dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology In order to evaluate the use of BIM-based DT and XR technologies to improve maintenance operations, a systematic literature review methodology was used. The main aim is to observe how these technologies are used in the building industry for maintenance purposes and to identify the gaps in the literature. Then, this research can be used to provide future fields of research. Multiple databases were selected to perform this research: Moreover, a manual search of Grey Documentation was performed to avoid relevant papers available on the Internet and published by non-academic institutions. This research was performed using the software Harzing's Publish or Perish (https://harzing. com/ resources/publish-or-perish (accessed on 5 February 2021)), which helps to perform a cross-database research.
The reference manager software utilised was Zotero (https:// www. zotero. org/ (accessed on 5 February 2021)) due to its large community and the automatic citation add-in for Microsoft Word. We decided to focus the research on the combined use of the terms "Maintenance", "BIM", "Extended Reality OR XR" and "Digital Twin". This research was conducted over a time window from 2005 to February 5, 2021, to observe the most recent developments and implementation of these technologies. A Venn diagram of the terms combinations has been created (see Figure  2 ). The size of the different coloured areas does not correspond to the number of items found for each combination but helps to identify the different combinations used. The different numbers are used to show the various combinations used: 1 for "Maintenance", 2 for "BIM", 3 for "Extended Reality OR XR" and 4 for "Digital Twin" However, to avoid missing any relevant paper, the term "Extended Reality OR XR" was broken down in its various subtypes: "Augmented Reality OR AR"; "Mixed Reality OR MR" and "Virtual Reality OR VR".
• Google Scholar (https://scholar. google. fr/) (accessed on 5 february • WebOfKnowledge (https://www. webofknowledge. com/) (access 2021) • Scopus (https://www. scopus. com/) (accessed on 5 february 2021) Moreover, a manual search of Grey Documentation was performed papers available on the Internet and published by non-academic research was performed using the software Harzing's Pu (https://harzing. com/resources/publish-or-perish), which helps to database research. The reference manager software utilise (https://www. zotero. org/) due to its large community and the automa for Microsoft Word. We decided to focus the research on the combined use of the ter "BIM", "Extended Reality OR XR" and "Digital Twin". This research was time window from 2005 to February 5, 2021, to observe the most recent implementation of these technologies.
We also decided to include papers that were not specific to the construction industry but whose methodology or results could be transposed to it. The criteria were applied separately for each searching string to narrow down their respective numbers. First, they have been applied during the initial result of 242 papers before excluding the duplicates of the different databases. Then, the criteria were applied to the title, abstract and introduction of each remaining paper before merging the different strings results. Finally, the new duplicates, due to the different research strings, were excluded. This methodology can be observed in Figure  3. The result of this methodology is a pool of 68 relevant papers, including conceptual projects, use cases and reviews.
Improvements for Monitoring One of the first improvements highlighted by the researchers concerns the global monitoring of the building and its equipment. Thanks to the centralised database provided by the DT, the different stakeholders obtain easier access to data related to the equipment they want to monitor  [16, 22, 26, 34, 35, 39, 43, [46] [47] [48] [49] [50] [51] [52] [53]. For example, Peng et al. created a specific platform that allowed them to visualise in real time both the status of all the equipment in a hospital and the occupation of its different rooms  [35]. This centralisation also helps to filter the large number of data that can be collected through the use of smart sensors  [49, 52]  and to select meaningful data depending on the role of the user  [47, 51]. The specification of the data needed to effectively evaluate the system can be performed by the different actors of the project using the centralised DT.
It allows for collaboration, allowing all the actors to contribute their own expertise in the data they need to effectively monitor the building  [26, 34, 44, 51]. Varé et al. have proposed a digital representation of a nuclear container that can be used by different teams (research, measurement and engineering) to exchange the information they need  [51]. The centralisation of data also helps the stakeholders to monitor the building together and communicate their different analyses to the others by using the shared database and the link between the data and the equipment. This help in communication between the different actors throughout the project lifecycle provides the possibility to prevent isolation of data in their respective phases and thus a possible loss of important knowledge for further phases  [22, [33] [34] [35] 51].
