dataesr/scientific-paragraphs-categorization huggingface dataset:
Development of the SkinVision App Service The history of the SkinVision app service is shown in Table  1. It went through several upgrades throughout its history, modifying the camera, the algorithm and its evaluation, type of lesions analyzed, and communication of the algorithm result to its user. One of the major initial challenges was related to image acquisition. In the beginning, there was no filter on the images sent for analyses, which meant that a significant proportion of the pictures taken by users was of insufficient quality to be analyzed by the disease classification algorithm or did not even contain a lesion to be analyzed. Since version 3 of the SkinVision app (2014), a special camera module  [23]  has been embedded, which only lets the camera take a photo after certain minimal quality conditions are met.
Compared to unfiltered images taken with a standard smartphone camera, the camera module reduces the number of blurry photos by about 52% on an average (determined using 2018 data). Altogether, improvements in the camera module (namely, image quality checks) and the algorithm pipeline led to a reduction in the number of assessments that failed to produce a risk rating, from 26% in 2016 to 2% in 2018 on an average. An overview of studies on the diagnostic accuracy of the SkinVision app is shown in Table  2. Diagnostic accuracy is evaluated based on two measures: sensitivity (proportion of lesions correctly classified as high risk) and specificity (the proportion of lesions correctly rated as benign). The first algorithm for skin lesion assessment was a rule-based fractal algorithm  [24]. Initially, it was focused on pigmented skin lesions and only able to analyze whether MM was present in the lesion, and it was tested based on clinical review of images.
The Munich University Hospital study was the first peer-reviewed publication where the SkinVision app algorithm was evaluated against histopathology  [24] , and thereafter, the algorithm achieved 73% sensitivity (83% specificity). During the Catharina Hospital Eindhoven study  [25] , the algorithm was recalibrated to analyze pigmented and nonpigmented lesions. Currently, it can detect several types of skin cancer (MM, SCC, and BCC) and skin conditions that can lead to skin cancer, namely, actinic keratosis and Bowen disease. It achieved 80% sensitivity (78% specificity) after inclusion of user clinical information. Although in the Eindhoven study, only 233 lesions were used for calibration, in 2018, the SkinVision app assembled a training dataset of more than 130,000 images that were risk classified by a dermatologist from the app's user database.
Training and Testing For training of the algorithm, we used images obtained from the user database (more than 130,000 pictures from 30,000 users), which received either a low-or high-risk tag during quality control of the algorithm by a dermatologist affiliated with SkinVision. A selection of cases clinically validated as low risk were randomly selected from the user database, while all cases rated as high risk or with a histopathological report were used, since there were considerably fewer of them. For testing the sensitivity, we used 285 skin lesions derived from both previous clinical studies (Munich and Catharina Hospital, 195 skin lesions, containing most common forms of skin cancer) and the user database (90 cases of melanoma that received histopathological confirmation from users).
Not reported Galway University Hospital Not reported, Dorairaj et al, 2017 [41] e a All lesions had a benign final histopathology diagnosis with the exception of one melanoma in situ. b Ngoo et al 2017  [40]  reported the results per type of operating system: iOS/Android. c DermNetZN is a publicly accessible skin lesion image database from New Zealand containing about 20,000 images. Images collected within the Los Angeles county were collected by the app company. No reference to a clinical site of the data collection was given in the publication. d Algorithm was only tested on "large lesions" defined as melanomas with a diameter ≥10 mm. e Despite the study being published in 2017, the study took place in 2012.
Improving the Diagnostic Accuracy of Mobile Health Apps A promising avenue to improve the diagnostic accuracy of mHealth apps is to train machine learning algorithms on large databases of skin cancer images. Several algorithms for skin cancer classification were recently developed based on clinical or dermoscopic images, with algorithm accuracy routinely on par with a dermatologist  [21, 22, 43]. For mHealth apps, the task of skin lesion classification is more difficult, as the images are taken by the users themselves, with variability in angle, luminosity, and smartphone model. The SkinVision app showed that skin lesion classification based on smartphone images can also achieve high accuracy (Table  2 ; A Udrea et al, PhD, unpublished material, 2019).
Summary and Conclusions Given the difficulties in access to high-quality care for early detection of skin cancer, there is considerable interest in developing algorithms and apps for skin cancer lesion assessment. Although mHealth apps have been criticized in the past due to their poor accuracy, the SkinVision app has a high accuracy to evaluate the risk of skin lesions for skin cancer. This was achieved due to improvements in the processing of images taken with the smartphone camera and a large risk-labeled image database from users, which was used to train a machine learning algorithm. However, there are still many open questions regarding the usage of mHealth apps. National health authorities need to decide where to position these apps in the health care system (lay population, GPs, or dermatologists).
