dataesr/scientific-paragraphs-categorization huggingface dataset:
CV) and the combination of selected features is retained if the predictive performance is improved. When all features are tested Data Preprocessing and the signature is identified, the model is evaluated on other BioDiscML supports multiple input files (e. g. , clinico- cross-validation/sampling procedures (see Model Evaluation). pathological information with omics data), as the condition Once all classifiers are tested, we end with a set of feature- that sample identifiers exist in all files to perform joining. The optimized models with their associated performances metrics input datasets are assumed to be clean and consistent, in a (see Model Evaluation) and associated features, for each model. flat file format, table-like structure with samples in rows and In total, about 8,500 models for classification and about 1,800 features in columns (Figure 2).
Field separator symbols (e. g. , for regression are tested, but a large part will not be computed tabulation, comma, semicolon) are automatically detected based because of non-supported data (see Supplementary Table S1). on the first lines of the file. Feature and instance duplicate names Once all models are generated, the program executes the best are not allowed. Where multiple datasets are submitted, only model(s) selection section. The average performance among one must contain the class label. File contents are composed some computed metrics (see Model Evaluation) are used to of instance identifiers (e. g. , samples, patients) associated to estimate the most efficient model (see Best Model Selection), and numerical and/or nominal features (e. g. , high/medium/low, correlated features are retrieved from the original dataset (see effect_A/effect_B, Drug_1/Drug_2).
Let be a set of q datasets Checkpoint FIGURE 1 | BioDiscML pipeline. Preprocessing and feature selection procedures are fully parallelizable, When all features-optimized models are computed, the model selection starts. The program can be also started from the checkpoint at any moment during the execution. *TheSet of ML classifiers is the set of pre-configured commands in classifiers. conffile. All classifiers are listed in the Supplementary TableS1. **Criterions are optimized metrics, evaluated by 10-folds cross validation (10 CV), used to assess if a model is improved, such as accuracy, balanced error rate, Matthew's correlation coefficient, area under the curve, sensitivity, specificity,.
Algorithm 1 : 1 Dimension reduction by Information Gain and ReliefF Input: train instances of D (merged datasets), classifierType (classification or regression) Output: Dataset with ranked best features S for each feature array A do if classifierType = classification then meritScore a = Compute information gain value of A with respect to classes Y else meritScore a = Compute ReliefF value of a with respect to classes Y end if if meritScore a = 0 then add | meritScore a |to meritScores end if end for SortedFeatures = Sort meritScores from largest to smallest values if SortedFeatures ≤ 1000 then S = SortedFeatures else S = SortedFeatures{A 1 , A 2 ,. , A 1000 } end if return S.
Algorithm 2 : 2 Identification of features subsets and featureoptimized models Input: Dataset with ranked best features S, set of ML classifiers with various hyperparameters, set of criteria, datasets D Output: Feature-optimized models list L with their identified features subset if newCriterionScore rule CriterionScore then discard A from selectedFeatures else keep A in selectedFeatures criterionScore = newCriterionScore for each selectedFeature from before last kept feature to the first selected feature in selectedFeatures do function EVALUATE(model, selectedFeatures, dataset D, list of remove selectedFeature from selectedFeatures models L) subModel = Train using classifier with trainSetEvaluation = Evaluate model using 10CV, LOOCV, selectedFeatures Bootstrap, Repeated Holdout, 0. 632+ estimator on train set subNewCriterionScore = perform 10CV testSetEvaluation = Extract selectedFeatures from test evaluation instances of dataset D and perform holdout evaluation if subNewCriterionScore rule with model NewCriterionScore then performances = trainSetEvaluation, testSetEvaluation discard selectedFeature from selectedFeatures add model with performances and selectedFeatures to L NewCriterionScore = subNewCriterionScore return L else end function keep selectedFeature in selectedFeatures end if for each classifier in classifiers do end for for each criterion in criteria do end if for each featureSearchMethod in featureSearchMethods{FSS, end for BSS, FSSBSE, BSSFSE) end if do L = EVALUATE(model, selectedFeatures, A, L) if criterion must be maximized end for (see Supplementary Table S2) then end for criterionScore = 0 # create models without stepwise feature subset selection rule = "lesser than" approaches else selectedFeatures = k first features criterionScore = 1000 model = Train using classifier with selectedFeatures rule = "greater than" from dataset S end if L = EVALUATE(model, selectedFeatures, A, L) if featureSearchMethod = FSS or BSS then end for if featureSearchMethod = BSS then return L S = invert feature rank order of S end if for each feature A in S do Add A to selectedFeatures MCC on the test set (TEST_MCC, see readme program file), or on the best bootstrapping using merged training and testing sets (TRAIN_TEST_BS_MCC).
