dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiment In this section, we present the experiment we conducted to assess the capability of our SGIM-PB algorithm. The experimental setup features the 7 DOF right arm of an industrial Yumi robot from ABB which can interact with an interactive table and its virtual objects. Figure  1  shows the robot facing a tangible RFID interactive table from  [47]. The robot learns to interact with it using its end-effector. It can learn an infinite number of hierarchically organized tasks regrouped in 5 types of tasks, using sequences of motor actions of unrestricted size. The experiments have been carried out with the physical industrial robot ABB and with simulation software provided with the robot, Robotstudio. While the software provided by the robot can provide static inverse kinematics, it can not provide movement trajectories.
Teachers To help SGIM-PB in the simulation setup, procedural teachers (strategical cost K(σ) = 5, compared to K(σ) = 1 for autonomous strategies) were available for every outcome space except Ω 0. Each teacher gives on the fly a procedure adapted to the learner's request, according to its domain of expertise and according to a construction rule: • ProceduralTeacher1: Ω 1 → (Ω 2 0 ); • ProceduralTeacher2: Ω 2 → (Ω 2 0 ); • ProceduralTeacher3: Ω 3 → (Ω 1 , Ω 2 ); • ProceduralTeacher4: Ω 4 → (Ω 1 , Ω 2 ). We also added different action teachers (strategical cost of K(σ) = 10), each expert of one outcome space: In the physical setup, we want to delve into the differences between action and procedural teachers. So as to put them on an equal footing, we used for all teachers demonstration datasets of limited sizes.
Choosing between autonomous exploration of the action space Π N and mimicry of any action teacher; • SGIM-PB: interactive learner driven by intrinsic motivation. Has autonomous exploration strategies (of the action Π N or procedural Ω 2 space) and mimicry ones for any procedural teacher and ActionTeacher0; • Teachers: non-incremental learner only provided with the combined knowledge of all the action teachers. For the physical setup, we are only comparing 1 run of SGIM-ACTS and SGIM-PB (20,000 iterations or one month of trials for each), as to delve deeper into the study of the interactive strategies and their impact. The codes used are available at https://bitbucket. org/smartan117/sgim-yumi-simu (simulated version), and at https://bitbucket. org/smartan117/sgim-yumi-real (physical one).
Data Efficiency of Active Learning: Active Imitation Learning vs Batch Transfer In this section, we explore the possibility to transfer a set of procedures as a batch at the beginning of the learning process, as opposed to active requests from the learner throughout the learning process. We consider a learner and a teacher with different embodiments working on the same tasks. While transfer of knowledge of actions can not be reused straightforward, how can the knowledge of procedures be exploited? In our example, we consider new learners trying to explore the interactive table using the left arm of Yumi, while benefitting from the knowledge acquired from a right arm Yumi. We call this simulation setup the left-arm setup. We extracted a dataset D 3 composed of the procedures and their corresponding reached outcomes ((ω i , ω j ), ω r )) taken from the experience memory of a SGIM-PB learner that has trained on the right arm for 25,000 iterations (taken from the runs of SGIM-PB on the simulated setup).
• Left-Yumi-SGIM-PB: the classical SGIM-PB learner using its left arm, using from the exact same strategies as on the simulated setup, without any procedure transferred; • Left-Yumi-SGIM-TL: a modified SGIM-PB learner, benefiting from the strategies used on the simulated setup, and which benefits from the dataset D 3 as a Transferred Lump at the initialisation phase: Memo ← {((ω i , ω j ), ω r )} at the beginning their learning process. No actions are transferred, and the transferred data are only used for computing local exploration of the procedural space, so they don't impact the interest model nor the test evaluations reported in the next section. All procedural teachers propose the same procedures as in Section 4. 1 for the righthanded SGIM-PB learner. The ActionTeacher0, proposing action demonstrations for reaching Ω 0 outcomes was changed to left-handed demonstrations.
