dataesr/scientific-paragraphs-categorization huggingface dataset:
Randomization Randomization of study subjects will be performed by a randomization website which has been developed by the Data Management department of the Julius Center, University Medical Center Utrecht, the Netherlands. This website is a password protected online application that has been used for randomization in over 40 studies. It has been developed in VB. Net with access to a SQL Server back end database. The application implements stratification, multiple sites, double blind randomization and the minimization method for randomization. Access to the system (individual username and password) is provided by the Data Management department of the Julius Center. Non-responders after four weeks of amisulpride treatment will be randomized to double-blind treatment out of two treatment arms (switching to olanzapine OR continuation of amisulpride).
Opened envelopes will be kept in the Investigator Site File at the center. After closing of the data base every site will be informed about each patient's assignment to a treatment group. Follow-up visits Follow-up visits are scheduled after the pharmacotherapy phase is completed or when a patient is discontinued, to assess the current status of the patient after that part of the study; one follow-up visit is specifically intended for patient that did not meet remission criteria during phase III, and a second follow-up visit is intended for all patients that have entered phase I. During the final follow-up visit, investigators are requested to provide the most recent clinical diagnosis of the patient.
Rehospitalisation All of these scales will be re-administered at 3 and 12 months post-randomisation by an independent assessor who will be blind to treatment allocation. Blindness will be maintained by a range of measures including geographical separation of raters and password protected databases. At the end of the 6 weeks series of motivational interviewing sessions, the CALPAS Therapeutic Alliance Measure will be completed by the patient. Following randomisation, participants and, where possible, family members in the experimental treatment arms will meet with the research nurse on two (or up to four if needed) occasions either at home or at the clinic to learn how to (i) access and navigate the psychoeducation website and (ii) operate the SMS and mobile phone interface and complete the rating scales.
Site requirements Each site must have access to an MRI scanner, preferably, but not necessarily 3T. 1. 5T scanners may also be used. Volumetric imaging data will be acquired using the ADNI protocols. These have been specifically developed for multi-centre imaging studies, and can be run on most recent scanners from Siemens, Phillips, and GE. The WP leader centre (IOP) already has experience in successfully implementing this protocol in another multi-centre MRI study funded by the EU to assess brain processes related to addiction in a sample of 2000 subjects (FP6; I MAGEN). Each MRI site will be assisted by our subcontractor IXICO (www. ixico. com), a company that specializes in MRI data handling in multi-centre studies. This will help to ensure quality control, and appropriate data collection and data transfer.
Image acquisition The scanning sequences are not complicated and the scanning session is likely to take about 45 minutes in total. Data will be acquired using standard localiser ('scout') images, followed by 2D T2-weighted and FLAIR scans on different planes with parameters suitable for radiological assessment. Scan times for these sequences will differ slightly between scanner manufacturers, but should total less than 10 minutes at all sites. This will be followed by a 3D ('volumetric)' T1-weighted MP-RAGE scan, based on the protocol devised and validated by the ADNI consortium. 112 This will take a further 9½ minutes. Data will be archived by each site according to existing local procedures, and will also be transferred (using the DICOM protocol supported by all scanners) to a project-specific computer, where, following basic consistency checks, it will be anonymised for transfer to the central coordinating/analysis site using the IXICO software (which will be provided to all sites).
Standardisation of data quality across sites To ensure high quality data, standard QA protocols (using the ACR MRI phantom) will be run at regular intervals (monthly) at all sites. Data will be analyzed locally (to allow a rapid 'turn round' if any issue is uncovered requiring servicing of the scanner), and also transferred to the central analysis site, where they will also provide information for inter -site calibration of voxel sizes. A group of healthy volunteers (n=5) will be scanned at a subgroup comprising the largest participating sites using the standardised acquisition protocol. This will establish the comparability of the MRI data across centres, and provide measures of within-and between-scanner variability.
Data analysis Radiological assessments will be performed at the coordinating site for MRI assessments (Institute of Psychiatry, King's College Londen) by one of two experienced neuroradiologists who are blind to the nature and purpose of the study. Using software provided by IXICO, they will assess all MRI images for any abnormalities as they would in conventional clinical practice, and will be asked to specify whether abnormalities require clinical action. They will be blind to whether subjects are patients or controls. The neuroradiological evaluation will assess: a) Absence or presence, location and number of lesions scored on a scale of 0 to 3 (0=none; 1=single lesion; 2 more than one lesion in the same hemisphere; 3=more than one lesion in both hemispheres) b) The ventricular system size and shape; Subarachnoid spaces and basal cisterns.
