dataesr/scientific-paragraphs-categorization huggingface dataset:
An inherent strength of the GP paradigm is its symbolic representation that can be adapted to a wide range of problems. For example, GP can be used either for feature selection, feature extraction, or classification. In  [37] , a GP multi-tree classifier was used in combination with Empirical Mode Decomposition (EMD) in an EEG dataset for epilepsy detection. A follow-up of this work with modified search operators was presented in  [38]. A decision-tree-based model was introduced in  [39] , where arithmetic and logical rules were inferred in a multi-level GP representation. Although electrocorticogram (ECoG) was used instead of EEG by the authors in  [40] , GP was employed as a classifier to solve an epileptic seizure recognition problem. In  [41] , the authors proposed a multi-layered population GP where each layer evolves a discriminant function upon a training set; consequent layers evolve on top of the previous, improving the overall classification accuracy.
A learning assembly was evolved by GP to build discriminant functions in the work by Chien et al. [42]. More closely related to this work, Guo et al. [25]  built a feature extraction model using GP for an epilepsy EEG dataset. In  [43] , GP was used similarly, as this was a feature extraction task. These cited works are further discussed in Section 3. 1, since there are some differences worth reviewing compared with our proposal. Apart from feature extraction methods, GP can be used as a simple feature selection tool like in  [44]. In  [45] , although it is not applied to EEG, GP was used to build feature transformation models where the individual is represented with a single root node, thus producing a single new feature. The remainder of this paper is organized as follows. In Section 2, the reference system based on CSP and LDA is described and analyzed.
Acquisition Protocol A group of individuals (critical personal information was protected) participated in the experiments during a 2011 campaign at the Université de Bordeaux, France. Each person was subjected to the same procedure, which is summarized in the following paragraph, with more details given in  [9]. The participants were put inside a soundproof room where the experiments were performed. A recording cap with 58 electrodes was placed over the scalp of each person. Then, a special session was executed, commonly referred to as Contingent Negative Variation (CNV) protocol  [46] , where the goal is to determine if the subject is truly in the appropriate physiological state. Two CNV tests were used, each one corresponding to a specific mental state. After each CNV test, the data used in this study were recorded for each subject.
Subjects The experiment involved 44 subjects (mixed gender), non-smoking, aged from 18 to 35 years old. All are right-handed to avoid variations in the characteristics of the EEG due to handedness linked to functional inter-hemispheric asymmetry. After the CNV test, several subjects were rejected, since the CNV test determined that they did not reach the expected mental state. Therefore, only 13 valid individuals were used to build the dataset used in this work.
Therefore, our dataset is stored in a matrix R 58×(26×22)×2048. Recorded EEG signals usually contain noise and mixed frequencies. These mixed frequencies are partly due the oscillatory rhythms present in normal brain activity  [47]. The most studied rhythms are alpha  (8) (9) (10) (11) (12) , beta  (19) (20) (21) (22) (23) (24) (25) (26) , gamma (1-3. 5 Hz), and theta (4-8 Hz), which are associated with different psycho-physiological states. The alpha waves are characteristic of a diffuse awake state for healthy persons, and can be used to discern between the normal and relaxed states. Actually, in some recordings, alpha waves begin to appear when the subject is starting to relax. In this work, band-pass filtering is applied to discriminate frequencies outside the alpha and beta bands. Again, an analysis is required to find out which cutoff frequencies are useful to build the filter by sweeping a range of candidate values.
Common Spatial Patterns Like other techniques that derive a projection or transformation matrix based on some specific requirements (e. g. , Independent Component Analysis (ICA) or PCA), the CSP is a technique where a matrix W is built that simultaneously maximizes the variance of a multivariate subset with an arbitrary label and minimizes the variance for another subset with a different label. Projected data are given by F = XW, where X is the data transformed from the original dataset R 58×(26×22)×2048 ∈ R 3 into X 58×1171456 ∈ R 2 with dimensions p × (n × 26 × T), where p is the number of channels, n is the number of trials, and T the trials' length for all subjects and two classes. W is a filter matrix, as described below. This technique is useful in a binary classification problem because data are projected onto a space where both classes are optimally separated in terms of their variance.