DT Improvements Brought about by eXtended Reality Technologies The use of DT proves to be helpful to improve maintenance procedures. Nevertheless, it is necessary to provide users with an easy and intuitive interface to display the data included in the DT. The most studied technologies in the I4. 0 to solve this problem are the eXtended Reality (XR) devices, such as Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality  [61, 72, 75, 76]. In recent years, many researchers have proposed a conceptual or practical use of these technologies to improve the use of DT. Of the 68 papers collected for this research, we identified 23 that show us these improvements. However, only 39% of them are use cases. Interestingly, only 22% of these papers explore the use of a BIM-based DT. As we see in Table  6 , we can observe that these devices can bring improvement to the visualisation and management of data but also provide new methods of interactions to the user.
Conclusions In this review, we have observed that most of the advantages of using DT and XR technologies to improve industrial performance can also be applied to improve building exploitation, especially when using data extracted from the BIM of the building to create the DT. A BIM-based DT can be likened to a centralised database where the real-time collected data of an equipment and its static data, such as maintenance history and technical documentation, are linked to its 3D representation, which facilitates its retrieval when needed. During the exploitation of the building, these data can then be used to monitor the building and its equipment, improve inspection operations while facilitating access to equipment data, and use the prediction algorithms of the DT to plan the maintenance when needed.
Following a narrative approach, we asked the residents about the benefits via openended questions  (Arias-Arévalo et al. , 2017). Because this is a suitable method for capturing multiple values, including nonmaterial benefits  (Arias-Arévalo et al. , 2017; Ladle et al. , 2016) , it also verifies if the ES concept captures key benefits comprehensively. We conducted an online survey involving the residents of the Hyogo and Kagawa Prefectures, which surround the Harima Sea, to collect data for the demand-side analysis (see SI for the questionnaire). We recruited participants through an Internet survey company; the company announced the survey and collected responses until the number of respondents reached the target volume. We asked that the sample follow the target population (i. e. , residents in the Hyogo and Kagawa Prefectures) in terms of gender ratio, age composition ratios, and the size of the population in each prefecture.
Each respondent was asked to choose the most and least important ES from a series of such sets. We analyzed the data obtained from the BWS questions using counting and econometric analyses. In the former, we noted the number of times each ES was chosen as being the most or least important for the total-best (ΣB i ) and total-worst (ΣW i ) numbers, respectively. The best-worst (B-W) score of ES i was calculated by subtracting the ΣW i of ES i from the ΣB i of ES i. The standardized B-W score of an ES i was calculated by dividing the B-W score of ES i by the number of times ES i appears in all questions (T i ) (Eq. (  1 )). The standardized B-W adjusts the influence owing to the differences in the number of respondents between groups. A higher standardized B-W score implies that an ES is evaluated as being more important.
In appendix in Sec. 7. 3, we show in a simple setting that as long as the correlation between two predictive features is less than 0. 8, it is possible to recover both features. However when the correlation between features is more than 0. 9, only one of the two features can be identified. In Sec. 7. 4, we show that in standard fMRI datasets neighboring voxels can have a correlation greater than 0. 9. Thus applying Desparsified Lasso at the voxel level certainly leads to many false negatives. However, since Desparsified Lasso is applied to the clustered problem, we have to consider correlation between clusters instead. In Sec. 7. 4, we show on HCP data that such inter-cluster correlation is almost always lower than 0. 8 and always lower than 0. 85. This means that data structure assumptions for EnCluDL are sustainable.
Implementation The Python code that implements Thr-SVR, Perm-SVR, Ada-SVR and EnCluDL can be found on https://github. com/ja-che/hidimstat. Our algorithms are implemented with Python = 3. 6. 8 and need the following packages Numpy = 1. 16. 2 [  Van der Walt et al. , 2011] , Scipy = 1. 2. 1  [Virtanen et al. , 2020] , Scikit-Learn = 0. 21  [Pedregosa et al. , 2011] , Joblib = 0. 11 and Nilearn = 0. 6. 0 [Abraham et al. , 2014].
