dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design The cVEDA is a cohort of individuals aged 6-23 years across 7 Indian sites. It draws upon existing research systems with well-established tracking and follow-up mechanisms, previously employed in studying the impacts of varied risk factors on non-communicable diseases  [82] [83] [84] [85]. Cohorts recruited at each centre are followed-up 1 and 2 years after baseline assessments. The study employs a planned missingness  [86]  (Fig. 2 ) approach in which participants are randomised to follow-up either 1 or 2 years after enrolment. Such a design permits three waves of data collection to be achieved while reducing the cost of measurement per wave. Since participants are randomly assigned to be present/missing at each follow-up, missing data are completely at random and hence parameters of interest can be estimated without bias.
Timeline The cVEDA study started in February 2016. After 9 months spent in study set up (staff recruitment, translations of study instruments into 7 Indian regional languages, setting up digital data capture platform, training of recruitment and assessment teams, and quality control exercises), recruitment started in October 2016. Recruitment, baseline assessments and randomized followup have been continued in parallel. Under the current funding cycle, we have completed recruitment and baseline assessments; and 1 and 2 year follow-ups on a part of the sample (Fig. 3 ).
Establishment of a repository data and biobank All cVEDA assessments are run on a digital platform using Psytools software (Delosis Ltd. , UK). Data is first synchronized with the Psytools server, from where it is accessed by data management teams in India, UK and France. The final data storage server (Dell power edge R530 Rack server) is located at NIMHANS, along with a back-up safety system and a mirror data storage system in France. The data management team conducts sanity checks and feedback is regularly sent to recruitment sites. Biological samples form part of a biobank located at NIMHANS. The databank and the biobank are a resource to facilitate research by consortium partners as well as collaborators investigating other areas of mental health, its interface with physical health, and crosscultural comparisons.
Quality control Quality control measures have been incorporated in the study protocol right from the beginning. Interviewer training, on-site and online, followed by mock interview assessments and feedback was conducted in the preparatory phase of the study. Following start of study on the field, India and UK based study coordinators conduct weekly recruitment meetings during which recruitment progress and completeness of data entry are reviewed. Standard operating procedures (SOPs) ensure consistency in biological samples collection and neuroimaging. Additionally, quality control procedures for neuroimaging are implemented at each site: (i) a phantom  [120]  is scanned to provide information about geometric distortions and signal uniformity related to hardware differences in radiofrequency coils and gradient systems, (ii) healthy volunteers are regularly scanned at each site to assess factors that cannot be measured using phantoms alone, and iii) after every subject scan, a quick 2-min script (https://github.
Discussion This paper presents the background and protocol of the cVEDA study. The study is designed to answer questions about the developmental, genomic, and environmental underpinnings of psychopathology. This has implications for preventive and early interventions for mental disorders. Biological samples collected from all participants significantly enrich exposome characterization in the sample and this will aid in discovering biomarkers of exposure and early disease through omic technologies (epigenomics, adductomics, proteomics, transcriptomics and metabolomics). Whilst it is beyond the scope of the current funding to carry out extensive omic analyses, we are establishing an integrated exposome database and biobank to facilitate future analyses. The cVEDA cohort is a substantial addition to, and provides comparative and cross-cultural datasets for international studies -IMAGEN that looks at biological and environmental factors affecting reinforcement related behaviours in teenagers; SCAMP looking at effects of mobile phone use on cognition in adolescents; ALSPAC with several decades of longitudinal data on developmental, environmental and genetic factors affecting a person's overall health and development.
It also makes a large databank rapidly available to researchers working in the field. Through collaboration with institutions in the UK, research scholars also have the opportunity to partner with international researchers and faculty to build their research repertoire. In conclusion, the cVEDA has established the largest neurodevelopmental database in India. The identification of environmental risk factors that contribute to vulnerabilities for psychiatric morbidity could have huge implications in public health interventions and prevention of psychiatric morbidity, in India. This unique database will facilitate international research collaborations, to study cross-cultural variations in the determinants of psychopathology. details in the paper. BH, RDB, GJB, and CA coordinated the neuroimaging assessments in the cVEDA study and contributed to the imaging protocols in the paper.
