dataesr/scientific-paragraphs-categorization huggingface dataset:
Data split The train-test split was performed over the 25 patients dataset, training dataset used only for hyperparameter tuning, model selection and model fitting, and a testing dataset from which we get the predictions of fitted models and use them as priors in the VEP pipeline. Not all of the 25 patients had all the necessary data for the VEP pipeline, so we put those patients into the train dataset. The final training dataset contained 16 patients (9 in the testing dataset). We observed heterogeneous 23 Na-MRI feature patterns at the individual level, which initially provided weak performance. So we opted to split the train set to train the models on data with different patterns. We arbitrarily choose to split into 2 different datasets using spectral embedding  (Luxburg, 2007)  with 2 dimensional projected subspaces.
The patients with the mean value in the EZN of the first eigenvector over 0 were included in the training dataset 1, and the others in the training dataset 2, making two sets of 8 patients each. Briefly, spectral embedding is a non linear dimensionality reduction using Laplacian eigenmaps, which preserves the local geometry. Here, we used a graph of nearest neighbors to construct the affinity matrix. After this stage, Laplacian decomposition is applied to the corresponding graph Laplacian. The eigenvectors for each data point provide the resulting transformation.
Polynomial transformation was followed by a data standardization step, needed for the majority of machine learning models. The next step was permutation feature importance which corresponds to the decrease in the model score when a feature values are randomly permuted  (Breiman, 2001). This procedure can be applied multiple times on repeated permutation of a feature (repeated 10 times here). For each model and on both training sets, this approach was validated using nested cross validation. Model Selection Procedure and priors estimation Feature permutation outcomes are used as predictors in the following steps. Models hyper-parameters were tuned using a grid search function, which searches over a specified set of parameters values (Table  1 ) the best mixture of parameters. Each mixture provides a model that will fit on the training dataset, and then provide a performance score, here balanced accuracy.
The likelihood function is given by Balanced accuracy, the imbalanced data adapted accuracy, is the arithmetic mean of sensitivity and specificity  (Kelleher, Namee, & D'Arcy, 2015). The classic accuracy score tends to be inflated due to the imbalanced nature of the dataset, which balanced accuracy prevents. The F-beta score used in this project is the harmonic mean of recall and precision Baeza-Yates and Ribeiro-Neto (1999) (Figure  6 ). As we are more interested in the precision than in recall, we gave more importance to precision than to recall, setting beta = 0. 5. These two metrics were first used in combination during parameter tuning procedure as scoring functions, as well as to evaluate VEP pipeline EZN estimation. The model selection is based on balanced accuracy. P (ν[t] ∼ x, θ) ∼ N (ν, ϵ ν ) -23- == D R.
VEP outcome analysis Over the 9 patients of the test dataset that we virtualized using all the described priors (VEP-EI, VEP-M, VEP-W, Na-MRI-prior 1, Na-MRI-prior 2, VEP-no-prior), the VEP pipeline provides results for a total of 26 seizures. In order to evaluate the performance of each prior we compute the balanced accuracy and the F 0. 5 -score for each seizure, using VEP-EI results as reference. Next, we compared the resulting balanced accuracy and F 0. 5 -score with a bootstrapped (1000000 resampling) paired t-test comparing each pair of priors. We used the classical threshold of 0. 05 for the p-value, also considering the threshold of 0. 01. Epileptor phenomenological model based on a system of coupled nonlinear differential equations generating epileptic dynamics. Neural Mass Models (NMM) A straightforward approaches to modelling the activity of neuronal populations, the so-called neural masses, described as groups of neurons with a common function and with similar or equal in-going and out-going connections.
SMOTE The Synthetic Minority Oversampling TEchnique is an oversampling used for data augmentation of imbalanced dataset. SMOTEENN The combination of over-and under-sampling using SMOTE and Edited Nearest Neighbours (ENN). Spectral Embedding A non linear dimensionality reduction using Laplacian eigenmaps, which preserves the local geometry. Nested Cross-validation Also called double cross validation, this procedure uses two k-fold type loops, meaning that each training fold is also folded. Azilinon et al. , 2022; Grimaldi et al. , 2021; Ridley et al. , 2017). -38-    The confusion matrix (A) is used to compute Balanced accuracy and F 0. 5 -score, the two performance evaluation metrics we explored. Data-driven VEP simulation through the priors (B) provided simulations from the priors and simulation from the CH, confronted altogether to evaluate the performance.
