dataesr/scientific-paragraphs-categorization huggingface dataset:
• MinPts was started from 20 points as the initial value and then gradually increased (firstly 50, then 100, and finally by 100 until the maximum number of points for each respective NC party event) until a maximum of NC party events were detected as clusters. • Time window: due to the typology of current NC parties, the clusters analysis was performed by filtering the NC database by day, by week, and by month, on a total duration that includes all the NC points (i. e. , if an NC party took place over a period between 2 months, the 2 months concerned were fully considered). • GPS accuracy: the DBSCAN process was performed with two settings for the GPS accuracy: "Off", meaning that the GPS accuracy is not considered; "On", meaning that measurements with a GPS accuracy strictly greater than 15 m are removed from the data.
Results All the results have been summarised in a table, with the processing parameters and the detection rates of the NC parties, both in tracks and in points. More than 600 trials were performed. Results of the 600 trials are summarized in Table  2. Some of the results for these 600 trials are presented in Table  2. For example, line #1 refers to the analysis carried out on the NC party N°1 (Figure  3 ), inside the spatial area "Pays de la Loire" (PdL, i. e. , an administrative zone) in France (FR), made of 10,700 points within 113 tracks, using the DBSCAN parameters Eps = 50 and MinPts = 20, after filtering the database in order to retain the data collected during 1 month only (i. e. , the month in which the NC party measurements were taken). Using these parameters, 7 clusters are found (Figure  4a ): 93.
Preliminary Results of DBSCAN in Some Countries At this stage, one can already apply the DBSCAN method to the NC database. One of the objectives of this first application is to determine if the method is able to detect clusters and then to identify the typology of the clusters that are obtained by this approach and their possible interest. As mentioned above, the values for Eps and MinPts parameters will condition the relevance of the detected clusters. The role of the expert is therefore important in the choice of these parameters. In the present case, the parameters are fixed to Eps = 3000 m and MinPts = 200 points, in order to limit the number of detected clusters and focus on larger ones. Indeed, the use of the previously identified set of parameters (Eps = 3000 m and MinPts = 20 points) on a large spatial area leads to a number of clusters that is too large to allow a relevant analysis.
• The next application was realized in the United Kingdom, one of the top contributors to the NC database with 4693 tracks (4th in tracks contribution) and 2,067,182 points (3rd in points contribution). Applying the DBSCAN method leads to 440 clusters. The cluster with the highest number of points (126,533 points with 33 tracks) took place in October 2019, close to the city of Stevenston in Scotland (Figure  8 ). This cluster was collected by one user only during 12 days, with a few tracks per days; the highest number of the data collection was performed between 22:00 and 01:59 (25. 8% points) and between 06:00 and 06:59 (8. 5% points), which could suggest that an objective of the measurements was to evaluate the noise distribution during late night and early morning. The metadata show that the smartphone was calibrated for all the measurements, but the calibration value (40 dB) seems excessive in relation to what can normally be expected.
• Italy was also considered, since it is also one of the major contributors to the NC database with 2654 tracks (ranked 11th) and 364,613 points (ranked 18th) and because few NC parties have been organized. Applying the DBSCAN method gives 151 clusters, among them, the two known NC party events, which took place in Fisciano in May 2018 and May 2019  [42, 43]  (Figure  9 ). • Lastly, the DBSCAN method was also applied to France, where most of the NC parties were carried out and potentially, some non-official events. A total of 1852 clusters were found, among them 211 during the month of September 2017, which corresponds to the first month of the application's existence. The cluster with the most tracks and points (1358 tracks/32,0850 points by 429 contributors) is observed in Paris in September 2017.
• Clusters of data collected by the same users in the same location but at different times. Based on the previous parameters for DBSCAN, it is clear that the number of detected clusters can be very important once applied to the whole NC database. Moreover, based on this typology of clusters, and depending on the final goal of the clustering, not all clusters have the same importance. Coming back to the initial objective of the present study, i. e. , the constitution of a reference database, type A and B clusters are certainly the most relevant due to a much higher density of measurement points, expressing a willingness to collect data over a given spatial extent. On this basis, the higher the value of the parameter MinPts will be, the larger the number of measurement points that will be important and the more the number of concerned clusters that will decrease (as shown by Table  4 ).
