dataesr/scientific-paragraphs-categorization huggingface dataset:
oup. com/database/article-abstract/doi/10. 1093/database/baz099/5585578 by INRA -Institut National de la Recherche Agronomique user on 07 November 2019 developmental stages (mainly in a context of meat production) or immune response (comparison of healthy animals with those suffering from mastitis, a widely-studied pathology in ruminants; Figure  5 ). The different issues addressed in the publications were implemented as filtering options in the RumimiR database. Thus, 40 different breeds (17 bovine, 14 caprine and 9 ovine breeds, both meat and dairy) are represented along with about 30 tissues and body fluids such as milk  (29, 30) , adipose tissue (4), mammary gland  (31) (32) (33)  and ovaries  (22, 34, 35). Around half of the microRNAs described in the RumimiR database are associated with a single tissue (these 'specific' microRNAs are highlighted by the symbol ' ').
While the probability of a microRNA being a false positive decreases if it is described in several studies, some of the microRNAs described only once can be specific to a breed, tissue, physiological stage or particular condition. Age was mentioned in 38% of the publications, and more than 30 different ages have been listed. Some studies described the differential expression of microRNAs throughout development, and at different ages, which is why several ages might be considered in a single publication. The same was true of lactation stages. All these figures will increase as the database is updated. The numbers of microRNAs common to all three ruminant species, or sequences common to these species and to human and mouse sequences, or specific to each species, are presented in Figure  6. Between 80.
24% and 88. 82% are speciesspecific microRNAs. RumimiR is limited to three ruminant species, unlike miRBase, which is the most widely used and complete Downloaded from https://academic. oup. com/database/article-abstract/doi/10. 1093/database/baz099/5585578 by INRA -Institut National de la Recherche Agronomique user on 07 November 2019  database in terms of the number of species covered (plants, animals and viruses). However, this restriction enabled us to generate a more detailed database containing all the microRNAs described in different publications as well as numerous features. Six times more microRNAs are listed in the RumimiR database for bovine, caprine and ovine species than in miRBase: 10 715 in RumimiR versus 1614 in miRBase. Although the latest miRBase release (v22) is quite recent (October 2018), it included only 5% of the data available on deep sequencing of small RNA  (14).
The risk of assembling all microRNAs mentioned in the literature is still to include false-positive microRNAs, which is why this risk has been evaluated for each sequence. Thus, RumimiR offers a complete and more precise microRNA database for cattle, goats and sheep and should therefore be of value to scientists working on livestock species. The RumimiR database is extensive because it lists all the features mentioned in the publications (unlike miRBase and other databases), the data are homogenized (with blast on the same reference genomes for example), and because information is included about the identity, small RNAs etc. In addition, the RumimiR database combines the results of a multitude of studies in a single location.
User interface The web interface is user friendly, allows visualization of all the microRNAs described in bovine, caprine and ovine species and provides at minimum the genomic position Downloaded from https://academic. oup. com/database/article-abstract/doi/10. 1093/database/baz099/5585578 by INRA -Institut National de la Recherche Agronomique user on 07 November 2019 (chromosome, start and end) of each microRNA. Some or all the columns can be selected to obtain the sequence, name, tissue or other features of the study and of the microRNAs, by clicking on the appropriate name in the 'show/hide columns' box (Figure  7 ). The features available for the various microRNAs, such as the sequence, related publications, name, isomiRs, family, tissue origin, breed, condition studied and others are listed in the 29 columns shown in Table  1.
Users can also visualize those microRNAs of solely personal interest by selecting the corresponding column (sequence and/or tissue and/or breed etc. ) and then using the 'search' box to define different options such as 'by position' (chromosome, start and end), 'by source' (species, breed and tissue) or 'by feature' (conditions, method and software; Figure  7 ). The above-mentioned criteria can also be applied to filter all the mature microRNAs listed. A sequence or key word can also be entered in the 'Search' box to extract a list of the relevant data in the results table. The name of each microRNA in the 'name' column is the one given in the original publication. If specific requests are selected, the appropriate filtered data appear and can be downloaded in several formats (Excel, CSV, Fasta or GFF).