This is given by F1 i = (µ C1 i -µ C2 i ) 2 σ 2 C1 i + σ 2 C2 i i=1. r , ( 14 ) where µ C1 , µ C2 , σ C1 , and σ C2 are the means and standard deviations corresponding to class one and two for the ith feature  [61]. For any given tree K, r local fitness measures are calculated. It was found that the use of the local fitness in other operators did not help the search. (b) Global fitness measure. The calculation of global fitness is the result of a two-tier process. In the first level, an initial regularization is performed over the tree output using the training data; then, the classification accuracy (Acc) is calculated. In detail, this is done by artificially changing the covariance of transformed training data (i. e. , the training data in the new feature space created by an evolved transformation), defined by β i (x) = [α C1 i ({K(x)| C1 } -µ C1 i ) + µ C1 i , α C2 i ({K(x)| C2 } -µ C2 i ) + µ C2 i ], (15) and α C1 i = 1 + 2σ C1 i max({K(x)| C1 }) -min({K(x)| C1 }) , ( 16 ) α C2 i = 1 + 2σ C2 i max({K(x)| C2 }) -min({K(x)| C2 }) , ( 17 ) where {K(x)| C1 } denotes the tree output corresponding to all the data samples from class C1 (normal), and {K(x)| C2 } is the same but corresponding to class C2 (relaxed).
Experimentation and Results In this section, we present experimental details regarding the proposed system. The reported algorithms were implemented in MATLAB. Specifically for the + FEGP algorithm, the GPLAB (http://gplab. sourceforge. net/) [62]  toolbox was used as a starting point for the implementation. The experiments were executed in a setup with an Intel Xeon at 2. 4 GHz with 32 GB of RAM. Given the stochastic nature of GP, a series of multiple runs were executed in order to statistically determine the algorithm performance. Following scheme for data partitioning and validation, the experiments involved 30 independent runs for each fold. The results presented in this work contain 390 runs in total (13 × 30). Details of the values used for filtering, CSP, and LDA were mainly discussed in Section 2.
All individuals were started with three features at the beginning of the runs. The evolved models produced a minimum of six and a maximum of 33 new features, with an average of 17. 35. As mentioned earlier in Section 3, there is an inherent flexibility in terms of the classifier selection after using the + FEGP. Therefore, additional classifiers were evaluated: Tree Bagger (TB), Random Forests (https://github. com/ajaiantilal/randomforest-matlab) [64]  (RF), k-NN, and SVM. All of them produce non-linear decision functions, as opposed to the relatively simpler LDA. With the exception of RF, all classifiers are based on their MATLAB implementations. The hyperparameters were optimized using a bayesian approach, and are shown in Table  5. The classification accuracies are summarized in Table  6 , including the specificity, recall, and F-score metrics per class.
Funding: Funding for this work was provided by  CONACYT (Mexico) Basic Science Research  Project No. 178323 , the  FP7-Marie Curie-IRSES 2013 European Commission program  through project  ACoBSEC  with contract No. 612689 , TECNM with contract No. 8027. 20-P, and  CONACYT  project  FC-2015-2/944  " Aprendizaje evolutivo a gran escala ". Finally, the first author was supported by  CONACYT  scholarship No. 294213.
The ADAM (A Surface Reflectance Database for ESA's Earth Observation Missions) product (a climatological database coupled to its companion calculation toolkit) enables users to simulate realistic hyperspectral and directional global Earth surface reflectances (i. e. , top-of-canopy/bottom-of-atmosphere) over the 240-4000 nm spectral range (at 1-nm resolution) and in any illumination/observation geometry, at 0. 1 • × 0. 1 • spatial resolution for a typical year. ADAM aims to support the preparation of optical Earth observation missions as well as the design of operational processing chains for the retrieval of atmospheric parameters by characterizing the expected surface reflectance, accounting for its anisotropy. Firstly, we describe (1) the methods used in the development of the gridded monthly ADAM climatologies (over land surfaces: monthly means of normalized reflectances derived from MODIS observations in seven spectral bands for the year 2005; over oceans: monthly means over the 1999-2009 period of chlorophyll content from SeaWiFS and of wind speed from SeaWinds), and (2) the underlying modeling approaches of ADAM toolkit to simulate the spectro-directional variations of the reflectance depending on the assigned surface type.
