dataesr/scientific-paragraphs-categorization huggingface dataset:
Logically, none of the points in the AUGMECON2 frontier is  dominated by points of the approximation frontier and therefore, the C metric equals 0 in all the datasets. The percentage of approximation frontier dominated by the corresponding exact frontier varies from 2. 87% in the GC dataset to more than 50% for the WBC instance. It means that some of the points of the approximation frontier are not dominated by points in the exact frontier. As regard the extent given my the  3 metric, it is always higher in the frontiers given by the metaheuristic, except for the last dataset, Iono, in which they are rather similar. The  metric indicates a better performance for AUGMECON2 in 3 of the 4 the datasets, that is, the larger hole between adjacent points, is nearly always present in the approximation frontier, but in most cases, the differences are not very large.
Looking at the new metrics + does not permit us to compare results, given that it measures the distance from exact and metaheuristic frontiers and we have only one metaheuristic method in our analysis. However, we can observe that the distance is rather low in all the instances, which indicates that the approximation frontier is quite near of the Pareto-optimal front. These values permit other researchers to compare new methods with our proposal in future research. Finally, the   metric indicates that the portion of Pareto-optimal front covered by the approximation frontier is always higher than 50% and in two of the four datasets, more than 85%. On the other hand, the number of support vectors is different for each Pareto front solution and it is usually large. The smallest dataset in terms of # vectors is the Iono instance, having 225 vectors in the positive class and 126 in the negative class.
For this small instance, the number of positive class support vectors is in the interval  [99, 178]  and the number of negative class support vectors is in the interval  [0, 99]. From these results we can conclude that, although the metaheuristic has been designed with the aim of being used to solve large instances in which the exact method cannot obtain the Pareto-optimal front, it has also demonstrated a good performance in small instances, here the exact method is capable of generating the optimal front. Fig. 6  shows, as example, the frontiers given by both methods, AUGMECON2 with 1 h of computation time and NSGA-II with 1/7 h (in one of the three runs) when solving the WBC dataset. Now, none of the points given by the exact method is dominated by a point of the heuristic frontier. However, we can observe that the metaheuristic frontier presents larger holes ( = 50.
Measurements of daytime radiometry in the ocean are necessary to constrain processes such as photosynthesis, photo-chemistry and radiative heating. Profiles of downwelling irradiance provide a means to compute the concentration of a variety of in-water constituents. However, radiometers record a non-negligible signal when no light is available, and this signal is temperature dependent (called the dark current). Here, we devise and evaluate two consistent methods for correction of BGC-Argo radiometry measurements for dark current: one based on measurements during the day, the other based on night measurements. A daytime data correction is needed because some floats never measure at night. The corrections are based on modeling the temperature of the radiometer and show an average bias in the measured value of nearly 1 × 10 -4 W m -2 nm -1 , an order of magnitude larger than the reported uncertainty of 2.
The Argo program is a global array of profiling floats funded by national agencies. Since its first deployment in 1999, the array of Argo floats has grown to nearly 4000. These profile from the surface to 2000 dbar every 10 days, collecting CTD data. The project has expanded into Biogeochemical (BGC)-Argo by including optical, oxygen, nitrate and pH sensors on some floats  [3]. Because the floats experience a dramatic range of temperature and pressure, and the sensors are not calibrated after deployment, it is essential to investigate the dynamics of sensor behavior. Without the retrieval of the sensors post-deployment, this must be done through investigation of the collected data. Radiometers report a non-negligible output, known as the 'dark current', even in the complete absence of ambient light.
