dataesr/scientific-paragraphs-categorization huggingface dataset:
The World Health Organization has endorsed ACT as first-line treatment where the potentially life-threatening parasite Plasmodium falciparum is the predominant infecting species  [6]. This strategy has been implemented in Côte d'Ivoire since 2005  [22] , where the proportion of patients with abnormal hemoglobin attending dispensaries for malaria is largely unknown and probably underestimated. HbS and HbC genotypes were found in all the regions of Côte d'Ivoire with the highest prevalence (27%) in the Northern region  [20]. The main purpose of this study was to assess in vivo the effect of artemisinin on parasite clearance according to erythrocyte variants. Data collected during in vivo drug efficacy surveys conducted on behalf of the National Malaria Control Program were analyzed retrospectively.
The clearance rate (k) was defined as the decrease of parasitemia per unit of time (per hour). Decreased response of treatment or the potential resistance of parasites are defined as the presence of parasites in blood on day 3 or by parasite half-life greater than 5 h. Pearson's v 2 test and Fisher's exact test were used for comparison of proportions, whereas the Mann-Whitney test was used for parasite densities and parasite clearance data. Descendant logistic regression was used to define risk factors associated with the occurrence of treatment failures with p-value > 0. 2 as cut-off of exclusion of the variables. Parameters used included region, parasitemia at day 0 (D0), gender, age, type of hemoglobin and parasitemia decay (quoted as more or less 5 h). Data analysis was done on R, version 3 environment and GraphPad Prism version 5.
Atomistic machine learning (AML) simulations are used in chemistry at an everincreasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neuralnetwork-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types.
Now, this part comprises the Fortran core of MLatom called MLatomF. Later, MLatomF added a Python wrapper called MLatomPy implementing multi-step tasks such as Δ-learning  [11]  and self-correction  [12]. We have implemented these and other methods developed by ourselves, such as structure-based sampling  [12] , the KREG model  [12] , ML-nuclear ensemble approach (ML-NEA) for precise absorption spectrum simulations  [13] , as well as selected literature methods, such as those based on the Coulomb matrix descriptor  [14, 15] , in MLatom for development purposes, tighter integration, and higher efficiency (see "Native Implementations"). We have used these native implementations also for developing methods for improving QC Hamiltonian  [16] , accelerating ML nonadiabatic excited-state dynamics  [17] , and for PES construction with spectroscopic accuracy by introducing a hierarchical ML (hML )  [18]  approach.
Sometimes the documentation is very poor, and only interaction with experienced users or developers enabled us to use some packages. Often, some critical functionality, such as hyperparameter optimization, is missing. Thus, as a pragmatic solution, we have provided the community with an integrated platform based on MLatom that interfaces the selection of popular thirdparty software packages via MLatomPy written in Python 3. 6 +  [23, 24]. This platform is released as MLatom 2 with all Python interfaces available as opensource, free software for non-commercial use. Importantly, the same input and output structure can now be used for many state-of-the-art, third-party ML models (see Interfaces). We have implemented the interfaces with sGDML  [19, 25]  (symmetrized gradient-domain ML), GAP and QUIP (providing GAP  [26] -SOAP  [27]  method), TorchANI  [28]  (providing ANI  [21]  methods), DeePMD-kit  [29]  (providing the DPMD  [30]  and DeepPot-SE  [31]  methods), and PhysNet  [22]  programs.
This selection of methods covers popular representatives of various types of methods, ranging from those based on kernel methods (KMs) to neural networks (NNs). Our implementation also supports hyperparameter optimization using Bayesian methods with Tree-structured Parzen Estimator (TPE)  [32]  via the hyperopt  [33]  package. The modular structure of MLatom allows easy extension to other models in the future, as it requires only writing a separate independent module for converting MLatom input to the input of the third-party software and parsing the latter's output. A similar approach is also used for interfacing various QC software packages  [34]. This differs, however, from an alternative approach where some packages offer only part of an ML model, e. g. , only a descriptor of a molecule to be used as an input for ML, as in DScribe  [35].
