dataesr/scientific-paragraphs-categorization huggingface dataset:
The end result of the overall analysis procedure is simply a list of events which can be used as the main ingredient for time delay or anomalous spectral feature searches. To be complete, it is necessary to stress that some of the methods listed below also require to take into account detector response functions such as energy resolution and effective area. 5. 1. 3. 1. Basic time profile analyses. The goal of time profile analyses in the context of searches for QG induced time delay effect, is mainly to look for energy-dependent time delays. This type of search has been performed in many different ways, either starting directly from individual photon properties or from energy and time distributions obtained from a larger data set. A simple method consists in comparing directly the time when the highest energy photon in the sample is detected with the time when the detector is triggered  [1212, 1213].
Then the sharpness of the corresponding light curve is computed as S(τ n ) = N -ρ i=1 ln ρ t i+ρ -t i , (61) where ρ is a parameter of the method and N the number of photons in the sample. It allows to probe the light curve with different levels of details and has to be carefully chosen, usually using a large number of simulated data sets. The best estimate of τ n is obtained maximizing function S (Figure  10 , right). in the case of a linear LIV effect. Left: distribution of spectral lags l i,j. A kernel density estimate (black curve) is used to locate the maxima of the distribution. Right: sharpness as measured by the sharpness-maximization method algorithm. The maximum of the curve corresponds to the best estimate for the LIV parameter. Reprinted with permission from  [1218]. In the case of the ECF method  [1212, 1213, 1215] , the idea is to restore the lost power of an EM pulse due to its propagating trough a linearly-dispersive medium.
Mrk 501 is a blazar at z = 0. 034. The usual Jacob-Piran formula for the relative energy-dependent time delay is confronted to the data, using the Maximum Likelihood Method. Since the data have a large signal-to-background ratio, the contribution of the background is neglected. Events whose energy is in the range 1. 3-3. 25 TeV are used to fit the emission time distribution at the source while the events with energy above 3. 25 TeV serve to calculate the likelihood and obtain the best estimate for the time delay. Statistical as well as systematic uncertainties are taken into account. The obtained 95% CL limits are E QG,2 > 8. 5(7. 3) × 10 10 GeV in the subluminal (superluminal) case. 5. 1. 4. 2. Tests with pulsars. Pulsars (see Sec. 3. 2. 6) have the following advantages for LIV searches: they are not random events thus allowing for crosschecks with different instruments and observations for a extended periods of time, which increase the statistics and improves the limits.
Moreover, the origin of the delays, intrinsic or induced by LIV, can in principle be determined since intrinsic lags should be constant when normalized by the rotation period. In addition, pulsars observed are generally nearby galactic sources, so that the time delay do not depend on a particular lag-redshift relation. The best limits from Crab pulsar data for linear and quadratic departures (both subluminal and superluminal) from dispersion relations have been recently reported by MAGIC collaboration in  [1271]. The Crab pulsar, located at 2. 0 ± 0. 5 kpc, has a rotational period of 33. 7 ms, slowly increasing, is a well-studied pulsar due to its brightness and large spectrum coverage. Its emission involves a main pulse (P1), an inter-pulse (P2) and a bridge region between P1 and P2. The energy spectrum of both pulses can be described by power-laws, extending to ∼ 1.
This is seen in many studies focused on generating new model structures and algorithms that can reduce errors based on generic isolated datasets. This is often a problem with DL due to the disconnect between modelling and real-world applications where the connection of important problems to the larger world of science and society is lost  [9]. This often happens because DL researchers are not always experts in the relevant technical areas and therefore focus more on the model structure rather than their adaptation. Assisting the adaptation to real-world implementation is this study's primary goal to help deliver models that are context-driven and understood by practitioners and not just researchers. This study, therefore, develops distress detection models using road images from secondary roads in France to build models and then carry out experimental testing to improve performance and understand the effects of different practical workflow changes.
The most researched area is Pavement Image analysis in which the focus is split between image classifications, where images are classified based on a distress in the image; and object detection, where distresses are detected using bounding boxes or masks in the image. Image classification has issues as multiple distresses frequently occur in an image making it impossible to label a particular image with one label. With object detection, multiple overlapping objects within an image can be identified, making it is possible to detect multiple overlapping pavement distresses. This is key for road asset databases. Many studies have focused on developing models to indicate the presence or lack of a distress  [20]. Crack detection has largely been seen as the most researched category. They are seen as the most common distress  [21]  and are easier to measure, with the simple required metrics such as the crack's width and length.
