dataesr/scientific-paragraphs-categorization huggingface dataset:
iii. Nuclear magnetic resonance (NMR) NMR spectra of ELP-containing samples in D2O were acquired at 298 K on an AVANCE III HD 400 spectrometer operating at 400. 2 MHz for 1 H. The solvent signal was used as the reference signal. Data processing was performed using Topspin software. coli for 21 hours after induction by isopropyl-β-D-thiogalactoside (IPTG). Soluble proteins were extracted from the cell lysate. The ELP was then purified by inverse transition cycling (ITC), dialyzed extensively against ultrapure water (UP) and lyophilized to provide pure protein in 150 mg/L culture yields. The purity of the ELP was assessed by SDS-PAGE (Figure  S2 ). MALDI mass spectrometry were performed to determine an experimental mass of 17,040 Da in agreement with the theoretical value (Figure  S3 ). The protein was characterized by 1 H NMR spectroscopy, confirming the ELP structure (Figure  S4 ).
Energies 2021, 14, 8325 2 of 15 Therefore, this work aims at the application of a hybrid method, combining regression techniques and classical statistic outlier methods focusing on detecting outliers of a dataset that contains measurements of electrical energy consumption of a tertiary building. The random forest  [12]  method as a regression technique to construct a model was used in this work. Afterward, all measured samples were compared with the model instances, resulting in an error. The statistical outlier detection methods were then implemented to search high error values in order to classify them as potential outliers. This combination is called the forecast error method. The construction of a predictive model of energy consumption in a building can be of great importance for energy managers.
Through these models, it is possible to plan from the short term optimization of energy consumption costs to the allocation of assets in case of preventive maintenance with low impact on the building's normal activity. In addition, the implementation of an algorithm that can detect anomalies integrated into a building management system can facilitate the identification of potential energy consumption reduction or even the need to perform corrective maintenance on an asset that may present a defect in real time. The following section exposes the statistical outlier detection methods employed in this work. Afterward, the regression (forecast) method employed and the error metrics that can be used to assess the performance of this method are briefly introduced. The combination of these techniques is applied in two datasets.
Methods for Outlier Detection An outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism  [14]. This type of sample can indicate malfunctioning of the metering system or even in a load itself. In addition, if this data quality problem is too persistent it can affect the accuracy of eventual machinelearning algorithms using this dataset. Therefore, its identification and correction are important steps in the data pre-processing. Standard outlier detection consists of two main components. The first one is calculating an outlier score for every data instance. The outlier score can be the value itself or even the difference between the value and its prediction  [15] , considering that the prediction model was generated based on healthy data.
Mean Absolute Deviation (MAD) Because of its sensitivity to the presence of outliers, the mean value is not the most suitable measure of central tendency to be used in the outlier detection. The median value, another measure of central tendency, is more adapted to this task due to its insensitivity to the existence of outliers in the dataset. The median is defined as the value associated with the mean rank after sorting the data ascendingly. The median absolute deviation  [20]  is then defined as the median of the absolute deviation from the median, and can be described as follows: 𝑀𝐴𝐷 = 𝑏 * 𝑚𝑒𝑑(|𝑥 𝑖 -𝑚𝑒𝑑(𝑋)|) (3) In this equation, b is a constant, suggested as 1. 4826,   represents each sample and  is the vector that contains all samples. The upper () and lower () bounds can be calculated by the application of the following equations: 𝑈𝑃𝐵𝑀 = 𝑚𝑒𝑑(𝑋) + 3 * 𝑀𝐴𝐷 (4) 𝐿𝑊𝐵𝑀 = 𝑚𝑒𝑑(𝑋) -3 * 𝑀𝐴𝐷 (5).
