dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018) ,  Ervan and Temeltas (2019)  also proposed a sampling algorithm based on a modified tensor voting framework to preserve geometric primitives while down sampling dense areas taking most salient points as representatives. Only qualitative results are given on only one scan, and no comparison have been made. The method presented in this paper is able to both reduce the number of point and retrieve the geometric information in large sparse noisy point clouds. Contrarily to other strategies, we presents a large-scale evaluation of these strategies along with our method in context of large-scale 3D environments. Although several new strategies have been proposed in the recent years, the most used point cloud processing software such as CloudCompare, the Point Cloud Library (PCL), or libpointmatcher, a popular modular library implementing the Iterative Closest Point (ICP), mostly still rely on either random, uniform or spatial sampling to reduce the number of points.
During the data filtering step, we applied the evaluated filter on both the reading and the reference. The data association is conducted by matching the two closest neighbors. We rejected the outliers in the matching process according to a trimmed distance. We limited the scope our experiments to a point-to-plane version of ICP, as it tends to perform better in those datasets as highlighted by  Pomerleau et al. (2013). The minimization process stops when the number of iterations reached 150, or when the differential translation error is less than 1 cm and the differential rotation error is less than 0. 001 rad.
Details on the Real-World Datasets Working under the hypothesis of robotics applications, this paper presents an in-depth evaluation of sampling algorithms on (1) structured with 45 pairs of scans, (2) semi-structured with 32 pairs of scans, and (3) unstructured point clouds with 32 pairs of scans, using the datasets "Challenging data sets for point cloud registration algorithms"  (Pomerleau et al. , 2012)   2. The structured environment is a map of an apartment with approximate dimension of 17 × 10 × 3 m, with an average 365 k points per scan. The semi-structured environment is a map of a park where there is grass, paved small roads, sparse trees, and containing a gazebo made of rock walls and a wood ceiling covered with vines trees. The dimension are 35 × 45 × 16 m, with an average of 170 k points per scan.
Finally, the unstructured environment is a map of a wood, mainly constituted of vegetation (tree, bushes, etc. ). The only structured element is a small paved road that crosses the wood. The dimension are 36 × 60 × 22 m, with an average of 178 k points per scan. The used sensor is an Hokuyo UTM-30LX, a time-of-flight sensor with a minimum range of 0. 1 m and a maximal range of 30 m. As indicated by  Pomerleau et al. (2012) , the specifications of the sensor give a range accuracy varying from ± 1 cm for distances within [0. 1, 10) m to ± 3 cm for distances within [10, 30] m. The evaluated methods are similar to the most used strategies available in point cloud processing software such as CloudCompare and PCL. For each method, we resumed in Table  3  the range a; b of parameters influencing the number of points, where a gives the smallest number of points, here, 1.
In the first situation, with more points, the dense areas predominate in the minimization process, leading to less accurate results. In the last situation, with only a few points, the minimization process cannot converge efficiently as not enough information is available to constrain the process. Both situations lead to less accurate results. The evaluated methods are significantly more accurate than the baseline except NSS, CovS, and SSNormal, which perform worse than Random, with a median error of 20 cm against 7 cm for the translation. This confirms their inability to manage uneven density and largescale point cloud performing poorly for all types of environment. These algorithms need to be adapted for an application in the context of robotics, with large-scale, uneven environments. Spatial methods perform well on all datasets, providing a median translation and rotation errors which tend to the minimal reachable errors for the ICP algorithm on these kind of maps.
Preserving Details and Making the Density Uniform on Real-World Large-Scale Data Finally, we evaluate qualitatively SpDF on real-world large-scale datasets. Figure  4  illustrates that SpDF is able to make the density uniform while preserving each geometric primitive, from an original point cloud of 370 k points where most points are concentrated in a small area, to a uniform point cloud of 40 k points, i. e. , a compression ratio of 89 %. Edges and corners have been preserved, while dense surfaces have been made uniform. We evaluated the proposed method along with one of the top three methods, Voxel sampling with a spatial extent of 0. 8 m on a real-world large-scale outdoor environment. Figure  1  shows the qualitative result of a sampled point cloud from 4. 65 million points to 100 k points, i.