Overview The philosophy behind MLatom is to provide the community with a black-box-like program that allows a variety of calculation tasks required for ML atomistic simulations to be performed (Fig. 1 ). The program provides only with user-friendly and intuitive input, and no scripting is required. Under the hood, MLatom is built of modules designed to be independent of each other as much as possible to the extent that many modules can be used as stand-alone programs entirely independent from the main program. As needed, the modules are combined to create a seamless workflow eliminating step-by-step manual calculations. The calculation tasks in MLatom are ML tasks and data set tasks. ML tasks are calculations involving training and using ML models. Our implementation includes a wide range of such tasks from basic to multi-step procedures: using an existing ML model from a file, creating an ML model and saving it to a file for later use, estimating ML model accuracy (model evaluation to determine the generalization error), Δ-learning  [11] , self-correction  [12] , learning curve and nuclear-ensemble spectrum generation  [13].
While both conventions are valid, we prefer to call them sub-training points both here and in MLatom. All data points that are used in creating the ML model we call the training set. This set includes the subtraining and the validation sets in our nomenclature. This convention allows for a fairer and more straightforward comparison of ML models trained on the same number of training points as the validation set, which is used indirectly in training, to be accounted for. For example, if the model is trained on 1000 points, but used another 1000 points for validation, then the reference data is needed for all 2000 points, and such a model cannot be compared to another model trained on only 1000 points without using such a validation set. When additional information such as derivatives of properties is available, it can be included into the training set too.
Estimating Accuracy of ML Models MLatom also provides the means to estimate the accuracy (generalization error) of the ML model (Fig. 3 ). Since the validation set has already been used to create the ML model, it is good to use another independent test set to assess the model's performance  [36]. The entire data set can be split into training and test sets, and points can be sampled into these subsets using procedures similar to those used for hyperparameter tuning (model selection; see "Splitting and Sampling"). Naturally, hyperparameter tuning and model evaluation can be combined in a single calculation task with MLatom.
Thus, we provide a dedicated task to automate this procedure (Fig. 4 ) in MLatom 2. In the learning curve task, the accuracy of each training set size requested is examined in multiple repeats, where different training points are sampled. Testing with repeats helps to reduce the bias introduced by a specific combination of training and test sets, investigates the statistical variance, and reflects the robustness of an ML model. The results (RMSEs, wall-clock training and prediction times) from each test are recorded in the. csv database file.
The user can also provide available pre-calculated data, such as output files with normal-mode calculations or nuclear ensemble geometries. Existing reference QC data can also be provided. MLatom has an interface to Gaussian 16, which automatically invokes and parses the QC calculations' output to get the equilibrium geometry and frequencies. Then, it passes the required data to the interface to Newton-X  [43, 44]  to generate a nuclear ensemble of 50k conformations sampled from a harmonic-oscillator Wigner distribution of the nuclear coordinates  [45]. QC properties for training ML models are calculated iteratively. The number of training points is increased at each iteration to train 2N fs models for excitation energies (ΔE 0n ) and oscillator strengths (f 0n ) of transitions from ground-state to each excited state n.
