dataesr/scientific-paragraphs-categorization huggingface dataset:
Controls had no history of mental disorders and were statistically matched for age to the ASD group at each site. In the ABIDE-I and -II datasets, there were no differences in age and sex between controls and ASD. ABIDE-I and -II datasets are based on studies approved by local IRBs, and data were fully anonymized (removing all HIPAA protected health information identifiers, and face information from structural images). MRI acquisition. High-resolution T1-weighted images (T1w) and resting-state functional MRIs (rs-fMRI) were available from all sites and in both discovery and replication datasets. Images were acquired on 3 T scanners from Siemens (NYU, USM, PITT) or Philips (IP, TCD). NYU data were acquired on an Allegra using 3D-TurboFLASH for T1w (TR = 2530 ms; TE = 3. MRI processing. The following processing was applied to discovery and replication samples.
github. io/) and included slice-time correction, head motion correction, skull stripping, and intensity normalization. Statistical corrections removed effects of head motion, white matter and cerebrospinal fluid signals using the CompCor tool, based on the top 5 principal components  73  , as well as linear/quadratic trends. After band-pass filtering (0. 01-0. 1 Hz), we co-registered rs-fMRI and T1w data in MNI152 space through combined linear and non-linear transformations. Surface alignment was verified for each case and we interpolated voxel-wise rs-fMRI time-series along the mid-thickness surface. We resampled rs-fMRI surface data to Conte69, a template mesh from the Human Connectome Project pipeline (https://github. com/ Washington-University/Pipelines) and applied surface-based smoothing (FWHM = 5 mm).
MRI quality control was complemented by assessment of signal-to-noise ratio and visual scoring of surface extractions for structural MRI, and evaluation of temporal derivatives and framewise displacement for rs-fMRI  33  (Supplementary Figure  1 ). Cortical surface extraction was visually inspected. Subjects with severe faulty segmentations, other artifacts or more than 0. 3 mm framewise displacement were excluded (discovery: n = 76/287, replication: n = 74/177). Segmentation inaccuracies of all remaining cases were manually corrected. Connectome gradient analysis. Surfaces and their features were downsampled to 10k vertices per hemisphere for computational efficiency. We generated cortex-wide connectome gradients for each participant based on open software (https://github. com/NeuroanatomyAndConnectivity/gradient_analysis).
As a previous study in healthy individuals  17  , we focused on the first two components which explained 37% variance (Supplementary Figure  2  also shows components 3-20). Components explained similar variance in ASD and controls (i. e. , no statistical difference; two-tailed Student's t-test; t < 1. 30, p > 0. 1). These components, initially defined in connectivity space, were then mapped back onto the cortical surface to visualize macroscale transitions in overall connectivity patterns at the system-level. Gradient component scores were statistically compared between ASD and controls using surface-based linear models implemented in SurfStat (http://www. math. mcgill. ca/keith/surfstat/) for Matlab. Models included effects of age, site, and group. Surface-based findings were corrected for family-wise errors due to multiple comparisons using a random field theory of p FWE < 0.
05 (p FDR < 0. 05). To integrate SFC findings with rich-club hierarchy, we tracked the proportion of rich-club, feeder, and local nodes at every step, and computed a ratio of rich-clubvs-local nodes. This ratio was compared between groups using two-sample t-tests (p FDR < 0. 05). Relation to connectivity distance. We calculated geodesic distances along the cortical mantle using the Fast Marching Toolbox between all pairs of surface points (https://github. com/gpeyre/matlab-toolboxes/tree/master/). We binarized the above thresholded functional matrix, taking only positive connectivity and multiplied it with the resulting distance matrix. We finally calculated row-wise sums of this matrix and divided it by the overall node degree, which conceptually provides the average distance of significant functional connections for a given region.