Users can thus obtain all the data that they require, appropriately filtered and rapidly. They can also download the entire database without applying any filters. For clarity, some statistics and graphs, which summarize the data presented in the database (Supplementary Figure  4 ), are provided in the online version. These include, among others, the date of the latest update, the number of sequences listed and the number of publications involved. The distribution of microRNAs by species and by breed is presented graphically, as is the distribution by tissue origin. RumimiR also includes an alignment tool so that sequences can be submitted for analysis and the corresponding microRNAs sorted according to the resulting hits.
2. 0). RumimiR has been successfully tested on Chrome (version 49 and later) and Firefox (version 57 and later). The data, in JSON format, Downloaded from https://academic. oup. com/database/article-abstract/doi/10. 1093/database/baz099/5585578 by INRA -Institut National de la Recherche Agronomique user on 07 November 2019.
Acknowledgements The authors are grateful to  C. Gaspin ,  C. Leroux  and  S. Marthey  for their critical reading of the manuscript and to  M. Boussaha  for his helpful discussions. Downloaded from https://academic. oup. com/database/article-abstract/doi/10. 1093/database/baz099/5585578 by  INRA -Institut National de la Recherche Agronomique  user on 07 November 2019.
Localizing objects is a key challenge for robotics, augmented reality and mixed reality applications. Images taken in the real world feature many objects with challenging factors such as occlusions, motion blur and changing lights. In manufacturing industry scenes, a large majority of objects are poorly textured or highly reflective. Moreover, they often present symmetries which makes the localization task even more complicated. PoseNet is a deep neural network based on GoogleNet that predicts camera poses in indoor room and outdoor streets. We propose to evaluate this method for the problem of industrial object pose estimation by training the network on the T-LESS dataset. We demonstrate with our experiments that PoseNet is able to predict translation and rotation separately with high accuracy.
Moreover, challenging objects (namely: objects without texture, objects with metallic materials and symmetric objects) have to be correctly handled. In this paper we are interested in localizing industrial objects with high precision for augmented reality applications in the manufacturing industry. We aim to deploy such solutions on augmented reality headsets. To that extent, we selected PoseNet  (Kendall et al. , 2015)  as the backbone architecture and trained it on the T-LESS dataset  (Hodaň et al. , 2017)  as it features 30 industrial objects and it consists in very detailed training data which perfectly suits our needs in order to train a CNN to localize objects. In this particular setup, we will focus on the influence of the two parameters of PoseNet in order to find the best weighting to ensure optimal pose estimation.
These two parameters weigh the importance of the translation estimation and rotation estimation respectively. Therefore, we provide a thorough evaluation of their impact on the training using PoseNet when applied to the T-LESS dataset. Our experiments will show that the network is not able to correctly learn both modalities together as one modality always overtakes the other. Overview of the Paper. In the next part we present the state of the art in object localization and available datasets. Then we detail the experimental protocol used measure if translation and rotation can be learned jointly, starting with the dataset: T-LESS  (Hodaň et al. , 2017) , the network proposed by A. Kendall and al: PoseNet (Kendall et al. , 2015) , the loss function optimized during training, and the accuracy measurement performed.
Therfore, we chose the PoseNet architecture as the backbone of the presented work. The Extented ACCV  (Crivellaro et al. , 2015b ) dataset is designed for training methods on 3D models. However, it has shown limitations in its usage as each image has to be rendered to generate training dataset. Datasets for autonomous driving such as  (Geiger et al. , 2012)  have been widely used because of their high quality. However, such datasets focus on outdoor scenarios at metric precision. On the other hand, the T-LESS  (Hodaň et al. , 2017)  dataset focuses on industrial objects with centimetric precision in the groundtruth. Moreover, it depicts objects without textures that are typically found in industrial environment (electrical parts). Thus, the T-LESS dataset perfectly fits our requirements in terms of precision and object diversity.
