wanyu/IteraTeR_v2 huggingface dataset:
Original:  We also show how a `` gap-preserving '' compression theorem for commuting operator strategies would imply that approximating the commuting operator value is complete for \Pi_1.
Revised:  We also show how a `` gap-preserving " compression theorem for commuting operator strategies would imply that approximating the commuting operator value is complete for \Pi_1.
Edit intent: fluency
Original:  Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, ~%DIFDELCMD < \ie%%%  , structural relation graph and semantic relation graph.
Revised:  Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, %DIFDELCMD < \ie%%% , structural relation graph and semantic relation graph.
Edit intent: fluency
Original:  Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, ~%DIFDELCMD < \ie%%%  , structural relation graph and semantic relation graph.
Revised:  Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, ~%DIFDELCMD < \ie%%% i.e. , structural relation graph and semantic relation graph.
Edit intent: clarity
Original:  Using on-chain data from the period after its launch, we evaluate the impact of EIP-1559 on  user experience and market performance.
Revised:  Using on-chain data from the period after its launch, we evaluate the impact of EIP-1559 on the user experience and market performance.
Edit intent: fluency
Original:  Our empirical findings suggest that while EIP-1559 achieves its goals on average, short-term behavior is marked by intense  oscillations in block sizes  ] and slow adjustments during periods of demand bursts (e.g., NFT drops).
Revised:  Our empirical findings suggest that although EIP-1559 achieves its goals on average, short-term behavior is marked by intense oscillations in block sizes ] and slow adjustments during periods of demand bursts (e.g., NFT drops).
Edit intent: fluency
Original:  Our empirical findings suggest that while EIP-1559 achieves its goals on average, short-term behavior is marked by intense  oscillations in block sizes  ] and slow adjustments during periods of demand bursts (e.g., NFT drops).
Revised:  Our empirical findings suggest that while EIP-1559 achieves its goals on average, short-term behavior is marked by intense oscillations in block sizes ]) and slow adjustments during periods of demand bursts (e.g., NFT drops).
Edit intent: fluency
Original:  We then study the effect of different learning rates and propose an alternative mechanism that uses a variable learning rate .
Revised:  To address this issue, we propose an alternative mechanism that uses a variable learning rate .
Edit intent: clarity
Original:  Our simulations show that the latter outperforms the standard EIP-1559 mechanism under various demand scenarios.
Revised:  Our simulations show that the latter robustly outperforms the EIP-1559 mechanism under various demand scenarios.
Edit intent: clarity
Original:  Our simulations show that the latter outperforms the standard EIP-1559 mechanism under various demand scenarios.
Revised:  Our simulations show that the latter outperforms the standard EIP-1559 protocol under various demand scenarios.
Edit intent: clarity
Original:  These results provide evidence that such mechanisms may constitute a promising direction for future research and contribute to the ongoing discussion on EIP-1559 (and EIP-1559 based) mechanisms and to design of more efficient transaction fee markets.
Revised:  These results provide evidence that such mechanisms may constitute a promising alternative to the default EIP-1559-based format and contribute to the ongoing discussion on EIP-1559 (and EIP-1559 based) mechanisms and to design of more efficient transaction fee markets.
Edit intent: clarity
Original:  These results provide evidence that such mechanisms may constitute a promising direction for future research and contribute to the ongoing discussion on EIP-1559 (and EIP-1559 based) mechanisms and to design of more efficient transaction fee markets.
Revised:  These results provide evidence that such mechanisms may constitute a promising direction for future research and contribute to the ongoing discussion on the design of more efficient transaction fee markets.
Edit intent: coherence
Original:  Its main research goal  is to retrieve persons with the same identity from different cameras.
Revised:  The main research goal is to retrieve persons with the same identity from different cameras.
Edit intent: fluency
Original:  However, traditional person Re-ID methods require manual marking of person targets, which consumes a lot of labor costs .
Revised:  However, traditional person Re-ID methods require manual marking of person targets, which consumes a lot of labor cost .
Edit intent: fluency
Original:  To facilitate researchers to better understand the latest research results and future development trends in this field.
Revised:  Therefore, this paper is to facilitate researchers to better understand the latest research results and future development trends in this field.
Edit intent: coherence
Original:  To facilitate researchers to better understand the latest research results and future development trends in this field.
Revised:  To facilitate researchers to better understand the latest research results and the future trends in the field.
