wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This work suggests that (1) macro-factors in the Emergent Period  have led to persistent growth in US  approvals enjoyed since 1974, (2) the CHE may be a valued method to explore the constancy of extrinsic pressures on time series data, and (3) adds further evidence that innovation-related economic cycles exist (as supported via the proxy metric of US drug approvals).
Revised:  This work suggests that (1) macro-factors in the Emergent Period have led to persistent growth in US drug approvals enjoyed since 1974, (2) the CHE may be a valued method to explore the constancy of extrinsic pressures on time series data, and (3) adds further evidence that innovation-related economic cycles exist (as supported via the proxy metric of US drug approvals).
Edit intent: fluency
Original:  This work suggests that (1) macro-factors in the Emergent Period  have led to persistent growth in US  approvals enjoyed since 1974, (2) the CHE may be a valued method to explore the constancy of extrinsic pressures on time series data, and (3) adds further evidence that innovation-related economic cycles exist (as supported via the proxy metric of US drug approvals).
Revised:  This work suggests that (1) macro-factors in the Emergent Period have led to persistent growth in US approvals enjoyed since 1974, (2) the CHE may be a valued method to explore influences on time series data, and (3) adds further evidence that innovation-related economic cycles exist (as supported via the proxy metric of US drug approvals).
Edit intent: clarity
Original:  This work suggests that (1) macro-factors in the Emergent Period  have led to persistent growth in US  approvals enjoyed since 1974, (2) the CHE may be a valued method to explore the constancy of extrinsic pressures on time series data, and (3) adds further evidence that innovation-related economic cycles exist (as supported via the proxy metric of US drug approvals).
Revised:  This work suggests that (1) macro-factors in the Emergent Period have led to persistent growth in US approvals enjoyed since 1974, (2) the CHE may be a valued method to explore the constancy of extrinsic pressures on time series data, and (3) innovation-related economic cycles exist (as supported via the proxy metric of US drug approvals).
Edit intent: clarity
Original:  This work suggests that (1) macro-factors in the Emergent Period  have led to persistent growth in US  approvals enjoyed since 1974, (2) the CHE may be a valued method to explore the constancy of extrinsic pressures on time series data, and (3) adds further evidence that innovation-related economic cycles exist (as supported via the proxy metric of US drug approvals).
Revised:  This work suggests that (1) macro-factors in the Emergent Period have led to persistent growth in US approvals enjoyed since 1974, (2) the CHE may be a valued method to explore the constancy of extrinsic pressures on time series data, and (3) adds further evidence that innovation-related economic cycles exist (as viewed via the proxy metric of US drug approvals).
Edit intent: clarity
Original:  Then, we use the well-trained features to automatically learn the adaptive spherical decision boundaries for each known intent . Specifically, we propose a new loss function to balance both the empirical risk and the open space risk.
Revised:  Then, we use the well-trained features to automatically learn the adaptive spherical decision boundary for each known intent . Specifically, we propose a new loss function to balance both the empirical risk and the open space risk.
Edit intent: fluency
Original:  Then, we use the well-trained features to automatically learn the adaptive spherical decision boundaries for each known intent . Specifically, we propose a new loss function to balance both the empirical risk and the open space risk.
Revised:  Then, we use the well-trained features to automatically learn the adaptive spherical decision boundaries for each known class with the aid of well-trained features . Specifically, we propose a new loss function to balance both the empirical risk and the open space risk.
Edit intent: clarity
Original: A self-contained autonomous navigation system is desired to complement the Global Navigation Satellite System (GNSS) for land vehicles, for which odometer aided inertial navigation system (ODO/INS) is a classical solution.
Revised: A self-contained autonomous dead reckoning (DR) system is desired to complement the Global Navigation Satellite System (GNSS) for land vehicles, for which odometer aided inertial navigation system (ODO/INS) is a classical solution.
Edit intent: meaning-changed
Original: A self-contained autonomous navigation system is desired to complement the Global Navigation Satellite System (GNSS) for land vehicles, for which odometer aided inertial navigation system (ODO/INS) is a classical solution.
Revised: A self-contained autonomous navigation system is desired to complement the Global Navigation Satellite System (GNSS) for land vehicles, for which odometer-aided inertial navigation system (ODO/INS) is a classical solution.
Edit intent: fluency
Original:  In this study, we use a wheel-mounted MEMS IMU (Wheel-IMU) to substitute the conventional odometer, and further, investigate three types of measurement models, including the velocity measurement, displacement increment measurement, and contact point zero-velocity measurement, in the Wheel-IMU based dead reckoning (DR ) system.
Revised:  In this study, we use a wheel-mounted MEMS IMU (Wheel-IMU) to substitute the odometer, and further, investigate three types of measurement models, including the velocity measurement, displacement increment measurement, and contact point zero-velocity measurement, in the Wheel-IMU based dead reckoning (DR ) system.
Edit intent: clarity
Original:  In this study, we use a wheel-mounted MEMS IMU (Wheel-IMU) to substitute the conventional odometer, and further, investigate three types of measurement models, including the velocity measurement, displacement increment measurement, and contact point zero-velocity measurement, in the Wheel-IMU based dead reckoning (DR ) system.
Revised:  In this study, we use a wheel-mounted MEMS IMU (Wheel-IMU) to substitute the conventional odometer, and further, investigate three types of measurement models, including the velocity measurement, displacement increment measurement, and contact point zero-velocity measurement, in the Wheel-IMU based DR system.
