, the term "forecasting" is generally used while the term "nowcasting" is used for very short-range forecasting.
Trajectory-Gated Recurrent Unit (TrajGRU) To overcome some limitations of ConvLSTM and Convolutional GRU (ConvGRU) models, the authors in  [41]  designed a new model and benchmark (dataset, training loss, and evaluation protocol) for precipitation nowcasting tasks. They designed a new TrajGRU model based on ConvGRU. It is able to actively learn the location-variant structure for recurrent connections. Unlike in classical convolutional RNN, recurrent connections are dynamically determined over time in the TrajGRU architecture  [41] , which is defined by Equations (  8 )-  (12). U t , V t = γ(X t , H t-1 ) (8) Z t = σ(W xz * X t + L ∑ l=1 W l hz * warp(H t-1 , U t,l , V t,l )) (9) R t = σ(W xr * X t + L ∑ l=1 W l hr * warp(H t-1 , U t,l , V t,l )) (10) H t = f (W xh * X t + R t • ( L ∑ l=1 W l hh * warp(H t-1 , U t,l , V t,l ))) (11) H t = (1 -Z t ) • H t + Z t • H t-1 (12) where: L represents the number of allowed links; U t , V t ∈ R L×H×W denote the flow fields; W l hz , W l hr , W l hh are the weights; warp(H t-1 , U t,l , V t,l ) is the function that selects the positions pointed out by U t,l , V t,l from H t-1 ; H t , R t , Z t , H t represent the memory gate, reset gate, update gate, and new information, respectively; X t is the input; f is the activation function; and • denotes the Hadamart product.
Require: θ f 0 , the initial LSTM's parameters. Train GAN with Algorithm 1 Cut the generator (G) of the trained GAN and attach it to the LSTM ( f lstm ). for number of training iterations do sample {v (i) } m i=1 ∼ p data : a batch from the real data. where v (i) = ∼ v i-j+1 , ∼ v i-j+2 ,. , ∼ v i. g θ f ← ∆ θ f [ 1 m ∑ m i=1 MSE(G( f lstm (v (i) ), ∼ v i+1 ] θ f ← θ f -α. Optimizer(θ f , g θ f ) end for In a similar manner, in order to ameliorate the performance of the classical ConvGRU for precipitation nowcasting tasks, a hybrid architecture similar to the GAN-LSTM model was designed in  [58] : the Generative Adversarial-ConvGRU (Ga-ConvGRU). The proposed model has two adversarial learning systems: a ConvGRU-based generator and a convolution neural network-based discriminator. The two systems of the model allow for more realistic and more accurate yield extrapolation  [58].
The proposed model had an encoder-decoder structure with convolutional LSTM units. As shown in Figure  15 , the last states and output cell of the encoder are copied to the decoder network. The encoder and decoder can consist of multiple layers of LSTM units stacked together, with the output of one layer being the input of the next one  [43]. The MLP or feed-forward multi-layer neural network is the first and simplest DL architecture. Numbers of neurons in each layer are variable and neurons are all fully connected. These networks are generally used for regression and classification problems, with a very large amount of training data. Due to the presence of clouds on the optical images or the malfunctions that may occur on satellite engines, for example, data sequences are often incomplete.
CNN-Based Approaches To overcome the shortcomings of MLP networks in computer vision tasks, the CNN architecture has been designed by scientists. They are specially used for data in two or more dimensions. When working on image processing problems, the CNN consists of four basics layers: the convolution, pooling, flattening, and fully connected layer. Unlike MLP, layers are not fully connected in CNN. In SITS prediction problems, one can notice that the CNN is generally used for data fusion or missing data reconstruction. For example, in  [61] , the authors explored the fusion of optical and radar images time series(Sentinel 2 and Sentinel 1) for the reconstruction of missing NDVI from synthetic aperture radar (SAR) data. To this end, they proposed a CNN-based model. The results of their study showed that there is a strong relationship between the radar data and the NDVI, which can be captured by a CNN model.
The Stochastic Gradient Descent (SGD) In the SGD, all the parameters are updated for each occurrence individually instead of computing the gradient of the cost function for the entire training dataset. The core of this approach is to minimize a function that can be written as the sum of differentiable functions. It is suitable for unconstrained optimization problems and has proven to be very effective in a variety of deep neural networks  [44, 46, 49]. Equation (  14 ) describes the SGD update rule. W k+1 = W k -η∆J(W k ) (14) where W k+1 is the updated value after the k-th iteration, W k is the initial value before the k-th iteration, η is the step size, and ∆J is the gradient of J.