Analysis of Data MLatom calculates several built-in statistical metrics for analyzing data, particularly for comparing ML estimations to the reference values. They are overviewed below for the sake of completeness. For N estimated values ŷ and reference values y: • Mean absolute error (MAE): • Mean signed error (MSE, not to be confused with mean squared error): (8) MAE = 1 N N ∑ i | | ŷi -y i | | 1 3 27 Page 12 of 41 • Root-mean-squared error (RMSE): • Arithmetic means of estimated and reference values, respectively: • Largest positive and negative outliers as judged by ŷy. • Linear regression coefficients a and b in ŷ = a + by, their standard errors (SEs) with the corresponding correlation coefficient R and its squared value R 2 found by least-squares fitting  [46] : where (9) MSE = 1 N N ∑ i ŷi -y i (10) RMSE = √ √ √ √ 1 N N ∑ i ŷi -y i 2 (11) ŷ = 1 N N ∑ i ŷi (12) y = 1 N N ∑ i y i (13) b = ss yŷ ss yy (14) a = ŷ -b y (15) SE(a) = s √ 1 N + 2 y ss yy (16) SE(b) = s √ ss yy (17) R 2 = ss 2 yŷ ss yy ss ŷŷ (18) ss yy = -N 2 y + N ∑ i y 2 i Analogous expressions are used for derived properties, such as partial derivatives; in the latter case, each partial derivative is treated as a data point, e.
Native Implementations This section overviews the theory and provides technical details behind the native implementations available in MLatom. All native ML models are currently based on KRR, so this approach is described first, and then we describe the details behind the KREG model and approaches based on the Coulomb matrix. The code for the KREG model, hyperparameter grid search, farthest-point, and structurebased sampling was optimized for efficient computing, while no such efforts were necessarily made for other implementations.
λ is called the regularization parameter and is an external hyperparameter not entering the approximating function itself but used for model selection. This system of equations has an analytical solution that makes it very attractive. The solution is, however, computationally costly for large data sets as it involves some kind of matrix decomposition that scales as O(N tr 3 ), followed by solving the system of equations, which scales as O(N tr 2 ). MLatom uses the very computationally efficient Cholesky decomposition by default. Bunch-Kaufman and LU decomposition approaches are also available, which are sometimes necessary when Cholesky decomposition fails  [47]. The solution of the above system of equations also requires calculation of the kernel matrix of size N tr 2 , which can become very large and no longer fit in the available computer memory.
All of them have the length-scale parameter σ, which is an internal hyperparameter. The Matérn kernel function has an additional integer hyperparameter n. The choice of the kernel function depends on the application, and it can be considered a hyperparameter itself, although MLatom does not automatically make this choice. MLatom performs automatic optimization of the hyperparameters λ and σ on the nested logarithmic grid  [10]. Alternatively, the hyperparameters can be optimized using a third-party hyperopt package (see "Interfaces" and its subsection "Hyperopt" below). As discussed above, we are often interested in the derivatives of properties. Once the KRR approximating function is trained on reference values of properties, it can be differentiated to obtain the required derivatives with respect to the d dimension of the input vector x: Thus, calculating the approximating function derivatives requires calculation of kernel function derivatives.
Model using uses third-party software to get estimated values from the trained model. The interfaces are modularized Python 3 scripts stored in the subfolder interfaces in the MLatom program directory. The currently interfaced third-party software packages are listed in Table  1  and will be introduced in the sub-sections below. Note that some programs provide several ML model types other than the listed ones. Using them through the interfaces might already be supported in MLatom or needs some slight modifications in the interface modules. However, only the listed model types are tested and will be discussed in this paper. Generally, we follow the strategy of making no modifications in the interfaced programs so that they can be taken "as is" and only the path to their executable should be known to MLatom.
The only exception is the PhysNet, which uses TensorFlow v1. To make it compatible with the newer version of Python and TensorFlow  [54] , names of some legacy functions need to be changed to their equivalent in TensorFlow v2. Also, it is important to note that the main hyperparameters of each interfaced model can be modified easily through the standard input file of MLatom such that no modifications to the third-party software are required. Besides the third-party ML software, we also implemented the interface to a hyperparameter optimization library called hyperopt  [33] , to provide a solution for hyperparameter optimization, which is unavailable in most of third-party software packages.