ACKNOWLEDGMENTS The authors would like to thank  L. Pini ,  C. Costes , and  V. Gimenez  for data acquisition and study logistics. We would also like to thank  M. Woodman ,  V. Sip ,  A. Vattikonda  and  M. Hashemi  for helpful discussions. This work has received support from the  French government  under the " Programme Investissements d'Avenir", Excellence Initiative of Aix -Marseille University -A * MIDEX -25-.
preserves a relative imbalance, managed by model class weights. The decision about the sets of parameters to use is based on their respective models performance, using the mean cross-validated validation score and the scores difference (mean CV train score -mean CV validation score). The models should have a mean cross-validated score above 0. 65 to remove all the models with a performance close to the chance-level. A score difference lower than 0. 1 removes all models that overfit, i. e. train score is much higher than the validation score. The parameters combinations retained provide 12 models. In order to reduce the number of models, we have chosen a model for each training dataset -as all models reached similar performance -Na-MRI Derived Priors Support Estimation of EZ Using Model Methods considering the mean test balanced accuracy maximum value.
The parameters and scores of the selected models are listed in the table. Model validation consists in comparing model performance on real data compared with surrogate data (shuffled targets, shuffled training dataset targets and shuffled testing dataset targets). This last stage defines the models used for the prediction/priors estimation stage. Predictions are made from prediction probabilities defined by each model. These probabilities are threshold in order to optimize the test score and obtain the best prediction possible from those models. The thresholding binarized the probabilities, in order to be integrated into the VEP framework, and more specifically into the Epileptor model. The predictions are then converted into x 0 , the neural mass excitability parameter. Optimization MAP pipelineTo infer the epileptogenicity parameter and source time series for each seizure, we apply a Bayesian modeling approach.
Parameters Values Symbols Maximum power (W) 145 Pmp Temperature coefficient of Isc (A/K) 0. 0065 ksc Maximum Current (A) 4. 2 Imp Short-circuit current (A) 4. 7 Isc Maximum voltage (V) 34. 4 Vmp Series cells 72 Nsc Temperature coefficient of Voc (V/K) -0. 3609 koc Open-circuit Voltage (V) 43. 5 Voc Parallel cell 1 Np To determine the MPP of the Solarex Solex FSM 145W-24 PV panel, the decisive step is to determine the power -voltage and current -voltage characteristics. I-V and P-V characteristics of solar photovoltaic modules under the STC are the basic requirements for monitoring the MPP using any of the MPPT techniques or algorithms. Therefore, the characteristic curves of the Solarex Solex FSM 145W-24 PV panel in Fig. 3  and Fig. 4  are simulated in the Matlab/Simulink environment. For different weather conditions, the electrical characteristics of the PV cell are generally represented by the power versus voltage (Ppv -Ipv) and current versus voltage (Ipv -Vpv) curves.
These data are used to verify the performance of different MPPT methods. The tracking efficiency η is an important parameter in the MPPT algorithm. This value is calculated as follows: 0 0 ( ) 1 00 ( ) t m ppt t m ax P t d t P t dt      (8) The ripple rate of the power τo is the ratio between the efficiency value and the average value of the ripple: _ pv ond o pv P P   , ( 9 ) where Ppv_ond is the effective power of PV panel. The average power Pm is the PV output power under the control of MPPT over some period of time T. It is calculated as follows: 0 1 ( ) T m mppt P P t dt T   (10) The response time τr corresponds to the time needed to reach the new MPP value.
B. Implementation aspects The experimental evaluation of the MPPT algorithms performance is verified by using the test bench that is illustrated in Fig. 7  (designed and implemented at the IRIMAS laboratory, located in the IUT of Mulhouse of the University of Haute Alsace in France). The test bench consists of the following elements. A photovoltaic solar panel Solarex Solex FSM 145-24 is placed outside of the building. With a south-east orientation (The PV characteristics are given in Table  I ). TSL2591 and DHT11 sensors are used to record respectively irradiance and temperature. The current and voltage sensors are used to acquire the current and voltage output of the PV panel. Both of these data are used as input variables for the MPPT controller to produce a Pulse Width Modulation (PWM) signal.
