dataesr/scientific-paragraphs-categorization huggingface dataset:
Unfortunately, extracted contours are split in several strokes which can be problematic to produce accurate polygons filling in a vector-based format. A third approach consists in creating layers containing full objects with the most effective cuts  [10]. This facilitates the edition of SVGs in a graphics editing software but at a cost of longer rendering times to compute the depth order of the different layers. As a result we chose to adapt the approach proposed by Eisemann et al. [11]  which provides fill regions support by using the 2D arrangement package of the CGAL 11  library to extract 2D regions formed by 2D strokes. In that way, it is possible to extract at the same time line contours and fill polygons.
Note that keeping hidden edges is crucial as it can be required to illustrate some interactive scenarios (typically instances of back-of-device interaction). Step 3: Computing and associating filling regions Once all the edges of interest of the different objects have been grouped, we compute the regions formed by the 2D projection of the visible ones. As  [11] , we use the 2D arrangement package provided by CGAL, which provides the regions formed by the 2D projection of the edges. Step 4: Associating 2D regions to their corresponding 3D objects We associate a region to its corresponding object in the scene by picking for a random point inside the region (using the GPC library  12  ) and raycasting this point to the 3D scene to identify the corresponding 3D object. This step is used to group regions of a same object, as well as select a default filling colour for each region.
As CGAL 2D arrangement package returns regions under a hierarchy, regions inside other regions can be considered as holes and thus, be drawn in a counter-clockwise way. Projecting UIs on screens. Our algorithm also needs to project possible images of graphical user interfaces on their corresponding screens in the 3D scene. For that, we use numpy linear algebra solver to compute the planar homography from the four corners of the user interface to the four corners of the screen object in the scene. However, considering SVG does not currently support perspective matrix transformations, we apply the homography to all the geometric objects of the user interface by computing the new coordinates. Some elements like ellipses, arcs or text first need to be discretized into segments or Bezier curves in order to be projected.
Method Participants and apparatus. We recruited 8 participants (x=33, σ=10), all researchers in HCI. Three were academic researchers, three PhD students and two Post-graduate students. None of these participants had previous experience with the Blender software. The experiment was conducted with Blender v2. 79 with the Esquisse add-on installed, running on an iMac 27' 5K display with i7 4Ghz. Three input devices were available: a Logitech G9 Laser computer mouse, an Apple Magic Trackpad 2 and a LeapMotion. Adobe Illustrator and Inkscape were also installed and available to use at anytime if the participants wanted to complete the vector graphics file produced with Esquisse. Procedure. Participants were invited to sit in front of the computer and were instructed that the experiment consisted in producing trace figures.
The latter was adopted by [P5] to more easily change the hand orientation. Overall, only two participants used the Leap Motion for changing the hand posture, the other one [P2] was illustrating AuraSense and used it to define an index pointing posture for the right hand. Finally, all the participants used the camera snapping functionality and no one moved using the 3-axis manipulator. SVG post-editing. While all participants were invited to do so, only three participants [P4,P5,P7] post-edited the figure produced with Esquisse in a vectorgrahics editing software. While two performed only minor adjustments, [P5] built a 3 images storyboard and overlaid an arrow depicting motion over one figure.
Participants Subjective Feedback. Learning and use. Overall, participants appreciated Esquisse, reporting that it was "easy to use and fun" [P4, P5, P8] and "fast" [P5, P7, P8], even if Esquisse requires a learning phase for objects manipulation and Blender interface [P1, P3, P8]. Participants quickly understood all the functionalities by just watching the 10 minute tutorial video [P1, P3, P4, P5]. Even better, [P1] was "impressed" and said it was "fast to learn and master the different functionalities", making Esquisse "interesting considering [his] poor artistic skills". Post editing. Globally, participants were satisfied with the Esquisse rendering results, but some still prefer to refine the figure afterwards in a graphics editing software [P4, P5, P7]. More importantly, participants saw Esquisse as a rapid prototyping tool "for making quick illustrations using complex objects to be modified afterwards" [P5, P7].
Discussion and Conclusion Trace figures are powerful and frequently used materials for illustrating HCI research papers. Based on a characterization of trace figures used in the HCI community, we designed Esquisse, a tool that implements a novel workflow based on 3D objects manipulation to stage a 3D scene and export it as a vector trace figure. We contributed interaction techniques to facilitate this workflow (including the use of anchor points to help the manipulation of complex articulated objects such as hands and the integration of stroboscopic effects) and a rendering pipeline to extract the different contours, fill them and finally generate a vector-based SVG file. We implemented Esquisse as an add-on for the open source 3D modelling software Blender and evaluated its usability in a qualitative experiment with 8 participants.