Mean Absolute Deviation (MAD) Because of its sensitivity to the presence of outliers, the mean value is not the most suitable measure of central tendency to be used in the outlier detection. The median value, another measure of central tendency, is more adapted to this task due to its insensitivity to the existence of outliers in the dataset. The median is defined as the value associated with the mean rank after sorting the data ascendingly. The median absolute deviation  [20]  is then defined as the median of the absolute deviation from the median, and can be described as follows: MAD = b * med(|x i -med(X)|) (3) In this equation, b is a constant, suggested as 1. 4826, x i represents each sample and X is the vector that contains all samples. The upper (UPBM) and lower (LWBM) bounds can be calculated by the application of the following equations: UPBM = med(X) + 3 * MAD (4) LWBM = med(X) -3 * MAD (5).
Skewed Boxplot The boxplot method, described in the previous section, is better suited for the detection of outliers in a dataset whose distribution is symmetric. When the distribution is skewed, some samples that exceed the upper and lower bounds defined by that method may be misclassified as outliers  [23]. For this reason, a correction is necessary in the calculation method of the upper and lower bounds. There are some ways to adjust the boundaries towards the asymmetrical data. In 1990, Kimber  [18]  proposed a method to consider the skewness of distribution in the search for outliers. The following equations define the upper () and lower () bounds used in this method. In these equations,   is the upper interquartile range,   is the lower interquartile range and  2 represents the median of the evaluated series (or the second quartile).
Skewed Boxplot The boxplot method, described in the previous section, is better suited for the detection of outliers in a dataset whose distribution is symmetric. When the distribution is skewed, some samples that exceed the upper and lower bounds defined by that method may be misclassified as outliers  [23]. For this reason, a correction is necessary in the calculation method of the upper and lower bounds. There are some ways to adjust the boundaries towards the asymmetrical data. In 1990, Kimber  [18]  proposed a method to consider the skewness of distribution in the search for outliers. The following equations define the upper (UPBS) and lower (LWBS) bounds used in this method. In these equations, SIQR U is the upper interquartile range, SIQR L is the lower interquartile range and Q 2 represents the median of the evaluated series (or the second quartile).
Results and Analysis This section presents the results obtained by applying the forecast error method in the search for outliers in power consumption data of a tertiary building. Firstly, the regression methods results are shown, quantifying their performance through error metrics. Afterward, using these regressions, the forecast error method was applied. For comparison, the classic statistic methods for outlier detection were also applied so that the results from both techniques are presented. The code used to perform these tasks was developed in python language in a Jupyter Notebook, available in an online open repository  [31]. The data used in this work were adapted from the dataset available for downloading at the open science platform Mendeley Data  [32]. That data comes in CSV (comma-separated values) files that contain the timestamp and the cumulative electricity consumption with 10 min sampling.
The dataset also has files with data of the external temperature, which has influence in the building energy consumption because of the nature of the cooling loads. The data were then resampled as the hourly consumption, resulting in 8760 samples. This dataset contains power consumption data of the GreEn ER, a building located in Grenoble, France. It houses the Grenoble-INP Ense3 engineering school, the G2Elab laboratory, besides training and research platforms. The building has more than 22,000 m 2 of surface area, which is divided over 6 floors and the roof. About 1500 students and several hundred professors, researchers, and staff frequent it. As it is a large building, its electricity consumption is also important. On typical days, the active power can amount to more than 300 kW. It is also a massively monitored and controlled building with more than 1500 sensors, including about 330 electricity meters.
These meters measure the consumption of the different loads in the building, such as lighting, electrical outlets, air handling units (AHUs), chillers, pumps, etc. [33]. The measured data are used to control the internal conditions and to monitor the energy consumption. Two different data series were used to test the forecast error method. Firstly, some known outliers were inserted in synthetic healthy data, without outliers or any other data quality problem in order to establish a benchmark. This series was called adapted data. In a second stage, the technique was employed in a data series without any pre-treatment regarding data quality. This second dataset was called real data.
The information of these samples ble 1, and some of these outliers are highlighted in Figure  4  too. This info used as ground truth and compared with the results obtained to assess t of each sample in true positive, false positive and false negative. From the data exposed in the previous figure, it is possible to notice different consumption patterns for different periods. It can be seen that the periods of higher consumption match with those of higher occupation, during daytime on the weekdays. Outside these periods, during nighttime on the weekdays, weekends, holidays and vacations, the consumption reduces drastically. In addition, it is possible to notice a relation with the temperature since the highest consumption occurs during summer. In order to test the outlier detection techniques, twelve outliers, both upper and lower, were manually introduced in the series presented in Figure  3 , resulting in the dataset presented graphically in Figure  4.
