dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, in cases when SMC is used, we essentially iterated the parameter estimation step by SMC. We kept the top five percent (equal to the tolerance level) of simulations from the testing dataset that best match the observed data. We then used the parameters of those simulations to update our prior range and sent it for next iteration until convergence was reached (Figures  S3  and S4 ). Before testing our primary hypothesis on real sequence data, we tested whether our approach (DLS) can reproduce the known results. The predicted parameters for real sequence data (see below for more details) are consistent with previous works from the literature  17, 19, 71  (Table  S7 ). We also tested our method on simulated data with known parameters under various models (model S, B, and M, see below for more information; simulation parameters coming from Tables 2, S5, and S6) and found that our approach with SMC correctly predicted the model in all the cases tested, suggesting it can find the correct model.
Model selection To test our hypothesis, we simulated three OOA models, simple model (model S), back to Africa model (model B), and mix model (model M), and all the models had introgression from Neanderthal to all OOA populations,  63  Denisova or unknown to Asia,  20, 47, 64  African archaic to Africa,  16 ,67,68,72  and European Neolithic farmers to Africa 69 (NDXF) (see material and methods for more details, Figure  1  and Table  1 ). We used the 1000 Genomes Project high-coverage genomes  44  (see material and methods for more details) of five Yoruba (African), five Utah residents with Northern and Western European ancestry (European), and five Han Chinese (East Asian) individuals as our real dataset. Next, we used three different methods to choose between the competing models: (1) RF that combines random forests with ABC;  43  (2) NN and ABC together (DL), which is an analogous but improved version of our previously published method, ABC-DL; 20 and (3) the method introduced here, DLS, which expands the DL method with SMC.
Although all three methods identified the back to Africa model as the most probable one, the prediction certainty varied between methods (Tables  3  and 4 ). Both DL and DLS returned >95% probability for model B although RF gave lower support. The DLS results were reproduced under different data filtering strategies, different datasets (Table  S2 ), and different total length with different per block length of simulated regions (Table  S4 ). We also tested whether our assumption of pulse migration events (three archaic introgression scenarios and recent migration of Neolithic farmers) could affect our inference. We tested different models with (1) no introgression and no farming migration (NI), (2) Neanderthal and Denisova introgression (ND), (3) Neanderthal, Denisova, and Africa Archaic introgression (NDX), and (4) Neanderthals and Denisova introgression with farming migration (NDF) using only DLS.
Acknowledgments We kindly thank  Michael Zody  of  New York Genome Center  for giving us access to high-coverage 1000 Genome data. These data were generated at the  New York Genome Center  with funds provided by  NHGRI  grant  3UM1HG008901-03S1. This research was supported by the  European Union  through  Horizon 2020 research and innovation program  under grant no. 810645 , the  European Regional Development Fund  Project no. MOBEC008 ,  2014-2020. 4. 01. 16-0030 , and  2014-2020. 4. 01. 16-0024. Data analyses were carried out in part in the  High-Performance Computing Center of the University of Tartu.
44 However, identification of such molecules is not trivial due to the large and complex surface through which OLIG2 interacts with itself and other partners, which is uncharacteristic and with no hydrophobic pockets. 18, 19 [47]  ■ EXPERIMENTAL SECTION Optical Setup for Massively Parallel Fluorescence Correlation Spectroscopy Integrated with Fluorescence Lifetime Imaging Microscopy (mpFCS/FLIM). The optical design of the mpFCS/FLIM system and important features are shown in Figure  1A-C  3. Information about optical alignment, calibration, data acquisition, analysis, image rendering, and fitting of temporal autocorrelation curves (ACCs) using eq S1 is provided in the Supporting Information (Section S1, Figures  S1-S5 ). Software for mpFCS/FLIM. mpFCS/FLIM data acquisition, analysis, and graphical presentation were carried out using our own software, into which the Micro Photon Device (MPD) software for running the 2D SPAD array was integrated.
