dataesr/scientific-paragraphs-categorization huggingface dataset:
E. Exploration of Hierarchical Co-clusters and Structure We use a sampled dataset D sam from the SMART collection to illustrate the hierarchies of co-clusters and the connections of sub-clusters inside a co-cluster. D sam contains 30 documents (10 from each class) and 51 terms (after preprocessing). With the aid of (c), we are able to know that in (b) the bottomright co-cluster is in fact closer to the top-left one and farther from the middle-situated co-cluster. (d) presents the content of the left-most sub-dendrogram in (c), i. e. , the bottom-right co-cluster in (b). Keeping the same structure as it is in the dendrogram, (d) allows us to know which documents and terms co-exit and how they are connected in their co-cluster.
whileThe above procedure of SHCoClust outputs a dendrogram of n + m leaves and n + m -1 internal nodes. If input D n×m is a doc-term matrix, the dendrogram grows by merging a pair of doc-doc, term-term or doc-term in each iteration. Cutting the dendrogram by a desired number of clusters K results in flattened co-clusters, each contains a sub-dendrogram of documents and terms. In Section IV-E, visualization of a sampled dataset is presented.
GPGPU Implementation The proposed computational methods for activation times and ECG were designed to run on GPGPU architectures. The power of GPGPU computation lies in a massive parallelism that can hide long-latency operations such as memory accesses. The latency hiding is possible thanks to the warp schedulers, which continuously switch among warps whose instructions are ready for execution. The solvers were implemented in C and C++ with CUDA extension  (Nickolls et al. , 2008; Cheng et al. , 2014) , version 8. 0. In all our experiments a single precision floating point representation of numbers was used, sufficient for stability and accuracy of the solvers. The eikonal Equation (  6 ) is solved by means of the Fast Iterative Method (FIM), proposed by  Jeong and Whitaker (2008). Although a popular alternative for solving the eikonal equation is the Fast Marching Method (FMM)  (Sethian, 1996) , we opted for the FIM for two reasons: first, to increase the parallelization of the code and to make it suitable for GPGPU.
Performance and Scaling The runtimes of the proposed eikonal solver and ECG solvers on the GPU were tested for six patient-specific geometries with 1-mm resolution. We tested our code on two different Nvidia GPUs: a lowend (LE) GeForce GT 650M on a laptop (384 cores, 950 MHz clock, 0. 73 Tflops of theoretical peak performance), and a highend (HE) GeForce GTX 1080 (2,560 cores, 1,607 MHz clock, 8. 2 Tflops of peak performance) on a local cluster node. In Figure  10  runtimes of the Fast Iterative Method (FIM) for solving the eikonal model, and the Fast Method (FM) and Simple Method (SM) for the ECG computation are shown, on both the LE and HE GPU. In Table  5  we report the speedup of HE GPU vs. LE GPU for FIM, FM and SM, and the speedup of the FM vs. SM for ECG computation. The reported variability in the computational time was due to the size of the heart and to the number of EASs.
• We compared the simulated ECGs with those of a bidomain torso model and identified the most important causes of differences. • We discussed how the proposed methods can be implemented on a GPGPU. • We showed that the proposed methods can simulate a highly realistic ECG in a few seconds. "CATION, " project code 14. 158. The authors gratefully acknowledge financial support by the Theo Rossi di Montelera Foundation, the Metis Foundation Sergio Mantegazza, the Fidinam Foundation, and the Horten Foundation to the Center for Computational Medicine in Cardiology. The authors also thank the Swiss PASC initiative (Platform for Advanced Scientific Computing) for their support. This work was also partially supported by a restricted grant of Biologic Delivery Systems, Division of Biosense Webster a Johnson & Johnson Company.
