dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS Training set of multi-redox voltammograms. The dataset that yields EchemNet includes simulated multi-redox voltammograms, conducted via finite-element methods using COMSOL Multiphysics v5. 5 (Supplementary Note 1). What we sought is to establish a dataset of simulated voltammograms that sample the majority of if not the whole numerical parameter space for each mechanism as defined in textbooks  2, 3  (Supplementary Note 2). Each data point in the dataset includes voltammograms of up to 6 scan rates and up to 4 redox events ({v, i(E)}n, n = 1 to 6; z = 1 to 4). 8 common mechanisms in electrochemistry (Fig. 1B ) have been included following the textbook definitions (Supplementary Note 3):  2, 3  (1) the singleelectron quasi-reversible homogeneous electron transfer (E); (2) a single-electron quasi-reversible homogeneous oxidative electron transfer followed by a chemical reaction of the oxidant in the solution (ECa); (3) a single-electron quasi-reversible oxidative electron transfer preceded by a chemical reaction of the reductant in the solution (ECb, the anodic variant of the classical CE mechanism in Savéant's textbook 3 that is the counterpart of ECa); (4) the single-electron heterogeneous electron transfer following the Tafel kinetics (T); (5) the twoelectron homogeneous electron transfer, in which a singleelectron transfer is followed by an irreversible chemical and a disproportionation steps (DISP-1); (6) a similar two-electron homogeneous electron transfer, in which a single-electron transfer is followed by an irreversible chemical step and a thermodynamically less demanding single-electron transfer (ECE); (7) the homogeneous electrocatalysis, in which a singleelectron transfer is followed by a chemical step that regenerates the redox-active catalyst (ECcat or EC'); (8) the interfacial single-electron transfer when the redox species follows the Butler-Volmer kinetics and is bound on the electrode surface (SR).
Here the categorization of ECa and ECb mechanisms, instead of the classical EC and CE ones in textbooks  2, 3  , is because an anodic/cathodic EC mechanism is mathematically equivalent to a cathodic/anodic CE one, respectively. A multi-step process is developed to establish the dataset of simulated multi-redox voltammograms. First, the parameter space of each mechanism, for example the value ranges for scan rate (v), exchange current density (i0), equilibrium constant (K), and forward kinetic rate constant (kf) in the ECa mechanism, is carefully defined following textbooks and prior literature  2, 3  (Table  S1 , Supplementary Note 3). Second, we randomly sampled about 3,000 parameter combinations following the constraints defined in Table  S1 , for each mechanism type with up to 6 different scan rates (n = 1 to 6).
The use of Elow and Ehigh to represent the voltage window without information of current density i is consistent with our design of onedimensional (1D) object-detection model (see below). Last, a certain extent of Gaussian noise, with a dimensionless standard deviation σtrain = σtest = 0. 01 unless otherwise noted, was applied to the normalized current density inormalized (Supplementary Note 5) following the same protocol as our previous work. 12  The voltammogram data {v, i(E)}n and the corresponding ground truth Elow and Ehigh were normalized before being deployed for the model's training, validation, and testing (Supplementary Note 5). Design of deep-learning (DL) architecture. A customdesigned Faster R-CNN architecture was needed to establish the EchemNet model. The presence of multiple electrochemical mechanisms within a single cyclic voltammogram precludes the use of image classification algorithms such as ResNet 19 alone.
The developed DL model was evaluated for its performance, in a protocol similar to our previous report, precision (P) and recall (R) of both metrics are calculated to evaluate the predictability and sensitivity, respectively, of the DL model (Fig. 3A ). Calculating the harmonic means of P and R in both metrics lead to the F1 scores, an overall measure of a model's performance  21. As shown in Fig. 3B , the F1 scores in Metrics I and II reach 0. 952 and 0. 937, respectively, illustrating strong performance by the RPN (Metric I) and overall balanced performance with high values of both precision and recall (Metric II). Such a performance is satisfactory to say the least, based on the standard of image recognition,  21  within our aforementioned assumptions and our dataset of simulated voltammograms.
