dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational resources and runtime. All analyses presented in the paper are run in a workstation with 40 Gb RAM memory, 10 cores of 2. 5 GHz Intel Xeon Platinum 8255C CPU, and a Nvidia Tesla T4 GPU with 8 Gb memory. The runtime of different cell typing methods in this workstation are shown in Table  3. modalities       Table  2  The control methods. The supervision-based methods employ reference scRNAseq datasets as trainset to train models. The robustness indicates the variations of performance across different datasets. The usability indicates the degree of easy-to-use. Figure Legends The efficiency indicates the running efficiency. Table  3  The average time cost (mins) per sample of Spatial-ID and control methods. Notably, the supervision-based methods (i. e. , ScNym and SciBet) do not count the time of models training.
and HDST do not have single-cell spatial resolution, where each spot contains 1 to 10 cells. With the continued improvement of spatial resolution, newly emerging SRT technologies such as Seq-Scope 50  and Stereo-seq can produce high-throughput subcellular SRT data with a large number of cells in large field tissues in subcellular spatial resolution. Here, we generate single-cell spatial gene expression profiles of 3 adjacent coronal sections (10μm thick, without intervals) along the anterior-posterior axis (Bregma -3. 56 to -3. 66) of right mouse brain hemisphere (Fig. 5a) using Stereo-seq. After the standard data processing and quality control (See Methods), 140,816 cells are retained for these 3 sections. The single-cell mouse brain atlas of cell types from the Linnarsson Lab 51  is employed as the reference dataset.
Qualified reads (Q score ³ 10) were then aligned to the mouse reference genome Ensembl GRCm38 v86 via STAR. Mapped reads with MAPQ ≥ 10 were annotated, and counted by handleBam (available at https://github. com/BGIResearch/handleBam). A resulting CID-containing expression profile matrix was thus generated for each section. To assign the captured RNAs to individual cells, we transformed the CID-containing gene expression matrix to an image by summing the MID counts at every pixel, and aligned with the corresponding ssDNA image based on patterned track lines in both images. Cell segmentation was performed on the registered ssDNA images by a UNet-like deep convolutional neural network (Extend Fig.
Clinical depression or Major Depressive Disorder (MDD) is a common and serious medical illness. In this paper, a deep recurrent neural network-based framework is presented to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, expanding training labels and transferred features are considered. The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accuracy of 76. 27% and a root mean square error of 0. 4 in assessing depression, while a root mean square error of 0.
The fusion of the two modalities: audio and video leads to better depression prediction results  [6]   [8]. But fusing three modalities: audio, video and text, decreases performances  [7]. Speech can be measured cheaply, remotely, non-invasively and non-intrusively  [8]  leading to a potent impact in recognizing depression. For this reason, the proposed study in this paper focuses on detecting depression using only speech recordings. The use of various depression datasets and depression estimation approaches make it hard to decide which acoustic features show a better performance for the assessment of depression  [9]. The Mel Frequency Cepstral Coefficients (the MFCCs) have proven their high efficiency in detecting depression compared to other audio features in shallow-based approaches  [9]   [10].
Method overview The deep learning based approach proposed in this paper aims to assess depression and predict its severity levels using the Mel Frequency Cepstral Coefficients and the Long Short Term Memory network. The steps followed throughout this study are summarized in Fig. 1. First, the audio signals are preprocessed (Section 3. 1. 1). Next, the low-level audio descriptors are extracted and normalized (Section 3. 1. 2 and Section 3. 1. 3). The low-level features are the MFCC features. In the following step, these MFCC features are fed to the deep neural network for depression prediction (see Section 3. 1. 4). Depression datasets available to assess depression from speech are relatively small. To overcome this challenge, data augmentation is performed and described in Section 3. 1. 5 and knowledge transfer from related task is eventually carried out in Section 3.