Furthermore, in the False Positives column, the number of normal samples misclassified as outliers are shown. Therefore, the sum of the true positives and the false negatives should be equal to the number of outliers present in the dataset, in this case, twelve. The sum of the true positives and false positives is equal to the potential outliers detected and the sum of both false positives and negatives gives the total of samples misclassified by each method. The results indicate that the MAD and the adjusted boxplot were the most successful methods in detecting outliers, having found half of them; however, they still misclassified several other samples, reducing their precision. Thus, even detecting some outliers, their poor recall, with several false positive samples classified as outliers, show that these methods alone are not the best suitable to detect outliers, especially local ones, such as those inserted in this dataset.
As the classical statistical methods failed to detect several outliers in the study dataset, the forecast error method, which compares the results of previous regression models with measurements was employed. The statistical methods for outlier detection are then applied on the resulting error. Table  4  shows the number of outliers detected by each method considering the deviation between the actual values and the predictions. The results presented in Table  4  indicate that the all the methods were able to detect most of the outliers inserted in the dataset, using the forecast error. However, the poor precision of the MAD, the boxplot, and the skewed boxplot misclassifying several samples indicates that they are not well suitable for this task in this dataset. The other two, threesigma rule and adjusted boxplot, perform better and similarly, with a small advantage for the adjusted boxplot.
Real Data The forecast error method was also tested in a dataset, available for downloading at the open science platform Mendeley Data  [32] , with no pre-treatment regarding data quality. This dataset was extracted directly from the GreEn-ER Building Management System and contains several problems of data quality, inherent to this type of monitoring. Figure  7  illustrates the power consumption data of the GreEn-ER building in which it is possible to visualize, for example, some outliers, values that extrapolate the scale of the graph, at the end of the year. In that period, both upper and lower outliers can be seen. A human agent looked through all samples and classified them into normal samples and upper (values higher than the normal instances) and lower (values lower than the normal instances) outliers, establishing the ground truth to which the results are compared to determine the true positives, false positives, and false negatives.
Outlier Detection As previously shown in Table  5 , the outliers present in the series were manually classified to establish a benchmark for comparing the performance of the outlier detection algorithms. Two hundred and twelve outliers were found, eight of which are upper outliers and the other two hundred and four, lower. The global search was performed using the three-sigma rule, the boxplot, the skewed boxplot and the adjusted boxplot methods. The results are shown in Table  7. The presented results indicate that none of the tested methods were able to detect all the outliers. Furthermore, although the adjusted boxplot has initially pointed out more outliers than actually exist, it failed to detect forty outliers, and misclassified ninety-nine normal samples as abnormal data instances. Therefore, the results corroborate that those classical statistical methods applied to the value itself are not suitable to detect outliers, especially for local ones, such as the lower outliers present in this dataset.
As shown in the previous section, the forecast error method was also employed in the real data. Although they found all outliers of the dataset, the results presented in Table  8  corroborate the notion that the MAD, the boxplot, and the skewed boxplot are not the most adapted methods to detect outliers using the forecast error in this dataset, as they misclassified several samples as outliers. Furthermore, the three-sigma rule failed to detect most of the outliers, flagging only the obvious upper outliers. Finally, the adjusted boxplot performed better, but still misclassified some samples. This method was able to detect 192 out of 212 outliers and misclassified another 13 samples as outliers, resulting in 33 misclassifications. This better performance of the adjusted boxplot can be seen by observing the F-score.