Evaluation Performance Figure  17  shows the global evaluation of Left-Yumi-SGIM-PB and Left-Yumi-SGIM-TL. We can see that, even though the learning process seems quicker before 500 iterations for the Left-Yumi-SGIM-TL learner, both learners quickly learn at the same rate as shown by their overlapping evaluation graphs. If we look at Figure  18  which shows this evaluation broken down for each task, we see the same phenomenon for Ω 2 , Ω 3 and Ω 4. This seems to indicate, that active imitation using the small demonstration datasets are enough for Left-Yumi-SGIM-PB to tackle this setup, while the huge dataset of transferred procedures don't give Left-Yumi-SGIM-TL an edge other than a slightly better performance for the complex tasks at the initial phase.
Procedures Learned To analyse what task hierarchy both learners have discovered at the end of their learning process, we plotted Figure  19. We can see that no learner is clearly better at discovering the setup task hierarchy (see Figure  2 ): Left-Yumi-SGIM-PB decomposes more often tasks Ω 1 as (Ω 0 , Ω 0 ), but slightly less often Ω 3 or Ω 4 as (Ω 1 , Ω 2 ). For Ω 3 , Left-Yumi-SGIM-PB also uses another procedure: (Ω 2 , Ω 1 ) which is also valid. To position both objects, the robot can start by either object 1 or object 2. However, if we take into account the task hierarchy learned by the transfer dataset which is fed to Left-Yumi-SGIM-TL before its learning process, we can see that Left-Yumi-SGIM-TL has learned almost exactly the same task hierarchy than the transfer dataset, which indicates its influence owing to the large number of procedures transferred: procedures from 25,000 iterations were transferred compared to the new procedures explored by Left-Yumi-SGIM-TL during only 10,000 iterations.
Hence Left-Yumi-SGIM-TL has the same defects and qualities than the transfer dataset in terms of the task hierarchy discovered. For instance, Left-Yumi-SGIM-TL uses the inefficient procedure (Ω 1 , Ω 2 ) to reach Ω 1 more often than Left-Yumi-SGIM-PB. It wasn't able to overstep the defects of the transfer dataset.
Procedures Used during Learning If we look at the procedures that were actually tried out by the learner during its learning process for reaching each goal outcome (see Figure  20 ), we can see first that for all versions of the algorithm and all types of tasks except Ω 1 , the procedures most used during learning phase correspond to the ground truth. Thus intrinsic motivation has oriented the exploration towards the relevant task decompositions. Besides, for all types of task, Left-Yumi-SGIM-TL tends to explore more the procedures in (Ω 1 , Ω 2 ) than Left-Yumi-SGIM-PB. This difference can also be explained by the predominance of this procedure in the transfer dataset D 3. Both of them also tried a lot of procedures from the Ω 2 0 procedural space. It confirms that Left-Yumi-SGIM-TL was not able to filter out the defects of its transfer dataset in terms of the task hierarchy provided.
Strategical Choices Analysing the learning process (Figures  21 22 23 24 ) of both learners, we can see that they are very similar. Both learners start by performing a lot of imitation of the available teachers coupled with an exploration of all outcome types, until 1500 and 2000 iterations for respectively Left-Yumi-SGIM-TL and Left-Yumi-SGIM-PB. This difference in timing can be caused by the transferred dataset D 3 , but is not very significant. Then they focus more on the autonomous exploration of the action space to reach the Ω 0 outcome subspace before gradually working on more complex outcome spaces while performing more an more autonomous exploration of the procedural space. However, the Left-Yumi-SGIM-TL learner seems to abandon its initial imitation phase faster than Left-Yumi-SGIM-PB (about 1000 iterations faster), and also quickly starts working on the more complex Ω 3 outcome space with the strategy autonomous exploration on procedures.
Discussion The experimental results highlight the following properties of SGIM-PB: • procedures representation and task composition become necessary to reach tasks of higher hierarchy. It is not a simple bootstrapping effect. • transfer of knowledge of procedures is efficient for cross-learner transfer of knowledge even when they have different embodiments. • active imitation learning of procedures is more advantageous than imitation from a dataset provided from the initialization phase. The performance of SGIM-PB stems from its tackling several aspects of transfer of knowledge, and relies on our proposed representation of compound actions that allows hierarchical reinforcement learning.
