dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical Data Source and Study Population We retrieved electronic health records for 3,055 COVID-19-positive inpatient admissions at five hospitals between March 9, 2020 and April 11, 2020 within the Mount Sinai Health System (MSHS). These data included patient demographics, past medical history, and admission vitals and labs (Table  1 , Supplementary Table  2 ). Relevant patient events (intubation, discharge to hospice care, or death) were recorded and subsets were constructed at 3, 5, 7, and 10 day intervals after admission (Figure  1 ). Of these patients, 17. 0% to 31. 6% had a critical event (intubation, discharge to hospice care, or death) and 6. 0% to 21. 5% died over the observed time frames (Supplementary Table  1 ). In contrast, the control group consisted of patients with all other discharge dispositions and those that were still hospitalized.
However, over time, age may become a better marker for critical event prediction, by offering a more stable container of clinical information, given its invariance to change relative to other features. As such, the classifier becomes optimized for predicting severe events earlier in the course of illness. The results of our models should be considered in light of several limitations. First, we base our predictions solely on a patient's admission labs (i. e. within 36 hours); while this restriction encourages the use of this model in patient triage, events during a patient's hospital stay after admission may drive their clinical course away from the prior probability. Furthermore, not all patient labs are drawn at admission, which introduces an element of missingness in our dataset. For example, unlike the general patient population, patients on anticoagulation therapy, who likely have comorbidities increasing their baseline risk, will have coagulation labs (PT, PTT) taken on admission.
However, collinearities between some categorical features in this dataset may be present with other continuous features, as exhibited by covariance strength between hypertension and systolic BP and creatinine in Supplemental Figure  4 , which can then serve as vehicles for capturing these categorical pieces of information. In conclusion, the COVID-19 pandemic unequivocally represents an unprecedented public health crisis. Healthcare institutions are facing extreme difficulties in managing resources and personnel. Physicians are treating record numbers of patients and continuously expose themselves to a highly contagious and virulent disease with varying symptomatology. Only few therapeutic options have demonstrated improvement to patient outcomes. As COVID-19 moves outside of the current epicenter in New York City, healthcare institutions will see a larger influx of affected patients and can benefit from immediate insights regarding assessment of disease severity  (29, 30).
Clinical Data Source and Study Population In this study, patient data came from five hospitals within the Mount Sinai Hospital We included patients who were over 18 years old that had a laboratory-confirmed COVID-19 infection, and were admitted between March 9 and April 11, 2020 to any of the hospitals previously mentioned. A confirmed case of COVID-19 was defined by a positive reverse transcriptase polymerase chain reaction (RT-PCR) assay of a nasopharyngeal swab. We excluded patients who had a positive COVID-19 RT-PCR result more than two days after admission. Additional exclusion criteria are presented in Figure  1. Full patient characteristics by site are provided in Supplementary Table  1.
Study Data Demographics included age, sex, as well as reported race, and ethnicity. Race was collapsed into seven categories based off of the most recent US census race categories: American Indian or Alaskan Native, Asian, Black or African-American, Other, Native Hawaiian or Other Pacific Islander, Unknown, and White. Ethnicity was collapsed into three categories: Hispanic/Latino, Non-Hispanic/Latino, and Unknown. We obtained demographics, diagnosis codes (International Classification of Diseases-9/10-Clinical Modification (ICD-9/10-CM) codes and procedures), as well as vital signs and laboratory measurements during hospitalization. A pre-existing condition was defined as the presence of ICD-9/10-CM codes associated with specific diseases. We chose to include as covariates conditions that have been previously reported to have increased incidence in hospitalized COVID-19 patients, specifically: atrial fibrillation, asthma, cancer, coronary artery disease, chronic kidney disease, chronic obstructive pulmonary disease, diabetes mellitus, heart failure, hypertension, and stroke  (15, (23) (24) (25) 31).
We included laboratory measurements and vital signs near the time of admission for prediction. Specifically, because records for laboratory values and vitals may appear with some lag, only the first available value within 36 hours of admission was included, otherwise the value was assigned as missing. Height was absent in a large percentage of the patients (18. 2%). Because height is generally invariant in the adult population, and given the resource constraint of the pandemic, it was common for triage nurses to use the height from a previous and recent admission. In an effort to be as cohesive in our data gathering methods as possible, these earlier records were not retrieved for this dataset. However, weight was used as the next approximate proxy for body habitus, with additional information being presented through sex and age for body habitus as well.