Edit intent: clarity
Original:  Firstly, we compare traditional and deep learning-based person Re-ID methods, and present the main contributions of several person Re-ID surveys , and analyze their focused dimensions and shortcomings .
Revised:  Firstly, we summarize the main study of several recently published person re-identification surveys and try to fill the gaps between them .
Edit intent: clarity
Original:  Secondly, we focus on the current classic deep learning-based person Re-ID methods  , including methods for deep metric learning, local feature learning, generate adversarial networks, sequence feature learning , and graph convolutional networks.
Revised:  Secondly, We propose a multi-dimensional taxonomy to categorize the most current deep learning-based person Re-ID methods , including methods for deep metric learning, local feature learning, generate adversarial networks, sequence feature learning , and graph convolutional networks.
Edit intent: clarity
Original:  Secondly, we focus on the current classic deep learning-based person Re-ID methods  , including methods for deep metric learning, local feature learning, generate adversarial networks, sequence feature learning , and graph convolutional networks.
Revised:  Secondly, we focus on the current classic deep learning-based person Re-ID methods , including methods for deep metric learning, local feature learning, generate adversarial networks, sequence feature learning and graph convolutional networks.
Edit intent: fluency
Original:  Furthermore, we subdivide the above five categories according to their technique types, analyzing and comparing the experimental performance of part subcategories of the method .
Revised:  Furthermore, we subdivide the above five categories according to their technique types, discussing and comparing the experimental performance of part subcategories of the method .
Edit intent: clarity
Original:  Furthermore, we subdivide the above five categories according to their technique types, analyzing and comparing the experimental performance of part subcategories of the method .
Revised:  Furthermore, we subdivide the above five categories according to their technique types, analyzing and comparing the experimental performance of part subcategories .
Edit intent: clarity
Original:  Finally, we discuss the challenges that remain in the field of person Re-ID field and prospects for future research directions  .
Revised:  Finally, we conclude this paper and discuss future research directions .
Edit intent: clarity
Original:  Finally, we discuss the challenges that remain in the field of person Re-ID field and prospects for future research directions  .
Revised:  Finally, we discuss the challenges that remain in the field of person Re-ID field and prospects for future research directions for person Re-ID .
Edit intent: clarity
Original: This paper proposes a deconvolution-based network (DCNN) model for DOA estimation of direct source and early reflections under reverberate scenarios.
Revised: This paper proposes a deconvolution-based network (DCNN) model for DOA estimation of direct source and early reflections under reverberant scenarios.
Edit intent: fluency
Original:  We use the covariance matrix of high order Ambisonics (HOA) signals in  time domain as the input feature of the network, which is concise while contains precise spatial information under reverberate scenarios.
Revised:  We use the covariance matrix of high order Ambisonics (HOA) signals in the time domain as the input feature of the network, which is concise while contains precise spatial information under reverberate scenarios.
Edit intent: fluency
Original:  We use the covariance matrix of high order Ambisonics (HOA) signals in  time domain as the input feature of the network, which is concise while contains precise spatial information under reverberate scenarios.
Revised:  We use the covariance matrix of high order Ambisonics (HOA) signals in time domain as the input feature of the network, which is concise while containing precise spatial information under reverberate scenarios.
Edit intent: fluency
Original:  We use the covariance matrix of high order Ambisonics (HOA) signals in  time domain as the input feature of the network, which is concise while contains precise spatial information under reverberate scenarios.
Revised:  We use the covariance matrix of high order Ambisonics (HOA) signals in time domain as the input feature of the network, which is concise while contains precise spatial information under reverberant scenarios.
Edit intent: fluency
Original:  We have carried out a series of experiments based on simulated and measured data under different reverberate scenarios, which prove the robustness and accuracy of the proposed DCNN model.
Revised:  We have carried out a series of experiments based on simulated and measured data under different reverberant scenarios, which prove the robustness and accuracy of the proposed DCNN model.
Edit intent: fluency
Original: Terms such as leader, follower, and oppressed sound equally well in the description of a pack of wolves, an insect community, a street protest crowd  or a business team , and have very similar meanings.
Revised: Terms such as leader, follower, and oppressed sound equally well in the description of a pack of wolves, a street protest crowd or a business team , and have very similar meanings.