EXPERIMENTAL SETUP We aim to find the 3D position of industrial objects in RGB video for augmented reality applications using deep learning techniques. The goal of the experiments reported in this paper are to show that the Euclidean loss does not allow training the  (Kendall et al. , 2015)  network to predict accurate pose estimation of translation and rotation on the selected dataset. In this section we first present in depth the dataset we use: T-LESS  (Hodaň et al. , 2017) , then we describe the architecture of the network proposed in  (Kendall et al. , 2015) , followed by the loss function used to train the network, and finally the accuracy metrics that are applied in our experiments. T-LESS Dataset. The T-LESS dataset  (Hodaň et al. , 2017)  features objects with no discriminating colors, metallic parts, intern similarities and symmetries.
Therefore, it is a very challenging dataset to work on. The training set only depicts a single object on a black background in each image. On the opposite, the testing set consists in many objects on various backgrounds for each image. As we are focused on selecting the optimal α and β parameters of the loss function Equation (  1 ), the network is trained on a split of the training set and accuracy is computed on the rest of the training set. PoseNet Architecture. PoseNet  (Kendall et al. , 2015)  is among the first networks that successfully tackled the problem of camera re-localization. It is a 20 modules deep convolutional neural network. The fact that it based on GoogleNet  (Szegedy et al. , 2015)  gives it interesting properties: low memory usage (only 4 million parameters), fast evaluation and the ability to work with features at different scales.
One way to compute the rotational accuracy is using the angle at τ: Acc o ( q,q,τ) = N ∑ i=0 1 N if angle( q, q) ≤ τ×π 180 0 otherwise (3) angle( q, q) = arccos(2 × q || q|| 2 , q ||q|| 2 2 -1) (4) A predicted rotation q (quaternion) is considered correct if the angle (in degree) between the prediction q (quaternion) and the label q (quaternion) is less than a given threshold τ (in degree). Note that angle( q, q) gives an angle in radians thus we convert the threshold τ to radians in the above equation. Complete Setup. We train the PoseNet network  (Kendall et al. , 2015)  to regress translation (3D vector) and rotation (4D quaternion) by optimizing an euclidean loss as defined in Equation (1) like in  (Kendall et al. , 2015)  on data from the T-LESS dataset  (Hodaň et al. , 2017). The images of a subset of the training set of the dataset are re-sized to a fixed size of 224x224 (RGB).
CONCLUSIONS Regressing both translation and rotation using a single regression branch is a very hard task for a network as it has to model different concepts (with different units) at the same level of abstraction. They are no values of the weighting parameters α and β that enable the network to learn jointly rotation and translation. If α > 0, conflicts between translation and rotation occur, and translation overtakes rotation. The only effect of increasing β was to delay the overtaking of the translation. The fact that we can see so clearly the PoseNet  (Kendall et al. , 2015)  forgetting one of the two modality in favor of the other in the results proves that when used on the T-LESS dataset it is failing to learn translation and rotation jointly. This is mainly because the scale, and the range of viewpoints of the data is not the same as the original use case.
Pose-Net is meant for large scene where object are bigger and observed from further away and from less point of views. In our case the positional data in millimeter leads to large translation error that is not bounded. The rotational loss on the other hand is bounded by definition. Thus the translation always takes over in the training at some point. Perspectives. To solve the problem of the euclidean loss not being able to learn jointly the translation and rotation, one could investigate the use of other losses that already exist, such as 3D geometrical loss for application that require absolute precision, or 2D geometrical loss computed in the image plane that are suitable for augmented reality applications  (Brégier et al. , 2016). These losses are very interesting because they can be modified to include symmetries.