What is interesting is that the extent of tolerable noise (σtest) can be slightly larger than the noise during the training process (σtrain) (Fig. 5B  to 5D), suggesting that the DL architecture per se is pretty resistant against noises. Similar robustness is observed towards the magnitude of current density (i) of the redox feature, in this work represented as the dimensionless factor Sz. Although Fig. 5F  suggests that smaller redox features are more prone to be missed during redox-event detection (fn cases), the effect is relatively mild and highly dependent on the extent of noise in the dataset. This presumably illustrates the benefits of our 1D adaptation of the DL architecture, which only consider a redox's voltage window and presumably minimizes the possible bias towards large redox features.
Although our existing dataset of simulated voltammograms ensures sufficiently separation among redox events (Supplementary Note 4), one venue of future research is to examine and potentially improve the sensitivity of detecting small redox events in close proximity with big redox features, a frequently encountered challenge in manual inspection of voltammograms. As credible experimental data are not expected to be significantly noisy (say, σ < 0. 02), the developed DL model is presumed sufficient towards smaller redox features in the voltammograms. One interesting issue that we discovered during our research is the quantitative descriptors to store the information of electrochemical potentials for a specific redox process. Our recent efforts in DL analysis of voltammograms  8, 12  suggest that the mechanism propensity vector y serves well as the descriptor for mechanism classification in a probabilistic manner.
Our work highlights the necessity of a database composed of experimental cyclic voltammograms. Although the simulated training set for the DL model benefits from the exhaustive sampling of mechanisms' parameter space, it remains critical to introduce experimental data during the model's training, evaluation, and future refinement processes. The confusion matrix (Fig. 3C ) illustrates that ECa, ECb and DISP-1 mechanisms are more challenging than others for the DL model to categorize. We propose to use experimental data to address the model's "confusion" among those 3 mechanisms. The low values of E mechanism's propensity (yE) for Co II TPP (79. 1% in Fig. 4E ) and nitroxyl derivatives (75. 4% and 75. 8% in Fig. 4H  and 4I, respectively) are another concerning point, as we had expected much larger yE values for those classical quasireversible electron transfers.
While simulated voltammogram of E mechanism precludes any side reactions, experimentally it is likely to have trace side reactions, for example from impurities or trace moisture in anhydrous solvents, which may slightly deviate voltammograms from theoretical ones. Thus the DL model trained by simulated voltammograms is deemed too restrictive on the conditions of an E mechanism, and it is necessary to use experimental data to further refine the model. One challenge for future development of DL model in electrochemistry is the absence of any datasets with annotated ground truths of "voltage windows" and mechanism assignments. In the research field of image recognition, multiple public datasets of 10 3 ~ 10 7 curated real-life images are available for researchers to test, evaluated, and improve their developed models.
26, 27  Historically, such publicly available datasets greatly propelled the development of DL models for image recognition. Yet to date there is not such an equivalent dataset available for the electrochemistry community. We hypothesize that a well-curated dataset of electrochemical characterizations will have a similar effect over the research of artificial intelligence in electrochemistry. Such an effort is currently undergoing. In the future, we envision a hybrid approach of model development, by feeding both simulated and experimental data during the training/refinement processes. Finally, we note EchemNet's potential broader impacts towards the research community of electrochemistry and beyond. The development of DL model that automates electrochemical mechanistic analysis does not obviate human researchers and experts' opinion in mechanistic studies.
The EchemNet model's capability of detecting an arbitrary number of redox events is commensurate with a data analysis process that accommodates a wide range of redox events, expected or unexpected, with little if any a priori chemistry knowledge. The DL model's probabilistic approach of mechanistic classification avoids deterministic mechanistic assignments, undesired when only finite information is available during the experimental exploration, and allows for decision-making process based on the analyzed propensity distribution. Our EchemNet model will augment the productivity of human researchers. Additional research is needed in order to achieve the aforementioned functionality in an autonomous electrochemistry platform. In particular, additional deployment of the DL model towards a large dataset of experimental voltammograms with diverse mechanisms is desired to further evaluate if not validate the model's utility in real-life applications.