Limits Related to Training Dataset Availability It is necessary to use a large training dataset to obtain satisfactory results with DL algorithms. However, contrary to models that use conventional images, there is a significant lack of labeled remote sensing data. Even if some training datasets are available for the remote sensing community, the problem regarding the quantity of elements in these databases persists. This issue of limited training samples is more important for working with Earth observation SITS. It is important to constitute a long time series of images to achieve accurate predictions  [71]. There are several satellite missions that provide a large quantity of data. However, the collection and preprocessing of remote sensing images to build a usable dataset for SITS prediction is not a simple task  [38].
In addition, Earth observation images are complex and varied (particularly because of the geographical metadata). The images are large, unlike traditional imagery datasets that are constituted of relatively small amounts of data. Thus, the download and storage costs are two aspects that make data availability difficult  [71]. Considering there is a lack of preprocessed training datasets for SITS prediction, other types of images are often used for model validation in the literature. These include, for example, the MNIST, the Moving MNIST, and the KTH Action databases. Low resolution images such as meteorological series and radar echo datasets are, therefore, most often used in the domain of SITS prediction. To solve this problem, scientists are encouraged to publish methods and algorithms for downloading automatic Earth observation images.
Preprocessing of Datasets Due to the satellites that orbit the Earth, it is possible to obtain images with excellent temporal resolutions. In their initial state, these SITS contain a great deal of information. However, thsse images are not all free and, in practice, it is not possible to use them in their raw state with DL algorithms. In fact, each image of the final sequence must be downloaded and preprocessed to extract useful information the end user can work with  [72]. For example, various indexes and classifications are performed before the images are used as input datasets. Figure  17  presents the entire remote sensing data process-ing flow  [72]. First, images are acquired and recorded from Earth observation satellites. Then, data are processed through three main operations, including the preprocessing step (radiometric, atmospheric, or geometric corrections, etc.
Although there are some techniques to overcome these limitations, the quality of the resulting data is often altered. This also leads to decreased prediction accuracy. Therefore, an alternative to cloudy images is the use of radar images. However, this solution is also a challenge for the community because the prepossessing steps for radar images seem to be more complex than those for optical images and SAR images are also noisy and grainy  [73]. Thus, precise workflows and scripts should be made public to facilitate the preprocessing step of satellite images.
Architecture of DL-Networks In some cases, deeper architectures with a large amounts of training data allow models to achieve satisfactory results on complex problems and to generalize well. However, SITS are made up of extremely large images. A significant resizing of these images will decrease the quality of the predicted data. However, the quality of output images in terms of resolution is important, for example, in land change predictions. In fact, for a better understanding of the studied phenomenon, output images must maintain the maximum amount of information and have the best resolution possible. Therefore, the use of some DL architectures with SITS could be an issue regarding the computing resource consumption. For example, it has been shown in  [74]  that the use of the ConvLSTM architecture is not advisable when the size of images and length of sequences increase.
Complexity of SI The conditions of acquisition of SI (such as atmospheric conditions, the effect of sunlight, the angle of view of the sensor, the type of sensor (radar, optical, etc. ), and the complexity of the images in terms of the numbers of pixels and channels) make the exploitation of SITS very difficult  [73]. In addition, from one geographical area to another, objects do not have the same appearance on the images, and over seasons, images of the same area can appear different because of the effect of seasonality. This makes it difficult to build linear datasets and to design DL models capable of learning how changes occur over time. As a solution, it is therefore necessary to design powerful frameworks capable of taking into account these diversities to produce good predictions. One way to deal with large image sizes is often to divide the images into smaller areas that are used as input datasets for the model training.
Generalization of DL Models Concerning natural images, DL models are generally reusable without too much difficulty regardless of the type of image. However, this is not the case with SI. For example, a model trained on optical images having 30 m of resolution will not be able to produce the same results with radar images of different resolutions. Moreover, models trained on the same type of data but corresponding to different geographical areas will not produce the same results. To this end, it is necessary first to design frameworks dedicated to the preprocessing of SITS for prediction. Then, meta-learning concepts have to be applied  [75, 76] , which will allow for the consideration of different geographical areas as different spots and to design models that can be reused for new areas.
Despite the promising results obtained with the proposed models, the SITS prediction community is still confronted by many challenges. Some examples of the issues encountered are the lack of available training datasets, the complexity of satellite images, the need to first preprocess images, the difficulty to generalize and reuse the models, and the configuration of DL networks. DL algorithms have been shown to be effective for predictions in several areas. For applications such as future land cover change prediction (deforestation forecasting, for instance), it will be necessary to design implicit models that generalize well, are capable of using large quantities of Earth observation data, and can address large-scale, real-world problems. Regarding future research directions, one can consider the need to deepen studies and propose new solutions on the following topics:.