Transfer Learning The core challenge to overcome throughout this study is the limited depression data available as an input to the MFCC-based RNN. The second proposed solution is to transfer knowledge from an independent, yet related, previously learned task. For that, a pretraining is first applied to pretrain the MFCC-based RNN on a related task. In this study, emotions recognition from speech is chosen as the related task. Second, fine-tuning on the target task, depression recognition from speech, is performed. The number of neurones of the third dense layers in the MFCC-based RNN model is modified and it is chosen to be as the number of emotions in the related task dataset. Once the MFCC-based RNN is pretrained on the emotions recognition dataset, the optimum weights of the model are used as a starting point during the fine-tuning of the model on depression recognition dataset.
DAIC-WOZ corpus The main dataset used in this paper to assess depression is the DAIC-WOZ depression dataset  [26]  which is used in the AVEC2017 challenge  [21]. The DAIC-WOZ corpus is designed to support the diagnosis of psychological distress conditions: depression, post traumatic stress disorder (PTSD), etc. It provides audio recordings of 189 clinical interviews of 189 participants answering the questions of an animated virtual interviewer named Ellie. Each recording is labeled by the PHQ-8 score and the PHQ-8 binary. The PHQ-8 score defines the severity level of depression of the participant and the PHQ-8 binary defines whether the participant is depressed or not. For technical reasons, only 182 audio recordings are used. The average length of the recordings is 15 minutes with a fixed sampling rate of 16 kHz.
The repartitions of the participants by their gender, depression, and depression severity level are shown in Fig. 3. Almost half of the participants are females (46%) (Fig. 3b ) and the third of the participants are labeled depressed (Fig. 3a ). Among the depressed participants, almost half of them are females (29 out of 54 participants) (Fig. 3c ). According to the repartitions in Fig. 3 , the dataset is gender-balanced. However, it is classimbalanced as the number of non-depressed participants is three times higher than the number of depressed participants. After data preprocessing, 80% of the audio segments are used for training, 10% of them are used for validation and 10% for testing.
RAVDESS dataset The experiment of transfer learning described in Section 3. 1. 6, uses a related task dataset. In this study, RAVDESS dataset  [27]  is used for this purpose. It is a dataset for emotions recognition  [28, 29, 30]. It contains audio recordings of 24 actors expressing eight different emotions: neutral, calm, happy, sad, angry, fearful, disgust, and surprised. For each actor, eight trials per emotion are recorded for two different tasks: speaking and singing. The average length of the audio recordings within the dataset is five seconds.
AVi-D dataset The generalization of the proposed model to other dataset is eventually evaluated in Section 4. 3. 6. For that, the performance of trained MFCC-based RNN is evaluated on the AVi-D corpus which was introduced during the AVEC2014 challenge  [22, 31]. The AVi-D dataset is depression and affects database. 300 audio recordings are collected from 292 participants who go through two different tasks: the Northwind task (reading task) and the Freeform task (answering questions). Approximately, 150 audio recordings are available per task. The AVi-D corpus is based on the commonly used depression assessment test: The Beck Depression Inventory-II for assessing the severity levels of depression. The audio recordings are labeled by the BDI-II scores ranging from 0 to 63. A BDI-II score of 14 is the threshold to use for depression assessment.
Data augmentation evaluation The data augmentation experiment is only evaluated over the depression assessment task. After training the MFCC-based RNN on the augmented dataset, the validation accuracy increased by 6. 39% to reach 74%. While, the validation loss dropped by 0. 08 and reached an RMSE of 0. 42 ( important number of input data and their diversification. The confusion matrix in Fig. 7a  shows that 66% of the samples are correctly identified as non-depression. But, only, 7. 9% were correctly identified as depression. Taking into consideration that the input data is imbalanced and that only the third of the participants are labeled as depressed (Fig. 3a ), the F1 score should be evaluated. The F1 score is based on the harmonic mean that punishes the extreme values caused by imbalanced data.
Gender effect assessment Throughout this experience, the input data is loaded into two separate groups: one for Male participants and one for Female participants. The purpose of this experience is to assess depression among each group. Thus, the MFCC-based RNN is trained for each one separately. The gender effect assessment is evaluated by comparing the Depressed/Non-Depressed accuracies for both genders. Initially, the validation accuracy for the Female group reaches 85% with a validation loss of 0. 32 compared to 83% for Male with a validation loss of 0. 34 (Table. 2). The network is highly-performing in detecting depression for both genders. The validation accuracy increases by an average of 33. 8% compared to the initial results in the validation set with 67. 61% for depression assessment.
