dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the success of visual features-based approaches is very dependent on the quality of the visual vocabulary, and in turn on the prior data and the reliability of extracting the same visual keypoints and descriptors in images with similar viewpoints. The latter is particularly problematic when there are large lighting variations and scene appearance changes due either to seasonal changes or to day and night cycles. Significant performance improvements over Fab-MAP under extreme lighting and atmospheric variations have been achieved within the SeqSLAM system, which relies on the use of a whole image regardless of its content by searching in a pre-learnt database of images for the most similar sequence to the query sequence  [1, 2]. SeqSLAM proved its success with very low resolution imagery  [2, 13] , on large-scale environments of 3000 km over four seasons  [6] , with UV imagery  [4]  and with ConvNet features  [5].
Illustrated in  [27]  is the fusion of visual odometry, used to track the position of a camera-equipped Micro Aerial Vehicle (MAV) flying in urban streets with an air-ground image matching algorithm using a cadastral 3D city model by means of a Kalman filter. Additionally, our approach is based on the fusion of VO and the appearance-based place recognition algorithm SeqSLAM. We adapted SeqSLAM for use in the context of long-term navigation for loop closure detection in order to reduce the drift caused by VO. The database of images is, therefore, created online. However, in the case of learning a geo-referenced database beforehand, the method remains valid and more accurate estimates could be obtained in the global reference frame without assuming a loopy trajectory.
Images are, beforehand, resolution-reduced and patch-normalised to enhance contrast and are, therefore, converted into visual templates. Then, to compare each query image I q from the query sequence Q = (I 1 ,. , I Q ) where Q = |Q| to each database image I D from the database D = (I 1 ,. , I D ) where D = |D|, SeqSLAM calculates the difference score d d = 1 R x R y |I Q -I D | (1) where R x and R y are the horizontal and vertical image dimensions, respectively. The difference scores are assembled into the so-called difference matrix. A next key processing step is to normalize the image difference values (d) within their (spatially) local image neighborhoods. Subsequently, a search for diagonals of low difference values is performed over the defined sequence length (Q) as depicted in Figure  1.
However, one of SeqSLAM's most significant drawbacks is its lack of viewpoint invariance inherited from the use of global image matching. To compensate for a small viewpoint invariance, variable offset image matching and distance-based template learning have been used in the SMART system built upon SeqSLAM  [28]. In fact, to improve performance on traverses with small shifts in lateral pose, a variable offset image matching is used where each query frame (template) is compared to each database frame (template) at a range of offsets with the sum of absolute differences (SAD) performed on the overlapping region and the minimum difference score is used: d = min x max le f t ≤u≤x max right y max up ≤v≤y max down d(u, v) (2) d(u, v) = 1 |X A ||Y A | ∑ x a ∈ X A x b ∈ X B ∑ y a ∈ Y A y b ∈ Y B |A x a ,y a -B x b ,y b | (3) where u and v are the coordinates in the difference score, X A , Y A , X B and Y B are vectors representing the overlapping region of images a and B and x a and x b are the pixel coordinates in a and B respectively (Figure  2 ).
Combined methods give, therefore, an increased robustness to the system. The template library can be loaded beforehand (when a learning has already been done) or created in real-time (without prior learning). In such a case, the template database is populated with the acquired frames from the monocular stream converted into visual templates along with their poses acquired from the VO module. The query sequence is a FIFO buffer holding the n last templates. In fact, when a new frame is captured, features are detected and matched with the last acquired frame and the relative motion is estimated using the VO module, which will subsequently allow the pose of the vehicle to be predicted. The frame is also converted into a visual template and added to the query sequence with the aim of matching with a database template.
Loop Closure-Based Measurement Update In the SeqSLAM algorithm, the input is the query sequence that refers to the last acquired images converted to visual templates. The algorithm selects the path in the difference matrix that connects the query templates to the database ones. The observations are the sequence of n query images I q , and the state space S is the set of database images I d. For each observation there is a state corresponding to one of the database images in the state space as depicted in Figure  4  where primitive high-resolution images are used instead of low-resolution templates for the purpose of clarity. Thus, the observables here are the query and database templates, which are continuously calculated from the monocular sequence and the tracked state x k = (x, y, θ) is hidden.
