dataesr/scientific-paragraphs-categorization huggingface dataset:
Data acquisition Some recent numerical facial reconstruction methods rely on the a-priori information contained in a database of coupled skull and face templates. The acquisition of both skull and face is accomplished by head CT scans of living subjects, allowing a good visualization of hard tissues. Standard segmentation tools and 3D reconstruction algorithms lead to the definition of dense surfaces of both skull and face from CT data. Unfortunately the invasiveness of this technique causes serious legal and ethical problems, preventing the constitution of a large database of healthy subjects. Due to this difficulty, several studies have proposed to exploit the relationship between soft and hard tissues by means of average soft tissue thickness measurements  [6] [7] [8]. Soft tissue depth tables are usually used in combination with a large database of face templates  [4].
Facial templates can be acquired by non-invasive techniques such as stereophotogrammetry  [9] , making the constitution of a large database painless and easy. However, despite the acquisition of dense surface templates for describing the outer face, the average values of soft tissue thickness are systematically measured on a sparse set (<53) of (manually positioned) anatomical landmarks. See  [10]  for a review of recent 3D landmarkbased facial reconstruction methods. Since the manual measurement is time consuming and requires expertise in correctly identifying the landmarks, it is actually infeasible to extend these measurements to a dense distribution of points. See  [11]  for a discussion about the use of sparse soft tissue measurements for facial reconstruction purposes. Several authors have claimed the importance of using a dense representation of the soft tissue information, for example by describing the intra-subject correlation in terms of the volume between the two boundary surfaces representing the face and the underlying skull  [12, 13].
Image segmentation Data segmentation consists in identifying the bone and soft tissues on stacked 2D grey-level images. This procedure has been carried out in a semi-automated way with the help of the software Amira  [15]. First, the CT slices are automatically pre-segmented using a multi-threshold technique. This step consists in partitioning the original images into subdomains whose boundaries are identified by given intensity values. The bone and soft tissues threshold values used are described  [14]. From the pre-segmentation step, two sets of binary imagesrespectively, for bone and soft tissuesare obtained. By stacking these slices one can essentially detect the 3D structures. However, the intensity-based segmentation is not enough to ensure a correct separation of the tissues of interest, due to the presence of noise on the data and artefacts occurring during the acquisition process.
Inter-subject shape matching Shape morphing or matching arises in a wide variety of situations in areas ranging from biomedical engineering to computer graphics. Beyond the specific stakes to each particular application, the general issue is to find one transformation from a given "template" shape V 0 into a "target" V T (Figure  7 ). Such a transformation may be used as a means to appraise how much V 0 and V T differ from one anotherfor instance in shape retrieval, classification or recognitionor to achieve physically the transformation from V 0 to V T (in shape registration, reconstruction or shape simplification). See for instance  [24]  and references therein for an overview of several related applications. In the facial reconstruction context, shape matching is a key ingredient for studying the shape database, making possible the generation of average shapespossibly weighted according to their "similarity"as well as for driving the registration of the craniofacial template onto the unknown skull.
Registration of the craniofacial template onto the unknown skull The parametrization of skull, face and soft tissue information depends strongly on the nature of the database. Using CT scans of living subjects allows a dense representation of the craniofacial information. If closed surface models of both skull and face are available, then the craniofacial template may be described in terms of the 3D domain delimitated by the outer face and the inner skull boundaries, generating a 3D "mask" that incorporates the soft tissue information. A single craniofacial template may be used for producing templates of all individuals in the database. The procedure relies on the shape matching technique, generating a set of vector fields, one for each individual, each of them allowing to deform the template shape onto a specific shape of the database.