Secondly, we evaluate ADAM simulation performances over land surfaces. A comparison against POLDER multi-spectral/multi-directional measurements for year 2008 shows reliable simulation results with root mean square differences below 0. 027 and R 2 values above 0. 9 for most of the 14 land cover IGBP classes investigated, with no significant bias identified. Only for the "Snow and ice" class is the performance lower pointing to a limitation of climatological data to represent actual snow properties. An evaluation of the modeled reflectance in the specific backscatter direction against CALIPSO data reveals that ADAM tends to overestimate (underestimate) the so-called "hot-spot" by a factor of about 1. 5 (1. 5 to 2) for barren (vegetated) surfaces.
In this context, the use of current Lambertian reflectance or albedo databases is reduced, because of their limited ability to reproduce the spectral and/or directional variations. The spectral information most commonly used is surface albedo databases which are also limited to the few broad-band channels of the instrument(s) they derive from, and these are provided for a typical observation geometry. This holds for the "classical" Lambertian equivalent reflectance (LER) databases  [23] [24] [25] [26] , as well as several databases of (i) multi-spectral albedos (which differ from LER by the integration of the surface reflectance over the entire hemisphere) either inferred from MODIS  [27] , Multi-angle Imaging Spectroradiometer (MISR)  [28] , MERIS  [21]  or by the fusion of several instruments (e.
g. , GlobAlbedo,  [29] ), and (ii) multi-spectral reflectances corrected for the surface anisotropy every eight days from MODIS Collection 5  [30]. Multi-spectral/multi-directional datasets derived from POLDER (Polarization and Directionality of the Earth's Reflectance)  [31, 32]  or reflectance anisotropy parameters derived from MODIS (MCD43A1 BRDF (Bidirectional Reflectance Distribution Function)/Albedo Model Parameters Product  [27] ), characterizing the surface anisotropy, remain limited to specific broad-bands in the visible to near-infrared range. Another example is the daily MODIS spectral BRDF climatology derived at 1 km from Collection 6 for the time period 2000-2016  [33]. More recently, a land surface atlas was derived from MODIS data to simulate hyperspectral/directional reflectance within the 400-2500 nm range  [1] , but both this and the previous reference only apply to land surfaces.
It was therefore concluded that a monthly mean climatology of hyperspectral surface reflectances is needed for the whole Earth's surface, which led to the initiation of the European Space Agency (ESA) study ADAM (A Surface Reflectance Database for ESA's Earth Observation Missions). In ADAM, a monthly climatological database and a companion toolkit were designed to simulate realistic spectro-directional variations of the Earth's surface reflectance at the global scale with a moderate 0. 1 • × 0. 1 • resolution (~10 × 10 km 2 ). The ADAM database contains climatological data, which are used as input by ADAM stand-alone calculation toolkit application programming interface (API) to calculate hyperspectral/bidirectional reflectance (or bidirectional reflectance factor according to Schaepman-Strub and colleagues  [34] , which is unitless), over the 240-4000 nm spectral range, with a spectral resolution of 1 nm and given any illumination/observation geometry.
ADAM (database and API) is freely available online, after registration, at http://adam. noveltis. fr. Different datasets and processing algorithms are applied depending on the surface type. For land surfaces (soil/vegetation and snow), the ADAM database contains a climatology (monthly means) of normalized reflectances for a reference illumination/observation geometry in the seven MODIS land broad-bands, which are processed by the FondsDeSol (FDS) processing chain  [30]  from the MODIS MOD09 products (representative of year 2005) derived from Aqua and Terra observations. For water surfaces, the database is made of climatological data (over the 1999-2009 period) of (i) chlorophyll content derived from SeaWiFs (Sea-viewing Wide Field-of-view Sensor onboard OrbView-2) and (ii) surface wind speed from SeaWinds (onboard QuikSCAT).