Applying the DBSCAN Method to the Full NC Database As a second application of the DBSCAN approach, the full NC database is considered (197,568 tracks, 48,901,719 points, 50,868 contributors, 195 countries), using the parameters Eps = 3000 m and MinPts = 5000 points, filtering the data weekly. On a laptop (Intel(R) Core(TM) i5-10210U CPU 64-bit Processor), the method takes about 16 h to process without any special optimization. Overall, 2046 clusters were found in 68 countries. The United States showed the most clusters (975 clusters), followed by France (297 clusters) and the United Kingdom (111 clusters), which is an expected result since these three countries are considered among the top three contributors to the NC database  [8]. Among these 2046 clusters, 1567 clusters (76. 59%) were collected by one contributor each (found in 40 countries, by 1155 different contributors).
The data collected during NC parties can thus be considered as reference data and can be used to learn models. However, these reference data are insufficient in number to ensure the quality of learning; additional data are then required in the reference database. The spread of the NC application around the world has also allowed other participants to organize NC party-like events, which would complete the reference database as soon as this data can be identified in the database. In general, the realization of these events generates an increase in the spatial and temporal density of the measurement points, which suggests that clustering-type methods would be well-suited to detect them. Among them, the DBSCAN method was retained in the present paper and tested in several configurations. The method was first applied in order to verify that the known NC parties could be detected.
By judiciously choosing the two main DBSCAN parameters, the search radius of the measurement points (Eps) and the minimum number of points (MinPts), it is thus possible to find 100% of the known NC parties in the form of clusters. In a second step, the method was applied to a selection of countries in order to analyze the typology of the detected clusters. Several events similar to the NC party were indeed detected, but, depending on the value of the processing parameters, a greater number of additional clusters can also be detected without being able to associate them in an obvious way with a particular event. Finally, the method was applied to the whole NC database, with a set of parameters aimed at detecting the most important clusters. More than 2000 clusters were detected in the world, some of which could be associated with events organized in the framework of research published in the literature.
g. , date/time of measurements or spatial directions, for example). The OPTICS method can give better results than DBSCAN, but it increases the computational time due to the initial ordering of data. Instead of focusing on points to build clusters, it could also be interesting to consider trajectories (since a set of measurement points comes from the same track), using line/trajectory spatial clustering methods. Among them, the TRACLUS method  [52]  would seem to be well adapted in this case since it allows us to group trajectories with common sub-trajectories in the form of clusters. This is usually the case in practice when an NC party is organized locally, since all contributors start collecting data from the same location (from the same street, for example). Considering the initial objective (i.
e. , to build a reference database), post-processing may be carried out after the clustering, for example by merging some detected clusters. Indeed, some measurements can belong to the same event but may be grouped into different clusters because they are performed in different places or different periods. This can be solved by investigating the contributors of each cluster to see whether they are the same, or by detecting if the area of the clusters is overlapping (this is the case, for example, when the measurements are collected in a same space but at a different period). In addition, the reference database may be increased by selecting the most important/relevant clusters and then associating the data that are produced independently by the participants to these events. Finally, this would allow the construction of a larger reference database that could be suitable for the use of supervised machine learning methods to develop quality control protocols for the NC data.
e. , the pleasantness), and other additional information. If the user has given his consent, the anonymous data are transferred to a remote server, controlled and then integrated into an open database, which is available for free. In addition, the raw data from the entire community are aggregated into a hexagonal based noise map and displayed on a public web page [10] , as shown in this figure. Graphical contents of this figure are part of the Noise-Planet project website [11] , licensed under CC BY-NC-SA 4. 0.
Objective: The aim of this study was to evaluate a novel smartwatch-and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD. Methods: This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts-the HD Natural History Study (NCT03664804) and the Digital-HD study.
This Study In this study, a smartwatch-and smartphone-based remote digital monitoring platform was developed to assess motor, cognitive, behavioral, and functional domains in HD using frequent active and continuous passive monitoring  [27, 28]. This platform was applied to individuals with premanifest HD (individuals genetically confirmed to have HD but not having diagnostic motor symptoms of HD), manifest HD (individuals with diagnostic motor symptoms of HD), and control participants to determine its feasibility, reliability, and cross-sectional validity for monitoring motor and cognitive features, which are key domains that change with clinical progression across the continuum of adult HD  [1]. Digital-based outcomes were compared at baseline with analogous in-clinic tests during the screening period from 3 independent studies (a recently completed open-label extension [OLE; NCT03342053] of a tominersen phase I/IIa study and 2 untreated natural history cohorts: the HD Natural History Study [NHS; NCT03664804] and the University College London Digital-HD study) to cross-sectionally validate the Roche HD Digital Monitoring Platform, with results reported herein.