• How to transfer? We showed that decomposition of a hierarchical task, through procedure exploration and imitation, is more efficient than learning directly action parameters, i. e. , interpolation of action parameters. This confirms the results found in a more simple setup in  [35]. • When to transfer? Our last setup shows that an active imitation learner asking adapted demonstrations, as its competence increases, performs almost better than when it is given a significantly larger batch of data at initialisation time. More generally for a less data-hungry transfer learning algorithm, the transferred dataset should be given to the learner in a timely manner so that the information is adapted to the current level of the learner, i. e. , its zone of proximal development  [52]. This advantage has already been shown by an active and strategic learner-SGIM-IM  [53] , a simpler versions of SGIM-PB-which had better performance than passive learners for multi-task learning using action primitives.
The dramatic decrease in R ad resulted from the improved adsorption ability with increasing the Cu doping because it mainly took place at the Cu electrocatalytic active sites. Hence, Cu doping provided many active sites and high surface energies. The lowest resistances were obtained at the Cu 0. 04 Zn 0. 96 O/ITO electrode. This is in agreement with the cyclic voltammetry results and confirms that Cu 0. 04 Zn 0. 96 O/ITO electrodes are good candidates for the electrochemical sensing of glucose. The EIS data were fitted using ZView software (North Carolina, USA) to determine these parameters. An example of the Nyquist and Bode diagrams' fits of the Cu0. 04Zn0. 96O/ITO electrode is presented in the Supplementary Information (Figure  S2 ). This figure shows that the phase plot presented a large phase angle maximum, which proved that the two-phase angle maxima merged toward each other.
(7)  In addition, the dislocation density δ of the NPs was calculated using Williamson and Smallman's equation (Equation (  8 )): δ = n/D 2 , ( 8 ) where n is a constant that is usually close to one  [81]. The morphology and the structure of the synthesized NPs were investigated using HR-TEM with a JEOL JEM 2100F microscope (TEM, Tokyo, Japan) operating at 200 kV (point-to-point resolution of 0. 19 nm). The samples were prepared by evaporating a diluted suspension of NPs in deionized (DI) water on a carbon-coated copper grid. About 150 NPs were counted to estimate the average size (Image J software, 1. 52a, NIH, MD, USA). FTIR measurements were recorded on a Bruker IFS 28 (Billerica, MA, USA) using the OPUS software (version 3. 1) in the 4000-400 cm -1 wavenumber range, with a resolution of 4 cm -1 and a total of 10 scans per measurement.
Electrochemical Characterizations The electrochemical measurements were performed using a Metrohm Potentiostat Autolab PGSTAT 20 (ECO CHEMIE Ultrecht, The Netherlands) running the Frequency Response Analysis (FRA) and General Purpose Electrochemical System (GPES) software (North Carolina, USA). The measurements were carried out in an electrochemical cell involving a conventional three-electrode system that was equipped with a platinum plate as a counter-electrode, a saturated calomel electrode as the reference electrode, and a sensitive membrane based on Cu x Zn 1-x O NPs as the working electrode. All experiments were carried out at 25 • C using 0. 1 M NaOH as the aqueous electrolyte, which was usually selected for non-enzymatic glucose detection  [72, 82]. This alkaline medium facilitated the formation of higher oxidized species, such as Cu(OH) 2 and CuOOH, which significantly affected the glucose electro-oxidation.
Cyclic voltammetry measurements were done with a scan rate of 50 mV•s -1 by cycling the potential from 0 to 0. 6 V. Electrochemical impedance spectroscopy was performed with a sinusoidal excitation signal with a 10 mV amplitude and the impedance was recorded in the frequency range from 100 to 0. 1 Hz. The optimal working potential was adjusted to 0. 7 V (the optimization of the polarization potential is described in supporting information section (Supplementary Materials Figure  S3 )). The experimental data of the EIS Nyquist plots were fitted with the ZView software. Differential pulse voltammetry peak currents were recorded between 0 and 0. 6 V at a scan rate of 50 mV•s -1. For the glucose detection, multiple solutions of different glucose concentrations were made by following serial dilutions from an initial stock concentration of 10 -2 M.