Various methods have been proposed for deforming the craniofacial template onto the unknown skull. On the one hand, template deformation methods use generic mathematical transformations such as radial basis   functions  [13]. Care has to be taken because no knowledge of facial anatomy is incorporated in these transformations leading sometimes to awkward looking faces. On the other hand, face-specific methods use principal component analysis on a facial database to generate a statistical shape model  [19, [27] [28] [29] [30]. The advantage is that faces are then deformed in a face-specific manner within statistical boundaries. The disadvantage is that the deformations are dependent on the database and can be too restrictive in case of small databases. Here, the method lies on the "physical" deformation of the craniofacial template onto the unknown skull.
Craniofacial template generation Let (S i ) i = 0, ÁÁÁ, N and (F i ) i = 0, ÁÁÁ, N be the collection of skulls and faces of the database. Thanks to the procedure described above, theses shapes can be at this point considered as closed surfaces. Then, the volume between S i and F i can be defined, generating a 3D mask M i (called craniofacial template). The craniofacial templates are first roughly aligned by an iterative closest point algorithm. Then, a reference craniofacial template, say M 0 , is chosen and filled with an elastic material (Figure  11 ). It is matched onto all the M 1 , …, M N (with global displacements u 1 , …, u N ) thanks to the shape matching process (Figure  12 ), that is M i ¼ I þ u i ð Þ M 0 ð Þ for all i ¼ 1; Á Á Á ; N: (8) Note that this process generates meshes for all the craniofacial templates M 1 , …, M N that share the same number of elements (vertices and tetrahedra) and the same connectivity.
Use of facial reconstruction techniques in forensic science An essential deal of the work of a forensic scientist regards the identification of postmortem bodies. In the special case in which the facial remains are no longer available, the forensic scientist can resort to various techniques to achieve a positive identification of the dead: DNA assessment, dental record analysis, the identification of a tattoo, the serial number of a prosthesis and other accepted methods of identification. All these techniques are based on a principle of comparison (with respect to a sample or to a database) and can indeed fail in the absence of comparative elements. For instance, at the Forensic Medicine Institute in Paris, this situation shows up for 2 up to 5 cases per year. In this context, a facial reconstruction is then the last resort for reaching a positive identification.
Validation The validation of a method for facial reconstruction is of tremendous importance to legitimate its use during a criminal investigation. The natural way to address this problem is the "leave-one-out scheme". One individual is removed from the database and the method is employed to reconstruct his/her face given the sole skull. Then, the predicted face is compared with the available "unknown face". The discrepancy between the two shapes can be evaluated mathematically by computing distances between them. However, since the final purpose of the method is a positive identification, a recognition test can be also employed for revealing the power of prediction of the method, as it is done in  [4]. A recognition test consists in showing the predicted face together with a sample of faces that contains the unknown face.
Then, the human volunteer indicates the face (or faces) that is (are) closest to the predicted one. The positive outcome will then correspond to the identification of the unknown face among the sample. Such a study for the method proposed in this article is under process and results will be published in a forthcoming paper. Hereafter three examples of reconstruction are presented. According to the leave-one-out scheme, one individual is removed from the database and a prediction of his face is generated. Figures  15 16 17 show the results obtained by computing the mean of all the faces transported onto the unknown skull. Figure  18  shows the distribution of the error (signed distance function) over the surface for the three reconstructions. Remark that in the regions of interest, excluding ears, neck and eyes in particular, the error is less than 1 mm for most points.
Also notice that in these regions, the thickness of the tissue is underestimated or overestimated. Using the information coming from the body mass index (BMI) of the individuals in the database could be a way to propose different reconstructed faces corresponding to different BMI of the unknown individual. The discrepancy D between a surface G and a reference surface G 0 can also be evaluated by the following mean error:   where jGj is the measure of G and d(¢, G 0 ) is the Euclidean distance to G 0. This error estimator is used for evaluating the vicinity between: D ¼ 1 jGj Z G d 2 x; G 0 ð Þdx 1 2 : (11) (1) the skull templates of the database and the unknown skull; (2) the face templates of the database and the unknown face associated with the unknown skull; (3) the face templates after deformation onto the unknown skull and the unknown face associated with the unknown skull; (4) the face templates of the database and the reconstructed average face.
