dataesr/scientific-paragraphs-categorization huggingface dataset:
ASPP To verify whether ASPP can enhance the ship detection model's ability to detect multi-scale ships, we added ASPP separately to the baseline. As shown in Table  2 , the ship detection model with ASPP added achieved an improvement of 3. 4% in AP, 1. 95% in P, 1. 66% in R, and 0. 01 in F1 compared to the baseline. To further illustrate the effectiveness of the proposed ASPP module, a visual comparison of detection results is provided in Figure  11. The ships in this image vary slightly in scale, which greatly tests the model's ability to detect different sizes of ships simultaneously. As shown in Figure  11a , in the baseline, the small ship in the lower left corner of the image is ignored because the model did not consider the detection of multi-scale ships. However, after adding ASPP to the baseline, the ship detection model correctly detects the ships (as shown in Figure  11b ).
AFBM To verify whether AFBM can improve the performance of the ship detection model, we added AFBM to the baseline model separately. As shown in Table  2 , the ship detection model with AFBM achieved an increase of 4. 5% in AP, 1. 95% in P, 2. 79% in R, and 0. 03 in F1 compared to the baseline. To further demonstrate the effectiveness of our AFBM, we provide visual comparisons of detection results in Figure  12. The image in Figure  12  contains a complex coastal environment, which poses a challenge for the ship detection model to have better robustness. As shown in Figure  12a , the baseline did not detect the ship in the image, but after adding AFBM to the model, the ship was correctly detected (as shown in Figure  12b ). This fully demonstrates that our adaptive weighted feature fusion method, by balancing the language and location information of features, can enhance the performance of the ship detection model.
To verify whether AFBM can improve the performance of the ship detection model, we added AFBM to the baseline model separately. As shown in Table  2 , the ship detection model with AFBM achieved an increase of 4. 5% in AP, 1. 95% in P, 2. 79% in R, and 0. 03 in F1 compared to the baseline. To further demonstrate the effectiveness of our AFBM, we provide visual comparisons of detection results in Figure  12. The image in Figure  12  contains a complex coastal environment, which poses a challenge for the ship detection model to have better robustness. As shown in Figure  12a , the baseline did not detect the ship in the image, but after adding AFBM to the model, the ship was correctly detected (as shown in Figure  12b ). This fully demonstrates that our adaptive weighted feature fusion method, by balancing the language and location information of features, can enhance the performance of the ship detection model.
Combination of Different Modules To investigate the potential negative impact of combining different modules on the ship detection model, we first added pairwise combinations of PPAM, ASPP, and AFBM modules to the baseline. As shown in Table  2 , adding two modules simultaneously to the baseline led to a slight decrease in P compared to adding a single module. However, the combined use of two modules always outperformed the single use of any one module when considering the comprehensive evaluation metric AP. Finally, when all three modules were added to the baseline, AP increased by 4. 96%, R increased by 4. 66%, P increased by 7. 75%, and F1 increased by 0. 06%. The experiments combining different modules further validated the effectiveness of the three modules in enhancing the performance of the ship detection model.
Validation of Module Advancement In this section, we conducted comparative experiments on the proposed PPAM and AFBM modules with commonly used attention and feature fusion modules in the SAR ship detection field on the SSDD dataset. The experimental results are shown in Tables  3  and 4. Compared with CBAM, ECA, and SE, PPAM achieved improvements of 1. 88%, 1. 25%, and 1. 33%, respectively, in terms of AP. In terms of P, PPAM achieved improvements of 1. 74%, 2. 96%, and 3. 1%, respectively, compared with CBAM, ECA, and SE. Compared with CBAM, ECA, and SE, PPAM achieved improvements of -0. 08%, 1. 04%, and 0. 89%, respectively, in terms of R. PPAM achieved improvements of 0. 01, 0. 02, and 0. 01 in terms of F, respectively, compared with CBAM, ECA, and SE. Compared with PANet and FPN, AFBM achieved improvements of 1.
Comparison with Other Advanced Ship Detection Models In order to verify the effectiveness of PPA-Net for SAR ship detection, we conducted comparative tests with other advanced algorithms (YOLOV4, YOLOV5, HR-SDNet, Det-Net). The comparative experiment was conducted on the SSDD and HRSID datasets, and the results are shown in Tables  5  and 6. The experimental results on the SSDD dataset show that compared with YOLOV4, YOLOV5, HR-SDNet, and DetNet, PPA-Net improved the AP by 3%, 2. 26%, 1. 45%, and 2. 51%, respectively. The precision was improved by 2. 36%, 5. 14%, 1. 18%, and 1. 68%, respectively, while the recall was improved by 6. 87%, 4. 58%, 1. 01%, and 1. 32%, respectively. The F1 score was improved by 0. 05, 0. 05, 0. 01, and 0. 04, respectively. The experimental results on the HRSID dataset show that compared with YOLOV4, YOLOV5, HR-SDNet, and DetNet, PPA-Net improved the AP by 7.