To assess associations between gradient changes in autism and connectivity distance, we correlated the surface-based distance map with the t-statistical map of the between-group difference in the principle functional gradient. Furthermore, we tracked average connectional distance across all SFC steps. Symptom severity prediction. We used supervised pattern learning to associate our imaging markers to ASD symptom severity. Symptom severity was indexed by ADOS total and sub-scores (i. e. , social interaction, communication, repeated behaviors/interests). We tested the ABIDE-I dataset in this analysis since all participants in this sample had ADOS scores collected. Training and performance evaluation employed nested 5-fold cross-validation with 100 repetitions. The classifier operated on the first score of the principal gradient and four SFC maps that were averaged across each level of functional hierarchy (see stepwise connectivity estimation, above, for details).
As for the rich-club analysis, we averaged features using a 1004-ROI parcellation, yielding 5020 (1004 × 5) features for each individual. Cross-validation incorporated training and testing. Training: 4 folds were split into 3 internal training and 1 validation subfold. A feature selection procedure was carried out based using an elastic net (alpha = 0. 5, to weight LASSO [least absolute shrinkage and selection operator] and ridge regression equally), which generated at every iteration a subset of imaging features that correlate to ADOS scores in the training dataset. Selected features trained the main classifier, for which support vector regression was chosen  32. 4-fold internal cross-validations effectively trained the main classifier to optimally predict ADOS scores during validation. Testing: The remaining unseen 'test' fold was used to calculate prediction accuracy.
Again, we corrected the features for age and site effects prior to the prediction experiments. Robustness analyses. To demonstrate robustness of our main gradient and SFC findings with respect to several methodological choices, we performed several replication analyses. • Given ongoing discussions on the effects of Global Signal Regression (GSR) on rs-fMRI data  30  , we did not perform GSR for our main analyses. We nevertheless repeated gradient and SFC analyses using GSR-processed data from Preprocessed Connectomes repository (http://preprocessedconnectomes-project. org/abide/). • A recent study suggested that unspecific differences in overall connectivity strength may affect case-control network analyses  31. We thus repeated our main analyses after additionally correcting for average whole-brain connectivity.
• We repeated surface-wide comparison of gradient scores using nonparametric permutation tests  28. Briefly, we randomly shuffled group membership (i. e. , ASD, controls) for each case and performed a cortex-wide comparison (cluster defining threshold α = 0. 01). Repeating this procedure 10000 times, we aggregated the maximum cluster size as an empirical null distribution. We estimated the significance of our findings by placing the cluster sizes of the actual between-group comparison into the permutation distribution. As in the main analysis, we statistically corrected for age and site prior to any permutation. Reproducibility analysis. Studying the replication dataset, we compared the principal gradient scores and SFC between ASD and controls. As our ABIDE-II sub-selection also included women, sex was additionally entered into all statistical models.
Acknowledgements We would like to thank each site (http://fcon_1000. projects. nitrc. org/indi/abide/)and  NITRC  (http://www. nitrc. org)for providing the data sharing platform for the ABIDE initiative. Funding sources for the ABIDE-1 subsample: The NYU site was supported by  NIH  ( K23MH087770 ;  R21MH084126 ;  R01MH081218 ;  R01HD065282 ),  Autism Speaks , The  Stavros Niarchos Foundation , The  Leon Levy Foundation , an endowment provided by  Phyllis Green  and  Randolph Cowen. The PITT site was supported by  Autism Speaks  Grant  04593 ,  KO1 NIMH MH081191 ,  NIMH   MH67924 ,  NIH HD55748. National Institutes of Health  (grant numbers:  K08 MH092697 ,  RO1MH080826 ,  P50MH60450 ,  T32DC008553 ,  R01NS34783 ). The USM site was supported by  Autism Speaks   Mentor-based Predoctoral Fellowship  (grant number:  1677 ),  University of Utah Multidisciplinary Research Seed Grant ,  NRSA Predoctoral Fellowship  (grant number:  F31 DC010143 ),  Ben B.