The software was written in Embaracadero C++ Builder 10. 2 (Embarcadero Technologies). Detailed information about data acquisition, analysis, and image rendering are given for mpFCS in Section S1b and for FLIM in Section S1c. Phasor plot analysis is presented in Section S1d. Cell Culture and Transfection. Procedures for cell culturing and transfection for mpFCS/FLIM measurements (Section S2a), pharmacological treatment of cells (Section S2b), and cell culture for FRET-FLIM measurements (Section S2c) can be found in the indicated sections in the Supporting Information. Dissociation Constant Assessment. Procedures for calculating the apparent dissociation constants of OLIG2-eGFP dimers (Section S3) and Olig2-eGFP-DNA complexes (Section S4) can be found in the indicated sections in the Supporting Information.
Standard Solutions for mpFCS/FLIM Calibration. Relevant information about standard solutions used for mpFCS/FLIM system calibration can be found in Section S5. Statistical Analysis. All values are presented as mean ± standard deviation (SD). Two-tailed Student's t-test was used to compare two groups. The correlation analyses were reported using the probability value (p-value). Differences between two groups were considered to be significant when p < 0. 05. Pearson's sample correlation coefficient r was used to assess the strength of a linear association between two variables. Statistical analysis was performed using the Origin 2018 program for interactive scientific graphing and data analysis and/or Excel. During data analysis, data from a few pixels (<5%) were disregarded due to the extremely high background in these SPADs.
* sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acs. analchem. 1c02144. S1: Optical setup for mpFCS/FLIM, data analysis, and image rendering; S2: Cell culture and transfection procedures; S3: Determining the apparent dissociation constant of OLIG2-eGFP homodimers; S4: Determining the apparent dissociation constant for OLIG2-eGFP binding to chromatin DNA by FCS; and S5: Standard solutions for mpFCS/FLIM system calibration (PDF) ■ AUTHOR INFORMATION.
■ ACKNOWLEDGMENTS The authors thank  Dr. Hideji Murakoshi ,  Supportive Center for Brain Research, National Institute for Physiological Sciences, Aichi, Japan , for providing the ShadowY and eGFP-ShadowY expression plasmids. Test compounds were obtained through the  National Cancer Institute Chemotherapeutic Agents Repository, Bethesda, MD. Financial support by the  Swedish Research Council  ( 2018-05337 );  Olle Engkvists Foundation  ( 199-0480 );  Magnus Bergvall Foundation  ( 2019-03381 ,  2020-04043 );  Karolinska Institutet 's Research Foundation Grants ( 2020-02325 );  Swedish Foundation for Strategic Research  ( SBE13-0115 );  NIH/NIAAA  ( R01AA028549 );  Nakatani Foundation for Advancement of Measuring Technologies in Biomedical Engineering; Strategic Research Program in Neuroscience (StratNeuro) ;  Yoshida Foundation for Science and Technology ;  ERASMUS+: European Union Programme for Education, Training , Youth, and Sport;  Qatar National Research Foundation  ( PPM 04-0131-200019 ); and  Science Fund of the Republic of Serbia  (call  PROMIS , Grant no.
Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applications, such as real-time visualization and inter-subject clustering, need fast and high-quality intra-subject clustering algorithms. This work proposes a parallel algorithm using a General Purpose Graphics Processing Unit (GPGPU) for fiber clustering based on the FFClust algorithm. The proposed GPGPU implementation exploits data parallelism using both multicore and GPU fine-grained parallelism present in commodity architectures, including current laptops and desktop computers.
INTRODUCTION In order to discover relevant information in tractography datasets, the research community has proposed several unsupervised approaches based on clustering algorithms to identify the white matter (WM) main structures based on shapes and streamline point positions. Some schemes used well known clustering algorithms such as spectral clustering  (O'Donnell and Westin, 2007; O'Donnell et al. , 2017) , hierarchical clustering  (Guevara et al. , 2011b (Guevara et al. , , 2012;; Siless et al. , 2018) , label fusion clustering  (Jin et al. , 2014) , and fuzzy c-means  (Li et al. , 2010). Recent methods use sampling, variation of distance metrics, multi-core parallelism, and different algorithm approaches and representations  (Garyfallidis et al. , 2012 (Garyfallidis et al. , , 2014 (Garyfallidis et al.