Conclusions This work aimed to employ a hybrid method, called forecast error, to detect outliers in the power consumption of a tertiary building. This method combines regression methods with statistical outlier detection techniques. The random forest algorithm was used as the regression method and the three-sigma rule, the median absolute deviation, the boxplot, the skewed boxplot, and the adjusted boxplot were chosen as outlier detection techniques. In a global search, using only the statistical methods to the data instances themselves, none of them presented the expected performance. On the other hand, when the adjusted boxplot was applied to the forecast error (difference between the actual measurement and the forecast) better performance was obtained. Considering both datasets tested, this combination has presented the best F-score (higher than 0.
90 in the real data dataset), but it was not perfect. Hence, a human-in-the-loop approach  [34]  is still needed, with the forecast error outlier detection method pointing out potential outliers and a human agent validating them. Thus, the effort would be less costly with the application of the method presented in this work. In addition, this approach relies on high quality predictions, which may be improved. One way to improve the forecasts is using more features in the training phase. The consumption of one week earlier, in the case of the datasets pattern presented in this paper, is a common feature used. However, in the present dataset, with several subsequent samples with data quality problems, the use of the past consumption could degrade the model. On the other hand, in a real-time application, this feature could be of great help in the definition of a good predictor and would significantly improve the outlier and anomaly detection.
Funding: The  Carnot Énergies du Futur Institute , under the projects ORCEE and interebat, the  French Centre National de la Recherche Scientifique (CNRS) , the  Grenoble-INP , the  Université Grenoble Alpes (UGA) , and the  Federal University of Santa Catarina (UFSC)  funded this research. This work has also been partially supported by the  ANR (Agence Nationale de la Recherche ) project eco-SESA (https://ecosesa. univ-grenoble-alpes. fr/ (accessed on  17 November 2021) )  ANR-15-IDEX-02.
Data Availability Statement: The data presented in this study are openly available in Mendeley Data at DOI: 10. 17632/h8mmnthn5w. 1, reference number  [32]. A resumed dataset can also be found in Supplementary Materials at: https://gricad-gitlab. univ-grenoble-alpes. fr/martgust/powerconsumption-data-quality (accessed on 17 November 2021).
The levels of purified protein are greater than those achieved by the immobilized metal ion affinity chromatography (IMAC) (data not shown). This may be because RAC has a higher binding capacity (365 mg of protein per gram of RAC) than any other commercial resin used to purify proteins (10-40 mg of proteins per gram of resin)  [26]. However, the purification conditions tested were not optimized, explaining why a lot of enzyme was not retained by the cellulose resin. Changing some purification parameters such as the ratio of crude extract protein to resin would improve the procedure.
Acknowledgments:  Reinaldo Fraga Vidal  and  Roberto C. Aríticas Ribalta  were supported by a  Development Research Project  financed by the  Development Cooperation Program of ARES (Académie de Recherche et d'Enseigment Supérieur)  from  Belgium  ( 2017-2022 ). Reinaldo Fraga Vidal thanks  William David Rau  ( MS Rau Antiques, New Orleans, LA, USA ) for his kind support. We thank  Magali Remaud-Simeon  and  Pierre Monsan  for their technical support. We also thank  Joan Combie  for the style correction of the manuscript.
For instance, consider the following project investing scenario where a company plans to invest in the purchase of machines for the production of new products. Each candidate product has a profit, while the composing parts of the product needs new machines to be acquired. The goal is to determine the set of products to be produced such that the profit will be maximized while ensuring that the cost of the purchased machines does not exceed a given budget. This problem can be conveniently formulated by the SUKP model where an item corresponds to a product with its profit and an element is a machine with its purchasing cost (element weight). Then, solving the project investing problem is equivalent to finding the optimal solution to the resulting SUKP problem. Generally, SUKP appears in the context of flexible manufacturing  [1] , financial decision making  [2] , applied cryptography  [3] , database design  [4] , data allocation in cyber systems  [5]  and key-pose caching systems  [6].
