dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability METIS workflows for different applications used in this study run as Google Colab Python notebooks and are free open source tools available at https://github. com/ amirpandi/METIS. All scripts used in this study were written in Python 3. Packages used in the development of the workflow are pandas (1. 1. 4) and numpy (1. 18. 5), matplotlib (3. 2. 2) and seaborn (0. 11. 0), scikit-learn (0. 22. 2. post1), xgboost (0. 90), and Keras (2. 3. 1) using TensorFlow backend.
The DTW score and the sum of the diagonal in this case are again different to zero, but the warping path is the diagonal itself. In Figure  4 , we follow the same procedure as in Figure  3 , but for sequences from our data set. We employ Wind data from the time period 2017 December 3 to 2017 December 30 (CR 2198). In Figure  4 (a), we show how DTW behaves when comparing the observed solar wind bulk speed with the same data set, assuming the ideal scenario in which the observed and predicted time series are exactly the same. The x-axis in the time series plots corresponds to the index number of the elements that actually describe time, thus the "time elements" label. In Figure  4.
In Figures  12(a ) and (b), we present the time series for two of the three periods for which the SSF and Skill Score showed opposite results. In both cases, the 27 day persistence model predicts the Wind data better than EUHFORIA, not only because of the number of HSSs that it captures, but also because it better reproduces the HSS amplitudes. In Figures  12(c ) and (d), we further show two cases for which the SSFs and Skill Scores agree. In the former case, the 27 day persistence model performs better than EUHFORIA, and that is reflected in the SSF and Skill Score numbers. In the latter case, EUHFORIA performs better than the persistence model, with both the SSF and Skill Score numbers being lower than unity.
Results We propose that the mechanistic inference framework of the VAB can causally link white-matter degeneration to age-related functional changes. By means of the computational framework of TVB, we designed virtual brains, i. e. data-driven brain network models integrating imaging data on SC to constrain network parameters, and we systematically generated a personalized ensemble of functional data (including FC and FCD variations) under various degrees of interhemispheric SC decrease. We specifically defined this process of artificial decline and assessment of the functional changes as "virtual aging". Figure  1  summarizes the purposes and the steps of the VAB pipeline, which was meant to confirm the role of white matter degeneration in determining functional and cognitive changes during senescence as follows.
Starting from various aging theories  (Cabeza, 2002; Festini et al. , 2018; Goh, 2011; Reuter-Lorenz & Park, 2014)  that link the interindividual variability during senescence and the personal trajectories of age and cognition with each individual's phenotype (comprised of structure and function, Figure  1. A), we designed individual brain-network models in the 1000BRAINS dataset in 2 scenarios. First, we estimated a model for each subject by constraining the brain network model parameters via the empirical connectome ( ) and we assessed whether we could generate realistic   functional data (Figure  1. B). Second, we tested the process of "virtual aging" for the SCs of the 50 youngest subjects of 1000BRAINS by artificially decreasing their interhemispheric connections and hence generating a new set of connectomes (labeled as , Figure  1.
, 2000)  (Figure  2. A). Specifically, in-silico experiments were conducted in two scenarios, represented by the connectomes in Figure  2. B and 2. D. On the one hand, the brain network model was constrained by the empirical connectomes and we referred to the simulated   data obtained with this model as age-simulation (also labeled as ). On the other  hand, we constrained brain network models via the connectomes of the 50 youngest subjects whose interhemispheric SC were homogeneously decreased from 0% to 100% by effectively masking the antidiagonal edges of the structural connectivity matrix (Figure  2. D). We referred to the functional data obtained under this variation of interhemispheric SC as -simulation and virtually-aged data (also labeled as α ). To assure that our brain network models could reproduce age-related α - functional changes found in the empirical data, we calculated on both in-silico functional datasets ( and ) a set of summary statistics that represents the  α - interhemispheric communication and its dynamical flexibility and compared them to those from the empirical functional data of 1000BRAINS.
