dataesr/scientific-paragraphs-categorization huggingface dataset:
7 below, it allows to reduce the vocabulary size of drugs while keeping the same predictive performance. As for subsequent encoding, we consider only tokens that occur at least 50 times in the training dataset, so that the encoder will see a total of 9 271 medical codes. On the left-hand side of Figure  5 , we show, for this final cohort, the distribution of n i , namely the number of patients with a given total number of drugs prescriptions (related to urinary problems) during    In the left-hand side of Figure  6 , we show the distribution of the observation period of each patient before T i. We observe that it is highly heterogeneous: the maximum is reached at 1 274 days while the average is 486 days, and some patients have a very short observation period. This variability is related to the variability of the medical practice itself: some patients are treated for urological problems with drugs for a long time before the surgery, while other patients have surgery sooner.
Conclusion and future works Future works will consider a multi-task version of ZiMM ED (several types of relapses, or other types of events), and other improvements using alternative architectures for attention modeling  [37, 27, 38, 70]. The considered dataset and the introduced methodology was developped for non-clinical claims data, which is fundamentally different from clinical datasets. A future effort will be done with the aim of adding a clinical and genomic data to th cohort. In this case a lot of additions analysis can be done (  [12] ). This work addresses the problem of predicting the blurry relapses of the TURP surgery, which is the first step towards an evidence-based approach using machine-learning to help the clinical decision. The next step is to exploit these predictions to help to decide the timing of the surgery: given the current health pathway of the patient, what is his probability of a relapse, so that it can help the clinician to decide when to perform the surgery, or to consider alternative treatments.
In this work, we propose ZiMM Encoder Decoder (ZiMM ED), an end-to-end deep learning model trained against the negative log-likelihood of the new ZiMM (Zero-inflated Mixture of Multinomial) model, for the modeling of long-term and blurry relapses. This deep learning model is trained on a cohort based on a large electronic health record database, that contains only claims and no clinical data, from the whole French population.
Acknowledgments This research benefited from the  National Health Insurance Fund (CNAM ) partnership with Data Science initiative at École polytechnique. We thank  Maryan Morel ,  Dinh Phong Nguyen ,  Youcef Sebiat ,  Dian Sun , researchers and engineers working on this project. Yiyang Yu  was supported by grants from  Région Ile-de-France. Anastasiia Kabeshova  was supported by  CNAM-Polytechnique  research partnership. This research is also supported by the  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program  (reference  ANR-19-P3IA-0001 ;  PRAIRIE 3IA Institute ). Finally, we wish to thank  Pr. Eric Vicaut , for his help in the understanding and the usage of the database.
This paper presents a deep-learning-based algorithm dedicated to the processing of speckle noise in phase measurements in digital holographic interferometry. The deep learning architecture is trained with phase fringe patterns including faithful speckle noise, having non-Gaussian statistics and non-stationary property, and exhibiting spatial correlation length. The performances of the speckle denoiser are estimated with metrics, and the proposed approach exhibits state-of-the-art results. In order to train the network to de-noise phase fringe patterns, a database is constituted with a set of noise-free and speckled phase data. The algorithm is applied to de-noising experimental data from wide-field digital holographic vibrometry. Comparison with the state-of-the-art algorithm confirms the achieved performance.
23 The best filtering scheme for denoising phase data from digital holographic metrology was found to be the two-dimensional windowed Fourier transform (WFT2F). 25 he main drawback of this algorithm is the computation time. For example, on a laptop personal computer (PC) Intel core i5 at 2. 30 GHz with MATLAB, the processing of the image sized 1024 × 1024 pixels with the WFT2F requires 63 s, 230 s, and 1600 s for kernel sizes at 10, 20, and 40 pixels, respectively. Over the past 10 years, deep learning-based artificial intelligence has become a very effective tool for signal and image processing. 26 It follows that alternative approaches to classical filtering schemes, exhibiting almost the same performance, but the reduced computational time could be of very high interest. Section III describes the method we followed in this paper to de-noise phase fringe data, including high speckle noise with a deep learning approach.
II. We also consider their transposed and phase shifted version with a π/4 phase shift. This leads to a grand total of 40 images. The noise level for the 40 images was set to 2 pixels per speckle grain in the simulator, which corresponds to realistic on-line digital holographic recording conditions (refer also Sec. V). Depending on the local fringe density in patterns, the signal-to-noise ratio of its cosine, in the overall database, varies from 7. 31 dB to 11. 45 dB. From the database, 384 patches per image were considered, leading to a grand total of 15 360 patches. These 384 patches, extracted from each image, were randomly selected for each of the 1920 epochs of the training. Furthermore, speckle noise realizations of all images were regenerated (see Sec. II) before each new training epoch and no data augmentation was performed.
