wanyu/IteraTeR_v2 huggingface dataset:
Original:  For QML tasks , QuantumNAS is the first to demonstrate over 95\% 2-class, 85\% 4-class, and 32\% 10-class classification accuracy on real quantum computers .
Revised:  For QML , QuantumNAS is the first to demonstrate over 95\% 2-class, 85\% 4-class, and 32\% 10-class classification accuracy on real quantum computers .
Edit intent: clarity
Original:  For QML tasks , QuantumNAS is the first to demonstrate over 95\% 2-class, 85\% 4-class, and 32\% 10-class classification accuracy on real quantum computers .
Revised:  For QML tasks , QuantumNAS is the first to demonstrate over 95\% 2-class, 85\% 4-class, and 32\% 10-class classification accuracy on real QC .
Edit intent: clarity
Original:  It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD baselines .
Revised:  It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD .
Edit intent: clarity
Original:  This set of optimal isometries admits many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular a member of this set which admits an asymptotic projective "tour" property  .
Revised:  This set of optimal isometries reveals many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular a member of this set which admits an asymptotic projective "tour" property .
Edit intent: clarity
Original:  This set of optimal isometries admits many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular a member of this set which admits an asymptotic projective "tour" property  .
Revised:  This set of optimal isometries admits many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular member of this set which admits an asymptotic projective "tour" property .
Edit intent: fluency
Original:  This set of optimal isometries admits many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular a member of this set which admits an asymptotic projective "tour" property  .
Revised:  This set of optimal isometries admits many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular a member of this set which admits an asymptotic "tour" property .
Edit intent: clarity
Original:  Finally, we consider the unit-length 3D curves (filaments) induced by these 2D Andrew's plots, where the linear isometry property preserves distances as "relative total square curvatures".
Revised:  Finally, we construct unit-length 3D curves (filaments) induced by these 2D Andrew's plots, where the linear isometry property preserves distances as "relative total square curvatures".
Edit intent: clarity
Original:  Prior work established a minimality result: every process typable with standard session types can be compiled down to a process typable using minimal session types: session types without  sequencing construct.
Revised:  Prior work established a minimality result: every process typable with standard session types can be compiled down to a process typable using minimal session types: session types without the sequencing construct.
Edit intent: fluency
Original:  This new minimality result for the session \pi-calculus can be obtained by composing existing results.
Revised:  Remarkably, this new minimality result for the session \pi-calculus can be obtained by composing existing results.
Edit intent: coherence
Original:  This new minimality result for the session \pi-calculus can be obtained by composing existing results.
Revised:  This new minimality result can be obtained by composing existing results.
Edit intent: clarity
Original:  This new minimality result for the session \pi-calculus can be obtained by composing existing results.
Revised:  This new minimality result for the session \pi-calculus can be obtained by composing known results.
Edit intent: clarity
Original: Recovering an underlying image from under-sampled measurements, Compressive Sensing Imaging (CSI) is a challenging problemand has many practical applications . Recently, deep neural networks have been applied to this problem with promising results, owing to its implicitly learned prior to alleviate the ill-poseness of CSI.
Revised: Compressive imaging aims to recover a latent image from under-sampled measurements, Compressive Sensing Imaging (CSI) is a challenging problemand has many practical applications . Recently, deep neural networks have been applied to this problem with promising results, owing to its implicitly learned prior to alleviate the ill-poseness of CSI.
Edit intent: clarity
Original: Recovering an underlying image from under-sampled measurements, Compressive Sensing Imaging (CSI) is a challenging problemand has many practical applications . Recently, deep neural networks have been applied to this problem with promising results, owing to its implicitly learned prior to alleviate the ill-poseness of CSI.
Revised: Recovering an underlying image from under-sampled measurements, Compressive Sensing Imaging (CSI) is a challenging problemand has many practical applications . Recently, deep neural networks have been applied to this problem with superior results, owing to its implicitly learned prior to alleviate the ill-poseness of CSI.
Edit intent: clarity
Original:  However, existing neural network approachesrequire separate models for each imaging parameter like sampling ratios, leading to training difficulties and overfitting to specific settings.
Revised:  However, existing neural network approachesrequire separate models for different imaging modalities and sampling ratios, leading to training difficulties and overfitting to specific settings.
Edit intent: clarity
Original:  However, existing neural network approachesrequire separate models for each imaging parameter like sampling ratios, leading to training difficulties and overfitting to specific settings.
Revised:  However, existing neural network approachesrequire separate models for each imaging parameter like sampling ratios, leading to overfitting to specific settings.