The results of this experiment suggest that users, even when not familiar with Blender nor used to produce trace figures, managed to quickly produce trace figures illustrating interaction techniques from the literature with little to no experience in the production of trace figures or use of a 3D modelling software. Esquisse thus provides an alternative workflow to produce trace figures, that can be used by users who believe they do not have the skills required to perform manual photo-tracing. Esquisse can also benefit users who usually rely on manual photo-tracing, for instance, as a rapid-prototyping tool as reported by participants in our experiment. A 3D scene staged with Esquisse can also be exported as is as a model for manual photo-tracing, which could be useful in situations where one has to illustrate a "complex and heavy" interactive scenario (for instance involving tabletops, large displays, several users, etc.
Because of this, however, the interaction with Esquisse is constrained by what the Blender environment allows add-ons to do (e. g. , the design of the user interface for add-ons is limited). In that respect, we designed Esquisse by trying to provide the best user experience and interaction that Blender allowed us to design. For example, we added dedicated slider controls to ease the definition of hand and body poses and implemented a direct LeapMotion integration to help users to manipulate hand poses. Similarly, inspired by DemoDraw  [9] , we plan to implement a Microsoft Kinect integration to ease the manipulation of body poses. Most trace figures overlay meta annotations (such as texts or arrows). Besides contact points, Esquisse does not currently support a way to add these annotations and the user still has to open the produced SVG file in a graphic editing software to add them afterwards.
Motivation: During development, progenitor cells undergo multiple rounds of cellular divisions during which transcriptional programs must be faithfully propagated. Investigating the timing of transcriptional activation, which is a highly stochastic phenomenon, requires the analysis of large amounts of data. In order to perform automatic image analysis of transcriptional activation, we developed a software that segments and tracks both small and large objects, leading the user from raw data up to the results in their final form. Results: MitoTrack is a user-friendly open-access integrated software that performs the specific dual task of reporting the precise timing of transcriptional activation while keeping lineage tree history for each nucleus of a living developing embryo. The software works automatically but provides the possibility to easily supervise, correct and validate each step.
Introduction Live imaging of developing organisms provides the unprecedented opportunity to access to nuclei/cellular genealogy. The combination of lineage information and transcriptional history permits to decipher the extent of transcriptional memory, i. e. the influence of the transcriptional status of mother nuclei on that of their daughters  (Ferraro et al. , 2016). Here we present MitoTrack, a user-friendly and open source software, able to automatically track nuclei and their transcriptional activity across multiple cell divisions. The tool has been primarily developed for images of living Drosophila embryos, in which labeling nascent transcription has become popular  (Garcia et al. , 2013; Lucas et al. , 2013; Pichon et al. , 2018). However, MitoTrack could also be used for genealogy and memory tracking in other organisms, such as zebrafish embryos, mouse early embryos or even in organoids.
Software description The typical data processed with MitoTrack are images from early Drosophila embryos where nuclei are labeled with a histone-RFP transgene and reporter mRNA detected upon binding of the MCP-GFP to MS2 containing nascent mRNAs (sample data can be downloaded following this link)  (Fernandez and Lagha, 2019). Most of MitoTrack algorithms are not novel and several more sophisticated programs are similarly able to perform lineage tracking in living organisms  (Amat et al. , 2014; Tinevez et al. , 2017; Tran et al. , 2018; Wolff et al. , 2018). However, MitoTrack was developed to fulfill the specific task of extracting lineage information and the timing of transcriptional activation directly from the raw data. All the developed algorithms work in a single package, embedded into a user-friendly graphical user interface (GUI) (Fig.
The last part of MitoTrack consists in merging the three aforementioned sections in order to update nuclei labels for the entirety of the analyzed movie encompassing mitosis. Moreover, in this step, the software attributes sub-labels to properly distinguish the two daughters from each other. In addition, MitoTrack allows for a spatial analysis and hence records the timing of transcriptional activation within user-defined domains. All the extracted quantitative data are organized into an. xls file.
Results and perspectives The described program represents a robust analysis platform to quantify the timing of transcriptional activation prior and after mitosis while keeping the genealogy information. Using MitoTrack software, we can now quantify the extent of transcriptional memory in hundreds of nuclei from various genotypes, as recently performed in  (Dufourt et al. , 2018). One of the main advantages of MitoTrack resides in the various tools it provides for manual correction of nuclei tracking during mitosis (Fig. 1c ). However, mainly depending on nuclei crowding and temporal resolution, MitoTrack is able to automatically track all nuclei during mitosis with near 100% accuracy (for example in mitosis from nc12 to nc13 with a temporal resolution of 20 s/frame, or in mitosis from nc13 to nc14 with images every 4 s, as the sample data accompanying the provided tutorial).
