dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This document was prepared by the DUNE collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. The DUNE collaboration also acknowledges the international, national, and regional funding agencies supporting the institutions who have contributed to completing this Technical Design Report. -563 --563 --563 -.
Gb/s, with temporary detector module data). Laboratory tests peak throughput of will allow to 100 Gb/s. demonstrate the performance reach. SP-DAQ-3 DAQ readout 10 µs < readout Storage of the complete Implementation window: The DAQ window < 100 s dataset for up to 100 s is techniques to be shall support storing required by the SNB physics validated on the triggered data of one studies; the typical readout ProtoDUNE setup or more APAs with a window of 5. 4 ms is defined and in test labs. variable size readout by the drift time in the window, from few µs detector; calibration triggers (calibration) to 100 s can be configured to readout (SNB), with a typical data much shorter time readout window for intervals. triggered interactions of 5. 4 ms. -381 --381 --381 -.
490 DC direct coupling. 142, 147, 154, 170, 573, 578 DUNE construction database (DCDB) Database used by DUNE to track the history and testing of all parts of each detector module. 497, 548 DCS Distributed Communications System. 450 DAQ data receiver (DDR) The portion of the DAQ front-end fragment that accepts data from the FER, emits trigger candidates produced from the input trigger primitives, and forwards the full data stream to the DAQ primary buffer. 567 DUNE detector safety system (DDSS) The system used to manage key aspects of detector safety. 16, 175, 207, 431 detector module The entire DUNE far detector is segmented into four modules, each with a nominal 10 kt fiducial mass. 19-21, 60, 62, 64, 74, 75, 81, 82, 86, 87, 125, 131, 132, 164, 223, 270, 277, 303, 304, 362, 366, 375, 376, 388, 392, 393, 396, 423, 429, 431, 432, 434, 436, 438, 440, 442, 446, 447, 451, 457, 458, 461, 464, 469, 473, 476, 489-494, 496, 504, 506-509, 523, 525, 555-558, 568-570, 579, 580detector unit A portion of a detector module may be further partitioned into a number of similar parts.
, 207, 209-211, 214, 215, 217, 219-221, 223, 517, 526, 527, 538, 550, 576 A material or process by which incident photons are absorbed by a material and photons are emitted at a different, typically longer, wavelength. 8, 129, 233, 237, 238, 240, 261, 265, 267-270, 273, 278, 279, 288, 299, 311, 312, 576, 577, 579, 581 warehouse management system (WMS) Commercial software package used to track shipments and interface to freight forwarders. This includes a database for shipping. 493, 497, 498 White Rabbit (WR) A component of the timing system that forwards clock signal and time-of-day reference data to the master timing unit. 397 X-ARAPUCA Extended ARAPUCA design with WLS coating on only the external face of the dichroic filter window(s) but with a WLS doped plate inside the cell. 9, 228, 229, 232-234, 236-241, 243, 251, 252, 259-270, 273, 275, 278, 279, 293-295, 304, 311, 313, 428 -580 --580 --580 --581 --581 --581 - wavelength-shifting (WLS).
Patient Inclusion and Clinical Data Patients with proven NSCLC were prospectively included in an exploratory cohort. Eligibility criteria were an Eastern Cooperative Oncology Group performance status (PS) of 0-2, no use of anticoagulation and no clotting disorders. All patients started (a new line of) treatment at time of inclusion. Informed consent was obtained from all patients. The study was approved by the Medical Ethical Committee (2015/602) and was registered in the Dutch trial register (NL55754. 042. 15/NL5423).
Single-Cell Whole-Genome Sequencing Single isolated CTCs were stored in freeze buffer after isolation. We performed scWGS as described previously with some minor modifications  [49]. In short, upon MNase treatment, de-crosslinking was performed by incubation at 65 • C for 1 h in the presence of Proteinase K (0. 025U) and NaCl (200 mM), followed by AMPure XP bead purification and subsequent end repair and A-tailing. During PCR, indexes were introduced to each DNA fragment allowing multiplexing of the libraries for sequencing. All libraries were sequenced with the Illumina NextSeq 500. Data analysis was performed using the AneuFinder package  [49, 50].