Data availability All data analyzed in this manuscript were obtained from the open-access ABIDE-I and ABIDE-II initiatives, in either raw (http://fcon_1000. projects. nitrc. org/indi/abide/) or processed form (http://preprocessed-connectomes-project. org/abide/). Gradient mapping analyses was based on open-access tools (https://github. com/ NeuroanatomyAndConnectivity/gradient_analysis). Stepwise functional connectivity and rich-club analyses made use of the brain connectivity toolbox (https://sites. google. com/ site/bctnet/home). Surface-wide statistical comparisons were carried out using SurfStat (http://www. math. mcgill. ca/keith/surfstat/).
The program will build upon past development of ocean observing. Ocean Observations are critical to public services, facilitate commercial activity, support scientific research, foster well-being, and protect lives and property. While much has been accomplished, there remain gaps in fully meeting user needs at the basin-scale. By 2030, we foresee that AtlantOS will have the following attributes: • AtlantOS is the All-Atlantic part of the Global Ocean Observing System, • All designated AtlantOS data providers are coordinated, integrated and cooperating to efficiently make accessible all relevant Essential Ocean Variables (EOVs) as part of the Global Ocean Observing System and the Group on Earth Observations Blue Planet programme, • User needs are met by keeping connected with users and understanding their evolving requirements, • A fully functioning governance framework is in place and provides a forum for coordination, resource mobilization, review and decision making, and • Long-term sustainability has been achieved through national and stakeholder commitments with a goal of reaching 75% of sustained resources, in line with present meteorological observational support.
We should share information widely, opening data to many uses and the greatest value. We will save time, money, and energy by working together. The ocean observing community has produced a Framework for Ocean Observing (Task Team for an Integrated Framework for Sustained Ocean Observing, 2012 -FOO), a guide to help us decide what to measure in our ocean and why. The FOO has been a useful guide in the writing this visionary strategy and will be helpful as we shift focus to the Implementation Plan.
, 2016)  and as considered in the FOO. Adapting this approach, we suggest four key criteria to consider in determining how to organize existing activities into the basinscale AtlantOS system: 1. Location and scale -it must take place in the Atlantic. This includes both coastal and open ocean areas but likely not the regional seas such as the Baltic and Mediterranean and the northern and southern boundaries are purposely vague. 2. There should be an intention for operation over multiple years. There is no expectation that funding is guaranteed but there should be a plan for ongoing operations. 3. There must be some formal governance structure including a management team, defined standards and plans and a multi-year strategy for operations. 4. There should be a policy for free and open data that is available in a timely manner.
Using and expanding the system of portals that are either EOV-based (primarily for delayed mode data) or platform based (for near real-time data but also scientifically assessed data) supports a rigorous and effective quality assessment service for data providers. Ocean observations must be available to users on a free and unrestricted basis. There is global recognition that open access encourages wide use of data, and the development of new data products. A key component of data management is close monitoring and quality control of data streams. Communication between the observing system operators and data managers is important. Data centers should give priority to using modern information and communication technology. Data and associated metadata have to be discoverable through portals, catalogs and search engines (Figure  7 ).
Principles and Approaches to Data The FAIR principles must guide the Atlantic Ocean data management system and should be central to the AtlantOS approach (e. g. ,  Tanhua et al. , 2019). There are four FAIR principles. First, data should be Findable -this goal requires globally unique and eternally persistent identifiers for each dataset, rich standardized metadata attached to each dataset, and that data be searchable through standardized discovery services. Second, data should be Accessible -this goal will be achieved by establishing free and unrestricted access to data, ensuring full and open exchange of data, metadata and products, all with minimum delays. Third, data should be Interoperable and understandable -observations should always be provided with at least a minimum set of metadata, to enable traceability.