We have constructed a new timescale, TT(IPTA16), based on observations of radio pulsars presented in the first data release from the International Pulsar Timing Array (IPTA). We used two analysis techniques with independent estimates of the noise models for the pulsar observations and different algorithms for obtaining the pulsar timescale. The two analyses agree within the estimated uncertainties and both agree with TT(BIPM17), a post-corrected timescale produced by the Bureau International des Poids et Mesures (BIPM). We show that both methods could detect significant errors in TT(BIPM17) if they were present. We estimate the stability of the atomic clocks from which TT(BIPM17) is derived using observations of four rubidium fountain clocks at the US Naval Observatory. Comparing the power spectrum of TT(IPTA16) with that of these fountain clocks suggests that pulsar-based timescales are unlikely to contribute to the stability of the best timescales over the next decade, but they will remain a valuable independent check on atomic timescales.
THE DATA SET AND ANALYSIS The pulsar timing analysis is a well-developed process of fitting a model, which describes both the pulsar and the propagation of the pulses to Earth, to a set of observed pulse ToAs (see  Hobbs et al. 2006  for details of how this process is implemented within the tempo2 software package). For this work, we use ToAs from the first IPTA data release by  Verbiest et al. (2016)  and  Lentati et al. (2016). The data set consists of observations of 49 pulsars from the EPTA telescopes (Lovell, Effelsberg, Westerbork and Nançay), the NANOGrav telescopes (Arecibo and Green Bank) and the Parkes telescope. Of the 49 pulsars 1  , 13 are solitary and 36 are in binary systems 2. The pulsars have been observed in numerous observing bands from around 300 to 3000 MHz. The observations are performed with an irregular observing cadence of 2 to 4 weeks (depending on the pulsar and the telescope).
The covariance matrices of all sources of noise must be estimated to optimise the least-squares (or maximum-likelihood) fits and to obtain valid uncertainties on the parameters of the timing model. Recent reviews of such issues in pulsar timing residuals in relation to PTA experiments were recently published by  Hobbs & Dai (2017)  and  Tiburzi (2018). Verbiest et al. (2016)  provided the first IPTA data release in three different forms: (A) a "raw" format with only minimal pre-processing, (B) a format usable for standard tempo2 analysis and (C) a data set using the Bayesian parameterisation for temponest analysis. These data sets are available from http://www. ipta4gw. org. The primary difference between these three data sets is the way in which the noise models are included. For our analysis we started with the IPTA data combination 'A', which is the raw form without any noise models.
The observational coverage is shown graphically in Figure  1. Appendix A describes our scripts for processing the data and forming the initial noise model, with Table  A1  listing the noise model parameters. We note that, as described in the Appendix, some of the PPTA observations were missing from the IPTA data release. These observations were included in our frequentist-based analysis. With the final parameter and arrival time files, we applied the  Hobbs et al. (2012)  technique to derive the common mode, CM(t), clock signal using a simultaneous global fit to all pulsar data sets 4. We constrained the fit for the clock signal so that it does not include an offset, linear or quadratic component. We also switch off fitting for the pulsar parameters (apart from the pulse frequency and its first time derivative).
The clock, CM(t), is parameterised by a grid of samples (separated by 0. 5 yr intervals) and linear interpolation between the samples 5. During the work described here, the software tempo2 (available from https://bitbucket. org/psrsoft/tempo2) was updated and the default least-squares solver was changed to a scheme using the QR decomposition 6  from the previous scheme based on the singular value decomposition (SVD). The QR based solver is faster, but we found a small difference in the clock estimate for the full sample of pulsars. The worst case difference was 0. 6 times the error bar. When we used only the eight most significant pulsars the difference between QR and SVD based schemes became insignificant. Further testing revealed that the covariance matrix of the parameters had full rank, but the condition number of that matrix had increased by a factor of 40 when all pulsars were used.