However, to each database template is connected a pose where this image has been taken and to the query template to be matched is connected the predicted state. Hence, the unobservable state variables are directly obtained from the observable templates and the transformation from the state space to the observation space is a simple linear model. y = Hx. ( 15 ) where H = I 3. In fact, when a place is recognized, the corresponding information of the node (matched in the database) can be used as an observation of the filter.
The Experimental Dataset The Kitti odometry dataset  [15] , recorded from cars driven in urban and rural areas and on highways, consists of 22 sequences where the first 11 are provided with ground truth. In order to evaluate our method, we conducted experiments on three data sequences from the odometry category with a loopy trajectory. Together, these sequences, presented in Figure  5 , include 1249 images of loop closures. The total driving distance for loop closures in these sequences is 1169. 6 m and are highlighted in blue in Figure  5. The locations of loop closures for the Sequence 00 in terms of the amount of travelled meters from the first reference pose are presented in Table  1. We recall that the Kitti dataset provides challenging benchmarks to the computer vision community that are available online www.
Parameters The parameters used for SeqSLAM are those reported in the literature and presented in Table  2. L , X R , Y T , Y R 1, 1, 1, 1 Shift offsets In order to calibrate SeqSLAM and evaluate the uncertainty of the position determination, we used almost 50% of the 1249 images providing two traversals of the same route. The first traversal was used as a database sequence and the second as a query sequence. We assimilated the measurement noise ν k = N (0, R). The matching results were compared to the ground truth. In the case of true positives, the deviations have been chosen to be respectively σ x = 1, σ y = 1 and σ θ = 0. 1 so that the returned result is always within 3 standard deviations from the ground truth.
Timing and Storage In this section, we briefly describe the storage and computational requirements of the system. In fact, all the templates are down-sampled from 1241 × 376 to 64 × 16 (0. 22%) pixels. Consequently, the storage and computational requirements are greatly reduced. To deal with the computational complexity of building a difference matrix between the query and all the database images, a CUDA-based solution was designed in  [36] , allowing less than 30 ms to be achieved for the query and database sequences used in our experiments. This timing is based on the use of a mid-range GPU, the CUDA NVIDIA GeForce GTX 850M running at 876 MHz with 4096 MB of GDDR device memory. The acceleration is mainly based on the allocation of the three major steps of SeqSLAM, namely: the difference matrix computation, the difference matrix contrast-enhancement and the route searching to three GPU kernels that exploit the parallel CUDA threads and thread-blocks and the fast shared memory.
Discussion The template library is created online using navigation information from the VO. That is, when there is not a match in the database, a node is added to this latter with the template and the corresponding information of pose obtained from the VO module. The sequence searching strategy used in this work is based on a regular time interval but could be optimized for a regular distance interval between templates to make the localized sequence searching algorithm more efficient as reported in  [28]. In fact, we have not relied on the regular distance strategy as we have used the Kitti dataset for evaluation. a regular distance consists in taking templates at a regular distance of 1 m for example. Moreover, this work is a proof of concept that an enhancement could be obtained by only detecting loop closures without jointly optimizing over landmarks and poses as a SLAM system does.
However, the greatest interest would be generated when a learning of a geotagged database is already performed which would allow an absolute localization in the global reference frame. The SeqSLAM algorithm could also be adapted to work with different camera systems to make it possible to share the database of templates between different users. Furthermore, even though the drift has been greatly reduced, other methods could be fused with SeqSLAM and VO to make the output suitable for autonomous cars.
Conclusions This paper presented a solution to localize a vehicle in urban environments by means of the integration of VO and SeqSLAM. SeqSLAM is a well-known successful approach for place recognition in varying conditions such as seasonal changes and day and night cycles when not dealing with a viewpoint change. The localization was performed using a single on-board camera and the template library was created online using navigation information from the VO. The integration was performed with a Bayesian filtering through the use of an EKF. That is, when a place is recognized, the corresponding information is used as an observation of the filter, otherwise the prediction is performed by the VO module. The performance of the system can be increased by using a pre-learnt geo-tagged database of images to develop a reliable alternative to satellite-based global positioning.
