dataesr/scientific-paragraphs-categorization huggingface dataset:
2021b ) code, which is an updated version of the original code presented by  Kubyshkina et al. (2019a,b). Briefly, PASTA constrains the evolution of planetary atmospheres and of the stellar rotation rate by combining a model predicting planetary atmospheric escape rates based on hydrodynamic simulations (this has the advantage over other commonly used analytical estimates in that it accounts for both extreme ultraviolet(XUV)-driven and core-powered mass loss;  Kubyshkina et al. 2018) , a model of the stellar high-energy (X-ray plus XUV) flux evolution  (Bonfanti et al. 2021b ), a model relating planetary parameters and atmospheric mass  (Johnstone et al. 2015) , and stellar evolutionary tracks  (Choi et al. 2016). PASTA works under two main assumptions: (1) planet migration did not occur after the dispersal of the protoplanetary disk; and (2) the planets hosted at some point in the past or still host a hydrogen-dominated atmosphere.
PASTA returns realistic uncertainties on the free parameters (i. e. , the planetary initial atmospheric mass fractions at the time of the dispersal of the protoplanetary disk, and the indexes of the power law controlling the stellar rotation period that is used as a proxy for the stellar XUV emission) by implementing the atmospheric evolution algorithm in a Bayesian framework  (Cubillos et al. 2017 ), using the system parameters with their uncertainties A183, page 8 of 14  as input priors. All details of the algorithm can be found in  Bonfanti et al. (2021b). The only difference with respect to the analysis of the systems considered by  Bonfanti et al. (2021b)  is that here we fit the planetary atmospheric mass fraction given in Sect. 5. 1 instead of the planetary radius. This enables the code to be more accurate by avoiding the continuous conversion of the atmospheric mass fraction into planetary radius, given the other system parameters (see e.
Acknowledgements. We would like to thank the anonymous referee for the careful reading and comments that improved the quality of the manuscript. CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from Austria,  Belgium ,  France ,  Germany ,  Hungary ,  Italy ,  Portugal ,  Spain ,  Sweden , and the  United Kingdom. The  CHEOPS Consortium  would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. CHEOPS data analysed in this article will be made available in the CHEOPS mission archive (https://cheops. unige. ch/archive_browser/) K. G. I. is the  ESA CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme.
We acknowledge the use of public TESS data from pipelines at the  TESS Science Office  and at the  TESS Science Processing Operations center. Resources  supporting this work were provided by the  NASA High-End Computing (HEC)   Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research centre  for the production of the SPOC data products. D. G. and L. M. S. gratefully acknowledge financial support from the  CRT foundation  under Grant No. 2018. 2323 " Gaseous or rocky? Unveiling the nature of small worlds ". Ph. B. and N. T. acknowledge support by the  DFG Schwerpunkt SPP 1992 "Exploring the Diversity of Extrasolar Planets"  (grant  TO 704/3-1 ) and by the  DFG Research Unit   FOR 2440 "Matter under planetary interior conditions " (grant  PA 3689/1-1 ). A. C. C. and T.
G. W. acknowledge support from  STFC  consolidated grant numbers  ST/R000824/1  and  ST/V000861/1 , and  UKSA  grant number  ST/R003203/1. This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional Competitividade e Internacionalização  by these grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020 ;  PTDC/FIS-AST/32113/2017  &  POCI-01-0145-FEDER-032113 ;  PTDC/FIS-AST/28953/2017  and  POCI-01-0145-FEDER-028953 ;  PTDC/FIS-AST/28987/2017  and  POCI-01-0145-FEDER-028987 ,  2022. 06962. PTDC. Y. A. , M. J. H. and J. A. E. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. A. B. was supported by the  SNSA. L. D. is an F. R. S. -FNRS  Postdoctoral Researcher.