OBSERVATIONS AND DATA REDUCTION We carried out eight observing campaigns from 2005 to 2019 using four different telescopes. A log of observations is shown in online table 1. The 1. 5-m telescope at Sierra Nevada Observatory in Granada (Spain) has a CCD VersArray with 2048 × 2048 pixels, while the field of view (FoV) is 7. 92 ′ × 7. 92 ′ and the image scale is 0. 232 ′′ /pixel. Observations were obtained in 2 × 2 binning mode. The typical seeing was ∼ 1. 5 ′′ , being the point spread function well sampled for the photometric goals. The 1. 23-m and the 2. 2-m telescopes at Calar Alto Observatory are located in Almería (Spain). The 1. 23-m telescope uses a DLR-MKIII camera with 4096 × 4096 pixels, while the FoV and the image scale are 21. 5 ′ × 21. 5 ′ and 0. 315 ′′ /pixel, respectively. At the 2.
6 ′ × 8. 6 ′. Bias and twilight flat-field calibration images were taken at the beginning of each observation night. The science images were processed by subtracting a median bias and dividing by a median flat-field. Using the Interactive Data Language (IDL), we developed our own code to perform these reduction steps, as well as the synthetic aperture photometry for the time series data. We performed the synthetic aperture photometry using different aperture radii, as described in  Fernández-Valenzuela et al. (2016) , and choosing the one in which the dispersion of the photometry is minimal. To construct the light curves, Varuna was compared to a set of reference stars located as close as possible to the object. Whenever possible during each campaign, we employed the same set of comparison stars each night in order to minimize systematic photometric errors.
ACKNOWLEGEMENTS EFV acknowledges UCF 2017 Preeminent Postdoctoral Program (P 3 ). Part of the research leading to these results has received funding from the European Unions Horizon 2020 Research and Innovation Programme, under Grant Agreement No. 687378 (SBNAF). We would like to acknowledge financial support by the Spanish grant AYA-2017-84637-R and the financial support from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709). This work is partially based on observations made with the Italian Telescopio Nazionale Galileo (TNG) operated on the island of La Palma by the Fundación Galileo Galilei of the INAF (Istituto Nazionale di Astrofisica) at the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias.
Introduction Business Intelligence & Analytics (BI&A) is defined as "a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions"  [37]. Data warehouses (DW) stand as the cornerstone of BI&A systems. Inmon  [21]  defines the DW as "a subject-oriented, integrated, time-variant, non-volatile collection of data in support of managements decision-making process". Figure A. 1 shows BI&A architecture where data is gathered from company operational databases and external data. Gathered data is heterogeneous, and has different types and formats. Before being loaded into the DW, this data is transformed and integrated using the ETL process  [34]. The latter performs three basic functions: (i) extraction from data source; (ii) data transformation where the data is converted to be stored in the proper format or structure for the purposes of.
Introduction Business Intelligence & Analytics (BI&A) is defined as "a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions"  [37]. Data warehouses (DW) stand as the cornerstone of BI&A systems. Inmon  [21]  defines the DW as "a subject-oriented, integrated, time-variant, non-volatile collection of data in support of managements decision-making process". Figure A. 1 shows BI&A architecture where data is gathered from company operational databases and external data. Gathered data is heterogeneous, and has different types and formats. Before being loaded into the DW, this data is transformed and integrated using the ETL process  [34]. The latter performs three basic functions: (i) extraction from data source; (ii) data transformation where the data is converted to be stored in the proper format or structure for the purposes of querying and analysis (e.
Data quality defects within the ETL process Many reasons stand behind the need of data integration phase within the decision system: (i) heterogeneous formats; (ii) data format can be difficult to be interpreted or ambiguous; (iii) legacy systems using obsolete databases; and (iv) data source's structure is changing over time. All these characteristics of data sources make DQ uncertain. A variety of studies were conducted in the sake of identifying different quality issues within the data integration process. The majority of them agree that DQ faces different challenges. Indeed, ETL is a crucial part in the data warehousing process where most of the data cleansing and curation are carried out. Hence, we propose a classification of typical DQ issues according to ETL stages, i. e. , extract, transform and load.
As depicted in Table  1 , each one of these stages is prone to different quality problems in both schema and instance level. DQ issues found in the ETL process are the focus of DQ improvement phase. In practice, the improvement phase is often a prerequisite for DQ assessment. The process of integrating DQ into the ETL process is an indicator of the gap between the quality obtained and that expected. Furthermore, overcoming all the DQ problems is still challenging. In the rest, we classify the pioneering approaches that integrate and improve DQ in the ETL process. [27]  Rule that defines the consistency of a given data or dataset in the database (e. g. , Primary key, uniqueness). Example of uniqueness violation: Two customers having the same SSN number customer 1= (name="John", SSN="12663") , customer 2= (name="Jane", SSN="12663").