sGDML sGDML  [19]  is a symmetrized variant of the gradient-domain machine learning (GDML  [48] ) model type and is interfaced to MLatom through the sGDML program  [25]. The sGDML method is a KRR model with the descriptor of unnormalized inverse distances (the ID descriptor) Thus, it has the same basic properties as the RE descriptor, i. e. , the ID descriptor is also a global and complete descriptor ensuring rotational and translational invariance, but not permutational invariance of homonuclear atoms. In contrast to the KREG model, the GDML model learns only from the energy gradients. It also uses the Matérn kernel function, whose expression differs slightly from that implemented in MLatom. For learning from gradients, the common KRR approximating function for scalar properties is modified by using covariance between derivatives to predict the XYZ components of energy gradients: As usual, the linear system of KRR equations is solved with the kernel matrix K, now of a size 3N at N tr × 3N at N tr : (44) T = ⋯ 1 r a,b ⋯ (45) f ( ) M at = N tr ∑ j=1 N at ∑ b=1 3 ∑ u=1 j,bu 2 k , j M at M j,bu.
When the energies are needed, they are obtained by integrating the energy gradients, and the integration constant is found by fitting to the reference energy values: The sGDML tackles the permutational invariance by using a modified, unnormalized permutational invariant kernel with permutations chosen automatically to minimize the matching cost between pairs of training points. The sGDML program also supports automatic hyperparameter optimization via cross-validation. The sGDML method achieves remarkable accuracy given only a very small number of training points, as was shown for molecular PESs visited during dynamics  [19]. Because of the large size of the kernel matrix, the method is practically applicable only for hundreds, up to several thousand training points. Note that when we refer to a single "training point," we mean all the associated information for one geometry, and the real number of reference values available for sGDML is 3N at times larger (the number of Cartesian energy gradient components).
TorchANI TorchANI  [28]  is a Python library that implements the ANI model type  [21] , with PyTorch  [58]  as its backend for NN construction. ANI is the abbreviation for ANAKIN-ME, which was back-engineered to Accurate NeurAl networK engINe for Molecular Energies. The ANI atomic environmental vector used in this model is a local descriptor, and is derived from the descriptor in Behler and Parrinello's work  [59]. The ANI descriptor �� ⃗ G X i for element X 's i th atom contains a radial and an angu- lar part, and both parts are further subdivided into subAEVs, in which the atoms taken into consideration will be limited by the same element. For each element, the radial subAEV consists of input vector elements G R k ∈ for different values of radial shift hyperparameters R (k)  s : Similarly, for each pair of elements, the angular subAEV consists of input vector elements G A p,q ∈ for different combinations of angular shift hyperparameter (q) s and another set of radial shift hyperparameters R (p) s : In the equations above, f c is the cutoff function used in Behler-Parrinello NN potentials  [59]  and similar to that used in GAP-SOAP, , R (k) s ,R (p) s , (q) s and are predefined hyperparameters.
Parameters are defined separately for radial part and angular part similarly to R s. After being computed, each atom's AEV will be plugged into its own NN as the input vector to predict atomic energy, and the atoms of the same element share the same NN structure to ensure the permutational invariance of the trained model. The total energies are obtained by summing all atomic energies, while atomic forces are generated by differentiating atomic energies using PyTorch's automatic differentiation engine. TorchANI reads HDF5 files, where training data are organized and stored. As shown in Table  3 , many hyperparameters can be tuned in ANI descriptors, not to mention the hyperparameters of NNs. However, as a Python library, TorchANI provides neither default values nor optimization method for hyperparameters, but only the basic core functions of ANI model type as building blocks.
g. , hyperparameters for network architecture, learning rate schedule, etc. ) are not listed. However, this package cannot optimize those hyperparameters. Also, DeePMD-kit does not include in its native implementation the regularization scheme called early stopping often required in NN models to control the number of iterations performed during (62) s r ij , x ij r ij s r ij , y ij r ij s r ij , z ij r ij s r ij , (63) s � r ij � = ⎧ ⎪ ⎨ ⎪ ⎩ 1 r ij , r ij < r cs 1 r ij � 1 2 cos � 𝜋 r ij -r cs r c -r cs � + 1 2 � , r cs < r ij < r c 0, r ij > r c ( 64 ) i jk = g k s r ij (65) i = i1 * ̃ i ̃ i * i2 , training, to stop the simulation before the model can reach an overfitting stage. Thus, we provide an external early stopping function as part of the interface module that monitors the training progress (based on the loss for the validation set) in the MLatom/DeePMD-kit output to stop the simulation when the criterion defined in the input has been reached.