Edit intent: clarity
Original:  Theoretical discussion and field tests illustrate their feasibility and equivalence in overall positioning performance, which have the maximum horizontal position drifts  less than 2\% of the total travelled distance.
Revised:  Theoretical discussion and field tests illustrate their feasibility and equivalence of the three measurements in terms of the overall DR performance. The maximum horizontal position drifts less than 2\% of the total travelled distance.
Edit intent: meaning-changed
Original:  The catheter model is constructed based on the nonlinear beam element and flexural rigidity distribution . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building  contour map of force on the arterial wall , and estimating structural stress of catheter.
Revised:  The model is constructed based on the nonlinear beam element and flexural rigidity distribution . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building contour map of force on the arterial wall , and estimating structural stress of catheter.
Edit intent: clarity
Original:  The catheter model is constructed based on the nonlinear beam element and flexural rigidity distribution . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building  contour map of force on the arterial wall , and estimating structural stress of catheter.
Revised:  The catheter model is constructed based on the nonlinear beam element and flexural rigidity profile over the tool length . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building contour map of force on the arterial wall , and estimating structural stress of catheter.
Edit intent: meaning-changed
Original:  The catheter model is constructed based on the nonlinear beam element and flexural rigidity distribution . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building  contour map of force on the arterial wall , and estimating structural stress of catheter.
Revised:  The catheter model is constructed based on the nonlinear beam element and flexural rigidity distribution . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building a contour map of force on the arterial wall , and estimating structural stress of catheter.
Edit intent: fluency
Original:  The catheter model is constructed based on the nonlinear beam element and flexural rigidity distribution . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building  contour map of force on the arterial wall , and estimating structural stress of catheter. Shape estimation error was within 2\% range.
Revised:  The catheter model is constructed based on the nonlinear beam element and flexural rigidity distribution . During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building contour map of force on the arterial wall and estimating tool structural stress.
Edit intent: clarity
Original:  The presented online monitoring tool delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance of clinicians , robotic control vascular system, and optimizing interventional device design.
Revised:  The presented online monitoring system delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance of clinicians , robotic control vascular system, and optimizing interventional device design.
Edit intent: clarity
Original:  The presented online monitoring tool delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance of clinicians , robotic control vascular system, and optimizing interventional device design.
Revised:  The presented online monitoring tool delivers insight into the intraluminal behavior of endovascular tools and is well suited for intraoperative visual guidance of clinicians , robotic control vascular system, and optimizing interventional device design.
Edit intent: meaning-changed
Original:  The presented online monitoring tool delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance of clinicians , robotic control vascular system, and optimizing interventional device design.
Revised:  The presented online monitoring tool delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance for the clinician , robotic control vascular system, and optimizing interventional device design.
Edit intent: clarity
Original:  The presented online monitoring tool delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance of clinicians , robotic control vascular system, and optimizing interventional device design.
Revised:  The presented online monitoring tool delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance of clinicians , robotic control of vascular procedures and research on interventional device design.
Edit intent: clarity
Original:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Revised:  In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Edit intent: meaning-changed
Original:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Revised:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on Chest CT scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Edit intent: meaning-changed
Original:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Revised:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 Chest CT images from the dataset entitled "A COVID multiclass dataset of CT scans," with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Edit intent: meaning-changed
Original:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Revised:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 was identified as the best model with an accuracy of 0.9931 +/ -
Edit intent: fluency
Original:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -
Revised:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an F1 score of 0.9769 +/ -
Edit intent: meaning-changed
Original:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -0.0046, accuracy of 0.9759+/-0.0048, sensitivity of 0.9788+/-0.0055, specificity of 0.9730+/-0.0057, and precision of 0.9751 +/- 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845+/-0.0109, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .
Edit intent: meaning-changed
Original: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised: 0.0021, F1 score of 0.9599 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .
Edit intent: meaning-changed
Original: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised: 0.0021, F1 score of 0.9931 +/ -0.0251 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .
Edit intent: meaning-changed
Original: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9682 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .
Edit intent: meaning-changed
Original: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0099, specificity of 0.9883 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .
Edit intent: meaning-changed
Original: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0150, and precision of 0.9526 +/- 0.0523 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .
Edit intent: meaning-changed
Original: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .
Edit intent: fluency
Original: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
Revised: 0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools . With a prediction speed of under 0.1 seconds on GPUs and 0.5 seconds on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19 .
Edit intent: meaning-changed
Original:  Along with the findings of the analysis results, we propose the attentive factors incorporated in an application's future design that could intervene in user attention and avoid potential harm caused by the spread of misinformation.
Revised:  Along with the findings of the analysis results, we propose that attentive factors are incorporated into a social media application's future design that could intervene in user attention and avoid potential harm caused by the spread of misinformation.
Edit intent: clarity
Original: Environmental management should be based on a long-run sustainable viewpoint. A stochastic control problem optimizing a long-run objective function is an ergodic control problem whose resolution can be achieved by solving an associated  Hamilton-Jacobi-Bellman (HJB) equation having an effective Hamiltonian.
Revised: Environmental management optimizing a long-run objective function is an ergodic control problem whose resolution can be achieved by solving an associated Hamilton-Jacobi-Bellman (HJB) equation having an effective Hamiltonian.
Edit intent: coherence