Many studies have developed specific DL models for crack detection and analysis using 2D and 3D imagery  [20, [22] [23] [24] [25] [26] [27]. Comparisons are often made to results from image-based systems for crack detection such as CrackIT  [28]  and other available crack databases  [29, 30]. CrackIT is a Matlab based system created to detect and characterize cracks. It must be noted, however, that whilst the major category of focus has been cracks; this is only one distress category. Other studies have considered multiple distress categories and datasets of multiple types. A German team developed a model for pavement distress application based on imagery across the German road network using a mobile mapping system  [31]. The German Asphalt Pavement Distress (GAPS) dataset was developed by this team, which was utilized to detect and analyse six different distress categories based on German road manuals considering their developed neural network.
Recently, the dataset was further updated with better annotations and a larger database  [32]. Other teams have considered the use of street images where DL models are used to detect distresses, mainly cracks  [33]. Using Google street view images, others have developed models to detect multiple distresses using the You Only Look Once (YOLO) model and network  [34]  and subsequently used image segmentation to detect the crack size  [35]. Studies have also been carried out considering fourteen distress categories with the application of semantic segmentation and object detection algorithms on a dataset in Naples, Italy  [36]. This was used to create a decision support system based on the occurrence of predicted distresses. There is also a large dataset of road surfaces called the KITTI dataset  [37] , however, this dataset was created primarily for assisting with automated driving research.
Other studies have considered the use of transfer learning using models initially trained in the ImageNet database. Using these available models, models were developed to detect cracks  [20]. The use of smartphones for capturing datasets has also been considered with one of the applications being the use of a DL model on image patches collected from a smartphone to detect cracks  [38]. There has also been the development of a Japanese road distress database using smartphones  [39]. This has provided the basis for competitions such as the Global Road Damage Detection Challenge  [40] , where different models are submitted to obtain higher performances using different configurations. This has led to different configurations using different base networks and models having the same goal to detect distresses  [41, 42].
Data collection A database containing 56,783 images captured across approximately 100 km of secondary roads in France was used. The images were taken during road surveys using a webcam affixed to a survey vehicle and aimed directly at the pavement's surface in a top-down view as shown in Fig. 1. The webcam used was a Logitech C920 Pro with a max resolution of 1080p equipped at a vertical distance of 1. 8 m from the surface. This means the road surface represents 2 mm/pixel on an image with the actual space taken being 1. 5 m in length and 2 m in width. This configuration offers a good compromise between the minimum image resolution and ground measurement area and importantly accordingly with the needs of road managers for rural French roads. After an overview of the database, it was determined that five distress categories would be considered.
Examples of these are shown in Fig. 2. The next step was analyzing the complete dataset to pinpoint images containing the relevant distresses. This was done manually and the distresses were identified and recorded using the LabelImg open-source software  [44]  in the Pascal VOC  [45]  xml format. For annotations Complex cases (such as alligator cracking inside block cracking) are not labelled as it is considered as the same in the French methodology for secondary roads The distresses identified are detailed in Table  1. This dataset amounted to 2325 images with 3280 annotations (1. 4 per image) out of the complete set (4. 09% of images).
The largest difference between the models is that the first three take a single-stage approach using only one snapshot of the image to detect multiple objects within the image, whilst the final two use a region proposal approach wherein two steps are employed, the first being to generate region proposals and then the second where the predictions are made. For the training, Fig. 3  highlights the key stages of the process, wherein the files necessary for the training are developed using various scripts to be used in the TensorFlow environment. The tf record format which is used for the model development is key as the information on the annotations and their locations are embedded within these files. Additionally, it is important to point out that the dataset was split for training, validation and testing in the ratio of 70:20:10 which is typical for this type of model development  [50].
The split was made ensuring that a similar distribution of the distresses was in each dataset to avoid overfitting or inflated results at any point. The percentage image split of each dataset is provided in Table  2. From this, it can be seen that the distributions are done to ensure no set is inflated with any particular category. The distributions cannot be exact, because of multiple distresses in images and overlaps, which is how they appear in the real world. The critical stage in the process is the tuning, which occurs, based on the evaluation results of the model during the training process. This is monitored using Tensorboard, which allows the user to check the losses and other metrics of evaluation during the training and evaluation stages of the model. The workstations utilized for modelling was a Windows 10 PC with an NVIDIA Quadro P4000 GPU (8 GB ram) and total CPU memory of 32 GB @ 3.