Robustness to Noise The robustness to noise of the MFCC-based RNN framework is evaluated by adding noise to the baseline dataset used. Gaussian noise is added to the validation set which is 20% of the input data. First, noise is added to a portion of 10% of the validation data and then to the whole set. The gaussian noise is generated with a mean of 0 and a sigma of 0. 1. As summarized in Table. 7, with 10% of noise added to the validation set, the binary depression classification (depression/nondepression) accuracy drops by 8. 27% compared to the one obtained with transfer learning. The accuracy achieves 68% which is 0. 39% higher than the baseline accuracy achieved with the MFCC-based RNN model before data augmentation and transfer learning. The drop of the overall accuracy is mainly caused by the 19% drop of the depression F1 score that achieves 20%.
The F1 score of non-depression drops by 5% only to achieve 80%. 20% of gaussian noise to the whole validation dataset. When adding noise to the dataset, the MFCC-based RNN performances slightly decrease by 8%. Its ability to well-classify depression decreases more than its ability to well-classify non-depression. When increasing the noise within the validation data, the performance of the model stays stable and does not deteriorate more. The proposed network is robust to noise and performs even better than the baseline model trained without augmenting the data and without performing transfer of knowledge.
Generalization of the MFCC-based RNN model to other dataset The proposed recurrent framework is tested on the AVi-D dataset. A good performance means that the MFCC-based RNN generalizes well to other datasets. The model is trained to classify the PHQ-8 binary while it is tested to classify the BDI-II binary, an other similar clinical test for depression assessment. The scores of the BDI-II test range differently from the scores of the PHQ-8 test. Therefore, the evaluation is performed only for binary classification of depression/non-depression. The model is tested over the AVi-D data of the two tasks Freeform and Northwind seperately. Next, the model is tested over the whole set of data of both tasks combined. Table. 8 describes the final results of the generalization experiment. The best classification accuracy is achieved with the Freeform task which reaches 56%.
For both tasks combined, the confusion matrix in Fig. 7c  shows that 12. 69% of the samples are correctly identified with depression. The depression F1 score drops by 11% with the new dataset to reach 35% for both tasks combined whereas it drops by 21% with non-depression compared to the F1 scores obtained with transfer learning. The classification accuracy of the generalization drops by 20. 43% while comparing it with the one achieved with transfer learning. However, the MFCCbased RNN model performs better in the generalization experiment in correctly identifying samples of depression: 12. 69% of the samples are correctly identified with depression while classifying the BDI-II binary. Whereas, only 10% of the samples are correctly identified with depression with transfer learning. These results are explained by the different recording conditions of the participants speech.
Conclusion and future works In this study, an MFCC-based Recurrent Neural Network is proposed to detect depression and to assess its severity levels. The audio recordings are preprocessed and the MFCC features are then extracted and normalized. The MFCC coefficients are fed to deep recurrent neural network of successive LSTM layers. To overcome the lack of training data and overfitting problems, two approaches are considered: augmenting the training data and transferring knowledge from another related task. The proposed architecture is evaluated on the DAIC-WOZ corpus and promising results are achieved. For the future work, we plan to add a gender recognition step and to balance data classes in the proposed framework. A web-application could be also designed to automatically diagnose clinical depression without any medical assistance.
Empirical algorithms require a training dataset composed of sounding points in order to statistically calibrate the inversion model. These approaches are site-and timedependent. Moreover, their accuracy depends on the spatial heterogeneity of substrate and water column optical properties. To overcome these limitations, empirical approaches are now associated with machine learning and multi-temporal techniques  [20, 21]. However, in most cases, regardless of approach, SDB suffers from a lack of precise quantification of uncertainty, limiting the massive scientific and operational exploitation of this product. Uncertainty can be defined as a statistical parameter "characterizing the range of values within which the true value of a measurement is expected to lie as defined within a particular confidence level"  [22].