The Chorea test captured the degree of chorea by recording upper body physical movements as the seated participant held the smartphone as still as possible in their outstretched arm and hand while wearing the smartwatch on the preferred wrist. As a dual task, the participant counted backward aloud during the test; these data are not included in these analyses as the methodologies needed to analyze these data are still under development. The Balance test assessed the participant's static balance function by recording movements as the participant stood as still as possible while wearing the smartphone and smartwatch. The U-Turn test was designed to assess gait and lower body bradykinesia. The participant walked and turned at least five times between 2 points that were at least four steps apart while wearing the smartphone and smartwatch.
Digital Test Outcomes The raw data for each test were converted into a single predefined readout, hereafter referred to as a feature. The values reported here are the medians for each feature over 2-week intervals. If, for a participant, less than n observations that passed the quality criteria (Statistical Analysis) for a given test in an interval were available, the data for that participant and interval were considered as missing. The value of n was 1 for the SDMT and SWR test and 3 for all other tests. The interval length of 2 weeks was found to be the optimal trade-off between period length and robustness against missing values. The following active test features were prospectively selected based on their face validity as tests of relevant cognitive and motor function in HD and digital surrogates of existing in-clinic tests: number of correct answers (SDMT), number of correctly read words (SWR), and mean intertap interval (Speeded Tapping).
Pearson correlation was used when both variables were normally distributed and a linear relationship between them was expected to exist. All analyses were conducted with Python (version 3. 6; Python Software Foundation) scripts using the pandas and SciPy libraries. Known-groups validity was assessed by comparing the feature values between the 3 cohorts from the Digital-HD study (premanifest HD, manifest HD, and controls) and the 2 manifest HD cohorts from the OLE study and HD NHS. Comparison was done by first fitting for each value a mixed linear effect model with a fixed effect for age and a random intercept for study. The residuals of this model were then compared using Kruskal-Wallis test and pairwise Mann-Whitney U tests between these 5 groups. cognitive and motor performance, passive monitoring of daily life, and electronic patient-reported outcomes), we relied on custom Android apps built specifically for these studies deployed on smartphones and smartwatches.
51, HD NHS: ρ=-0. 16; P=. 18, Digital-HD [manifest HD cohort]: ρ=-0. 20; P=. 32), and step frequency variance while walking for 2 minutes with UHDRS-TMS (OLE: ρ=0. 71, HD NHS: ρ=0. 26, and Digital-HD [manifest HD cohort]: ρ=0. 47). Using data from participants with premanifest HD in the Digital-HD study, the digital SDMT and SWR tests were strongly associated with the in-clinic SDMT (r=0. 64; P=. 002) and SWR (r=0. 91; P<. 001) tests, and sway path during the Chorea test showed moderate association with the UHDRS Chorea item when using the D hand (r=0. 58; P=. 01).
The high proportion of failed tests observed with the Balance, U-Turn, and Walking tests was due to the exclusion of data that were collected from participants who performed the tests with the smartphone in their trouser pocket instead of the provided running belt; these data were excluded to account for any possible influence of sensor placement differences on the digital measures. All sensor-based features had excellent test-retest reliability (ICCs≥0. 8). Generally low QC failure, high reliability, and good adherence indicate that these measures possess the properties required to be used as outcome measures in clinical trials. The difference in adherence overall between studies is likely due to the difference in study design, where participants in an interventional trial, in this case the OLE study, are more likely to be motivated compared with participants in observational trials, as with the HD NHS and Digital-HD study.
Strengths and Limitations In summary, data from remote patient-driven digital monitoring systems have the potential to advance insights into HD disease features and progression that may enable improved clinical trial design and disease management. As demonstrated in this study, the Roche HD Digital Monitoring Platform appears to fulfill the criteria of cross-sectional validation required for a novel platform to be useful in this context. An important limitation of this study is the cross-sectional nature of the data and the lack of a comprehensive evaluation of the platform's clinical validity. Accordingly, the next goal for the platform is to demonstrate sensitivity to clinical change over time and ability to measure drug effect. Such additional longitudinal data are critical to judge the true value of the digital approach versus the standard approach, and these data are presently being generated across the Roche tominersen clinical development program in interventional and observational contexts.
Acknowledgments The authors thank all the patients who participated in our studies and their families. The authors also acknowledge  Jens Schjodt-Eriksen ,  Luis Matos ,  Hans Van Wesenbeeck , and  Sarper Guerel  for their contributions in implementing data capture and processing solutions;  Timothy Kilchenmann  and  Eduardo Arteaga Bracho  for study management;  Wei-Yi Cheng ,  David Slater , and  Gregoire Pointeau  for contributing to feature development; and  Alexis Dorier  for conducting data analyses.
