dataesr/scientific-paragraphs-categorization huggingface dataset:
Secondly, the patients included all carried SVs detected by karyotype or array-CGH, with a resolution limited to 30Kb. Nevertheless, it has been described that in case of chromothripsis, small insertions and deletions can be detected  (Gu et al. , 2016; Kurtas et al. , 2019; Slamova et al. , 2018). This limitation has to be considered in a diagnostic setting. It is also important to stress that in Loupe software, phasing in the linked-read helps the identification of breakpoints. For 4 patients, the detected SVs disrupted at least one gene which could be involved in the clinical presentation of the patient (patients 5, 7, 9, and 13) (Table  S4 ); one of them was identified using linked-read strategy. More studies are, however, needed to prove the implication in the clinical presentation, but the present study highlights the importance of using WGS for ABCR mapping and precise characterization in diagnosis.
The study of watermarks is a key step for archivists and historians as it enables them to reveal the origin of paper. Although highly practical, automatic watermark recognition comes with many difficulties and is still considered an unsolved challenge. Nonetheless, Shen et al. [2019]  recently introduced a new approach for this specific task which showed promising results. Building upon this approach, this work proposes a new public web application dedicated to automatic watermark recognition entitled Filigranes pour tous. The application not only hosts a detailed catalog of more than 17k watermarks manually collected from the French National Archives (Minutier central) or extracted from existing online resources (Briquet database), but it also enables non-specialists to identify a watermark from a simple photograph in a few seconds.
). We don't know how many watermarks have existed in the West since the 13th century. Probably tens of thousands until 1500, hundreds of thousands until the French Revolution: the development of printing, the progress of the government in writing and the growing literacy of the population has indeed led to an increase and multiplication of producers in all countries. In this work, we introduce a new web application for automatic large-scale watermark recognition based on the approach developed by  Shen et al. [2019]. This application enables archivists and historians not only to search for a particular watermark in our large database of watermarks, but also to identify a watermark from a simple photograph. Our database is composed of a new photograph dataset manually collected from the Minutier central des notaires de Paris (more than 2,250 photos of almost 400 watermarks) as well as the well-known Briquet design catalog  (16,112 watermarks).
II MOTIVATIONS Since the end of the 19th century, many researchers have sought to establish the most comprehensive watermarks catalogs possible on the basis of the paper stored in archives and libraries in Western Europe. There has been many attempts to build a general database. While they have always remained unfulfilled, the first of them was a masterstroke. Charles-Moïse Briquet (1839-1918, the greatest man in the history of Switzerland after Guillaume Tell and Roger Federer, according to Neil Harris, professor of Bibliography at the University of Udine in Italy and passionate filigranist) visited 235 archives and collections of manuscripts and printed books, worked on 30,840 volumes and 1,432 unrelated documents (particularly letters). He made 45,930 tracings, which he reproduced on a more robust paper now in the Briquet archives in Geneva.
). The Bernstein project 3  brings them together with another Austrian edition (Wasserzeichen des Mittelalters) in one searchable database on the website of the Austrian Academy of Sciences. Much of the ingenuity developed by these online databases lies in the search for the best possible semantic description of watermarks to offer as quickly as possible the answer to the questions posed by the discovery of one design or another. The drafting of thesauri and description standards followed different paths. In 2002, the International Association of Paper Historians (IHP) indexed the descriptive terms of watermarks in six international Paper languages. In 2012, the Austrian project Bernstein published on its website a glossary of Watermark-Terms: Vocabulary for Watermark Description, in seven languages.
Rauber et al. [1997]  for instance have used histogram-based methods to compare the similarity between a query watermark image to their database of binarized digitized watermarks. But their approach relies on the ability to separate watermarks from other lines in the paper, which is itself a complex task, and thus requires many pre-processing steps. Others like  Picard et al. [2016]  have used Machine Learning based methods such as dictionary learning, where an image is seen as a collection of regions where each region is encoded on a dictionary; similar regions are then encoded by similar atoms of the dictionary. These approaches differ from ours as their objective is not to find the exact matching design of a given watermark photograph, but to find a similar watermark design given an input design.
A more similar approach to the one of  Shen et al. [2019] , which we based our work on,  Pondenkandath et al. [2018]  used convolutional neural networks to classify a non-public dataset of more than 100k watermarks into 12 categories. But their approach differs from our work as our objective is not to classify into a fixed number of coarse categories. Instead, we are trying to find the exact class for each watermark, making each one of the 16,112 watermarks in the Briquet database a single class. While we believe the association of the photograph of a watermark on a paper to its corresponding design from the Briquet catalog is of great help to anyone looking to identify a watermark, other prior information the user might have about the original paper would be complementary to fully identify and date it, and future work could consider how to properly integrate it to the proposed matching algorithm.