Statistical Analysis Our primary model was fit with the Extreme Gradient Boosting (XGBoost) implementation of boosted decision trees on continuous and one-hot encoded categorical features. The XGBoost algorithm provides state-of-the-art prediction results through an iterative process of averaging in decision trees (we used 100) fit to the residual error of the prior ensemble. While each tree is too simple to accurately capture complex phenomena, the combination of many trees in the XGBoost model accommodates non-linearity and interactions between predictors. Missing data values are routed through split points based on the direction to minimize loss. XGBoost models were trained and evaluated using 10-fold stratified cross validation. For each fold, hyperparameter tuning was performed by randomized grid searching directed towards maximizing the sensitivity metric over 2,000 discrete grid options.
Four main models were generated: 1) Logistic regression using only Age 2) Generalized Additive Model using only Age 3) Logistic Regression with all available features 4) Generalized Additive Model using all available features. Since these models do not have a built in method of dealing with missing data, we dropped features with over 70% missingness and samples that lacked values for the remaining feature space. The models were trained and evaluated using 10-fold stratified cross validation on patient data from MSH and subsequently evaluated on patient data from the other hospitals. The same metrics were recorded for these models; however, this model was only trained at outcome prediction on Day 3, which was the time frame at which model AUC-ROC was highest for the XGBoost classifier. Performance results for this classifier are represented in Supplementary Table  5.
System (MSHS): the Mount Sinai Hospital (MSH) located in East Harlem, Manhattan; Mount Sinai Morningside (MSM) located in Morningside Heights, Manhattan; Mount Sinai West (MSW) located in Midtown and the West Side, Manhattan; Mount Sinai Brooklyn (MSB) located in Midwood, Brooklyn; and Mount Sinai Queens (MSQ) located in Astoria, Queens. The dataset was obtained from different sources and aggregated by the Mount Sinai COVID Informatics Center (MSCIC).
The pre-processed images will be then sorted with a competitive coding scheme using MLBP; where the final image will be composed of the winning code from the different MLBP images. The matching process for making-decision is then performed using Kullback-Leibler divergence and Jaccard distance. The experimental results obtained on MS-PolyU database has shown that the proposed method achieves improved performances for both identification and verification modes up to 99. 64% in terms of CIR for the identification and 0. 00078% in terms of EER for the verification; and also outperforms the state-of-the-art methods.
In fact, the palm vein differs from the other biometric modalities by its intrinsic nature where the vein structure is not visible to the naked eye which reduces the possibility of stealing. Access to this biometric modality requires sophisticated hardware such as multispectral sensors that enable the acquisition of near-infrared images. The palm vein recognition can also represent some points of complexity, such as low contrast or image quality and the visibility of the venous structure that complicated the recognition phase. Faced with these problems, the efficiency of feature extraction method is paramount. Several palm vein methods have been proposed in literature  [4]  that can be regrouped into five main categories : (i) Structural-based approaches which are based mainly on the geometry of the vein, such as the minutiae, the curvatures and the lines  [5, 6] , (ii) Appearances-based approaches are methods that do not require a deep knowledge of the palm vein; and are often associated with meta-data analysis methods, such as Linear discriminant analysis (LDA), Independent component analysis (ICA) and Principal component analysis (PCA)  [7, 8] , (iii) Statistical approaches are divided into two sub-methods: a local representation which consists of dividing the palm vein image into several regions, a global representation where the whole palm vein image is taken into consideration  [9, 10, 34] , (iv) Coding-based approaches encode the multi-scale representation of palm vein images using a binary coding; where this kind of techniques is often implemented using Wavelet and Gabor filters  [11, 12, 13, 14, 15, 30] , and (v) Hybrid approaches consist of the combination of two or more techniques from the previous approaches that allow the benefits of each method to be tested, but it can be at the expense of time and performance  [16, 33].
The matching process for making-decision is then performed using Kullback-Leibler divergence and Jaccard distance. The experimental results obtained using MS-PolyU database have shown that the proposed method allows to achieve interesting performance in terms of the Correct identification rate (CIR) up ti 99. 64% and up to 0. 00078% in terms of Equal error rate (EER); and also outperforms some state-of-the-art methods. The rest of the paper is organized as follows: Section 2 presents the related work. Section 3 describes our proposed palm vein recognition method while Section 4 illustrates the results of our experiments. Finally, Section 5 concludes the paper and presents some directions for future work.
The LGPDP represents the palm vein feature derived from the Gabor magnitude response where each pixel of this response has a neighboring number of pixels. First, the pixels are quantized in different regions, then the Local maximum difference (LMD) is applied. This operator is to compare a central pixel with its environment and returns the maximum difference direction in the form of a binary sequence. The LGXP has been implemented to encode the Gabor phase response (GPR). Similar to the LGPDP operator, the LGXP is obtained by calculating the central pixel and its surrounding pixels. The same authors proposed in  [13]  a novel palm vein recognition scheme based on a coding method. The idea of their approach is to divide the normalized palm vein image into several non-overlapping sub-regions and then the optimal parameters of the Gabor filter will be determined from each sub-region.
