dataesr/scientific-paragraphs-categorization huggingface dataset:
vs other shapelet methods We compare our proposal to Fast Shapelet or FS  [15]. We also compare our proposal to methods that learn shapelets, namely Learning time series Shapelets or LS  [17]  and ELIS++  [16]. The accuracy of ELIS++, FS and LS are taken from the ELIS++ paper and we considerer the same 35 datasets they used (marked with a plus sign in Table  4 ). The average accuracies of these models on the 35 datasets are 0. 78 ± 0. 14, 0. 81 ± 0. 14, 0. 83 ± 0. 13 and 0. 85 ± 0. 14 for FS, LS, SAST and ELIS++ respectively. Fig. 15  shows a pairwise comparison of these methods and the critical different diagram in Fig. 16  shows how significant is each method compared to the others in terms of accuracy. LS, ELIS++ and SAST are not significantly different in terms of accuracy, however, they outperform FS.
SAST vs other types of methods We also compare SAST to other state of the art algorithms that are not necessarily based on shapelets, nor on one type of features. ROCKET  [26] , HIVE-COTE  [24]  and TS-CHIEF  [25]  are to our knowledge the most accurate methods for TSC. The results of these models are taken from the UEA & UCR repository  [35]. Among the 72 datasets on which we have SAST results, ROCKET, HIVE-COTE and TS-CHIEF do not have results for the datasets DodgerLoopDay, DodgerLoopGame, DodgerLoopWeekend, Fungi and MelbournePedestrian; so we excluded these 5 datasets from this comparison. Elsewhere, we believe that the comparison we are doing here is not fair since these methods are not based on only shapelet features. However, considering the no free lunch theorem  [47] , SAST could outperform these models on some datasets and the goal of this experiment is to see how SAST stands w.
r. t to these methods that are based on combination of features. Although our model uses only shapelet features, it manages to outperform ROCKET on 5 among the 67 with 4 draws (Fig. 17(a) ). Elsewhere, SAST respectively outperforms HIVE-COTE and TS-CHIEF on 10 and 9 datasets among the 67 with 4 and 3 draws. Since SAST can perform better than HIVE-COTE on some datasets, replacing the shapelet module in HIVE-COTE with a SAST based model could increase HIVE-COTE accuracy and could reduce its time complexity since the shapelet module is the most time consuming one in HIVE-COTE. When TS-CHIEF was proposed, their authors decided not to exploit shapelet features because of their computation time. With the core shapelet recognition task we introduce in this work, we believe that shapelet features could be added in TS-CHIEF at low cost and that this could increase the accuracy of this model.
The Wilcoxon statistical test failed to reject the null hypothesis with a 𝑝-value of 0. 05, meaning that these four models are not significantly different on the considered 67 datasets. In fact, SAST, ROCKET, HIVE-COTE and TS-CHIEF respectively achieve an average accuracy of 0. 84 ± 0. 12, 0. 88 ± 0. 11,. 88 ± 0. 11 and 0. 88 ± 0. 12. These average scores clearly show that SAST is comparable to ROCKET and HIVE-COTE in terms of accuracy, and in addition SAST is more interpretable as it is a shapelet based method  [5, 6].
Model accuracies per dataset type The datasets on the UEA & UCR archive are categorized in problem types. Among the 72 datasets we have experimented on, there is 1 electric device problem, 4 ECG problems, 1 High Resolution Melt (HRM) problem, 25 image problems, 9 motion recognition problems, 1 power consumption problem, 16 sensor reading problems, 7 simulated dataset problems, 6 spectrograph problems and 2 traffic problems. We would like to see the method that is more appropriate for each problem type. However, the observations should be taken wisely since the number of datasets per problem type is relatively small to be representative. We compute these statistics among three groups of methods as in the previous subsections: the first group is SAST, STC-1 and STC; the second group is SAST, ELIS++, FS and LS; and the last group is SAST, ROCKET, TS-CHIEF and HIVE-COTE.
For each group and for each problem type, the percentage of times each method achieves the highest accuracy is computed. These statistics are shown as stacked bar plots with problem types on the 𝑥-axis and the number of times the highest accuracy is achieved on the 𝑦-axis. Above each bar, the number of datasets in the corresponding problem type is displayed. Since more than one model can achieve the highest accuracy on the same dataset, summing the percentage in a bar could be greater than 100% and the value above a bar can be less than the bar height. Fig. 18  shows the percentage of times SAST, STC-1 and STC achieve the highest accuracy per problem type. STC-1 achieves the highest accuracy on the image dataset MiddlePhalanxOutlineAgeGroup and on the sensor dataset Earthquake. STC is the only method that achieves the highest accuracies for ECG and Power.