7 12 A2DE BSSFSE AUC 93. 3 98. 6 11 HT FSSBSE AUC DLBCL 68 9 RF 100 93 6 A1DE FSSBSE MCC 98. 7 98. 3 6 NB FSSBSE AUC Prostate cancer 95. 2 158 SVM 100 91 12 VFI BSSFSE ACC 99 95. 7 10 NB FSSBSE AUC Feature Selection Procedures in BioDiscML Are Fast and Scalable Omics datasets are generally composed of a thousands of features. To simplify input datasets and save computation time BioDiscML implements a feature ranking and dimension reduction procedure. Many approaches exist (Chandrashekar and Sahin, 2014) and most are applicable to biological problems.
2017; Bauböck et al. 2019). NICER pulse-profile modeling can therefore also help to constrain the characteristics of the hot regions. In this paper we undertake pulse-profile modeling of NICER X-ray Timing Instrument (XTI ) observations of the rotation-powered MSP PSR J0030+0451. Discovered as a radio pulsar by  Lommen et al. (2000)  and then identified as an X-ray pulsar  (Becker et al. 2000) , PSR J0030+0451 has a spin frequency of 205 Hz and lies at a distance of 325 ± 9 pc  (Arzoumanian et al. 2018). There are no independent prior constraints on either mass or radius. Our analysis uses the X-ray Pulsation Simulation and Inference package (X-PSI 8  v0. 1; Riley & Watts 2019, submitted to ApJS). X-PSI is a software package for Bayesian modeling of astrophysical X-ray pulsations generated by the rotating, radiating surfaces of relativistic compact stars.
X-PSI couples X-ray pulsation likelihood functionality to open-source statistical sampling software for use on high-performance computing systems; we apply nested sampling  (Skilling 2006)  in our analysis (refer to Appendix A). The work presented here is based on usage of a 500, 000 core-hour grant on the Dutch national supercomputer Cartesius. 9 ection 2 outlines the modeling choices and introduces details specific to this analysis, including issues associated with the PSR J0030+0451 surface radiation field parameterization, instrument response, prior definition and implementation, and the consequences for computational efficiency of posterior sampling. In particular we restrict this analysis to models with two distinct hot regions with various structures. While our choices are physically motivated, it is important to emphasize that our inferences are conditional upon these choices.
We term one member as ceding, and the other as superseding. As discussed in the appendix of  Bogdanov et al. (2019b) , the X-PSI implementation (referred to as the AMS code in  Bogdanov et al. 2019b ) of a radiating region is specialized for fast likelihood function evaluation when said function is a callback for sampling processes: whilst numerical approximations are necessary in general, it is relatively inexpensive to ensure that the proper area of each finite-element (discretised) radiating region is computed to a precision that (almost) exactly matches their mathematical definition. 24 When two members overlap to form a hot region: (i) the area of a discretely-represented superseding member is (almost) exact; and (ii) the area the discretely-represented, non-superseded subset of a ceding member is also (almost) exact.