MP, AC, SD, KamK and GK coordinated and supervised the biological sample acquisitions in the study and contributed to the methodological details in the manuscript. GSF, JH, MH and KT conceptualized the study design and drafted the relevant sections of the manuscript. PJ, MR, MT and KesK conceptualized and coordinated the behavioral and neuropsychological assessments for cVEDA. DPO and DJ set up the data acquisition pipeline and databank. PM, SJ, MV, DB, SBN, RK, SSK, KalK, MK, RLS, LRS, and KarK lead and supervise the various cVEDA recruitment sites. GS and VB conceptualized and lead the cVEDA study, and critically reviewed several drafts of the paper. All authors read and approved the final manuscript.
Third, we conduct a correlation analysis to identify whether any socio-economic characteristic(s) associated with the catchment or hydrogeomorphological characteristic(s) of the lake corresponded to the magnitudes of changes in any of the climate and land-use drivers that were associated with a 25% catch decrease. To improve clarity, Fig. 3  presents a flowchart that summarizes the procedures of these three analyses and Table  1  provides a list of symbols used in the development of BNM. We demonstrate that climate and land-use changes can have strong effects on CATCHs across lakes at a global scale, but the effects can be either positive or negative. The subsequent 60°N 30°N 0°3 0°S 60°S 120°W 60°W 60°E 120°E 0°F ig. 1 Global distribution of the 31 study lakes. The map was generated by using R  58  and the R package rworldmap  62  to plot the centers of study lakes (as given in Supplementary Data 2) on a base world map, of which layers, including country borders, continent outlines, global lakes, and latitude-longitude grids, were obtained from a public domain map dataset Natural Earth (www.
Although the 31 study lakes were selected partly because of good data availability, there were still large within-lake differences in data availability among model variables (as shown in Supplementary Data 3). For example, we obtained 9, 16, and at least 32 years of data for CHL, WT, and all the other variables in Lake Chilwa, respectively. Therefore, the BNM's capability of taking all model inputs into account for the estimation of every model coefficient was important to maximize the statistical power of our analysis. Model development. To mathematically formulate our BNM based on our hypothesized causal processes (Fig. 2 ), we assumed that (1) each response variable in the BNM has a normal distribution, with a mean that can be expressed as a function of predictor variables based on the primary literature described below, and (2) annual time-series data are independent observations, such that the mean value of each response variable in 1 year is dependent upon the values of predictor variables in the same year, but independent from all of its mean values in the other years.
Computationally, we derived empirical posterior distributions for all BNM coefficients by using a Gibbs sampling method to run Markov chain Monte Carlo (MCMC) simulations  55. We ran three random-starting MCMC chains by implementing the program JAGS  56  and the package runjags 57 in R  58. After discarding 20,000 iterations for burn-in and adaptation, we considered that the convergence of three MCMC chains was achieved at 50,000 interactions as autocorrelations were close to 0 and Gelman and Rubin's convergence diagnostics  59  were <1. 005 for all coefficients. Our empirical posterior distribution of each BNM coefficient was based on 50,000 samples from each converged MCMC chain. Effects between variable pairs. As shown in Fig. 2 , there were 13 pairs of predictor and response variables in our BNM.
Data availability Data used to derive model inputs for variables associated with lake environment, fish catch, fish stocking, and fishing effort (i. e. , WT, ΔWL, CHL, CATCH, ST, and EFF) are either publicly available or available upon requests to corresponding authorities, as given in Supplementary Data 3. Data used to derive model inputs for variables associated with climate and land use (i. e. , AT, PRE, PE, and LUag) are from publicly available global databases, as described the Supplementary Methods. The source data underlying Figs. 4a-g, 5a-e, 6a-e, 7, and 8 are provided as a Source Data file.