Discussion on the Choice of the Sampling Method Our results show that sampling strategies designed for computer graphics applications cannot be extended to real-world large dataset. CovS and NSS cannot handle large scale map with noise, while SSNormal and Random suffer from their inability to deal with varying densities. Considering real-world large scale environments with spurious and noisy measurements, Voxel, MaxDensity, and SpDF can be used to sample the point cloud. In applications where computation time is critical, Random can still be considered as a strong solution. However, if the precision is critical, it is better to use Voxel, MaxDensity, or SpDF as they provide errors within the range accuracy of the used sensor. Here, in the context of large scale environments, it represents less than 2 cm in translation and less than 0.
FUNDING This work has been supported by the  French program WOW! Wide Open  to the World in the context of the project  I-SITE  Clermont  CAP 20-25 , by the  AURA Region  and the  European Union (FEDER)  through the  MMII  project of CPER 2015-2020 MMaSyF challenge, and by a public grant overseen by the  French National Research Agency as part of the Investissements d' Avenir  through the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-0016 ), the  IDEX-ISITE initiative CAP 20-25  ( ANR-16-IDEX-0001 ), and the  RobotEx Equipment of Excellence  ( ANR-10-EQPX-0044 ).
This allows one to give an insight about how poorly the existing simplified models can predict the OWT natural frequencies. Another objective of the work is to tune the mostly used simplified foundation models, making use of the developed 3D FEM-based model. A final objective of this work is to employ the developed 3D FEM-based model to determine the effect of the different parameters of the soil and the foundation on the first natural frequency, the existing simplified models being unable to explicitly include the soil, and foundation design parameters. In the present study, a full 3D FEM-based model of the 10 MW DTU wind turbine is developed using the commercially available finite element (FE) code Abaqus/Standard. The geometrical properties of the wind turbine components (blades, tower, transition piece, and monopile) are explicitly considered, and the soil is modeled as a 3D continuum.
In their studies, the OWT structure was simply modeled by a beam with a lumped mass at its top as a representation of the nacelle/blades system. Notice that the used modulus of subgrade reaction was the one derived by Ashford and Juirnarongrit,  14  which was originally developed by Vesic. 15 It is given as follows: K = E 0 1 -ν 0 2 E 0 D 4 E P I P " # 1=12 , ð2Þ where D, E P , and I P are respectively the diameter, the Young modulus, and the moment of inertia of the monopile; and E 0 and ν 0 are respectively the soil small-strain Young modulus and Poisson ratio. The individual spring stiffness at a given depth is obtained by multiplying the corresponding K value by the distance separating two consecutive springs. Another approach was used by Prendergast et al. 13  to derive the spring stiffness distribution in sand from py curves based on cone penetration test (CPT) data making use of the expression proposed by Suryasentana and   Lehane.
| Development of the 3D FEM-based model The 3D model of the DTU 10 MW wind turbine was developed in this paper using the FE code Abaqus. As natural frequency is concerned with very small amplitude vibrations, linear modal analysis is sufficient (cf. Letcher 38  ). The deformation of the foundation will be small, and F I G U R E 1 DTU 10 MW OWT (front view) consequently, the consideration of the initial foundation stiffness would suffice for this purpose. Practically speaking, the use of the small-strain properties of the soil is adopted in this paper for the linear modal analysis of the OWT. In this section, one presents the modeling of the different structural components and the soil-monopile system.
