dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, a subset of frames in each image sequence is labeled with the location of some object. We apply as input to the CNN both the target object (from the previous frame) and the search region from the current frame, centered in the same position as the target. The search region has twice the size of reference object bounding box (Figure  2 ). The output of the convolution layers is a set of features that capture the image high-level representation. These features are further applied as input to the fully connected layer. By comparing the features associated to the object with those extracted from the search area, we can determine the novel position of the target (Figure  2 ). We adopted the CNN architecture of the first five layers of the traditional CaffeNet framework  [18]. The layers are pre-trained using the ImageNet database  [19].
The layers are pre-trained using the ImageNet database  [19]. In the training phase, we adopted a learning rate of 1 × 10 -5 and we constrained all other parameters to the default values of CaffeNet. The outputs from all the convolutional layers are concatenated in one vector that is applied to the fully connected layers. The CNN has four outputs, which represent the x,y coordinates corresponding to the object bounding box upper left and lower right corners. Let us emphasize that the CNN training is performed uniquely in the offline stage. In the online phase, the network weights are frozen and no fine-tuning is required. Because the proposed tracker learns generic relationships between the object's appearance model and the motion patterns, various objects can be tracked without requiring any category-specific information about the target.
The Benchmark The proposed obstacle detection and tracking approach is evaluated on the challenging dataset considered by the Visual Object Tracking (VOT) international contest in 2016. The VOT'2016 database is composed of 60 video sequences exhibiting several challenging situations including camera motion, object occlusion, deformation, aspect ratio and size change, illumination variation, clutter or blur. All image sequences have a per-frame visual attribute labeling provided by human observers, which serves as ground truth. The VOT protocol performs two types of evaluation: unsupervised, where the tracking system receives as input the object bounding box from the first frame of the video sequence (the object bounding box can be provided by a human annotator or by an obstacle detection system) and then no human intervention is allowed; and supervised, where the tracker is locally re-initialized with the object bounding box (from the ground truth) if the target element is lost (tracking failure).
The failure rate considers the evaluation problem in a supervised manner and involves a human observer which reinitializes the tracker at each failure. The average number of manual interventions per 30 frames is recorded and used as a comparative score. To be able to evaluate separately the influence of the various conditions that can appear in the videos (e. g. , camera motion, illumination changes, object occlusion, size change, target motion change), we have adopted the globalization protocol considered by the VOT'2016 challenge. Here, the videos of the database are split into smaller videos, with a size of N s frames, corresponding to each of the considered situations. The N s parameter takes values within a [N li , N hi ] interval that is automatically generated by the VOT'2016 toolkit.
The DEEP-SEE system is composed of a regular smartphone device (Samsung S8+, Samsung Electronics Co. , Suwon, Korea) functioning as a video acquisition device, Bluetooth bone conducting headphones (AfterShokz Bluez 2 Voxlink LLC, East SyracuseOnondaga, New York, NY, USA) used to transmit the acoustic warning messages to the VI user, an ultrabook computer (Dell XPS running on an Nvidia GTX 1050 GPU, Nvidia Corporation, Santa Clara, CA, USA) used as backpack processing unit, a smartphone waist belt and a backpack. The total weight of our system is inferior to 2 kg. At the software level, the DEEP-SEE architecture adopts a modular framework, composed of the following three units: object detection and recognition (cf. Section 3. 1), object tracking (cf. Section3. 2) and acoustic feedback (Figure  9 ).
However, after consulting VI users from several blind associations, we decided to use acoustic warnings rather than tactile stimulation. Most of the VI users consider the tactile display invasive because they require an actual physical contact with the person's skin. In addition, the information acquired from the haptic sense is insufficient to capture the overall semantics of the scene. In the context of DEEP-SEE, the semantic interpretation of the recognized objects and the effective transmission of information to VI people are the key elements. The acoustic feedback At the software level, the DEEP-SEE architecture adopts a modular framework, composed of the following three units: object detection and recognition (cf. Section 3. 1), object tracking (cf. Section 3. 2) and acoustic feedback (Figure  9 ).
Finally, regarding the user interaction aspects, DEEP-SEE provides an intuitive and limited feedback by using verbal warning messages. We introduce an acoustic warning protocol able to prioritize, based on the degree of danger of the recognized obstacles, the acoustic alerts and transmit a message only when such an action is required (i. e. , in order to avoid collision). At the software level, the DEEP-SEE architecture adopts a modular framework, composed of the following three units: object detection and recognition (cf. Section 3. 1), object tracking (cf. Section3. 2) and acoustic feedback (Figure  9 ). Finally, regarding the user interaction aspects, DEEP-SEE provides an intuitive and limited feedback by using verbal warning messages. We introduce an acoustic warning protocol able to prioritize, based on the degree of danger of the recognized obstacles, the acoustic alerts and transmit a message only when such an action is required (i.