S. C. C. B. acknowledges support from FCT through FCT contracts nr. IF/01312/2014/CP1215/CT0004. X. B. , S. C. C. B. , D. G. , M. F. and J. L. acknowledge their role as ESA-appointed CHEOPS science team members. This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by NOIRLab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by NSF. H. J. D. acknowledges support from the Spanish Research Agency of the Ministry of Science and Innovation (AEI-MICINN) under grant PID2019-107061GB-C66, DOI: 10. 13039/501100011033. This project was supported by the CNES. O. D. S. D. is supported in the form of work contract (DL 57/2016/CP1364/CT0004) funded by national funds through FCT. B. -O. D. acknowledges support from the Swiss National Science Foundation (PP00P2-190080).
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (project FOUR ACES; grant agreement No 724427). It has also been carried out in the frame of the National Centre for Competence in Research PlanetS supported by the Swiss National Science Foundation (SNSF). DE acknowledges financial support from the Swiss National Science Foundation for project 200021_200726. A. De. acknowledges the financial support of the National Centre of Competence in Research PlanetS supported by the Swiss National Science Foundation under grants 51NF40_182901 and 51NF40_205606. M. F. and C. M. P. gratefully acknowledge the support of the Swedish National Space Agency (DNR 65/19, 174/19, 174/18). M. G. is an F. R. S. -FNRS Senior Research Associate.
K. W. F. L. acknowledges support by DFG grants RA714/14-1 within the DFG Schwerpunkt SPP 1992, "Exploring the Diversity of Extrasolar Planets". This work was granted access to the HPC resources of MesoPSL financed by the Region Ile de France and the project Equip@Meso (reference ANR-10-EQPX-29-01) of the programme Investissements d'Avenir supervised by the Agence Nationale pour la Recherche. M. L. acknowledges support of the Swiss National Science Foundation under grant number PCEFP2_194576. R. L. acknowledges funding from University of La Laguna through the Margarita Salas Fellowship from the Spanish Ministry of Universities ref. UNI/551/2021-May 26, and under the EU Next Generation funds. P. M. acknowledges support from STFC research grant number A183, page 11 of 14 A&A 675, A183 (2023) ST/M001040/1.
To standardize and improve the usefulness of field-captured images of plants,  Weaver and Smith (2023a)  report the development and implementation of FieldPrism, a system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers. They also developed FieldStation, a mobile imaging system that records images, GPS location, and other metadata on multiple storage devices. The combined use of FieldPrism and FieldStation will facilitate the rapid and standardized capture of fieldbased plant traits. The application of a standard protocol for capturing field images can also facilitate downstream image analysis and modeling, allowing the creation of three-dimensional (3D) models of plants. These models allow the digital preservation of the shape, size, and architecture of an organism, as these features would otherwise be lost when captured only via pressed specimens or two-dimensional photographs.
A fourth paper using field-captured photographs focuses on the analysis of color using images available on iNaturalist. To allow the rapid generation of color data,  Luong et al. (2023)  present a computational pipeline developed using R scripts and showcasing the utility of R shiny apps for enhancing iNaturalist collections and aiding users, including students, in natural history research. As an example, the authors analyze variation in Erysimum capitatum, a native North American species that exhibits a wide range of flower colors. The pipeline they developed allowed the testing of interesting hypotheses related to color spatial autocorrelation, climate correlation, and elevational gradients. This work highlights the enormous potential of citizen/participatory science data sets to increase the breadth of sampling for scientific research.
Semi-supervised clustering is a machine learning technique that was introduced to boost clustering performance when labeled data is available. Indeed, some labeled data are usually available in real use cases, and can be used to initialize the clustering process to guide it and to make it more efficient. Fuzzy ART is a clustering technique that is proved to be efficient in several real cases, but as an unsupervised algorithm, it cannot use available labeled data. This paper introduces a semi-supervised variant of the FuzzyART clustering algorithm (SSFuzzyART). The proposed solution uses the available labeled data to initialize clusters centers. In another hand, to deeply evaluate the characteristics of the proposed algorithm, a clustered binary data generation algorithm with controlled partitioning is also introduced in this paper.