Experimental procedures 4. 1 Data To validate empirically the statistical guarantees of the four algorithms -Thr-SVR, Perm-SVR, Ada-SVR and EnCluDL-described in Section 3, we perform several experiments on resting-state fMRI and task fMRI data. We also show some results for Univ-OLS to highlight the complementarity of univariate and multivariate analyses, in particular when studying predictive patterns on real data. We focus on three datasets: HCP900 resting-state fMRI, HCP900 task fMRI and RSVP task fMRI. HCP900 resting-state fMRI data. HCP900 resting-state fMRI dataset  [Van Essen et al. , 2012 ] contains 4 runs of 15 minutes resting-state recordings with a 0. 76s-repetition time (corresponding to 1200 frames per run) for 796 subjects. We use the MNI-resampled images provided in the HCP900 release.
For this dataset the number of samples is equal to 1200 (only one run is used) and the number of voxels is 156 374 after gray-matter masking (the spatial resolution being 2 mm isotropic). HCP900 task fMRI data. We also use the HCP900 task-evoked fMRI dataset  [Van Essen et al. , 2012] , in which we take the masked 2 mm z-maps of the 796 subjects from 6 tasks to solve 7 binary classification problems: emotion (emotional face vs shape outline), gambling (reward vs loss), language (story vs math), motor hand (left vs right hand), motor foot (left vs right foot), relational (relational vs match) and social (mental interaction vs random interaction). We consider the fixed-effect maps for each outcome (or condition), yielding one image per subject per condition (which corresponds to two images per subject for each classification problem).
Then, for each problem, the number of samples available is 1592 (= 2 × 796) and the number of voxels is 156 374 after gray-matter masking. Unmasked RSVP task fMRI data. We also use activation maps obtained from a rapid serial visual presentation (RSVP) task of the individual brain charting dataset  [Pinho et al. , 2018] , augmented with 9 additional subjects performing the same task, under the same experimental procedures and scanning parameters. No masking is used for this dataset, so that out-of-brain voxels are not withdrawn from preprocessing. We consider the unmasked 3 mm-resolution statistical z-maps of the 6 sessions of the 21 subjects for a reading task with 6 different contrasts that have been grouped into 2 classes: language (words, simple sentences, complex sentences) vs pseudo-language (consonant strings, pseudo-word lists, jabberwocky).
The images are all registered to MNI space and per-condition effects are estimated with Nistats v0. 0. 1 library  [Abraham et al. , 2014]. For this dataset the number of samples available is equal to 756 (21 subjects × 6 runs × 6 images per run) and the number of voxels is 173 628 (unmasked images resampled at 3-mm resolution). We run the inter-subject experiment described in Sec. 4. 4 with this dataset.
Statistical control on semi-simulated data A first series of experiments study whether the four different methods exhibit the expected δ-FWER control and are competitive in terms of support recovery, as measured with the precision-recall curve. To do so, we have to construct the true weight map w *. We generate "semi-simulated" data: generating signals from estimates on real data. To avoid circularity in the definition of the ground truth, we used two different tasks: one to build w * and another one to define X. Building a reference weight map from HCP900 motor hand dataset. To construct an underlying weight map, we use the motor hand (MH) task of the HCP900 task fMRI dataset described in Sec. 4. 1. Specifically, we build a design matrix X MH ∈ R n×p from the motor hand task z-maps of all subjects associated with a binary target index y MH.
To obtain an initial weight map w SVC MH we regress X MH against y MH by fitting a linear Support Vector Classifier (SVC)  [Cortes and Vapnik, 1995]. From w SVC MH we only kept the 10% most extreme values ensuring that the connected groups of non zeroweight voxels have a minimal size of 1 cm 3 by removing small clusters. We chose this map (represented in Fig. 3  and Fig. 4 ) to be the true weight map w * ∈ R p for the whole simulated experiments. Simulating responses with HCP900 emotion dataset. We then take X to be the set of z-maps from the emotion task of the HCP900 task fMRI dataset described in Sec. 4. 1. To generate a continuous response vector y, we draw a Gaussian random noise vector ε ∼ N (0, σ 2 ε I n ) and use the linear model introduced in (1), where σ ε = 0. 2 to reach SNR y = 10, where SNR y is given by: SNR y = X w * 2 nσ 2 ε.