These boxes should be consistent with the location, shape and size of the objects that the model is attempting to detect. This is important as the overlap between the boxes generated and the annotated training set is used for the model to understand the particular features of the annotations. As a result, it is important to establish a clear understanding of the space occupied by the distresses to produce effective models. The traffic direction may for example be different in different countries causing distresses to be localized in different lanes. This is another clear justification for the inclusion of this study on anchor-box location for practical applications. For this, the images within the used database were analyzed. To this end, the CSV files generated from the annotated images containing the information on the bounding box location and sizes were analyzed.
Anchor-box optimizations In performing the anchor-box optimizations as described in section 3, the process resulted in optimized aspect ratios and sizes, which are shown along with the default values in Table  4. An example of the new anchor-boxes, when visualized on the image, is given in Fig. 6. From the image, it is seen that the anchor-boxes are more suitable to the shape and size of the annotated distresses as they appear more similar in size. This is key to quicker training and higher developed accuracies for practical application. Future work will carry out further testing on the effect of using optimized anchor-box configurations. After the setup and in the first instance of testing, the results of the full dataset with the different base models and utilizing the five previously referenced classes are shown in Fig.
7. Considering the results of these models, there were high levels of precision achieved for the majority of distresses but lower levels of recall, particularly for the longitudinal cracking and block, cracking    categories. It was also noted that the base models of ssd_mobile-net_v1_fpn_shared_box_predictor and ssd_mobilenet_v1_ppn_shar-ed_box_predictor both performed quite poorly with the former of two seeming unable to produce usable results at least under the used configurations of the models. This may be due to the models trying to speed up detection compared to the regular SSD configuration  [54]  and missing the smaller detections of pavement distresses contained in this dataset. It should not be ruled out that other configurations could yield usable results and therefore further test of these models could remain an option.
Case studies to test the use of augmentations In an attempt to increase the dataset size and produce better results (given the tendency of DL to function better with more data  [55] ), augmentations were considered and applied to the images before training. Augmentations are variations of an image, wherein particular image characteristics are modified. These changes can vary from the color, size or rotation of the images and this process is used to increase the size of the dataset. Augmentation is generally assumed to increase the variability and diversity of the data which will enable producing a more robust DL model, considering the many variances that can exist in the specific data  [56]. This study aimed to explore this and understand the effects of using various augmentations for the given dataset.
The images show how light can make a significant difference in identifying the presence of distresses. In total, nine different situations were considered for the case study with results shown in Fig. 10  and which were done using Python scripts to add the changes to the images. It should be noted that horizontal and vertical flips were performed but these flips are mirror flips wherein the orientation of the transverse or longitudinal cracks are not interfered with and instead the crack would simply appear in the other half of the image. This is important as the use of an incorrect flip or rotation could result in a transverse crack noted as a longitudinal one or vice versa. When analyzing the results and changes of the augmentations, the changes varied across the distress types and the results are shown in Fig.
When examining photos, it was seen that this augmentation showed mixed performance as some images appeared better when brightened whereas some in their original instance suffered from too much light and therefore darkening helped. This led to the consideration of exposure, contract and pixel value changes as these alterations focus on the particular allocation of pixels within the image and not a generic application to the entire dataset. These augmentations are however not natural and therefore particular attention has to be paid to their use. iii. The use of horizontal and vertical flips appeared to provide a marginally positive response in performance. iv. A combination of flips with random changes to the contrast proved to be the best combination of augmentation for this test. v. The augmentations generally had little positive effect on increasing the performance of the models to detect longitudinal cracking.
Conversely, the faster-rcnn based model had more false positives that could be removed and therefore increasing the confidence allowed the model to reduce the errors. It must be restated here that the models were all tested using the same test dataset, which was not revealed to the model until the testing phase and therefore represented a test akin to what could happen in practice. This is stated to establish that the effect was directly related to the models' performance on these types of images and detections.
The main goals of this work are to understand the impacts of changing particular aspects of the DL workflow in a practical and effective manner, especially for these secondary road types. To do this, several DL models are tested with varying optimization and sensitivity tests to understand the effects on the performance when detecting particular distresses for secondary roads. The experimental results from the case studies demonstrate the value of testing strategies considering a real-world approach and with real datasets, and showcase the performance under different practical situations. The case studies help to provide an understanding of how DL models react to different changes in the context of pavement engineering and application. The results can be used to create better workflows for practical implementation and they offer a new view of the typical DL implementation and workflow, showing the novelty of the research.