I. INTRODUCTION Decision support techniques play an essential role in today's business environment. Since the 17th century, statistical reasoning (SR) has been used extensively to shape business decisions  [1]  and it was the earliest method to draw insights from data. With the emergence of decision support systems (DSSs) in the 1970s  [2] , SR is frequently used in DSSs and decision support tools, just take SPSS (Statistical Package for the Social Sciences)  [3]  or SAS (Statistical Analysis System)  [4]  as examples. With the rise of information technology in the 1990s, online analytical processing (OLAP)  [5]  The associate editor coordinating the review of this manuscript and approving it for publication was Wei  Wang. and association rule mining (ARM)  [6]  have emerged as powerful decision support techniques (DSTs)  [7] , both with their specific rationales, objectives, and attitudes.
Over the years, both OLAP and ARM have gained massive ground in practice (Cognos, SAP-BW resp. RapidMiner, Orange -to name a few) and, similarly, massive attention in the research community. Unfortunately, both OLAP and ARM have been introduced together with their own genuine mathematical formalizations and developed their specific terminologies. This makes it hard to reuse results from one domain in another; in particular, it is not always easy to unlock the potential of statistical results in OLAP and ARM application scenarios. OLAP represents relational data  [8]  in multi-dimensional views using roll-ups, drill-downs, slices, dices, etc. In contrast, ARM relies on the notion of itemsets and frequent itemsets  [9]  in transaction databases. The correspondences between OLAP and ARM might seem rather simple, but it is neither fully elaborated in the state of the art nor implemented in practice.
The tool's outcomes establish semantic correspondence between SR, OLAP, and ARM and provide various useful data visualization methods. The tool is implemented on ASP. NET. In the tool, we use 'all combinations of influencing factors' (ACIF) function to select the target column and influencing factors to generate all possible combinations of data items. The programming code and other instructions on how to use the proposed tool are available in the GitHub repository  [15]. We have named the tool grand report  [12] ,  [16] ; a grand report provides a complete printout of generalized association rules, which can also be seen as the entire unfolding of a pivot table  [17]. An instance of the tool is hosted and available on the web. 1 The tool is straightforward to use, and it provides unified usages of DSTs.
The key contributions of the paper are as follows: 1) Elaboration of semantic correspondences between the three DSTs, i. e. , SR, OLAP, ARM, and their foundations, i. e. , probability theory, relational algebra, and the itemset apparatus, respectively. 2) We characterize to what extent and how far SR, OLAP, and ARM can be considered synonymous. 3) A novel framework for the unification of DSTs is presented to develop next-generation decision support tools. 4) A sample tool is presented to implement the unification of DSTs. The tool provides unified usages of DSTs. 5) The tool is tested on various datasets and compared to a state-of-the-art decision support tool. The comparison and the tool's outcome demonstrate the tool's superior performance. The paper is organized as follows: In Sect. II, we review current work related to the unification of SR, OLAP, and ARM.
II. EXISTING WORK In this section, previous work related to semantic correspondences between DSTs and various approaches for the integration of DSTs is explored. 1 http://grandreport. me The classical DSSs  [2]  were developed to assist managerial decisions by presenting several combinations of information. With the emergence of OLAP  [5] , knowledge discovery in databases (KDD)  [36]  and ARM  [6] ,  [37] , many authors have proposed a variety of advanced DSSs. In the 1990s, webbased DSSs have been very popular  [38]. Later, organizations have started taking advantage of different DSTs in DSSs  [19]. We examine eighteen different research articles that discuss the integration of DSSs with different DSTs. A summary of these articles is given in Table  3. Wang  [18]  presented a novel architecture to integrate KDD techniques into existing DSSs.
In 1998, Ng et al. [41] , and Zhu  [10]  have proposed different ways to integrate ARM and OLAP together; however, their research was centered towards multi-dimensional ARM, automatic OLAP, and other specific sets of problems. The mainstream ARM was developed to find frequent items, while OLAP represented a multi-dimensional view of data using different OLAP operations. Therefore, the popularity of ARM for transactional datasets and the progress of OLAP  [44]  in a multi-dimensional environment attracted many authors to propose possible ways to integrate the ARM and OLAP. In 1997, Kamber et al. [11]  first addressed the relationship between ARM and OLAP and proposed a meta-rule-guided mining approach for mining association rules from a multi-dimensional data cube. In this paper, Kamber et al.
In our opinion, this also explains the emergence of ARM and OLAP, which are two immensely successful approaches that complement, extend (but also overlap) the established SR toolkit. Moreover, the journey has just begun, as the current interest in data science proves -in 2015, Donoho  [49]  showed the evolution of data science from statistics. In Table  5 , we provide different combinations of data used in SR, OLAP, and ARM. R is used to represent numerical type data, D is used to represent discrete type data and B is used to represent bitmap data.