We call for the establishment of a public database of curated experimental voltammograms with a wide range of mechanisms. Such a public database will not only help benchmark future models' performance but also provide the training set for additional model refinement. A synergistic combination of simulated voltammograms that numerically exhaust all possible mechanistic variations and experimental ones that offer the taste of real-life scenarios is hypothesized to yield an artificial intelligence of electrochemical mechanistic deciphering that rivals if not surpass human intelligence.
ACKNOWLEDGMENTS J. Y. Y. , Q. G. and C. L. acknowledge the  National Science Foundation (NSF)  ( CHE-2247426 ), W. Z. acknowledges the  UCLA Dissertation Year  Fellowship, C. C. acknowledges the partial support from  Agence Nationale de la Recherche (Labex ARCANE ,  CBH-EUR-GS ,  ANR-17-EURE-0003 ). The authors thank  Prof. Matthew Sigman  and  Dr. Avijit Hazra  for constructive discussions. C. L. is grateful for the feedbacks from the attendees in the 2023 Telluride Workshop " Molecular Transformation through Proton-Coupled Electron Transfer for Energy Storage  and Conversion". C. L. thanks the constructive interactions with "echem", a Felis catus at his residence that inspires the naming of EchemNet. (^ↀᴥↀ^).
This fusion technique not only improves the accuracy of the system but also enhances the ability to adapt to dynamic environmental changes, thus ensuring the stable movement of the robotic arm. In addition to shoulder joints and elbow joints, we also collect data on wrist joints and finger joints through data gloves to control the manipulator. This meticulous data collection allows the robotic hand to mimic the movements of the human hand, giving it a high degree of dexterity and precision, especially when grasping movements are required. This paper uses a previously developed robotic arm, coupled with a dexterous hand, as shown in Figure  2 , as the execution part of the entire system. The robotic arm has five degrees of freedom, which are composed of two joint angles of the shoulder, two joint angles of the elbow, and one joint angle of the wrist.
The fusion curve was plotted, and the smoothness of the curve of single sensor and fused angles was compared. The trajectory smoothness of the curve under these four groups of actions was statistically calculated. The calculation formula is as follows. The smaller the data, the smoother the curve is. The results obtained are shown in Table  3. The experimental results of action A are shown in Figure  12. Because the movement speed was too fast, the IMU data lagged behind the joint angle of Kinect, so it should not be too fast during the control process. The shoulder abduction angle collected by Kinect changed from 0 degrees to around 180 degrees during the collection period. This phenomenon is theoretically correct, but this mutation needs to be removed in control. In the position, the shoulder, elbow, and wrist are approximately in a straight line; thus, the shoulder roll calculated by Kinect is incorrect and is very different from that calculated by the IMU.
Data Glove Angle Verification In this experiment, we aimed to evaluate the performance of an anthropomorphic control system in the control of fine hand movements. The action of grabbing a disposable paper cup filled with water and pouring water was designed, and the rotation angle of the wrist and thumb, as well as the bending angle of the metacarpophalangeal joints of the five fingers, were collected through the data glove. Both the rotation angle and bending angle of the thumb were mapped to [0, 90], which is more convenient to control the dexterous hand. The experimental results are shown in Figure  16. The wrist servo of the robotic arm can rotate up to 180 degrees, and the starting position was set to 0 degrees in the neutral position. Therefore, we set the wrist rotation angle value range to [-90, 90], which is easier to map to the robotic arm.
