dataesr/scientific-paragraphs-categorization huggingface dataset:
If y = (y i ) 1≤i≤N denotes the true dataset to estimate and ỹ = (ỹ i ) 1≤i≤N the corresponding estimates, the modeling efficiency ǫ  Wallach (2006)  (also called the coefficient of determination) and the accuracy α with which the leaf area has been estimated can be calculated as:    ǫ = 1 -N i=1 (y i -ỹi ) 2 / N i=1 (y i -ȳ) 2 α = 1 N N i=1 ỹi y i ( 19 ) where ȳ is the mean value of y. The higher the modeling efficiency and the closer to one the accuracy, the better. Different such criteria were computed for completeness: one per individual leaf to account for the fact that the mean leaf area can be of different orders of magnitudes for different leaves, one per genotype for the different leaves, one per leaf for the different genotype and a global one taking into account all the data available.
Methods: Using a somatic cellular model, we have traced the pattern of karyotype evolution comparing gene and transcriptome profiling. Specifically, key evolutionary transitions (e. g. immortalization, transformation, metastasis, and drug resistance) were monitored by both single cell and population profiles. Further synthesis was performed to search for novel types of inheritance above gene/epigene and to characterize fuzziness for chromosomal or karyotype coding. [1]  Conclusion: Data analysis and their synthesis with the genome theory have led to the following conclusions: 1) chromosome or karyotype codes for the address of genes within the nuclei. Such system inheritance differs from gene-coded "parts inheritance", defining the network structure. Most cancers belong to different genome defined systems; 2) genomic coding is less precise; rather, it is fuzzy.
This suggested that either the tumors had overcome the mutations that provoked the constant chromosome missegregation or that the recurring copy number changes were outcompeting the missegregation rate. To test this, we performed single cell sequencing of primary tumor cells and developed AneuFinder, a dedicated software tool to quantify intratumour karyotype heterogeneity  [3]. AneuFinder analysis of our murine aneuploid lymphoma revealed dramatic intratumour heterogeneity, while confirming selection for the recurrently gained/lost chromosomes, indicating ongoing chromosome missegregation can yield seemingly stable aneuploid tumors that display high-grade intratumour heterogeneity. We are currently analyzing human tumors and our preliminary findings are indicating that intratumour heterogeneity rates can be very different between aneuploid tumors.
According to the most recent account of the National Cancer Institute's NCI-Mitelman database all 66,872 cancers recorded by November 2016 had individual sets of chromosomes or karyotypes  [6]. No theory to explain the individuality of the karyotypes of all cancers Despite the abundance of evidence for individual karyotypes in individual cancers, there is no theory to explain the individuality of cancer karyotypes in current textbooks  [3, 7]. In view of this endless variety of cancer karyotypes most cancer researchers abandoned Boveri's theory in favor of the theory of causal mutations, and thus regarded the individual karyotypes of cancers as "epiphenomena"  [3, [8] [9] [10]  or "Folgeerscheinungen"  [11]  or "consequences"  [7]  of the "instability" of cancer karyotypes  [12, 13]. This confirms Gunther Stent's famous theory for experimental scientists: "The best results are useless, if they cannot be confirmed by theory!" In view of this, Is it possible that cancer researchers have overlooked an established model for the generation of new functions and phenotypes by altering the karyotypes? Speciation theory of cancer There is, indeed, a classic biological mechanism of altering phenotypes via karyotypes in countless variationsnamely speciation.
These subpopulations include neural stem-like cells and tumor mass cells. Previous studies have demonstrated that these subpopulations have different phenotypes, responses to radiotherapy, and levels of oxidative stress. In this study, fluorescence lifetime imaging microscopy (FLIM) on the metabolic coenzyme, reduced nicotinamide adenine dinucleotide (NADH) was performed. NADH is an essential cofactor for oxidative phosphorylation and glycolysis, therefore the NADH lifetime can be used as an indicator for metabolic states, with longer lifetimes correlating to oxidative phosphorylation and shorter lifetimes correlating to glycolysis. By analyzing the data in the phasor space, the neural stemlike cells and tumor mass cells can be identified using this label-free, live cell, imaging technique.
Acknowledgements I'd like to acknowledge  Peter Duesberg  ( UC Berkeley ) for critical review and mentorship during this project and  Peter Walian  ( Lawrence Berkeley Laboratory ) for helpful comments and discussion. Bob Leppo  ( San Francisco ) is specifically thanked for his philanthropic support, which made this research and conference possible. Acknowledgements We authors would like to thank: the staff of the  Cesare Maltoni Cancer Research Center, Ramazzini Institute ;  Linda Nguyen  and  Laura Neumann ,  George Washington University ;  Peter Duesberg  and  Amanda McCormack ,  UC Berkeley ;  Giovanni Pierini ,  University of Bologna ;  Francesco Ceci  and  Paolo Castellucci ,  Department of Nuclear Medicine, S. Orsola-Malpighi Hospital, Bologna ;  Tiziana Balbi ,  Institute of Surgical Pathology, S.