Theodorou et al. [32]  proposed an automatic data generator called Bijoux. Based on an ETL process logic model, this tool extracts the semantics of data operations and analysis the constraints they imply over input data. Roughly speaking, Bijoux allows users to generate a set of data to test the different execution scenarios. To cover all test scenarios, an algorithm is proposed to enumerate different paths existing in the ETL process which is modeled as a graph. For every path, Bijoux analysis the different constraints applied to ETL operations in order to generate the testing datasets. This approach has been shown to be useful for measuring the performance of different implementations of the same ETL process. On the other hand, they proposed a tool called POIESIS that generates alternative flows by adding flow patterns to the initial flow.
Syntactic oriented approaches An important quality default is related to data duplication. If a company has different customer databases related to subsidiaries and would like to combine them, it would be an actual challenge to identify matching records, since the same data is presented in different ways. This problem known as Entity resolution problem, is defined as the process of identifying and merging records judged to represent the same real-world entity. In  [5] , Benjelloun et al. proposed three algorithms based on the comparison of features in order to identify matching records. Likewise, Term Frequency-Inverse Document Frequency (TF-IDF) metric has been used for duplicates detection. Moyer et al. [18]  have proposed a new method that extends TF-IDF to address not only duplicates but also missing entries.
ISNULL(row1. Column1) if the considered column type is Integer or by using the expression !input row. Column0. equals("") whenever the considered column type is String). However, none of these tools have considered techniques (e. g. , Imputation) that handle missing values according to the context of use. • Accuracy: the extent to which data are correct, reliable, and certified error-free  [35]. This criterion assesses the percentage of data without data errors  [31]. After delving the considered ETL tools, we noticed that most of them provide components that check the validity of email addresses. Unlike the rest of the studied tools, Informatica Mapplet (i. e. , a set of reusable components) is not only reduced in structural validity of email address using regular expression, but also ensuring email addresses validity at the domain level.
Furthermore, it validates an address against a set of reference address databases. • Interpretability: degree to which user can understand data that they get  [31]. To enhance DQ according to this dimension, it is absolutely necessary to integrate semantic recognition operations to the ETL process. This quality dimension is deemed in Informatica by providing a data domain discovery. Since many important data remains undiscovered in data sources or it does not have a significant column name, the data domain can be a predefined or a user-defined repository based on the semantics of columns. • Representational conciseness: the extent to which data are compactly represented (i. e. , brief in presentation, yet complete and to the point)  [35]. In fact, data sources are likely to have embedded values which lead to have overwhelming data.
tUniqRow Our objective is to check to which extent DQ is achieved in TDQ components. We split our example into two scenarios named S1 and S2. For both of them, we consider an excerpt of an ETL process wherein we perform a join operation using TDI components as shown in Figure B. 2. In our example, data are extracted from XML file and MySQL table. The XML file holds data about customers that are matched to their orders in the MySQL table through an inner join on the customer ID. We can see that among customers data in the XML file, there are two different elements having the same primary key (i. e. , customer Id equals 2). In the sake of ensuring DQ, TDQ tool offers a DQ component that compares entries and removes duplicate one from the input flow. Subsequently, we used tUniqRow taking as input customers data stored in the XML file.
tUnit We performed a unit operation using tUnit component (see Figure  B. 3) which have two input tables stored in a MySQL database. The source tables hold data about customers (i. e. , phone, email and gender). They have the same column names but a different column order. In the output file created after running the Talend Job, we point out that data in the second column email of the first table is unified with the second column phone of the second table. Additionally, Gender column is united with the email column, etc. Hence, merging data from various sources based on common schema but having different column order produces certainly an incorrect result. Moreover, Talend user must be alerted so that (s)he can prevent the spread of wrong data throughout the integration process. Thus, the above mentioned example proves that this component needs a further semantic enrichment in order to detect that columns order of the two data sources is different.
tAggregateRow In our example illustrated through Figure B. 4, we intend to aggregate the students thorough scores based on students names. Thus, we create a Talend Job using the tAggregateRow component as an intermediary step, since it requires input and output. The input data about students and their associated scores are extracted from a table stored in a MySQL database. The output data is diplayed in the Talend console using the tLogRow component. The view setting of the tAggregateRow component offers the option to edit the type of the output data. In our example, the score type is an Integer, whereas the Max column type in the output is Double. In this case, running the Talend Job rises an exception as depicted in Figure B. 4. The incompatibility of input and output types presents a major limit related to the tAggregateRow component.