Lesions will be scored on a rating scale from 0 to 6, as proposed by Scheltens with separate scores for each of the cerebral lobes, the periventricular, the subcortical regions, and the infratentorial brain structures. Perivascular spaces will be disregarded. f) Height, width, and anteroposterior diameter of the pituitary gland will be reported together with morphological characteristics of the pituitary stalk. Measurements and ratings will provide data for inter-rater reliability calculations.
Data analysis for objective: Finding evidence for pharmacotherapy guidelines in first episode schizophrenia The primary outcome will be the number of patients in symptomatic remission according to  Andreasen et al. 2005. The proportion of patients meeting remission criteria will be estimated at the end of phase I, phase II, and phase III. Logistic regression analyses will be used to test whether the probability of remission is significantly different between the amisulpride and olanzapine treatment arms at the end of phase II. Duration of untreated psychosis, age, and gender will be included as covariates in this analysis. The secondary outcome measure is all-cause treatment discontinuation, which will also be compared between treatment arms with survival analyses including Cox regression analyses and Kaplan-Meier functions.
This PG-chip has come into production early 2009 (Pr Beaune INSERM). DNA will also be used to characterize the methylation profiling before and after treatment with amisulpride. DNA collected before and after treatment will be converted using bisulfite treatment and hybridized on HumanMethylation27 BeadChips on Illumina platform (Integragen). These BeadChips contain 27,578 CpG islands spanning more than 14,000 genes. Methylation data analysis will be analysed using the BeadStudio software, which will allow the integration with gene expression data (Dr Jamain, INSERM).
Metabolomics analysis Plasma will be portioned and snap-frozen as soon as possible after collection. Plasma (50µL) is required for a single assay. Samples will be processed in a fully automated manner in multiwell plates using Hamilton robotics station. Metabolite spectrum is designed to monitor the metabolism of sugars, acylcarnitines, amino acids, glycerophospholipids, sphingolipids and many other analytes. The resulting dataset will be subject to several levels of data analyses, starting with metabolite identification and quantification based on the raw multiplexed MS/MS spectra and the knowledge of the spiked isotope reference markers. In this step, BIOCRATES Life Sciences MarkerIDQ™ software shall be used as provided with the AbsoluteIDQ™ kit. In a second step, correlations within the metabolite dataset will be combined with external biochemical knowledge (e.
Only qualified and authorised collaborators of the study sponsor will enter the pseudonymous data into a computerised database. The acquired data will be used without participants' names for scientific analysis and can be used in related or other future studies. Participants' names will not be mentioned in any publication of study results. To ensure data accuracy, the study coordinator will conduct a direct comparison of the data with medical records available to or assembled by the investigators at the study site. These documents will only be checked by qualified and authorised personnel. Persons monitoring the data are required to keep information confidential and to respect data privacy. Participants have the right to look into their data and can check their data in accordance with the relevant judicial regulations and procedures.
Quality control of data acquisition Study sites and associated investigators will be carefully selected and comprehensively informed and trained regarding Good Clinical Practice (GCP), all study procedures and the required examinations and documentation. The quality of data acquisition will be ensured by regular monitoring visits. After data have been submitted to the study center, another thorough inspection of the completeness and plausibility of entries will be conducted. If needed, questions for clarification will be addressed to the sites. Only after all questions regarding data quality have been answered, the database will be locked (data-lock).
European countries and Israel consisting of general hospitals and psychiatric specialty clinics. (Clinicaltrials. gov identifier is NCT01248195). Patients with schizophrenia or schizophreniform disorder were treated for four weeks with up to 800 mg/day amisulpride in an open design. Patients who did not meet symptomatic remission criteria at four weeks were randomized to continue amisulpride or switch to olanzapine (max 20 mg/day) during a six-week double blind phase. Patients who were not in remission at ten weeks were given clozapine (max 900 mg/day) for an additional 12 weeks in an open design. Data were analyzed using a generalised linear mixed model, with a logistic link and binomial error distribution. Findings Participants were recruited between May 26, 2011 and May 15, 2016 with 481 signing informed consent.
Data Analyses Data analyses will be conducted in a timely fashion in a manner that ensures the study's scientific integrity. The final analysis will be done after all patients have completed the follow up visits, queries have been solved and the database is locked. No interim analysis is planned. Data will be analysed first for the comprehensive sample. After the first publication of the comprehensive sample has been published, reports of individual sites' data may be developed. Data from the individual sites may be used in the development of grant applications at any time provided it doesn't compromise the study's integrity.