Edit intent: clarity
Original: Terms such as leader, follower, and oppressed sound equally well in the description of a pack of wolves, an insect community, a street protest crowd  or a business team , and have very similar meanings.
Revised: Terms such as leader, follower, and oppressed sound equally well in the description of a pack of wolves, an insect community, a street protest crowd , or a business team , and have very similar meanings.
Edit intent: fluency
Original: Terms such as leader, follower, and oppressed sound equally well in the description of a pack of wolves, an insect community, a street protest crowd  or a business team , and have very similar meanings.
Revised: Terms such as leader, follower, and oppressed sound equally well in the description of a pack of wolves, an insect community, a street protest crowd or a business team and have very similar meanings.
Edit intent: fluency
Original:  Use of these terms implies presence of some general law or structure that govern collective behaviour.
Revised:  This indicates the presence of some general law or structure that govern collective behaviour.
Edit intent: clarity
Original:  Use of these terms implies presence of some general law or structure that govern collective behaviour.
Revised:  Use of these terms implies presence of some general law or structure that governs collective behaviour.
Edit intent: fluency
Original:  We picked up the most common parameter for all levels of organization - motion.
Revised:  To reveal this, we selected the most common parameter for all levels of organization - motion.
Edit intent: coherence
Original:  The pattern observed in the network can be easily interpreted and are in agreement with expected behaviour.
Revised:  The patterns observed in the network can be easily interpreted and are in agreement with expected behaviour.
Edit intent: fluency
Original:  This is specified through a few target utterances and achieved by modelling the relationship between the target subset and the selected subset using submodular mutual information functions.
Revised:  This is specified through a few target utterances and achieved by modeling the relationship between the target subset and the selected subset using submodular mutual information functions.
Edit intent: fluency
Original:  We also compare with a skyline where we specifically pick from the target and  generally outperforms the oracle in its selections.
Revised:  We also compare with a skyline where we specifically pick from the target and our method generally outperforms the oracle in its selections.
Edit intent: clarity
Original:  In this paper, we propose MEL-ONS , a melody generation framework based on a graph representation of music structure which consists of eight types of bar-level relations.
Revised:  In this paper, we propose MELONS , a melody generation framework based on a graph representation of music structure which consists of eight types of bar-level relations.
Edit intent: coherence
Original:  IFP is most familiar in neural systems, our primary motivation, but it appears everywhere from bacterial signal transduction to the human immune system.
Revised:  IFPs are most familiar in neural systems, our primary motivation, but it appears everywhere from bacterial signal transduction to the human immune system.
Edit intent: clarity
Original:  IFP is most familiar in neural systems, our primary motivation, but it appears everywhere from bacterial signal transduction to the human immune system.
Revised:  IFP is most familiar in neural systems, our primary motivation, but they appear everywhere from bacterial signal transduction to the human immune system.
Edit intent: clarity
Original: In this study, we use a reservoir computing based echo state network (ESN) to predict the collec- tive burst synchronization of neurons.
Revised: In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons.
Edit intent: fluency
Original:  However, in the case of assortative network, training the machine with the information (i.e time series) of low-degree nodes gives better results in predicting the burst synchro- nization .
Revised:  However, in the case of assortative network, training the machine with the information (i.e time series) of low-degree nodes gives better results in predicting the burst synchronization .
Edit intent: fluency
Original:  Motivated by statistical scalability to high dimensions, this paper proposes sliced MI (SMI) as a surrogate measure of dependence.
Revised:  Motivated by statistical scalability to high dimensions, this paper proposes sliced MI (SMI) as a surrogate measure of dependence.
Edit intent: fluency
Original:  Manual labeling often has many false negative labels (abnormal labels ), which will seriously harm the training process.
Revised:  Manual labeling often has many false negative labels (abnormal annotations ), which will seriously harm the training process.
Edit intent: clarity
Original:  In this work, we find that region-based loss significantly outperforms distribution-based loss when dealing with falsenegative labels, therefore we propose Mask Dice loss (MD loss), which is the first reported region-based loss function for training 3D image segmentation models using sparse 2D slice labels.
Revised:  In this work, we find that region-based loss significantly outperforms distribution-based loss when dealing with falsenegative labels, therefore we proposed Mask Dice loss (MD loss), which is the first reported region-based loss function for training 3D image segmentation models using sparse 2D slice labels.