Elsewhere STC seems more appropriate for simulated datasets. SAST tends to be generally the best choice for electric device, HRM, image, motion recognition, sensor and is always the best for spectrograph problems compared to STC approaches. When comparing SAST to other shapelet methods (ELIS++, FS and LS), we can see in Fig. 19  that SAST always achieves the highest accuracy on spectrograph problems and is therefore a good choice for this problem type. Elsewhere it achieves the highest accuracy on more than 25% of image and sensor datasets. ELIS++ is more suitable for ECG, image, motion, and sensor problem types. LS is a good choice for simulated datasets. Finally, Fig. 20  reveals that ROCKET, TS-CHIEF and HIVE-COTE win on more datasets than SAST, but with a relatively small difference in accuracy.
ROCKET seems to be the most promising method for ECG, motion, sensor, simulated, spectrograph and traffic datasets, while HIVE-COTE is a good choice for image and power datasets. TS-CHIEF is a fair option for device. Although SAST achieves the highest accuracy than ROCKET, HIVE-COTE and TS-CHIEF on some datasets, it sometimes obtains the same average accuracy as these methods. In fact, Table  2  gives the mean and standard deviation of each model accuracy per dataset type. We can see that SAST achieves the same average accuracy as the state of the art methods on spectrograph and is on average relatively closed on many other data types, except device (but there is only one dataset of that type). This results emphasize the fact that SAST can achieve accuracy equal to or closed to the state of the art method accuracy while offering easier interpretability.
Time series length Here we use the dataset HouseTwenty from the UEA & UCR repository  [35]. It is a binary dataset of electricity usage in houses. The training set has 34 time series and of length 3000 each. We vary the series length starting at 32 and only the first time steps up to the current length are used to train our models. More precisely, we consider the HouseTwenty dataset with time series truncated at length 2 5 , 2 6 , 2 7 and finally 2 8. The running time of each model is given in Fig. 21(a). For each of the four models, the running time increases with the length of time series in the dataset. However, SAST models are much more scalable than STC, and SASTEN-A is the most scalable of all, since it uses a fixed number of shapelet candidates whatever the length of time series. For SASTEN-A, increasing the length of the time series only increases the computation time of the similarity between time series and shapelet candidates.
More specifically, STC takes about 1 hour and 40 minutes to train on a dataset of 34 time series of length 64, while SAST, SASTEN and SASTEN-A take about 13 seconds, 27 seconds and 8 seconds respectively. For the same number of time series but now of length 256, STC takes a bit more than a day, while SAST, SASTEN and SASTEN-A take about 14 minutes, 26 minutes and 2 minutes respectively. Therefore, even our slowest method SASTEN is 55 times faster than STC. SASTEN-A and SAST are respectively 1440 times and 102 times faster than STC.
Interpretability The predictions of a SAST model trained on a dataset are explained by identifying and visualizing the shapelets that have been learned for that dataset. This is how the explanation of shapelet methods is given in the literature  [5, 48]. This is done using feature importance analysis (see Proposition 2). Each feature is related to a shapelet candidate extracted from a time series whose class label is known. Shapelet candidates related to the most important features are the top best shapelets. We say that any shapelet candidate is from the class of the time series from which it has been extracted. Therefore, the class label of a time series can be interpreted by looking at the class labels of the shapelet candidates to which it is the most similar. Let us interpret the predictions of SAST-RF and SAST-Ridge trained on the Chinatown dataset.
We consider this dataset because it has only two classes and time series of length 24, it is therefore easy to visualize this dataset. However, what we are doing here is applicable to any dataset. Since SAST-RF uses a tree based classifier, information gain is used as feature importance. With SAST-Ridge, the importance of feature is given by the absolute value of the corresponding learned weight. Although feature importance is computed differently for both models, we show that their predictions are interpretable in the same manner. In order to predict the class label of a test time series, SAST identifies the most important features similar to the time series. In other words, SAST checks if the time series contains subsequences  that are similar to the most important features. Fig. 24  shows the matches between the top-5 most important features learned by SAST-Ridge and two randomly selected test time series.