Conceptualization, M. T. , K. C. A. , T. J. N. H. , W. T. and H. J. M. G. ; methodology, M. T. , T. J. N. H. , M. J. and W. T. ; formal analysis, M. T. , K. C. A. , H. v. d. B. and W. T. ; resources, H. J. M. G. , M. J. and L. W. M. M. T. ; data curation, M. T. , K. C. A. and H. v. d. B. ; writing-original draft preparation, M. T. ; writing-review and editing, K. C. A. , T. J. N. H. , E. S. , H. v. d. B. , M. J. , D. C. J. S. , P. M. L. , W. T. , L. W. M. M. T. and H. J. M. G. ; supervision, T. J. N. H. , W. T. , D. C. J. S. , P. M. L. , L. W. M. M. T. and H. J. M. G. ; funding acquisition, L. W. M. M. T. and H. J. M. G. All authors have read and agreed to the published version of the manuscript. Funding: The authors are part of the CANCER-ID consortium, which has received support from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement No 115749.
Acknowledgments: During the study,  R. Smith ,  J. Wheeler  and  J. Ladtkow  ( Terumo BCT, Lakewood, CO, USA ) provided advice and key insights into the apheresis procedure and technology. Procedures were run with the assistance of the personnel from Sanquin (Sanquin,  Sanquin Bloedvoorziening , Groningen, The Netherlands). We are very grateful for all of their expertise and efforts.
Author Contributions: Conceptualization, M. T. , K. C. A. , T. J. N. H. , W. T. and H. J. M. G. ; methodology, M. T. , T. J. N. H. , M. J. and W. T. ; formal analysis, M. T. , K. C. A. , H. v. d. B. and W. T. ; resources, H. J. M. G. , M. J. and L. W. M. M. T. ; data curation, M. T. , K. C. A. and H. v. d. B. ; writing-original draft preparation, M. T. ; writing-review and editing, K. C. A. , T. J. N. H. , E. S. , H. v. d. B. , M. J. , D. C. J. S. , P. M. L. , W. T. , L. W. M. M. T. and H. J. M. G. ; supervision, T. J. N. H. , W. T. , D. C. J. S. , P. M. L. , L. W. M. M. T. and H. J. M. G. ; funding acquisition, L. W. M. M. T. and H. J. M. G. All authors have read and agreed to the published version of the manuscript. Funding: The authors are part of the CANCER-ID consortium, which has received support from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement No 115749.
Furthermore, none of the aforementioned methodologies is able to deal with the systematics introduced by the parallax spatial correlations present in the Gaia data (see Sect. 5. 4 of  Lindegren et al. 2018). Following the guidelines provided by  Luri et al. (2018) , in this work we attempt to solve the aforementioned issues in the specific context of stellar clusters by providing the astrophysical community with the free open code Kalkayotl 2. It samples the joint posterior distribution of the cluster parameters and stellar distances, given their Gaia astrometric data and a set of cluster oriented prior families. Our approach is different from that adopted in the aforementioned works. While  Bailer-Jones et al. (2018) ,  Wright & Mamajek (2018)  and  Anders et al. (2019)  analytically or numerically find statistics of the stars posterior distance distributions using a unique Galactic prior, Kalkayotl obtains samples of the posterior distribution of the star and cluster parameters for a set of cluster oriented prior families by means of Hamiltonian Monte Carlo  (Duane et al.
Methodology Kalkayotl is a free python code 3  designed to simultaneously sample the joint posterior distribution of cluster parameters and stellar distances. In addition, the user can decide to only sample the stellar distances by fixing the cluster parameters, and/or perform the sampling on the parallax space (i. e. sampling the cluster and sources true parallaxes). The latter can be useful when the subsequent analyses need to be done on the parallax space. Although the methodology can be applied to any parallax measurement, Kalkayotl is specifically designed to work with Gaia astrometric data. The users will also be able to run the code through the Spanish Virtual Observatory, although with limitations.
