dataesr/scientific-paragraphs-categorization huggingface dataset:
Basic OS-ELM Since ELM learns a big amount of data at once, the output weights β of the hidden layer are also tuned for once and cannot be updated. Therefore, a new problem of weights updating appears when data change with time. Consequently, ELM releases the OS-ELM as one of its variants that depend on an online learning attitude with chunks in varied or fixed size, as it also could learn data that come consecutively in one by one instance  [23]. OS-ELM experimentally has given the training rules as explained in the following steps: For a given small dataset N 0 = (X 0 , T 0 ) X 0 = {x 1 ,. , x n }, T 0 = {t 1 ,. , t n } , initialization of the network parameters: • Generate randomly all the input weights and hidden layer biases (A, BI) A = {a 1 ,. , a s }, BI = {b 1 ,. , b l }. The parameters of hidden nodes (weights and biases) must be normalized between {-1,1}  [24].
Signature patterns have been introduced to model repetitive behavior, e. g. , of customers repeatedly buying the same set of products in consecutive time periods. A disadvantage of existing approaches to signature discovery, however, is that the required number of occurrences of a signature needs to be manually chosen. To address this limitation, we formalize the problem of selecting the best signature using the minimum description length (MDL) principle. To this end, we propose an encoding for signature models and for any data stream given such a signature model. As finding the MDL-optimal solution is unfeasible, we propose a novel algorithm that is an instance of widening, i. e. , a diversified beam search that heuristically explores promising parts of the search space. Finally, we demonstrate the effectiveness of the problem formalization and the algorithm on a real-world retail dataset, and show that our approach yields relevant signatures.
Our first main contribution is a problem formalization that defines the best signature for a given dataset, so that the analyst no longer needs to choose the number of segments. By considering the signature corresponding to each possible number of segments as a model, we can naturally formulate the problem of selecting the best signature as a model selection problem. We formalize this problem using the minimum description length (MDL) principle  [4] , which, informally, states that the best model is the one that compresses the data best. The MDL principle perfectly fits our purposes because 1) it allows to select the simplest model that adequately explains the data, and 2) it has been previously shown to be very effective for the selection of pattern-based models (e. g. ,  [11, 7] ). After defining the problem using the MDL principle, the remaining question is how to solve it.
Minimum description length (MDL). Let us now briefly introduce the basic notions of the minimum description length (MDL) principle  [4]  as it is commonly used in compression-based pattern mining  [7]. Given a set of models M and a dataset D, the best model M ∈ M is the one that minimizes L(D, M ) = L(M ) + L(D|M ), with L(M ) the length, in bits, of the encoding of M , and L(D|M ) the length, in bits, of the encoding of the data given M. This is called two-part MDL because it separately encodes the model and the data given the model, which results in a natural trade-off between model complexity and data complexity. To fairly compare all models, the encoding has to be lossless. To use the MDL principle for model selection, the model class M has to be defined (in our case, the set of all signatures), as well as how to compute the length of the model and the length of the data given the model.
Algorithms The previous section presented how a sequence is encoded, completing our problem formalization. The remaining problem is to find the signature minimizing the code length, that is, finding S M DL such that S M DL = argmin S∈S L(α, S). Naive algorithm A naive approach would be to directly mine the whole set of signatures S and find the signature that minimizes the code length. However, mining a signature with k segments has time complexity O(n 2 k). Mining the whole set of signatures requires k to vary from 1 to n, resulting in a total complexity of O(n 4 ). The quartic complexity does not allow us to quickly mine the complete set of possible signatures on large datasets, hence we have to rely on heuristic approaches. To quickly search for the signature in S that minimizes the code length, we initially rely on a top-down greedy algorithm.