Its accurate quantification remains a major issue for most ocean color products  [23]. For SDB applications, uncertainty is depth-dependent and requires expressing it as a function of depth. However, in the scientific literature, metrics used to describe the SDB uncertainty are most often computed for all data without differentiating between the specific ranges of depth. Furthermore, the impact of extrapolating models beyond the domain of the calibration dataset on uncertainty is rarely addressed despite being a major issue due to often incomplete spatial data coverage  [20]. Finally, the uncertainty associated with SDB is essential information for data interpretation, and, therefore, must be quantified when analyzing and interpreting bathymetric changes. Objectives of this study are, therefore, to evaluate the performance and robustness of well-established SDB empirical models and to propose and validate an uncertainty model based on inferential statistics, and using a multi-scene approach for a mixed energy coastal environment.
Ground Reference Bathymetry Data Echo sound bathymetric data of the Arcachon Bay inlet were collected every year from April to October by the Direction Départementale des Territoires et de la Mer (DDTM) and the Syndicat Intercommunal du Bassin d'Arcachon (SIBA). These surveys were mainly carried out for the monitoring of navigation channels and the repositioning of navigation buoys; thus, bathymetric data generally covered only part of the study site. Due to the large lateral extent (almost 10 km from North to South), strong tidal currents and energetic waves, and shallow water areas (spit platform, inter-channels banks), the Arcachon inlet was a very challenging site to survey with conventional tools, which explained the lack of complete synchronous coverage. A comprehensive dataset of 450,837 echo sound data points collected between 2013 and 2020 was used in this study (Table  1 ).
Echo sound data were acquired with an Odom Hydrotrac single-beam echosounder co-located with a Leica DGPS during low or high tide slack. The observed depths were then corrected for roll, pitch, yaw, GPS latency, and theoretical tide computed at the Arcachon-Eyrac reference tide gauge at the time of acquisition. Finally, the bathymetry (noted, Z situ ), which was associated with the corrected depth reduced within the appropriate vertical datum, was obtained after the correction of the local chart datum of the Lowest Astronomical Tide (LAT). Accuracy on Z situ was assumed to be within 0. 2 m. The Landsat-8 satellite mission was launched on 11 February 2013 by the National Aeronautics and Space Administration (NASA). Its payload comprised the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS).
OLI provides enhanced instrument features for aquatic surface observation compared to former Landsat sensors  [33]. OLI operates in the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) for 9 spectral bands with a 12-bit radiometric resolution. Its spatial resolution was 30 m for multispectral images and 15 m for the panchromatic band. Landsat-8 Collection 1 Level 1 products were downloaded freely on https://earthexplorer. usgs. gov (accessed on 31 March 2022). Sentinel-2A and 2B were twin polar-orbiting satellite missions launched in June 2015 and March 2017, respectively, by the European Space Agency (ESA). The multispectral instrument (MSI) mounted on the Sentinel spacecraft was very similar in design and requirements to OLI  [34, 35]. MSI acquired data in 13 spectral bands from VIS to SWIR with a spatial resolution ranging from 10 to 60 m and a radiometric resolution of 12 bit.
In step 1, Z max and Z min values associated with the different parameterizations of X are computed (Figure  3c ). In step 2, the calibration dataset is filtered to remove all values of Z situ higher than the lowest Z max values (noted Z max-ref ). In step 3, the coefficient of determination (r 2 ) of the linear regression models calibrated for the different parameterizations of X are computed. In step 4, if the r 2 value of the parameterization of X associated with Z max-ref (noted X ref ) is higher than all other X parameterization r 2 values, the associated LRM is selected for the range of Z situ values comprised between Z min-ref and Z max-ref. The calibration dataset is then filtered to remove all values of Z situ lower than Z max-ref. X ref is no longer considered in the procedure and the algorithm returns to step 2.