Edit intent: clarity
Original:  In this paper, we present a dynamic proximal unrolling network (dubbed DPUNet)  , which can handle a variety of measurement matrices via one single model without retraining.
Revised:  In this paper, a dynamic proximal unrolling network (dubbed DPUNet) , which can handle a variety of measurement matrices via one single model without retraining.
Edit intent: coherence
Original:  Specifically, DPUNet can exploit both embedded physical model via gradient descent and imposing image prior with learned dynamic proximal mapping leading to joint reconstruction.
Revised:  Specifically, DPUNet can exploit both the embedded observation model via gradient descent and imposing image prior with learned dynamic proximal mapping leading to joint reconstruction.
Edit intent: clarity
Original:  Specifically, DPUNet can exploit both embedded physical model via gradient descent and imposing image prior with learned dynamic proximal mapping leading to joint reconstruction.
Revised:  Specifically, DPUNet can exploit both embedded physical model via gradient descent and imposed image priors by learned dynamic proximal mapping leading to joint reconstruction.
Edit intent: clarity
Original:  Specifically, DPUNet can exploit both embedded physical model via gradient descent and imposing image prior with learned dynamic proximal mapping leading to joint reconstruction.
Revised:  Specifically, DPUNet can exploit both embedded physical model via gradient descent and imposing image prior with learned dynamic proximal operators, achieving joint reconstruction.
Edit intent: clarity
Original:  A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at  inference stage and make it adapt to any given imaging setting . Experimental results demonstrate that the proposed DPUNet can effectively handle multiple CSI modalities under varying sampling ratios and noise levels with only one model, and outperform the state-of-the-art approaches.
Revised:  A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at the inference stage and make it adapt to any given imaging setting . Experimental results demonstrate that the proposed DPUNet can effectively handle multiple CSI modalities under varying sampling ratios and noise levels with only one model, and outperform the state-of-the-art approaches.
Edit intent: fluency
Original:  A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at  inference stage and make it adapt to any given imaging setting . Experimental results demonstrate that the proposed DPUNet can effectively handle multiple CSI modalities under varying sampling ratios and noise levels with only one model, and outperform the state-of-the-art approaches.
Revised:  A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at inference stage and make it adapt to different imaging settings . Experimental results demonstrate that the proposed DPUNet can effectively handle multiple CSI modalities under varying sampling ratios and noise levels with only one model, and outperform the state-of-the-art approaches.
Edit intent: clarity
Original:  A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at  inference stage and make it adapt to any given imaging setting . Experimental results demonstrate that the proposed DPUNet can effectively handle multiple CSI modalities under varying sampling ratios and noise levels with only one model, and outperform the state-of-the-art approaches.
Revised:  A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at inference stage and make it adapt to any given imaging setting . Experimental results demonstrate that the proposed DPUNet can effectively handle multiple CSI modalities under varying sampling ratios and noise levels via only one trained model, and outperform the state-of-the-art approaches.
Edit intent: clarity
Original:  A tree-structured predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality.
Revised:  A branching predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality.
Edit intent: clarity
Original:  To solve this problem, we proposed an alternating optimization (AO) technique based on difference-of-convex (DC) programming and successive convex approximation (SCA).
Revised:  To solve this problem, we propose an alternating optimization (AO) technique based on difference-of-convex (DC) programming and successive convex approximation (SCA).
Edit intent: fluency
Original:  Our results show that existing models do not directly transfer from one disaster type to another but using labeled emotional corpora for domainadaptation is beneficial .
Revised:  Our analyses suggest that cross-domain information transfers occur, yet there are still significant gaps. We propose semi-supervised learning as a way to bridge this gap, obtaining significantly better performance using unlabeled data from the target domain .
Edit intent: clarity
Original:  However, the theoretical solution provided by transportability is far from practical for UDA, because it requires the stratification and representation of an unobserved confounder that is the cause of the domain gap.
Revised:  However, the theoretical solution provided by transportability is far from practical for UDA, because it requires the stratification and representation of the unobserved confounder that is the cause of the domain gap.
Edit intent: fluency
Original:  The combination of the proposed constitutive law and mortar meshtying approach is validated on  three-dimensional examples with rather complex geometry , representing a first step towards part-scale predictions.
Revised:  The combination of the proposed constitutive law and mortar mesh tying approach is validated on three-dimensional examples with rather complex geometry , representing a first step towards part-scale predictions.
Edit intent: fluency
Original:  The combination of the proposed constitutive law and mortar meshtying approach is validated on  three-dimensional examples with rather complex geometry , representing a first step towards part-scale predictions.