, 2015; Combrisson et al. , 2019). However, most of the clustering algorithms based on traditional clustering methods applied on large datasets are usually computationally expensive. Hence, recent works have proposed clustering schemes to improve computational time while maintaining high-quality results  (Garyfallidis et al. , 2012 (Garyfallidis et al. , , 2014 (Garyfallidis et al. , , 2016;; Vázquez et al. , 2020). With the increase in data volumes and parallel architectures, parallel computing has become a powerful paradigm to improve performance in various applications. Building parallel clustering algorithms include different platforms and strategies. Some strategies use sampling  (Guha et al. , 2001) , data partitioning and the MapReduce paradigm  (Dean and Ghemawat, 2008; Banharnsakun, 2017).
A recent survey  (Dafir et al. , 2020)  provides a discussion of parallel clustering algorithms and platforms. For applications based on tractography datasets, some methods introduce parallelism using GPUs to improve the execution times for visualization of bundles and streamlines  (Guevara et al. , 2015; Combrisson et al. , 2019) , visualization of fused DTI/HARDI data  (Prckovska et al. , 2011) , efficient tractography compression, storage, and visualization  (Haehn et al. , 2020) , fiber segmentation  (Ros et al. , 2011; Labra et al. , 2017) , dMRI non-linear model fitting and probabilistic tractography calculation  (Hernandez-Fernandez et al. , 2019) , geodesic fiber tracking  (van Aart et al. , 2011) , and connectome pruning  (Kumar et al. , 2019). In the context of fiber clustering, FFClust is a state-of-theart fast method that builds high-quality clusters  (Vázquez et al.
, 2020). The algorithm consists of four steps where it combines data parallelism on multicore architectures using local clustering, information aggregation, refinement, and graph representation to produce global clusters. This work proposes a GPGPU parallel algorithm for FFClust. The parallel algorithm exploits multicore and GPU fine-grained parallelism. The proposed method provides a variation of the Kmeans++ algorithm, includes highly parallel patterns, memory optimization using constant, shared, and coalescing memory for high performance. The algorithms are implemented in C++, the thrust library, and the Compute Unified Device Architecture (CUDA) language. The experimental evaluation shows that our approach obtains clustering results with a quality that is similar to FFClust. In addition, it can attain an execution time of 3.
Tractography Datasets This study uses the ARCHI database  (Schmitt et al. , 2012)  containing high-quality MRI data acquiered on a Tim Trio 3T MRI system with a 12-channel head coil (Siemens, Erlangen). The MRI protocol includes a T1-weighted image at 1 mm isotropic spatial resolution using an MPRAGE sequence, a B0 field map to correct artifacts, and a single-shell HARDI SS-EPI sequence with 60 optimized diffusion weighted directions, b = 1, 500 s/mm 2 and isotropic spatial resolution of 1. 7 mm. The HARDI dataset was corrected for artifacts produced by eddy currents, susceptibility effects, spikes, and noise. Then, the analytic qball diffusion model  (Descoteaux et al. , 2007)  was calculated. A robust brain white matter propagation mask based on a T1-weighted segmentation was also calculated  (Guevara et al.
, 2011a)  and whole-brain regularized streamline deterministic tractography  (Perrin et al. , 2005)  was computed on the diffusionweighted (DW) space, based on the propagation mask, using a step of 0. 2 mm and a maximum curvature angle of 30 •. We used the BrainVISA / Connectomist-2. 0 software to processed all data  (Duclap et al. , 2012). Resulting tractography datasets contain about one million fibers per subject (1,019,160 fibers on average). Also, we performed a post-processing step to resample all fibers using 21 equidistant 3D points. Therefore, each fiber consists of 21 3D points. Several previous approaches used the same representation  (Guevara et al. , 2012; Vázquez et al. , 2020).
On the other hand, Kmeans++ aims to select the initial centroids from data points that are far away from one another. This scheme aims to select data points from different clusters as initial centroids, which aids in building better clusters than Kmeans. Algorithm 1 shows the main algorithm for implementing Kmeans++. For a selected position in all fibers in the tractography dataset T, the algorithm first defines an initial centroid C 1 by choosing a data point at random. Then, it computes the array D with the Euclidean distance for every data point x i ∈ T to the centroid C 1. Note that here the Euclidean distance is the distance between fiber 3D points (d P in Equation  1 ). Next, the algorithm defines as the next centroid a data point x i , which is the farthest away (maximum distance) point to all previous centroids (lines 6 and 7 in the algorithm).