Acknowledgements This research was funded by the  US Geological Survey National Climate Adaptation Science Center. We thank help from  A. Sakas  for data compilation and email communication at the beginning of this project. We thank  E. Politi  for global database support and  C. Paukert  for comments on an earlier draft of this manuscript. Personal funding supports, data providers, and data sources are acknowledged in Supplementary Note. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  US Government.
In this paper we present MILPIBEA, a novel hybrid algorithm which combines the scalability of a genetic algorithm (IBEA) with the accuracy of a mixed-integer linear programming solver (IBM ILOG CPLEX). We also study the behaviour of our solution (MILPIBEA) in contrast with SATIBEA, a state-of-the-art algorithm in static software product lines. We demonstrate that MILPIBEA outperforms SATIBEA on average, especially for the most challenging problem instances, and that MILPIBEA is the one that continues to improve the quality of the solutions when SATIBEA stagnates (in the evolving context).
These evolutions appear as an adaptation of the FM from a version to another. For instance, Saber et al. [7]  have shown in their study that the large FM representing the Linux kernel evolves continuously. They have also shown that a new version of the kernel is released every few months with a successive difference that can go up to 7%. In this paper, we propose to leverage the evolution context when performing optimisations of feature configurations. It seems odd to generate random bootstrapping populations for SATIBEA in the presence of well-performing solutions for similar problem instances. At the same time, it might be beneficial to exploit the fact that the FM has evolved and that configurations generated previously are close enough and can be adapted. This paper presents our approach, MILPIBEA, which was initially designed to address the problem of feature selection in a multi-objective context when the FMs evolve, but proved to be better than SATIBEA both when the FMs evolve and when they do not.
Evolution in SPL Evolution of SPLs and the corresponding FMs is known to be an important challenge, since product lines represent long-term investments  [13]. For instance, in Section 3 we describe a study of a large-scale FM, the Linux kernel by Saber et al. [7]  which shows that every few months a new FM is released with up to 7% modifications among the features (features added or removed). In this paper, we show a potential approach for this optimisation problem which utilises the evolution from one FM to another. The relationship between two versions of a feature model is expressed as a mapping between features. Let us assume an FM F M 1 evolved into another FM F M 2. Some of the features f 1 i ∈ F M 1 are mapped on to features f 2 i ∈ F M 2 (treated as the same), whereas other features f 1 i ∈ F M 1 are not mapped onto any features in F M 2 (f 1 i has been removed), and features f 2 i ∈ F M 2 have no corresponding features in F M 1 (f 2 i has been added).
Benchmark for Evolving FMs Our work is based on the largest open-source FM we could find in the literature: the Linux kernel version 2. 6. 28 containing 6,888 features and 343,944 constraints. Saber et al. [7, 16]  studied the demographics (features/constraints) and evolution pattern of 21 successive versions of the Linux kernel (going from 2. 6. 12 to 2. 6. 32). They observed that on average there was only 4. 6% difference in terms of features between a version and the next (out of those changes, 21. 22% were removed features and 78. 78% were added features). They also evaluated the size of the clauses/constraints in the problem, as we need to know how the constraints we add in the problem should look and found that a large proportion of the FMs' constraints have 6 features (39%), 5 features (16%), 18 features (14%) or 19 features (14%).
System and Algorithms Set-up We use the source code provided by SATIBEA's authors and make MILPIBEA publicly available at https://github. com/takfarinassaber/MILPIBEA. The tests are performed on a machine with 62GB of RAM and 12 core Intel(R) Xeon(R) 2. 20GHz CPU. We use the following parameters for our genetic algorithm: -Population size: 300 individuals. -Offspring population size: 300 individuals. -Crossover rate: 0. 8. Represents the probability of two individuals in the population to perform a crossover (an exchange of their selected features). -Mutation rate: 0. 001. Represents the probability for each bit (true if a feature is selected, 0 otherwise) of an individual to be flipped. -Solver mutation rate: 0. 02. Represents the probability of using the solver to correct a solution during the mutation process.