Data and processing for the Bayesian analysis The frequentist method, as described above, relies on the manual modelling of the noise properties, as well as the careful subjective checking of the resulting data sets and fits. The Bayesian method is independent: it fits both the timing model and the noise models for the pulsars simultaneously. This avoids the iteration in the frequentist method, however, it requires that the analyst make a choice of prior distributions for the model parameters. For the Bayesian analysis, we used the IPTA data set without excluding any data from specific observing systems or including the additional PPTA data used in the frequentist method. We did, however, keep the same time-spans as in the frequentist analysis. Because of the significant computational cost in carrying out the Bayesian analysis, we used a restricted list of eight pulsars.
The four panels in this Figure provide noise spectra for important pulsars in the sample. The white noise spectrum corresponding to the measured ToA uncertainties that have been corrected for the EFAC and EQUAD parameters (see Equation  A1 ) are shown as the horizontal black, dotted line for the four listed pulsars. The red noise model as used in the frequentist analysis is shown as the black, solid curve and that from the Bayesian analysis as the red curve. For comparison we also show the red-noise model for J1713+0747 and J1744-1134 given in the IPTA data release B (blue dotdashed curve). The red-noise estimates for PSR J0437-4715 in the IPTA B data set is negligible and no red-noise model was provided for PSR J1909-3744. The frequentist noise modelling includes a corner frequency in the power spectrum; see Equation A2.
Around this time the IPTA data set contains observations from both the Parkes and Effelsberg telescopes and they both indicate a dip of around 2µs. The observations of PSR J1713+0747 around this time are complex. The Parkes data were recorded at multiple observing frequencies from 1285 to 1704 MHz and there is very sparse sampling around the centre of the dip. However, it does seem that an event occurred around this time that is not included in our noise modelling procedures. As we only have a few pulsars contributing to the clock signal at this time, this does lead to an apparent significant clock offset. We know that the timing residuals for PSR J1713+0747 occasionally show unusual behaviour (see, e. g. ,  Lentati et al. 2016 ) that cannot be modelled using simple, power-law red noise models.
This is because  Shannon et al. (2015)  showed that PPTA observations at high frequencies, which were not corrected for DM variations, produced whiter (and lower rms residual) Figure  3. Power spectrum of the IPTA clock signal (light blue) with its mean and 95% confidence levels (horizontal light blue dashed lines) overlaid. Each panel also contains the red noise models for the specified pulsar from this paper (black for the frequentist analysis and red for the Bayesian analysis) and in the IPTA data release (dot-dashed, blue). The spectral density corresponding to the ToA uncertainties for each pulsar scaled by the white noise error factors is shown as the dotted line. The purple dot-dashed line indicates the power spectrum of the clock signal after the specified pulsar has been removed from the sample.
The spectral frequency corresponding to f 0 = 1/[data span] for the specified pulsar is shown as a vertical dashed line. data sets compared with the DM-corrected lower-frequency data. Lam et al. (2017)  also identified excess, observingfrequency-dependent noise in the timing residuals for this pulsar. We therefore believe that uncorrected interstellar medium effects, or instrumental effects are leading to the observed red noise in the IPTA data set for this pulsar. We repeated the frequentist data processing to re-form the clock waveform after removing the IPTA data-set for PSR J1909-3744 and replacing it with the  Shannon et al. (2015)  data set. Variations of 250 ns are seen comparing this clock signal with that shown in Figure  2  between the years 2005 and ∼2008, which covers the range where this pulsar was observed by the Green Bank Telescope.
This ranking highlights that the pulsars with the largest influence on the resulting clock signal are PSRs J1713+0747, J1744-1134, J0437-4715, J2145-0750 and J1909-3744 each producing variations to the pulsar timescale at a level >100 ns for at least one time sample. PSR J1713+0747 has the largest contribution because the IPTA data release includes the early pre-NANOGrav Green Bank and Arecibo data for this pulsar providing a very long span of high quality data. There are 22 pulsars whose removal only changes the resulting timescale by <10 ns. Removing all 22 causes no visible change (the maximum change being 13 ns) to the pulsar-based timescale. The analysis described above shows that the removal of some pulsars changes the resulting clock signal. It does not indicate whether the removal of a pulsar improved the pulsar timescale, or not.