These residuals are derived using the observations and following the procedure outlined in  Ludlam et al. (2018) , modified to use the appropriate number of detectors, ARF, RMF, and sun-angle cut consistent with the PSR J0030+0451 data set (refer to Section 2. 2). We acknowledge that the Crab is a remarkably different source to PSR J0030+0451: the expected operation of NICER (and X-ray instruments in general) in response to incident radiation fields is a function of its properties. The Crab exhibits a very different spectrum to PSR J0030+0451, being harder, more absorbed, and subject to astrophysical features; the Crab is also an extended source, not a point source, and is much brighter than the rotation-powered MSPs targeted by NICER. The calibration product is a channel-by-channel vector R of ratios of observed Crab count numbers to count numbers derived using a theoretical incident spectrum and the reference response matrix R.
The blue ring indicates the simplest model that can generate data that is visually comparable to the real data set (refer to Section 3 for discussion). The red annotations are to denote where we learn that a given model is not performing adequately or has unhelpful complexity. A strikethrough denotes that a model was not applied: (i) a solid strikethrough denotes that the model is considered as being incapable of generating synthetic event data that resembles the real XTI event data, based on performance of a simpler model with which some degree of symmetry is shared; (ii) a dotted strikethrough denotes that the model is considered to include unhelpful complexity for at least one hot region. at ∼ 0. 5 rotational cycles. Moreover, for both ST+CST and ST+CDT, the secondary region is formed (not necessarily by trivial union) from two members-one or both of which radiate-that are concentric and thus share a single colatitude parameter.
Another option would be to define a conditional prior distribution for each background parameter, where the upper-limit of the support is a function of the source parameters-e. g. , some fraction of the phase-average source counts in a channel. We did not consider at the outset of this work the possibility of jointly modeling NICER and XMM event dataa strategy both tractable and arguably more rigorous given that we do not have likelihood function information nor posterior information from  Bogdanov & Grindlay (2009)  that is compatible as a prior information to be updated conditional on the NICER data. We could thus consider constructing a joint likelihood function over observations with both telescopes (requiring definition of at least one more nuisance parameter). On the other hand, the NICER event data comprise far more photons than do the XMM data, and so inferences may be dominated by the (backgroundmarginalized) NICER likelihood function.
Furthermore, the microcalorimeter spectral resolution of X-IFU (∼ 2. 5 eV resolution for photons energies < 7 keV) will permit precise measurements of various edges of the interstellar medium (e. g. , oxygen K edge at 0. 55 keV, iron L edge at 0. 72 keV), thereby enabling derivation of independent tight constraints on the hydrogen column density N H for a given MSP. Archival NICER MSP data, on the other hand, will remain valuable far into the future because the principal NICER mission science objectives focus purely on MSPs; NICER will have compiled O(10 6 ) s integrated exposures on the primary MSP targets, meaning that despite the smaller total effective area, the size of the event data set will be synergistic with an advanced multi-faceted mission such as Athena whose science objectives are broader.
2013; Guillot & Rutledge 2014; Guillot 2016) , but the addition of new data and the use of more recent globular cluster distance measurements resulted in higher values, R eq in the 9. 9-11. 2 km range for a 1. 5 M NS (2σ credible interval;  Bogdanov et al. 2016). Combining qLMXBs and Type-I X-ray bursts, other works have found radii in a wide range of values: R eq ≈ 10. 4-12. 9 km (95% credible interval;  Steiner et al. 2010 Steiner et al. , 2013)) , R eq ≈ 10. 5-12. 7 km (90% credible interval;  Lattimer & Steiner 2014) , and R eq ≈ 9. 8-11. 4 km (95% credible interval,  Özel et al. 2016) , for a 1. 4 M NS. However, these analyses may have been affected by systematics, such as those due to the modeling of piled-up X-ray photons in the Chandra data 82 -which was not considered for all qLMXBs in these early analyses-or those due to the choice of atmospheric composition for qLMXBs (generally H versus He).
A longer total exposure time could also certainly be accumulated (and indeed the NICER team anticipate doing this). Previous studies that have examined how posterior estimation of mass and radius is sensitive to factors such as geometry, spin rate, and the number of source counts in the event data, indicate that constraining power increases as the square root of the number of counts  (Lo et al. 2013; Psaltis et al. 2014). However, those studies all assumed a single circular single-temperature hot spot, not a more complex hot region configuration such as those we have considered and inferred here. While it is likely that gathering more data will improve the joint constraint on mass and radius (without reference to an EOS model), the precise observing time required to achieve a given level of precision cannot be estimated robustly without further study.
