dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All SARS-CoV-2 whole-genome sequences produced by NGS-SA are deposited in the GISAID sequence database and are publicly available subject to the terms and conditions of the GISAID database. The GISAID accession numbers of sequences used in the phylogenetic analysis, including Omicron and global references, are provided in the Supplementary Table  1. Raw reads for our sequences have also been deposited at the NCBI Sequence Read Archive (SRA) (BioProject: PRJNA784038). Other raw data for this study are provided as a supplementary dataset at our GitHub repository (https://github. com/krisp-kwazulu-natal/ SARSCoV2_Omicron_Southern_Africa). The reference SARS-CoV-2 genome (MN908947. 3) was downloaded from the NCBI database (https://www. ncbi. nlm. nih. gov/). Other publicly available data used in this study are as follows: NCBI SARS-CoV-2 Data Hub (https://www.
ncbi. nlm. nih. gov/sars-cov-2/), Protein Data Bank coordinate set 7A94 (https://www. rcsb. org/), Nexstrain global build (https://nextstrain. org/ncov/gisaid/global), Covid-19 Re repository (https://github. com/ covid-19-Re), daily Covid-19 case numbers from the Data Science for Social Impact Research Group at the University of Pretoria (https:// github. com/dsfsi/covid19za), daily case numbers from OWID (https:// github. com/owid/covid-19-data) and the Virus Pathogen Database and Analysis Resource (ViPR) (https://www. viprbrc. org/).
Code availability All input files (such as raw data for figures, alignments or XML files), along with all resulting output files and scripts used in the present study are publicly shared at GitHub (https://github. com/krisp-kwazulu-natal/ SARSCoV2_Omicron_Southern_Africa). 50. Marivate, V. et al. Coronavirus disease (COVID-19) case data-South Africa. Zenodo https://doi. org/10. 5281/zenodo. 3819126 (2020).
Epidemiological dynamics We analysed daily cases of SARS-CoV-2 in South Africa up to 14 December 2021 from publicly released data provided by the National Department of Health and the National Institute for Communicable Diseases. This was accessible through the repository of the Data Science for Social Impact Research Group at the University of Pretoria (https:// github. com/dsfsi/covid19za)  49, 50. The National Department of Health releases daily updates on the number of confirmed new cases, deaths and recoveries, with a breakdown by province. Daily case numbers for Botswana were obtained through Our World in Data (OWID) COVID-19 data repository (https://github. com/owid/covid-19-data). We obtained test positivity data from weekly reports from the National Institute for Communicable Diseases (NICD)  51.
1 to highlight the gradual discovery of the motion components. Structure classification (Fig. 3 ). The stimulus data and human responses were released by  [17]  on GitHub. The experiment is described in detail in  [17]. There were 12 participants with each participant performing 200 trials. Each trial consisted of three dots moving on a circle for 4 s. Dots had different colors to prevent their confusion, but colors did not convey any information on the dots' roles within the structure. No data was excluded. Trials were generated stochastically from the same generative model that is considered in this work, with uniform probability for each of the four structures (Independent, Global, Clustered, Hierarchical) to underlie the trial. Motion strengths were chosen such that all dots had identical marginal velocity distributions, p(v k ), across all structures-leaving motion relations as the only distinguishing information (see  [17] , for detailed stimulus parameters and λ-values of all structures).
Funding Information This research was supported by grants from the  NIH  ( NINDS U19NS118246 , J. D. ), the  James S. McDonnell Foundation   (Scholar Award for Understanding Human Cognition , Grant  220020462 , J. D. ), the  Harvard Brain Science Initiative (Collaborative Seed  Grant, J. D. & S. J. G. ), and the  Center for Brains, Minds, and Machines  (CBMM; funded by  NSF   STC award   CCF-1231216 , S. J. G. ).
0. 3 m/px) were neither too dark nor too bright, with almost no clouds. A home-made Python snippet collected tiles 256 × 256 px in size over the targeted geographical extent. They were then merged to produce larger images of 640 × 645 px (corresponding to 187. 5 m × 188. 5 m), a size suitable for further application of deep learning ( Fig. 3 a ). Reconstructed GeoTIFF images were made to overlap each other by 80 pixels (ca. 25 m) in all directions, to ensure that all houses are complete at least once  [34]  ; see houses 1 and 2 in Fig. 3  b, truncated with a blue square, but complete with yellow and red squares. Due to overlap, some complete houses are seen on two or more images (e. g. houses 3 and 4 in Fig. 3 b ). They were automatically removed using a simple rule: if the centres of their bounding boxes were less than 6 m apart (a value lower than the minimum size of a house), only the most probable item was kept.