The minimal, maximal and mean values of D for the 25 templates of the database are reported in Tables  1 2 3 in each of the third cases. In particular, the discrepancy between a face template and the unknown face is smaller after deformation for all the individuals. Thus, the elastic transformation used in the method seems to be a good tool to transport the faces close to the unknown face. Moreover, the discrepancy between the unknown face and the predicted face is smaller than the discrepancy with any individual in the database, so this measure D can be used for an automatic numerical identification.
Comparison with previous works A comparison with previous facial reconstruction methods is a quite hard task. The reliability of two methods should be compared starting from the same database and the same test cases, to avoid the dependencies of the methods on the data. Such a comparison is out of the scope of this paper. Here, we limit ourselves to compare the results of the test cases with the outcomes of some statistical facial reconstruction methods  [6, 17, 19]. A statistical method is developed for the purpose of estimating a facial outcome using landmarks  [19]. The experiment is carried out from a database of 118 individuals. The mean reconstruction error registered equals 1. 4 mm, with a standard deviation of 1. 04 mm. A statistical method based on implicit surfaces was employed for the facial reconstruction test using a database of 20 individuals  [17].
The mean error registered equals 1. 9 mm with a standard deviation of 1. 7 mm. A local technique based on surface patches is used for statistically predicting some facial regions  [6]. The experiment is carried out using a database of 47 individuals. For both chin region and nasal region, the mean reconstruction equals 0. 99 mm, ranging from 0. 58 to 1. 83 mm for the nose, and from 0. 21 to 2. 41 mm for the chin. The mean error registered with the method proposed in this paper is reported in Tables  1 2 3. Note that most of the error is concentrated on the ears, the neck, the eyes and lips regions. The distribution map of Figure  18  shows that for the three studied test cases the mean error over the remaining regions is smaller than 1 mm. The result is similar to the one reported in  [6]  (which only deals with the reconstruction of two facial regions) and improves the outcomes of  [17, 19] , at least when considering the regions of interest.
Conclusion The proposed reconstruction method lies on the "physical" deformation of templates of coupled faces and skulls onto the unknown target skull. In practice, the acquisition of full head scans of healthy subjects is still a difficult process. Most of the time one can have access to clinical data, meaning that the patients present morphological anomalies or that the scans are only partial (in the case of maxillo-facial examination for surgery purposes). The access to an adequate database of full heads of healthy subjects would enormously improve the final product of the method. Even if the experiments were carried out on a small collection of 26 individuals, the preliminary results produced are very promising. The proposed method for shape matching allows an accurate registration. The method is simple to implement and does not require any a-priori landmark correspondence, allowing for an automatic processing of the database.
Acknowledgments The authors would like to kindly thank  F. Tilotta ,  Y. Rozenholc  and  J. Glaunes  for providing the CT head data [14],  L. Uro  and  S. Slimani  for segmenting part of the DICOM images,  P. Frey ,  M. Friess  and  P. Deuflhard , whose comments improved the quality of the manuscript,  C. Dapogny ,  L. Norgeot , the ISCD team and the partners of project FaciLe for fruitful meetings and  D. Baum ,  S. Zachow  and the ZIB team for interesting discussions.
Acknowledgments We thank  Jun Ye  from  JILA, University of Colorado  for useful discussions. We acknowledge support from  Defense Advanced Research Projects Agency  ( W31P4Q-15-1-0015 ), the  Air-Force Office of Scientific Research  ( FA9550-15-1-0303 ), and  National Science Foundation  ( ECS-0335765 ,  ECCS-1306035 ). This work was performed in part at the  Cornell Nano-Scale Facility , a member of the  National Nanotechnology Infrastructure Network , which is supported by the  National Science Foundation (NSF)  (grant  ECS-0335765 ).
