dataesr/scientific-paragraphs-categorization huggingface dataset:
SURVEY METHODOLOGY We conducted an exhaustive literature review by applying the Systematic Literature Review (SLR) process proposed by  Kitchenham & Charters (2007)  and preferred reporting items for systematic reviews and meta-analyses guidelines  (Moher et al. , 2009)  (Fig. 1 ). We followed the six stages of the review protocol used in  Wen et al. (2012)  but added a previous stage, which aims to detect the need for a review (Fig. 2 ). First, we searched for other reviews to formulate questions related to the aim of this review, and then we selected the search strategy based on key terms and available databases. In the next step, we defined the exclusion criteria for filtering relevant articles that could contribute to answering the questions from the first stage. For this, we applied several filters in the Quality Assessment Checklist (fourth stage) to choose articles to be included in the following steps.
Identification of the need for a review The strategy used was based on the guidelines proposed by  Wen et al. (2012)  and used in  Franco-Bedoya et al. (2017)  and  Reinel, Raul & Gustavo (2019). To define the need for a systematic review, we searched through published and available reviews (secondary studies) on the topic of interest. We used Eq. (  1 ) to search in the literature databases referenced in Table  1. ð"transposable element" OR retrotransposon OR transposonÞ AND ð"machine learning" OR "deep learning"Þ AND ðreview OR "systematic review" OR overview OR "state of the art" OR "systematic mapping"Þ (1) The keywords were selected based on the following: (i) type of information that we aimed to retrieve (TEs as well as retrotransposons and transposons classes), (ii) techniques addressed in this review (ML and DL) and (iii) keywords related to secondary studies.
Search design and study selection Once we identified the need for a review and formulated the research questions, we developed the search strategy to find research articles, chapter books, conference proceedings and other review articles in the databases shown in Table  1. Similar to search Eq. (1), we used general keywords related to (a) type of genomic data and (b) techniques used. We did not use specific keywords (such as specific algorithms or architectures) and any time limitations, because few relevant results were found on this topic. Major keywords were separated by the "AND" operator, and related keywords were linked using the Boolean operator "OR", as shown in Eq. (2): ð"transposable element" OR retrotransposon OR transposonÞ AND ð"machine The literature search using Eq. (  2 ) retrieved 403 candidate articles of which were eliminated those that were: (a) repeated (the same study was found in different databases); (b) of different types (books, posters, short articles, letters and abstracts); (c) written in other languages (languages other than English).
Data extraction and synthesis In this stage, we first wholly read the selected publications (Table  2 ) to extract information to answer the research questions. Then, we registered the article into a data extraction card proposed by  Wen et al. (2012)  with some adaptations to our study. The card contained information on publication identifier, year, publication name, related research questions, and the information itself. In the final step, we synthesized all of the collected information and obtained evidence to answer the research questions. Interestingly, more than 50% of the selected studies were published between 2017 and 2019 (Fig. 4 ), demonstrating a growing interest in this topic in the last years. We identified 35 relevant publications after the SLR (see Table  2 ). These articles were published between 2009 and 2019.
Using the Random Forest (RF) algorithm,  Schietgat et al. (2018)  were able to improve results obtained by popular bioinformatics software (which followed a homology-based strategy) such as Censor, RepeatMasker, and LTRDigest  (Schietgat et al. , 2018)  in the detection of LTR retrotransposons. The authors proposed a framework called TE-Learner LTR , which outperformed LTRDigest in recall and RepeatMasker and Censor in terms of precision. Machine learning techniques also obtain better results than traditional methods regarding TEs classification. Using ML, it is possible to classify non-autonomous TEs (specifically derived from LTR retrotransposons) using features other than coding regions (which are commonly used in classification processes), including element length, LTR length, and ORF lengths  (Arango-López et al.