The DC-DC converter, designed to operate in continuous inductive current mode (whose specifications are given in Table  II ), is directly connected to the PWM controller, which receives and amplifies the signal of the slave port I/O PWM of the DS1104 control board. This signal will be utilized to control the IGBT (electronic switch) of the DC-DC boost converter, which in turn will shift the operating power to the MPP and achieve maximum operating efficiency. The load connected to the output of the DC-DC boost converter is a resistance of 120 Ω. The DS1104 control board is connected to the computer equipment with the ControlDesk software and Matlab/Simulink software. The studied MPPT algorithms are implemented in Matlab/Simulink. Then, exploiting the features of the real-time interface toolbox (i.
e. , the RTI data block with a sampling frequency of 10 kHz) available in the Simulink libraries, the implemented MPPT algorithms are interfaced with the hardware. The ControlDesk software allows managing the process hosted on the control board. It has been used as a system to first acquire, store the measured signals, then give access to the display of the respective curves and finally to facilitate the real-time analysis of the performance of the MPPT method in the control of the PV system. The hardware platform is permanently connected to the computer for interactive control, status monitoring and code download.
Deep neural networks have demonstrated their ability to automatically extract meaningful features from data. However, in supervised learning, information specific to the dataset used for training, but irrelevant to the task at hand, may remain encoded in the extracted representations. This remaining information introduces a domain-specific bias, weakening the generalization performance. In this work, we propose splitting the information into a task-related representation and its complementary context representation. We propose an original method, combining adversarial feature predictors and cyclic reconstruction, to disentangle these two representations in the single-domain supervised case. We then adapt this method to the unsupervised domain adaptation problem, consisting of training a model capable of performing on both a source and a target domain.
Before deep learning became prevalent,  [40]  presented a method using bi-linear models able to separate style from content. More recently, methods based on generative models have demonstrated the ability to disentangle factors of variations from elements of a single domain  [30, 26, 7, 13, 33]. In a cross-domain setting,  [12]  use pairs of images with the same labels from different domains to separate representations into a shared information common to both domains and a domain-exclusive information. We note that these approaches do not explicitly aim at respecting all conditions listed in Section 2. Additionally, most require labeled datasets (and in some cases even paired datasets) and thus do not address the unsupervised DA problem. One approach to UDA consists in aligning the source and target distributions statistics, a topic closely related to batch normalization  [18].
Single-domain disentanglement We evaluate the disentanglement performance of DiCyR by following the protocol introduced by  [26]. Since we do not use generative models, we only focus on their two first items: swapping and retrieval. We evaluate DiCyR on the SVHN  [27] , and 3D Shapes  [3]  disentanglement benchmarks. The task is predicting the central digit in the image for the SVHN dataset, and the shape of the central object in the scene for the 3D Shapes dataset. Swapping involves swapping styles between samples and visually assessing the realism of the generated image. It combines the task-related information τ i of a sample x i with the style σ j of another sample x j. We use the decoder to produce an output xij. Figure  2  shows randomly generated outputs on the two datasets. DiCyR produces visually realistic artificial images with the desired styles.
Retrieval concerns finding, in the dataset, the nearest neighbors in the embedding space for an image query. We carry out this search for nearest neighbors using the Euclidean distance on both the task-related and the style representations. A good indicator of the effectiveness of the information disentanglement would be to observe neighbors with the same labels as the query when computing distances on the task-related information space, and neighbors with similar style when using the style information. Figure  3  demonstrate that the neighbors found when using the task-related information are samples with the same label as the query's label and that the neighbors found using the style representation share many characteristics with the query but not necessarily the same labels. We ran a quantitative evaluation of disentanglement by training a neural network classifier with a single hidden layer of 32 units to predict labels, using either the task-related information alone, or the style information alone.
The trained classifier reaches accuracies (Table  1  rightmost columns) that are very close to a random guess, thus validating the disentanglement quality. Without proper disentanglement, the features extracted by neural networks may contain context information that is specific to the training data distribution and unrelated to the task at hand. This context information may comprehend bias, introduced during the collection of the data for example, which can strongly affect generalization. To illustrate this phenomenon, we train a network to classify zeros and ones extracted from the MNIST dataset. During training, we modify the dataset so that all ones have a yellow color and all zeros have a blue color while varying the intensity of the We then measure the accuracy of this network on a test dataset in which the colors of the ones and zeros are inverted (Figure  4 ).