To accurately characterize oceanographic processes at depth or in low-light conditions, where uncertainties in the radiometric measurements may be significantly impacted by uncertainties in the blank, a correct calibration which includes a correction for the temperature-sensitive dark current is essential  [4, 6]. Here, we investigate the dependence of the dark measurements (where measured irradiance is expected to be zero) on sensor temperature T s for radiometers on BGC-Argo floats and provide a quality control (QC) framework for correcting radiometer dark measurements for the instrument temperature dependence dE dark /dT s , [W m -2 nm -1 • C -1 ] and dPAR dark /dT s , (µmol photons m -2 s -1 • C -1 ) so that it can directly be applied by users. The analysis is done with data collected on floats characterized for this effect, which, as we show here, varies between individual radiometers in both magnitude and sign.
We note that another paper with the same aims has been recently published, to which we have contributed  [5]. However, the methods presented here are different and are intended to be applied directly to BGC-Argo s-files, rather than additionally using the Argo B-and transmission files (these contain data at float park depth, which we do not use here). Furthermore, unlike  [5] , we found no significant sensor drift over the lifetime of the floats analyzed once the temperature-dependent correction was applied.
Materials and Methods Data from 218 BGC-Argo floats equipped with OCR504 radiometers, downloaded from https://www. ifremer. fr/erddap/tabledap/ArgoFloats. html (accessed on 28 March 2022), were investigated in this study. E d at three wavelengths, 380 nm, 412 nm, and 490 nm (W m -2 nm -1 ) and the instantaneous photosynthetically available radiation (iPAR, µmol photons m -2 s -1 from 400-700 nm) were used. The floats were located across the global ocean, samplng a range of conditions, from continental shelves to open ocean gyres and from high to low latitudes. Radiometers may sample every 10 meters from 1000 m to 250 m, though many record no radiometric measurements at all in this interval. Starting at 250 m, radiometric measurements are made every 1 m, and from 10 m to the surface every 0.
2 m. The average number of profiles taken per float in this dataset was 200. The average number of "good" daytime radiometry profiles per float was ninety, as determined following QC procedures outlined in  [7] , namely taken during consistent wave and cloud conditions and with sun elevation above 15°to the horizon. The average number of nighttime profiles taken per float, defined as sun elevations below the horizon, was six. The average temperature range experienced by these floats over their lifetime during good radiometric profiles in this dataset was 12. 44 • C. With this dynamic temperature range and given that  [4]  showed the existence of a significant temperature response for these instruments, a temperature-dependent radiometric dark correction is necessary to accurately quantify or model processes occurring at low light levels.
In our dataset, 28% of nighttime values and 8% of daytime values were removed by this filter. Following these QC steps, all the accepted profiles of a specific float and wavelength were compiled into a sensor-specific temperature versus irradiance database to determine a float-specific, wavelength-specific, temperature-dependent dark correction, which is assumed to be invariant in time. That is, the dark-current and temperature sensitivity were assumed constant throughout the life of a float, as we observed no evidence to the contrary. Daytime deep profiles and nighttime profiles were kept separate. The compiled profiles were then further subjected to the following two tests: (a) Temperature range test: the temperature range of the compiled dark profiles must be greater than 2. 5 • C. This test is important as the dE d /dT s is small (-3 × 10 -5 to 3 × 10 -5 W m -2 nm -1 • C -1 ) and hence not detectable relative to other environmental processes if the temperature gradient in a profile is too small.
Discussion and Summary For this BGC-Argo dataset, the mean absolute temperature corrections on E d using night and day profiles are 8 × 10 -5 and 9. 3 × 10 -5 [W m -2 nm -1 ] and maximum absolute corrections are 4. 4 × 10 -4 and 6. 14 × 10 -4 [W m -2 nm -1 ], respectively (Table  2 ). These corrections are more than an order of magnitude larger than the known sensitivity of the sensors (2. 5 × 10 -5 W m -2 nm -1 ), are consistent with what has been observed in the lab by  [4] , and hence are significant. The average correction is O (10%) of the 0. 1% light level, while the maximum is O (40%) of that value. We further investigated whether the corrections had a significant impact on the diffuse attenuation coefficient: K d = - 1 E d (z) dE d dz , (6) for profiles corrected with both methods using a center difference scheme.