Among these, the global modulation represents the overall σ  2  influence of the SC-mediated input from the other regions on each node and we considered this parameter as an index of the modulation between structural and functional connectivity. Figure  3  shows the results of the summary statistics as obtained for the empirical data, the simulation data and the virtually aged simulation data. In terms of homotopic FC, we found a negative age-trend for all three scenarios, the empirical data (Figure  3   the dataset for one target subject (out of 50, the remaining panels of can be  α  α found in the supplementary materials, Figure  S3 ). Specifically, the FCD variance is shown as a heatmap for each considered in the sweep procedure for each  subject's age (Figure  3. G) and for each alpha of a virtually aged subject (Figure  3  , Figure  3.
In line with previous studies focusing on age-related SC differences  (Puxeddu et al. , 2020; Zhao et al. , 2015) , the interhemispheric SC is the most prominent structural factor showing the strongest age-related decreases. This anatomical driver was used to virtually reproduce an age trajectory for each individual by masking interhemispheric structural connections. Applying the computational models to both, the empirical dataset   as well as the virtually reproduced datasets revealed decreases in the homotopic  α FC as well as lower FCD for all simulated functional data. The functional connectivity between homotopic regions has been previously reported to be highly age-characteristic  (Chan et al. , 2014; Senden et al. , 2017; Tzourio-Mazoyer et al. , 2016; Zuo et al. , 2010)  including the dynamical interplay between the two hemispheres  (Escrichs et al.
Hence, it could also track the evolution of this complex interaction during senescence. Thanks to the capacity to handle the dynamic and static properties of FC and the possibility of a nonlinear decline of artificial interhemispheric functional data, the VAB approach underpins a nonlinear relationship between SC and FC, which comes closer to linear or 1:1 modulation of structure over function with age and cognitive decline. To validate whether the brain network models could actually estimate the relationship between structure and function, we applied a Bayesian framework (SBI) to infer the full posterior of parameters from the joint distribution of empirical functional data and a low-dimensional set of summary statistics  (Cranmer et al. , 2020; Gonçalves et al. , 2020; Hashemi et al. , 2020).
By considering the same premises on the metastable behavior of the brain, we employed a simulation-based inference that retrieves model parameters with the same age-declining FC and FCD features. Unlike the parameter-sweep, the SBI could rely on the individual estimate of global modulation by both considering the SC and functional data in a low dimensional space. Therefore, the Bayesian approach independently provides a ground truth value of change of global modulation, which becomes interpretable through the mechanistic framework virtual aging, in which we separate coexisting influences of global modulation and structural deterioration. Importantly, we systematically demonstrate the direction of the scaffolding and dedifferentiation process, which always was directed towards increasing global modulation.
Figures Figure  1:  The mechanistic framework steps of the VAB to casually link wihte matter degeneration with age-related functional changes. (A) To explain the heterogeneity of cognitive performance during senescence, we designed personalized brain-network models in two scenarios. (B) First, we estimated virtual brains based on the empirical connectomes of 1000BRAINS dataset to generate functional data. (C) Second, we virtually age the brain models of the 50 youngest subjects structural connectomes by artificially declining the interhemispheric SC. (D) For both scenarios, we verified that the network models could realistically mimic in-vivo data by comparing the static and dynamical functional connectivity of the simulated data. (E) Thanks to the individual brain network models, we could derive an index of the causal link between structural connectivity and brain function, here labeled as  index to explain the heterogeneity of cognitive decline.
By verifying that the simulated data could α replicate the empirical functional changes, we defined the masking process as "virtual aging". (E) We specifically test if we could replicate the decline of homotopic FC (magenta line in left panel) and the FCD variance difference, which is the difference of the variance of entries between the blue and the magenta triangle (right panel). See methods for details. Eventually, we could derive for each model a global parameter of modulation of SC onto the brain dynamics, which has been selected as the representative index of the causal tethering between SC and FC.