Self-reported demographic characteristics of the SkinVision app users. The data are from the SkinVision app proprietary user database, accessed in September 2018. Numbers are based on users, who made a picture that was evaluated by the algorithm, and filled the online questionnaire. Some users may be health care providers taking pictures of multiple patients, so this is likely an underestimate. For the gender and age categories, about 75% and 44%, respectively, did not fill any data.
Introduction In this paper, we propose a system to remove one or several objects from a video, starting with only a few user annotations. More precisely, the user only needs to approximately delimit in the first frame the objects to be edited. Then, these annotations are refined and propagated through the video. One or several objects can then be removed automatically. This results in a flexible computational video editing tool, with numerous potential applications. Removing unwanted objects (such as a boom microphone) or people (such as an unwanted wanderer) is a common task in video post-production. Such tasks are critical given the time constraints of movie production and the prohibitive costs of reshooting complex scenes. They are usually achieved through extremely tedious and time-consuming frame-by-frame processes, for instance using the Rotobrush tool from Adobe After Effects  [2]  or professional visual effects softwares such as SilhouetteFX or Mocha.
For this, we employ two strategies: a motion-based pixel propagation for static background and a patchbased video completion for dynamic textures. Both methods rely heavily on the knowledge of segmented objects. This interplay between objects segmentation and the completion scheme improves the method in many ways: it allows for better video stabilization, for a faster and more accurate search for similar patches, and for a more accurate foreground/background separation. These improvements yield completion results with very little or no temporal incoherence. We illustrate the effectiveness of our system through several challenging cases including severe camera shake, complex and fast object motions, crossing objects, and dynamic textures. We evaluate our method on various datasets, in both objects segmentation and objects removal.
For the semi-supervised task, where a first frame annotation is available, methods mostly differ in the way they train the networks. The One Shot Video Object Segmentation (OSVOS) method, introduced in  [8] , starts from a pre-trained network and retrains it using a large video dataset, before fine-tuning it per-video using the annotation at the first frame to focus on the object being segmented. With a similar approach,  [62]  relies on an additional mask layer to guide the network. The method in  [7]  further improves the results from OSVOS with the help of Multi Networks Cascade (MNC)  [20]. All these approaches work image-per-image without explicitly checking for temporal coherence, and therefore can deal with large displacements and occlusions. However, since their backbone is a network used for semantic segmentation, they cannot distinguish between instances of the same class or between objects that resemble each other.
In  [37] , the networks introduced in  [74]  are adapted to the purpose of multiple objects segmentation through the heavy use of data augmentation, still using annotation of the first frame. The authors of this work also exploit motion information by adding optical flow information to the network. This method is further improved in  [46]  by using a deeper architecture and a re-identification module to avoid propagating errors. This last method has achieved the best performance in the DAVIS-2017 challenge  [66]. With a different approach, Hu et al. [31]  employ a recurrent network exploiting the long-term temporal information. Recently, with the release of a large-scale video object segmentation dataset called YouTube Video Object Segmentation (YouTube-VOS) [83], many further improvements have been made in the field.
The next paragraphs focus on our networks' architectures and on semi-supervised training techniques that we use to circumvent the training difficulty. Multi-OSVOS network. The training technique of our semantic segmentation networks is mainly inspired from the OSVOS network  [8] , a breakthrough which achieved the best performance in DAVIS-2016 challenge  [63]. The OSVOS network uses a transfer learning technique for image segmentation: the network is first pre-trained on a large database of labeled images. After training, this so-called parent network can roughly separate all foreground objects from the background. Next, the parent network is fine-tuned using the first frame annotation (annotation mask and image) in order to improve the segmentation of a particular object of interest. OSVOS has proven to be a very fast and accurate semi-supervised method to obtain a background/foreground separation.
Our Multi-OSVOS network uses a similar transfer learning technique, yet with several important differences: • Our network can identify different objects separately (instead of a simple foreground/background segmentation) and provides a smart dilation mask, i. e. a smart border which covers the interfaces between segmented objects and the background, and reduces a lot the number of false negative pixels. The ground truth for this smart dilation mask is defined in the fine-tuning step by a 7-pixels wide dilation of the union of all object masks. • Unlike OSVOS, which uses a fully convolutional network (FCN)  [47] , our network uses the Deeplab v2  [10]  architecture as the parent model since it outperforms FCN in some common datasets such as PASCAL VOC 2012  [25]. • In the fine-tuning training step we adopt a data augmentation technique in the spirit of Lucid Tracker  [37] : we remove all objects from the first frame using Newson et al's image inpainting algorithm  [55] , then the removed objects undergo random geometric deformations (affine and thin plate deformations), and eventually they are Poisson blended  [65]  over the reconstructed background.