Acknowledgments: We would like to express our deep gratitude to  Herve Claustre  from LOV and his team, who are responsible for the majority of the floats whose data are analyzed here and whose vision and determination resulted in radiometry being measured on the BGC-Argo floats. These data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. (https://argo. ucsd. edu (accessed on 28 March 2022), https://www. ocean-ops. org (accessed on 28 March 2022)). The  Argo Program  is part of the  Global Ocean Observing System. We thank  Andrew Thomas  for help editing this manuscript. Comments by three reviewers helped improve this paper.
However, only a few studies have rigorously validated this temporal periodicity for particular species and commonly only for some portion of lifespan (see review by  Cailliet, 2015). To further complicate this, recent studies suggest that growth zone deposition (i. e. , vertebral band pairs) in sharks and batoids is closely related to somatic growth and body shape, rather than time  (Natanson et al. , 2018; James & Natanson, 2020). As an alternative, theoretical ages may be obtained indirectly from length via growth models, such as the von Bertalanffy growth model, which is the most common growth model utilized for fishes. Mark-recapture data can provide information on size increments over time, which are used as inputs to these growth models. Rearing in research laboratories or public aquariums such as Association of Zoos and Aquariums (AZA) accredited institutions also provides the invaluable opportunity to advance scientific knowledge of the animals in their care and enhance the conservation of wild populations (Association of Zoos and Aquariums, 2022a).
, 2014). Incorporating auxiliary information on growth parameters, even basic information such as a biologically feasible range for the disc width, can help the estimation process in such situations (e. g. Dureuil et al. , 2022). Building on previously published work by  Bassos-Hull et al. (2014)  and incorporating data from individuals housed in aquarium and zoo facilities, this study re-estimated growth parameters in this endangered ray species using both a frequentist and Bayesian modeling framework. In the latter, auxiliary information was formalized in prior distributions assigned to growth parameters. In addition, important life history parameters such as size at birth, age at maturity, growth, maximum size and longevity of wild and aquarium-housed A. narinari were examined and compared.
Animal housing and monitoring in the study aquarium Current and historical morphometric data from these 19 aquarium-housed A. narinari were included in this study. All A. narinari at GAI were housed in the Ocean Voyager exhibit, in a 24-million-liter saltwater habitat containing about 8000 individuals of 60 other species of sharks, rays, and teleost fishes. Water temperature in the exhibit was maintained between 24-25°C year-round. Juvenile and adult rays were target fed a daily ration between 1. 5-3% of their body weight (BW), with pups receiving higher rations (3-5% of BW/d). Current diets consist of surf clam (Spisula solidissima), hardshell clam (Mercenaria mercenaria), Jonah crab (Cancer borealis), knobbed whelk (Busycon carica), Atlantic sea scallops (Placopecten magellanicus), whiteleg shrimp (Litopenaeus vannamei) and blue mussels (Mytilus edulis), but have varied historically.
Aquarium survey Aetobatus narinari is managed in public aquariums through the Species Survival Plan ® (SSP) of the Association of Zoos and Aquariums. The cooperatively-managed SSP programs oversee the population management and enhance conservation of select species in AZA member institutions. SSP programs are led by AZA member volunteers who work collaboratively with AZA committees, Scientific Advisory Groups, and AZA-accredited institutions to maintain healthy, genetically diverse, and demographically varied populations through strategic management and planning (Association of Zoos and Aquariums, 2022b). To collect data for this study, a survey was sent to the AZA A. narinari stakeholders. In the survey, institutions were asked to provide the following information, as available, regarding animals of each sex in their collection: maximum size (DW in cm) recorded with corresponding age, maximum lifespan recorded and corresponding size, all sizes at birth recorded and ages at first reproduction recorded for sexually mature rays.