• For the soil/vegetation pixels, the spectral interpolation/extrapolation of the MODIS broad-bands/normalized reflectances between 240 and 4000 nm is performed using empirical orthogonal functions (EOFs) derived from spectral reflectance databases of soil/vegetation/leaf optical properties (similar to  [1] ). The Ross-Li-HS kernel based BRDF model  [35]  is used to calculate the reflectance spectrum in any illumination/observation geometry. A separate processing scheme is applied for snow covered surfaces: It relies on the Asymptotic Radiative Transfer (ART) model  [36] , fitted to the normalized reflectances, to simulate the spectro-directional variations of snow reflectance. Moreover, it is possible to calculate the uncertainty attached to the land surface reflectance: the calculation relies on the variance covariance matrix of the reflectance values between the seven MODIS bands for each 0.
• Over water surfaces, the reflectance is simulated by a combination of three components: (i) the water column and (ii) foam, that mainly shape reflectance spectral variations, and (iii) the specular reflection that essentially drives reflectance directionality. The water column reflectance is parameterized as a function of the chlorophyll content and specular reflection (also referred to as sunglint), which mostly depends on the wind speed. In Section 2, we describe the reflectance models used in the ADAM API. Section 3 presents the data used for building the ADAM monthly climatology database over land and water surfaces and their processing (including the gap-filling approaches). In Section 4, we perform an evaluation of ADAM simulations of spectro-directional reflectances over land surfaces.
ADAM Spectral and Directional Calculation Models (API Toolkit) Depending on the surface type, specific models are implemented in the ADAM calculation toolkit to simulate the spectro-directional variations of surface reflectance. Over land, the main input to the ADAM models is the ADAM climatological broadband MODIS-FDS reflectances (see Section 3. 1) that are provided in a standard illumination and viewing geometry. The standard geometry is defined for a sun zenith angle θ s of 45 • and a viewing at nadir (i. e. , the view zenith angle θ v is 0 • , in which case the relative azimuth angle φ is meaningless). Further, we define the backward scattering direction as φ = 0 • while the forward scattering direction corresponds to φ = 180 •. The API calculation toolkit is written in Python3 and requires one input configuration file (in Python) to be provided by users.
Spectral Modeling Spectral calculation relies on the assumption that the reflectances for soil and vegetation are correlated across the 240-4000 nm range and that a few spectral measurements in the visible and near-infrared provide sufficient information on the reflectance in the UV (below 400 nm) and shortwave infrared (from 2500 nm to 4000 nm)  [1, [38] [39] [40]. That assumption is further supported by statistical analysis (results not presented here) of tens of spectra available from several databases, containing measured spectral reflectances from soil and vegetation samples (presented in Section 3. 4. 1). The simulated reflectance spectrum ρ(λ)over the 240-4000 nm spectral range in the standard observation geometry is expressed as a function of an averaged spectrum ρ EOF (λ) and a few EOF eigenvectors ρ EOF j v (λ) (centered), which are both derived from these databases (presented in Figure  1 ): Only the first four eigenvectors (which contain 99% of the measured variance) were considered as their performance in terms of model-data reconstruction (over the measured reflectance spectra) was globally better compared to cases where either three or five eigenvectors were used instead.
The weights were calculated on a pixel/month basis in order to minimize the misfit between the simulated reflectances and the normalized MODIS-FDS reflectances in the seven broad-bands considered. D is the matrix of the four eigenvectors sampled at the MODIS band centers,  ( ) is the averaged spectrum sampled at the same wavelengths, and  ( ) is the MODIS-FDS surface reflectance measurements from the ADAM climatological database. The vector of the optimal weights is estimated as: ρ(λ, 45 • , 0 • , 0 • ) = ρ EOF (λ) + M j=1 α j ρ EOF j v (λ) (1 =. ( ( ) - ( )) with = ( ) (2) For each pixel/month, the uncertainty on the spectrally extrapolated spectrum can be calculated using the variance/covariances of the reflectances in the seven MODIS channels (provided at all land pixels) (see Section 3.
cos -) + 1 + cos 2 cos cos (4) Only the first four eigenvectors (which contain 99% of the measured variance) were considered as their performance in terms of model-data reconstruction (over the measured reflectance spectra) was globally better compared to cases where either three or five eigenvectors were used instead. The weights α j were calculated on a pixel/month basis in order to minimize the misfit between the simulated reflectances and the normalized MODIS-FDS reflectances in the seven broad-bands considered. D is the matrix of the four eigenvectors sampled at the MODIS band centers, ρ EOF (λ MODIS ) is the averaged spectrum sampled at the same wavelengths, and ρ ADAM (λ MODIS ) is the MODIS-FDS surface reflectance measurements from the ADAM climatological database. The vector of the optimal weights is estimated as: ( , , ) = 4 3 1 cos + cos 2 - cos  + sin  × 1 + 1 +   - 1 3 (5) α = P.