Background With the advent of modern biophysical techniques and devices  [1, 2] , the need for image analysis (IA) of the recordings and automatic processing of obtained data has been growing. Versatile IA open software solutions, e. g. ImageJ  [3] , Icy  [4] , Vaa3d  [5]  and cellProfiler  [6] , were developed to provide the broad scientific community with tools for analysis of general experimental data. Open-source developers and research groups involved in those software projects contributed many powerful and accessible plug-ins for widely used technologies and common IA tasks. In addition, a plethora of specialized algorithms were designed to process the primary image data of particular experimental devices. The specialized algorithms are however typically complex, their implementation often lacks a graphical user interface (GUI) and is seldom publicly available or documented.
Therefore, user-friendly dedicated IT tools are still lacking for specific biophysical techniques; one of such techniques is the Biomembrane Force Probe (BFP). This technique could become more accessible if robust post-experiment analysis were readily feasible-it would decrease the need for specialized equipment, relax constrains on experimental procedure and imaging quality, and allow repeated and more sophisticated analysis. Due to technical constraints (limitations on the bandwidth of video recording and on data file size), such an approach was not practical in the early days of the BFP technique (1990s), but has become readily feasible with current standard equipment. The aim of this work is to provide a Matlab-based processing and analytical tool with intuitive GUI precisely for the BFP.
Controlled movement of the RBC-mounted pipette (RMP) or of the studied sample imposes a load F on the studied bond/interface, mediated by the bead and transduced by the RBC. The RBC undergoes a deformation proportional to the load, x ∝ F. The deformation x can be measured by tracking the centre of the circular bead and a specific point on the pipette tip; the change in the distance of these two tracked points corresponds to the RBC deformation. Surprisingly, no freely-available dedicated processing software is available to analyse BFP recordings. In  [11, 14] , the bead tracking was performed in real time during the experiment using custom software, fitting a Gaussian blob onto the bead image intensity profile. An alternative method was developed in the proprietary software LabView  [15] , detecting the bead fringe as a minimum in contrast along the probe axis.
Implementation The tool is presented in the form of a Matlab application package, bundled into an application installation file BFPTool. mlappinstall. It can be easily installed into the Matlab application dashbar and run by clicking the icon. The application was tested in Matlab version 2015b and 2016a with Image Processing Toolbox, under Linux (Ubuntu 16. 04), Windows 10, and Mac OS 10. 9 and later. It is accompanied with a user guide and essential documentation, which are embedded in the installation file; after the first run of the application, the documentation can be reached directly from the home page in the Matlab help browser, and is fully searchable for keywords. During the application installation, the source M files are unpacked into Matlab application folder; the code has been commented extensively and can be readily modified by users to suit their own needs.
Computational functions The main purpose of the application is to calculate the applied force time course, F(t). This is implemented by the Matlab class BFPClass (child of the native handle superclass). The instances of this class are constructed with initial information about the experiment (e. g. geometry, pipette and bead tracking settings, list of frames for processing), they store results (e. g. bead/pipette coordinates, log of bad frames, force) and perform some of the complementary tasks (e. g. plotting, stiffness calculation). The class calls two external functions to perform the tracking of the bead and the pipette tip and calculates the force based on the returned results. We assume that the system is approximately axial (centres of the bead and RBC, and the pipette anchor, are all on one axis), so the extension or compression of the RBC along the axis, x(t), is equal to the change in distance x(t) between the bead centre and the pipette tip anchor, compared to such distance when the RBC is under zero load, x 0 (see Fig.
1 ). The positions of the bead centre and the pipette tip anchor at the given time therefore determine the RBC deformation and consequently the force, F(t). The bead tracking algorithm is based on Matlab's circle detection function imfindcircles from the Image Processing Toolbox, which implements a circular Hough transform, either two-stage or phase coding method. Both algorithms are applied on each frame, on the area around the last confirmed presence of the tracked bead. A list of potential candidates is built from scores (above a threshold) based on the imfindcircles detection metric M b,i (where the index i numbers the candidates) and the distance d i of candidate centre from the centre of the last known position of the bead, with candidates' radii within the range (R < , R > ). The bead metric is the value of the accumulator array for the given pixel returned by the imfindcircles method, usually in the range M b,i ∈(0,3).