Therefore, for each individual, we remove bits representing removed features and add bits with random values for each new feature. Then, we compute their objective functions. We give the new resulting individuals as an initial population to SATIBEA that will run normally on F 2. Our hope is that initial individuals will be better than random solutions. We tested this approach on all the modified versions of the Linux Kernel and all of the results are equivalent: as expected, when supplied with an initial seed SATIBEA converges within a short time (i. e. , less than 100s) whereas the classical SATIBEA needs 700s to reach the same hypervolume. This approach also has some limits: with a modified version of 20% features and 10% constraints, classical SATIBEA reaches a slightly better hypervolume than the one with seed.
On the Multi-Objective Feature Selection Problem Figures 4 show the evolution using SATIBEA or MILPIBEA in terms of hypervolume when applied on our 10 generated models: each of them is a modification of the 2. 6. 28 version of Linux kernel represented by a couple (x y) where x is the percentage of features modified and y the percentage of constraints modified. This hypervolume is measured based only on the individuals of the current population. We are not seeding the initial population: the problem studied in these result is the multi-objective feature selection problem, without the notion of evolution. The initial population is generated randomly for both algorithms. Our results indicate that MILPIBEA outperforms SATIBEA with an improvement of 41. 2% hypervolume on average. Figure  4  also indicates that MILP-IBEA is more efficient on the most constrained problems (i.
With Evolved Feature Models We now compare MILPIBEA and SATIBEA in the case of the multi-objective feature selection problem in evolving FMs. As described in Section 2, the notion of evolution is represented by features/constraints modifications in the FM. In our case, the Linux kernel 2. 6. 28 is the original FM, and we generated 10 modified versions. Because of evolution, the original FM has been optimised, and its solutions as given as initial population to SATIBEA and MILPIBEA: the purpose is to improve the quality of results on modified FMs as fast as possible. Figures 5 show the hypervolume of the individuals at every new generation for both SATIBEA and MILPIBEA when given the solutions of the original FM as initial population. We see that both algorithms start from a relatively good hypervolume, which shows the quality of the initial population.
We segmented each participant's MRI and then constructed the source space based on a warped Montreal Neurological Institute (MNI) anatomical grid template, which we used for group analysis. After preprocessing the individual MEG recordings, we computed the source modelling and cortico-acoustic coupling using the Dynamical Imaging of Coherent Sources (DICS) beamformer  (41). Lastly, for statistical analysis we applied non-parametric randomization, cluster-based permutation statistical tests across participants for each frequency of interest and condition in five bilateral regions-of-interest (ROIs). We achieved the statistical assessment of cortico-acoustic coherence by comparison to two control conditions: coherence obtained either using surrogate data (trial shuffling) or using pre-stimulus data.
Time-compressed speech was compressed at the same syllable rate as in the natural fast rate condition (mean syllable rate 9. 15 syll/s). Noise Normal and Noise Fast correspond to noise stimuli modulated with the amplitude envelopes of normal rate and fast rate sentences, respectively. The depicted spectra represent parametric model fits of the PSD (Power Spectral Density), that consist of aperiodic and periodic components computed using the FOOOF algorithm (42) (B) Periodic components of the full model shown in (A), i. e. after removal of the aperiodic (the so-called 1/f) component of the spectra. The spectral power peaks in the speech stimuli for each condition occur at frequencies that match the corresponding mean syllable rates calculated using the Praat software (see supplementary Table  S1  and   The ROI-based analysis using shuffled data as control revealed a shift in the frequency domain of cortical oscillations to align to the syllable rate of naturally-produced heard sentences.
Stimuli We created 288 meaningful sentences (7-9 words) following the same syntactic structure: Determiner -Noun 1 -Verb -Determiner -Noun 2 -Preposition -Determiner -Noun 3 (e. g. , "Sa fille déteste la nourriture de la cantine" / His daughter hates the food at the canteen). Sentences were recorded in a sound-attenuated booth by a French native professional theatre actor who was able to produce sentences at the required fast rate while remaining intelligible. We recorded each sentence twice (44. 1 kHz, mono, 16 bits) using ROCme! software  (85) , first at a normal and then at a fast rate. The procedure was the following: the sentence was first displayed on a computer screen in front of the speaker who was instructed to silently read it and to subsequently produce it aloud as a declarative statement at his normal speech rate.