Most digital libraries that provide user-friendly interfaces, enabling quick and intuitive access to their resources, are based on Document Image Analysis and Recognition (DIAR) methods. Such DIAR methods need ground-truthed document images to be evaluated/compared and, in some cases, trained. Especially with the advent of deep learning-based approaches, the required size of annotated document datasets seems to be ever-growing. Manually annotating real documents has many drawbacks, which often leads to small reliably annotated datasets. In order to circumvent those drawbacks and enable the generation of massive ground-truthed data with high variability, we present DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled ground truth.
Introduction Almost every researcher in the field of Document Image Analysis and Recognition (DIAR) had to face the problem of obtaining a ground-truthed document image dataset. Indeed, many DIAR tools (image restoration, layout analysis, text-graphic separation, binarization, OCR, etc. ) rely on a preliminary stage of supervised training. Moreover, ground-truthed document image datasets are needed to evaluate these DIAR tools. Digital curators are the first users of these tools, e. g. , for announcing expected OCR recognition rates together with automatic transcriptions of books  [1]. One common solution is to use ground-truthed training and benchmarking datasets publicly available on the internet. For document images, the following databases are the most commonly used. For printed documents: Washington UW3  [2] , LRDE  [3] , RETAS-OCR  [4] , PaRADIIT  [5] , etc.
; for handwritten documents IAM database  [6] , RIMES  [7] , GERMANA  [8] , etc. ; for graphical documents: chemical symbol database  [9] , logo databases  [10, 11] , architectural symbol database  [12]  or musical symbol database CVC-MUSICMA  [13] ; camera-based document image analysis  [14, 15]. The International Association for Pattern Recognition, for instance, gathered some interesting datasets  [16]  mostly used for different conference competitions over the last two decades. The main international conference in document image analysis, ICDAR, references on its websites many contest datasets. However, very few of them are reliably annotated, copyright-free, up-to-date or easily available to download. An alternative for researchers and digital curators is to create their own ground truth by manually annotating document images.
In order to assist them in the tedious task of ground truth creation, multiple software have been proposed during the last two decades. As detailed in Table  1 , some are fully manual stand-alone software (Pink Panther (1998)  [17] , trueViz (2003)  [18] ), while others provide semi-automatic annotation modules (GEDI (2010)  [19] , Aletheia (2011)  [20, 21] ). Some of the most recent solutions are based on an online collaborative platform (Transcriptorium (2014)  [22] , DIVADIAWI  [23]  (2015),  [24]  (2016), Recital manuscript platform  [25]  (2017)). Among non open-source solutions, some have an academic licence:  [20, 26]. These software assist the user in creating the ground truth associated with real documents, intrinsically limited in number because of acquisition procedures and copyright issues.
Moreover, despite the use of such software, manual annotation remains a costly task that cannot always be performed by a non-specialist. Another solution is available for getting (quickly and with lower human cost) large ground-truthed document image datasets. This solution, investigated since the beginning of the nineties  [27] , is to generate synthetic images with controlled ground truth. The authors of  [28, 29]  propose two similar systems. They consist of using a text editor (e. g. , Word-office, Latex, etc. ) to automatically create multiple documents with varied contents (in terms of font, background, layout). Alternative approaches consist of re-arranging, in a new way, elements extracted from real images so as to generate (manually, semi-automatically or automatically) multiple semi-synthetic document images  [12, 30].
Recently, in particular with the advent of deep learning techniques which require huge masses of training data, the need for synthetic data generation seems to be ever-growing. In  [31] , among the 60,000 character patches that were used to train a convolutional network for text recognition, only 3000 were real. In this paper we present DocCreator, an open-source and multi-platform software that is able to create virtually unlimited amounts of different ground-truthed synthetic document images based on a small number of real images. [23]  XML n/a online yes n/a 2015 Recital  [25]  no yes online yes yes 2017.
Software for semi-automatic ground truth creation and data augmentation capabilities DocCreator XML yes online/desktop yes no 2017 As illustrated in Figure  1 , DocCreator can handle the creation of ground-truthed synthetic images from a limited set of real images. Various realistic degradation models can be applied on original document images, the resulting images being called semi-synthetic images in the rest of the paper. If there is no ground truth associated to the real images, DocCreator can create, with a given text, synthetic images that look like the real ones and their associated ground truth. Depending on the needs and expertise of the user, DocCreator can be used in a fully automatic mode, or in a semi-automatic mode where the user can interact with the system and tune its parameters.