Common analyses between MEG and sEEG datasets Software. All analyses were done using MNE-Python  (Gramfort et al. , 2014) , FreeSurfer (http://surfer. nmr. mgh. harvard. edu/) and custom scripts written in Python. Statistical analysis. Analyses of sEEG data were done following a fixed effect strategy, that is, considering all patients' electrodes collectively in an average brain. Individual differences of brain morphology and connectivity were therefore neglected. This strategy was chosen as each patient had a unique electrode implantation map, thus rendering group statistics inappropriate. For MEG data, the strategy was to reduce the normalized signal estimated at the source-level (vertex) to a statistics across participants before subsequent analyses, typically a t computed from a one-sample t test against 0 across participants.
Most of the analyses were thereafter consistent between sEEG and MEG data, as having a single average brain with multiple electrodes is similar to having a single average brain with multiple vertices. The only difference was that the sEEG metrics corresponded to z values, and the MEG metrics to t values. The fixed-effect strategy we used for the sEEG data has a low generalization power alone. However, replicating the effects in the MEG-independent dataset and using a random-effect strategy for the MEG data greatly strengthen our confidence in the generalization of the results. Align-bin-and-count (ABC) pipeline: estimation of the duration of a cyclic activity. We developed a method to estimate the number of cycles of oscillatory activity present in a neural signal, relative to the number of cycles present in the stimulus.
sEEG Participants. Sixteen patients (5 females, mean age 26. 9 years, range 9-46 years; see Table  1 ) with pharmaco-resistant epilepsy took part in the study. They were implanted with depth electrodes for clinical purposes at the Hôpital de La Timone (Marseille, France). Neuropsychological assessments conducted before sEEG recordings indicated that all participants had intact language functions and met the criteria for normal hearing. None of them had their epileptogenic zone, including the auditory areas as identified by experienced epileptologists. Patients provided informed consent before the experimental session, and the study was approved by the Institutional Review Board of the French Institute of Health (IRB00003888). Data acquisition. Depth electrodes (0. 8 mm, Alcis) containing 10-15 contacts were used to perform the functional stereotactic exploration.
Contacts were 2 mm long and spaced from each other by 1. 5 mm. The locations of the electrode implantations were determined solely on clinical grounds. During the recording session, participants lay comfortably in a chair in a sound-attenuated room. sEEG signals were recorded at a sampling rate of 1000 Hz using a 256 channel BrainAmp amplifier system (Brain Products) and bandpass filtered between 0. 3 and 500 Hz. A scalp electrode placed in Fz was used as the recording reference. Anatomical localization of electrodes. Anatomical localization of electrodes was performed using a local software developed at the Hôpital de La Timone  (Medina Villalon et al. , 2018). First, an automatic procedure coregistered electrode location on the scanner and the patient's MRI. Second, the morphing matrix computed by Freesurfer used to project one individual's brain onto the average brain was applied directly on these locations.
Sources with. 70% rejected epochs were entirely removed, as they most likely contained epileptic discharges. Finally, sources showing low voltage epileptic activity were removed based on visual inspection. Overall, 2648 of 3139 sources were kept, with on average 82 of 100 epochs (Table  1 ). Time-frequency decomposition. Trial-by-trial time-frequency decomposition was conducted in a range of 100 frequencies, logarithmically ranging from 2 to 150 Hz. Morlet wavelet transform was applied to the data using the MNE-python function time_frequency. tfr_morlet, with parameter n_cycles = 6. From the resulting complex representation, both ITPC and power were extracted as follows: ITC t;f j ¼ 1 N X N n¼0 e i:arg z t;f j;n À Á Power t;f j ¼ 1 N X N n¼0 z t;f j;n 2 where z t;f j;n designates the complex time-frequency representation of source j at trial n over N, frequency f and time t.
Primary auditory cortex (PAC) was defined by the presence of a P20/N30 complex, and secondary auditory cortex (SAC) by the presence of a P40/N50 complex  (Liégeois-Chauvel et al. , 1991 , 1994). Responses of PAC and SAC were averaged, as no main differences were found in any of our analyses. Definition of canonical frequency bands was then based on the profile of the spectrum of ITPC and power (averaged across time during the stimulus duration, from 0 to 6. 24 s). It resulted in an empirical definition of d (2-3. 5 Hz), u (4-7 Hz), a (8-11 Hz), and g bands (50-110 Hz). Although not prominent in either the sEEG dataset or in the MEG dataset, we added a b band (12-22 Hz) based on existing literature. Indeed, previous works have shown coupling of the beta band power with the phase of the stimulus envelope at similar paces  (Fujioka et al.
