wanyu/IteraTeR_v2 huggingface dataset:
Original: Transient stability assessment (TSA) , a cornerstone for resilient operations of today's interconnected power grids , is a grand challenge yet to be addressed since the genesis of electric power systems .
Revised: Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids , is a grand challenge yet to be addressed since the genesis of electric power systems .
Edit intent: fluency
Original: Transient stability assessment (TSA) , a cornerstone for resilient operations of today's interconnected power grids , is a grand challenge yet to be addressed since the genesis of electric power systems .
Revised: Transient stability assessment (TSA) , a cornerstone for resilient operations of today's interconnected power grids .
Edit intent: clarity
Original:  We devise a quantum TSA (qTSA) method , a low-depth, high expressibility quantum neural network, to enable scalable and efficient data-driven transient stability prediction for bulk power systems . qTSA renders the intractable TSA straightforward and effortless in the Hilbert space, and provides rich information that enables unprecedentedly resilient and secure power system operations.
Revised:  We devise a quantum TSA (qTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems . qTSA renders the intractable TSA straightforward and effortless in the Hilbert space, and provides rich information that enables unprecedentedly resilient and secure power system operations.
Edit intent: coherence
Original:  We devise a quantum TSA (qTSA) method , a low-depth, high expressibility quantum neural network, to enable scalable and efficient data-driven transient stability prediction for bulk power systems . qTSA renders the intractable TSA straightforward and effortless in the Hilbert space, and provides rich information that enables unprecedentedly resilient and secure power system operations.
Revised:  We devise a quantum TSA (qTSA) method , a low-depth, high expressibility quantum neural network, to enable scalable and efficient data-driven transient stability prediction for bulk power systems . qTSA renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations.
Edit intent: coherence
Original:  Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of qTSA . qTSA underpins a solid foundation of a quantum-enabled, ultra-resilient power grid which will benefit the people as well as various commercial and industrial sectors .
Revised:  Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of qTSA .
Edit intent: coherence
Original:  With the remarkable success of representation learning in providing powerful predictions and data insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analysis , and learning with networks.
Revised:  With the remarkable success of representation learning in providing powerful predictions and insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analysis , and learning with networks.
Edit intent: clarity
Original:  With the remarkable success of representation learning in providing powerful predictions and data insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analysis , and learning with networks.
Revised:  With the remarkable success of representation learning in providing powerful predictions and data insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analyzing , and learning with networks.
Edit intent: fluency
Original:  In this review, we put forward an observation that long-standing principles of network biology and medicine -- while often unspoken in machine learning research -- can provide the conceptual grounding for representation learning, explain its current successes and limitations, and inform future advances.
Revised:  In this review, we put forward an observation that long-standing principles of networks in biology and medicine -- while often unspoken in machine learning research -- can provide the conceptual grounding for representation learning, explain its current successes and limitations, and inform future advances.
Edit intent: clarity
Original:  We synthesize a spectrum of algorithmic approaches that, at their core, leverage topological features to embed networks into compact vector spaces .
Revised:  We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces .
Edit intent: clarity
Original:  Representation learning techniques are becoming essential for identifying causal variants underlying complex traits, disentangling behaviors of single cells and their impact on health, and diagnosing and treating diseases with safe and effective medicines.
Revised:  Representation learning techniques are becoming essential for identifying causal variants underlying complex traits, disentangling behaviors of single cells and their effects on health, and diagnosing and treating diseases with safe and effective medicines.
Edit intent: fluency
Original:  Representation learning techniques are becoming essential for identifying causal variants underlying complex traits, disentangling behaviors of single cells and their impact on health, and diagnosing and treating diseases with safe and effective medicines.
Revised:  Representation learning techniques are becoming essential for identifying causal variants underlying complex traits, disentangling behaviors of single cells and their impact on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines.
Edit intent: clarity
Original: Nearest Neighbor Search (NNS) over generalized weighted distance is fundamental to a wide range of applications.
Revised: Nearest Neighbor Search (NNS) over generalized weighted distances is fundamental to a wide range of applications.
Edit intent: fluency
Original:  The problem of NNS over the generalized weighted Square Euclidean distance has been studied in previous work.
Revised:  The problem of NNS over the generalized weighted square Euclidean distance has been studied in previous work.
Edit intent: fluency
Original:  However, numerous studies have shown that the Manhattan distance could be more practical than the Euclidean distance for high-dimensional NNS  . To the best of our knowledge, no prior work presents a sublinear time solution to the problem of NNS over the generalized weighted Manhattan distance .