22 ncerning the apparent fixity model, two different approaches are considered in this study. The first one is the traditional AF approach. This approach considers a single cylinder whose properties (length L AF and flexural rigidity EI AF ) were determined from the diagonal terms of the stiffness matrix as given in Equation  4. The second one is the improved AF approach proposed by Løken and Kaynia,  27  which considers two consecutive cylinders as shown in Figure  8. The parameters (length and rigidity) of the two cylinders can be determined from both the diagonal and off-diagonal terms of the stiffness matrix (cf. Løken and Kaynia  27  ). Notice that the stiffness matrix of the soil-monopile system used in the present study to extract the parameters in both AF approaches is derived from force-displacement curves obtained from FE simulations using Abaqus software.
It should be noted here that a 3D representation was considered for the cylinder(s) used within the two AF approaches. Concerning the distributed spring model considered in this section, it makes use of three different approaches to determine the distribution of the spring stiffness along the monopile embedded depth. The three approaches are based on the API py curves (Equation  1 ), the modulus of subgrade reaction (Equation  2 ), and the CPT data (Equation  3 ). It should be noted that the API-based py curves are derived based on the friction angle φ of the sand. In this study, this angle was calculated from the sand relative density D r using the relationship given by Das and Sobhan  51  as follows: Within the distributed spring model, a 3D representation of the monopile was adopted when implementing this approach in Abaqus.
Concerning the tuning of the coupled spring model, the stiffness of the coupled lateral and rotational springs (K HH , K RR , K RH ) correspond to the terms of the same stiffness matrix used to calibrate the parameters of the improved AF model. The value of the first natural frequency obtained using this tuned model (0. 197 Hz) was found to be very close to the one obtained by the 3D developed model (0. 201 Hz), the difference being smaller than 2%. Finally, the tuning of the springs within the distributed spring model was obtained from FE simulations using Abaqus software. Notice that the springs stiffness distribution along the monopile embedded depth (of 45 m) were derived from the distribution of the lateral force and the corresponding lateral displacement shown in Figure  11. A total of 90 springs [for each lateral (x or y) direction] equally spaced at 0.
User data can be acquired from various domains and is characterized by a combination of demographics such as age and occupation, and user actions such as rating a movie or recording one's blood pressure. User data is appealing to analysts in their role as data scientists who seek to conduct large-scale population studies, and gain insights on various population segments. It is also appealing to users in their role as information consumers who use the social Web for routine tasks such as finding a book club or choosing a physical activity. User data analytics usually relies on identifying group-level behaviors such as "Asian women who publish regularly in databases". Group analytics addresses peculiarities of user data such as noise and sparsity to enable insights. In this survey, we discuss different approaches for each component of user group analytics, i.
1. UGA starts from raw user • Affiliation. Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble France / Institute of Engineering • E-mail. firstname. lastname@univ-grenoble-alpes. fr data and discovers groups that reflect the behavior of a set of users, e. g. , "Asian women who publish regularly in databases". User data must first be cleansed and enriched  [8]  before group discovery (i. e. , Preparation component in Fig. 1. ) Once groups are discovered, there are still two challenges before turning results into insights. • First, there may be millions of groups. An exhaustive scan through all groups is not possible for analysts  [9]. The group exploration component is designed to address this challenge. It operates on the results of group discovery in an iterative manner. It provides means to analysts to navigate in the space of resulting groups.
USER GROUP MODEL AND USE CASES Given a set of users U and a set of items I, we define user data as a database D of tuples u, i, v which represent a value v ∈ R induced by an action such as browsing, tagging, or rating, of user u ∈ U, on item i ∈ I. The notation used in this paper is summarized in Table  1. This generic data model describes multiple user datasets in the literature, such as 1. 7M research publishing actions of database researchers 1  [18] , 5B tweets  [19] , 300M customer receipts from a retail chain of 1,800 stores  [20] , 10M rating records from MOVIELENS 2  [21] , 50M artist ratings from LASTFM 3  [22] , 1M electronic health records (EHR)  [23]  and about 200K book ratings from BOOKCROSSING 4   [24]. For instance in MOVIELENS, the tuple John, Titanic, 4 describes that the user John rated the movie Titanic with a score of 4.