, 2013). Regular calibration measurements in both modes allow the computation of the offset value to apply to the data to reach this factor two. Although no significant drift in the quadrature component could be observed during the surveys, a clear continuous drift in the in-phase component is visible in the data (Fig. 2 ). The above calibration procedure took a few minutes at each location which actually accentuated the in-phase drift by introducing steps in the visible drift at each calibration location (Fig. 2 ). The data were processed in order to reduce the effect of this drift. The investigated areas are located a few meters north west from the current excavation site (Fig. 1 ). The EMI survey was performed with vertical dipoles along profiles with 50 cm intervals.
Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB R software for further analysis using AI techniques.
For the purpose of fault classification and decision making once the feature-set has been developed, AI based techniques are very instrumental  [21] ,  [22]. Generally, prior to classification stage, the type of features used is of utmost importance. A good feature set should be able demonstrate high variability and totality of the main  [13] ,  [23] ,  [24]. This enables better training of classifiers and helps in ruling out the true negatives and false negatives. In literature, many feature selection and dimensionality reduction techniques demonstrate its capability in extracting noteworthy features to be ready from the raw/standardized dataset for faster processing when it comes to training the classifiers  [25] ,  [26]. The classification is normally done in either supervised or non-supervised fashion: under supervised learning, the classes are labelled whereas they are unknown under unsupervised learning.
III. METHODOLOGY A. EXPERIMENTAL TEST RIG To develop the database, an experimental setup has been constructed. The setup includes a three-phase squirrel cage IM of 1. 1 kW rating that is connected to a SEMIKRON IGBT Voltage Source Inverter (VSI) which is of 12 kVA. The parameters of the IM used is described in Table  3. For acquisition of current signals, the LEM (LA 55-P) current transducers have been used in conjunction with DS1104 card (dSPACE). The data was acquired at a sampling frequency of 10 kHz. Fig. 2  shows the experimental setup used in this study.
B. STATOR FAULT EXPERIMENTATION: DATA ACQUISITION AND DESCRIPTION In order to develop a database, the three-phase stator current signals were acquired from two identical IMs (parameters listed in Table  1 ) with similar manufacturing conditions. For both the IMs, data was acquired for inverter fed condition via scalar control method  [41]. For both the cases (healthy and faulty), data was acquired for 0%, 25% and 40% loading conditions (full load torque is 7 Nm). The inter-turn short circuit has been introduced in one of the coils of Phase C as shown in Fig. 3. To quantify the percentage of inter-turn short circuit (ITSC) in a phase, The level of fault (% ITSC) ranges from 0% to 10%, according to the connected shunt resistor R F. This shunt resistor R F has been used to restrict the circulating currents in the shorted portion of the stator winding under each level of fault severity.
19-21. Under 0% loading condition, the data cloud aligns itself onto the bisector (Fig. 19 ), however, the presence of thick cloud around it represents the noise in data   which is typical for the case of IMs. The point thickness around the origin depicts some element of non-linearity in the dataset with respect to short distances. The manifold is a slightly curved five-dimensional flat (a ''flat'' is a subset of n-dimensional space that is congruent to a Euclidean space of lower dimension). Fig. 20  obtained with 25% load conditions, resembles Fig. 19 , but a large amount of noise is present. Nevertheless, it is apparent that short distances are better preserved in the projection. Middle distances under the bisector can be interpreted as the unfolding of the original dataset. It means that the corresponding manifold is a slightly folded five-dimensional flat.
C. STATOR FAULT EXPERIMENT: DATA AND GCCA QUANTIZATION TOGETHER WITH BRIDGES Having determined the intrinsic dimensionality in part B above, GCCA has been applied to the three datasets (no load, 25% load and 40% load), by projecting to dimension five. At this aim, input to the GCCA are the temporal features explained in Table  3  for Phase C current. Under this experiment, following are observed: (a) Transition from healthy to faulty state (fault detection) (b) Transition between the fault severities Figures  22 23 24 show the GCCA quantization in the original 15-dimensional space, using the first three principal components for visualization. Red segments represent the bridges. The healthy cluster is well separated under all loading conditions. Notice the presence of long bridges when there is a transition from one level of fault severity to another.