These should not be exactly the same as the output masks, otherwise the network would learn to perform a trivial operation ignoring the RGB information. Such inaccurate input mask predictions are created by applying relevant random degradations to ground truth masks, e. g. , small translations, affine and thinplate spline deformations, followed by a coarsening step (morphological contour smoothing and dilation) to remove details of the object contour; finally, some random tiny square blocks are added to simulate common errors in the output of multi-OSVOS. The ground truth output masks in the training dataset are also dilated by a structuring element of size 7 × 7 pixels in order to have a safety margin which ensures that the mask does not miss any part of the object.
Results We first evaluate our results for the segmentation step of the proposed method, for which we provide quantitative and visual results and comparisons with state-of-the-art methods. We then provide several visual results for the complete object removal process, again comparing with the most efficient methods. These visual comparisons are given as isolated frames in the paper and it is of course more informative to go for the complete videos in the supplementary material, see https://object-removal. telecom-paristech. fr/. We consider various datasets: we use sequences from the DAVIS-2016  [63]  challenge, from the MOViC  [14] , and from the ObMIC [86] datasets; we also consider classical sequences from the papers  [26]  and  [54]. Eventually, we provide several new challenging sequences containing strong appearance changes, motion blur, objects with similar appearance and possibly crossing, as well as complex dynamic textures.
Implementation details For the segmentation part, we use the Deeplab v2  [10]  architecture for the multi-OSVOS and refining networks. We initialize the network using the pretrained model provided by  [10]  and then adapt it to video using the training set of DAVIS-2016  [64]  and train-val set in DAVIS-2017  [66]  datasets (sequences from which we exclude the validation set of DAVIS-2016). For the data augmentation procedure, we generate 100 pairs of images and ground truth from the first frame annotation, following the same protocol as in  [37]. For the patch-based mask propagation and mask linking, we evolved from the implementation of  [68]  and  [21] , respectively. For the video inpainting step, we use the default parameters from our previous work  [39]. In particular, the patch size is set to 5, and the number of levels in the multi-scale pyramid is 4.
Object segmentation For the proposed object removal system, and as explained in detail above, the most crucial point is that the segmentation masks shall completely cover the considered objects, including motion and transition blur. Otherwise, unacceptable artifacts remain after the full object removal procedure (see Figure  13  for an example). In terms of performance evaluation, this means that we favor recall over precision, as defined below. This also means that the ground truth provided with classical datasets may not be fully adequate to evaluate segmentation in the context of object removal, because they do not include transition zones induced by, e. g. , motion blur. For this reason, recent video inpainting methods that make use of these databases to avoid the tedious manual selection of objects, usually start from a dilation of the ground truth.
Again this makes sense since our method has learned a dilation whose equivalent radius is 15. Results are displayed in Figure  8. As can be seen from this Quantitative evaluation of our object segmentation method compared to other state-of-the-art methods, on two multiple objects datasets (MOVICs  [14]  and ObMIC [86]) figure, our method is the best in terms of recall, and the recall is increasing significantly with respect to the dilation size. With the sophisticated onAVOS method, on the other hand, the recall increases slowly, and the precision drops drastically as the dilation size increases. Basically, these experiments show that the performances achieved by our system for the full coverage of a single object (that is, with as few missed pixels as possible) cannot be obtained from state-ofthe-art object segmentation methods by using simple dilation techniques.
Multiple objects segmentation Next, we perform the same experiments for datasets containing videos with multiple objects. Since the test ground truth was not yet available (at the time of this writing) for the DAVIS-2017 dataset and since our network was trained on the train-val set of this dataset, we consider two other datasets: MOViCs  [14]  and ObMIC  [86]. The datasets include multiple objects, but only have one label per sequence. To evaluate the multiple object situations, we only kept sequences containing more than one object, and then manually re-annotated the ground truth giving different labels for different instances. Observe that these datasets contain several major difficulties such as large camera displacement, motion blur, similar appearances, and crossing objects. Results are summarized in Table  3.
From this table, roughly the same conclusions as in the single object can be drawn, namely the superiority of our method in term of recall score, without sacrificing much the precision score. Some qualitative results of our video segmentation technique are shown in Figure  7. In the first two rows, we show some frames corresponding to the single object case, on the DAVIS-2016 dataset  [63]. The last three rows show multiple objects segmentation results on MOViCs  [14] , ObMIC [86] and Granados's sequences  [26]  respectively. We observe on these examples that our approach yields full object coverage, even with complex motion and motion blur. This is particularly noticeable on the sequences KITE-SURF and PARAGLIDING-LAUNCH. In the multiple objects cases, the examples illustrate the capacity of our method to deal with complex occlusions.