Introduction Clustering techniques are useful to group similar unlabeled data instances together. It is used to explore unlabeled datasets and to discover similarity relationships between its instances. In some real cases, it is possible to have access to labeled data instances. These instances are, usually, not enough to learn a classifier. In fact, the available labels could represent a subset of possible labels, on only a tiny fraction of the dataset, preventing the training of a learner. However, usual clustering algorithms are not designed to consider this information about labels. In such cases, semi-supervised techniques can use the labeled instances to guide the clustering process. Fuzzy Adaptive Resonance Theory (Fuzzy ART) is a classic clustering algorithm. It was introduced by Carpenter et al.
These datasets are used to test and validate the effectiveness of binary data partitioning solutions. Being able to control the characteristics of the data generated also makes it possible to study the applicability of these methods and their weaknesses. These characteristics are also useful for choosing a machine learning technique. Indeed, the choice will be guided by the characteristics of the data and tests in circumstances similar to real cases. In addition, a study comparing dimension reduction algorithms was carried out to show the usefulness of the generated data in the choice of a dimension reduction algorithm, under the angle of the conservation of the characteristics of the clusters in space. The contributions of this paper are the following:  (1)  the proposition of a semi-supervised version of the Fuzzy ART, SSFuzzyART, clustering algorithm.
SSFuzzyART uses a set of available labeled instances to initialize the clusters centers. Also, a set of experiments is made to evaluate the proposed algorithm and to study its strength and weakness. (2) the proposition of a data generation algorithm and the study of the characteristics of the generated data and the use of the generated data to deeply study SSFuzzyART algorithm characteristics. This paper is organized as follows: Section 2 presents an overview of the current state of the art on the semi-supervised clustering, Fuzzy ART and clustering data generation. Section 3 details the classic Fuzzy ART algorithm and the proposed SSFuzzyART. Section 4 presents the clustered binary data generation algorithm, and evaluates the generated clusters through a set of experiments. Section 5 introduces the considered evaluation measures, evaluates the characteristics of SS-FuzzyART using the generated data, compares SSFuzzyART clustering to FuzzyART clustering and discusses the experiment results.
The generation of this data considers the ''cluster'' aspect in the generation process. A cluster/group is defined by the fact that the measures within each cluster are correlated, but the measures of different clusters are independent. These generation methods are different from our solution. Indeed, these approaches do not control intra-and inter-cluster distances, but rather the correlation of data in each cluster. Synthetic binary data has been used to experiment and validate clustering algorithms, like in the work of  [8]. However, the generated data used did not allow significant experimentation in terms of data partitioning. Indeed, the binary data generation method used only allowed the generation of high-dimensional data, but these data do not present detectable clusters. Thus, these datasets only allowed the study of the scalability of the proposed clustering algorithm.
Semi-supervised Fuzzy ART (SSFuzzyART) In real world problems, some labeled data instances may be available. Fuzzy ART algorithm is not able to consider this available data in its clustering process. To resolve this problem and make Fuzzy ART output more efficient in such cases, this paper introduces the SSFuzzyART algorithm. This section details first the Fuzzy ART algorithm. Next, it introduces the SSFuzzyART.
SSFuzzyART algorithm The semi-supervised Fuzzy ART (SSFuzzyART) is introduced to consider the case where some labeled data instances are available. These instances may represent all possible labels in a given problem, or just a subset of these labels. Consequently, this data is useful to make Fuzzy ART clustering more efficient and to get better quality results. The SSFuzzyART has the same steps as the classic Fuzzy ART algorithm, and it differs from it in the algorithm initialization. In fact, Fuzzy ART starts the clustering process with one neuron in the 𝐿 2 layer, which is the commitment neuron. This initialization makes it sensitive to the order of data entry. Indeed, the first coming instances will be used to initialize the clusters and then, will influence the clustering performance.
2. In the second scenario, the labeled data is used to initialize category neurons (layer 𝐿 2 ). Next, their labels are discarded, and initialization instances are clustered with the rest of data regardless of their initial labels. 3. In the third scenario, the labeled data is used to initialize category neurons (layer 𝐿 2 ). Next, initialization instances are clustered with the rest of the data, but their labels are set and cannot change during the clustering process, thus guiding the clustering process. The choice of the scenario depends on the data to be clustered and the problem to be solved. For example, if there is some doubt about the labeled instances, it could be useful to apply the second scenario and to keep SSFuzzyART cluster these instances again. In cases where the quality of labeled instances is high, then it is recommended to consider the first or the third scenario.