25  top) which is a shapelet candidate of class 1. The other top best shapelet candidates, which are all from class 2 do not match with the first time series. This explains why the predicted class label for the first time series is class 1 and not class 2. The first, third, fourth and fifth top best shapelet candidates, which are all from class 2 have near perfect matches with the second time series, while the second top best shapelet candidate, which is from class 1 does not match (see Fig. 25 bottom ). Hence, we can interpret why the class label of the second instance is predicted as class 2 and not class 1. Therefore, we have proved experimentally in this Section that SAST-RF and SAST-Ridge automatically learn to put more attention on the subsequences that are shapelets for any given dataset.
Conclusion In this paper, we have shown that the number of shapelet candidates in a shapelet algorithm can be reduced considerably without losing accuracy. We have also shown that it is not always necessary to learn shapelets beforehand of classification. We introduced the Scalable and Accurate Subsequence Transform (SAST) algorithm which is interpretable, accurate and a more scalable alternative to the Shapelet Transform algorithm. Furthermore, SAST is comparable in terms of accuracy to the state of the art methods ROCKET, HIVE-COTE and TS-CHIEF, especially for the spectrograph dataset type, while offering easier interpretability. Our experiments have revealed that a good trade-off between accuracy and scalability can be found by ensembling different SAST models, each one focusing on different shapelet candidates.
Algorithm 1 : 11 𝐷 13 𝑐𝑙𝑓 11113 ShapeletTransformK Input: 𝐷: dataset, 𝑘: nb of instances per class, 𝐶: classifier, 𝑙𝑒𝑛𝑔𝑡ℎ_𝑙𝑖𝑠𝑡: subsequence lengths, 𝑚𝑖𝑛_𝑖𝑔: min IG 1 begin 2 𝐷 𝑐 ← 𝑟𝑎𝑛𝑑𝑜𝑚𝑙𝑦𝑆𝑒𝑙𝑒𝑐𝑡𝐼𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠𝑃 𝑒𝑟𝐶𝑙𝑎𝑠𝑠(𝐷, 𝑘) 3 𝑆 ← 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑆ℎ𝑎𝑝𝑒𝑙𝑒𝑡𝐶𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑠(𝐷 𝑐 , 𝑙𝑒𝑛𝑔𝑡ℎ_𝑙𝑖𝑠𝑡) 4 𝑆 ← 𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑆ℎ𝑎𝑝𝑒𝑙𝑒𝑡(𝑆, 𝐷, 𝑚𝑖𝑛_𝑖𝑔) 5 𝐷 𝑓 ← ∅ 6 for 𝑖 ← 1 to 𝑛 do 7 𝑥 𝑖 ← [] 8 for 𝑗 ← 1 to |𝑆| do 9 𝑥 𝑖 [𝑗] ← 𝑑𝑖𝑠𝑡(𝑇 𝑖 , 𝑆 𝑗 ) 10 end 𝑓 ← 𝐷 𝑓 ∪ {(𝑥 𝑖 , 𝑐 𝑖 )} 12 end ← 𝑡𝑟𝑎𝑖𝑛𝐶𝑙𝑎𝑠𝑠𝑖𝑓 𝑖𝑒𝑟(𝐶, 𝐷 𝑓 ) 14 return (𝑐𝑙𝑓 , 𝑆) ; // the trained classifier and the shapelet candidates 15 end.
Figs. 22 22 and 23 show the top-5 best shapelets plotted on the reference time series for the Chinatown dataset with respect to SAST-Ridge and SAST-RF respectively. The top rows of the figures are the reference time series selected from class 1, while the second rows are the reference time series selected from class 2. A perfect match between a shapelet candidate and a reference time series means that the shapelet has been extracted from that reference time series. Hence, the top-5 best shapelets learned by SAST-Ridge are from class 1. The second of the top-5 best shapelets learned by SAST-RF is from class 1, while the four others are from class 2.
W power ). An average of three measurements was calculated for data reproducibility. The relative abundance of clay (i. e. < 2 m), silt (2-63 m) and sand (> 63 m) fractions was calculated for each sample. The silt fraction was subdivided into fine (2-10 m), medium (10-30 m) and coarse silt (30-63 m) fractions. Mineralogical analysis was performed by X-ray diffraction (XRD) using a Bruker® D8-Advance Eco diffractometer (CuKα radiance, =1. 5418Å, 40 KV, 25 mA) coupled with a Lynxeye XE detector (AGEs, University of Liège). The sampling resolution reaches up to 1 cm for LTa19-3 (n=72) and 4 to 5 cm for LLEs19-3 (n=10) and LTe19-4 (n=22). The dried (40°C) bulk sediment sample was ground by hand with an agate mortar. The powder was sieved at < 150 m and transferred to a plastic holder using the backside method  (Moore and Reynolds, 1989)  and scanned from 2 to 70° 2 with a step size of 0.