ACKNOWLEDGMENTS This manuscript is the output of the "BluePrint for an Integrated Atlantic Ocean Observing System" team that has received funding and support from the  European Union 's  Horizon 2020 Research and Innovation Programme Project AtlantOS  (Grant Agreement No. 633211 ). We thank  P. Lehodey  who provided the operational tuna example shown in Figure 3. The team has also received support from the  EuroGOOS , the pan-European network that is part of the  Global Ocean Observing System (GOOS)  of the  Intergovernmental Oceanographic Commission (IOC)  of the  UNESCO  and from the  Marine Environmental Observation Prediction and Response (MEOPAR)  program funded through the  Networks of Centers of Excellence Program in Canada  and from the  Integrated Oceanography and Multiple Uses of the Continental Shelf  and the  Adjacent Ocean Integrated Center of Oceanography (INCT-Mar COI ,  CNPq ,  Proc.
This paper will be based on the same representation to include a tri-dimensional modeling of the environment. The main contribution of this approach is that the classification of dynamic objects in a 2. 5D grid is done according to an evidential fusion of multi-grids. The second aim was to extract an object-level representation according to the detected dynamic cells for tracking purposes. The objective is to achieve the dynamic detections on an object-level rather than on a cell-level which is commonly done in literature. The objects are built based on the clustering of mobile cells using the DBSCAN algorithm. The third contribution of this work is a quantitative evaluation according to a measure of average precision of the detection results based on a KITTI dataset for comparison purposes.
The paper is structured as follows: Section II covers a survey on multiple object detection based on occupancy grids as well as the definition of a 2. 5D representation. Section III introduces the different steps allowing the transition from a grid-level representation to the dynamic object detection. This includes the definition of an evidential grid as well as mobile cells labeling (cf. Fig. 1 ). The segmentation algorithm is also specified in order to extract dynamic objects. Section IV illustrates the dataset used for evaluation as well as a quantitative result analysis. Section V concludes the paper.
A. Dataset and Performance Evaluation The used data is extracted from the KITTI database  [16]. It is a widely used dataset in research for autonomous driving as it provides a large set of images, GPS/IMU recordings and laser scans raw data as well as labeled scenes. In this study, sequence 17 from the raw data set is used considering that annotations with the GT on detected objects as well as the vehicle pose are available. A total of 114 frames are used for which 59 contain annotated moving cars. The used data for this approach are the point cloud recorded by a Velodyne HDL-64 which is characterized by 64 horizontal layers and a 360 o horizontal field of view and a 26. 9 o vertical field of view. The GPS data is also used to obtain the vehicle's pose. The images are not exploited for this application but are used for visualization.
V. CONCLUSION The approach presented in this paper aims at the detection of multiple objects based on LiDAR data according to an evidential 2. 5D grid. The main contributions of this paper is the use of an evidential elevation map and the evaluation of conflict for the determination of mobile objects. Another contribution is the clustering to have an object-level representation for tracking purposes. The detection of dynamic objects is evaluated according to ground truth given by a set of annotations of KITTI dataset. The approach is shown to be efficient according to its high average precision. The perspectives for future work are the identification of the clustering algorithm parameters in order to identify many classes of objects. Extending these first results by testing the approach on more complex scenarios including occluded objects is a second work perspective.
Parameter Settings and Experimental Protocol To assess and compare the performance of the IDTS algorithm, we use the ve most recent state-of-the-art Min-Di DP algorithms in the literature as our main reference algorithms: the construction and improvement heuristic (CIH)  [3] , the evolutionary path relinking (EPR) algorithm  [13] , the variable neighborhood search (VNS) algorithm  [22] , the iterated local search (ILS) algorithm  [33] , and the solution-based tabu search (TS) algorithm  [27]. Our IDTS algorithm and all the reference algorithms were implemented in the C++ programming language. and compiled using the g++ compiler with the -O3 ag as in  [27, 33]. For the CIH, EPR, VNS algorithms, the new versions implemented by the authors of  [27]  were used in our comparisons, since the new implementations of these algorithms have a much better performance than the original ones according to experimental results in  [27].