First possibility: if a researcher has real document images but without any ground truth, DocCreator can generate synthetic images that look like the real ones, and of course, with the associated ground truth. Second possibility: a researcher has a ground-truthed database but it is too small or not heterogeneous enough. DocCreator provides several degradation algorithms to augment the dataset. By degrading text ink, paper shape or background colours it is possible to create a representative document image database where many defects are present. This complete database is finally useful for very precise performance evaluation or to provide multiple cases for retraining processes (in algorithms embedding a learning step). DocCreator features compared to existing software are highlighted in Table  1.
First of all, DocCreator is the only one that can create synthetic documents that mimic real ones. Besides, as it includes several degradation models, it provides an integrated solution to carry out data augmentation. DocCreator thus makes quickly available ground-truthed databases. It makes DocCreator a unique software that can be seen as a complementary tool to those mentioned in Table  1. This paper is organized as follows. In Section 2, we present the methods used to extract document characteristics and to generate synthetic documents, while in Section 3 document degradation models are discussed. Section 4 highlights the advantages of DocCreator on various DIAR tasks, both for benchmarking and for retraining DIAR tools using data augmentation.
As word segmentation is already available via Tesseract OCR (but not the complete document layout), we use a hybrid approach proposed by  [45]. With only one parameter to adjust the number of extracted blocks, this method ensures a good layout segmentation of many different classes of typewritten documents. DocCreator, as an interactive software, leaves once more the possibility to adapt to the wished segmentation results. This method has the advantage of a very low computational cost, without any preprocessing training required. At this point, the three characteristics used in the synthetic image generation process have been extracted (background, font and layout). The next step is to assemble these elements with a given text in order to build the final output, which is the created synthetic image and the associated XML ground truth.
Document Degradation Models Physical degradation due to ageing, storage conditions or poor quality of printing materials may be present on documents. DocCreator currently proposes seven degradation models. As detailed in Figure  1  (right part), all these models can be applied on real images to extend any document image database. The user can interact with DocCreator in order to set the quantity of defects to generate. In the following sections, we describe the main ideas of these seven degradation models. As DocCreator is an open source software, readers can consult the source code to get more details about the implementation of these models.
Bleed-Through With DocCreator it is possible to add bleed-through defects. This algorithm is directly inspired from  [49]  that initially proposes an algorithm for erasing the bleed-through from a document image. By just giving an input recto image, an input verso image and the amount of wished degradation, a physical model is applied. This model mimics the verso ink that seeps through the recto side. This model simulates an anisotropic diffusion and each pixel at time t + 1 is modified according to the pixels values at time t with the following equation: I t+1 i,j = I t i,j + λ * (c t N i,j • N I t i,j + c t S i,j • S I t i,j + c t E i,j • E I t i,j + c t W i,j • W I t i,j ) where I is the recto image, V is the verso image, lambda a constant value in [0; 0. 25] and N, S, E, W are the mnemonic subscripts for North, South, East, West.
Adaptive Blur The blur defect is a very common defect encountered during digitization campaigns. The difficulty here is to create a realistic blur defect that mimics the very slight blur that appears when the scanner is incorrectly set (a large blur is easily detected by scanners). To do so, we propose a method inspired by the blur detection from  [50]. First, the user chooses a blur image to mimic among a real blur example available in DocCreator. Then, using a dichotomic algorithm, we compute the size of the kernel of a Gaussian blur that, once applied on the input image, produces a blur similar to the chosen real blur image. In this method, the Fourier Transform of the image is first computed. Then the module of the Fourier Transform is binarized according to its mean. The resulting binarized image produces a disc for images with only text.
Document Image Generation for Performance Evaluation The segmentation system proposed by  [36]  is based on a texture feature extraction without any a priori knowledge on the physical and logical document layout. To assess the noise robustness of their system, they used DocCreator and applied the character degradation model. From 25 simplified real document images, they generated a semi-synthetic database of 150 document images. This database is made up of several subsets where the degradation levels are different. The performance evaluations presented in  [36]  highlight that the texture descriptors are slightly perturbed by the degradations. When characters are highly disconnected (our algorithm has erased important character ink areas), a drop of the segmentation performances was observed.
DocCreator was also used during the ICDAR contest: staff-line removal from musical scores. The 3D distortion and the character degradation models were used in order to generate an extended database from the 1000 images of the MUSCIMA database  [13]. As a result, the extended database contains 6000 semi-synthetic grayscale images and 6000 semi-synthetic binary images. This database has been used in the second edition of the music score competition ICDAR 2013  [37]. Five participants submitted eight methods. Participants were given a training set of 4000 semi-synthetic images and then 2000 semi-synthetic images to test their methods on. Regarding the results on the 3D distortion set, the submitted methods seem less robust to global distortion than to the presence of small curves and folds. For more details about the participants, the methods and the contest protocol, refer to  [37].