of Treatment and Management of Schizophrenia in Europe version 2. 4, dated July 6, 2015 Study protocol (overall protocol of WP 1, 2, 3 and 5) We strongly recommend reading this statement in conjunction with the CONSORT 2010 Explanation and Elaboration for important clarifications on all the items. If relevant, we also recommend reading CONSORT extensions for cluster randomised trials, non-inferiority and equivalence trials, non-pharmacological treatments, herbal interventions, and pragmatic trials. Additional extensions are forthcoming: for those and for up to date references relevant to this checklist, see www. consort-statement. org. Figure 2 13b For each group, losses and exclusions after randomisation, together with reasons 10-12 and Figure 2 Recruitment 14a Dates defining the periods of recruitment and follow-up 10 14b Why the trial ended or was stopped 10 Baseline data 15 A table showing baseline demographic and clinical characteristics for each group Table 1 Numbers analysed 16 For each group, number of participants (denominator) included in each analysis and whether the analysis was 10-13 and by original assigned groups Tables 2-4 Outcomes and 17a For each primary and secondary outcome, results for each group, and the estimated effect size and its 10-13 and estimation precision (such as 95% confidence interval) Tables 2-4 17b For binary outcomes, presentation of both absolute and relative effect sizes is recommended 11 Ancillary analyses 18 Results of any other analyses performed, including subgroup analyses and adjusted analyses, distinguishing n/a pre-specified from exploratory Harms 19 All important harms or unintended effects in each group (for specific guidance see CONSORT for harms) 12-13, table 4 and appendix Discussion Limitations 20 Trial limitations, addressing sources of potential bias, imprecision, and, if relevant, multiplicity of analyses 14-18 Generalisability 21 Generalisability (external validity, applicability) of the trial findings 15-18 Interpretation 22 Interpretation consistent with results, balancing benefits and harms, and considering other relevant evidence 14-18 Other information Registration 23 Registration number and name of trial registry 3 & 6 Protocol 24 Where the full trial protocol can be accessed, if available n/a Funding 25 Sources of funding and other support (such as supply of drugs), role of funders 4 & 9 EudraCT-Number: 2010-020185-19 Protocol-Code-Number: KP7 242114 *Protocol OPTiMiSE study, version 2.
Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets.
Then, membership matrix values u ik (see Equation  5 ) are assigned to each data point in the dataset. Centroids of clusters c k are updated based on Equation  6 until a termination criterion is reached successfully. In FCM, this criterion can be a fixed number of iterations t, e. g. , t = 100. Alternatively, a threshold ǫ can be used, e. g. , ǫ = 0. 0001. Then, the algorithm stops when the relative difference of objective function < ǫ. u ik = 1 K j=1 ( xi-c k 2 xi-cj 2 ) 1 m-1 (5) c k = n i=1 (u m ik )x i n i=1 (u m ik ) (6) When using FCM, an important point is the way of choosing K data points as initial centroids (seeds). An efficient initialization method should be linear, so that the FCM algorithm stays linear, too. Then, the initialization method must be evaluated using validity indices that are well suited to the fuzzy case.
To obtain a good validated clustering result, one has to minimize intra-cluster distance (compactness) and at the same times, one has to maximize inter-cluster distance (separability). The more often, proposed clustering validity indices associate a compactness index with a separability index. Thence, we propose in this paper (1) a linear and efficient initialization method for FCM clustering called MaxMin Linear. Moreover, to compare our proposal with several initialization methods from the literature, we also propose (2) a new clustering validity index called Transformed Standardized Fuzzy Difference (TSFD), which is tailored to the fuzzy case. We perform validation experiments on several numerical real-world and artificial datasets. The remainder of this paper is organized as follows. Section 2 presents initialization methods for iterative clustering and several clustering validity methods proposed in the literature.
Related Works Most initialization methods are studied through K-Means clustering  [2]  concepts. We have reviewed various works from the literature, including much-cited papers  [6, 7, 8]. In our study, we make use of commonly mentioned linear methods from these three papers. The first initialization method by  [2]  uses the first K data points as centroids. This method is sensitive to the order of data. It is used by default in SPSS  [9]. The second method by MacQueen (MacQueen2 ) takes K random data points as centroids. Moreover,  [10]  proposes to perform multiple relaunches of MacQueen2. Among the different relaunches, the one that optimizes F W (Equation  1 ) is considered the best candidate. This method is the standard way for initializing clusters. Its main disadvantage is that already selected points are not considered when a new seed is chosen.
The second disadvantage is that outliers can be chosen. On the other hand, multiple runs ensure to improve the quality of the chosen sample. Hand et al. [11] , propose an extension of Faber's method that starts with a random set of seeds. It suggests iteratively modifying the partition by randomly moving some points to other clusters. The partition minimizing F W is chosen as the best candidate. To move each data point to another random cluster, a probability α, e. g. , α = 0. 3, must be set. The method is only interesting if parameter α is fixed for different datasets. Bradley and Fayyad's method  [12]  starts by randomly partitioning the dataset into J subsets. Then, each subset is clustered with the K-Means algorithm using MacQueen2 initialization. MacQueen2 produces J sets of centers, each containing K points.