For this purpose, we used the available function make_blobs 2   2 make_blobslink from the python library scikit learn. The generated samples follow the normal distribution. Then, the idea is to generate a set of real-valued random clusters. Next, a threshold is used to transform these real-values to binary values. Then, if a given real value is lower than the threshold, the binary value will be 0 else it will be 1. This algorithm is called Baseline data generation in the experiments. We note that the algorithms and all the experiments are implemented with Python. Fig. 4  shows a visualization of three data sets and corresponding distance matrices with three-bit swapping rates 𝐵𝑠 = 0. 1%, 𝐵𝑠 = 20% and, 𝐵𝑠 = 50% respectively. Besides, Fig. 5  shows a visualization of two data sets generated with the Baseline data generation algorithm.
Experiments Several experiments are made to evaluate the proposed SSFuzz-yART. All experiments presented in this paper are implemented with Python3. Then a set of evaluation measures is introduced in Section 5. 1. A first set of experiments evaluates the characteristics of SSFuzzyART (Section 5. 2. Finally, an evaluation of SSFuzzyART and a comparison with FuzzyART using three available benchmarks is presented in Section 5. 3.
Evaluation measures Four evaluation measures are considered to evaluate the proposed SSFuzzyART algorithm, and to compare it to the classic Fuzzy ART. The considered measures are defined as follows: • Adjusted Rand index 3  : it is a similarity score between two clustering in the range [-    predicted clustering has exactly the same number of clusters as the target clustering. This measure is useful to evaluate Fuzzy ART and SSFuzzyART, as both do not use the number of clusters as input. • Cluster purity ratio: the ratio of pure clusters, where a cluster is considered pure if at least 70% of its elements have the same label. • Label detectability ratio: the ratio of detectable labels, where a label is considered detectable if at least 70% of data instances having that label are in the same cluster.
SSFuzzyART characteristics study on generated data SSFuzzyART is tested on a simulated dataset generated using the algorithm introduced in Section 4. 2. It consists of binary vectors grouped in clusters whose variance can be controlled with the bit-swapping parameter. This parameter ranges from 0 to 1 and the cluster variance is the highest when the bit-swapping rate is equal to 0. 5 (in most ''normal'' cases). This means that clusters are created with an assigned root cause. As the binary vector dataset is simulated, the cluster in which each point belong is known. This allows for assessing the ability of SSFuzzyART to effectively cluster binary vectors. The quality of the SSFuzzyART clustering is assessed using the Adjusted Rand Index, which compares the SSFuzzyART clustering with the known vector assignment from the generation process.
To summarize, provided clusters are well-defined and separated, SSFuzzyART can retrieve the clustering structure of binary vectors In the second experiment, the effect of the number of features (the binary vector length) is studied by computing the Adjusted Rand Index for several values of the feature number  (200, 500, 1000, 2000)  and different values of bit swapping. The generated dataset contains 40 clusters with 15 vectors each, and the vectors have 200 features. Besides, 75 initialization vectors having 15 labels (5 vectors per label) are considered in the initialization set of SSFuzzyART. Results are displayed in Fig. 10. According to Fig. 10 , the feature number has a relatively small impact on the Adjusted Rand Index, mainly for vigilance parameter values that are less than 0. 5. Consequently, the SSFuzzyART clustering process can be achieved for different values of feature numbers.
The feature number has no significant impact on clustering results. The effect of the cluster number to detect (the number of root causes) is displayed in Fig. 11. The number of features of the binary vector is 200, the number of vectors per cluster is 15, and 75 initialization vectors having 15 labels (5 vectors per label) are considered in the initialization set of SSFuzzyART. According to Fig. 11 , the number of clusters has little impact on the behavior of SSFuzzyART. Consequently, SSFuzzyART's capacity to cluster data is not dependent on the number of clusters, but more on the relative distance between clusters (accounted by the bit-swapping parameter). In Fig. 12 , we investigate the effect of the occurrence probability of 1 in binary vectors. In fact, real data are known to be sparse, with more 0 values than 1 values in their feature vectors.