Revised:  The combination of the proposed constitutive law and mortar meshtying approach is validated on realistic three-dimensional examples with rather complex geometry , representing a first step towards part-scale predictions.
Edit intent: clarity
Original:  The combination of the proposed constitutive law and mortar meshtying approach is validated on  three-dimensional examples with rather complex geometry , representing a first step towards part-scale predictions.
Revised:  The combination of the proposed constitutive law and mortar meshtying approach is validated on three-dimensional examples , representing a first step towards part-scale predictions.
Edit intent: clarity
Original: By lumping all dangling nodes of a web graph into a single node , PageRank computation can be proceeded with a much smaller matrix on the case that the number of dangling nodes is large .
Revised: On the case that the number of dangling nodes is large , PageRank computation can be proceeded with a much smaller matrix on the case that the number of dangling nodes is large .
Edit intent: clarity
Original: By lumping all dangling nodes of a web graph into a single node , PageRank computation can be proceeded with a much smaller matrix on the case that the number of dangling nodes is large .
Revised: By lumping all dangling nodes of a web graph into a single node , PageRank computation can be proceeded with a much smaller matrix through lumping all dangling nodes of a web graph into a single node .
Edit intent: clarity
Original: We propose a multi-channel speech enhancement approach with a novel two-level feature fusion method and a pre-trained acoustic model in a multi-task learning paradigm.
Revised: We propose a multi-channel speech enhancement approach with a novel two-stage feature fusion method and a pre-trained acoustic model in a multi-task learning paradigm.
Edit intent: clarity
Original:  In the first fusion level , the time-domain and frequency-domain features are extracted separately.
Revised:  In the first fusion stage , the time-domain and frequency-domain features are extracted separately.
Edit intent: clarity
Original:  To fully integrate the rich information of multi-channel speech, i.e. time-frequency domain features and the array geometry, we apply a third 2-D convolutional layer in the second level of fusion to obtain the final convolutional features.
Revised:  To fully integrate the rich information of multi-channel speech, i.e. time-frequency domain features and the array geometry, we apply a third 2-D convolutional layer in the second stage of fusion to obtain the final convolutional features.
Edit intent: clarity
Original:  In the time domain, the multi-channel convolution sum (MCS) and the inter-channel convolution differences (ICDs) features are computed and then integrated with a 2-D convolutional layer, while in the frequency domain, the log-power spectra (LPS) features from both original channels and super-directive beamforming outputs are combined with another 2-D convolutional layer.
Revised:  In the time domain, the multi-channel convolution sum (MCS) and the inter-channel convolution differences (ICDs) features are computed and then integrated with a 2-D convolutional layer, while in the frequency domain, the log-power spectra (LPS) features from both original channels and super-directive beamforming outputs are combined with a second 2-D convolutional layer.
Edit intent: clarity
Original:  To fully integrate the rich information of multi-channel speech, i.e. time-frequency domain features and the array geometry, we apply a third 2-D convolutional layer in the second stage of fusion to obtain the final convolutional features.
Revised:  To fully integrate the rich information of multi-channel speech, i.e. time-frequency domain features and the array geometry, we apply a third 2-D convolutional layer in the second fusion stage to obtain the final convolutional features.
Edit intent: clarity
Original:  On the Task1 development dataset of the ConferencingSpeech 2021 challenge, a PESQ improvement of 0.24 and 0.19 is attained compared to the official baseline and a recently proposed multi-channel separation method.
Revised:  On the Task1 development dataset of ConferencingSpeech 2021 challenge, a PESQ improvement of 0.24 and 0.19 is attained compared to the official baseline and a recently proposed multi-channel separation method.
Edit intent: fluency
Original: This paper uses the panel data of Chinese listed companies from 2007 to 2019, uses the relaxation of China's margin trading and Short-Selling restrictions as the basis of quasi experimental research, and then constructs a double difference model to analyze whether the margin trading and Short-Selling will encourage enterprises to engage in green technology innovation activities.
Revised: This paper uses the panel data of Chinese listed companies from 2007 to 2019, uses the relaxation of China's margin trading and short selling restrictions as the basis of quasi experimental research, and then constructs a double difference model to analyze whether the margin trading and Short-Selling will encourage enterprises to engage in green technology innovation activities.
Edit intent: fluency
Original: This paper uses the panel data of Chinese listed companies from 2007 to 2019, uses the relaxation of China's margin trading and Short-Selling restrictions as the basis of quasi experimental research, and then constructs a double difference model to analyze whether the margin trading and Short-Selling will encourage enterprises to engage in green technology innovation activities.