A distance factor f i = max(d i ,R < ) R < is used to decreases the weight of candidates that moved too far; the final sorting scores are M b,i f i = M b,i R < max(d i ,R < ). The highest score, M b = max M b,i f i , is selected as the match, the position is logged and used to calculate d i for the next time frame. The centre of the bead is detected with sub-pixel precision, based on the accumulator array result. The major advantage of this method is that it detects also partially obscured objects, however the more edge pixels are obscured the lower the precision is. The detection accuracy depends on the bead size and image quality, and is typically in the range 30-50 nm. The pipette tracking algorithm uses pattern matching, based on evaluating the normalized cross correlation (Matlab normxcorr2 function) of a user-delineated template of the pipette tip with sub-frames of the video.
Basic analysis Preliminary analysis of the data can be performed in several direct steps, without setting any particular parameters. Please refer to Fig. 2  to see the functions in context of the workflow; the basic program features form an integrated set, coloured in red and labelled by capital letters. The analysis begins with loading of a video of the BFP experiment (C in Fig. 2 ). Standard video formats compatible with Matlab are supported (including AVI, MP4, MOV, WMV-a complete list can be found at MathWorks website  [18] ); Matlab also supports TIFF stacks through LibTIFF library  [19]. Note that TIFF metadata fields are not always reliably populated-in case the program cannot detect the framerate parameter, the user is prompted to provide the information during the video import or The blue nodes provide the optional means of adjustment and optimisation (labelled by lower-case letters).
The nodes are referred to by their labels at the appropriate sections of the text, where more details are provided change it in the GUI. The implemented wrapper class vidWrap provides a unified interface for all types of media files; it contains several verification procedures and maintains basic information about the video file and its contrast metric. Once the video file has been opened, GUI buttons are enabled and the user can select a bead to track (D in Fig. 2 ) and delineate a pattern of pipette tip (E in Fig. 2 ) to match. Having chosen the interval (F in Fig. 2 ) of frames to process, the tracking procedure can be started (B in Fig. 2 ), the BFPClass object is constructed and the functions invoked. The program tracks the bead and the pipette, one after the other. Several performance metrics are evaluated to ensure robust tracking.
d Illustration of BFPtool's internal treatment of data. On the left, the probe under no load, RBC undeformed (x 0 , x=0). The stiffness of the probe is calculated after the geometrical parameters of the probe (R c , R 0 , R p ) are measured, yielding k = 274 pN μm. The RBC acts as a spring in the probe. On the right, the probe at any later time, at 4. 2 s and 7. 2 s as examples. Note the same pipette pattern is shared by both intervals. The RBC is deformed, the deformation corresponds to the change of distance between the bead centre and the pipette anchor, x(t) = x(t)x 0 , as illustrated. The force is then calculated as F(t) = k • x(t). Total computational CPU time 152 s. The video file used for this analysis, interrupted. avi, is included in the program repository probe with a bead attached and no load applied; under such circumstances, the distance between the pipette anchor and the bead corresponds to the reference distance x 0.
The inefficiency could be removed by inserting an intermediate function, which would read each frame only once and provide it (in read-only mode) to the tracking methods, cutting the number of read frames by the factor of two but resulting in increased need for management of returned and forwarded data and number of function calls, making the code more difficult to maintain and debug. Redesigning this part of the program would somewhat improve the computation speed, but would require significant changes to already tested code. The analysis becomes substantially more demanding for recordings of low quality, containing frames for which the tracking quality metrics do not exceed the preset thresholds. In such cases, the program attempts to refine the detection by repeating the analysis on a larger sub-field of the subthreshold frame, with dilated or eroded pipette pattern, higher edge sensitivity etc.
Conclusion The present BFPTool application is (to our knowledge) the first freely available software for automated analysis of BFP image data and of the exerted force. It has been made with the end-user in mind: a comprehensive and comprehensible GUI was implemented, permitting a fast-track use supplemented by optional advanced functions, running extensive verifications and generating reports and warnings. We made the best efforts to avoid crashes and exceptions and to instead provide the user with assertions and reports, with limited impact on performance. The program offers tools to selectively analyse recorded videos of BFP experiments. The user is guided in several ways to exclude faulty segments of the recording and concatenate the remaining parts in an optimal manner, to obtain coherent results.