Eligibility for MRI measurements included any history of neurosurgery, cardiac pacemakers, coronary artery stents, surgical implants or prostheses in head or trunk, tattoos or permanent make-up on the head. Participants gave written informed consent prior to inclusion in 1000BRAINS and MR imaging was only conducted as there were no dental implants found which could cause artefacts in the brain images and as participants did not experience claustrophobia. The study protocol of 1000BRAINS was approved by the Ethics Committee of the University of Essen, Germany. In the current work we focused on particularly older adults (above 55 years, ), from which a total of  = 970 871 had functional scans available. 16 participants had to be excluded due to insufficient data quality. Of these 855 subjects, 718 also had diffusion data available, from which 649 were of good quality (see more details under Imaging).
M. 2. 1 Structural Image Processing Using the CAT12 toolbox implemented in SPM12  (Ashburner, 2009) , we created tissue probability maps (TPM) for grey matter (GM), white matter (WM) as well as corticospinal fluid (CSF) from the participant's T1 data. These brain masks were used to optimally extract the brain from the T1 data by superimposing and thresholding them at 0. 5 (small enclosed holes were filled). The T1 brain was bias field corrected, rigidly aligned to MNI152 template space and resampled to 1. 25 mm isotropic voxel size. We corrected the diffusion MRI data for eddy current and motion artifacts including interpolation of slices with signal dropouts  (Andersson et al. , 2016; Andersson & Sotiropoulos, 2016). Subsequently, all diffusion data were visually controlled for ghosting, remaining signal dropouts or very noisy data and volumes or datasets that were considered as suboptimal were excluded from further analysis ( ).
For dMRI -T1 alignment, the first b0 images from each dMRI data with  =-69 b1000 and b2700 were extracted and rigidly aligned to T1 dataset using mutual information as cost function  (Wells et al. , 1996). Based on the estimated transforms, the dMRI data were transferred to the individual T1 space, separately for both b-values. Implicitly, the realignment resamples the data to 1. 25mm and rotates the b-vectors according to the corresponding transformations. As there are no field maps or b0 volumes with opposite EPI readout directions available in the current study, we computed Anisotropic Power Maps (APM) from the b2700 dMRI data in 1. 25 mm space to account for susceptibility artifacts and optimize image registration  (Dell'Acqua et al. , 2014). APM contrasts are very similar to those of the T1 image and therefore provide an optimized base for image registration.
Hence, APMs were used to compute the non-linear transformation from diffusion to anatomical space, thereby taking EPI induced distortions into account. These non-linear transformations were then used to transform the TPMs to diffusion space. Finally, the two datasets with b1000 and b2700 were merged into one single file and corrected for different echo times. This correction was computed by a voxelwise multiplication of the b2700 data with the ratio of the non-diffusion-weighted data respectively for the two datasets. Lastly, local modeling and probabilistic streamline tractography were performed using the MRtrix software package version 0. 3. 15 (Tournier et al. , 2012). We computed the constrained spherical deconvolution (CSD) local model using multi-tissue CSD of multi-shell data  (Jeurissen et al.
M. 4 The virtual aging brain pipeline For the investigation of the causal link between white matter degeneration and functional changes and its association with the heterogeneity of cognitive decline, we performed a two-fold analysis. In the first part, we addressed the questions whether realistic brain function can be modeled via a simulated SC decline. After we verified that the presence of a decline in the interhemispheric connectomes in empirical SC dataset, we designed virtual brains, brain network models constrained by the imaging data of SC, to predict brain function from the empirical SC dataset ( )   as well as one virtually aged participant ( ) (M. 4. 1) and again, compared the  α derived (in-silico) functional data between each other as well as to the empirical (in-vivo) functional data (M.