Revised:  However, numerous studies have shown that the Manhattan distance could be more effective than the Euclidean distance for high-dimensional NNS . To the best of our knowledge, no prior work presents a sublinear time solution to the problem of NNS over the generalized weighted Manhattan distance .
Edit intent: clarity
Original:  However, numerous studies have shown that the Manhattan distance could be more practical than the Euclidean distance for high-dimensional NNS  . To the best of our knowledge, no prior work presents a sublinear time solution to the problem of NNS over the generalized weighted Manhattan distance .
Revised:  However, numerous studies have shown that the Manhattan distance could be more practical than the Euclidean distance for high-dimensional NNS . To the best of our knowledge, no prior work solves the problem of NNS over the generalized weighted Manhattan distance .
Edit intent: clarity
Original:  In this paper , we propose two novel sublinear time hashing schemes (d_w^{l_1},l_2)-ALSH and (d_w^{l_1},\theta)-ALSH to solve the problem .
Revised:  In this paper , we propose two novel sublinear time hashing schemes (d_w^{l_1},l_2)-ALSH and (d_w^{l_1},\theta)-ALSH .
Edit intent: clarity
Original: We propose an enhancement technique of the Class Activation Mapping methods like Grad-CAM or Excitation Backpropagation, which presents visual explanations of decisions from CNN-based models.
Revised: In this paper, an enhancement technique of the Class Activation Mapping methods like Grad-CAM or Excitation Backpropagation, which presents visual explanations of decisions from CNN-based models.
Edit intent: coherence
Original: We propose an enhancement technique of the Class Activation Mapping methods like Grad-CAM or Excitation Backpropagation, which presents visual explanations of decisions from CNN-based models.
Revised: We propose an enhancement technique for the class activation mapping methods such as gradient-weighted class activation maps or excitation backpropagation is proposed to present the visual explanations of decisions from CNN-based models.
Edit intent: clarity
Original:  Instead of producing a coarse localization map highlighting the important predictive regions in the image, our method outputs the specific shape that most contributes to the model output.
Revised:  Instead of producing a coarse localization map that highlights the important predictive regions in the image, our method outputs the specific shape that most contributes to the model output.
Edit intent: fluency
Original:  Instead of producing a coarse localization map highlighting the important predictive regions in the image, our method outputs the specific shape that most contributes to the model output.
Revised:  Instead of producing a coarse localization map highlighting the important predictive regions in the image, the proposed method outputs the specific shape that most contributes to the model output.
Edit intent: clarity
Original:  Thus, it improves the accuracy of saliency maps.
Revised:  Thus, the proposed method improves the accuracy of saliency maps.
Edit intent: clarity
Original:  Effect has been achieved by  gradual propagation of the crude map obtained in  deep layer through all preceding layers with respect to their activations.
Revised:  The effect has been achieved by gradual propagation of the crude map obtained in deep layer through all preceding layers with respect to their activations.
Edit intent: clarity
Original:  Effect has been achieved by  gradual propagation of the crude map obtained in  deep layer through all preceding layers with respect to their activations.
Revised:  Effect has been achieved by the gradual propagation of the crude map obtained in deep layer through all preceding layers with respect to their activations.
Edit intent: fluency
Original:  Effect has been achieved by  gradual propagation of the crude map obtained in  deep layer through all preceding layers with respect to their activations.
Revised:  Effect has been achieved by gradual propagation of the crude map obtained in the deep layer through all preceding layers with respect to their activations.
Edit intent: fluency
Original:  In validation tests conducted on a selected set of images, the proposed method significantly improved the localization detection of the neural networks ' attention .
Revised:  In validation tests conducted on a selected set of images, the faithfulness, interpretability, and applicability of the method are evaluated. The proposed technique significantly improves the localization detection of the neural networks ' attention .
Edit intent: clarity
Original:  Furthermore, the proposed method is applicable to any deep neural network model.
Revised:  Furthermore, the proposed method is applicable to a variety deep neural network model.
Edit intent: clarity
Original:  A new type of step length is also proposed with good properties for nonlinear mappings .
Revised:  A new type of step length is proposed with good properties for nonlinear mappings .
Edit intent: clarity
Original:  A new type of step length is also proposed with good properties for nonlinear mappings .
Revised:  A new type of step length is also proposed with good properties for nonlinear applications .
Edit intent: clarity
Original:  The methods require no specific adaptation and are especially efficient for high-dimensional problems.
Revised:  The methods require no problem-specific adaptation and are especially efficient for high-dimensional problems.
Edit intent: fluency
Original:  The methods require no specific adaptation and are especially efficient for high-dimensional problems.
Revised:  The methods require no specific adaptation and are especially efficient in high-dimensional problems.
Edit intent: fluency
Original:  Performances are on par  or better than  alternatives.