56%, 3. 34%, 2. 62%, and 6. 06%, respectively. The precision was improved by 4. 44%, 4. 68%, 1. 69%, and 6. 03%, respectively, while the recall was improved by 11. 9%, 2. 66%, 1. 56%, and 7. 56%, respectively. The F1 score was improved by 0. 08, 0. 05, 0. 02, and 0. 07, respectively. To further ensure the reliability of the model performance, three additional experiments have been conducted on the SSDD dataset, and AP values were reported for all models in each experiment. We ensured that all models used the same training and testing sets for each experiment. The experimental results are shown in Table  7. The superiority of PPA-Net over other ship detection models has been evaluated using multi-scale detection metrics for object detection in the MC COCO. These metrics are AP [IoU = 0. 50:0.
95] and AR [IoU = 0. 50:0. 95]. IoU = 0. 50:0. 95 represents the average precision and average recall at different IoU thresholds (ranging from 0. 50 to 0. 95 with an interval of 0. 05). The experiment was conducted on the SSDD dataset, and the results are shown in Table  8. Compared with other object detection algorithms, PPA-Net achieved an improvement of 0. 063-0. 118 in detection precision and 0. 042-0. 073 in recall for small objects. For medium objects, PPA-Net achieved an improvement of 0. 021-0. 032 in detection precision and 0. 043-0. 056 in recall compared with other object detection algorithms. For large objects, PPA-Net achieved an improvement of 0. 128-0. 204 in detection precision and 0. 015-0. 024 in recall compared with other object detection algorithms.
Discussion This study proposes two novel modules, PPAM and AFBM, for improving the performance of the SAR ship detection model. Our experiments on the SSDD dataset demonstrate that these two modules outperform commonly used attention and feature fusion modules. First, we evaluate the superiority of PPAM by comparing it with SE, ECA, and CBAM. The results show that PPAM achieves 1. 25-1. 88% higher ship detection accuracy than SE, ECA, and CBAM. Furthermore, the improvement of PPAM over ECA can be.
Discussion This study proposes two novel modules, PPAM and AFBM, for improving the performance of the SAR ship detection model. Our experiments on the SSDD dataset demonstrate that these two modules outperform commonly used attention and feature fusion modules. First, we evaluate the superiority of PPAM by comparing it with SE, ECA, and CBAM. The results show that PPAM achieves 1. 25-1. 88% higher ship detection accuracy than SE, ECA, and CBAM. Furthermore, the improvement of PPAM over ECA can be attributed to the pooling operation that suppresses the impact of noise on weight generation. This finding confirms the previously mentioned issue that noise can affect weight generation in attention mechanisms. However, compared with CBAM, the recall rate of PPAM decreases by 0. 08% due to the potential damage to ship features caused by the introduction of pooling operation.
Second, we evaluate the superiority of AFBM by comparing it with other commonly used feature fusion modules. The results show that AFBM achieves 1. 41% and 2. 56% higher ship detection accuracy than PANet and FPN, respectively. This advantage is due to the ability of AFBM to balance the semantic and positional information of ships through weighted feature fusion. However, the limitation of AFBM is the increased computational cost caused by using convolutional operations to automatically learn the contribution of different feature maps to the output features. Furthermore, through comparative experiments, we found that the improvement of the ship detection model can be better reflected in large-scale datasets because larger datasets provide a more diverse range of ship image variations, including changes in size, shape, and orientation.
With more data, the model can better learn complex features and patterns that distinguish ships from backgrounds, which helps to improve the accuracy of the model. In summary, our proposed PPAM and AFBM achieve state-of-the-art performance in SAR ship detection. Although they have limitations compared with commonly used attention and feature fusion modules, they have more significant advantages. Our future work will focus on optimizing these modules to address their limitations and further improve the performance of ship detection models.
Conclusions This paper introduces a robust ship detection model, named PPA-Net, to improve SAR ship detection. Specifically, considering the influence of noise and background information on ship detection, PPAM is designed and added to the backbone of the ship detection model to reduce the influence of background noise and complex background on ship detection. Different from previous attention modules, the structural design of PPAM takes into account the influence of background information on weight generation. Next, we proposed the AFBM module, which adopts the weighted feature fusion method to make the neural network better balance the location information and semantic information in feature fusion. Finally, the ASPP module is introduced to enhance the detection ability of multi-scale ships.