Genetic structure. When analysing the whole dataset with STRUCTURE  26  , the software found that a division into 2 clusters best represented the data (ΔK2 = 131. 82). While most of the sampling sites were assigned to a group with an average membership coefficient >0. 9, three sampling sites (SW1, SW2 and SE2) presented more admixture than the other sites with an average membership coefficient of 0. 80, 0. 61 and 0. 74 for SW1, SW2 and SE2, respectively. Apart from these three sites a clear separation was revealed by STRUCTURE between the northern sites (NW1, NW2, NW3 and NE1) and most of the southeastern sites (SE1, SE3, SE4 and SE5) (Fig. 2a ). Within each cluster, additional levels of substructure were found. For the northern and southwestern sites, three genetic clusters were detected by STRUCTURE (ΔK3 = 58.
1. The same goes for the pairwise F st inside the southeastern group, with weak F st lower than Figure  1. Location of the 11 sampling sites (SE1 to SE5, SW1, SW2, NW1, NW2, NW3 and NE1), the square and dots are the locations of the sites, for southern group and the northern group respectively (groups were assigned with the software STRUCTURE), and each colour represents a different cluster as defined by the software STRUCTURE. The black triangles show the major cities in the area, the black lines represent regions borders and the grey line is the Loire River. The figure was created using ArcMap 10. 4 (Esri, Redlands, CA, USA, http://www. esri. com/arcgis/about-arcgis). ) and observed (H obs. ) heterozygosity. 0. 1. The results of STRUCTURE and DAPC show similar patterns as pairwise F st between the northern group and the southeastern group, with strong F st always higher than 0.
Nine were developed specifically for the marbled newt  24  and one (Tcri27) was developed for the crested newt  57. PCR and genotyping were performed by the Gentyane INRA platform (Clermont-Ferrand, France). We analysed the genotyped data with Genemapper v4. 0 (AppliedBiosystems ™ ). Data treatment. Deviation from Hardy-Weinberg equilibrium was calculated using GENEPOP v4. 2  58  and significance levels were adjusted with Bonferroni correction. A possible Wahlund effect for populations presenting a deficit in heterozygotes was tested for the correlation between F is and F st per locus. F is and F st per locus were obtained with the R package hierfstat v0. 04-22  59  implemented in R v3. 3. 2  60  , then a linear regression was calculated in order to observe the relationship between both variables  25.
Expected heterozygosity (H epx. ), observed heterozygosity (H obs. ), and average number of alleles per locus were calculated with the software Genetix v4. 05. 2  61. The allelic richness (Ar. ) was plotted using allele rarefaction curves, as calculated by the package PopGenKit v1. 0 (R package, R Core Team), implemented in R v3. 2. 2  62. After visual verification, the minimum sample size was set to 14 in order to have a good estimation of the number of alleles. Pairwise F st and significance probabilities were obtained with the software Arlequin (Ver 3. 5)  63. Only sample sites (n = 8) with more than 20 individuals were kept for this analysis. This minimum was set according to Kalinowski  64  , as it give good results for F st larger than 0. 01. F st significance was corrected for multiple testing with the Bonferroni method.
Bayesian estimations of population structure were inferred with the software STRUCTURE (2. 3. 4) 26 using a burn-in period of 150,000 followed by 10 6 Markov chain Monte Carlo (MCMC) iterations. Default settings were used (Admixture model) for all parameters but for the LOCPRIOR option (the sample location was used as a prior), which was enabled as it tends to perform better than models without LOCPRIOR enabled  65. For each analysed dataset, 10 replicate runs were performed for each value of the number of clusters (K) from 1 to 10. The most likely number of clusters (K) was determined using the calculation of delta K (ΔK) described in Evanno et al. 66  , as implemented by the web interface of STRUCTURE HARVESTER  67  , but also visually directly from STRUCTURE's bar plots (Fig. 2 ). Sampling sites were assigned to a specific population according to the highest average membership coefficient.