Algorithm runtime and code length analysis We here analyze the runtimes and code lengths obtained by variants of Algorithm 1. 3000 customers having more than 40 baskets in the Instacart 2017 dataset are randomly selected  6. Customers having few purchases are less relevant, as we are looking for purchase regularities. These 3000 customers are analyzed individually, hence the algorithm is evaluated on different sequences. Code length analysis To assess the performance of the different algorithms, we analyze the code length yielded by each algorithm on each of these 3000 customers. We evaluate different instances of the widening algorithm with different beam widths w and diversity constraints β. The resulting relative mean code For each customer, the relative code length is computed with regard to the smallest code length found for this customer.
Conclusions We tackled the problem of automatically finding the best number of segments for signature patterns. To this end, we defined a model selection problem for signatures based on the minimum description length principle. Then, we introduced a novel algorithm that is an instance of widening. We evaluated the relevance and effectiveness of both the problem formalization and the algorithm on a retail dataset. We have shown that the widening-based algorithm outperforms the beam search approach as well as a naive baseline. Finally, we illustrated the practical usefulness of the signature on a retail use case. As part of future work, we would like to study our optimization techniques on larger databases (thousands of transactions), like online news feeds. We would also like to work on model selection for sets of interesting signatures, to highlight diverse recurrences.
How all A better understanding of how resistance emerges and the factors that drive parasite gene flow across geographic regions are key to guiding interventions that aim to contain resistance. To obtain actionable information from genetic data, additional work is needed, including the generation of molecular and clinical databases of parasite resistance across multiple geographic regions, the validation of molecular markers linked to resistance in different geographical settings (eg, markers associated with piperaquine and lumefantrine resistance), and the definition of appropriate sampling strategies and genotyping methods, to optimise routine surveillance and to build capacity for agile evidence-driven decision making in malaria-endemic countries. We recall Sun Tzu's precept (The Art of War): "know our enemy, if we want to win the battle without loss".
applanata and R. belangeriana. Metabolite segregation amongst all studied Roccella species, including R. montagnei and R. belangeriana, gave some insight into the taxonomy of the latter two species, which we interpret as separate species. An additional analysis focusing on R. montagnei samples revealed chemical differences along both a latitudinal and ecological gradient (from Europa Island to São Tomé and Príncipe). Three mass spectra databases were built to dereplicate the ions, which gave an overview of the factors that could drive quantitative and qualitative metabolite composition in lichens. Additionally, several new Roccella species records are reported for the Scattered Islands, as well as São Tomé and Príncipe.
Mass Spectral Data processing Each MS1. datx file was exported as *. cdf and mass spectral data were processed using the MZmine 2. 52 freeware (http://mzmine. sourceforge. net/) (Pluskal et al. 2010 ). The mass detection was performed with a noise level set at 2E6. ADAP chromatogram builder  (Myers et al. 2017)  was run using as parameters a minimal number of scans of 4; group intensity threshold and min highest intensity of 2E6 and m/z tolerance of 0. 8 m/z. Then deconvolution was applied using the ADAP Wavelets algorithm. Dereplication was realized through three personal databases built in the lab. The first one, called HLDB, uses standards injected according to the same chromatographic protocol; identification was done by comparison of their retention time and MS1 data. The second one, called RoccellaDB, was realized through an extensive bibliographic study of the metabolites found in Roccellaceae family coupled with their molecular weight obtained from Huneck Data's  (Huneck & Yoshimura 1996) , as well as prediction of their retention time (predicted RT).
These predicted RT (text S1) were determined by a linear regression based on their lipophilicity using the online AlogPs software  (Tetko 2005 ). The third one, called LDB-MS1, was a database built by Damien Olivier-Jimenez (in prep. ); it contains a large array of lichen metabolites together with their structures, along with their calculated m/z (here for [M-H] -) and their predicted RT. All data were exported as. csv files for further multivariate analysis.