Document Image Generation for Retraining Task The IAM-HistDB  [58]  database contains 127 handwritten historical manuscript images together with their ground truth. This database consists of three sets: the Saint Gall set containing 60 images (1. 410 text lines) in Latin, the Parzival set containing 47 images (4. 477 text lines) in Medieval German, and the Washington set containing 20 images in English. The authors of  [39]  used the character degradation model to create two extended databases of the IAM-HistDB. The first one is composed of 17. 661 images degraded with the ink model. The 1. 524 images from the second dataset have been created using the 127 original images and transformed using our 3D distortion model. The tests presented in  [39, 59]  confirm the conclusion of  [60]  about the impact of the degradation level on re-training, either for a task of character recognition or layout extraction.
Increase the Prediction Rate of Predictive Binarization Algorithm In  [61] , we have presented an algorithm to predict error rates of 11 binarization methods on given document images so that the best binarization method is automatically chosen for any image depending on its quality. This method requires ground-truthed data as input of the training step. The DIBCO database  [62]  was used. However, the DIBCO database contains only 36 images. We propose here to extend the original DIBCO database by using the ink degradation model. Since the DIBCO database contains 36 images, we extend it with the same number of semi-synthetic document images. This extended dataset is then used to train the prediction model of  [61]. Our retraining tests show that the use of this extended dataset allows one to increase the performance of the prediction model of  [61].
It is noteworthy to mention that the fonts were extracted thoroughly, in particular to include even characters not recognized by the OCR, or even to adjust margins of correctly labeled characters. (2) An adapted Lorem ipsum text is randomly generated and used to create synthetic images with the font and background previously extracted. This adapted Lorem ipsum is generated with accentuated characters (é, à, ù, etc. ) and old characters (ff, fi, s, fl, ffi) if the original text contains such characters. Generating such characters is important to have a representative dataset for fair OCR testing. As a result, images like the one presented in Figure  2  are generated with the associated XML ground truth. (3) An OCR (Tesseract) is finally used to recognize the text on these synthesized images.
We compute the mean of the Levenshtein distances for the N documents of each database. Using this Levenshtein distance, the difference between the OCR text recognition rate computed on real images and the one computed on "Lorem ipsum" version (Table  2  Column 1 and Column 2) is, on average, only overestimated by 0. 04. The difference between the real OCR rate and the one computed on the synthetic versions (Table  2  Column 1 and Column 3) is, on average, only overestimated by 0. 03. Most of the success of different existing OCR prediction methods (  [63] [64] [65] [66] ) are related to the quality and quantity of the needed ground truth. Our prediction method presented here provides comparable results with the ones form the state of the art.
Conclusions DocCreator gives to DIAR researchers a simple and rapid way to extend existing document image databases or to create new ones avoiding the tedious task of manual ground truth generation. DocCreator embeds many fonts, backgrounds, meshes and realistic degradation models which, when combined, result in an interesting combination of ground-truthed databases. The experiments detailed in this paper show semi-synthetic and synthetic documents created with DocCreator are useful for performance evaluation, retraining tasks or performance prediction. In future work, we plan to improve the synthetic document creation to avoid to have too different characters in the composed document. For example, we should investigate if adding some constraints on the font extraction phase or taking into account the context when adding new characters to the synthetic document may lead to more realistic synthetic documents.
We also consider to set up a cognitive experiment to evaluate the perceived realness of the degraded documents or even the created synthetic documents. We are also planning to investigate how the generation of highly diversified data can improve the results of tasks based on deep learning methods. DocCreator (source, Linux, Mac, Windows packaged versions), all the databases used for the tests, a video and an extra database (31. 000 synthetic images generated with William Shakespeare sonnet text files) are available at [http://doc-creator. labri. fr/].
(2) font, background and layout are automatically extracted from original images; and (3) font, background and layout are finally used with the transcribed texts to automatically generate synthetic versions of the original ones. The whole database is composed of 93 document images containing 18. 240 words and 115. 622 characters. (a) (DB1) Contemporary french typewritten document. (b) (DB2) Old french typewritten document. (c) (DB3) Old french Manuscript document.
Farmers are classified within a given type using a classification algorithm that is implemented under MOSAICA. In this algorithm, each farm is considered to remain within the same type or is moved to another type in the simulated mosaics, depending on the activities selected by the optimization process  (Chopin et al. , 2015a). The process of assigning activities to fields recorded in a geographical database is driven by several types of constraints that are implemented at different spatial scales (Figure  2 ; see also  Chopin et al. 2015a). For instance, at farm level, the amount of workforce available limits the adoption of labour intensive cropping systems. This information for the process of allocating activities is determined for each field within the geographical database. The simulated allocation process is spatially explicit because cropping systems are allocated to a given field within the map of the territory, based on the sets of equations implemented at different spatial levels within the model.