For each cluster detected by STRUCTURE, analysis was run again with the same parameters in order to detect more subtle substructure  68  (Fig. 2b, c ). When the STRUCTURE bar plot showed that individuals could not be assigned to different clusters and the ΔK was low, the dataset was considered as one cluster. Discriminant analysis of principal components (DAPC) was run using the dapc function in the R package adegenet  69  , as a complementary analysis for population structure. This method applies a discriminant analysis on data previously transformed using a principal component analysis (PCA). DAPC optimises the genetic variance between groups while minimising the variance within groups, in order to show better separation of the different groups. For the PCA calculation, after visual interpretation, we decided to retain 35 PCs as it conserved 96% of the variance.
Regarding the discriminant analysis, the first two eigenvalues were kept, after visual interpretation, as they were carrying most of the information. The results of the DAPC were gathered in a scatterplot, with individuals represented as dots and the different groups as inertia ellipses. The smallest clusters detected by STRUCTURE were used as predefined groups for the AMOVA in order to detail our population structure. The AMOVA was calculated with the software Arlequin (Ver 3. 5)  63. Isolation by distance among populations was tested with a Mantel correlogram using the matrix of pairwise Euclidian distance (in kilometres) between sample sites and the matrix of pairwise standardised genetic distances between sampling sites (F st /1 -F st ). The calculation was performed with the R package vegan (vegan v 2.
Landscape analysis. We tested the influence of the agricultural landscape on genetic distance by analysing habitat types between sampling sites. To do so we created, on Arcgis 10. 4 (Esri, Redlands, CA, USA, http://www. esri. com/arcgis/about-arcgis), a 10 km wide corridor between every sampling site. We then used the CORINE (Coordination of Information on the Environment) land cover data from Copernicus land cover monitoring services  70  to record the different habitat types within each corridor. The amount (in percent) of different habitat types within each corridor was then calculated with the software FRAGSTATS (v4. 2)  71. In order to detect the influence of large field crops on genetic distance we used the proportion of arable land (code 21) within each corridor and accounted for the distance between sites.
We chose arable land as it is the CORINE land cover habitat that best represents the large field crops and intensive agricultural activities. In our sampling area we only had one type of arable land, non-irrigated arable land (code 211), and so for the rest of the article we will refer to it simply as arable land. We then created a linear model with pairwise F st as a response variable and proportion (in percent) of arable land, and pairwise distance as explanatory variables. We also included the interaction between the two explanatory variables in order to account for the combined effect of both pairwise distance and amount of arable land. The three candidate models were then compared using Akaike's Information Criterion corrected for sample size (AIC c )  72  with the function model. sel from the R package MuMIn  73.
Acknowledgements Financial support for this work came from  Région Pays de la Loire ,  Centre Beautour , Chinon  SNB  project (stratégie nationale pour la biodiversité 2011, French minister of ecology),  Office National de la Chasse et de la Faune Sauvage  ( Pôle Bocage  et  Faune Sauvage ) and  Nouvelle-Aquitaine Region. We thank  Alexis Viaud  ( Bretagne Vivante ),  Cédric Baudran (réseau herpétofaune de l'Office National des Forêts) ,  Didier Montfort ,  Mickael Ricordel  ( réseau herpétofaune de l'Office National des Forêts ),  Philippe Evrard ,  Sylvain Courant ,  Yohann Jaumouillé  ( Office National des Forêts ),  Jérémy Souchet ,  Jérôme Lallemand  ( Conservatoire des Espaces Naturels Poitou-Charentes ),  Florian Doré  ( Deux-Sèvres Nature Environnement ) and  Fabrice Conort  ( Office National de la Chasse et de la Faune Sauvage ) for their investment in the project and their help in sample collection that made this study possible, also we are grateful to  ONF-Centre Val de Loire.
The UnionO f operator is defined as a relation between sets of classes. The defined logical property states that if an instance belongs to a class x or an instance belongs to a class y then it belongs to the class z belonging to the UnionO f relation (axiom axm4 of figure  5 ). To obtain the definitions of all the formalisation rules defined for the deep modelling process, the reader may refer to these references  [6, 7]. In figure  6 , we give an extract of the ontology of diplomas we have formalised as instances of the generic concepts previously introduced. The defined ontology illustrates the subClassO f , Equivalence and UnionO f relationships. The OntoEventB plug-in  [10]  has been developed to automatically support the translation of ontologies models, described using ontology description languages such as OWL  [11]  or PLIB  [8] , into Event-B Contexts  [1].