Multivariate analysis All MZmine files (MZattributes. csv) were uploaded to MetaboAnalyst 4. 0 software (https://www. metaboanalyst. ca/MetaboAnalyst)  (Chong et al. 2018)  for multivariate statistical data analysis. The file comprised a list of features (m/z, retention times and intensities). Integrity was checked, missing values were replaced by very low values (half of the lowest), and data were filtered using the interquantile range (IQR) to remove variables close to the baseline. All data were normalized using log transform and scaled by the Pareto method (mean-centered and divided by the square root of standard deviation of each variable) (van den  Berg et al. 2006).
Metabolic profiles of the five Roccella species The LC-MS profiles of the five species were acquired and the mass chromatograms are provided in Figure  3. Four major compounds were identified according to their retention time and their m/z using the HLDB (Fig. 3   317 ), but they are distinguishable by the analysis of complete mass spectrum (Fig. 4 ). R. fuciformis contains only lepraric acid ([M-H] -= 361. 1; main m/z detected: 317. 0); whereas R. phycopsis, R. applanata, R. belangeriana and R. montagnei only contain lecanoric acid ([M-H] -= 317; main m/z detected: 167. 0). The untargeted and automated processing of these LC-MS data sets, including alignment and clustering of all mass signals into so-called reconstructed chromatograms, resulted in a data matrix of 101 features for all samples analyzed.
Discussion Improved knowledge of Roccella chemistry The establishment of metabolic profiles of the five Roccella species included in this study (Table  2 ) improves our knowledge of these species' chemistry and according to the data contained in RoccellaDB (our bibliographic database focused on metabolites found in Roccellaceae), five compounds were detected for the first time in this genus: 2,4-dihydroxyphthalide (R. phycopsis; French mainland); orcinyl lecanorate (R. phycopsis; French mainland); roccellaric acid (R. montagnei; Grande Glorieuse); cholesta-3,5-dien-7-one (R. belangeriana; Europa Island); and oleic acid (R. belangeriana; Europa Island). Moreover, the chemistry of R. applanata (Europa Island) was profiled for the first time, resulting in the identification of three compounds (all monophenolic compounds or depsides), plus four requiring further investigation (all probably polyphenolic compounds).
fuciformis  (Parrot et al. 2014; Sweidan et al. 2017) , was not found here. However this aliphatic compound is not expected to be detected under UV, and appears to be hardly ionized. Therefore, we analyzed the extracts of R. fuciformis with HPLC-MS using an universal Evaporation Light Scattering Detector (ELSD) and confirmed the presence of a peak at RT = 23. 30 min corresponding to acetylportentol (data not shown). Nevertheless, R. fuciformis can be easily identified by its other metabolites, such as lecanoric and lepraric acids. Moreover, given the high intensities of the peaks for the main compounds, it was difficult to detect picrorocellin  (Marcuccio & Elix 1983 ) and 6-hydroxy-methyleugenitin in R. fuciformis  (Huneck 1972). Three acids were detected or suspected: roccellic acid was detected in R.
R. montagnei-belangeriana complex The studied dataset showed that a clear segregation occurs in metabolite composition between specimens considered in this publication as R. montagnei (thallus C+ red, generally sterile, with soralia, rarely with apothecia) and R. belangeriana (thallus C+ red, fertile, without soralia). These two taxa are considered as synonyms by  Tehler et al. (2010) , although they could not integrate the holotype into their phylogenetic analysis of Roccella species from the Paleotropics (because they were unable to receive the holotype on loan). Prashanth et al. (2008)  also asserted that R. montagnei and R. belangeriana are the same species, basing their conclusion on the analysis of ITS sequences and indicating that these two taxa are similar in both morphology and secondary chemistry.