Also for the tweets dataset, the tuple Tiffany, tweet id , Hemophilia describes that Tiffany tweets about Hemophilia. Attributes. Users and items have attributes drawn from a set A. Instances of user attributes are age, gender, diet, political orientation, and occupation. Instances of item attributes are book author, movie director, tweet language, and treatment duration. The choice of what constitutes an attribute is application-dependent. For instance, a user on a collaborative rating site may have an "age" and "gender", and a book has an "author" and "publisher". Social links. A social link u, u , link type depicts the potential bond between a pair of users u and u where link type may represent co-authorship  [25] , affinity  [26]  or friendship  [27].
Note that query formulation requires a knowledge of the dataset and the query language, which is not always the case. Implementations of by-query exploration are as follows. Explore next returns next possible groups for a given query. In  [66] ,  [68] , a query is formulated in terms of constraints, and the next available group satisfying the constraints will be returned. In  [69] , the next random group is returned which is in line with feedback constraints of the analyst. Explore best region forms a query based on location attributes. In  [65] ,  [70] , a spatial rectangle is given as query constraints and all groups within that rectangle are returned. There exist many applications for the explore best region implementation, including location-based advertisement targeting  [63]  and signage localization for marketing a new product  [70].
Online community search. In  [71] ,  [72] ,  [73] , the problem of online community search is addressed, where the query is a set of users, and the task is to find a densely connected subset of U that contains those users. By-Facet. The parameter θ consists of attribute-value pairs (i. e. , facets) which appeal interesting to the analyst. The exploration system returns groups whose members satisfy requested facets. For instance, if gender, female is given as an attribute-value pair in θ, a group of female students would be a good candidate for exploration. In the next iterations, the analyst adds or removes facets to receive new groups. Comparing to by-query exploration, by-facet exploration reduces the burden on the analyst specifically on datasets with many attributes  [74] ,  [75]. One implementation of by-facet exploration is Split which admits as input an attribute a ∈ A and returns one group per a's value  [13] ,  [76].
Most dimensionality reduction methods suffer from two drawbacks. First, they do not scale to real-size user data. Second, they do not incorporate time of user actions. We review dimensionality reduction improvements as follows. Efficiency in dimensionality reduction. Visual results should be rendered fast so that the analyst can quickly verify discovered groups in a readable format and request a rediscovery if needed. Most dimensionality reduction methods are designed for small datasets and hence are slow on real-size user datasets  [109]. CLOUDVISTA  [110]  is an efficient distribution-based approach for visualizing groups as multidimensional clusters. It performs dimensionality reduction on visual frames, i. e. , a subset of user data bounded to the resolution of the visualization. The size of visual frames is much smaller than the original data, hence faster to compute.
Inspired from previous visualization query languages  [145] ,  [146] , analyst's needs are expressed in terms of a SQL-like language called ZQL (Zenvisage Query Language). ZQL operates on top of a visual algebra which defines the logic behind the mapping function between user groups and visual variables. By defining a restrictive yet expressive schema for ZQL, Zenvisage guarantees that the visualizations are clear. Data Tweening. In  [135] , a visualization view enables analysts to retain changes in exploration. It is often unclear for analysts how the exploration transitions from one iteration to another. Data Tweening visualizes additions, withdrawals and replacements of groups from one iteration to another, in an animated form. Data Tweening functions on a catalog of mapping functions, such as order, rotate, nest using which it visualizes the changes.
NSTRATION OBJECTIVES plemented in Java JDK 1. 8. Our demonstraaded with a few synthetic and real datasets olecules 2 ) with di↵erent sizes (up to 1. 3 mils). Example initial query graphs will be preoratory search. Users can also formulate their ough our gui. ective of the demonstration is to enable the eractively experience multiple reformulation subgraph query in a progressive manner to e underlying information space and identify s. com/info/plus/download-database.