RESEARCH SITE AND METHODS We took a historical perspective (Van de  Ven and Poole, 1990)  to deconstruct the process of BMA in an incumbent media organization. In particular, we conducted a longitudinal case study  (Yin, 2003)  of the Italian publisher GEDI covering the period 1997-2017. Given the scant understanding of the relationship between disruptive innovation and BMA, an inductive and field-based approach was particularly suited to develop a new theory  (Eisenhardt, 1989; Glaser and Strauss, 1967). We collected rich data about GEDI and its ecosystem from multiple sources (interviews, archival, and observations), and we tracked the major events and actions undertaken by the company to adapt. These data were particularly useful to develop our process model, given that process theorization This article is protected by copyright.
Research Site and Data Collection We studied GEDI and the newspaper industry for several reasons. First, digital disruption has devalued newspapers' business models worldwide, calling for a profound readjustment  (The Economist, 2011; Seamans and Zhu, 2014). The advertising revenues of the global newspaper industry (offline plus online) have decreased by about 42 percent between 2005 and 2015 (The Wall Street Journal, 2016), and the situation was similar in Italy (where GEDI operated). Between 2000 and 2012, the offline advertising revenues of Italian newspapers plummeted by 41 percent  (FIEG, 2001; FIEG, 2013) , and the new online ad revenues accounted for only 10-15 percent of the total ad revenues in 2015. The number of physical copies of newspapers sold declined by 33 percent between 2000 and 2012, but their audience and reach have grown with the internet  (Audipress, 2012).
6 million euro). The main reason for this difference was that GEDI had a higher propensity to innovate, and top management who supported experimentation, which made their company ideal for a BMA study. Of course, mistakes were also made by the company and we examined them to discuss the possible sources of tension and failure during an adaptation process. We had access to all sorts of primary data at GEDI, from in-depth personal interviews to internal and confidential archival documents (all types of reports, business plans, and financial records), and observational data. The period of study was 1995-2017. Archival data were collected from 2013-2017, and interviews and observational data were gathered from 2013-2015. Our multiple data sources were constantly triangulated to improve accuracy  (Jick, 1979).
The Italian entrants (Populis, Banzai, YouReporter, CityNews) were all using similar models: enabling external bloggers and citizens to produce content and controlling specialized platforms to monetize via advertisements. As anticipated above, GEDI and the other publishers perceived these entrants as real threats, because Google alone in 2013 was capturing around 60 percent of the Italian online advertising market (previously contended by publishers). From a theory standpoint, entrants in phase 2 introduce disruptive business models because they effectively create and capture value in a different way (see our definition above). They create value by orchestrating external sources of knowledge (rather than producing knowledge through internal know-how, as incumbents were doing). They capture value by controlling specialized platforms and customers' data (rather than controlling specialized assets along a vertical value chain, as in the incumbents' prior model).
The BMA develops by incorporating new forms of value creation and capture from the disruptor, thus avoiding the risks of stand-alone experimentation and the difficulties of early-stage competition and conflicts. In addition to alliances, a related mechanism to speed up and secure the BMA process in phase 2 is the acquisition of entrants employing disruptive models. Acquisitions and alliances are related governance mechanisms on a same continuum  (Capron and Mitchell, 2012). The company made several acquisitions during phase 2, but the most relevant from a BMA standpoint was the acquisition in 2013 of 51 percent of an online open community for movies: mymovies. it. This community was the largest platform and online database of films in Italy, created in 2000 through the contributions of normal viewers of films, and having an installed base of three million monthly unique users in 2013.
In fact, disruptive entrants using platform-based open models are common across many sectors (e. g. , Uber for taxis; Airbnb for accommodation). Platform-style responses by incumbents are also abundant: Coursera, Hulu, and Paym are all controlled by incumbents, and Spotify is owned by incumbent record labels. Finally, the identified tensions in BMA are also generalizable. The innovation in Ryanair's business model of exploiting external pilots taken from external agencies has generated conflicts (Independent, 2018). Facebook's model of granting control to third-party apps (Cambridge Analytica) has created tensions with customers' data  (Financial Times, 2018). In the remainder of the paper, we present additional important contributions. Bower, 1996 , Danneels, 2004 ) is illuminated by considering the type of technological disruption in this case.