Mini-batch size and initial learning rate were set to 128 and 0. 0006, respectively, as summarized in Table  I. Therefore, a total of 230 400 iterations were used for the training. Figure  3  shows the illustrations of the selection of the phase fringe patterns and patches from the database. The cost function, defined as the root-mean-square error (RMSE), was minimized using the stochastic gradient descent. The total duration of the training was about 32 h with 8 core desktop PC including nvidia gtx1080 GPU. Before the training stage, the model was initialized with values corresponding to the original DnCNN model of Zhang,  7  which corresponds to de-noising of natural images with Gaussian noise. In order to validate the model, an evaluation was performed with a second database including 25 phase fringe patterns that have not been seen by the network in the training stage.
This database has been used in author's previous works related to phase de-noising in digital holography. 23, 29 The model was also compared with two other ones: the original DnCNN model from Zhang,  7  which is a model trained on sets of natural images with Gaussian noise, and the same pre-trained model retrained on the same fringe pattern database that was used for training with realistic speckle noise, but with added Gaussian noise. The behavior of the cost function vs the iteration number is reported in Fig. 4. One can observe that it exhibits a larger variance than in the case of usual network training with Gaussian noise. In order to improve performances, we also introduced iterations in the de-noising process, as shown in the processing scheme reported in Fig. 5. Therefore, 1-5 iterations were taken into account for the three models.
It follows that the same amount of error can be observed between two restored phase data when one of them may exhibit a very different perceived quality. However, the "quality index," Q index , criterion from Wang and Bovik 30 does account for the perceived quality of restored images. The value of Q index is included in the range [-1, +1], when +1 is reached and when the two images are quite similar. In this paper, the quality index will be used to check the perceived quality. Another metric that can be considered is the peak signal-to-noise ratio (noted PSNR), which is defined as the ratio between the maximum gray level in the image and the standard deviation between the initial and processed image. The PSNR is given in the following equation for images digitized with nbits: PSNR = 10 log 10 ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ (2 nbits -1) 2 1 MN ∑ i,j (s(i, j) -d(i, j)) 2 ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦.
( 5 ) FIG. 5. Scheme of the de-noising process used for the evaluation of the three models. The phase is re-calculated by using an arctangent formula of the sine and cosine images. In Eq. (  5 ), s(i, j) and d(i, j) refer to the initial noise-free phase data and the enhanced one, respectively. The set of the index values (i, j) relates to the matrix coordinates having M rows and N columns. Finally, a last metric, called the method error, 31 yields the value of the phase error generated by the de-noising algorithm itself when its input is composed of noise-free images. This error can be interpreted as the lower bound of the phase error relative to the database used for the evaluation. It is then computed as the mean value of phase errors when the inputs of the de-noising algorithm are the noise-free images from the database.
IV. RESULTS As the first results, we present comparisons of the three selected model networks that correspond to the DnCNN model trained with natural images with Gaussian noise, with noise-free fringe patterns with added Gaussian noise (an amount around 11 dB), or with realistic speckle noise in phase data from the simulator, respectively. These three models are called models "1," "2," and "3," respectively. Validation is achieved with the database used in Ref. 23, which is constituted with 25 phase fringe patterns with realistic speckle decorrelation noise and cosine signal-to-noise ratio varying from 3 dB to 12 dB. As such fringe patterns have also been used for the training, we have inserted a rotation of 90 ○ of the images of the validation database to avoid any similar patterns when de-noising.
11(a ). This could be ameliorated by decreasing the window size, but at the cost of increasing the standard deviation of the phase error. Finally, the compromise for the correct parameters and/or suitable algorithm is not so trivial and could be considered in the perspective of the expected application. As an example, in the field of vibroacoustics, for the problem of force identification,  36  the inverse problem is required to process data from full-field measurements, as provided in this section. The discussion related to the required overall quality and accuracy of the measured input data remains opened and needs to be deeply investigated in the future. From Figs. 11 and 12, we can conclude that the DL, trained on the database with realistic speckle noise, exhibits high efficiency and is state-of-the-art, when de-noising phase data that were not in the learning database.
VI. CONCLUSION This paper demonstrates that a state-of-the-art deep-learningbased algorithm trained to de-noise phase maps from digital holographic interferometry can be obtained when training the network with realistic noisy phase data. To do this, faithful speckle noise conditions, having non-Gaussian statistics and non-stationary property, being amplitude-dependent, and exhibiting spatial correlation length, must be simulated. As a result, training a network with data including simply added stationary Gaussian noise does not provide a robust de-noiser. In order to achieve the state-of-the-art de-noiser, the deep neural network was trained with a database constituted with 40 noisy phase maps, including various orientations and fringe densities. With three iterations, the performance obtained with the proposed approach yields comparable standard deviation to the 2D windowed Fourier transform algorithm and better peak-to-valley.