The results also confirm that the models, using only low-cost cameras, can be accurate for the intended purpose as opposed to typical visual inspections. The use of DL techniques is likely to take over the domain still held by common images segmentation methods in particular when the quality of images is quite low. Although the initial results in this study were interesting, better robustness will be sought through both an optimization of several parameters in the DL workflow and a further expanded sensitivity study. Future tests will consider comparisons to results using higher quality images, more datasets of similar roads conditions and the implementation of more distress categories such as glazing/bleeding, representing more visual road deformations.
Acknowledgments The authors would like to acknowledge and thank the county council of  La Manche (France)  for assisting with the data collection and  Mr. S Todkar , of  SII-COSYS, Université Gustave Eiffel , for help in proofreading the work. The research presented in this paper was carried out as part of the  SMARTI ETN  project under the  H2020-MSCA-ETN-2016. This project has received funding from the  European Union 's  H2020 Programme for research, technological development and demonstration  under grant agreement number  721493.
In the case of cancer-related studies, the lack of methods for tumor detection and segmentation specific for small-animal preclinical studies, results in manual and time-consuming segmentation by experts. In the case of lung tumors, the development and validation of automated solutions is further hampered by the fact that there are no public databases of mouse or rat MR lung images. Even for human lung images, to our knowledge, there are only two public databases (Lung Image Database Consortium image collection LIDC-IDRI and ELCAP Public Lung Image Database), both based on CT scans, which limits the development of AI approaches. In this study we developed a complete methodological pipeline for the automatised analysis of lung and metastases in mice, consisting of an MR sequence for imaging acquisition and a deep learning method for lung and metastases segmentation.
This deep learning method enables the measurement of metastases volume in lungs at a given time and the assessment of their longitudinal growth. To ensure reproducibility and foster the use of our method by a large scientific community, all relevant software resources and MR image data are made publicly available. As such, our full pipeline consisting of an MR sequence and downstream automatic image quantification with DeepMeta, constitutes the first step toward standardization of lung and metastases imaging and segmentation in mice, while our MR image database constitutes the first publicly available data resource to foster further methods development.
Image reconstruction For image reconstruction we followed the procedure described in  (Ribot et al. , 2015). Briefly, the amplitude of the FID signal was characteristic of the animal's respiration and made it possible to identify stable phases and peaks that reflected non-corrupted and motion-corrupted data, respectively. Peaks were identified using the "peak detection" function in MatLab and were then used to delete approximately 30% of the echoes distributed around these peaks. Stacks were then retrospectively reconstructed by deleting the respiration-corrupted k-space lines (or echoes) corresponding to respiration peaks. The different k-spaces obtained for each repetition were then averaged so that missing lines from one k-space could be filled by the k-space lines from another repetition.
Annotation RENCA dataset yielded a total of 24576 slices with 128 × 128 pixel size. Metastases were visible on 62 of the 186 3D stacks. Annotation of the RENCA dataset has resulted in a total of 8156 slices for the lungs segmentation, 1296 slices for the metastases segmentation and a total number of 5762 images with associated multiclass masks (see Table  1 ). Due to the imaging of the same mice at different timepoints during the development of metastases, large variation in their volume was observed, ranging from 0. 0188 mm 3 to more than 200 mm 3 (see Figure  2  for an example of slice annotation).
Data augmentation Notice that the resulting number of slices is quite small for training a deep neural network. Consequently, we created a data augmentation pipeline, composed of rotations and elastic transformations, which applied a factor 8 to the dataset volume (these numbers are recapitulated in the column "#Total after augmentation", in Table  1 ). Rotations (90 °, 180 °, 270 °) ensured that the network was exposed to mouse slices in every position and the elastic transformation ensured a better robustness of the network by providing a training dataset that contains slices subjected to small deformations. Together these augmentation steps helped to reduce overfitting and improved the quality of the segmentations.
Test dataset To create the test dataset, we selected four 3D stacks representative of the complete RENCA dataset (representing a total number of 512 slices), sampled from our dataset of 3D image stacks. It is composed of one control 3D stack without metastasis, two 3D stacks with small metastases and one 3D stack with large metastases, defined as under or above 0. 4 mm 3 , respectively. This test dataset was annotated by two different annotators in order to obtain a precise inter-observer variation and to quantify whether and to what degree the possible associated bias impacts the training.