The speaker then produced the sentences at a fast rate (i. e. as fast as possible while remaining intelligible) using the same procedure (i. e. no external pacing was imposed). We calculated the durations of the 2×288 sentences and the number of actually produced syllables for each sentence with Praat software  (40). The mean syllable rate was 6. 76 syllables/s (SD 0. 57) for natural normal rate sentences and 9. 15 syllables/s (SD 0. 60) for natural fast rate sentences. This led to an overall fast-to-normal ratio of 0. 74 (i. e. speed-up factor of 1. 35). Subsequently, we computed time-compressed sentences by digitally shortening them with a PSOLA (Pitch Synchronous Overlap and Add) algorithm  (86) , as implemented in Praat. We obtained compression rates for each sentence: we matched each individual time-compressed sentence in terms of syllable rate to its equivalent natural fast item.
MEG data acquisition and task design Participants were comfortably sitting in a sound-attenuated, magnetically-shielded recording room with a screen in front of them. We presented stimuli binaurally through air-conducting tubes with foam insert earphones (Etymotic ER2 and ER3). Prior to the MEG recording, we determined participants' auditory detection thresholds for each ear with a 1-minute pure tone of 44 kHz; the level was then adjusted so that we presented the stimuli at 50 dB Sensation Level with a central position (stereo) with respect to the participant's head. During the experiment, participants attentively listened to all stimuli from one of the two experimental lists while looking at a fixation cross at the center of the screen. Their task was to detect beep-sounds embedded in filler sentences by pressing a button (response button Neuroscan, Pantev) with their left index finger.
MEG data We first segmented the MEG data into periods of 3 s (from 1 s before stimulus onset to 2 s after onset) for preprocessing. We rejected data segments contaminated by eye blinks, heartbeat and muscle artefacts using a standard semi-automatic procedure available in the Fieldtrip toolbox as follows. First, we filtered the signals at 50, 100 and 150 Hz, we then re-sampled the data to 300 Hz and rejected the deviant trials from visual inspection. Second, we detected and rejected EOG artifacts, jumps and muscle artifacts and visually double-checked the trials. Finally, we In addition to computing cortico-acoustic coherence during the speech encoding period (200 to 1000 ms), we also computed cortico-acoustic coherence (a) for the baseline window (-1000 to -200 ms) and (b) for shuffled data, as control conditions for statistical analysis.
Acknowledgements We would like to thank  Damien Gouy  for recording the sentences,  Emmanuel Ferragne  for his help with Praat software, and  Mélanie Canault  and  François Pellegrino  for discussions on articulatory phonetics and signal processing respectively. We also thank  Sébastien Daligault  and  Claude Delpuech  from the  CERMEP of Lyon  for assisting in MEG data acquisition, and  Mainak Jas  for his implementation of the algorithm for automatic parameterization of neural power spectral densities (PSDs). V. B. was supported by funding from the  French National Research Agency (ANR)  for the  ODYSSEE  project ( ANR-11-JSH2 005 1 ) and by the  LabEx ASLAN  ( ANR-10-LABX-0081 ) of  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  ANR.
Among them we can mention the absence of a clear peak in the power spectrum density (PSD) of the phase, the choice of the filter bandwidth or the nonstationarity. To date, a handful of established brain data analysis toolboxes provide built-in functionalities to compute PAC, these include Matlab-based packages such as Fieldtrip  [52] , Brainstorm  [53]  and EEGLAB  [54]. The open-science Python research community has also proposed a few PAC tools, including pacpy (https://github. com/voytekresearch/pacpy/) or pactools  [38] , with the latter supporting MNE-Python inputs  [55]. While extremely valuable, each of the available tools covers a limited choice of PAC computations and visualization options. In addition, computational time remains a challenge that can severely limit PAC analyses at a time where data dimensionality is drastically increasing in neuroscience research.