The computation time of the proposed approach is smaller than that of the 2D windowed Fourier transform, especially for experimental noisy phase data. Processing of experimental data from widefield digital holographic vibrometry at a 100 kHz frame rate yielded promising results. Future works concern reducing the computation time, for example, by moving to the Tensorflow software environment, and deep investigation of the architecture could be carried out by testing other network configurations, for example, when reducing the number of layers. Finally, the overall quality and accuracy of the processed data must be investigated to determine which phase error could be tolerated in the related metrology problems.
Data from the two studies were then used to assess the relationship between usefulness, ease of use, and self-reported usage. Psychometricians emphasize that the validity of a measurement scale is built in from the outset. As Nunnally (1978) points out, "Rather than test the validity of measures after they have been constructed, one should ensure the validity by the plan and procedures for construction" (p. 258). Careful selection of the initial scale items helps to assure the scales will possess "content validity," defined as "the degree to which the score or scale being used represents the concept about which generalizations are to be made"  (Bohrnstedt, 1970, p. 91). In discussing content validity, psychometricians often appeal to the "domain sampling model,"  (Bohrnstedt, 1970; Nunnally, 1978)  which assumes there is a domain of content corresponding to each variable one is interested in measuring.
80 (Davis, 1986). Adding four additional items for each construct to allow for item elimination, it was decided to generate 14 items for each construct. The initial item pools for perceived usefulness and perceived ease of use are given in Tables retained, yielding 10 items for each construct. Next, a field study (Study 1) of 112 users concerning two different interactive computer systems was conducted in order to assess the reliability and construct validity of the resulting scales. The scales were further refined and streamlined to six items per construct. A lab study (Study 2) involving 40 participants and two graphics systems was then conducted. Data from the two studies were then used to assess the relationship between usefulness, ease of use, and self-reported usage. Psychometricians emphasize that the validity of a measurement scale is built in from the outset.
The initial item pools for perceived usefulness and perceived ease of use are given in Tables retained, yielding 10 items for each construct. Next, a field study (Study 1) of 112 users concerning two different interactive computer systems was conducted in order to assess the reliability and construct validity of the resulting scales. The scales were further refined and streamlined to six items per construct. A lab study (Study 2) involving 40 participants and two graphics systems was then conducted. Data from the two studies were then used to assess the relationship between usefulness, ease of use, and self-reported usage. Psychometricians emphasize that the validity of a measurement scale is built in from the outset. As Nunnally (1978) points out, "Rather than test the validity of measures after they have been constructed, one should ensure the validity by the plan and procedures for construction" (p.
The scales were further refined and streamlined to six items per construct. A lab study (Study 2) involving 40 participants and two graphics systems was then conducted. Data from the two studies were then used to assess the relationship between usefulness, ease of use, and self-reported usage. Psychometricians emphasize that the validity of a measurement scale is built in from the outset. As Nunnally (1978) points out, "Rather than test the validity of measures after they have been constructed, one should ensure the validity by the plan and procedures for construction" (p. 258). Careful selection of the initial scale items helps to assure the scales will possess "content validity," defined as "the degree to which the score or scale being used represents the concept about which generalizations are to be made"  (Bohrnstedt, 1970, p.
A lab study (Study 2) involving 40 participants and two graphics systems was then conducted. Data from the two studies were then used to assess the relationship between usefulness, ease of use, and self-reported usage. Psychometricians emphasize that the validity of a measurement scale is built in from the outset. As Nunnally (1978) points out, "Rather than test the validity of measures after they have been constructed, one should ensure the validity by the plan and procedures for construction" (p. 258). Careful selection of the initial scale items helps to assure the scales will possess "content validity," defined as "the degree to which the score or scale being used represents the concept about which generalizations are to be made"  (Bohrnstedt, 1970, p. 91). In discussing content validity, psychometricians often appeal to the "domain sampling model,"  (Bohrnstedt, 1970; Nunnally, 1978)  which assumes there is a domain of content corresponding to each variable one is interested in measuring.
Acknowledgements This research was supported by grants from the  MIT Sloan School of Management ,  IBM Canada Ltd. , and The  University of Michigan Business School. The author is indebted to the anonymous associate editor and reviewers for their many helpful suggestions. Acknowledgements This research was supported by grants from the  MIT Sloan School of Management ,  IBM Canada Ltd. , and The  University of Michigan Business School. The author is indebted to the anonymous associate editor and reviewers for their many helpful suggestions. Acknowledgements This research was supported by grants from the  MIT Sloan School of Management ,  IBM Canada Ltd. , and The  University of Michigan Business School. The author is indebted to the anonymous associate editor and reviewers for their many helpful suggestions.