Hardware configuration: All our experiments were conducted on Windows 10 with Pytorch 1. 10, CUDA11. 5, and RTX3090 with 24 GB of memory; (2) During the training process, a learning rate of 0. 01 and a batch size of 32 were used for all model training. The training was carried out iteratively for rounds; (3) The experiments were conducted on the SSDD and HRSID datasets, respectively. We randomly divided the SSDD and HRSID datasets into training and testing sets in an 8:2 ratio. Specifically, the SSDD dataset contains 1160 images, with 928 images used for training and 232 images used for testing. The HRSID dataset contains 5604 images, with 4483 images used for training and 1121 images used for testing. The partitioning of the datasets ensures that the images used for training the models are not used for testing them.
( 1 ) 1 Hardware configuration: All our experiments were conducted on Windows 10 with Pytorch 1. 10, CUDA11. 5, and RTX3090 with 24 GB of memory; (2) During the training process, a learning rate of 0. 01 and a batch size of 32 were used for all model training. The training was carried out iteratively for rounds; (3) The experiments were conducted on the SSDD and HRSID datasets, respectively. We randomly divided the SSDD and HRSID datasets into training and testing sets in an 8:2 ratio. Specifically, the SSDD dataset contains 1160 images, with 928 images used for training and 232 images used for testing. The HRSID dataset contains 5604 images, with 4483 images used for training and 1121 images used for testing. The partitioning of the datasets ensures that the images used for training the models are not used for testing them.
It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces. Biosignals have been classically used for studying the underlying physiology, for clinical diagnostics, and for monitoring. More recently, they have also been used for interfacing humans with external devices. For example, signals measured at the surface of the skin from skeletal muscle electrical activity, i. e. surface electromyography (sEMG), are used for the control of bionic limbs  1. In this application, the recorded electrical signals are converted into motion commands using machine learning  [2] [3] [4]. In recent years, with the development of deep-learningbased methods as well as wearable and cost-effective recording devices, there has been increased interest in using muscular signals as a basis for human-machine interfaces  5, 6.
The potential applications go well beyond the traditional clinical domains of prostheses and orthoses and range from robotic control to gaming and virtual reality  7. To be useful in real-world applications, particularly for the mass market, EMG-based muscle-computer interfaces need to be highly intuitive and functional. They also need to work for the vast majority of subjects with little or no user-specific calibration, regardless of their anatomy and physiology. Traditional machine learning techniques might work well for the relatively simple task of hand gesture classification, but they require training on a specific subject and potential regular recalibrations  1, 7. This is a major bottleneck for mass market applications, where obtaining subject-specific data is not possible. Deep learning methods could tackle these problems  8  , but large, annotated, and high-quality datasets are required.
To consider deep learning, training data needs to be recorded for different subjects, at different times, with high variability in electrode configurations and experimental paradigms. In addition, it is challenging and in some cases impossible to properly describe the underlying physiological or neural parameters (e. g. individual muscle forces, fibre physiological parameters, motor neuron impulse timings), which are crucial for the correct annotation of data samples. As a result, acquiring experimental EMG data in sufficient quantity and quality is not only expensive and time-consuming, but in many cases not possible. Data augmentation via simulation is an alternative approach to lengthy data acquisitions, and indeed, augmentation techniques have been recently introduced for electrophysiological signals  [9] [10] [11] [12].
Our results show that it is possible to simulate EMG signals for anatomically accurate conductor geometries and multiple muscles with tens of thousands of muscle fibres in a few seconds. This realistic and fast model allows, for the first time, the generation of synthetic EMG data in sufficient quantity and quality to train deep learning algorithms. Figure  1a  illustrates a general pipeline of using the Myoelectric Digital Twin to train EMG processing AI. At the training stage, the user can define a high variety of simulation parameters. The software generates a corresponding EMG dataset coupled with all the metadata that can potentially be used for annotation. The annotated data is then used to train a specific AI. This trained AI can then be used to process real EMG measurements and translate it into commands for real-world applications.
This information can then be used to interfaces with different devices with high precision and complexity of the gestures. This particular application is just an example. The same approach can be used for different tasks: categorical gesture classification, denoising, fatigue detection, and so on. Our approach is also agnostic to the exact neural network architecture. As an application scenario and a proof of concept, in this work, we demonstrate the use of this model for data augmentation by pre-training neural networks that decompose EMG into the underlying neural activity sent from the spinal cord to muscles  22. Note, that because large EMG dataset simulation was not possible before, the effect of using this data for AI training is not yet well studied. It includes all the potential advantages of using synthetic EMG data, as well as potential biases that it can introduce to the training.