The relatively  steady downward-facing views from the satellite facilitate this task, although some satellite pictures were also captured in oblique view, and then post-processed by orthorectification before diffusion. This process generated some noticeable deformation, transforming squares into diamonds, but did not affect identification or bounding box positioning (not shown here). When the triangular structure on the roof was not clearly identified, the houses were not labelled (see 1 and 2 in Fig. 4  ). The annotating step used La-belImg, a free software ( https://github. com/tzutalin/labelImg ). Note that bounding boxes of houses partly masked by vegetation were kept approximately square by including the supposed position of the building below the canopy. A total of 494 images for the region of Waikabubak produced 1 396 bounding boxes of traditional houses.
Results are aggregated, and redundant information is eliminated by applying a non-maximum-suppression algorithm, as with Faster R-CNN. The two-stage detectors may, however, provide better results at the expense of speed. The algorithm schematics of SSD and Faster R-CNN are available in Supplementary SM1, modified after  [23]  and  [44]. Although many of the available models have already been evaluated through various challenges, using for instance the Common Object in Context dataset (i. e. COCO, http://cocodataset. org/#home ;  [45]  ), eight different object detection models were tested here: two based on the SSD cat-.
Metrics (expressed in%) AP @. 50:. 95 AP @. 50 AP @. 75 AP @. 50:. 95 AP @. egory, and six on Faster R-CNN  [46]. They are listed in Table  1  , with different convolutional neural network backbones, codenamed inception  [47]  , ResNet  [48]  and inception-ResNet, with or without feature pyramid networks (namely FPN;  [49]  ) as feature extractors. The idea was to reuse these models, already trained for a different task, as the starting point for a custom dataset and specific problem  [50]. This approach, known as transfer learning, is expected to speed up the training step and to improve overall performance when sensitive parameters are fine-tuned  [38].
Data augmentation Deep learning skills are conditioned by the availability of the data feeding the model. While popular datasets used for challenges may contain tens of thousands of images, instances may be much rarer in cultural or archaeological studies. A simple technique, known as data augmentation, has been developed to enlarge the dataset artificially in such cases  [53]. This technique supplements the original set of images by new synthetically produced data, obtained by combining geometrical transformations and colour alterations sequentially  [54]. Here, six augmentations were tested: 90 °rotation, horizontal and vertical flipping, grey level conversion, random colour distortion, and random jitter of box corners by 1 to 4 pixels (see Supplementary Material SM2). A total of 64 experiments (i.
Operational settings For all the experiments, the maximum training epoch was set at 50 0 0 0, using an initial learning rate of 3. 10 -4 for the first 10 0 0 0 steps, 3. 10 -5 up to 20 0 0 0 steps, and then 5 10 -6. For Faster R-CNN models, a batch size of 1 (corresponding to a Stochastic Gradient Descent optimization algorithm,  [ 58 ] ) was set, together with a momentum of 0. 9, while a batch size of 4 was used for SSD. At a certain point, training loss may continue to decrease, but testing loss may start to increase. This is a clear sign of overfitting, where the model learns well from the training dataset, but fails to generalize this knowledge to make correct inferences on new instances. That is why training was always stopped before 50 0 0 0 iterations (often after 15 0 0 0 -45 0 0 0).
Implementation Object detection models were produced and evaluated using Python 3. 7 ( https://www. python. org/ ), and the free TensorFlow object detection API (v. 1. 13 including GPU capabilities). Pre-trained models are available at the Tensorflow detection model zoo homepage. The homemade Python snippet for assessing house size relies on the numpy, gdal, scikit-learn, scikit-image, csv, and pandas libraries, and a modified version of the min_bounding_rect. py snippet ( https://gist. github. com/kchr/77a0ee945e581df7ed25 ). Results are expressed as georeferenced polygon vector layers. The homemade snippet for DBSCAN relies on the scikit-learn library  [ 63 ]. The identification of geographical clusters by scan statistics used the 64-bit SaTScan v9. 6 software ( http://www. satscan. org/ ,  [60]  , see user manual for p -value calculation with a Monte-Carlo approach).
50:. 95 increases by 5-6%, AP@. 75 by 4-10%, and AP@. 50 by 1-2%. The Faster R-CNN model with a ResNet 101 backbone is retained as it greatly surpasses the other models whatever the number of instances used for training. Despite an increase in running time, an output stride for the extractor of 8 pixels is used, because houses must only be extracted once from satellite images. As no synthetic data augmentation is applied, the performances reported in Table  1  can be considered as the baseline.
Data augmentation strategy The question then arises whether the use of an appropriate data augmentation strategy can push significantly further the capabilities for a training dataset of fixed size. The results of the 16 experiments for the fractional design built to assess the effect of each individual factor (i. e. , horizontal and vertical flip, rotation, colour  adjustment, grey level conversion, and jitter of bounding boxes) are presented in Table  2. Note that training involved the lesser of the two configurations used in the previous experiment: the same sets of 480 and 124 instances of houses for training and evaluation, respectively. It appears that AP@. 50:. 95 increased notably, from no data augmentation (68. 4% for exp #1 in Table  2  ) to data augmentation applying all factors (71.