TabNet  [16]  is a simulated decision tree network specialized for processing tabular data. The classification of TabNet is slightly superior to that of Random Forest  [17]. The sample used in this paper also consists of tabular data, and its form is a one-dimensional feature vector trained in terms of features. Therefore, for these data, the approach discussed in this study relies on a model based on TabNet. Additionally, to further refine the prediction accuracy, the SimAM  [18]  module is added to improve the neurons in the feature matrix. It is known today that the characteristics of multiple strokes in writing contain more information. Therefore, the three-dimensional sample data, combined with the multi-shot features, are imported into the long short-term memory (LSTM) models. This allows the model to process the temporal features.
SimAM is integrated into CNN by boosting the weights of highly correlated features  [19]. In addition, the Mogrifier long short-term memory (MLSTM)  [20]  is proposed. This variant of LSTM allows the input data to be fully independent and interact with each other. Therefore, it can significantly improve the performance of the network. In this paper, multiple fusion classification models are proposed to recognize PHPs, and we propose the following five contributions: (1) Creation of a self-collected dataset, as there are no relevant public datasets to conduct in-depth experiments. This item requires developing a list of experimental tasks, processing raw data, and producing stroke features in writing; (2) Proposal and implementation of a TabNet model integrating SimAM for PHP recognition and classification of single strokes in writing, which we call single stroke classification (SSC); (3) Implementation of an MLSTM model, incorporating SimAM and CNN, applied to PHP recognition and classification of multiple strokes in writing, which we call multiple stroke classification (MSC); (4) Comparison of accuracy rates by F1-score values of the proposed models with other classification models; (5) Demonstrating the feasibility and stability of proposed models using relevant assessment tools.
They demonstrated the benefits of this method on public datasets. The research team also proposed a new learning strategy for visual classification problems that improves the convergence and learning efficiency of CNN using an improved elastic backpropagation algorithm, also known as the modified resilient backpropagation (MRPROP) algorithm  [22]. They achieved good results on public-facing datasets. SimAM is an out-of-the-box module that has no additional model parameters, which makes it particularly suitable for various deep learning recognition tasks. For example, Zhongwen Wang et al. [19]  applied dual-stream convolutional networks combined with SimAM modules for human behavior recognition. The results showed a 2. 6% improvement in the UCF101 dataset (action recognition dataset composed of realistic action videos from YouTube with 101 action categories) and a 11.
7% improvement in the HMDB51 dataset (dataset consisting of 6766 realistic video clips belonging to 51 action categories, each category containing at least 101 clips). Xiaoyi Qin et al. [23]  used the SimAM module to detect noise and were able to reduce the noise detection error rate to 0. 643%. TabNet is a versatile model that has been applied to various domains. Sabri Boughorbel et al. [24]  used TabNet to predict vacancy rates in hospitals and achieved better results than traditional machine learning models. Heng Shi et al. [25]  used TabNet to detect signaling enzymes and achieved a classification accuracy of 94. 51%. In the stock market domain, Jixiang Sun et al. [26]  used TabNet for stock market forecasting and found that it outperformed the LSTM, generative adversarial network (GAN), and gate recurrent unit (GRU) models.
Experimental Equipment The experimental environment includes the following hardware and software equipment: (1) Hardware environment: As shown in Figure  1 , a commercially available dot-matrix digital pen was used to collect the dataset. The high-speed camera on the front of the digital pen captures the movement of the pen tip at any time. The pressure sensor sends the pressure data back to the data processor, which then transmits all sorts of information to the outside world via Bluetooth or a USB cable. This information includes paper type, source, page number, position, handwriting coordinates, movement trajectory, pen tip pressure, processing time, etc. The hardware environment for the handwriting capture software is an octa-core Qualcomm Snapdragon 835 processor clocked at 2. 45 GHz.
The experimental environment for training the model uses the Windows 11 operating system and the AMD Ryzen 7 5800H processor. (2) Software environment: The software for collecting the dataset is developed by the user himself and is called Handwriting-collect. It is developed with Android Studio 4. 1. 3, and the application programming interface (API) version is Android API 29. The library of mathematical operations, numpy 1. 19. 5, is used for the Python module. Scikit-learn 0. 23. 2 is used for the machine learning module. For the deep learning module, PyTorch 1. 10. 2 is used.