MEG Participants. We collected data from 15 participants (8 females, median age 27 years, age range 23-40 years) after providing informed consent. All had normal hearing, reported no neurologic deficits, and received 40 euros for their time. The experiment was approved by the National Ethics Committee on research on human subjects. Data acquisition. MEG data were recorded with a whole-head 4Dneuroimaging system with 248 magnetometers. Participants were lying in horizontal position under the MEG dewar, facing a screen displaying a silent movie. MEG recordings were sampled at 678 Hz and bandpass filtered between 0. 3 and 500 Hz. Four head position coils (HPI) measured the head position of participants before each block. Before the session, 2 min of empty room recordings was acquired for the computation of the noise covariance matrix.
K E Y W O R D S amplicon sequencing, bacteria, fungi, holobiont, microbial diversity, microbiome, nontarget amplification, oomycete, protist and oomycete ITS)  (Agler et al. , 2016). Primers specific to a known, nontarget DNA template (blocking oligos) and nested inside the universal primer binding sites (Figure  1 ) were designed (see Supporting Information Files S1a and S1b for all oligo and primer sequences used in this study). To design oligomers with high specificity, we adapted the approach used by  Lundberg et al. , (2013)  for PNA clamps. In short, the region of interest (chloroplast/mitochondria 16S rRNA gene or ITS) from A. thaliana was divided up into "k-mer" sequences of length 30. We then used BLAST to search the k-mers against a blast-formatted target database. The BLAST search used the following parameters which allow weak matches: percent identity 25, word size 7, e-value 100,000.
Candidate 30-mer blocking oligos were selected that received a relatively low number of hits. For this study, we have developed an R package, "AmpStop", which automates this part of the process and suggests good candidates, and which is freely available on GitHub (https://github. com/magle r1/AmpStop). We then selected candidates which had a high T m (well above the universal primer binding temperatures) and which had low potential to form self-dimers or hairpins. Candidate oligomers were tested in single-step amplification of target and nontarget templates for nontarget specificity. The selected blocking oligomers (Supporting Information Files S1a and S1b) were always used in the first amplification step of library preparation (blocking cycles), resulting in shortened amplicons that could not be elongated with Illumina adapters in the second amplification step (Figure  1 ).
| Design of universal plant blocking oligos for 16S and 18S rRNA gene loci To design blocking oligos that could be used for multiple plant species we used the same approach as described above. In short, we used chloroplast sequences (to design 16S rRNA gene blocking oligos) or plant sequences (to design 18S rRNA gene blocking oligos) from multiple plant species that spanned the phylogeny of plants (Table  S2 ) as input for the AmpStop package. The 21 plant species were chosen because they are highly diverse, and many were available to us for testing the oligos. We checked where the results from the AmpStop package overlapped between species. Resulting blocking oligo candidates were tested in a one-step PCR protocol for their specificity against genomic DNA from various plant species and bacterial mixes (for 16S rRNA gene, see Figure  S1  and Tables  S3  and S4 ) or Trichoderma sp.
Sequences and details of all primers used can be found in Supporting Information File S1a and details on PCR, library preparation and sequencing, as well as the steps to generate operational taxonomic unit (OTU) tables and taxonomy from raw multilocus data can be found in the Supporting Information Methods (Protocol A). We summarized bacterial, fungal and oomycete OTU tables by taxonomic ranks, converted abundances to relative values and plotted the genus-and order-level taxonomic distribution directly from this data with the package ggplots2 in R. So that the plant fraction would stand out, the colour of chloroplast and mitochondria fractions in Figure  2a  were altered to grey tones in Illustrator. To analyse the percent reduction in host plant-associated reads when blocking oligos were employed, we considered the relative abundance of reads associated with the class "Chloroplast" or the order "Rickettsiales" in the 16S rRNA gene OTU tables and reads in the kingdom "Viridiplantae" in the ITS OTU tables in samples with A.
, 2016). Library preparation, sequencing and analysis was performed as in the optimized protocol with blocking oligos. To provide a complete and concise picture of the diversity of microbiota inhabiting A. thaliana, we combined the data from all samples. To visualize data, we assigned taxonomy to OTUs and generated two phylogenetic trees where branches represent unique genera. Trees were generated based on the taxonomic lineages (not phylogenetic relatedness of OTUs or genera) with the ape package in R and output as newick files  (Paradis et al. , 2004). The trees were uploaded to iTOl v3. 1  (Letunic & Bork, 2016)  to colour branches by taxonomy or by targeted regions. The first tree, for eukaryotes, includes data from the 18S rRNA gene and ITS targeted regions. The second tree includes data from the 16S rRNA gene targeted regions.