The accumulation of slightly influential factors by applying optimal data augmentation strategy as reported in Table  2  undoubtedly produces a sizable increase in AP values. Such improvements are observed systematically whatever the number of instances used for training (from 144 to 1033; Fig. 7  ). However, the larger the dataset, the lower the gain, because one cannot afford an explosive increase in terms of AP when numerous instances already illustrate most of the house variability encountered in the field. Note that even when 1033 instances are used for training, the AP value does not reach an asymptotic plateau ( Fig. 7  ). Slight but significant progress might be achieved using more data for learning.
Inference on all Sumba Island tiles The final model trained using 1033 instances of houses and data augmentation was used to make inferences on the entire collection of ca. 700 000 tiles covering Sumba Island. After a few days of computation, a total of 22 397 traditional houses was identified, with a confidence score above 0. 5 (Supplementary Material SM5). After duplicate removal, 19 143 items remained. Close examination in GIS, using BING satellite imagery as basemap, revealed the presence of several false positives, i. e. objects wrongly identified as traditional houses. These defects were essentially associated with low confidence scores close to (or barely above) 0. 5. A cut-off of 0. 8 was therefore applied, keeping false negatives (traditional houses missed) at a very low level. A few obvious false positives remained in open spaces, such as rice fields, with isolated trees producing shadows resembling those of house roof towers, or in river talwegs, where shadows from rocks have a similar appearance.
House size Table  4  presents the results of the five machine learning algorithms tested, and their performance in assessing house size from oriented gradient values. This was computed using either the complete raw dataset as input, or a reduced set obtained by Princi- pal Component Analysis (PCA) to capture maximum total variance with lower dimensionality. Using the entire set of 768 values as input data (i. e. without PCA transformation), performance scores are satisfactory, with high R 2 values, varying from 0. 67 to 0. 86, whatever the method. The best results are obtained with k -nearest neighbours. The score of all models tends to improve with data reduction, except for the k -nearest neighbour algorithm that remains steady. The biggest boost is observed for the artificial neural network, with a notable R 2 increase of almost 0.
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms July/August 2021, 8(4) ENEURO. 0475-20. 2021 1-33 Research Article: Methods/New Tools for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e. g. , lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.
e. , inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
Introduction One of the greatest challenges today is to develop approaches allowing the useful exploitation of large-scale datasets in biomedical research in general  (Margolis et al. , 2014)  and neuroscience and neuroimaging in particular  (Van Horn and Toga, 2014). Progress in this direction is made possible by the increasing availability of large public datasets in the domain of connectomics  (Van Essen et al. , 2013; Poldrack and Gorgolewski, 2014; Horien et al. , 2021). This is true, in particular, for research in Alzheimer's disease (AD), in which, despite decades of massive investment and a daunting literature on the topic, the partial and, sometimes contradictory nature of the reported results  (Patterson, 2018)  still prevents a complete understanding of the factors governing the progression of the disease  (Braak and Braak, 1991; Braak et al.
, 2006; Komarova and Thalhauser, 2011; Henstridge et al. , 2019)  or of the diversity of cognitive deficits observed in different subjects  (Iacono et al. , 2009; Mungas et al. , 2010; Allen et al. , 2016). In AD research, datasets that compile rich and diverse genetic, biomolecular, cognitive, and neuroimaging (structural and functional) features for a large number of patients are playing an increasingly important role  (Rathore et al. , 2017; Iddi et al. , 2019). Example applications include: the early diagnosis and prognosis by using magnetic resonance imaging (MRI) images  (Dennis and Thompson, 2014; Chiesa et al. , 2017; de Vos et al. , 2018) , the use of machine learning for automated patient classification  (Cuingnet et al. , 2011; Zhang et al. , 2012; Moore et al. , 2019) , or prediction of the conversion from early stages to fully developed AD  (Rombouts et al.
, 2005; Moradi et al. , 2015; Casanova et al. , 2018) , with signs of pathology difficult to distinguish from "healthy aging" effects  (Doan et al. , 2017) , the extraction of decision networks based on the combination of semantic knowledge bases and data mining of the literature  (Sanchez et al. , 2011; Kodamullil et al. , 2015; Iyappan et al. , 2016). Among the factors contributing to the performance of prediction and inference approaches in AD, and, more in general, other neurologic or psychiatric diseases  (Walter et al. , 2019)  or studies of aging  (Cole and Franke, 2017) , are not only the large size of datasets but also the multiplicity of features jointly available for each patient. Indeed, one can take advantage not only of the complementary information that different features could bring but also capitalize on possible synergies arising from their simultaneous knowledge  (Wang et al.