Primary source and database We first collected watermark photographs in the Minutier central des notaires de Paris, the department of the French National Archives where all the archives of the notaries practising in Paris (and in the old municipalities depending from the capital in 1860) from the end of the 15th century to the beginning of the 20th century are preserved. Notaries produced familiar and economic deeds or any other private deed with authentic value, so that the Minutier central des notaires de Paris holds today around 22 million deeds coming from 122 notary's offices. Notaries drew up tens of documents every month and conserved the minutes on paper in their archives, bound in registers or collected in sets of loose sheets. The abundance of daily and weekly deeds drawn up by the same office implies a significant consumption of paper, which is perfectly suited to the efficient collection of many samples of the same watermark required to train the recognition algorithm.
A database was thus created from original photos from the documents of the Minutier central des notaires de Paris, where the voluminous registers and the numerous loose sheets are made by consistent series of the same paper (that is the same watermark), and where the richness and variety of watermarks made it possible to collect easily and quickly significant series of the same pattern. As it is in the common sense for paper historians, we decided to define a class of watermark by all the watermarks produced using the same watermark mould, including the couple of inevitable twins. Indeed, in the regular process of hand-making paper, two paper moulds (where the twin watermarks were sewn on) were simultaneously and alternately used at the same vat by a couple of workers. In this way, one ream of paper contains the twin watermarks.
Enrichment with existing resources Since we wanted to address watermark photographs recognition from existing design collections, we also required paired data of photographs and designs to train and evaluate the recognition algorithm. We focused on the well-known repertory Les filigranes (1907), by the Swiss scholar  Charles-Moïse Briquet (1839 -1918). Apart from its authority and accuracy in the dating and location of paper and watermarks, Briquet's database was an interesting extension because it reproduces many different patterns, some of them already found at the Minutier central des notaires de Paris in a later version. We thus created another training and evaluation database for automatic recognition using the watermarks listed by Briquet. On one hand, all the 16,112 PDF versions of the original published tracings were retrieved from the Briquet Online website (built by the LAMOP in Paris and currently hold in Vienna by the OAW) and we semi-automatically removed information irrelevant for the visual classification algorithm.
IV WEB APPLICATION Our main goal is to introduce a mobile application for watermark recognition. Indeed, we believe the wide availability of smartphones can allow the general public, which has little knowledge about watermarks, to get relevant information right away when sorting through documents. The mobile application was also seen as the proper vector for crowd-sourcing knowledge about documents at diverse institutions  (Terras [2016] ). We adopted a set of recent mobile Web standards, also known as Progressive Web Apps, for developing and maintaining our native mobile application. In this section, we first introduce the concept of Progressive Web Apps. We then detail the technical design of the application and present its search, catalog and contribution workflows. The full code for the Web application is publicly available 4  , and scripts as well as Dockerfiles are provided to help and encourage future development.
g. , HTML, CSS, Javascript) and advanced Web standards supported in modern Web browsers (e. g. , service workers, manifests, local storage, notifications, geo-localisation and sensors). As the support for these advanced standards has dramatically increased in both desktop and mobile browsers (Chrome, Firefox, Safari, Opera,. ), developing native-like mobile applications from a single code base and deploying through a unique compilation and delivery process is now possible. Major services (e. g. , Uber, Tinder, Trivago) now leverage PWA for their mobile application as it, comparatively to native applications, successfully addresses heterogeneity (through wellsupported standards), produces smaller bundles (which improves download/update times) and enables to start a relationship with Web visitors (which addresses the issue of overcrowded App stores).
Architecture To build the application, we leveraged the Angular framework for the Web frontend, NodeJS for the Web backend and the Python Flask framework for the Search backend application, whose goal is to perform the actual matching of a given source watermark image to all the watermarks in the database. Both NodeJS and Flask backend applications expose a REST API to be used by respectively the Angular and NodeJS applications. These technological choices were first and foremost guided by their wide usage and support history in order to ease the maintenance. The full architecture is illustrated in Figure  1. The Web and the Search backend applications access a Mongo database currently storing the watermark images and associated metadata for both the Minutier and the Briquet repositories. The database may be extended either with individual contributions from registered users (i.
e. , crowdsourcing), or through the direct integration of additional repositories. For the Web frontend application, we leveraged a range of Web APIs 5. In addition to the Service Worker API that enables the execution of the code in the Web page as a stand-alone application on the smartphone, we also leverage the APIs for local storage and database, media access, push notifications and background tasks. The compiled application bundles are downloaded by the client's Web browser when visiting the project's website from a smartphone, and a request to install it as a mobile app on the phone's home screen is displayed. Once installed, the application can be launched directly from the home screen, even offline, and in a fullscreen mode (hiding the fact that the application is executed inside the phone's Web browser).