Land The input dataset for the ADAM database over land surfaces (soil, vegetation, and snow covered surfaces) is climatological values of normalized reflectances in the seven MODIS land spectral channels (centered at 469, 555, 645, 858, 1240, 1640, and 2130 nm), provided in a standard illumination/observation geometry. It is derived from the FondsDeSol (FDS) processing chain  [30] , which generates monthly normalized surface reflectances from MODIS Collection 5 MOD09A1 inferred from the Terra and Aqua instruments at the original 500 m and eight-day resolutions  [53]. The output gridded monthly FDS climatology of normalized surface reflectances was generated for one typical year (2005) at 0. 1 • × 0. 1 • , along with an associated variance-covariance matrix to quantify uncertainty. The details of the FDS processing scheme are described in more detail by Gonzalez and colleagues  [30] ; only differences from the original publication are reported here.
Sea Ice Sea ice pixels are masked in the MOD09A1 data (where they are frequently confounded with clouds) that are used for building the ADAM product over land, and in the SeaWiFS and SeaWinds datasets over the ocean. In ADAM, the corresponding sea ice pixels are considered as snow land pixels, and the gap-filling therefore consists of attributing realistic reflectance values from the FDS products (in the seven MODIS bands) and corresponding variance-covariances between channels to enable spectral uncertainty calculation. The sea ice pixels were identified using the CryoClim database  [57, 58] ), and in particular the osisaf-nh and osisaf-sh monthly sea ice climatologies at 10 km that cover the polar regions of the northern and southern hemispheres. Given that the ADAM product over land uses the MODIS-FDS climatological data of year 2005, we also used the CryoClim products for 2005 for consistency.
Land A total of 118 reflectance spectra of soil and vegetation surfaces (acquired either in the field or in laboratories) covering the 400-2400 nm spectral range was used to determine the empirical orthogonal functions (EOFs) for the spectral extrapolation model (see Section 2. 1. 1). These spectra were collected from several open-access databases: the Deutsches Zentrum für Luft-und Raumfahrt (DLR) spectral archive  [60]  which has measurements over the 350-2500 nm range; the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral library  [61] ; the U. S. Geological Survey (USGS) database  [62]  which contains in particular 18 spectra with measurements in the UV, below 400 nm (10 spectra for soil and vegetation with measurements going from 300 to about 2600 nm and eight spectra covering the spectral range 260-2980 nm but with similar values below 300 nm).
The various spectra were interpolated to 1-nm resolution using a simple linear interpolation. The 18 USGS spectra were used to gap-fill the other spectra between 300-400 nm, and the spectral extension down to 240 nm was performed assuming a linear variation of the reflectance between 240 and 300 nm (imposing a minimum reflectance value of 0. 01, corresponding to the average minimum of the measurements in the lower part of the UV). The gap-filling from 2400 nm to 4000 nm was done using the ASTER spectra and an additional database of 32 leaf reflectance measurements (personal communication of S. Jacquemoud)  [63] ; note that this database was used for gap-filling only and the spectra used to generate the EOF eigenvectors and mean spectrum were not included in the dataset). The spectral variation of snow optical properties (see Section 2.
ADAM Database Format The ADAM gridded climatology is provided in Plate Carrée projection. The ADAM products related to the normalized reflectance over land and to the ocean products (chlorophyll concentration and wind speed) are stored in a single netCDF file covering the whole globe for all twelve months. The size of this file is about 200 Mb. The file also includes quality flags associated with the processing of the ADAM input datasets, in particular, gap-filling. The gridded variance-covariance on the land surface reflectances is provided in separate netCDF files for each month, the size of each file being about 160 Mb. The total size of the ADAM gridded products is about 2. 1 Gb. The auxiliary spectral data described in Section 3. 4 are provided in separate ASCII files. The EOF elements used for the spectral interpolation (Section 2.