Revised:  Performances are on par with or better than alternatives.
Edit intent: fluency
Original:  Performances are on par  or better than  alternatives.
Revised:  Performances are on par or better than competitive alternatives.
Edit intent: clarity
Original:  In the instances designed by Kurokawa, Procaccia and Wang, the gap is exponentially small in n   .
Revised:  In the instances designed by Kurokawa, Procaccia and Wang, the gap is exponentially small in n . .
Edit intent: fluency
Original:  We examined the influence of three forest stand attributes: species, crown closure , and age  on the vertical distribution of aerial LiDAR returns of forested stands.
Revised:  We examined the influence of three variables, species, crown closure , and age on the vertical distribution of aerial LiDAR returns of forested stands.
Edit intent: clarity
Original:  We examined the influence of three forest stand attributes: species, crown closure , and age  on the vertical distribution of aerial LiDAR returns of forested stands.
Revised:  We examined the influence of three forest stand attributes: species, crown cover , and age on the vertical distribution of aerial LiDAR returns of forested stands.
Edit intent: clarity
Original:  We examined the influence of three forest stand attributes: species, crown closure , and age  on the vertical distribution of aerial LiDAR returns of forested stands.
Revised:  We examined the influence of three forest stand attributes: species, crown closure , and age , on the vertical distribution of aerial LiDAR returns of forested stands.
Edit intent: fluency
Original:  We examined the influence of three forest stand attributes: species, crown closure , and age  on the vertical distribution of aerial LiDAR returns of forested stands.
Revised:  We examined the influence of three forest stand attributes: species, crown closure , and age on the vertical distribution of airborne LiDAR returns from forest stands.
Edit intent: clarity
Original:  We modeled vertical distribution of LiDAR returns against the three attributes using a functional glm and a novel nonparametric graphical test of significance.
Revised:  We modeled the vertical distribution against the three attributes using a functional glm and a novel nonparametric graphical test of significance.
Edit intent: clarity
Original:  We modeled vertical distribution of LiDAR returns against the three attributes using a functional glm and a novel nonparametric graphical test of significance.
Revised:  We modeled vertical distribution of LiDAR returns against the three variables using a functional glm and a novel nonparametric graphical test of significance.
Edit intent: clarity
Original:  Results indicate that  aspen stands had the most uniform vertical distribution of LiDAR returns;
Revised:  Results indicate that LiDAR returns from aspen stands had the most uniform vertical distribution of LiDAR returns;
Edit intent: clarity
Original:  balsam fir and white birch distributions were similar and centered  around 50\% of the stand height ;
Revised:  balsam fir and white birch distributions were similar and centered at around 50\% of the stand height ;
Edit intent: fluency
Original:  balsam fir and white birch distributions were similar and centered  around 50\% of the stand height ;
Revised:  balsam fir and white birch distributions were similar and centered around 50\% of the stand height , and
Edit intent: fluency
Original:  black spruce and white spruce distributions were skewed  below 30\% of stand height (p<0.001).
Revised:  black spruce and white spruce distributions were skewed to below 30\% of stand height (p<0.001).
Edit intent: fluency
Original:  An increase in crown closure concentrated the distributions around 50\%  height.
Revised:  Increased crown cover concentrated the distributions around 50\% height.
Edit intent: clarity
Original:  The full model maximal R-squared was 0.47 around 10\% of stand height.
Edit intent: coherence
Original:  Results suggest that the stands vertical distributions of LiDAR returns depend on the three variables studied .
Revised:  Results suggest that the vertical distributions of LiDAR returns depend on the three variables studied .
Edit intent: clarity
Original: Event camera is an asynchronous, high frequencyvision sensor with low power consumption, which is suitable forhuman action understanding task.
Revised: Event camera is an asynchronous, high frequency vision sensor with low power consumption, which is suitable forhuman action understanding task.
Edit intent: fluency
Original: Event camera is an asynchronous, high frequencyvision sensor with low power consumption, which is suitable forhuman action understanding task.
Revised: Event camera is an asynchronous, high frequencyvision sensor with low power consumption, which is suitable for human action understanding task.
Edit intent: fluency
Original:  It is vital to encode the spatial-temporal information of event data properly and use standardcomputer vision tool to learn from the data.
Revised:  It is vital to encode the spatial-temporal information of event data properly and use standard computer vision tool to learn from the data.
Edit intent: fluency
Original:  In this work, wepropose a timestamp image encoding 2D network, which takes theencoded spatial-temporal images with polarity information of theevent data as input and output the action label.
Revised:  In this work, we propose a timestamp image encoding 2D network, which takes theencoded spatial-temporal images with polarity information of theevent data as input and output the action label.