The first derivative of the thermogravimetric curve (DTG) represents the rate of weight loss. It was calculated since DTG peak gives the specific temperatures characteristic of the different mineral components more accurately than the TG curve and indicates the characteristic temperature of the reactions occurring in the samples  (Földvári, 2011) , in particular dehydration and dehydroxylation for clay minerals. The STA data were processed using the evaluation software Metler Toledo STAR e SW16. 40. Fourier Transform Infrared Spectroscopy (FTIR) was carried out on 8 samples already analyzed by STA using a Nicolet NEXUS Spectrometer (Laboratory of Mineralogy, University of Liège). Pellets were made from 2 mg of powdered sample, ground to < 250 µm and 148 mg of potassium bromide. The measurement was made in the range of 4000 to 400 cm -1 with a resolution of 1 cm -1.
g. Frugone-Álvarez et al. , 2017). Measurements of fourteen elements (i. e. Al, Si, S, K, Ca, Ti, Mn, Fe, Co, Ni, Br, Sr, Zr and Pb) were done with voltages of 10, 30 and 50 kV and counting times of 15, 20 and 25 seconds, respectively. Semi-quantitative concentration profiles of the elements were plotted through the depth of the sediment core based on the variation of their peak areas measured on XRF spectra and expressed as counts per second (cps). A statistical approach was applied to the XRF dataset using version 4. 2. 2 of the R environment (R Development Core Team, 2013). First, the XRF count data were transformed to centred logratio (clr) using rgr package  (Garrett and Garrett, 2018). A Robust Principal Component Analysis (RPCA,  Candès et al. , 2011)  was then applied to the clr data using the package pcaPP  (Filzmoser et al.
, 2018)  as proposed by  Żarczyński et al. (2019). The 14 elements were included in the analysis. The principal components (PC) with an eigenvalue > 1 were retained as significant for the total variance of the dataset  (Davis, 2002). Sediment organic geochemistry, including organic carbon (C) and nitrogen (N) stable isotope ratios (δ 13 C, δ 15 N) and elemental concentrations, was characterized by Isotope Ratio Mass Spectrometry (IRMS)/Elemental Analyzer (EA) at LETIS (University of Liège, Belgium). The analysis was carried out at ~4 to 10 cm intervals using a VarioMicro Elementar analyser coupled to a precisION IRMS (LTe19-4). Prior to the analysis, the samples were acidified by exposing the freeze-dried and ground sediments to 37% HCl vapor for 10 hours to remove carbonates. Bulk N was considered to comprise primarily organic nitrogen given the generally low concentration of inorganic nitrogen in lake sediments.
Acknowledgments The fieldwork was done in June 2019 by  N. Fagel  and  I. Israde-Alcantara , with the help of  Luis Matilde Gacia ,  Julio Cañas ,  David Sanchez  and  Angel Zamudio. Thanks to  Arturo Chacon  for providing a boat location. Part of the analysis costs was supported by a  FNRS  funding for the  PDR  proposal  HolMecl 2020-2025. The authors thank  Joel Otten  and  Nicolas Delmelle  ( Geology, ULiège ) for their technical support with the sedimentological and mineralogical preparations and analyses. Subsampling and sedimentological analyses were done as part of the master thesis of  Gaelle Wanlin. Prof. Frédéric Boulvain  ( Sedimentology, ULiège ) brought his expertise on sedimentological features and core description. Prof. Frédéric Hatert  ( Mineralogy, ULiège ) helped with the TOPAS software.
Introduction: In the field of planetary remote sensing, terabytes of data from extraterrestrial planetary bodies are made available to the scientific community today. With the development of new missions, the volume and variety of these data continue to grow. This development has been largely facilitated through developments leading to higher integrated technology allowing to build compact and efficiently performing platforms and instrumentation. A likely other reason might be an increasing international competitive pressure which motivated new developments in sensor technology and mission designs. Along with the increase of raw data volume, the volume of derived research data continues to grow in parallel, albeit at a slower rate. While mission data, i. e. primary research data (PRD), as derived from instruments, are commonly well maintained within archives such as the Planetary Data System (PDS) and Planetary Science Archive (PSA), derived research data, i.
e. , secondary research data (SRD might not always share the same fate. Scientific studies, resulting in further derived data do not often receive the same attention. In order to accomplish efficient research data management (RDM) for both kind of RD and to provide tools across research domains it is important to develop appropriate and effective structures. In that context, research data infrastructures (RDI) have the primary goal of collecting existing data under uniform guidelines and to make data accessible in defined ways. RDI initiatives supports the establishment of research management practices, including adopted metadata standards, research lifecycle and associated infrastructures. E. g. The European Open Science Cloud  [1]  (EOSC) as RDI undertaking, "aims to federate existing and future RDI -across disciplines -under a single umbrella, and provides (open) services for the European researcher community.