Currently, a few hundreds of plant genomes are available to train algorithms, but this number will increase significantly in the near future due to massive genome sequencing projects such as the 10 K plant project (https://db. cngb. org/10kp/) or the Earth BioGenome Project (https://www. earthbiogenome. org). This large amount of data will help to produce more accurate and reliable software thought ML and DL. Additional to available training data, some plant genomes are very interesting to identify and classify TEs following ML or DL approaches, due to their huge genome and their composition. As examples, sugarcane, maize, and barley have large genomes (3 Gb, 2 Gb and 5. 1 Gb respectively) that are composed mainly by repetitive sequences (up to 80%,  (Rahman et al. , 2013) ). On the other hand, the process of supervised training of ML algorithms provides another advantage when training a model that increases true positives (TP) and decreases false positives results.
, 2016) , Theano  (Bergstra et al. , 2011)  or Pytorch  (Paszke et al. , 2017). These operations include matrix multiplication, convolution, and automatic differentiation  (Eraslan et al. , 2019) , allowing users to specify their models more easily and quickly. Another advantage is that users do not need to parallelize their codes since frameworks like tensorFlow can do it automatically. Convolutional neural networks have been widely used in genomics. Most of the publications shown in Table  4  take advantage of the ability of CNNs to extract high-level features directly from sequences (in most cases, using one-hot codification). These features were then passed to other layers (i. e. , fully connected layers) to calculate the final results. Recently, CNNs have been applied to the classification of TEs and have shown better results than conventional bioinformatics software such as PASTEC and REPCLASS.
Parameters and metrics applied in algorithms and architectures (Q3) To ensure that ML architectures do not exclusively learn patterns of the training dataset, there are several techniques used to split information into different datasets, such as the hold-out and k-cross-validation methods. These methods should be used in problems with information of any kind. Particularly with data of genomic origin, k-cross-validation seems to be the most popular  (Ma, Zhang & Wang, 2014). Using k = 10, different studies demonstrated high accuracies for long non-coding RNAs  (Chen et al. , 2018; Zhang et al. , 2018) , for selecting features for classification of biological sequences  (Kamath, De Jong & Shehu, 2014) , analyzing insertion sites of somatic LINEs in ovarian cancer  (Tang et al. , 2017) , and improving the classification of TEs  (Loureiro et al.
, 2013b   Nakano  et al. , 2018b). Meanwhile, k = 5 was used by  Segal et al. (2018)  to infer the importance of specific genes for growth under laboratory conditions. In ML tasks, it is essential to have curated datasets  (Loureiro et al. , 2013a). The quality of TE and other genomic datasets is complex to evaluate, and their nature can influence the final results. Databases such as PGSB contain genomic TE sequences from many species, while repetDB or RepBase comprise consensus sequences of TEs. Ashlock & Datta (2012)  proposed that, although consensus sequences have been used to train several ML algorithms (i. e. , TEclass  (Abrusan et al. , 2009)  and REPCLASS  (Feschotte et al. , 2009) ), this type of datasets caused poor results. The authors also recommended taking this into account for ML projects in genomics.
10 )  (Nakano et al. , 2017). Since TE classification systems follow a hierarchical topology, these metrics can contribute to improving the measurement of algorithms and architectures to classify TEs. In brief, because the detection and classification of TE can be covered from different approaches (binary problems, multi classes or hierarchical classification), multiple metrics can be applied. However, it is necessary to use those metrics that are not affected by unbalanced data sets, which is a problem linked to these types of data. Although in genomics, the k-cross-validation method is the most common, the k-value depends on the size of the training dataset. The articles evaluated in this review used values of k = 5 and 10. On the other hand, it was found that the nature of the data (genomic sequences or consensus) affects the performance of ML algorithms, so some authors recommend the use of genomic sequences.