A. 2. 2. Parameter kernel density estimation and credible regions We applied the post-processing module of the X-PSI package (v0. 1; Riley & Watts 2019, submitted to ApJS). X-PSI wraps-combines and adds functionality-to several other packages for statistical computation: relevant here for mode-separation algorithm are incompatible with the notion of process combination: the theory nor software implementation exists at the time of writing, and therefore we only display such a run-when relevant and available-in the posterior figures, but do not use it to calculate numerical estimators.
2006), PINT (photonphase; https://github. com/nanograv/PINT), MultiNest (Feroz et al. 2009), PyMultiNest (Buchner et al. 2014), GetDist (https://github. com/cmbant/getdist), nestcheck (Higson 2018;Higson et al. 2018 Higson et al. , 2019)), fgivenx ( Handley 2018 ), X-PSI (v0. 1; https://github. com/ThomasEdwardRiley/xpsi; Riley & Watts 2019, submitted to ApJS).
Background: Patients undergoing gynaecological surgery are known to experience anxiety. While the use of music selected by patients based on their personal taste has hardly been evaluated, a recent study suggests that musical preferences significantly alter the anxiolytic and relaxing effects of music. Our study aims to determine whether selfselected music decreases patient anxiety prior to gynaecological surgery, as compared with predetermined music from a software programme such as MUSIC CARE®. Methods: The study will consist of a clinical trial comparing the effects of self-selected music versus predetermined music on patient anxiety prior to gynaecological surgery. A minimum of 170 patients will be randomised in a 1:1 ratio. Inclusion criteria will be: women aged 18-55 years, awaiting scheduled gynaecological surgery under general/ local anaesthesia or under sedation; having created a personal 20-min playlist; and not having received anxiolytic drugs prior to surgery.
In 2018, another systemic review examined the effectiveness of music as an adjunct treatment in anxiety for patients undergoing hip and knee surgery and showed that music improved anxiety in six of the ten studies  [18]. Nevertheless, most published RCTs evaluate the effects of music therapy on preoperative anxiety by comparing patients who listen to researcher-selected or predetermined music (from a software programme such as MUSIC CARE®) with patients who listen to no music at all. The effects of self-selected music on patient anxiety have hardly been investigated  [19, 20]. One exception is a recent study on the anxiolytic and relaxing effects of music, which has highlighted the importance of preference and familiarity in eliciting the relaxation response  [21]. Perceptions of music styles, rhythms, and sounds are known to vary depending on cultural background, ethnicity, and personal experience.
However, music therapy in preoperative settings currently relies on software programmes that provide pre-selected, standardised music playlists. This is the case of MUSIC CARE®, the music therapy programme that has been in use in our gynaecology department for the past 10 months. Though the efficacy of MUSIC CARE® has been demonstrated, the playlists it provides (jazz, classical music, world music, etc. ) are not totally adapted to the specific cultural contexts of users. For example, in Reunion Island, a French overseas territory with a multiethnic and multicultural population, two popular music styles are Séga and Maloya. This type of music is not included in MUSIC CARE® software. Our study seeks to fill this gap by letting patients create their own, culturally adapted, music playlist. Our assumption is that a self-selected playlist composed of patients' preferred songs will have beneficial effects on preoperative anxiety.
Control group Patients will listen to a predetermined music playlist that they will have chosen among those available on the MUSIC CARE® software. Patients can choose among 25 playlists, including reverie, Cuban night, guitar ballad, film scores, folk guitar, etc. MUSIC CARE® is a non-drug alternative for the management of anxiety and pain that uses a form of music therapy known as the 'U sequence'. The latter is based on the hypno-analgesia principle, whereby progressive variations in tempo, rhythm, tune, and harmony bring patients into a state of deep psycho-muscular relaxation before the awakening phase. The objective of MUSIC CARE® is to promote wellbeing and to induce a relaxed state in patients by loosening their muscles and reducing their level of anxiety or pain. The U sequence is only for the control group and is composed of three phases: 1.