One example of the distribution is presented in Figure  3f. It is important to note that pixels assigned to classes associated with optically deep waters or to classes that do not have enough Z situ points for calibration (see the LRM procedure) will be automatically masked (Figure  3e, g ). Four statistical parameters were used to assess and compare the performance of the LRM, SM, and CBR methodologies applied to the 89 satellite images. The coefficient of determination (r 2 ), bias (Bias), mean relative absolute difference (MRAD), and root mean square error (RMSE) were computed from the matchup dataset (DS) composed of Z situ and its estimator Z sat. The number of observations (N DS ) varies with each pair of satellite images and field bathymetry dataset. Bias, MRAD, and RMSE are computed as follows: Bias = 1 N DS ∑ N DS i=1 (Z sat;i -Z situ;i ), (4) MRAD = 1 N DS N DS ∑ i=1 |Z sat;i -Z situ;i | Z situ;i × 100%, (5) MSE = 1 N DS ∑ N DS i=1 (Z sat;i -Z situ;i ) 2.
Assessment of SDB Uncertainty Using a Multi-Scene Approach The calibration dataset often only partially covers the study area (Figure  3g ). For pixels associated with areas that were not sampled and were, therefore, not included within the calibration dataset (here, referred as unsampled pixels), quantification of SDB uncertainty requires taking into account 2 terms (Figure  4 ). The first term was associated with the uncertainty of the SDB regression model. The second term was associated with the uncertainty generated by the extrapolation of the SDB model beyond the calibration domain. To estimate these 2 terms, an uncertainty model based on a multi-scene approach was proposed. We expressed the uncertainty as a positive value and assumed that error follows a normal distribution. Combination of Landsat-8 and Sentinel-2 enables to have several image acquisitions in a 1 month-interval around the bathymetric survey.
Assume we have N usable images for a given bathymetry survey. For each image, an SDB model was calibrated independently of other SDB models generating N bathymetry maps which can be associated with N random variables Z sat1 , Z sat2 ,. Z satN. Each variable was then separated into 2 parts comprising observations associated with the DS-matchup dataset, Z DS satj , and observations associated with the NS-unsampled pixels dataset, Z NS satj. uncertainty requires taking into account 2 terms (Figure  4 ). The first term was associated with the uncertainty of the SDB regression model. The second term was associated with the uncertainty generated by the extrapolation of the SDB model beyond the calibration domain. To estimate these 2 terms, an uncertainty model based on a multi-scene approach was proposed.
( 12 ) Validation of the uncertainty model was carried out from a dataset composed of 6 satellite images acquired in an interval of 1 month (S2A, 6 October 2017;_S2A, 11 October 2017;_S2A and L8, 16 October 2017;_L8, 25 October 2017; S2A, 31 October 2017) around a bathymetric survey characterized by one of the greatest spatial coverages. DS was randomly separated into 2 parts comprising a calibration dataset (DS-C) and a validation dataset (DS-V), which represented 67% and 33% of the data, respectively. DS-C allows to compute U bin , which was used to estimate TVU NS i for each observation of DS-V. DS-V allows to compute the error, ε DS-V i , associated with the SDB multi-scene model for each observation as: ε DS-V i = Z DS-V sat,i -Z DS-V situ. ( 13 ) The uncertainty model was finally evaluated by computing the percentage of absolute values of ε DS-V i lower than TVU NS i.
Hydrological Conditions and Spatio-Temporal Variability of R rs The dataset encompasses a wide range of hydrodynamic conditions. The number of images per tidal stage (TS) varies depending on flood (N = 29), ebb (N = 26), low tide (N = 19), and high tide (N = 15) conditions (Table  2 ). Tidal range (TR) and tidal level (TL) data were normally distributed with a mean value of 3. 10 ± 0. 84 m and 2. 16 ± 0. 92 m, respectively. Hs ranged from 0. 25 to 2. 20 m, with a mean value of 1. 14 ± 0. 50 m. Results showed that variables TL and TS (ANOVA p-value of 1. 10-11), and Season and Hs (ANOVA p-value of 0. 046) were not independent. However, there was a statistical relationship between TR and TS (ANOVA p-value < 0. 001), which represents a sampling bias with respect to hydrodynamic conditions. Low tide and flood were mainly associated with high TR values, while high tide and ebb show on average low TR conditions.