Prior families The prior distribution is supposed to encode the previous knowledge of the investigator about the plausibility of the parameter values in a model. In the case of stellar clusters, we know that their stars share common distributions of their astrophysical properties, like their distance to the observer, age, metallicity, etc. This a priori information is what we use to construct an informed prior. Nonetheless, given the variety of cluster morphologies, we believe that there is no universal prior for clusters. In Kalkayotl we propose two types of distance prior families, one based on classical statistical probability density distributions and another based on purely astrophysical considerations. The purely statistical ones are common distributions used in the literature, while the astrophysical ones are inspired by previous works devoted to the analysis of the luminosity (or number) surface density profiles of galactic and globular clusters.
Hyper-priors If the user decides to infer the cluster parameters, φ = {loc, scl}, together with the source parameters Θ, then a hierarchical model is created with the cluster parameters at the top of the hierarchy. In this case, a prior must be set for each parameter of the chosen A&A proofs: manuscript no. Kalkayotl cluster prior family. In the Bayesian jargon, this kind of prior is called hyper-prior, and its parameters, hyper-parameters. As hyper-prior for the location, loc, and scale, scl, we use the Normal(loc|α) and Gamma(scl|2,2/β) densities respectively, where α and β are their hyper-parameters. The Gamma distribution and its hyper-parameters are specified following the recommendations of  Chung et al. 2013. The specific choice of the rate parameter as 2/β in the Gamma distribution results in the mean of the latter at β.
We notice that this latter case should not be used in combination with the EDSD prior because it will be meaningless to infer the scale length of a Galactic prior based on data from the population of a single Galactic cluster. In Kalkayotl the posterior distribution is sampled using the Hamiltonian Monte Carlo method implemented in PyMC3 (Salvatier et al. 2016), which is a Python probabilistic programming framework. For details of the capabilities and caveats of Hamiltonian Monte Carlo samplers on hierarchical models we refer the reader to the work of  Betancourt & Girolami (2013). In particular, our model faces the typical problems of sampling efficiency associated with hierarchical models. Thus, following the recommendations of the aforementioned authors, Kalkayotl allows the user to choose between the central and non-central parametrizations (except for the GMM prior family, which contains more than one scale parameter).
In addition, it also makes trace plots of the cluster and source parameters. Although the automatic analysis made by PyMC3 usually suffices to ensure convergence, we strongly recommend the users to visually inspect the chains to ensure that no anomalies are present. As in most Bayesian inference problems, the investigator must face the decision of choosing the most suitable prior. The rule of thumb is that there is no universal prior, and the most suitable prior depends on the specific problem at hand. Thus, to help users decide which prior family might be the most suitable for their data sets, Kalkayotl offers a module to make comparison of models by means of Bayes factors, which are the ratio of the Bayesian evidence (Z in Eqs. 5 and 6) of each pair of models. Estimating the Bayesian evidence is a hard and computationally expensive problem, thus, in order to reduce the computation time extra assumptions are needed.
Validation In this section, we measure the accuracy, precision, and credibility of our methodology at estimating the true values of both the population and source parameters (further details and additional figures can be found in Appendix B). We measure accuracy and precision as the fractional error (i. e. the posterior mean minus the true value divided by the true value) and the fractional uncertainty (i. e. the 95% posterior credible interval divided by the true value), respectively. We define credibility as the percentage of synthetic clusters realizations in which the inferred 95% posterior credible interval contains the true value (i. e. the true value 10 https://github. com/agabrown/PyGaia is covered by the 2σ uncertainties). This definition of credibility measures the trustworthiness of the inferred value and its reported uncertainty.
Conclusions and future perspectives. We make public the free and open code Kalkayotl. It is a statistical tool for the simultaneous inference of star cluster parameters and individual distances of its stars. This tool utilizes distance prior families specifically designed for stellar clusters and takes into account the parallax spatial correlations present in the Gaia data. Upon convergence, Kalkayotl delivers high credibility (>90%) estimates of distances to stellar clusters located closer than ∼5 kpc, and cluster sizes up to ∼1 kpc, with these values depending on the number of cluster stars. The samples from the posterior distributions of both cluster parameters and source distances can be used to propagate their uncertainties into subsequent analyses. Although the general formalism of our methodology can be applied to parallax measurements of diverse origins, our methodology is tuned to deal with the parallax spatial correlations of the Gaia data.