Funding This work was supported by  Université Pierre et Marie Curie (UPMC) ,  CNRS ,  INSERM ,  Agence Nationale de la Recherche  (Grant  ANR-2010-BLAN-1401-01  to AT), and by  National Institute of Health  (Grant  5R01DC012441  to AT),  Czech Science Foundation  grant ( GACR 14-16755S  to MZ),  Grant Agency of Charles University  grant ( GAUK 396213  to DS and MZ),  Bourse Gouvernement Francaise-French embassy in Prague  (to DS),  MSMT Barrande  ( 7AMB12FR002  to MZ and AT), and institutional support RVO# 67985823. The AT team is affiliated with the  Bio-Psy Laboratory of Excellence ,  Investissements d'Avenir program  managed by the  Agence Nationale de la Recherche  under reference  ANR-11-IDEX-0004-02.
8 μm per pixel, for a period of 30 min. See Fig. 2  for initial and final state of a particle. The acquired images were processed with image analysis tools to determine the surface area of each particle as a function of time. Then, an equivalent circle was assigned to the surface area. This resulted in obtaining an equivalent particle radius (r i ) as a function of time (see Fig. 3A ). This data was fitted using Eq. (  9 ), where Q max and D were unknown parameters. Q max was determined by measuring the mass of many particles before and after swelling. The experiment was conducted as follows: approximately 2 g of dry SAP particles were put into a petri dish and subsequently hydrated. After 30 min, the mass of the swollen particles was measured. After repeating this experiment, we found Q max =216 g/g ±15%.
Harris's Methods for the Temporal Representation of Stratigraphic Units It is important to note US 12, which is a cut in the layer labeled as US 15. US 12 is a negative US because it is only a trace of an action of cut. US 12 now separates US 13 and 14, but the archaeological investigation must be able to understand if these two US before the cut of US 12 are the same US or not. If this is the case, we will add in the diagram the synchronous relation between US 13 and 14. This is followed by US 11, which fills US 12 visible at that time. The Harris formalism which allows one to manage and visualize these US, is well distributed throughout the community by software, mostly open source, which helps to visualize and store these graphs. Earlier software such as ABC FlowCharter 4. 0 (originally from Micrografx, in 1995 and then by iGraphx) was mainly presented as flowchart software with an adaptation for the Harris formalism.
Also in 1985, the old Strata software program from the Computing Laboratory, University of Kent at Canterbury, used a set of recorded observations of stratigraphic relationships as input and was the foundation stone of jnet software  [38]  based on semi-structured formalism (XML). In the same way, we can mention the Bonn Archaeological Software Package (BASP)  [39]  developed since 1973 included in many software packages dedicated to archeology (orthophoto, level curve, etc. ). None of these software packages are available any longer. Still used by the community, we can cite ArchEd from the University of Vienna, Austria  [40] , which allowed one to build stratigraphic graphs, but they were completely disconnected from the survey. More recently, Intrasis, a Swedish GIS software package dedicated to archeology, is able to manage 3D files coming from terrestrial laser scanners and linked using ArchEd  [41].
Dense Cloud of 3D Points and Meshes Since 2002, with one three-month campaign per year, more than 90,000 photographs have been taken of the Shawbak site, following the continuous progress of digital camera technology, as well as photogrammetry software. These campaigns have produced photographic sets acquired with calibrated digital cameras both in convergent and parallel coverage with the survey of control points using a total station and DGPS. These control points are used to reference the photogrammetric models in a common geodetic system. We are still processing these photographs, using the set of control points, in order to obtain a complete 3D model with dense point clouds of the entire site. This first 3D elaboration produces a huge quantity of 3D points and meshes with the help of several photogrammetric software programs mainly Photomodeler from EOS and Photoscan from Agisoft.