Data Collection and Processing We randomly selected 15 student volunteers, 8 men and 7 women. The volunteers were between 22 and 26 years old. They usually write with the correct PHP and with high frequency. The 15 volunteers were asked to write on a flat table under soft natural light. To enable the volunteers to perform the nine writing tasks thoroughly, they were asked to imitate the writing based on the images of the PHPs and their corresponding descriptions to obtain feature data. The number of volunteers may seem small, but it generated more than 16,000 samples of handwriting strokes, which is more than sufficient for the small-sample learning experiments conducted in this study. Once the 15 volunteers have finished writing, the data recorded in the pen is exported. This information includes X (X coordinate), Y (Y coordinate), f (pressure), S (status, including top, move, and bottom), and t (timestamp).
Traditional Machine Learning Models The traditional machine learning methods we have used have only been applied to SSC classification problems. In traditional machine learning, the original data must be filtered. Therefore, the correlation test  [36]  is used to filter features by combining the T and U tests  [37]. Based on these two different evaluation measures, features with strong correlations can be developed from the 48 original parts. We divided the original dataset. To avoid random results, the 10 fold cross-validation method  [38]  is used for each classifier and divides the dataset into ten copies. Nine of these copies are used for training and one for testing. Each test gives the corresponding accuracy (or error rate). The average of the ten results is used to recognize PHP. The traditional classification models used are random forest, XGBoost, and support vector machines (SVM).
The features are then processed for segmentation. A part of the data is taken as common features by the activation function. The other part is imported into the next step to learn the personality features of each step, until all decision-making steps are completed. SimAM-TabNet is based on the original TabNet with the addition of the SimAM module between the attention and the feature transformer. This allows TabNet to select the parts of the original features that best meet its needs. SimAM has the advantage of allowing simultaneous attention to the channel and space. To improve attention, this model needs to evaluate the importance of each neuron.
SimAM-CNN-MLSTM The network model first combines several SimAM modules, the number of which is equal to the number of strokes (n) in a multiple-stroke sample. Four CNN layers that independently extract features to ensure their temporal independence follow each SimAM module. When the input data and the feature vectors of multiple strokes, are fed into multiple identical SimAM-CNNs, 32-dimensional data are formed. By combining MLSTM, the convolution output can be fed sequentially into two MLSTM layers, with the output size of the first MLSTM layer being 256 and that of the second layer being 64. The output of the last state of the second layer is used as the input of the fully connected (FC) layer, and then the FC layer is used to transform the 64-dimensional data into two-dimensional data. Finally, the Softmax activation function is applied to transform the probability distribution vector to recognize PHPs.
The data dimension of the SimAM-CNN-MLSTM network for the multi-stroke datasets is shown in Table  3. In Table  3 , 48 × 1 represents a feature vector consisting of 48 stroke features. The complete flowchart of the SimAM-CNN-MLSTM network is shown in Figure  5 , where D represents the dimension. We will explain in detail the role of each module in SimAM-CNN-MLSTM, knowing that the SimAM module has already been presented in detail in Section 3. 2. 2. To further increase the weight of background features before MLSTM input features, this model incorporates SimAM and CNN, whose processes are shown in Figure  6. The samples presented in this paper are textual data. The width is convolved, while the height is not. Once the samples are captured, the data features are normalized, and the results are imported into the convolution layer.
In SSC, the accuracy and F1-score of TabNet are the best among the proposed models. LSTM is proposed to explore the temporal correlation of multiple hits. Time series are added to the dataset to improve its performance. As a result, the accuracy of LSTM is higher than that of TabNet in the SSC. In addition, the parameter-free attention mechanism is added to the proposed models to improve their performance. The accuracy and F1-score of SimAM-CNN-MLSTM are the highest among these classification models.