Experimental settings The proposed MSBTS algorithm was implemented in C++ and compiled using the g++ compiler with the -O3 option. All the experiments were carried out on an Intel Xeon E5-2670 processor (2. 5 GHz CPU and 2 GB RAM) running under the Linux operating system. The MSBTS algorithm used the same stopping conditions for the reference algorithms (see below), i. e. , 500 s for the Set I instances and 1000 s for the Set II instances. Each instance was solved 100 times independently with different random seeds. Note that contrary to the existing algorithms, our algorithm eliminates the need for tuning parameters. Among the existing algorithms for SUKP in the literature, we identify four best performing algorithms according to the reported computational results: hybrid jaya algorithm  [17]  (DH-Jaya, 2019), hybrid binary particle swarm optimization with tabu search algorithm  [21]  (HBPSO/TS, 2019), iterated two-phase local search algorithm  [9]  (I2PLS, 2019) and the kernel based tabu search algorithm  [22]  (KBTS, 2020).
Acknowledgments We are grateful to the reviewers for their useful comments and suggestions which helped us to significantly improve the paper. We would like to thank  Dr. Congcong Wu ,  Dr. Geng Lin ,  Dr. Xiangjing Lai  and their co-authors for sharing the codes of their algorithms [17,21,32]. Support from the  China Scholarship Council  (Grant  201706290016 ) for the first author is also acknowledged.
Approaches such as  [15, 16]  have extended the heuristics of IM with goal-oriented exploration, and proven to be able to learn fields of tasks in continuous task and action spaces of high but bounded dimensionality. More recently, IMGEP  [17]  and CURIOUS  [18]  have combined intrinsic motivation and goal babbling with deep neural networks and replay mechanisms. They could select goals in a developmental manner from easy to more difficult tasks. Nevertheless, these works did not leverage cross-goal learning but only used interpolation between parametrised goals over a common memory dataset.
More precisely, while in  [35]  we showed on a toy simulation faster learning and better precision in the control, in this article, we show on an industrial robot that task decomposition is pivotal to completing tasks of higher complexity (by adding more levels of hierarchy in the experimental setup), and we test the properties of our active imitation of task decomposition : it is valid for cross-learner transfer even in the case of different embodiments, and active imitation proves more efficient than imitation of a batch dataset given from initialisation. This use-case study enables deeper analysis into the mechanisms of the transfer of knowledge. The article is organized as follows: we describe our approach in Section 3; and present our setups on the physical simulator and the real robot of an industrial robot arm in Section 4.
Algorithm We here present the algorithm SGIM-PB (Socially Guided Intrinsic Motivation with Procedure Babbling). In Section 5. 5, we will also look at the variant named SGIM-TL (Socially Guided Intrinsic Motivation with Transferred Lump), which is provided at initialisation with a dataset of transferred procedures and their corresponding reached outcomes: {(ω i , ω j ), ω r }. The main differences between SGIM-PB and SGIM-TL are outlined and they are contrasted with former versions IM-PB and SGIM-ACTS in Table  1. SGIM-PB and SGIM-TL propose to learn both M and P simultaneously. (Ω i , Ω j ) ⊂ Ω 2 , defined as all possible pairs of two outcome subspaces. Our SGIM-PB agent is to collect data in order to learn how to reach all outcomes by generalising from the sampled data. This means it has to learn for all reachable outcomes, the actions or procedures to use to reach the outcome.
Algorithm 1 SGIM-PB and its SGIM-TL variant Input: the different strategies σ 1 ,. , σ m Initialization: partition of outcome spaces R ← i {Ω i } Input: CASE SGIM-TL: transfer dataset D 3 = {(ω i , ω j ), ω r } Initialization: CASE SGIM-TL: episodic memory Memo ← {(ω i , ω j ), ω r } 1: loop 2: ω g , σ ← Select Goal Memo ← Goal-Directed Procedure Optimization(ω g ) 7: else if σ = Mimicry of action teacher i strategy then ((ω di , ω dj ), ω d ) ← ask and observe demonstrated procedure to teacher i 12: Memo ← Mimic Procedure((ω di , ω dj )) At each episode, the robot and the environment are reset to their initial states. The learner uses the chosen strategy to build an action (based or not on a procedure), decomposed into action primitives which are then executed sequentially without getting back to its initial position.