The centers of clusters are combined into a superset. Then, the superset is clustered by K-Means J times. Each time, K-Means is initialized with a different center set, and members of the center set that give the smallest F W are selected as final centers. The PCA-Part method  [13]  uses a divisive hierarchical approach based on Principal Component Analysis (PCA)  [14]. The method starts with a single cluster containing the whole dataset. Then, it iteratively divides clusters with respect to F W. Clusters are divided into two sub-clusters by using a hyperplane that is orthogonal to the principal eigenvector of the cluster covariance matrix. The division process ends after K clusters are obtained. The K-Means++ method  [15]  selects centroids based on a distance probability to the nearest center.
V F Ratio = F B/F W (9) V F CH = F B/(K -1) F W/(n -K) = n -K K -1 F B F W (10) V F S = F W -F B (11) V XB = K k=1 n i=1 u m ik x i -v k 2 n * min j,k v j -v k 2 (12) Among all the above stated validity indices, there is no single validity index that gives the best result for any dataset. Thus, there is room for a new validity index that is specifically tailored for fuzzy validation. This is why we propose the Transformed Standardized Fuzzy Difference index.
Datasets Initialization methods are compared on 15 commonly used real-life datasets from the UCI Machine Learning Repository 1  and seven artificial datasets. Their characteristics are featured in Table  1. In the case of real-life datasets, the true number of clusters in each dataset is assimilated to the number of labels. Although using the number of labels as the number of clusters is debatable, it is acceptable if the set of descriptive variables explain the labels well. In artificial datasets, the number of clusters is known by construction. In addition, we created new artificial datasets by introducing overlapping and noise to some of the existing artificial datasets such as E1071-3, Ruspini original, and E1071-5 datasets (see Table  1 ,  ID 17, 18, and 21). To create the dataset, new data points are introduced and each must be labeled.
To obtain a dataset with overlapping, we modified the construction of the E1071 artificial datasets  [28]. In the original datasets, there are three or five clusters of equal size (50). Cluster i is generated according to a Gaussian distribution N (i; 0. 3). To increase overlapping while retaining the same cluster size, we only change the standard deviation from 0. 3 to 0. 4. Then, there is no labeling problem. Noise is introduced in each cluster by adding noisy points generated by a Gaussian variable around each label gravity center. First, for each label, we calculate the coordinates of centers, and the mean and standard deviation of each variable. With Gaussian variables, points mainly lie between "center +/two standard deviations". Noisy data are often generated by distributions with positive skewness.
For example, in a two-dimensional dataset, for each label, we add points that are far away from the corresponding gravity center, especially on the right hand side, which generally contains the most points. Then, we draw a random number r between 0 and 1. If r ≤ 0. 25, the point is attributed to the left hand side. Otherwise, the point is attributed to the right hand side. This method helps obtain noisy data that are 1 / 4 times smaller and 3 / 4 times greater, respectively, than the expected value for the considered label. We apply this process to the Ruspini dataset  [1].
Experimental Results In our experiments, we compare our method MaxMin Linear to all initialization methods from Section 2, on all datasets. We account for the following comparison criteria: number of iterations, V P C , V CL , F B, F W , F I, V F Ratio , V T SF D , V F S , and V XB. We also rank the initialization methods with respect to all criteria. Since presenting all results would take too much space, we only present three real-life datasets i. e. , WineQuality-Red (Tables  2, 3 , and 4), Glass (Tables  5, 6 , and 7), and Segmentation (Tables  8, 9 , and 10), as well as two of the artificial datasets we modified to introduce noise and overlapping, i. e. , Ruspini noised (Tables  11, 12 , and 13), and E1071-5-overlapped (Tables  14, 15 , and 16), respectively. Finally, the average ranking of initialization methods on all datasets is presented in Table  17.
Conclusion and Perspectives In this paper, we propose a new, fast, and easy to implement initialization method for FCM called MaxMin Linear. MaxMin Linear is compared to several initialization methods from the literature. It is experimentally shown that MaxMin Linear outperforms existing methods on 22 datasets. Moreover, we also propose an appropriate fuzzy validity index, TSFD, to evaluate initialization methods. In addition, MaxMin Linear can be applied to algorithms other than FCM, such as Fuzzy K-Modes and Fuzzy K-Medoids, which apply on categorical. In particular, MaxMin Linear allows decreasing the complexity of Park's Fuzzy K-Medoids implementation. In consequence, an immediate perspective to our work is to propose a new clustering ensemble method for heterogeneous datasets composed of both numerical and categorical data.