Experiments and results To evaluate our methods as well as the results obtained, we have used the MS-PolyU database described in the next subsection 4. 1. The results obtained for the verification and identification modes are firstly computed from samples of different LBP descriptors (i. e. LBP images in Figure  7 ). Then, the results obtained from the proposed method are analyzed and compared with some existing methods of the state-of-the-art.
Database MS-PolyU is among the popular multispectral palm-print database. It contains 6000 images from 250 volunteers, including 195 males and 55 females, with an age ranging between 20 and 60 years  [32]. In our study, we have exploited only the images from the NIR spectral band to exploit the palm vein structures. Indeed, the NIR band can cross the muscle tissues and detect the venous structure.
Identification and verification We have assessed the performance of our proposed method for the both verification and identification modes: (i) verification mode which consists in comparing the identity claimed by the person and his biometric data, (ii) identification mode which consists in comparing the biometric characteristics of entries To organize the database, we have considered that one of the collections represents the test and the second is the gallery. Each feature map from the test set was matched with all the maps in the gallery set using both distances defined in Eqs. 9 and 12. The following metrics: EER and CIR, are used to assess the recognition performances for the verification and identification modes. Noted that during the matching phase, we took into consideration the translation's effect that can occur at the time of the ROI extraction.
Speed performance The proposed method was implemented using Matlab R2015b on a MacBook Pro with OS X High Sierra, Intel Core i7 CPU (2. 5 GHz) and 16 GB RAM. Table  7  shows the results obtained in terms of execution time (average time over 20 runs). We can see that the preprocessing and feature extraction phases take more time compared to the matching phase but it remains below than 1s. The total processing time using the KL distance is about 0. 34s and 0. 35s for Jaccard distance which are much similar. With these processing times, the proposed method can be used within the framework of real-time application.
Conclusion A novel approach for palm vein recognition was proposed in this paper. The proposed method is based on a competitive coding scheme using MLBP; where the ACO was applied on the Table  7. Execution time of the proposed method. The execution time (average time over 20 runs) for feature matching step is given for the both distances. Step Execution time (s) Preprocessing with ACO 0. 126 Feature extraction (MLBP + Competitive coding) 0. 215 Feature matching (1) KL distance 0. 0016 (2) Jaccard distance 0. 0023 palm vein images in order to enhance the quality of their feature maps generated from MLBP. Two distance metrics based on KL and Jaccard have been proposed for the matching. The experimental results on MS-PolyU database have shown that the proposed method achieves improved performances for both identification and verification modes up to 99.
Participants were given 5 min to relax between trials. After completing the four 6-min trials, a maximal voluntary contraction (MVC) was recorded during a short sprint with a much higher resistance (approximately 500 W)  24. The aim of calculating the MVC was to normalise EMG signals in data post-processing. Experimental setup. For the first session, participants used an Excalibur Sport stationary bicycle (LODE, Netherlands) and wore an analyser gases system (Quark CPET, Cosmed, Italy), which was used to identify their VT 1. For the second session, participants used the road bicycle (Merida®) and the HomeTrainer Tacx Flux. The Rotorbike 2InPower crank, a dual-sided power meter, was used to measure the torque applied to each pedal independently (50 Hz), which was transmitted to a computer via ANT+ transmission.
Depending on the session-assigned auditory condition, when negative torque values were registered, a squeak sound was produced in Max/MSP (Cycling '74, USA) and online delivered to participants via AKG K702 headphones  20, 21. The duration of the squeak was proportional to the duration of the negative torque period. Forty-three markers (size: 19 mm; weight: 2. 5 g) for a three-dimensional kinematic motion analysis were placed on the participant's body, limbs, and head  31. Kinematic data were captured at 200 Hz using a ten-camera Qualisys motion analysis system. Before each data collection session, a calibration of the system was performed. Only calibrations that produced average residuals of less than 0. 8 mm for each camera were accepted prior to data collection. EMG data were recorded with a Delsys Trigno system (sampling rate: 1926 Hz).
Data analysis. Measurements recorded when the session-assigned auditory conditions were presented to participants were extracted and used for analysis. Torque signals were sampled at regular intervals (50 Hz), whereas pedal angles were sampled at lower interval (5 Hz). In order to calculate the completion of a cycle, the torque signals were used to interpolate and estimated pedal angles (0. 5 • steps) via the MATLAB cubic interpolation function interp1 (MATLAB 2016b, MathWorks Inc, USA). The performance of each cycle was assessed by calculating the torque effectiveness (TE), where τ + the total positive torque over the cycle and τ -the total nega- tive torque over the cycle (Eq. 1)  20, 21. TE was considered 100% if there was no negative torque during the cycle. Marker data were filtered using a Butterworth 6th order low-pass filter (cut-off frequency: 15 Hz).