M. 4. 1 Predicting functional connectivity: The Connectome-based Brain Network Model The approach to virtually age a structural connectome provides the basis to test whether a specific modification in SC affects brain function in a causal sense. Therefore, as a preliminary investigation, we inspected the empirical SC datasets with regards to age and specifically tested whether the previously reported strong age-related interhemispheric SC decline  (Jockwitz et al. , 2019; Puxeddu et al. , 2020; Roland et al. , 2017; Zhao et al. , 2015)  is also applicable to the current dataset of older adults (see Figure  2 ). Furthermore, we performed a virtual aging process on the 50 youngest participants (mean age, age-range) by homogeneously decreasing the interhemispheric connections. In particular, we applied a mask on the empirical connectome as  0 follows: 𝑊 = 𝑊 0 -α𝑀 𝑖𝑛𝑡𝑒𝑟 * 𝑊 0 , (3) where is the antidiagonal mask, is the normalized intensity of decrease and *   α represents an element-wise product (see Figure  2 ).
M. 4. 3 SC-FC tethering and its association to age, cognitive performance and sex The model-derived tethering between SC and FC ( modulation index) was then  statistically related to both demographic and behavioral factors. First, we performed an ANCOVA indicating whether the index is overall age-or  sex-related (corrected for education). Furthermore, to analyze age-related trajectories of the SC-FC linkage, we performed partial correlations between the  modulation index and age (corrected for sex and education). This was repeated after stratifying the group by sex, and the partial correlations (corrected for education) were statistically compared (Fishers-Z) indicating whether age-related modulation  index changes are different in males as compared to females. Lastly, we performed a Pearson's correlation to test the relation between the modulation index of the  virtually aged dataset and the alpha-variable.
The capacity to replicate empirical data does not only entail the ability to reproduce a specific property of the functional data, but reproduce a wider range of features or summary statistics, such as static FC and other FCD properties. Specifically, for each dataset we conducted a parameter sweep or grid search for the global parameter in the range 𝐺 = [1. 5 -3. 2] with sampling interval ∆𝐺 = 0. 05 and the noise variance in the range σ 2 = [0. 01 -0. 05] with ∆ σ 2 = 0. 01 to determine the optimal couple (𝐺, σ 2 ) for which SI is maximal. The direct numerical integration for the nodal equations (2) was performed via Heun-stochastic integration implemented in the Virtual brain open-source code. For each region, we integrated the equations with variable time steps (from 0. 005 ms to 0. 0005 ms) for 5 minutes and we downsampled the variables , 𝑖 (𝑡) 𝑉 𝑟 𝑖 (𝑡) to the sampling frequency.
As a major contributor to the global economy (13% of global Gross Domestic Product) and one that is expected to rise by 85% to $15. 5 billion globally by the year 2030  [9] , the construction sector is still under-achieving and inefficient, since 9 out of 10 global mega projects encounter delay and cost overruns  [10]. Scheduling software packages, such as Primavera and Microsoft Project  [11] , as well as planning techniques such as bar charts, time charts, and network approaches, are used to assist and help project managers in planning construction projects  [12]. However, these tools are still limited and insufficient when considering the massive amount of newly-available data (e. g. , feedback, images captured from smart devices, IoT sensors, etc. ) that can continuously be produced on every project.
Such information could include onsite spatial and topographical information, temporal and schedule information, and resources and cost information, among others  [16] [17] [18]. BIM models are characterised by a level of development (LoD) which varies from 100 to 500 (i. e. , from least to most developed) and serves to specify the appropriate amount of information required for specific uses  [19]. Such digital representation is multi-dimensional, or 'nD', where each dimension indicates an information-processing capacity for various aspects  [20]. The fourth dimension, known as 4D BIM, incorporates time-related information in the 3D information model to simulate and optimise the project construction process  [21]. Practically, this consists of linking units of work or elements in the form of objects from the geometric 3D model to the construction scheduling activities using proprietary software, such as Navisworks or Synchro Pro  [22, 23].
g. , to optimise and develop more effective strategies for construction project management through the automatic generation and simulation of different construction scenarios  [40]. To bridge this gap, this study first presents case-based evidence of the use of 4D BIM during the construction phase of a real project to understand how this tool can be practically implemented to support and assist the project participants in their mission. Second, to enable process automation and 4D tools development, this paper presents an ontology-known to be a useful AI tool in formalising specific domain knowledge including concepts, relations, and constraints  [41, 42] -dedicated to scheduling, planning, and 4D simulation, and demonstrates its application by populating the corresponding database and developing a digital tool for application within the context of deep renovation projects that are part of a large European research project known as the RINNO project.