Acknowledgements. We thank the anonymous referee for the comments that helped to improve the quality of this manuscript. This research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  682903 ,  P. I. H. Bouy ), and from the  French State  in the framework of the  "Investments for the future" Program ,  IdEx Bordeaux , reference  ANR-10-IDEX-03-02. L. C. and Y. T. acknowledge support from " programme national de physique stellaire" (PNPS) and from the "programme national cosmologie et galaxies" (PNCG)  of  CNRS/INSU. The figures presented here were created using Matplotlib (Hunter 2007). Computer time for this study was provided by the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Bordeaux  and of the  Université de Pau et des Pays de l'Adour.
Patient sample The current study used data from the Resilience Study, a prospective longitudinal, cohort study conducted in the Neonatal Intensive Care Unit (NICU) of the University Hospitals Leuven, Belgium. Parents of preterm infants born before 34 weeks gestational age (GA) and/or with a birth weight less than 1500 g were informed within the rst three days after birth. Exclusion criteria were parents age < 18 years, absence or limited knowledge of Dutch or English, medical (somatic or psychiatric) condition in the parent(s) that impeded participation, and the presence of a major congenital malformation or central nervous system pathology (grade 3 or grade 4 intraventricular hemorrhage or periventricular leukomalacia) at the time of consent. Preterm infants (n=136) were included in the study between July 2016 and 2018.
Data acquisition Patients' pain levels were daily recorded at the cot-side. Pain scores were assessed using the Leuven Pain Scale (LPS), a validated multidimensional pain scale for preterm infants  [17] ,  [18]. The LPS assigns scores (0,1 and 2) to seven categories (facial expression, cry-A c c e p t e d M a n u s c r i p t ing, irritability, drowsiness, muscle tension, comfort, and heart rate). Thus, LPS scores vary between 0 and 14, with higher scores corresponding to higher pain intensity. A score of 4 is regarded as the critical threshold for distinguishing a comfortable condition from an uncomfortable one (not pain-free and/or acute pain). The inter-rater reliability was 0. 88  [17] ,  [18]. Pain assessment was done routinely by bed-side nurses that were familiar with the LPS and noted it in the patient's electronic medical le.
A total of 245 recordings had corresponding pain scores available and were analyzed. Table  1  summarizes the clinical characteristics of patients at each measuring point. EEG data were collected according to the 10-20 system using nine monopolar electrodes (F p1 , F p2 , C 3 , C 4 , C z , T 3 , T 4 , O 1 , O 2 ) and monitored with the OSG system (OSG BVBA, Brussel). Each EEG signal was referenced to C z , which was then excluded from further analysis, leaving a total amount of 8 channels. ECG was then used to derive the tachogram or HRV signal as subsequent R-peak to R-peak intervals (RRi). The R-DECO toolbox  [19]  was used for R-peaks detection.
An ecient way to estimate the Hurst exponent H is based on the wavelet transform  [36]. Wendt proposed to estimate the spectrum of singularities via wavelet leaders, which measures the dierent Hurst exponents in the signal and their associated fractal dimension. The c 1 ,c 2 ,c 3 parameters respectively represent the location A c c e p t e d M a n u s c r i p t of the maximum, the width and the asymmetry of the singularity spectrum. Therefore, c 1 is usually considered the main Hurst exponent from the multifractal signal, while c 2 and the dierence between the maximal and minimal Hurst exponent are variational indices to represent the dispersion of fractals inside the signal. Further details of the methodology are reported in  [36] , which also describes the WLBFM toolbox to estimate the fractal parameters in MATLAB.
Neural features The dysmature EEG features can also be quantied by means of the NEURAL toolbox  [45] , available on GitHub. The approach proposed by the authors yields an exhaustive range of features that can be obtained from amplitude, burst and spectral information, connectivity analysis and the range EEG (rEEG). As mentioned by O'Toole  [45] , the range EEG (rEEG) was proposed as an alternative to amplitudeintegrated EEG (aEEG), since a clear denition of the amplitude integrated EEG is missing and dierent EEG machines implement dierent algorithms for its estimation. The range EEG represents a ltered, compressed and rescaled version of the EEG: it is normally band-pass ltered between  [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]  Hz, compressed and rectied in the time domain with a windowing procedure and rescaled in a linear-log amplitude scale.