However, after consulting VI users from several blind associations, we decided to use acoustic warnings rather than tactile stimulation. Most of the VI users consider the tactile display invasive because they require an actual physical contact with the person's skin. In addition, the information acquired from the haptic sense is insufficient to capture the overall semantics of the scene. In the context of DEEP-SEE, the semantic interpretation of the recognized objects and the effective transmission of information to VI people are the key elements. The acoustic feedback module is designed to be intuitive and does not require extensive and laborious training phases to understand the technical functionalities. The key principle that has been adopted respects the VI user recommendations and fulfills the following two rules: (1) the user should not be overwhelmed with too many and useless alerts; and (2) the system should minimize all interference with other senses such as auditory and touch.
VI Navigational Assistance: Experimental Evaluation The overall DEEP-SEE navigational assistant has been tested on multiple, complex outdoor environments by using the video dataset of  [16]  that was recorded with the help of visually impaired people. The dataset contains 30 video sequences with an average duration of 10 min, acquired at 30 fps and at a resolution of 320 × 240 pixels. Because the dataset was recorded with the help of visually impaired people in real urban scenes and not in simulated environments, the videos are trembled, cluttered and include important camera and background movement. In addition, various types of dynamic and static objects are present. In Figure  12 , we give some examples of static/dynamic objects existent in the video dataset that have been used for testing.
For the evaluation, we used the video dataset of  [16]  where the ground truth elements represent the number of objects, for each category, that need to be detected and classified. Table  3  summarizes the experimental results obtained. Using the ground truth data, the performance of the obstacle detection and classification modules are evaluated using traditional objective parameters such as Precision (P), Recall (R) and F1 score (F1), defined as: P = TP TP + FP , R = TP TP + FN , F1 = 2•P•R P + R ; ( 9 ) where TP is the number of true positive elements (correctly detected and classified objects), FP is the number of false positive (incorrect detected/classified objects) and FN false negative elements (missed detected/classified objects). For the evaluation, we used the video dataset of  [16]  where the ground truth elements represent the number of objects, for each category, that need to be detected and classified.
The user feedback is transmitted as a set of acoustic warnings, through bone conducting headphones. From a methodological point of view, the core of the approach relies on a novel object tracking method that uses two CNNs for taking into account both motion patterns and visual object appearances. We evaluated the proposed system on two video datasets: the Visual Object Tracking From the experimental results presented in Figure  13 , it can be observed that the DEEP-SEE prototype is able to correctly detect, track and recognize various object encountered by the VI user during the outdoor navigation. In addition, the system proves to be robust to the scene dynamics or to various changes in the light intensity or object appearance.
The user feedback is transmitted as a set of acoustic warnings, through bone conducting headphones. From a methodological point of view, the core of the approach relies on a novel object tracking method that uses two CNNs for taking into account both motion patterns and visual object appearances. We evaluated the proposed system on two video datasets: the Visual Object Tracking 2016 database composed of 60 videos. The experimental results obtained on the challenging VOT 2016 datasets demonstrate that the proposed detection and tracking system is capable to obtain state of the art performances by alternating between two CNNs, respectively, trained with motion and visual patterns. In terms of accuracy and robustness, the tracking system returns similar performances with the winner of the VOT2016 challenge while being four times faster.
Concerning the evaluation of the DEEP-SEE prototype for VI assistance purposes, we have considered the video dataset of  [16]  recorded with the help of VI users. The proposed methodology led to a gain of 5% in terms of F1 score when compared with  [16]. For further work and developments, we propose to integrate the proposed framework in a complete platform that allows additional functionalities, such as crossings detector, face recognition, shopping assistance, and guiding system, that may help a VI user to reach a desired destination. Next, we propose extending the proximity region and define two areas of interest, both with a trapezoidal shape, in the near surrounding of a user: one situated on the walking path and the other at the head level. Using this strategy, we can launch acoustic warning messages for obstacles situated at the head level, such as tree branches, hanging signs or other dynamic flying objects, and not solely for object located on the ground.