Software structure and user interface Our Myoelectric Digital Twin is a cloud-based software with a Python API which allows users a simple yet flexible way to define various simulation parameters and to control the simulation pipeline. User can define subject's anatomy (by providing surfaces of the muscles, bones, fat and skin), different tissue conductivities, the electrode configuration (electrode locations, sizes, shapes), individual fibre properties (location of neuromuscular junctions, tendon lengths, action potential propagation velocities), motor unit and recruitment model parameters, as well as the activations of each muscle, etc. Our software is not limited to individual muscle activations. Instead, it allows users to define arbitrary activation (% of MVC as a function of time) for each muscle of the model (e.
g. a forearm) simultaneously. The corresponding EMG signal is a superposition of contributions of each individual muscle. Figure  1b  shows a schematic representation of the user's input, main simulation modules and their interaction. Each step of the simulation pipeline depends on the output of the previous module and on a specific subset of the input parameters. This architecture, dictated by the mathematical properties of the model, allows efficient management of pre-computed data (i. e. changing fibre properties does not require recomputing the forward solver). Moreover, users can provide their own data as an input for each module instead of using automated tools of our software, if necessary (see Supplementary Movie for a video example of a simulation script).
Second, using the same muscle surfaces used to describe the volume conductor, individual fibre geometries can be automatically generated, if this data is not available from other sources (e. g. from diffusion magnetic resonance imaging). Moreover, the fibres are grouped into motor units (MUs) following the state-of-the-art models for MU physiology. This step does not depend on the forward computations, and thus altering the related parameters and producing new simulation is highly efficient. Third, the current source density propagating along the fibres is generated using a realistic intracellular action potential model. The contribution of individual fibres to the EMG recordings is obtained by discretizing each fibre into a set of points, integrating the current source density along its length, and projecting onto the sensor locations using the basis points computed in the first step.
This approach effectively decouples the number of fibres and their discretization from the conductor model, allowing the simulator to handle tens of thousands of fibres per muscle. Again, changing the fibre parameters (end-plate location, action potential propagation velocity, tendons length, etc. ) does not require recomputing the other blocks of the simulation. Fourth, given a muscle activation profile, we use the size principle to recruit MUs and their associated fibres. This allows a simple and easily interpretable input to the simulation that can be used to simulate EMG recordings associated to specific muscle contractions and their movements. As a result, our model is the first that allows the generation of highly realistic and arbitrarily large (because of its computational performance) datasets of simulated EMG signals that can be used for AI training.
The simulator reproduces analytical solutions To produce realistic EMG data, the simulator leverages a flexible representation of the underlying anatomy and physiology. This flexibility does not only allow the use of realistic and personalized models, but also permits reproducing simple conductor geometry used in analytical solutions. We compare our numerical solution with its Fig. 1  | Simulation pipeline. a General strategy of using Myoelectric Digital Twin to train an artificial intelligence (AI), that then can be used to process real surface electromyography (sEMG) signals in real-world applications. b Schematic representation of the software's input, main elements of the simulation pipeline and output. User can define a large set of simulation parameters describing anatomical and physiological properties of the tissues, geometry and montage of electrodes, fibres geometry and their physiological properties, motor unit (MU) distribution and their recruitment model, activation of individual muscles, etc.
Realistic and fast EMG simulations open unique perspectives for deep learning Here, we show a potential use of high volumes of simulated surface EMG data for deep learning, utilising the proposed model to generate data which can be used to pretrain neural networks. This methodology is used in other deep learning domains, such as the use of the Ima-geNet image database to pretrain object classifiers prior to adaptation to specific applications  26. The selected application was that of a time series classifier, which took as an input unwhitened high-density surface EMG (HD-sEMG) signals recorded from the dominant wrist of nine participants and detected discrete MU activation events (Fig. 5a ). Participants performed an isometric contraction at a constant force of 15% of the maximal voluntary contraction, as measured by a force cell and fed back to the user on a computer monitor.
By using reciprocity, Pereira Botelho et al. 20  reported a computational time of 1 h for simulating the activation of nearly 15,500 fibres for 5 electrodes. This time, however, remains impractical for simulating arbitrary large datasets for a variety of parameter values. The model we proposed in this paper significantly surpasses the computational efficiency reported in ref. 20. We achieved it by efficiently exploiting mathematical properties of the forward equations, in particular by introducing the concept of basis points and by separating model parameters and variables into independent computational blocks. Our approach does not only reduce the computational time for a full simulation, but also allows us to scale the solution, so that new solutions for the same volume conductor can be obtained without recomputing the volume conductor transformation.
Details of deep learning experiment To evaluate the effect of using the simulation-pretrained network, an experimentally collected high-density surface electromyography (HD-sEMG) signal dataset was used, originally created to test low-force First, either a MUAP template was placed in the centre of the window or it was left empty at a 50% probability. Then MUAP templates from other MU classes were added to the window at a random offset to generate superpositions. Finally, standard normal distributed noise was added to the window, with the central 80 samples then paired with the label for supervised learning. b The neural network architecture and pre-training methodology used to improve the performance of a deep learning-based HD-sEMG decomposition algorithm. The neural network consists of a single gated recurrent unit layer, with predictions made using a 20sample wide window of the hidden vector output, which is flattened before being passed to a sigmoid-activated densely-connected linear layer.