All the model estimation and inference described above were implemented in R version 4. 0. 2 (R Core Team, 2022). We made use of the R package Template Model Builder (TMB;  Kristensen et al. , 2016)  to code the joint likelihood of the Bayesian Fabens estimation and of the Fabens model with random intercept. For the former, we used the R packages tmbstan  (Monnahan & Kristensen, 2018)  and rstan  (Stan Development Team, 2020)  to link to the Stan Stan Development Team, 2022 statistical libraries which include the NUTS algorithm. We used TMB's efficient implementation of the Laplace approximation for integrating random effects both to evaluate the marginal loglikelihood in the random effects model and to evaluate the marginal densities in the BF computations. Our code is readily available on the GitHub page of the third author  (Aeberhard, 2022).
Data preparation In the wild population, only recaptures with times at liberty longer than 90 days were included in the growth analysis because of the large variability and influence on errors for shorter time periods  (Simpfendorfer, 2000; McAuley et al. , 2006). Successive recaptures of the same individual were merged into a single capture-recapture event to maximize the time at liberty and thus the observed growth. Two methods were then used to identify potential outliers, the influence plot  (Dureuil & Worm, 2015)  and the interquartile range  (Dureuil et al. , 2022). Any data point identified by both of these methods was considered an outlier. To estimate growth parameters in the aquarium population, we considered the first and the last measurement of each individual in order to maximize the observed growth.
Estimation and comparison of growth parameters Estimation of von Bertalanffy growth parameters in wild versus aquarium rays In the wild group, none of the eligible data points were considered an outlier using the methods previously described (influence plot and IQR). However, a single recapture of a small male (74 cm DW) showing null growth over five months (166 days) was removed from the dataset. The selection of recaptures with times at liberty longer than 90 days resulted in a final data set of 22 recaptures (15 males and seven females) used for growth analysis (Supplementary Material 7). Size at capture ranged from 60 to 155 cm in females, and from 51 to 159 cm DW in males. Size at recapture ranged from 134 to 185. 2 cm DW in females, and from 84 to 169. 5 cm DW in males. Time at liberty ranged 1.
Acknowledgments We thank  Mote Marine Laboratory's Marine Operations Team  ( P. Hull ,  D. Dougherty ,  G. Byrd  and  C. Cole ) and several field operations graduate students and interns (especially  Breanna DeGroot  and  Brianna Cahill ). We wish to thank the  Animal Health and Zoological Operations teams at Georgia Aquarium , and the  AZA Spotted Eagle Ray SSP  stakeholders who provided valuable growth and life history data. We are grateful to  Matthew Ajemian  and  Juan Carlos Peŕez-Jimeńez  for their helpful comments and constructive suggestions for improving the manuscript.
Motivation: In multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates this differentiation through the simultaneous learning of prediction tasks across cohorts. Since multicohort data can often not be combined into a single storage solution, there would be the substantial utility of an MTL application for geographically distributed data sources. Results: Here, we describe the development of 'dsMTL', a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. First, we derive the theoretical properties of these methods and the relevant machine learning workflows to ensure the validity of the software implementation.
Second, we implement dsMTL as a library for the R programming language, building on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data. Third, we demonstrate the applicability of dsMTL for comorbidity modeling in distributed data. We show that comorbidity modeling using dsMTL outperformed conventional, federated machine learning, as well as the aggregation of multiple models built on the distributed datasets individually. The application of dsMTL was computationally efficient and highly scalable when applied to moderate-size (n < 500), real expression data given the actual network latency.
g. diagnosis) associated patterns across datasets with dataset-specific, as well as shared, effects. MTL has numerous exciting application areas, such as comorbidity modeling, and has already been applied successfully for, e. g. disease progression analysis  (Zhou et al. , 2013). Here, we describe the development of dsMTL ('Federated Multi-Task Learning for DataSHIELD'), a package of the statistical software R, for Federated Multi-Task Learning (FeMTL) analysis (Fig. 1 ). dsMTL was developed for DataSHIELD  (Gaye et al. , 2014) , a platform supporting the federated analysis of sensitive individual-level data that remains stored behind the data owner's firewall throughout analysis  (Wilson et al. , 2017). We explored the utility of the dsMTL algorithms for comorbidity analysis based on simulation data.