SSFuzzyart evaluation and comparison with FuzzyART To evaluate the proposed SSFuzzyART algorithm and compare its performance to the classic FuzzyART, a set of experiments is made to compare Fuzzy ART results to the SSFuzzyART results using three benchmark datasets. The considered datasets were selected from UCI machine learning repository. 4 Table  2  presents the considered datasets. To evaluate SSFuzzyART, a set of experiments on three benchmarks (Table  2 ) is carried out. The clustering results of SSFuzzyART are compared to Fuzzy ART results according to the four considered evaluation measures. In each experiment, a sample from the dataset is selected to initialize the 𝐿 2 layer in SSFuzzyART. Fig. 13 , Fig. 14  and Fig. 15  presents the experiment results on respectively Iris, Wine and Breast cancer datasets.
In these experiments, the Adjusted Rand score, the number of clusters ratio, the purity ratio and the detectability ratio were measured in function of the vigilance parameter 𝜌 to compare the SSFuzzyART to Fuzzy ART. Moreover, to study the impact of the number of labels/category used in the initialization sample, a set of experiments was carried out using different samples containing a different number of labels, depending on the selected dataset. The number of selected instances depends also on the number of labels, to ensure that labels are equally present in the sample. For example, the Iris dataset contains three labels. The first sample out of the Iris Dataset contains five instances all having the same label, the second sample contains ten instances belonging to any of two different labels (five instances per label) and the third sample contains fifteen instances from the three available labels (five instances per label).
In a given sample, all considered labels are represented with the same number of instances. 4 https://archive. ics. uci. edu/ml/datasets. php  According to the results in Fig. 13 , Fig. 14  and Fig. 15 , the SS-FuzzyART leads to better results than Fuzzy ART when the vigilance parameter, 𝜌, values are low, and they yield similar results when 𝜌 values are higher. Indeed, when the 𝜌 parameter is high, the tolerance of the algorithm to create a new cluster is higher, which increases the number of clusters, then the initialization that is made to guide the clustering process is loosed. However, when the 𝜌 parameter is low, the tolerance to create a new cluster is low too. Then, the number of cluster is also low, and the algorithm will not be allowed to add many clusters, especially when most possible labels are represented with corresponding neurons in the 𝐿 2 layer.
Furthermore, a number of instances per label that is greater than 2 has always a positive impact on the Rand index, according to these results. In the previous experiments, the labeled instances that are used to initialize SSFuzzyART are clustered with the rest of the data. Then, a last experiment is carried out to compare the performance of SSFuzzyART on the labeled data (labeled instances that are used as seeds), the unlabeled data (the rest of the dataset) and the combined dataset. Iris dataset is considered in this experiment. Fig. 18  presents the results of this experiment. According to the results in Fig. 18 , the highest Adjusted Rand score is obtained on the labeled data. This is an expected result as the labeled data is used to initialize SSFuzzyART second layer neurons. The Adjusted Rand score on the unlabeled data is almost equal to the Adjusted Rand score of the entire dataset.
EEG signal is one of the main measurement tools for sleep and brain related diseases. Analysis of brain waves enables the direct identification of different sleep cycles, making it easier to conduct research on sleep quality. This property facilitates the quantitative research on sleep quality and makes the effect of auditory stimulation or music on sleep measurable in real time. [2]  Based on this context, our work aims to use Web technologies to transform smart devices into a brain-controlled music box. Our previous researches focused on web-based DSP techniques, sound design systems, visual programming languages for real-time audio processing and interactive music  [8, 9, 11] , and led to the design and implementation of a "patcher" web application named JSPatcher  [10] , a high-level visual language similar to the one of Max/MSP  [4, 5] , that enables users to visually, interactively, construct audio graphs using the Web Audio API.