Parallel Algorithm for Step 4 of FFClust This step uses a graph model, representing candidate clusters as vertices and defining edges based on a threshold of the maximum Euclidean distance of pairwise centroids. To improve performance, FFClust first groups centroids based on the membership of the middle point of fibers, and then over each group, it applies the graph model. Next, for each graph, the algorithm enumerates all maximal cliques, sorts them by size, and merges all clusters represented by the vertices in the same maximal clique. The FFClust implementation uses networkx python package to perform all graph operations. In this work, we follow the same ideas, but instead of using python networkx, we use C++ and the Eppstein et al. algorithm  (Eppstein et al. , 2013)  for listing maximal cliques, which is very efficient for sparse graphs.
Performance Results We evaluate the performance in terms of execution time and speedup. We implemented the parallel algorithms using C++, thrust library, OpenMP, CUDA 10 toolkit with O3 compiler optimization. All experiments were executed in a i5-9600K 6C12H CPU model, which has 6 cores working at 3. 7GHz; 32 GB of RAM and a Quadro P620 Nvidia GPU. datasets with about a million fibers each. As observed, Step 1 consumes the largest execution time in FFClust, whereas using fine-grain parallelism in all the presented alternatives improves the execution time of this step in more than one order of magnitude. This figure also shows that all parallel alternatives achieved a similar total execution time between 2. 5 and 4 s, while FFClust consumes about 40 s. The CefFFC alternative filters out all final clusters produced by CeFFC that have intra-cluster distance over 70 mm.
Performance Discussion Table  1  displays a comparison summary between FFClust and the best GPGPU implementation, CeFFC. For each of the steps, it shows the programming languages, libraries, computing platforms, and the execution times in seconds required. In addition, the last column of the table provides the speedup achieved by CeFFC in each Step. As observed, FFClust exploits CPU parallelism in all the Steps, where Step 1, 2 and 4 uses python Multiprocessing package for process-based parallelism. In Step 3, FFClust uses parallelism using C with OpenMP  (Vázquez et al. , 2019). The GPGPU implementation uses C++ and CUDA in Step 1, CUDA Thrust library in Step 2 and 3, and C++ and OpenMP in Step 4. As seen in the last column of Table  1 , the GPGPU implementation achieves acceleration in all the FFClust steps.
In Step 1, the FFClust algorithm uses MiniBachKMeans and Multiprocessing python packages, whereas the GPGPU alternative implements in C++ and CUDA the Kmeans++ with point retraction for centroid initialization. Moreover, since MiniBatchKMeans uses sampling, it is faster than the other Kmeans implementations in python. We also implemented sampling with Kmeans++, but we obtained lower quality results and the execution time saving was not significant and therefore not shown in this study. Table  1  shows that we achieve a speedup of 20. 30 in this Step. In Step 2, FFClust uses the Multiprocessing python package to parallelize the construction of a dictionary data structure to compute preliminary fiber clusters. In contrast, the GPGPU implementation uses the parallel thrust primitive sort_by_key() to compute the preliminary fiber clusters and the reduce_by_key() primitive to compute the size of preliminary fiber clusters required as input in the Step 3.
The parallelism of this step provides the highest speedup, which is about 80. The parallelization of the other three steps of FFClust uses the multicore architecture using OpenMP, the parallel thrust library, and a fast algorithm for listing maximal cliques. The overall speedup of the complete parallel method is about 11. 5, being able to process a tractography dataset of one million fibers in 3. 5 s. The quality of the resulting clusters  is about the same as FFClust measured in terms of the DB index metric.