Metric Representation Observations Tasks in which it was used  Most used DNA coding schemes (Q4) Accuracy ðTP þ TNÞ ðTP þ FP þ FN þ TP Â TN À FN Â FP ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðTP þ FNÞ Â ðTN þ FPÞ Â ðTP þ FPÞ Â ðTN þ One of the most critical tasks in ML algorithms is correct data representation. In contrast to other datasets, DNA nucleotide sequences are human-readable characters, C, T, A, and G, so it is necessary to encode them in a machine-readable form  (Yu, Yu & Pan, 2017). Table  6  shows coding schemes that can be applied to representing nucleotides by different approaches.
CONCLUSIONS AND FUTURE WORK ML is a powerful tool that can extract patterns, novel information, and relations from labeled data (supervised learning) or unlabeled data (unsupervised learning). These artificial intelligence approaches improve complex tasks and automate processes that would otherwise be done manually. Although ML and DL fields have been applied in areas such as genomics, human health, face recognition, and many others, the use of ML and DL in TEs is still limited. This is especially true for deep neural networks such as CNNs, which could provide opportunities to extract features that are undetected by conventional bioinformatics methods. Although TE detection and classification are very complex tasks because of the variability of these elements, there are databases with thousands of TE sequences that have been recently released.
These databases can contribute training sets for obtaining better and generalized models to improve the accuracy and reliability of the results. TEs are associated with many aspects in humans (such as diseases) and plants (like intra and inter-species diversity, adaptation to the environment, among others). Therefore, a broader understanding of these elements can provide better knowledge of our genomes as well as about important crops. Accordingly, this can lead to faster and reliable diagnostic and clinical treatments in diseases like cancer and more resistant and productive crops. Unquestionably, ML and DL can support novel methods to detect, classify, and analyze repeated sequences. To date, there are few publications on the application of DL in TEs, so the door is open to proposing innovative methodologies and architectures.
Taking into account this systematic review of literature, we propose the following ideas as future work: -To use Autoencoders to increase the size of the training datasets (data augmentation) on the TEs already classified and validated by the bioinformatics community, in order to obtain a better generalization of the ML and DL algorithms. -To use sets of simultaneous classifiers (SVM, RF, DT, LR, among others) in order to generate separation frontiers of classes more adapted to the nature of this type of problem.
e. only their product matters  (Llamosi et al. , 2016). Accounting for these parameters as separate entities may cause practical issues as well as erroneous interpretation of the results. Without loss of generality, in agreement with existing literature  (Llamosi et al. (2016)  and references therein), we, therefore, fix k m to 10 (min) À1 in both simulation and identification, focusing our analysis on the reduced parameter vector w v ¼ ðg v m ; k v p ; g v p Þ and the corresponding size-three parameters A, b and X. To simulate artificial datasets, starting from a single uninduced cell at time 0 (Generation 1), we simulate division of every existing cell into two daughter cells every 90 min over seven generations, thus obtaining a full cell tree. Single-cell parameters are simulated on the basis of model (2), with parameters A ¼ Diagð0:5; 0:5; 0:5Þ; b ¼ ½logð0:294Þ; logð0:947Þ; logð0:1Þ T and X ¼ Diagð0:1; 0:1; 0:1Þ.
Gene expression dynamics of every cell are simulated in accordance with model (1). Here, we assume that, besides measurement noise, the reporter protein concentration p coincides with the observed fluorescence intensity. For every cell, we assume that measurements of p(t) are taken every minute. Measurement noise is simulated by adding random Gaussian error with strength h ¼ 20, corresponding to a standard deviation in order of 10% of the simulated protein concentrations (as observed in real data). This simulation is repeated 20 times, each time with different cell parameters sampled from model (2) and different outcomes of measurement noise. Figure  2  reports an example of the simulated data from one of the 20 datasets. To assess identification performance, the ARME algorithm of Section 3 is run on every dataset separately, yielding 20 iterative estimation profiles for the unknowns h ¼ ðA; b; X; hÞ.