Unsupervised domain adaptation problem We evaluate DiCyR by performing domain adaptation between the MNIST  [20] , SVHN, and USPS  [17]  datasets, and between the Syn-Signs  [9]  and the GTSRB  [36]  datasets. Following common practice in the literature, we trained our network on four different settings: MNIST→USPS, USPS→MNIST, SVHN→MNIST, and Syn-Signs→GTSRB. We measure the classification performance in the target domain and compare it with state-of-the-art methods (Table  3 ). We also compare with a baseline classifier that is only trained on the source domain data. Values reported in Table  3   85. 1 73. 0 73. 9 88. 6 ADDA  [41]  89. 4 90. 1 76. 0 -DRCN  [11]  91. 8 73. 7 82. 0 -DWT  [31]  99. 1 98. 8 97. 7 -SEDA  [8]  98. 2 99. 5 99. 3 99. 3 SHOT  [23]  98. 4 98. 0 98. 9 -Table  3 : Target domain accuracy, reported as percentages Our method, without extensive hyperparameter tuning, appears to be on par with the best state-of-the-art methods.
B. 1 Single domain supervised disentanglement experiments This section and Table  5  describe the network architecture and the hyper-parameters used in the experiments of Section 5. 1. The encoder Π is composed of shared layers, followed by the specific single-layer task-related and style encodings Π τ and Π σ. We used the exact same network architectures for both the 3D shapes and SVHN datasets, the only difference being the dimension of the embeddings T and S. In all experiments, we applied a coefficient β reco = 5 to L reco and β cyclic = 0. 1 to L cyclic in the global loss. We also use a β inf o on L inf o ; this coefficient increases linearly from 10 -2 to 10 over the first 10 epochs and remains at 10 afterwards (see Appendix C for a discussion on this coefficient). Convergence was reached within 50 epochs.
B. 3 Computing resources and code release All the experiments from section 5 were run on a Google Cloud Platform n1-standard-8 virtual machine (8 virtual cores, 30Go RAM, Nvidia P100 GPU). The code corresponding to the experiments, a list of dependencies, and pre-trained models are available at https://github. com/SuReLI/DiCyR_code. Details about each experiment are reported in Table  4.
Time series classification using phase-independent subsequences called shapelets is one of the best approaches in the state of the art. This approach is especially characterized by its interpretable property and its fast prediction time. However, given a dataset of 𝑛 time series of length at most 𝑚, learning shapelets requires a computation time of 𝑂(𝑛 2 𝑚 4 ) which is too high for practical datasets. In this paper, we exploit the fact that shapelets are shared by the members of the same class to propose the SAST (Scalable and Accurate Subsequence Transform) algorithm which has a time complexity of 𝑂(𝑛𝑚 3 ). SAST is accurate, interpretable and does not learn redundant shapelets. The experiments we conducted on the UCR archive datasets showed that SAST is more accurate than the state of the art Shapelet Transform algorithm while being significantly more scalable.
In fact, the state of the art time complexity of shapelet based methods is 𝑛 2 𝑚 4 where 𝑛 is the number of time series in the dataset and 𝑚 is the length of the longest time series. This high time complexity is due to the large number of shapelet candidates that need to be evaluated in order to find the top best shapelets. A human brain is able to recognize a lot of variations of an object after seeing a single variant. For instance, we are able to recognize any model of car after seeing one of them, we can recognize many species of dog if we have ever seen a dog. This ability is called core object recognition  [8]. Inspired by this amazing behavior of our brain, we claim that a shapelet model should be able to recognize any variant of a shapelet if it knows one or a few number of its variants.
Simply defined, a shapelet is a pattern that is shared by the time series that belong to the same class. Therefore, any single instance of a class should contain all the shapelet candidates or at least a variant of each shapelet candidate for that class. Guided by this observation, we propose the Scalable and Accurate Subsequence Transform (SAST) algorithm, a TSC algorithm that is accurate, scalable and whose predictions are interpretable. Existing shapelet based methods use the whole dataset to generate shapelet candidates, then use information gain to select the top best shapelets before doing the classification using a supervised classifier. We claim that it is not necessary to generate the shapelet candidates from the whole dataset, only one or few instances per class is enough. We also claim that pruning shapelet candidates without taking into account the classifier can lead to inaccurate classification.