Measuring Artifacts and Querying the Model: Spatial Considerations Specific photogrammetry tools dedicated to measuring stones individually have been under development since 2002 to help archaeologists to easily produce photogrammetric surveys. These tools are now integrated in a more complex system, which allows automatic production of 2D or 3D representations from archaeological database queries. The graphic 2D documents produced through this process look like the handmade drawings done by archaeologists using ortho-rectified photos. Once all the photographs are oriented, the I-MAGE process (Image processing and Measure Assisted by GEometrical primitive), developed in 2001, is used to support the user during the measuring process in photogrammetric surveys. Users can make a 3D measurement using one single photograph, without altering the precision of the result.
The main problem is determining whether a block is an edge block when observing photogrammetric data. To do this, archaeologists need a document of the highest quality. We have tried developing an automatic edge detection system for ashlar blocks, but indeed, the problem is more related to expert knowledge. Since 2016, we have developed a new version of the I-MAGE tool using both an oriented image and the dense point clouds. The user can describe the block geometry observing a good quality photograph, and the 3D block geometry is computed using the dense cloud of 3D points. The extrude value is still given as an hypothesis by the archaeologist. Since 2014, we stopped using a database to store ashlar block measurements and are currently only using an ontology describing both the photogrammetric process and the archaeological knowledge related to the blocks and stratigraphy.
Photogrammetry, Knowledge and Time The 3D GIS, merging photogrammetry and ontologies, is the last step of this chain and aims at the automatic production of 3D (or 2D) models through ontological queries: these 3D models are in fact at the same time a graphic image of the archaeological knowledge and the current interface through which the user can edit the dataset. This approach enables automatic 3D thematic representation and new archaeological analysis, through bidirectional links between 3D representation and archaeological data. In this section, we will see in Section 4. 1 the ontology development, merging photogrammetry and archeology knowledge, then in Section 4. 2, we present briefly certain tools used to easily manage these ontologies. Section 4. 3 will show the link with stratigraphy and the use of time relations, and Section 4.
Ontology for Photogrammetry Process The ontology developed in the framework of our research takes into account the manufactured items surveyed, as well as the method used to measure them, in this case, photogrammetry. The surveyed item is therefore represented from the measurement point of view and has access to all the photogrammetric data that contributed to its measurement in space. Two ontologies are aligned in this context; one dedicated to photogrammetric measurement and the geo-localization of the measured items, whereas the other is dedicated to the measured items, principally the archaeological artifacts, describing their dimensional properties, ratios between the main dimensions and default values. These ontologies are developed closely linked to the Java class data structure that manages the photogrammetric process, as well as the measured items.
It was therefore important to ensure the coherence of the measured items and potentially propose a possible revision. This previous work was done in the context of underwater archeology with similar problems. For more information, see  [21] [22] [23] [24]. The extension of the CIDOC-CRM ontology is structured around the concept of Man-Made Object. The root of ItemMesurable developed in this project extends this concept (see Figure  10 ). The mapping operation is done in Java by interpreting a set of data held by the Java classes as a current identification of the object: 3D bounding box, specific dimension. These attributes are then computed in order to express the correct CRM properties. Several methodologies can be chosen regarding mapping two ontologies. For example, Nicola Amico and his team  [13]  chose to model the survey location with the concept of Activity in CRM.
Tools for Managing Ontologies The use of ontologies in archeology and cultural heritage, as we have already stated, is becoming increasingly widespread. Indeed, this formalism is particularly suitable for heterogeneous data and offers concepts and tools to manage incompleteness, updates and revisions of the involved knowledge. Therefore, the way ontologies are structured is far from a traditional relational database, and managing them can be difficult for a person without a solid background in computer science. In addition, even if many research papers are published in the field of ontology for cultural heritage, the use of ontologies is not yet a common or widespread tool for managing archaeological excavations. Much research shows that the ontologies are very suitable for archeology, but only a very few archaeological missions use ontologies to manage data coming from excavations.