Using data with variable degrees of cross-dataset heterogeneity aimed at quantifying the ability of the MTL algorithms to suitably characterize shared and specific biological signatures. We then showed the scalability of dsMTL methods for up to 20 servers compared to the federated ML methods. In addition, we analyzed actual RNA sequencing and microarray data in order to show that: (i) such analysis can be performed in an acceptable runtime using dsMTL given the real network latency, and (ii) federated MTL can capture more reproducible genes signatures compared to federated ML.
In a non-federated implementation, this method has previously been used to predict the response to different drugs, and the identified models showed a high degree of interpretability in the context of the represented drug mechanism  (Yuan et al. , 2016). dsMTL_Net incorporates a tasktask network as the shared structure in order to improve biological interpretability. In a non-federated version, this technique has Fig. 1. Schematic illustration of dsMTL using comorbidity modeling of schizophrenia and cardiovascular disease as an example. Multiple datasets stored at different institutions are used as a basis for federated MTL. dsMTL was developed in the DataSHIELD ecosystem, which provides functionality regarding data management, transmission and security. Data are analyzed behind a given institution's firewall and only algorithm parameters that do not disclose personally identifiable information are exchanged across the network.
Such modeling has been applied to explore dependencies in multi-omics data for biomarker identification  (Fujita et al. , 2018; Yang and Michailidis, 2016). In addition to the federated MTL methods, we also implemented a federated version of the conventional Lasso (dsLasso)  (Tibshirani, 1996)  in the dsMTL package due to its wide usage in biomedicine and as a benchmark for testing the performance of the federated MTL algorithms. The derivation of the mathematical properties, optimization procedure and machine learning workflows of each algorithm are summarized in the Supplementary Methods.
e. up-versus down-regulated). Here, we call these signatures 'heterogeneous'. In contrast, 'homogeneous' signatures relate to the same features and signs across datasets. We aimed to capture this shared information using the supervised dsMTL method dsMTL_L21 and assessed its predictive performance as well as model interpretability. In case study 2, we assumed that the outcome-associated signature was the sum of the comorbidity signature and two disease-specific signatures. This mixed structure can be disentangled by the unsupervised dsMTL method dsMTL_iNMF. A detailed description of the simulated data can be found in the Supplementary Material. In addition, a scalability analysis was conducted using simulation datasets to show the dependence of dsMTL's efficiency on the number of servers. The efficiency was quantified as the communication cost, i.
e. the number of network accesses, which has been recognized as the primary bottleneck of efficiency for federated applications  (Li et al. , 2020). We scaled the number of participating servers from 1 to 20 and trained the dsMTL_L21 (federated MTL) as well as dsLasso (federated ML) models for each given number of servers. The number of network accesses was counted as the basis for the comparison. Furthermore, we varied the number of subjects, in order to explore the dependency of the communication cost on the sample size. Two studies were performed on real gene expression datasets, in order to investigate the efficiency and interpretability of dsMTL. For supervised analysis, a three-server MTL analysis was conducted to assess the running speed. For the unsupervised analysis, we simultaneously investigated data from patients with schizophrenia and bipolar disorder, and quantified the running speed on two actual servers.
In order to explore the biological plausibility of the identified gene signatures, we performed a pathway enrichment analysis. Furthermore, in order to demonstrate the reproducibility of the gene signatures found by dsMTL, the overlap of the signatures found by dsMTL using different datasets for training was investigated. For this analysis, the ensemble of local models was used as the baseline. A detailed description of the preprocessing, as well as the analysis of real gene expression data can be found in the Supplementary Material.