Edit intent: fluency
Original:  In this work, wepropose a timestamp image encoding 2D network, which takes theencoded spatial-temporal images with polarity information of theevent data as input and output the action label.
Revised:  In this work, wepropose a timestamp image encoding 2D network, which takes the encoded spatial-temporal images with polarity information of theevent data as input and output the action label.
Edit intent: fluency
Original:  In this work, wepropose a timestamp image encoding 2D network, which takes theencoded spatial-temporal images with polarity information of theevent data as input and output the action label.
Revised:  In this work, wepropose a timestamp image encoding 2D network, which takes theencoded spatial-temporal images with polarity information of the event data as input and output the action label.
Edit intent: fluency
Original:  In addition, wepropose a future timestamp image generator to generate futureaction information to aid the model to anticipate the humanaction when the action is not completed.
Revised:  In addition, we propose a future timestamp image generator to generate futureaction information to aid the model to anticipate the humanaction when the action is not completed.
Edit intent: fluency
Original:  In addition, wepropose a future timestamp image generator to generate futureaction information to aid the model to anticipate the humanaction when the action is not completed.
Revised:  In addition, wepropose a future timestamp image generator to generate futureaction information to aid the model to anticipate the human action when the action is not completed.
Edit intent: fluency
Original:  Our future timestamp image generating model caneffectively improve the prediction accuracy when the action is notcompleted .
Revised:  Our future timestamp image generating model can effectively improve the prediction accuracy when the action is notcompleted .
Edit intent: fluency
Original:  Our future timestamp image generating model caneffectively improve the prediction accuracy when the action is notcompleted .
Revised:  Our future timestamp image generating model caneffectively improve the prediction accuracy when the action is not completed .
Edit intent: fluency
Original:  We also provide insight discussion on the importanceof motion and appearance information in action recognition andanticipation .
Revised:  We also provide insight discussion on the importance of motion and appearance information in action recognition andanticipation .
Edit intent: fluency
Original:  We also provide insight discussion on the importanceof motion and appearance information in action recognition andanticipation .
Revised:  We also provide insight discussion on the importanceof motion and appearance information in action recognition and anticipation .
Edit intent: fluency
Original: This paper models residential consumers' energy-consumption behavior by load patterns and distributions and reveals the relationship between consumers' load patterns and socioeconomic features by machine learning.
Revised: This paper models residential consumers' energy-consumption behavior by load patterns and distributions and reveals the relationship between load patterns and socioeconomic features by machine learning.
Edit intent: fluency
Original:  We further develop a customized deep neural network model to characterize the relationship between consumers' load patterns and selected socioeconomic features . Numerical studies validate our proposed framework using Pecan Street smart meter data and survey.
Revised:  The relationship between consumers' load patterns and selected socioeconomic features . Numerical studies validate our proposed framework using Pecan Street smart meter data and survey.
Edit intent: clarity
Original:  We demonstrate that our framework can capture the relationship between load patterns and socioeconomic information and outperform benchmarks such as regression and single DNN models .
Revised:  We demonstrate that our framework can capture the relationship between load patterns and socioeconomic features .
Edit intent: clarity
Original:  This learning happens in an online manner -- meaning that Tactician's machine learning model is updated immediately every time the user performs a step in an interactive proof.
Revised:  Learning happens in an online manner -- meaning that Tactician's machine learning model is updated immediately every time the user performs a step in an interactive proof.
Edit intent: clarity
Original:  This learning happens in an online manner -- meaning that Tactician's machine learning model is updated immediately every time the user performs a step in an interactive proof.
Revised:  This learning happens in an online manner , meaning that Tactician's machine learning model is updated immediately every time the user performs a step in an interactive proof.
Edit intent: fluency
Original:  Our study is motivated by the fact that  GNNs cannot be adopted for our problem since message passing to such edgeless nodes having no connections is impossible.
Revised:  Our study is motivated by the fact that existing GNNs cannot be adopted for our problem since message passing to such edgeless nodes having no connections is impossible.
Edit intent: clarity
Original:  The known network structure is used to train model parameters, whereas a loss function is established  in such a way that our model learns the network structure.
Revised:  The known network structure is used to train model parameters, while a new loss function is established in such a way that our model learns the network structure.
Edit intent: clarity
Original:  Benifiting from these works, the paper introduces a write-friendly flash-based disk cache system,  called WLFC (Write Less in  Flash-based Cache) which mainly makes three contributions.
Revised:  Benifiting from these works, the paper introduces a write-friendly flash-based disk cache system, which is called WLFC (Write Less in Flash-based Cache) which mainly makes three contributions.
Edit intent: coherence