Implementation: the Conquests package The computation of graph, stoichiometry and optimal-efficiency PEMs was performed by the Python package Conquests available at https://github. com/jlaniau/conquests. For the sake of application, each metabolic network was represented using its SBML file and incorporated into Conquests as an input, along with the identifier of a targeted reaction r T. The SBML file was then parsed with lxml Python package functions in order to identify the boundary seed compounds S and the list of substrates T of the targeted reaction. Sustainability-PEMs computation was performed using Answer Set Programming (ASP) and the pyASP Python package that calls upon the clingo4 grounder and solver. Finally, the CobraPy Python package  (Ebrahim et al. , 2013)  was used to check whether the network is producing a flux for the targeted reaction r T.
, 2012). It is a declarative approach oriented toward combinatorial (optimization) problem-solving and knowledge processing. ASP combines both a high level modeling language with high performance solving engines with the result that the focus is on the specification of a problem rather than on the algorithmic part. The basic idea of ASP is to express a problem as a set of logical rules (clauses). Problem solutions appear as particular logical models (so-called stable models or answer sets) of this set. Modern ASP solvers like clasp  (Gebser et al. , 2014)  support various combinations of reasoning modes, among them, regular and projective enumeration, intersection and union. An ASP program consists of Prolog-like rules h :b 1 ,. , b m , not b m+1 ,. , not b n , where each b i and h are literals and not stands for default negation.
Listing 2: Computing pruned graphs 1 p r un e (M) :-s c o p e (M) , r e a c t a n t (M, R ) , n o t s e e d (M) , n o t t a r g e t ( R ). 2 3 pruned ( R ,M) :-p r u ne (M) , r e a c t a n t ( _ , R ) , n o t r e a c t a n t (M, R ). Finally, according to its definition, a sustainability-PEM must be necessary to produce at least one substrate of the targeted reaction. In other words, there exists a reactant of the targeted reaction which does not belong to the scope of the set of seeds in the network pruned with respect to the sustainability-PEM. To model this property in the ASP program (listing 3), the scope of the set of seeds in a network pruned with respect to metabolite PM is computed (lines 1-3). A sustainability-PEM C is a metabolite that is necessary to produce a substrate TM of a target reaction, the latter being a metabolite that is no longer in the scope of the network when pruned with respect to C (lines 5-6).
The biomass component cannot be produced because of the non-balanced mass equation despite the fact that it is theoretically still able to be activated topologically. As shown in Fig. 8 , these compounds are mainly the co-products of essential reactions that need to be metabolized to ensure the biomass production. In the periplasmic compartment of the iAF 1260 network (Fig. 8 ), the compound murein5px4p (a biomass component) is a product of a reaction whose substrate is murein5p5p. Another product of this reaction is D-Alanine. When the export reactions of this co-product are removed Figure  8  Fine tuning of mass-balance equilibria: example of a producibility-SEM that is neither a sustainability nor a optimal-efficiency-PEM. In the iAF 1260 network, the production of the periplasmic murein5px4p (that is, the identifier of a biomass component in the BIGG database) is under the control of the export of its coproduct D-Alanine to the cytoplasm or to the external compartment.
Here we have defined three types of PEMs that can be distinguished on the basis of the modeling method used to compute them. Sustainability-PEMs are key components related to the graph-based structure of the graph and the initiation of production paths, whereas producibility and optimal-efficiency-PEMS are related to fluxes distribution at steady-state. The latter focuses on the optimal activation of the target reaction, thus pinpointing metabolites that enable its maximal rate. The computation of sustainability-PEMs was challenging because it relies on a difficult combinatorial problem being solved. The efficiency of the ASP solvers enabled an efficient and fast computation to be produced while allowing all PEMs computations to be contained in a single and easily distributable Python package.
We conclude that the systematic comparison of several modeling approaches (graph, stoichiometric and optimal flux-based analyses) of the same model may highlight components with different roles within the different formalisms. Our hypothesis is that these compounds often carry relevant information about system dynamics. This promotes the use of the Conquests package in two complementary tasks. Either the information about system dynamics is biologically irrelevant and this information may be used for finalizing the curation of genome-scale metabolic networks, or the information about system dynamics is biologically relevant and allows the role of internal cycle and mass-balance equilibria with respect to the production of targeted biomass reactions to be deciphered. Interestingly, the number of PEMs belonging to one or two of the three classes we described is particularly small in genome-scale models that usually contain more than a thousand metabolites.
While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as reanalysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not.