Statistical control under the global null with i. i. d. data In this experiment, we test whether the procedures control the FWER under a global null model. EnCluDL only controls the δ-FWER theoretically but, when the true weight vector w * is null, the δ-FWER and the classical FWER are identical. Then, all procedures should control the FWER. Here, we considered the tasks of the HCP900 task fMRI dataset described in Sec. 4. 1 keeping all the subjects (n = 1592). Then, to get a noise-only response, we (uniformly) randomly permute the original response vector. Similarly as in Sec. 4. 2, the i. i. d. hypothesis is legitimate, since the data correspond to z-maps of different subjects. For each task, we draw 100 different permutations of the response and check if the different methods enforce the chosen nominal FWER of 10%.
Statistical control of out-of-brain detections In this experiment we test the four procedures on an unmasked task fMRI dataset to verify that no spurious detection is made outside of the brain -up to the allowed error rate. Indeed, the non-null coefficients of the weight vector w * should all be contained in the brain since there is no informative signal in out-of-brain voxels. To do so, we take the unmasked RSVP task fMRI dataset, described in Sec. 4. 1 (with design matrix X containing n = 756 unmasked z-maps). Then, we report how frequently some voxels are detected outside the brain volume. For the sake of completeness, we also check the non-occurrence of out-of-brain detections with Univ-OLS.
Face validity on HCP dataset In this experiment, we consider the output of the procedures in terms of brain regions that are conditionally associated with the task performed by the subjects. Similarly as in Sec. 4. 3, we consider the tasks of the HCP900 task fMRI dataset described in Sec. 4. 1, keeping this time the true response vector. We run all the procedures on every task and report the statistical maps thresholded such that the FWER < 10% or the δ-FWER < 10% (for EnCluDL). For this, we use all the available samples (n = 1592). We also include Univ-OLS to compare the discriminative patterns obtained with a univariate inference.
Prediction performance Even if it is not the purpose of this study, we also checked the prediction performance of the decoders produced by each method. Since Thr-SVR and Perm-SVR rely on the same predictive function, there are three different decoders: SVR, Ada-SVR and EnCluDL. To perform this experiment, we consider the tasks of the HCP900 task fMRI dataset described in Sec. 4. 1. We run all the procedures on every task using a sample size n = 400, keeping the rest of the samples to test the trained model. For each task and each method, we take 100 different random subsamples to produce the results. This experiment being a side study, we give the results in appendix in Sec. 7. 12.
Estimating δ in HCP and RSVP datasets In all the experiments, unless specified otherwise, we run EnCluDL with the default choice C = 500. Reversing (12), we obtain a tolerance parameter of δ HCP = 5. 4 voxels for HCP900 and δ RSVP = 5. 6 voxels for RSVP, corresponding to δ HCP = 12 mm and δ RSVP = 18 mm respectively after rounding up. In Fig. 14  in appendix, we display the spatial tolerance of 6 voxels in the case of HCP data.
Insights on choosing the number of clusters Here, we report the results obtained of the experiment task-fMRI data (Sec. properties of EnCluDL for various sample sizes. These results are obtained with 100 repetitions for every sample and cluster sizes. In Fig. 9 , we notice that a lower C leads to improved recovery, according to the area under the precision-recall curves, for δ = 6 voxels (or 12 mm). However, when the number of cluster is lower, the average cluster radius increases and overcomes the spatial tolerance of δ, leading to inflated error rates (cf. Sec. 7. 6). More precisely, the δ-FWER is controlled when C ≥ 500. Note that for C < 500, it is possible to control the δ-FWER, even when n is small, provided a larger spatial tolerance δ > 6 voxels. To compute the requested δ, one can use (  12 ).