It takes as input an ontology description file and generates, according to the selected approach (shallow or deep), the corresponding Event-B contexts. The OntoEventB plug-in is developed according to an architecture composed of three components: Input, Pivot and Output Models (Figure  7 ). The Input Models component. This component is devoted to the processing of the input models described using different ontology description languages such as OWL, PLIB. It browses the input models files in order to extract ontological concepts descriptions (e. g. OWL classes, OWL data type properties and OWL object properties in the case of OWL models) and to send them to the Pivot Model component. CONTEXT IS A = {IsA|IsA ∈ CLASS ↔ CLASS ∧ (∀x, y•(x ∈ CLASS ∧ y ∈ CLASS ∧ x → y ∈ IsA ⇔ union({r•r ∈ HAS INSTANCES|ran({x} r)}) ⊆ union({r•r ∈ HAS INSTANCES|ran({y} r)}))))} axm3 : EQUIVALENCE = {EQo|EQo ∈ CLASS ↔ CLASS ∧ (∀x•(x ∈ CLASS ⇒ x → x ∈ EQo)) ∧ (∀x, y•(x ∈ CLASS ∧ y ∈ CLASS ∧ x → y ∈ EQo ⇒ y → x ∈ EQo)) ∧ (∀x, y, z•(x ∈ CLASS ∧ y ∈ CLASS ∧ z ∈ CLASS ∧ x → y ∈ EQo ∧ y → z ∈ EQo ⇒ x → z ∈ EQo))} axm4 : UNION OF = {unionOf |(unionOf ∈ (P(CLASS) × P(CLASS) ↔ CLASS)) ∧ (∀x, y, z•(x ∈ P(CLASS) ∧ y ∈ P(CLASS) ∧ z ∈ CLASS ∧ x → y → z ∈ unionOf ⇒ ∀instance•(instance ∈ INSTANCE ⇒ ∃hasInstance•(hasInstance ∈ HAS INSTANCES ⇒ (∀n, m•(n ∈ x ∧ m ∈ y ∧ (n → instance ∈ hasInstance ∨ m → instance ∈ hasInstance)) ⇒ z → instance ∈ hasInstance)))))} axm i :.
Background: Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs. Results: We developed DiNAMO (for DNA MOtif), a new software based on an exhaustive and efficient algorithm for IUPAC motif discovery. We evaluated DiNAMO on synthetic and real datasets with two different applications, namely ChIP-seq peaks and Systematic Sequencing Error analysis.
In this article, we present an exact discriminative method for IUPAC motifs discovery in DNA sequences. With this method, it is possible to efficiently search for weakly represented IUPAC motifs in a large signal dataset, compared to the control dataset, without any restriction on the motif. Our approach is exact because it takes into account all existing exact motifs, whether significant or not, to construct the degenerate motifs. It uses mutual information (MI) as an objective function to search for over-represented degenerate motifs. It proceeds in an exact way in a reasonable time, through the use of suitable data structures. The algorithm has been implemented in a software, called DiNAMO, and was evaluated on synthetic datasets as well as on real datasets for two different applications linked to next generation sequencing technologies, namely Chip-Seq analysis and sequencespecific errors detection (SSE).
Methods We work on the DNA alphabet {A, C, G, T}, and consider the IUPAC alphabet where each character corresponds to a non-empty subset of {A, C, G, T}. Thus the IUPAC alphabet has 2 4 -1 = 15 characters, that are represented in Fig. 1a. We say that a DNA word w of length L, called an L-mer, matches an IUPAC motif of the same length if, for each position, the associated nucleotide of w belongs to the IUPAC character. A word matching a given IUPAC motif is called an instance. For example, the IUPAC motif AWRT has four exact instances {AAAT, AAGT, ATAT, ATGT}. The DiNAMO algorithm takes as inputs two files in multi-fasta format, containing DNA sequences corresponding respectively to the positive (or signal) dataset P and the negative (or control) dataset N , and searches for all IUPAC motifs that are over-represented in P compared to N.