In our nonoptimized implementation in Julia  (Bezanson et al. , 2017) , one estimation run takes about 5 h on an Intel Xeon 3 GHz workstation. Statistics of the estimation process over the 20 datasets are shown in Figure  3. Estimated single-cell dynamics from the model identified on one dataset are shown in Figure  2. From Figure  3 , the first remark is the neat convergence of the iterative procedure around the true parameter values. Unreported results show that the observed convergence is robust to both initial parameter guesses and the randomness of the algorithm. Importantly, the iterative estimation sequences converge on average to the true values used in data generation, i. e. there is no estimation bias (the discrepancy in the estimation of the mean of k p can be explained by a small sensitivity of the model around its true value and does not exceed the first and third quartiles, see further comments below).
The variability of the estimates over the 20 datasets reflects variability in the data due to randomness in parameter inheritance and the realistically large measurement noise (Fig. 2 ). In general, estimation variability also depends on the richness of the dataset. To verify this, we repeated the same experiment with a higher number of observed cell generations (11 generations, Supplementary Fig. S2 ) and with a smaller measurement noise strength (h ¼ 10, Supplementary Fig. S1 ). Our algorithm converges nicely in all these cases, and the estimation uncertainty is decreased in both cases, as expected (the same Supplementary Figures also show that the discrepancy in the estimation of the mean of k p observed in Fig. 3  disappears for richer datasets). Additional simulations show that convergence holds for different parameters, notably for a non-diagonal matrix X (Supplementary Fig.
S3 ). This case corresponds to a non-diagonal matrix R, i. e. a more complex correlation structure among g m , k p and g p. A validation study also shows that the identified model is not overfit and predicts well single-cell parameters of a synthetic validation dataset (Supplementary Section S5). Finally, application of our method to simulated data with various degrees of intrinsic noise shows that inference is robust to small intrinsic noise levels, while estimation uncertainty increases for larger intrinsic noise levels, as expected (Supplementary Section S4). For the identification of the inheritance matrix A, estimation performance is expected to depend not only on the number of observed cells (i. e. jVj, the cardinality of set V) but also on the structure of dependencies W. In particular jWj, the number of mother-daughter pairs for which gene expression data is available, plays an important role (unrelated cells do not provide information about A).
A preliminary study of this question shows that indeed, for an equal number of cells jVj, a larger set of dependencies jWj favors estimation of A. promoter activity u. For every cell, true parameter vectors w v ¼ ½gm; kp ; gp that generated the data are displayed in square brackets Fig. 3. Iterative ARME identification of parameters A ¼ DiagðA1;1; A2;2; A3;3Þ, b and R ¼ DiagðR1;1; R2;2; R3;3Þ from the application of the algorithm to 20 simulated datasets Y for 80 search iterations plus 20 stabilizing iterations (100 iterations total). Identification is based on data simulated over seven generations with measurement noise level h ¼ 20. Horizontal black lines: true parameter values; blue lines: median of the iterative estimation profiles; and shaded blue region and dashed lines: at every iteration, 25% and 75% quantiles of the estimates over the 20 datasets, and extension of corresponding whiskers, as computed for the final parameter estimates in later boxplots (Fig.
We rely on the artificially generated datasets of the previous section. ARME estimates of h are those of the previous section, whereas indirect ME estimates are obtained for every dataset as explained above. In Figure  4 , we show boxplots of estimates ÂME , and analogous boxplots for the ARME estimates Â. The difference is apparent. Estimates Â are nicely centered around the true values and show little dispersion. On the contrary, despite a rather rich dataset, estimates ÂME are biased, a signature of poor estimation performance. Bias was also verified by non-parametric hypothesis testing. A sign test applied to ÂME 1;1 ; ÂME 2;2 and ÂME 3;3 rejected the hypothesis that the estimate is centered around the true value 0. 5 for all of them at 0. 05 significance (with P-values < 10 À5 , 0. 0026 and < 10 À5 , respectively), confirming bias, whereas the same test applied to the ARME estimates Â1;1 ; Â2;2 and Â3;3 did not reject this hypothesis (P-values 0.