Unlike previously described models trained with local descriptors (GAP-SOAP, ANI, DeepPot-SE), PhysNet may also take long-range interactions (e. g. , electrostatic and dispersion) into account. By default, dispersion corrections are enabled in the MLatom interface, while electrostatic corrections are disabled because their calculations require additional input (reference dipole moments). The official implementation of the PhysNet model is programmed in Python 3. 6 with TensorFlow v1, and the data need to be stored in the Numpy's npz format of a specific structure. Similar to TorchANI, using the PhysNet program also needs much manual work on script-writing and hyperparameter-tuning (see Table  5  for the list of the main hyperparameters). (68) l+2 i = l i + l+1 l l i + l + l+1 , ( 69 ) ̃ l i = l l i + l + ∑ j≠i l r ij • l l j + � , ( 70 ) l+1 i = l • l i + l l i + l ,.
Conclusions In this review article, we have described the MLatom 2 software package, which provides an integrative platform for AML simulations. Unlike other specialized AML packages, MLatom has been developed with the aim of facilitating the application of ML models to the wide variety of tasks often required in computational chemistry research. Its capabilities range from native implementations such as the KREG model and other KRR model types (with the Coulomb matrix or any other user-defined descriptors as well as the Gaussian, Matérn, Laplacian, and exponential kernel functions) to interfaces to the third-party packages with popular models. The latter models are overviewed here for the sake of completeness and include sGDML, GAP-SOAP, ANI, DPMD, DeepPot-SE, and PhysNet. Other AML model types can be implemented easily by using the modular approach adopted in MLatom for third-party interfaces.
It is also a useful package for educational purposes as it is used for teaching the basic and advanced concepts behind ML use in quantum chemistry (see, e. g. , the book chapter  [2] , and online tutorial at http:// MLatom. com). We are continually developing this platform based on the needs for practical AML computations such as dynamics, calculation of excited-state properties, and rovibrational spectrum simulations, improvement of QC methods, and materials design.
Online interface An interactive online interface was created, allowing model behaviour for different parameterisations to be visualised. It is available at https://plantdiseasevectorpreference. herokuapp. com/ (Fig  2 ). This interface allows its user to explore the results of a single model run, or to perform a one-way parameter scan. In both cases, the user has the flexibility to choose parameterisation of the model, either using the default settings for PT or NPT, or by selecting a custom set of parameters. Whenever the user changes values of parameters, the interface triggers a callback function, which calculates the model outputs or equilibria before returning the results in the form of tables and interactive figures. The interface was built using Python Dash  [58] , which is open source.
In the right panel of Fig. 2 , a typical energy spectrum is shown as it was acquired when studying the removal of 220 Rn daughters. The peaks of 212 Po and 212 Bi are clearly visible. The latter is overlapping with the 210 Po peak which is also present in the 220 Rn data as a background. It is originating from the contamination of the spectrometer and the PIN-diode themselves due to an earlier exposure of the detector to 222 Rn. During our studies, the 210 Po background was monitored and taken into account when determining the 210 Po reduction after the investigated procedures. The alpha peaks are fitted using the crystal-ball function (red line) as in  [24]. It consists of a gaussian part and a left-sided tail which accounts for the asymmetric peak shape. In order to determine the activity, we chose a selection window of +3 σ and -5 σ around the fitted gaussian mean of the 210 Po and 212 Bi peak, respectively.