Acknowledgements Aurélie Bernard  ( ISCR)  is acknowledged for her technical assistance. The authors warmly thank  Rémy Ferron  for his help in the set-up of the retention time prediction model used for the RoccellaDB and LDB-MS1 databases. We are also grateful to  Léonie Gourdon  who initiated the data analysis. RP gives special thanks to  Sylvie Chevalier  ( UMS 2006  PatriNat),  Mélanie Hubert  ( UMS 2006  PatriNat),  Laurent Poncet  ( UMS 2006  PatriNat),  Julien Touroult  ( UMS 2006 PatriNat ) and Serge Muller (ISYEB -MNHN) for their trust and mostly for their invaluable help in this project, and without whom none of this would have been possible. Sophie Marinesque  (TAAF),  Jonathan Grand  (TAAF) and  Cedric Marteau  ( TAAF ) who coordinated the consortium and the field campaigns, and invested countless energy and time for improving the Scattered Islands' natural heritage knowledge and conservation, are also warmly thanked.
They try to engage people and particularly sportsmen challenging them on their physical performance without using fantasy. This kind of approach consists in turning a solitary physical activity into a social activity. Moreover, research shows that the social presence in a competitive setting influences the participants' behavior and their performance. By focusing on bike-based exergames, Anderson-Handley et al. [26]  assessed the effect of virtual social presence on behavior while exercising. They used a system based on a stationary bike interfaced with "Netathlon" riding software. They showed that the presence of competitive avatars in the virtual world significantly increases pedaling intensity.
Method Our research aims to show the usefulness of extrinsic fantasy to address a wide audience and to engage people in exercising. To that end, we decided to experiment our system in a place where a wide audience is likely to be interested in testing a bike-based exergame. We took the opportunity of the 'Tour de France 2019' to recruit people who are likely to be interested in testing a bike-based exergame. We automatically log quantitative data thanks to an in-game digital tracker. A database, attached to the exergame, stores indicators related to the gaming sessions: participant's identity and data attached to a gaming session. Qualitative data are collected through a questionnaire and participants were equipped with a connected bracelet to track the heart beat. Participants were asked to answer to a questionnaire after they played.
The questionnaire is composed of demographic items, background items, graphical preferences and 10 items related to SUS questionnaire. To check the usability of the system, we use a standardized questionnaire System Usability Scale (positive version) (SUS)  [41]. This questionnaire is widely available, and has been used in many research studies. The figure  3  illustrates the procedure with diagram blocks. The data related to the heart beat is not presented in this paper. The user study was conducted in a real context (outside of the laboratory) to explore the participants' experiences which means the physical condition of the subjects was not under control. Both quantitative and qualitative data were collected to assess the usability and the acceptability.
We estimate the input entanglement by correcting on the losses ratio between the two interferometers. The raw data given in Table  I  allow to characterize the entanglement transfer performances. The first crucial parameter is the suppression of the two-photon component. It can be evaluated here by the ratio w = p 11 /(p 10 • p 01 ). This parameter amounts to w in = 0. 11 ± 0. 07 and w out = 0. 08 ± 0. 06 before and after storage respectively, thereby confirming the preservation of the single-photon FIG. 3 : Measurement of entanglement for the input and output entangled fields. The relative phase ϕ between the two modes is scanned and lead to interferences fringes for (a) before storage with visibility V = 0. 96 ± 0. 03 and (b) after storage and read-out with V = 0. 87 ± 0. 04 (V = 0. 94 ± 0.
CONCLUSION In conclusion, we have reported the first realization of a highly-efficient and reversible entanglement transfer between light and quantum memories, together with a strong and preserved suppression of the two-photon component. The demonstrated capability is an important step towards the development of scalable networking architectures. A central challenge remains to demonstrate a quantum link efficiency greater than unity  [37, 38] , i. e. , a preparation rate of entangled memories much larger than the decoherence rate, a cornerstone yet to be demonstrated in a cold atom setting. Combining high efficiency as shown here with longer lifetime  [31] [32] [33]  and with multiplexing in multiple degrees of freedom  [39] [40] [41] [42]  will be necessary. This work was supported by the European Research Council (Starting Grant HybridNet), the EU H2020 FETFLAG Quantum Internet Alliance (820445), and the French National Research Agency (quBIC project ANR-17-CE39-0005).