, 2015; Zimmermann et al. , 2016; Iddi et al. , 2019). Unfortunately, even gold standard publicly available datasets in AD, such as the datasets released by the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium  (Wyman et al. , 2013; Beckett et al. , 2015; Weiner et al. , 2017) , have severe limitations. Indeed, if they include neuroimaging features of different types, structural DTI and functional MRI (fMRI), these features are simultaneously available for only a substantial minority of the subjects in the dataset (i. e. , the feature coverage is not uniform over the dataset). In addition, if the number of subjects included is relatively large (hundreds of subjects), it still is too small to properly qualify as "big data. " Furthermore, the connectomic data themselves have an imperfect reliability, with a test/ retest variability that can be quite large, making potentially difficult subject identifiability and, thus, personalized information extraction  (Termenon et al.
, 2016). Here, we will introduce a new solution aiming at relieving the problems of partially missing features and limited sample size and illustrate their validity on the two independent example datasets. Specifically, we will focus on two examples of structural and functional neuroimaging datasets, as important proofs of concept: a first one addressing AD, mediated from the previously mentioned ADNI databases  (Wyman et al. , 2013; Beckett et al. , 2015) , and a second one investigating a cohort of healthy subjects over a broad span of adult age, to analyze the effects of normal aging  (Zimmermann et al. , 2016; Battaglia et al. , 2020). It is important to stress however that the considered issues may broadly affect any other connectomic dataset gathered for data mining intents. To cope with missing connectomic features (and "filling the gaps" in neuroimaging datasets), we propose to build on the quickly maturating technology of meanfield whole-brain network modeling (for review, see  Deco et al.
Such whole-brain simulations are greatly facilitated by the availability of dedicated neuroinformatic platforms, such as The Virtual Brain (TVB;  Sanz-Leon et al. , 2013 , 2015; Woodman et al. , 2014) , and data preprocessing pipelines  (Schirner et al. , 2015; Proix et al. , 2016) , enabling brain model personalization and clinical translation  (Jirsa et al. , 2017; Proix et al. , 2017). It thus becomes possible to complete the missing information in a dataset about BOLD fMRI FC by running a TVB simulation in the right regime, embedding the available empirical DTI SC (SC-to-FC completion). Analogously, algorithmic procedures based on mean-field modeling (MFM) steps ("effective connectivity" approaches by  Gilson et al. , 2016 Gilson et al. , , 2018)) , here used for a different purpose) can be used to address the inverse problem of inferring a reasonable ersatz of SC from resting state FC  (FC-to-SC completion).
In this study we will demonstrate the feasibility of both types of completion (SC-to-FC and FC-to-SC), applying alternative linear and nonlinear simulation pipelines to both the ADNI and the healthy aging proofof-concept datasets. Beyond a single step of virtual completion, by combining completion procedures, to map, e. g. , from an empirical SC (or FC) to a virtual FC (or SC) and then, however, to a "twice virtual" SC (or FC), we can generate for each given empirical connectome a surrogate replacement, i. e. , map every empirical SC or FC to a matching dual (bivirtual) connectome of the same nature. We show then that pairs of empirical and bivirtual dual connectivity matrices display highly correlated network topology features, such as node-level strengths or clustering and centrality coefficients  (Bullmore and Sporns, 2009).
We demonstrate along the example of relevant classification tasks [stratification of mild cognitive impairment (MCI) or AD patients from control subjects on the ADNI dataset and ageclass prediction on the healthy aging dataset] that close performance can be reached using machine learning algorithms trained on actual empirical connectomes or on their duals. Furthermore, empirical connectomes can be correctly categorized by classifiers trained uniquely on virtual duals. To conclude, we provide systematic recipes for generating realistic surrogate connectomic data via data-constrained MFMs. We show that the information that we can extract from computationally inferred connectivity matrices are only moderately degraded with respect to the one carried by the original empirical data. This opens the way to the design and sharing of veritable "virtual cohorts" data, ready for machine-learning applications in clinics, that could complement actual empirical datasets, facilitating learning through "data augmentation"  (Yaeger et al.
Two datasets for proof of concept We applied our data completion pipelines in this study to two different and independent neuroimaging datasets, from which SC and FC connectivity matrices could be extracted for at least a part of the subjects. A first dataset was obtained from the ADNI database (adni. loni. usc. edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. We refer in the following to this first dataset as to the ADNI dataset. A second dataset was generated by Petra Ritter and co-workers at the Charité Hospital in Berlin, with the aim of studying and investigating changes of structural and static and dynamic FC occurring through healthy aging.