The bit-vector sketch consumes |B|k bits of memory for the entire dataset and can be computed in a single pass of the data in time O(|B|nk). Furthermore, computing the estimated correlation coefficient between every pair of features takes O(|B| 2 k) time as opposed to O(|B| 2 n) time. Setting k to a value that is O(log 2 n) guarantees high accuracy while significantly reducing the time complexity of ranking and searching for correlation coefficients. Initial experiments (without parallelism) showed > 90% accuracy and 3x 4x speedup in preprocessing, with interactive speeds during exploration.
Usage Scenario We now describe how an analyst uses Foresight to explore a dataset containing wellbeing indicators for the OECD member countries. This dataset contains 25 distinct attributes (indicators) about 35 countries and is included in our demo as an illustration and for ease of comprehension. Foresight is intended to facilitate interactive exploration of datasets with data items of the order of 100K and attributes that number in the hundreds. Figure  2  shows the current interface design of AWARE with a risk controller, which incorporates the above ideas, running on a tablet. The user interface features an unbounded 2D canvas where chains of visualizations (such as the one shown in Figure  1 ) can be laid out in a free form fashion. A "risk-gauge" on the right-hand side of the display (Figure  2 (A) ) serves two purposes: it gives users a summary of the underlying procedure (e.
Hold-Out Dataset A possible method to deal with the multiple hypothesis error is to split the dataset D into a exploration D1 and a validation D2 dataset  [38]. D1 is then used for the data exploration process, whereas the validation dataset is used to re-test all hypotheses in order to validate the results of the first phase. In the following we will provide some examples which will clarify how, albeit useful, a hold-out dataset does not solve the multiple hypothesis testing problem. Let us consider a null hypothesis H, and let pD denote its associated p-value when H is evaluated with respect of the entire dataset D. Lets assume we perform a test with significance-level ↵. In this case the probability of wrongly rejecting H is at most ↵ Suppose now that we randomly split the dataset into two datasets D1 and D2.
For the same null hypothesis H we evaluate the p-values pD 1 and pD 2 each obtained by evaluating H on D1 or D2 respectively. We then run a a test with significance-level ↵ (like the one discussed above) for each of the datasets. We then decide to reject H if it has been rejected by both the testing procedures operating on the datasets D1 or D2. If both procedures operating on D1 and D2 have significance-level ↵, then the probability that the overall procedure ends up rejecting H is at most ↵ 2. For the common value of ↵ = 0. 05, the chance of a Type I error is thus reduced to 0. 0025, which is good news. Rather than fully handling the multiple hypothesis problem, what we have achieved trough this procedure is however just the lowering of the threshold for rejecting the null hypothesis (i.
e. , the significance level of the test). This fact appears clearly in the following scenario. Suppose that the user wants to evaluate multiple hypotheses (e. g. , 25) rather than just one. Assuming that these hypotheses, and their p-values are independent, the probability of observing at least one erroneous rejection using the test technique based on the use of the holdout dataset would be: pf = 1 (1 pD) 25 ⇡ 0. 06, which is higher than the desired ↵ significance level. Albeit the lowering of the achieved reduction of the significance level is indeed useful for reducing the chance of Type I errors, it comes at the cost of a significant reduction of the power of the testing procedure. Let us consider the following example scenario in which we aim to compare the means M1 and M2 of two samples one drawn from a population with expected value µ1 = 0 and the other from a population with µ2 = 1, both having a standard deviation of = 4.
In order to determinate weather the observed difference between M1 an M2 is actually statistically significant, we test the null-hypothesis "there is no significant difference between µ1 and µ2" using the one-sided t-test and a sample composed by 500 records from each population. Given the properties of the t-test (see  [13] ), the statistical power of our test would be 0. 99, and the probability of erroneously accepting the null hypothesis would be at most 0. 01. Suppose now that we divide the dataset into a dataset for exploration and one for validation each composed by 250 records. The statistical power for each of the individual t-test executed on the two dataset is now lowered to 0. 87, due to the reduction of the data C-Explorer Picasso Aware Foresight Fig. 5. DEV approaches. those components are integrated.