Change detection (CD) in SAR (Synthethic Aperture Radar) images has been widely studied in recent years and has become increasingly attractive due to the growth of available datasets. The potential of CD has been shown in different fields, including disaster monitoring and military applications. Access to multi-temporal SAR images of the same scene is now possible, and therefore we can improve the performance and the interpretation of CD. Apart from specific SAR campaign measurements, the ground truth of the scene is usually unknown or only partially known when dealing with open data. This is a critical issue when the purpose is to detect targets, such as vehicles or ships. Indeed, typical change detection methods can only provide relative changes; the actual number of targets on each day cannot be determined.
Introduction SAR (Synthetic Aperture Radar) images change detection is a promising solution for target detection when dealing with high cluttered environments, such as urban areas, forests, or harbors. Indeed, typical CFAR (Constant False Alarm Rate) detectors computed on single images also provide the scatterers that belong to the background and can fail to detect targets due to the high clutter level relative to the target response  [1]. SAR images change detection (CD) has been widely studied, for instance, in bi-date cases and for multi-temporal data in  [2, 3]. Typical bi-date based change detection approaches represent a relative change between the combination of acquisitions. As such, it might lead to misinterpretation, for example, if the target was present during the two acquisitions or when a target overlaps on two different dates.
Multi-Bidate Detection Approach This framework of change detection introduced by  [3]  performs a bi-date CD sequentially or combinatorially (all possible dates combinations). Then the output results represent different classes according to the sum of changes or the possible combination of changes occurring within the time-series. We present the representation of such a framework in Figure  1. This method, however, does not consider the temporal statistics of the pixels since they are computed for a bi-date combination. It is not possible to retrieve the current ephemeral objects at a specific date because it is a relative procedure. Finally, for each new acquisition, the whole dataset has to be recomputed, increasing the computational cost.
Singapore Region Study A more complex area was chosen in Singapore in the industrial harbor of Jurong Island. Figure  14  shows the FBR image computed using 83 Sentinel GRD (Ground Range Detected) Images of Singapore projected on Google Earth in order to validate the reference scene. The characteristics of the Sentinel data used in this study can be found in Table  3. In addition, the Figure  A2  represents the operations performed for each GRD image of the stack. The stack was afterward coregistrated according to the first image of the stack using the function «Coregistration» of SNAP (Sentinel Application Platform) software  [9]. Since the environment is constituted of man-made structures, we chose to use SAR images with always the same configuration of observation, and thus the same orbit number.
Ship Number Estimation within the Scene Using the FBR Procedure We focus now on the estimation of the number of ships for each acquisition. First, a mask was defined to exclude possible changes coming from the land areas, as shown in annex Figure  A3  in light blue. It is now possible to estimate the number of ships for each acquisition by using the FBR procedure and setting a threshold on the raw output CD for each acquisition. The change detection map can be superimposed with the reference image when the user is interested in a specific date to improve the interpretation, as shown in Figure  20b, d , and can evaluate the ship traffic for a given date. The MATLAB function bwconncomp was used to determine the number of binary objects forming a group of pixels detected within the scene. The estimated ship number for each acquisition date is presented in Figure  21  for VH.
Conclusions In this article, the concept of frozen background reference image based change detection was introduced for target detection. The simulations showed that the interpretation was improved as well as the detection capabilities for targets mixed with Gaussian noise in a SAR time-series. The use of several acquisitions improved the detection capabilities in the case of a low target SNR. An application on industrial harbor traffic monitoring was proposed using this method. In the future, testing on a dataset with known ground truth is needed to evaluate the performances of detection and extend it to a larger scale of applications. This concept can be applied in several configurations for target detection. The Sentinel data resolution here was suitable to detect ships. However, it would be interesting to test this method with a higher resolution in dense urban areas.