Aims: The overarching goal here is to develop a practical concept, and to address requirements to enable open, transparent and sustainable access to planetary maps as part of the open planetary spatial research data family. We here refer to maps as conventional cartographic visualization products which contain a classical map layout composed of the main map contents (the topics), map frame, map grid information related to at least one cartographic reference system, map scale information, map title and map legend, as well as other map-related metadata information  [3, 4]. These maps can be provided in different formats on various media.
Method and Results: In order to identify a practical approach to assess the current situation and to find a strategy to close the planetary research-data cycle, we first need to analyze the main elements along its current research path. (1)  We here first identify how the current situation, including users (actors) and data flows, is constituted and how main processes are characterized. (2) In a next step, additional as well as potentially alternative roles and data paths, respectively, are identified and further developed through user and system requirements. (3) Finally, based on the assessment of the current situation and a requirement analysis, potential solutions are highlighted that build upon existing Earth-based developments, especially the INSPIRE framework  [5]  or demonstrate which additional developments are required.
Especially the adaption of spatial research infrastructure developments shows how the planetary community could benefit from an adaption of existing infrastructure environments in order to Summary: The aim of this study is to characterize the relationships between different interest groups (stakeholders) and products as well as processes, and to discuss how processes could potentially be optimized and streamlined in order to re-insert planetary maps (and research information) into a healthy and sustainable research cycle. Developments like this could build a thematic bridge to Earth-based RD repositories like EOSC [6], Earth cube  [7]  and Pangea [8] in order to push the reuse of planetary maps forward. One way of realizing an approach targeted at improving the re-use of research data in the planetary sciences is by merging the existing alternatives and to integrate the structural benefits from the INSPIRE (or any other SDI) domain.
Acknowledgment These works have been partially funded by the  Bourgogne Franche-Comté région , by the  COLAMIR  project (contract  ANR-16-CE10-0009 ), by the  MiMedi  project funded by  BPI France  (grant No. DOS0060162/00 ) and the  European Union  through the  European Regional Development Fund of the Region Bourgogne-Franche-Comte  (grant No. FC0013440 ), and supported by the  EIPHI Graduate school  (contract  ANR-17-EURE-0002 ), the  French ROBOTEX network  (grant no. EQUIPEX+ TIRREX ANR-21-ESRE-0015 ) and the  French RENATECH  network through its  FEMTO-ST technological facility MIMENTO. The authors acknowledge  Thomas Huot ,  Chencheng Tang ,  Mélissa Blot ,  Patrick Rougeot ,  Johnny Beaugrand  and  Miguel Suarez  for their help in early developments and useful discussions.
V. -F. Drȃgoi was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI, project number PN-III-P1-1. 1-PD-2019-0285, within PNCDI III. B. Colombier was supported by the French National Research Agency in the framework of the "Investissements d'avenir" program "ANR-15-IDEX-02" and the LabEx PERSYVAL "ANR-11-LABX-0025-01".
Numerical optimization and related computations Numerical simulations are carried out using PyTorch. We solve the optimization problem in Eq. (  18 ) through stochastic gradient descent on the loss on a dataset with P = 5000 samples from p(x), divided in minibatches of size 128, with the Adam optimizer  [32]  with learning rate equal to 10 -4 and otherwise standard hyperparameters. The learning rate for β, η β , is set to 0. 1. The training is iterated over multiple passes over the data (epochs) with a maximum of 5000 epochs and it is stopped when the training loss running average remains unchanged (with a tolerance of 10 -5 ) for 100 consecutive epochs. The parameters are initialized as follows. The preferred positions, c i , are initialized as the centroids obtained by applying a k-means clustering algorithm (with k = N ) to the set of stimuli in the dataset.
Acknowledgments We thank  Luigi Gresele  and  Giancarlo Fissore  for useful discussions, and  Trang-Anh Nghiem  and  Luc Stebens  for comments on a earlier version of the manuscript. This work was supported by the  Alfred P. Sloan Foundation  through grant  G-2020-12680 , the  CNRS  through  UMR8023.