Thus, a key marketing issue was the extent to which the new product would compete favorably with established brands, such as Chart- Master. Participants were given one hour of hands-on experience with Chart-Master and Pendraw, using workbooks that were designed to follow the same instructional sequence as the user manuals for the two products, while equalizing the style of writing and eliminating value state- ments (e. g. , "See how easy that was to do?"). Half of the participants tried Chart-Master first and half tried Pendraw first. After using each package, a questionnaire was completed. The study involved evaluating two IBM PC- based graphics systems: Chart-Master (by De- cision Resources, Inc. of Westport, CN) and Pen- draw (by Pencept, Inc. of Waltham, MA). Chart- Master is a menu-driven package that creates numerical business graphs, such as bar charts, line charts, and pie charts based on parameters defined by the user.
g. , "See how easy that was to do?"). Half of the participants tried Chart-Master first and half tried Pendraw first. After using each package, a questionnaire was completed. The study involved evaluating two IBM PC- based graphics systems: Chart-Master (by De- cision Resources, Inc. of Westport, CN) and Pen- draw (by Pencept, Inc. of Waltham, MA). Chart- Master is a menu-driven package that creates numerical business graphs, such as bar charts, line charts, and pie charts based on parameters defined by the user. Through the keyboard and menus, the user inputs the data for, and defines the desired characteristics of, the chart to be made. The user can specify a wide variety of options relating to title fonts, colors, plot orienta- tion, cross-hatching pattern, chart format, and so on. The chart can then be previewed on the screen, saved, and printed.
We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies.
approach to reconstructing 18 tracts (8 left/right, 2 commissural) based on priors derived from an atlas. For each tract, manual tract labelling was performed in a training cohort and this provided prior information to be used in a Bayesian probabilistic framework for automated tractography. In  (de Groot et al. , 2013)  standardised protocols for probabilistic tractography were used to reconstruct 27 tracts (12 left/right, 3 commissural) in two datasets with varying quality. More recently  (Wassermann et al. , 2016)  proposed a framework for describing WM anatomy and tracts which uses subject-wise anatomical segmentation, clustering and a query language to extract 57 (25 left/right, 7 commissural) tracts from whole-brain tractography and a grey matter (GM) parcellation. Their approach reduced the definition of tracts to sets of logical rules with reference to the tract position and termination, the tract route and its spatial relationship to given brain parcels.
3 The contribution of the presented work is as follows: 1) we design tractography protocols for 42 tracts and we illustrate their robustness against data quality, using high-resolution data from the Human Connectome Project (HCP)  (Sotiropoulos et al. , 2013)  and more typical data from the UK Biobank  (Miller et al. , 2016) , 2) we illustrate generalisability of the tractography protocols to the macaque brain, 3) we derive high-quality tract atlases using these protocols both for the human brain (1000 HCP subjects) and the macaque brain (high-resolution, ex vivo datasets from 6 animals), 4) we perform indirect validation by assessing lateralisation of the extracted tracts (in humans), 5) we illustrate that, despite being template-driven, reconstructed tracts preserve individual variability as assessed via twinship analysis, and 6) we offer an open-source flexible framework for publicly exchanging tractography protocols available within FSL  (Jenkinson et al.
Data and preprocessing To assess robustness across varying data quality, we utilised data from the Human Connectome Project (HCP, cutting-edge diffusion MRI)  (Sotiropoulos et al. , 2013; Van Essen et al. , 2013)  and data from the UK Biobank  (Miller et al. , 2016)  (overall quality closer to that typically available through clinical scanners). To ensure generalisability of the protocols across species, we also utilised diffusion MRI data from the macaque brain. This data consisted of an extended set of animals used in  (Eichert et al. , 2019a; Mars et al. , 2018b). In total, the datasets we considered consist of 1065 subjects from the HCP (all available HCP S1200 subjects that had diffusion MRI data), 1000 subjects from the UK Biobank and ex vivo high-resolution datasets from 6 macaques.
, 2012) , to which the distortion-corrected diffusion MRI data were also linearly registered. Concatenation of the diffusion-to-T1 and T1-to-MNI transforms allowed diffusion-to-MNI warp fields to be obtained. For the macaque data, we combined data previously used in  (Mars et al. , 2018b)  with newly acquired data. Data were acquired locally on a 7T magnet with an Agilent DirectDrive console (Agilent Technologies, Santa Clara, CA, USA) using a 2D diffusion-weighted spin-echo protocol with single line readout (DW-SEMS, TE/TR: 25 ms/10 s; matrix size: 128 Â 128; resolution: 0. 6 Â 0. 6 mm; number of slices: 128; slice thickness: 0. 6 mm; diffusion data were acquired over the course of 53 h). 16 non-diffusion-weighted (b ¼ 0 s/mm 2 ) and 128 diffusion-weighted (b ¼ 4000 s/mm 2 ) volumes were acquired with diffusion directions distributed over the whole sphere.