7. 5). In practice, our choice of δ is conservative, and with current fMRI datasets, δ-FWER control holds for smaller δ, even in relatively large cohorts (n = 1200). In our experiments, the spatial tolerance is around 1cm. Given that the definition of spatial location is blurred by inter-subject variability in group studies, this tolerance does not seem problematic. The method can thus be used for inference in cognitive neuroscience and population studies in psychiatry, neurology or epidemiology. In addition, EnCluDL exhibits the best support recovery performance in the proposed semi-simulated experiments with fMRI data but also finds patterns with good face validity in more qualitative experiments plotted in Fig. 7. On the other hand, we also notice that EnCluDL tends to be over-conservative.
Taking into account the difficulty of the problem and the fact that the convergence results are only asymptotic, we do not recommend using EnCluDL with n < 100. In the present study, we have considered that the confounding variable effects have been removed during fMRI data preprocessing. However, it is still possible to include an additional confounding variable to the covariates before performing the inference. With regards to EnCluDL, we note that confounding variables should be handled separately from the clustered brain features. Although it is not the main purpose of this study, we also checked the prediction performance of the decoders produced by each method. It is important to note that EnCluDL has been designed for the recovery of conditional statistical associations, not for prediction.
In practice, the prediction performance is almost the same for SVR and Ada-SVR, and is slightly better than the one of EnCluDL (see Fig. 24 ). For prediction purpose, we recommend using Fast Regularized Ensembles of Models (FReM)  [Hoyos-Idrobo et al. , 2018] , which is a stable and computationally efficient decoder with stateof-the-art prediction performance. For pedagogical purpose, we have also considered a dataset where cross-sample independence is violated due to serial correlation, reproducing an experiment of  Eklund et al. [2016]. The ensuing loss of statistical control underlines the importance of the i. i. d. hypothesis. Hence, EnCluDL should not be used to make inference from intrasubject dataset recorded over one session. With these warnings in mind, we think that EnCluDL can be used safely in neuroimaging context.
Our code, implemented with Python 3, can be found on https://github. com/ja-che/hidimstat along with some examples. We have not considered the method proposed by  Nguyen et al. [2019]  based on the Knockoff filters  [Barber and Candès, 2015, Candès et al. , 2018 ] that yet appear to be an appealing procedure, as it can only control the FDR. In this study we have focused on δ-FWER control, and hence defer the analysis of FDR-controlling procedures to future work. Also, we have not benchmarked post-selection inference procedures  [Lee et al. , 2016 , Berk et al. , 2013] , as we found them challenging to run in high dimensional settings and prone to numerical underflows. Our empirical results clearly show that standard thresholding procedures, including classical permutation tests, are not reliable to infer regions importance on decoder maps, due to the high number of covariates.
First simulation: approximating the clustered data setting. In this simulation we set n = 100 and p = 500. We construct the design matrix X such that features are normally distributed and the first two features have a correlation equal to parameter ρ, while all the other features are independent. The weight w * is such that w * j = 1 for 1 ≤ j ≤ 10 and w * j = 0 otherwise. We also set σ ε = 1 giving approximately SNR y = 12 close to the SNR estimated in real fMRI datasets. To check the ability of Desparsified Lasso to identify two correlated features, we compare the smallest z-score of the first two first features ("correlated features") with the smallest z-score of the two following features ("control features") for different value of ρ ∈ (0, 1). While the minimum z-score of the control features should not vary significantly and corresponds to a control value, the minimum z-score of the two correlated features should decrease towards 0 when ρ increases to 1.
δ 0 being a distance in voxel unit close to the average radius of the clusters used in En-CluDL. However, when the setting is particularly favorable for inference, i. e. , if log(n)/C is large or σ ε is small, the choice of δ given by (  12 ) may be over-optimistic and we might need to correct this formula. We have found empirically that a suitable multiplicative factor, denoted by τ > 0, that could be used to correct δ 0 is given by: δ 0 = p 2C 1 τ = -45 log σ ε std(y) log(n) C , ( 22 ) where σ ε is the standard deviation of the noise ε. In practice σ ε has to be estimated; in the fMRI datasets we studied, estimates of σε std(y) were close to 0. 1. However, given the heuristic derivation of this quantity and the uncertainty about the value of τ , we do not recommend correcting δ 0 with a factor lower than 1 as it could lead to a dramatic under estimation of the valid δ.