Conclusion, limitations and discussion In this paper, we have provided a full system performing object removal in videos. The input of the system is made of a few strokes provided by the user to indicate the objects to be removed. To the best of our knowledge, this is the first system of this kind, even though the Adobe company has recently announced to be developing such a tool, under the name Cloak. The approach can deal with multiple, possibly crossing objects, and can reproduce complex motions and dynamic textures. Although our method achieves good visual results on different datasets, it still suffers from a few limitations. Fig. 13  Results of object removal using masks computed by OSVOS (top) and ours (bottom). From left to right: Segmentation mask, the resulting object removal on one frame, zooms.
Status of τ lepton decay Monte Carlo generator TAUOLA, and its main recent applications are reviewed. It is underlined, that in recent efforts on development of new hadronic currents, the multi-dimensional nature of distributions of the experimental data must be taken with a great care. Studies for H → τ τ ; τ → hadrons indeed demonstrate that multi-dimensional nature of distributions is important and available for evaluation of observables where τ leptons are used to constrain experimental data. For that part of the presentation, use of the TAUOLA program for phenomenology of H and Z decays at LHC is discussed, in particular in the context of the Higgs boson parity measurements with the use of Machine Learning techniques. Some additions, relevant for QED lepton pair emission and electroweak corrections are mentioned as well.
Introduction It is thirty years since the first versions of TAUOLA package  [1] [2] [3] [4]  for simulation of τ -lepton decays and PHOTOS  [5] [6] [7]  for simulation of QED radiative corrections in decays became public. The interfaces of these programs are designed in a way to fulfil needs of different groups of users. The bulk of the code remain written and maintained by the same (main) authors, but contributions originating from other researchers became integrated over the time too. These programs became parts of a wide range of applications, sometimes consisting of quite complicated simulation chains. Some versions of these codes at the moment require an independent maintenance. Some of those code modifications are not prepared and maintained by the program authors. This is often the case of TAUOLA.
Currents and structure of TAUOLA Monte Carlo The TAUOLA and PHOTOS represent long term projects, that is why no substantial changes were introduced since the τ conferences of 2014 and 2016  [8, 10]. Nowadays, the C++ became dominant in many segments of the code for programs and for the tests as well. The structure defining part of the code was modified, to prepare for more modular code organization. In fact only small changes in the text of the code were necessary, that is why, numerical tests if introduced changes were bug free were simple. Now, once decision to re-write the whole code into C++ is finally taken, it can be performed in quick and well controlled steps, each to be tested separately. See Ref. [9]  for more explanations. Let us point, that language change for TAUOLA is more complex than it was for PHOTOS  [11].
We expect it of importance for modeling of τ decays as well. Especially in this case, proper evaluation of multidimensional distributions used for fits and in particular systematic uncertainties may be of a great importance. We study if Monte Carlo matrix element dependent event weights may be useful. Finally let us recall some limitations on physics precision of the TAUOLA resulting from the program design. Up to a precision level of about 0. 2% choice of hadronic currents play the central role for systematic uncertainties. That is why, corresponding parts of the program represent well defined, easy to replace, building block. Confrontation of model's predictions with experimental data with the help of multi-dimensional distribution has to be central for the future project developments. This was pointed already in Ref.
PHOTOS Monte Carlo for bremsstrahlung: its systematic uncertainties Over the last two years no major upgrades for functionalities were introduced into PHOTOS Monte Carlo, except introduction of emission of lepton pairs. Published documentation of the program  [11] , correspond to the present day head version  [13]. Numerical tests for pair emission algorithm are published  [14] , and updates on the program necessary due to evolution how event records contents have to be understood are continuously updated, see changelog. txt of Ref. [13]. A possibility for the user modification of the Z → l + l -γ matrix element is prepared. The appropriate matrix element can be replaced by the user own. In this way, e. g. effects of non-standard-model matrix elements or leading contributions of the loop electroweak corrections, can be studied.
For the final states with τ leptons, methods to manipulate their spin state can be used to optimize the measurements. The τ is the only lepton of its spin accessible to the measurements. From the first paper  [15]  TauSpinner was oriented for such applications. The response due to changes in Z, W or H decays, represents a valuable information. Let us review the status. General structure of the tool was recently documented in  [16] , but let us nonetheless point to the general idea. The program is calculating weights corresponding to changes of the physics assumption. Ratios of matrix elements squared for the compared options is used for that purpose. As an input, events stored on the data file are used. No changes for kinematical configurations are introduced. In that respect nothing has changed since 2016 τ conference and remain as in Ref.