Sleep-based Classication A customized software tool was developed using MATLAB built-in machine learning libraries to classify patients into stress or absence of stress conditions. To summarize, the following groups of features were derived for each patient and for both QS and nQS: • Power features for both HRV and EEG: 7 features for each HRV time-series and 5 features for each EEG channel • Entropy features: 12 features for each EEG channel and HRV time-series • Regularity features: 6 features for each EEG channel and HRV time-series • NEURAL features for EEG: 30 for all EEG channels • EEG connectivity topological indices: 76 features for all EEG channels • Brain-heart connectivity topological indices: 18 features for all EEG channels and HRV combined and 10 directionality indices. Considering the number of channels, the dierent frequency bands and the dierent type of methodologies, 800 features in total were extracted.
Given that the number of features should be below one tenth of the training dataset size and the datapoints in the dierent datasets can go as low as 50, the subspace of features has been restricted to 5  [61]. However, before tuning of the model, a basic ltering approach was applied. Features were included in the subspace ensemble algorithm if intra-feature correlation was below 90% and they had the highest ratio between intra and inter variance ratio  [61]. In addition, the subject's PMA is an important feature covariate given the maturational changes in both EEG and HRV features. Therefore, if there was a signicant Pearson correlation between the selected feature and PMA A c c e p t e d M a n u s c r i p t (p < 0. 05), the linear correction on the generic feature F F corr = F -b 1 * age -b 0 was applied, where F corr was the baseline value deprived of age eects.
The stress classication was tested in the three datasets based on 3 monitoring groups (5days, 34w, P SG) and both sleep states. The stress classication was also tested on 4 dierent regroupings of the recordings based on PMA: ≤ 32 weeks,  (32 -34]  weeks,  (34 -36]  weeks, > 36 weeks. The reason to test the classication performance on those dierent groups is twofold. On the one hand, this classication might still be inuenced by age in the monitoring groups (especially in the rst group, where the frequency of hands-on care and the heterogeneity of PMA and gestational age are the highest). On the other hand, if a classier has to be implemented at the cotside, clinicians tend to be interested in a classier that considers the age of the patient instead of the monitoring group. This age division follows clinical and physiological denition.
Results The results for the stress classication for the monitoring groups are reported in Figure  1  and 2 , respectively for QS and nQS. The left panel of both Figures reports the AUC, while the right one shows the kappa score. The AUC, intended as measure for accuracy, is above 0. 7 for both QS and nQS. Specically, the AUC of two sleep states respectively lies in the range [0. 73-0. 97] and [0. 70-0. 90]. However, the description of the classier would be incomplete without reporting the agreement between the ground truth and the predicted labels, expressed by Cohen's kappa. The kappa scores span the range [0. 24-0. 68] for QS and [0. 18-0. 68] for nQS, which suggests a moderate association between features and the outcome variable, but also an increased AUC due to an unbal-A c c e p t e d M a n u s c r i p t anced design of the dataset.
In fact, the most noticeable aspect is that kappa does not necessarily increase with increasing pain threshold, but it can also decrease (as shown in the PSG group), which can be due to the presence of an unbalanced dataset. The results for age groups are reported in Figure  3  and 4. Similarly to the monitoring groups, AUC can respectively span in the range [0. 78-0. 93] for nQS and [0. 77-0. 96] for QS and the kappa score span the range [0. 18-0. 59] for nQS and [0. 20-0. 63] for QS. As mentioned in the monitoring groups, the AUC can increase beyond 0. 7, however Cohen's kappa shows a moderate association between features and outcome variable or the possibility of an unbalanced dataset. Moreover, kappa score can either increase or decrease in function of the pain score and it especially decreases in groups above 36 PMA weeks in nQS.
This research is supported by Bijzonder Onderzoeksfonds KU Leuven (BOF): The eect of perinatal stress on the later outcome in preterm babies (# C24/15/036). The research leading to these results has received funding from EU H2020 MSCA-ITN-2018: 'Integrating Functional Assessment measures for Neonatal Safeguard (INFANS)', funded by the European Commission under Grant Agreement # 813483. This paper reects only the authors' views and the Union is not liable for any use that may be made of the contained information. This research is supported by Flanders AI Research Program. M. L. is a SB PhD fellow at Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO), supported by Flemish government.