Construction Planning and the Advent of 4D BIM BIM makes it possible to design, build and operate a structure over its entire life cycle  [14]. It involves a collaborative process within the company and between external partners around a digital model. The latter can be described as a 3D representation of the physical and functional characteristics of a built asset  [81]. It is a technical database, made up of objects defined by their characteristics and the relationships between them. The whole forms a structured set of information about a built asset. The 3D model is useful for visualizing the planned project. However, on its own, this virtual structure remains static and does not allow a clear appreciation of the implementation of the construction process and the dynamic that characterises the sequence works.
Moreover, there is a dearth of studies that investigate how 4D BIM can be integrated with AI tools which will enable the development of more effective methods for construction project management by considering the massive amount of data produced on every project and thus enable the gaining of valuable insights  [40]. To capture the industry perspective on BIM-based 4D solutions and understand existing practices, a survey of French construction companies was performed. The results of the survey are described in the next subsection.
As shown in Figure  2 , Navisworks and Synchro Pro are the most widely-used BIMbased 4D planning softwares in France with 55% and 25%, respectively. Other 4D planning tools, such as Vico Control (6%), XD Builder (4%) and others (e. g. , ITwo 4. 0) are used by some French construction companies as well. However, not all the respondents had experienced the use of 4D BIM planning (6%), which demonstrates that the 4D BIM tool is not yet fully implemented and adopted within the French construction ecosystem. The most used 4D tool in France is Navisworks. It is easy to use and enables the linking of activities on a bar chart to corresponding 3D elements in the BIM model. Navisworks can be used for operation control, conflict analysis, construction sequences, and coordination between disciplines.
Indeed, it proposes a solution based around the Navisworks software and its TimeLiner module. By importing a Gantt schedule and a digital model of a project, it is possible to link the corresponding digital objects to each of the project tasks. This link can be performed manually or automatically if the 4D parameters of the digital model have been correctly created and entered. The second method is developed by the iBIM teams at the Vinci Construction France company. This method is based on the use of Synchro Pro software. It consists of remodelling and restructuring the BIM model into elementary objects, creating a task for each item in the schedule, and then linking each elementary object to its corresponding task. This results in a 4D schedule with a very high granularity, since the BIM objects in the original BIM model will be split into many new elementary BIM objects according to their actual onsite construction schedule.
Summary of the Identified Problems The difficulties encountered by survey respondents corresponded closely with the observations noted in the literature, summarised in Section 2. 3, above. This suggests that current methods of creating 4D tools from 3D BIM models are either (i) relatively easy but not suitable for the full range of construction site planning functions (as in the case of the Hardin and McCool approach); or (ii) reasonably suitable for such purposes in terms of their granularity but involve a laborious and time-consuming manual conversion process. The requirement for the CESI BIM team to undertake the implementation of 4D BIM planning on the Nanterre 2 project presented an opportunity to develop a workflow that addressed these concerns. The aim was to produce an approach to 4D BIM implementation that could produce a construction schedule of maximum functionality with minimum extra effort expended in data exchange.
The results, which are presented in the following section, could help project participants more fully exploit the potential of 4D BIM. Furthermore, to address the problem of how 4D BIM can best be coupled and used with AI technologies to exploit the massive and increasing amount of construction data and enable 4D BIM automation, Section 6 proposes an ontology and demonstrates its application by developing an automated 4D BIM process for application within the context of the RINNO research project.