Search The search workflow is illustrated in Figure  2. First, a document of watermark is scanned (2(a)) and square resized for better matching capacity (2(b)). Then, the matching algorithm is launched and once finished, the first 10 matching watermarks, both from the photographs and designs datasets, are transferred to the Web backend which automatically displays the results (2(c)). Finally, matching entries can be selected to display their details (2(d)) and explored further through image zooming (2(e)) or external links (2(f)). More specifically, as a mobile user takes a snapshot of a document with a watermark (or selects an existing snapshot), the photograph is sent to the NodeJS Web backend application, which in turns makes the relevant calls to the search backend. Indeed, the search backend exposes multiple API calls, each accessing a search algorithm dedicated to a specific dataset: the photograph dataset (currently corresponding to the Minutier central des notaires de Paris dataset for now) and the design dataset (currently corresponding to Briquet for now).
Contribution The contribution workflow in Figure  4  illustrates how a user can scan (4(a)) and annotate (4(b) and 4(c)) a watermark before it is added to the watermarks database on the Web backend side. Crowdsourcing is foreseen to extend the sets of watermarks that the matching algorithms search against (i. e. , to identify more watermarks), and also to gather descriptive metadata related to the document of the watermarks being added. Leveraging the storage and background tasks Web APIs, these steps may be undertaken offline (e. g. , when inside a building with no Internet access), waiting for later connectivity to upload the data. Once uploaded, the contributed watermark is not yet added to the database used for the matching algorithm. Indeed, the contributed data needs to be curated and validated.
Task and challenges Given a photograph of a watermark on an archive document, our goal is identify the watermark in order to find information about the document such as its origin or the time when it was written. In order to do so, we would like to compare this photograph to the largest possible database to find the corresponding watermark. As discussed above, we chose to use the database collected by Briquet which is available online and contains 16,112 engraved watermarks along with the one collected from the Minutier central des notaires de Paris which contains 2,250 photographs of almost 400 watermarks. The recognition task is challenging for several reasons. First, some photographs are very cluttered (Figure  5 ). This could be because the state of conservation of the archive document is bad, or simply because a lot of writing has overshadowed the watermark in the paper.
Network Training We used two different CNNs for recognition, the first one designed to match a query photograph to a photograph database, the second one to match a query photograph to a design database. The first CNN was a standard ResNet-18 network  (He et al. [2015] ) whose parameters were optimized to classify well a subset of our watermarks. More precisely, we selected 60 watermarks with 100 different photographs for each and optimized the network parameters so that the network could classify each photograph into one of the 60 classes. Our model reached 98% accuracy on new watermarks photographs from these classes. An intermediate representation learned by this deep network can directly be used to recognize new watermarks, as detailed in the next section. To match photographs to designs, we additionally fine-tuned our model on the joint photograph and design database.
Matching procedure Given a pair of images, for example a photograph and a design, as visualized in Figure  7 , we can compute their features using the network trained according to the procedure described in the previous section. We then need to compare the two features to decide if they correspond to the same watermark. We again followed the approach developed by  Shen et al. [2019]  and compare the content of the two images in two steps. The first step is to compute a global similarity score between the global features of each of the images. The second step aims at refining the matching by emphasizing spatial information and uses a local similarity score that can roughly be interpreted as the number of small patches that are similar in the two images. Figure  7 : Cross-domain matching. Given a source photograph of a watermark, we want to find its corresponding design in a database of 16,112 designs.
In the example of the Briguet catalog, we would like to find the matching design of the photograph of a watermark among 16,112 designs. We first compute the global feature similarity between the query photograph and the 16,112 designs (a few seconds). We then compute the more computationally expensive local similarity score only on the most similar designs. This allows to refine the ranking of the most similar designs in a reasonable time. Indeed, computing this local similarity on the full database takes 27 minutes for each query, while the two-steps approach leads to similar results in a matter of seconds. Following  Shen et al. [2019] , this matching is performed at multiple scales and takes rotations and flips into account as illustrated in Figure  8.