Edit intent: fluency
Original:  In this work, we find that region-based loss significantly outperforms distribution-based loss when dealing with falsenegative labels, therefore we propose Mask Dice loss (MD loss), which is the first reported region-based loss function for training 3D image segmentation models using sparse 2D slice labels.
Revised:  In this work, we find that region-based loss significantly outperforms distribution-based loss when dealing with falsenegative labels, therefore we propose Mask Dice loss (MD loss), which is the first reported region-based loss function for training 3D image segmentation networks using sparse 2D slice labels.
Edit intent: clarity
Original:  In addition, fault is an edge feature, and the current network widely used for fault segmentation downsamples the features multiple times, which is not conducive to edge characterization and thus requires many parameters and computational effort to preserve the features.
Revised:  In addition, fault is an edge feature, and the current network widely used for fault segmentation downsamples the features multiple times, which is not conducive to edge representation and thus requires many parameters and computational effort to preserve the features.
Edit intent: clarity
Original:  We propose Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort . Experimental results show that MD loss can clearly weaken the effect of anomalous labels.
Revised:  We proposed Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort . Experimental results show that MD loss can clearly weaken the effect of anomalous labels.
Edit intent: fluency
Original:  We propose Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort . Experimental results show that MD loss can clearly weaken the effect of anomalous labels.
Revised:  We propose Fault-Net, which uses a high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort . Experimental results show that MD loss can clearly weaken the effect of anomalous labels.
Edit intent: clarity
Original:  We propose Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort . Experimental results show that MD loss can clearly weaken the effect of anomalous labels.
Revised:  We propose Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computation are required . Experimental results show that MD loss can clearly weaken the effect of anomalous labels.
Edit intent: clarity
Original:  We propose Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort . Experimental results show that MD loss can clearly weaken the effect of anomalous labels.
Revised:  We propose Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort . Experimental results show that MD loss can clearly weaken the effect of false negative labels.
Edit intent: clarity
Original:  The Fault-Net parameter is only 0.42MB, support up to 528^3 (1.5x10^8, Float32) size cuboid inference on 16GB video ram, and its inference speed on CPU and GPU is significantly faster than other networks , but the result of our method is the state-of-the-art in the FORCE fault identification competition.
Revised:  The Fault-Net parameter is only 0.42MB, support up to 528^3 (1.5x10^8, Float32) size cuboid inference on 16GB video ram, its inference speed on CPU and GPU is significantly faster than other networks , but the result of our method is the state-of-the-art in the FORCE fault identification competition.
Edit intent: fluency
Original:  Manual labeling often has many false negative labels (abnormal annotations), which will seriously harm the training process.
Revised:  Manual labeling often has many false negative labels (abnormal annotations), which will seriously jeopardize the training process.
Edit intent: clarity
Original:  Meanwhile, in order to efficiently fuse multi-scale features, we decouple the convolution process into feature selection and channel fusion, and proposed a lightweight feature fusion block, Multi-Scale Compression Fusion (MCF).
Revised:  Meanwhile, in order to efficiently fuse multiscale features, we decouple the convolution process into feature selection and channel fusion, and proposed a lightweight feature fusion block, Multi-Scale Compression Fusion (MCF).
Edit intent: fluency
Original:  It works well on most of the open data seismic images, and the result of our method is the state-of-the-art in the FORCE fault identification competition.
Revised:  It works well on most of the open data seismic images, and the result of our approach is state-ofthe-art in FORCE fault identification competition.
Edit intent: clarity
Original:  Our learnable ACE (LACE) transforms the data into a new `` whitened" space that improves the inter-class separability and intra-class compactness.
Revised:  Our learnable ACE (LACE) transforms the data into a new " whitened" space that improves the inter-class separability and intra-class compactness.
Edit intent: fluency
Original: Purpose: Magnitude-only fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2* estimation where complex-based methods fail or when phase data is inaccessible or unreliable, such as in multi-centre studies.
Revised: Purpose: Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2* estimation where complex-based methods fail or when phase data is inaccessible or unreliable, such as in multi-centre studies.
Edit intent: clarity
Original:  To gain deeper insights into algorithm behavior, the paths on the likelihood functions are visualized and statistics describing correct minimization are generated.
Revised:  To gain deeper insights into algorithm behavior, the paths on the likelihood functions are visualized and statistics describing correct optimization are generated.
Edit intent: clarity