CoronaSurveys is an ongoing interdisciplinary project developing a system to infer the incidence of COVID-19 around the world using anonymous open surveys. The surveys have been translated into 60 languages and are continuously collecting participant responses from any country in the world. The responses collected are pre-processed, organized, and stored in a version-controlled repository, which is publicly available to the scientific community. In addition, the CoronaSurveys team has devised several estimates computed on the basis of survey responses and other data, and makes them available on the project's website in the form of tables, as well as interactive plots and maps. In this paper, we describe the computational system developed for the CoronaSurveys project. The system includes multiple components and processes, including the web survey, the mobile apps, the cleaning and aggregation process of the survey responses, the process of storage and publication of the data, the processing of the data and the computation of estimates, and the visualization of the results.
When these responses are analyzed collectively, across time and geographic locations, a combined view of the pandemic can be inferred. To the best of our knowledge, this is the largest scale NSUM system ever deployed and the only one to be collecting data continuously over a period of over a year using open surveys. In this paper, we present the main components of the current CoronaSurveys infrastructure, including the collection, processing and visualization methods used. The computational system powering CoronaSurveys has been designed as the aggregation of lightly coupled components that can be replaced and modified almost independently (see architecture in Figure  1 ). This has enabled the system to continuously adapt to the evolution of the COVID-19 pandemic with relatively low effort, demonstrating its extensibility, re-usability, and potential to be used for tracking future pandemic outbreaks.
Data Aggregation The back-end data collection engine was designed to provide seamless aggregation of the data in a consistent and structured format. Timeliness, consistency, and proper dissemination of the data were the three main pillars of the aggregation process. CoronaSurveys updates its estimates daily to provide a comparison with the estimates of officially confirmed cases, which are also updated once per day. This daily aggregation also serves as a privacy preserving measure, as we discuss in the next section. During aggregation, survey responses are classified by country and stored in individual files named as CC-aggregate. csv, where CC is the two letter ISO code of the country. Each row in the file corresponds to a single response and is composed of the elements that appear in Table  1 : the date of the response, the country for which the response reports, the country ISO code, the region in the country for which the response reports (if any), the region ISO code, the language used to fill the survey, the answers to the survey questions (Var 1 ,.
DATA ANALYSIS Based on the aggregated, anonymous data, CoronaSurveys employs several methods to produce estimates of the number of COVID-19 cases in all geographical areas for which sufficient data are available, comparing these estimates with those provided by the official authorities. The estimation methods are: • cCFR-based: This method is based on estimating the corrected case fatality ratio (cCFR), from the official numbers of cumulative cases and fatalities, and taking an estimation of the approximate number of cases with known outcomes into consideration. It is also assumed that a reliable value of the traditional case fatality ratio (CFR * ) is available (We use CFR * 1. 38% with a 95% confidence interval of 1. 23% and 1. 53%, as described in  (Verity et al. , 2020)   (Fan et al. , 2020).
Cookies allow us to make sure we only count the latest answer for each respondent in each aggregated batch (set of 300 responses for estimates-300, or last W days for estimates-W). The estimates obtained with the above methods are stored in the online public repository. Each method M stores the estimates in a folder named estimates-M/PlotData, and the estimates for   2. Once in the predefined format, the estimates are imported in a time-series database, from which we generate visualizations. Time-series databases are often used to store streaming data organized in measurements. For CoronaSurveys, each series of estimates obtained with a given method is one such measurement, while the date, the country, the region, and the population, are characteristics of the measurements, facilitating the localization of the estimates.
Coronasurveys Participation Statistics Figure  3  depicts the statistics of CoronaSurveys participation. In just over 12 months, CoronaSurveys has collected data from roughly 25, 000 participants worldwide, with Spain being the country with the most responses. This means that the absolute reach in Spain is significantly higher. However, the country with largest relative reach with respect to the population is Cyprus, with almost 1, 300 responses, an absolute reach of more than 30, 000, and a population of roughly 1 million. This figure also reflects the success of indirect reporting: with this method, we obtain the information of more than 50 times the number of survey responses, more than a million persons in total.
89. In addition, we observed that CoronaSurveys systematically underestimates the number of cases by a factor of 46%, possibly due to asymptomatic cases. This ratio is consistent with other survey implementations that used direct reporting instead  (Oliver et al. , 2020). Although further comparisons in other countries are necessary once we have sufficient data and similar serology studies are available, we believe this strongly supports the use of open surveys as an additional source of information to track the progress of pandemics.