Results and execution time Here, we present results obtained when matching a photograph of a watermark to designs from the Briquet database. This is much harder than matching a watermark photograph to another, as is done in the web application on data from the Minutier central des notaires de Paris. We expect much higher performances for this photograph-to-photograph retrieval task, but it was difficult to evaluate performances in a realistic setup. Our experiments indicate around 90% accuracy with the photographs we obtained from the Minutier Central, but these only include a limited amount of variations and are probably over-optimistic. However, obtaining images from very different sources is very challenging. We thus focus on the more difficult photograph-to-design experiments. The original matching method described in  Shen et al.
Qualitative Results Figures 9 and 10 show cases of challenging queries for the matching algorithm where there are multiple similar designs in the search database. Figure  9  highlights challenging cases where the algorithm was still successful, because enough details were present in the watermark and could be identified in the photograph. On the contrary, Figure  10  shows failure cases, but also highlights the extreme difficulty of our data which includes a wide variety of very similar designs where identifying the correct watermark in the photograph is very challenging.
VI CONCLUSION We introduced a new public and user-friendly web application aimed at making the recognition and study of watermarks easier and more efficient. First, the application hosts a large-scale annotated watermark database. Second, it incorporates an adaptation of a state-of-the-art automatic watermark recognition algorithm, enabling users to identify a watermark from a simple photograph in a few seconds. Third, although the feature is not public yet, the database can directly be enriched through user contributions. In the future, we plan to improve the matching algorithm to get even better recognition performance, and to extend our database through existing resources or individual contributions, which we ambition to collect by making our web application a go-to platform for watermark crowdsourcing.
Acknowledgements This work has received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by  ANR  with the references  ANR-10-IDEX-0001-02 PSL. It was also supported by the  ANR  project  EnHerit  ( ANR-17-CE23-00080 ), project  RAPID Tabasco and gifts from Adobe to Ecole des Ponts.
Tables and Figures The "Riple_DATA. txt" file contains the data from each instrument of the RIPLE platform. 5 The "Riple_SAV. txt" file contains the data that enable to remotely control the proper functioning of the RIPLE platform. camera in order to make a reliable archiving of the images (in case of malfunction of the remote transmission for example). "Config. ini" is the configuration file of the platform, which is generated when the user wants to change the configuration of the platform from a remote location using the RIPLE interface (section 5). A backup of the old configuration is made in the 10 "Config_old. ini" file. The data logger continuously retrieves data from the instruments integrated in RIPLE and stores the result in a table. 1. The data logger supplies power to the control camera for a few minutes, which automatically takes an image when it starts up.
11. A web page created under Shiny (R package) is updated after each new data transmission. The button « charger la configuration actuelle » triggers the reading of the "config. ini" file on the FTP by the data logger. This file contains the values of the variables currently loaded by RIPLE paltform. The button « charger l'ancienne configuration » triggers the reading of the file "config_old. ini" on the FTP by the data logger, in which there is a backup of (n-1) configuration of RIPLE platform (in case of false manipulation). The button « modifier la configuration » [ADMIN]: after entering the password, it allows to edit the value of each variable to change the RIPLE platform configuration.
In this article we present the use of a recently developed pre-processing approach, sequential pre-processing through orthogonalization (SPORT), to improve the predictive power of multivariate models based on NIR spectra of food materials. The SPORT approach utilizes sequential orthogonalized partial least square regression (SOPLS) for the fusion of data blocks corresponding to several spectral preprocessing techniques. The results were compared with commonly used preprocessing techniques in the analysis of food materials by NIRS. The comparison was made by analyzing 5 different datasets comprised of apples, apricots, olive oils and grapes associated with chemical properties such as dry matter (DM), Brix, lipids and citric acid. The datasets were from both reflection and transmission measurements.
The results showed that the fusion-based pre-processing methodology is an ideal choice for preprocessing of NIRS data. For four out of five datasets, the prediction accuracies (high R 2 pred and low RMSEP) were improved. The improvement led to as much as a 20 % increase in R 2 pred and a 25 % decrease in RMSEP compared to the standard 2nd derivative pre-processing. The pre-processing fusion was more effective for the reflection mode compared to the transmission mode. Multiple pre-processing techniques provided complementary information, and therefore, their fusion using the SPORT approach improved the model performance. The methodology is not only applicable to food materials but can in fact be used as a general pre-processing approach for all types of modeling of spectral data.
However, SNV has the defect of mathematical closure due to the division by the standard deviation of the whole spectrum, and this means that changes in the shape of one part of the spectrum can have an effect over the entire spectrum. Such a distortion can influence the robustness of the models as the b-coefficients no longer correspondent to the physical reality present in the NIRS spectra. In the present work, the SNV was implemented using the MATLAB codes explained in literature  (Roger et al. , 2020b).