Revised: This paper uses the panel data of Chinese listed companies from 2007 to 2019, uses the relaxation of China's margin trading and Short-Selling restrictions as the basis of quasi experimental research, and then constructs a double difference model to analyze whether the margin trading and short selling will encourage enterprises to engage in green technology innovation activities.
Edit intent: fluency
Original:  Firstly, our research results show that after the implementation of the margin trading and Short-Selling , the green technology innovation behavior of pilot companies will increase significantly.
Revised:  Firstly, our research results show that after the implementation of the margin trading and short selling , the green technology innovation behavior of pilot companies will increase significantly.
Edit intent: fluency
Original:  Secondly, the empirical results show that the implementation of margin trading and Short-Selling restrictions will significantly promote the number of green technology innovation of pilot enterprises, but will not  promote the quality of green technology innovation of pilot enterprises.
Revised:  Secondly, the empirical results show that the implementation of margin trading and short selling will significantly promote the number of green technology innovation of pilot enterprises, but will not promote the quality of green technology innovation of pilot enterprises.
Edit intent: clarity
Original:  Secondly, the empirical results show that the implementation of margin trading and Short-Selling restrictions will significantly promote the number of green technology innovation of pilot enterprises, but will not  promote the quality of green technology innovation of pilot enterprises.
Revised:  Secondly, the empirical results show that the implementation of margin trading and Short-Selling restrictions will significantly promote the quantity of green technology innovation of pilot enterprises, but will not promote the quality of green technology innovation of pilot enterprises.
Edit intent: clarity
Original:  Secondly, the empirical results show that the implementation of margin trading and Short-Selling restrictions will significantly promote the number of green technology innovation of pilot enterprises, but will not  promote the quality of green technology innovation of pilot enterprises.
Revised:  Secondly, the empirical results show that the implementation of margin trading and Short-Selling restrictions will significantly promote the number of green technology innovation of pilot enterprises, but will not significantly promote the quality of green technology innovation of pilot enterprises.
Edit intent: fluency
Original:  Furthermore, we analyze the difference of the impact of margin trading and Short-Selling restrictions on the number of green technology innovation of pilot enterprises in different periods.
Revised:  Furthermore, we analyze the difference of the impact of margin trading and short selling on the quantity of green technology innovation of pilot enterprises in different periods.
Edit intent: clarity
Original:  Finally, we find that the  yield gap between financial assets and operating assets, the risk of stock price decline , management shareholding, institutional shareholding ratio, weak product market competition and bull market will affect the role of short selling in promoting green technology innovation of pilot enterprises.
Revised:  Finally, we find that the yield gap between financial assets and operating assets, the risk of stock price decline , management shareholding, institutional shareholding ratio, product market competition and bull market will affect the role of short selling in promoting green technology innovation of pilot enterprises.
Edit intent: clarity
Original: Quantum degeneracy in error correction is a feature unique to quantum error correcting codes unlike their classically counterpart.
Revised: Quantum degeneracy in error correction is a feature unique to quantum error correcting codes , unlike their classical counterpart.
Edit intent: fluency
Original:  It allows a quantum error correcting code to correct errors even in cases when they can not uniquely pin point the error.
Revised:  It allows a quantum error correcting code to correct errors even when they can not uniquely pin point the error.
Edit intent: clarity
Original:  It allows a quantum error correcting code to correct errors even in cases when they can not uniquely pin point the error.
Revised:  It allows a quantum error correcting code to correct errors even in cases when they can not uniquely pinpoint the error.
Edit intent: fluency
Original:  Diagonal distance of a quantum code is an important parameter that characterizes if the quantum code is degenerate or not.
Revised:  The diagonal distance of a quantum code is an important parameter that characterizes if the quantum code is degenerate or not.
Edit intent: coherence
Original:  If code has  distance more than the diagonal distance  then it is degenerate  whereas if it is below the diagonal distance  then it is nondegenerate.
Revised:  If code has a distance more than the diagonal distance then it is degenerate whereas if it is below the diagonal distance then it is nondegenerate.
Edit intent: fluency
Original:  If code has  distance more than the diagonal distance  then it is degenerate  whereas if it is below the diagonal distance  then it is nondegenerate.
Revised:  If code has distance more than the diagonal distance , then it is degenerate whereas if it is below the diagonal distance then it is nondegenerate.
Edit intent: fluency
Original:  If code has  distance more than the diagonal distance  then it is degenerate  whereas if it is below the diagonal distance  then it is nondegenerate.
Revised:  If code has distance more than the diagonal distance then it is degenerate , whereas if it is below the diagonal distance then it is nondegenerate.
Edit intent: fluency