Determination of layer thickness, grating depth and width This optical simulations of the resonant structure are based on the rigorous wave-optical method RCWA (rigorous coupled wave analysis), using the commercial software "MC Gratings"  [29]. The goal is to find for room temperature a highly efficient reflection-resonance under normal incidence for TE polarization at a wavelength of λ = 1500 nm. For our first tests the period is imposed at Λ = 1 µm, with the duty cycle (ridge width / period) set to 0. 5. The height of the TiO 2 layer is set to d 2 = 70 nm, reusing already determined experimental layer height values of previously produced, anatase phase TiO 2 layer. Scanning the VO 2 thickness reveals a peak of the reflection at around d 1 = 25 nm in cold state (Fig. 3(a) ). Additionally, the grating depth has been varied from h = 100 nm to 400 nm in order to optimize the grating-waveguide coupling and increase the maximal reflection value, resulting in an optimal grating height of ∼200 nm.
Low-Voltage Power Distribution Grid The case study  [20]  is carried out on a simulated low-voltage power distribution grid (a residential neighbourhood) in the Occitania region in southern France, composed of approximately 600 households, 200 household PV installations of 4 kW each, amounting to a total capacity of 800 kW, a small-scale biogas plant (100 kW), and a water tower (100 kW). Figure  1  displays some grid load and PV power generation data (over a week in April) used in this work. Grid load is measured at the MV/LV transformer level of the low-voltage power distribution grid whereas PV power generation is inferred from GHI measurements taken by a pyranometer installed at PROMES-CNRS laboratory, which is located a few kilometres from the residential neighbourhood. In this section, models of the power distribution grid and the flexible assets used in this case study are presented, formulated over a forecast horizon H.
As part of the research activities of the PROMES-CNRS laboratory, a comparative study of models developed for multi-step-ahead GHI forecasting is carried out for intrahour and intraday forecast horizons using a two-year database of GHI measurements sampled at a 10-min rate. Results demonstrate that, although all considered models outperform the persistence model, there is no clear frontrunner in terms of nRMSE values, with a slight advantage for LSTM (long short term memory) artificial neural network (ANN) and Gaussian process regression (GPR) models when taking into account the quality of the forecasts' associated confidence intervals. Confidence intervals are a noteworthy perk of using GPR models in the forecast module of the smart management strategy since they are built-in in the models and do not require running a Monte Carlo simulation to be statistically inferred as is the case for other forecasting methods (like artificial neural networks).
These confidence intervals give the predictive controller supplementary information it uses to achieve a more robust control strategy. The development of GPR models for intraday GHI forecasting are detailed in  [46] [47] [48]. GPR models used herein are, in part due to the fact that the database used to update the model's parameters is a sliding one, sufficiently fast to be a valid candidate for the real-time control application at hand. On the downside, the predictive nature of the controller makes it dependant upon real-time access to measurements to be able to sustain the forecasting models' sliding databases and to update their parameters at each time step. This limitation is true for all machine-learning-based methods operating in real time, hence the growing interest in the development of state-of-the-art smart metering technologies that ensure quick and reliable data transfer.
The isotropic rational quadratic (RQ) kernel is given by: k RQ (x, x ) = σ 2 3 1 + (x -x ) 2 2 2 3 • α -α (14) where σ 3 is the amplitude, 3 is the correlation length and α defines the relative weighting of large-scale and small-scale variations. In addition to forecasts, the regression model provides an associated confidence interval within which measurements have a probability of 95% of staying. This interval (CI) is computed as follows: CI = µ * ± 1. 96 σ 2 * (15) where CI represent the confidence interval bounds, µ * is the predictive mean, and σ * is the predictive variance. During the training phase, the values of hyperparameters are optimised using a 2week database  [48]. During the test phase, at every time step, new measurements are incorporated in order to update values of the model's parameters using a sliding 24 h database.