Moreover, all the computational experiments and comparisons in this work are based on the same computing platform with an Xeon E5440 processor (2. 83 GHz and 2G RAM), running the Linux operating system, which makes it possible to make a direct and fair comparison between the proposed IDTS algorithm and these reference algorithms. Following the studies  [13, 22, 33] , our IDTS algorithm was run 20 times for each tested instance, with a time limit t max equaling n seconds for each run, where n represents the number of elements in the tested instance.
Acknowledgments We are grateful to the reviewers for their valuable comments which helped us to improve the paper. This work was partially supported by the  National Natural Science Foundation of China  (Grant No. 61703213 ), the  Natural Science Foundation of Jiangsu Province of China  (Grant No. BK20170904 ), six talent peaks project  in Jiangsu Province  (Grant No. RJFW-011 ), and  NUPTSF  (Grant No. NY217154 ).
Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labelling data is a timeconsuming and costly human (expert) intelligent task. Semi-supervised methods leverage this issue by making use of a small labeled dataset and a larger set of unlabeled data. In this article, we present a flexible framework for semi-supervised learning that combines the power of supervised methods that learn feature representations using state-of-the-art deep convolutional neural networks with the deep embedded clustering algorithm that assigns data points to clusters based on their probability distributions and feature representations learned by the networks. Our proposed semi-supervised learning algorithm based on deep embedded clustering (SSLDEC) learns feature representations via iterations by alternatively using labeled and unlabeled data points and computing target distributions from predictions.
I. Introduction SEMI-SUPERVISED learning has recently attracted enormous attention due to its capacity to reduce the need for large labeled datasets that are needed to efficiently train deep models based on artificial neural networks. Obtaining labeled data, in practice, can be associated with significant cost and/or require expertise. For instance, reliably labeling or segmenting large medical imaging data requires excessive amount of work by a group of expert radiologists or well-trained technologists. For example, manual segmentation of each brain MRI scan in the isointense infant brain MRI segmentation challenge (iSeg2017) took, on average, one week of a neuroradiologist's time  [1]. On the other hand, in many domains including medical imaging, getting access to large unlabeled data is relatively easy and inexpensive.
One of the main assets of deep learning methods over other machine learning algorithms is their great modelling capacity, which allows them to handle complex, high-dimensional datasets through feature representations [2],  [3]. Consequently, neural networks have achieved state-of-the-art results in computer vision where they have shown great success in making inference from high-dimensional image data. The majority of deep learning algorithms, however, are supervised as they learn to predict or classify from labeled training samples. These algorithms have been modified in various ways, some of them presented in the next section, to handle semi-supervised or unsupervised learning tasks. Conventionally, unsupervised learning methods are applied as pre-training for supervised learning tasks  [4].
Unsupervised learning or "clustering" algorithms handle unlabeled data through sorting them into a number of clusters based on similarities or distances between data points. These algorithms, however, are, by definition, uninformed of class labels. Moreover, while clustering algorithms often perform well for low-dimensional data, they often suffer from the "curse of dimensionality" when dealing with high-dimensional data. As data samples become distant from each other in high dimensional spaces, clustering algorithms would require an excessively large number of data points to effectively measure the effect of parameters on data to make inferences. As an example, the infamous K-Means clustering algorithm achieves an accuracy score of only about 55% on the MNIST dataset  [5]  which is one of the basic benchmark datasets used in computer vision.
In this paper, we take advantage of the recently developed deep embedded clustering (DEC) algorithm  [6]  to design a robust, accurate, flexible, and computationally efficient semisupervised deep learning method. DEC combines a deep stacked autoencoder with a clustering algorithm to iteratively optimize a cost function based on target probability distributions to refine cluster centroids. To this end, by adding a clustering layer to a deep convolutional neural network (CNN), we present a new training algorithm for a semisupervised method that learns feature representations from unlabeled data while keeping the model consistent with the labeled data. We apply this method, called Semi-Supervised Learning with Deep Embedded Clustering (SSLDEC) to benchmark image classification datasets that have been routinely used in the evaluation and comparison of semi-supervised learning algorithms, as well as a challenging medical image segmentation task, i.