50, 0. 82 and 0. 50). Bias of ÂME is also reconfirmed on other simulated datasets (Supplementary Figs  S4  and S5 ). Importantly, this bias generally depends on the true values of the unknown parameters under estimation as well as noise strength and observed population size (same Supplementary Figures); therefore, it cannot be easily compensated for. The bias is negative, i. e. traditional approaches systematically underestimate the degree to which parameters are inherited from mother to daughter cells. This is easy to explain: Methods that do not have a dependency model at the core of the inference approach assume a priori independence (no inheritance) of parameters of different cells. In sums, we showed that ARME identification provides unbiased estimates of inheritance and variability of gene expression parameters, whereas indirect methods are affected by an estimation bias that is hard to compensate for.
Fluorescence intensity gene expression measurements are collected for individually tracked cells about every 6 min. Single-cell gene expression data from these recordings are available online  (Llamosi et al. , 2016). Lineage information were provided to us by the authors for a set of 86 cells, corresponding to the cells observed in a single microfluidic chamber. An illustration of the known parental relationships among these cells is shown in Figure  5. Measurements are shown in Figure  5  on a time axis that also illustrates the delivered osmotic shocks. [In the whole section and figures, florescence measurements The experiments we consider are on budding yeast (Saccharomyces cerevisiae). In budding yeast, mother cells generate one newborn daughter cell at a time. Newborn cells are initially smaller than mother cells and do not replace them but rather coexist with them.
Based on (  8 ) and the inheritance model (2), we ran our ARME identification method to get estimates of the inheritance model parameters h pertaining the unknown individual-cell quantities g m , k p and g p (in view of the identifiability considerations of Section 4. 1, in agreement with  Llamosi et al. (2016) , k m is fixed to 10, while the remaining parameters k h and g h in (8) are known and fixed as above). Results from these estimates are reported in Figure  6. In Figure  5 , for various cells v, predicted single-cell dynamics corresponding to 30 values for u v sampled from the identified model pðu v j ĥ; W; YÞ are compared with the individual-cell measurements. Similar data fits for all cells of the dataset are reported in Supplementary Figures  S11-S13. From Figure  5 , it is apparent that the identified model provides an excellent explanation of the data.
Funding This work was funded in part by the  French national research agency (ANR)  via project  MEMIP  [ ANR-16-CE33-0018 ], and by the  Inria IPL CoSy. A. M. acknowledges partial support by the Chaire ' Modelisation Mathematique et Biodiversite' of VEOLIA-Ecole Polytechnique-MnHn-FX.
The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project's aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens' privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments.
This results in the expensive collection of a vast amount of rich data that have been unused thus far. This paper presents a new sensor, based on the edge-computing paradigm, for real-time traffic monitory leveraging the existing CCTV network data to address the issues of the network cost by adding new usages while respecting the privacy regulations. The remainder of this paper is organized as follows. Section 2 introduces the scope of the project, its requirements and methodology, which lead to the development of the sensor introduced in Section 3. Section 4 then focuses on the framework designed to collect, store and access the data. The performances of the sensor are assessed in Section 5. We next present in Section 3 two applications of the sensor. Concluding remarks and future work are discussed in Section 7.
While they were able to extract daily profiles, the need to set-up and optimize algorithms for each specific viewpoint is an obstacle for large deployment, mainly due to poor conditions of image acquisition such as low resolution, lighting conditions, combined with very dense crowds. The recent advances in neural network architectures now allows the training algorithms on large datasets to reduce the need for manual tuning. For example, Dimou et al. [22]  used a Faster R-CNN neural network architecture  [23]  to track objects in CCTV footage in order to take into account blur and Pan-tilt-zoom features of CCTVs. They showed that use of heterogeneous data and data augmentation with motion blur during the training phase can improve the performance of the detector. Peppa et al. [24]  evaluated the performances of the Single Shot MultiBox Detector MobileNet neural network, and two versions of Faster R-CNN trained on the KITTI  [25]  and the COCO  [26]  datasets on CCTV footage captured on different weather conditions.