The kinematics at hip, knee and ankle joints of the right lower limb were computed in the sagittal plane using Visual3D (v6 Professional, C-Motion, USA). Ankle, knee, and hip joint angles were calculated over each cycle. The crank angle was also calculated from the retro-reflective markers placed on the crank extremities. Based upon the detection of the vertical position of the right crank, joint kinematics and EMG data were split into cycles and time-normalised to 101 points over the pedalling cycle. To accurately measure the maximal muscle activity performed while cycling, the sprint method of normalization was used, as it has been proven to be reliable in nature to subsequent cycling trials  33. Thus, the EMG signals recorded during the short sprint that followed the four 6-min trials were used for normalization.
Statistical comparisons. To analyse the percentage of torque effectiveness, we opted for a sample size that reflected ones used in similar studies  27, 34, 35. In addition, we computed a priori power analysis for within-between participants Repeated Measures (RM) ANOVA (medium effect-size = 0. 5, α = 0. 05), which revealed that a sam- ple size of at least 8 was sufficient to reach a power of 0. 95. RM ANOVA was conducted with auditory condition and pedal factors. Where main effects and interactions were detected, post-hoc Bonferroni-adjusted t-tests were (1) Where the assumption of sphericity was violated, Greenhouse-Geisser adjustments are reported. Right leg-only kinematics and EMG analyses were performed with the statistical MATLAB package SPM1D (version M. 0. 4. 5)  36. Statistical Parametric Mapping (SPM) was developed initially in neuroimaging in the mid-1990s, but has more recently been applied in the field of biomechanics.
Abstract. The Observations for Model Intercomparison Project (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs (1) targets observed variables that can be compared to CMIP model variables; (2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output; (3) provides brief technical documentation for each dataset, designed for nonexperts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits, and limitations; and (4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and acces-sible via the same portals as the model output.
Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80; many are undergoing updates, with another 20 or so in preparation, and more than 100 are proposed and under consideration. A partial list of current global satellitebased datasets includes humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top-of-atmosphere longwave and shortwave radiation; and ozone (O 3 ), methane (CH 4 ), and carbon dioxide Published by Copernicus Publications on behalf of the European Geosciences Union.
D. Waliser et al. :  Obs4MIPs: status for CMIP6 (CO 2 ) products. A partial list of proposed products expected to be useful in analyzing CMIP6 results includes the following: alternative products for the above quantities, additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e. g. , FAPAR, LAI, burned area fraction), ice sheet mass and height, carbon monoxide (CO), and nitrogen dioxide (NO 2 ). While most existing obs4MIPs datasets consist of monthly-mean gridded data over the global domain, products with higher time resolution (e. g. , daily) and/or regional products are now receiving more attention. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including (1) an updated obs4MIPs data specifications document that provides additional search facets and generally improves congruence with CMIP6 specifications for model datasets, (2) a set of six easily understood indicators that help guide users as to a dataset's maturity and suitability for application, and (3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata.
1 ). A sister effort, "CREATE-IP" (initially conceived as ana4MIPs), has been advanced to make reanalysis data available with a similar objective  (Potter et al. , 2018). While the infrastructure advances made by CMIP had established an obvious precedent, the daunting prospect of dealing in a similar way with the plethora of observation quantities was challenging, even when only considering satellite data. Within the NASA holdings, for example, there have been over 50 Earth observation missions flown, each producing between 1 to nearly 100 quantities, and thus there are likely on the order of 1000 NASA satellite geophysical quantities that might be candidates for migration to obs4MIPs, with many more when accounting for other (EU-METSAT, NOAA, ESA, JAXA, etc. ) satellite datasets. Key to making initial progress was the recognition, illustrated in the right part of Fig.
1 , that only a fraction (perhaps about 10 %) of the available observation variables could be directly compared with the available CMIP output variables of which there are over a thousand. The aspirations and framework for obs4MIPs were developed with these considerations in mind. Since the initial implementation of obs4MIPs, there has been an intention to expand the breadth of datasets, including a better match of derived quantities and model output variables, e. g. , through using simulators (e. g. ,  Bodas-Salcedo et al. , 2011)  and an increased capacity to host the datasets, as well as to describe and disseminate them. In addition, for the first time in CMIP, evaluation tools are available that make full use of the obs4MIPs data for routine evaluation of the models  (Eyring et al. , 2016)  as soon as the output is published to the Earth System Grid Federation (ESGF) (e.