For each group An inclusion visit will take place the day before surgery. During this visit, the investigator will provide detailed information on the study and answer the patient's questions concerning the objectives, constraints, foreseeable risks, and perceived benefits of the study. The investigator will also be responsible for obtaining written informed consent from the patient. During the inclusion visit, a department nurse or care provider with training in MUSIC CARE® will explain the software to the study participant. Each patient will be asked to choose a predetermined music playlist from those available on the MUSIC CARE® software. The following day (i. e. the day of surgery), participants in the control group will be asked to listen to this music playlist, while those in the intervention group will be asked to listen to the music playlist they will have created at home.
Methods: Assignment of interventions Eligible patients will be randomised on the day of surgery using EnnovClinical®. Randomisation will be balanced in both groups, single-blinded, with a 1:1 allocation ratio, without stratification. The patient will know her allocation to the control group or to the intervention group, but the care-giver team will be unaware of it. The randomisation list will be established by the statistician from the methodological support department before the start of the research. The method used will be a randomised block approach, using the SAS® Proc Plan. We will build blocks of variable size. The investigator will perform the randomisation directly via the electronic case report form (eCRF) using the CS Random module of the Ennov Clinical® eCRF the day of surgery.
Data collection method Data will be collected using an eCRF. The database will be created by a data manager of the Methodology and Data Management Centre using EnnovClinical® software. Database access will be protected with a user name and password, and will include various safety levels according to role assigned. Research team members will enter the data. Data-entry agents who are used to working on the eCRF will be responsible for entering the participants' data. Data-entry and consistency checks will be carried out at the end of the inclusion period by a third party.
Data monitoring A clinical research associate (CRA) mandated by the study sponsor will visit the gynaecological surgery department twice during the study and once at the end. During these visits the CRA will verify: all consent forms; compliance with established protocol and procedures; quality of the data collected in the eCRF. The CRA will write a monitoring report after each visit. There will not be an independent body monitoring safety. Access to the database will be secured (access by personal ID and password) with different levels of security depending on the roles assigned. The data hosting is certified ISO 9001:2015. Ennov® hosts its customers' applications in a data centre located in two mirror sites offering optimal security conditions. Ennov Clinical® complies with Food and Drug Administration (FDA) recommendations regarding Computerised Systems for Clinical Trials Management ('Guidance for computerised systems used in clinical trials') and electronic signature ('21 CFR Part 11') and international standards.
Acknowledgements We would like to thank  Laurence Nicole  and  Christine Juhel  from the  Direction of the Clinical Research and Innovation of the CHU de la Réunion  who supervised the project management and the  Institutional Review Board  who supported the study. The authors are also grateful to  Karim Boussaïd ,  Nadège Naty ,  Sylvaine Porcherat , and  Corinne Mussard  from the  Clinical Investigation Centre in La Réunion.
Advances in uorescence microscopy enable monitoring larger brain areas in-vivo with ner time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CAIMAN, an open-source library for calcium imaging data analysis. CAIMAN provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identi cation, and registration across di erent sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CAIMAN is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data.
g. , independent component analysis (ICA)  (Mukamel et al. , 2009) , constrained nonnegative matrix factorization (CNMF)  (Pnevmatikakis et al. , 2016)   ( and its adaptation to one-photon data  (Zhou et al. , 2018) ), clustering based approaches  (Pachitariu et al. , 2017) , dictionary learning  (Petersen et al. , 2017) , or active contour models  (Reynolds et al. , 2017). Such spatio-temporal methods are unsupervised, and focus on detecting active neurons by considering the spatio-temporal activity of a component as a contiguous set of pixels within the FOV that are correlated in time. While such methods tend to o er a direct decomposition of the data in a set of sources with activity traces in an unsupervised way, in principle they require processing of the full dataset, and thus can be rendered intractable very quickly.