SAST is also more scalable than the state of the art shapelet methods. In particular, SAST took 1 second to classify the Chinatown dataset with an accuracy of 96%, while the state of the art shapelet based algorithm STC took 51 seconds and achieved an accuracy of 97% on the same computer. Furthermore our proposed method can successfully classify some datasets on which STC fails. • We give an open source implementation of our model that is compatible with the well known scikit-learn machine learning library. This allows anyone to easily use our model on its own data, and to reproduce our experiments. The code is available here https://github. com/frankl1/sast The rest of this paper is organized as follows: we start by setting the background and present related works in Section 2. In Section 3 we describe our proposed method SAST, which is inspired by the core object recognition capability of human brain.
However, any distance can be used to compute the similarity between subsequences. A time series is similar to a pattern if there is a subsequence of that time series that is similar to the pattern. Definition 3 (Similarity). The similarity between a time series 𝑇 of length 𝑚 and a subsequence 𝑆 of length 𝑙 is defined as follows: The patterns 𝑆 and 𝑅 are put on the same scale before the distance computation using a normalization technique. Beware that we are not using the Euclidean distance, but the square of the Euclidean distance. As a matter of fact, the square root can be seen as a change of the similarity scale and therefore does not change the similarity. 𝑑𝑖𝑠𝑡(𝑇 , 𝑆) = min ∀𝑅∈𝑊 𝑙 ({ 𝑙 ∑ 𝑖=1 (𝑟 𝑖 -𝑠 𝑖 ) 2 }), Among all the patterns contained in a time series dataset, some can be used as discriminative features in order to find the class label of an unseen time series.
Training a shapelet based decision tree consists of learning the best subsequence (i. e shapelet) to use at each node, and for each shapelet the best similarity threshold. This training is done in a top-down approach as in a classical decision tree using the information gain (IG) at each node to find the best split  [5]. Definition 4 (Shapelet). Given a dataset 𝐷 = {(𝑇 1 , 𝑐 1 ), (𝑇 2 , 𝑐 2 ), … , (𝑇 𝑛 , 𝑐 𝑛 )} of time series with their class labels 𝑐 𝑖 taken from a finite set of classes 𝐶, a shapelet 𝑆 ⋆ is a subsequence that maximizes the information gain 𝑆 ⋆ = argmax 𝑆∈𝑊 𝐼𝐺(𝐷, 𝑆), with 𝑊 being the set of all subsequences in 𝐷. (4).
Definition 5 (Information Gain (IG)). Let 𝐷 be a time series dataset and 𝑆 a shapelet. Let 𝐷 𝐿 = {𝑇 ∈ 𝐷 | 𝑑𝑖𝑠𝑡(𝑇 , 𝑆) ≤ 𝜖} and 𝐷 𝑅 = {𝑇 ∈ 𝐷 | 𝑑𝑖𝑠𝑡(𝑇 , 𝑆) > 𝜖}, then 𝐼𝐺(𝐷, 𝑆) = max 𝜖∈𝑆𝑃 ⊂R ( 𝐻(𝐷) - |𝐷 𝐿 | |𝐷| 𝐻(𝐷 𝐿 ) - |𝐷 𝑅 | |𝐷| 𝐻(𝐷 𝑅 ) ) , with 𝐻(𝐷) = - ∑ 𝑐∈𝐶 𝑝 𝑐 log 𝑝 𝑐 (5) 𝐻(⋅) is the entropy, 𝑝 𝑐 is the probability of having the class 𝑐 in the dataset 𝐷, 𝐶 is the set of classes in 𝐷, and 𝑆𝑃 is the set of possible split points. For a time series of length 𝑚, there are 𝑚 -𝑙 + 1 subsequences of length 𝑙 and 𝑚(𝑚+1) 2 subsequences in total for a single time series. Since any subsequence in the dataset is a shapelet candidate, and there are about 𝑂(𝑛𝑚 2 ) subsequences, evaluating them is very time consuming. Because of that, the time complexity of the shapelet decision tree algorithm is 𝑛 2 𝑚 4  [5].