To better discriminate neutral from causal variants (deleterious or protective), we incorporated a weight system in the computation of average allele frequencies. A similar approach was used in the Kernel-Based Adaptive Cluster (KBAC) test  [8] , on multi-locus genotypes. To assess the performance of DoEstRare, we compared its power and type I error to other existing tests by analyzing simulated data. We conducted simulations, based on the backward coalescent model implemented in the COSI program  [22] , under different scenarios varying the position distribution of DRVs. We considered three main scenarios: in the first scenario DRVs are uniformly distributed on the gene, in the second and third scenarios DRVs are respectively clustered in one and two areas. We also varied the proportion of causal variants which is linked to the window sizes of clustered areas.
From these simulations we show that our test is among the most powerful statistical tests and perform even better with the presence of one cluster of DRVs. We also applied association tests based on rare alleles on two real datasets to assess the consistency between significance results and evaluate the properties of our test in real settings. The studied pathologies were Brugada syndrome (BrS), with data from Le Scouarnec et al. (2015)  [23]  and early-onset Alzheimer's disease (EOAD), from  Nicolas et al. (2015)   [24]. Interestingly, we show that DoEstRare provides slightly different significance results from other tests. DoEs-tRare is indeed based on a different hypothesis and could be used to explore new research insights, involving variant positions.
Performance of DoEstRare We conducted simulations to assess the type I error and the power of DoEstRare and 14 other association tests on rare variants, covering a large spectrum of existing strategies. The tests we compared are summarized in the Table  1  with a categorization inspired by the review from Lee et al. (  2014 )  [25]. We simulated a 10kb gene with the backward coalescent model available in the COSI program  [22] , for a population of 10,000 haplotypes. We repeated the simulations to obtain 10,000 replicates of this haplotype set. The median number of simulated variants is 202 (2. 5% and 97. 5% quantiles: [172; 235]). The median number of variants with a MAF between 0. 001 and 0. 01 is 30 (2. 5% and 97. 5% quantiles:  [18; 46] ). From the population, we simulated the phenotype in order to obtain 1,000 cases and 1,000 controls.
Application of DoEstRare to real data We also applied our newly developed test and other association tests on rare variants to real data in order to study the significance similarities across the analyzed genes. We performed association tests on two pathologies, BrS and EOAD, in order to evaluate the stability of the comparison. Due to the number of tests under comparison, we used a multidimensional approach as Jeanmougin et al. (  2010 )  [27]. We analyzed the significance similarities using a Principal Component Analysis (PCA)  [28]  on -log 10 (p-values) data. BrS data. We applied 15 statistical tests (DoEstRare and 14 other tests) to BrS data. Sequence data for 163 candidate genes were available for 167 cases and 167 controls. Rare variants are defined as showing an MAF below 1%, and residing in coding DNA sequences (CDS) regions +/-10 bp.
The cumulative inertia explained by the first three axes of the PCA is about 88. 31% of the total inertia. From the BrS correlation circle ( EOAD data. We applied DoEstRare and 8 rare variant association tests to the EOAD dataset, which contain whole exome sequences from 431 cases and 555 controls. Rare variants are still defined with an MAF inferior to 1% and a location in CDS regions. As for the previous analysis on BrS, we removed from the PCA, genes presenting a missing p-value for at least one test. We analyzed 17,409 autosomal protein-coding genes with 9 statistical tests. These genes represent a total of 273,390 rare variants, corresponding to a median number of variants per gene of 11 (min: 2; max: 901). The first three axes explain 86. 72% of the total inertia (  present a different position distribution between cases and controls, and tend to cluster in a small genetic region in cases.
We could assess the power of our statistical test DoEstRare and usual tests, with the analysis of simulated data. However, to test their behavior on biological datasets, we have applied up to 14 association tests, in addition to DoEstRare, on rare variants to BrS and EOAD data and investigated the significance similarities between the results from the different tests. This comparison may help in choosing the best test combination when designing a rare variants project and in interpreting differential test results. Both BrS and EOAD PCA representations underlined the same tendencies. All statistical tests are correlated with the first principal component, which means that significance results provided by different tests show globally the same tendency. The second source of inertia in significance results is due to some statistical tests giving partially different results.
This may be related to the underlying genetic structure behind the disease. The main significance differences, according to the second principal component, are between the group including C-alpha and KERNEL, and the group including WSS and KBAC. The third component, which explains a low part of the inertia, opposes DoEstRare to CAST. Finally SKAT-O seems to be a good compromise as it provides similar results than other tests. We could observe some differences in the correlation structure when analyzing BrS data and EOAD data. For instance, the correlation of PODKAT and SKAT significance results with the other tests differs between BrS and EOAD datasets. One explanation is that correlation structure between statistical results may depend on the studied pathology, as statistical tests assume different biological models.