Patient informed consent was obtained for being registered anonymously in the database. We used a Siemens Artis zeego C-arm system in the operation room to obtain a CBCT for a patient during an EVAR. Four protocols are preset in this system: they are 5sDR protocol, 5sDSA protocol, 8sDR protocol and 20sDR protocol. Low-dose intra-operative CBCT was realized using a 5sDR protocol. The 5sDR protocol has 5-s acquisition time capturing 133 frames at 30 frames/second (f/s). The 5sDSA protocol is used for acquiring a digital subtraction angiography (DSA). The 5sDSA protocol has the same characteristics of the 5sDR but it realized two rotations, the first one is used to acquire the mask, and the second one is synchronized with the injection with the injection of contrast medium. Other acquisition parameters were: the distance from the X-ray source to detector is 1199 mm, the field of view (FOV) is 100 mm × 100 mm, the slice thickness is.
Results from real CBCT images In this section, we used the intra-operative CBCT data of an AAA (abdominal aortic aneurysm) patient to evaluate the effectiveness of the proposed filter. The original image of slice 152 is shown in Fig. 11 (a1) and the filtered results   obtained by PM, NCDF, BF, NLMF and LPMND are shown in Fig. 11 (b1-f1), respectively. The thresholds for the MAE in PM, NCDF and LPMND were all set to 2. The time step was 0. 24 and 0. 1 for PM and NCDF, respectively. The search window and similarity window in NLMF were set to 7 × 7 and 3 × 3, and the filtering parameter was 150, whereas in the BF, the standard deviations of closeness function and similarity function were 3 and 500, respectively. All of parameters in each filter were hand-tuned set to obtain possible best results based on trial errors.
In practice, the noise level changes across slices even in the same CBCT data, we therefore selected another slice (slice 29) that contains more artifacts to test the proposed filter. Figure  13  shows the comparison of processed images by different filters. In order to evaluate the consistent performance of different filters, the parameters in each filter were kept the same as those used for slice 152. It can be observed that PM, NLMF and BF cannot suppress artifacts effectively compared with NCDF and LPMND. On the other hand, the bony structures in the NCDF filtered image are a little blurred, whereas the LPMND filtered image has clear bony structures. Thus, the LPMND filter may have superiority for practical applications. We also calculated the average time for one CBCT data of 365 slices under the environment that OS: 64-bit; Windows 7; CPU: Intel Core(TM) i7, 8G RAM.
The proposed method can suppress noise and streak artifacts meanwhile protect low-contrast regions. We also compared the proposed method with other edge-preserving filters through quantitative analysis in simulation study and qualitative analysis in patient study. The MTF study shows that LPMND can produce good image resolution (Fig. 9 ), and the sharpness estimation shows that it works well on preserving edges of low-contrast regions (Fig. 10  and Table  3 ). Actually, we have verified that the proposed method performed well on clinical CBCT data with full slices. This method could be used to improve results of further processing (e. g. CT -CBCT registration). There are also some drawbacks of this proposed method. One is the selection of parameter k. Actually, k plays an important role in diffusion procedures and should correspond to the noise level.
Funding This work was partially conducted in the experimental platform  TherA-Image (Rennes, France)  supported by  Europe FEDER. This work was partially supported by the  French National Research Agency (ANR)  in the context of  Endosim  project (grant  n°ANR-13-TECS-0012 ), and in the framework of the  Investissement d'Avenir Program  through  Labex CAMI  (grant n°ANR- 11-LABX-0004-01 ). The funders had no role in the design of the study, the collection, analysis and interpretation of the data, and in writing the manuscript.