wanyu/IteraTeR_full_sent huggingface dataset:
Original: The research of two-level overlapping Schwarz (TL-OS) method based on constrained energy minimizing coarse space is still in its infancy, and there exist some defects, e.g. mainly for Poisson-like problems and too heavy computational cost of coarse space construction.
Revised: The research of two-level overlapping Schwarz (TL-OS) method based on constrained energy minimizing coarse space is still in its infancy, and there exist some defects, e.g. mainly for second order elliptic problem and too heavy computational cost of coarse space construction.
Edit intent: meaning-changed
Original:  In this paper, by introducing appropriate assumptions, we propose more concise coarse basis functions for general Hermitian positive and definite discrete systems, and establish the corresponding TL-OS preconditioned algorithmic and theoretical frameworks  .
Revised:  In this paper, by introducing appropriate assumptions, we propose more concise coarse basis functions for general Hermitian positive and definite discrete systems, and establish the algorithmic and theoretical frameworks .
Edit intent: coherence
Original:  In this paper, by introducing appropriate assumptions, we propose more concise coarse basis functions for general Hermitian positive and definite discrete systems, and establish the corresponding TL-OS preconditioned algorithmic and theoretical frameworks  .
Revised:  In this paper, by introducing appropriate assumptions, we propose more concise coarse basis functions for general Hermitian positive and definite discrete systems, and establish the corresponding TL-OS preconditioned algorithmic and theoretical frameworks of the corresponding TL-OS methods .
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Original:  As the first application of the frameworks, we prove that the assumptions hold for the linear finite element discretization of Poisson problem with high contrast and oscillatory coefficient  .
Revised:  As the first application of the frameworks, we prove that the assumptions hold for the linear finite element discretization of Poisson problem with high contrast and oscillatory coefficient and the condition number of the TL-OS preconditioned system is robust with respect to the model and mesh parameters .
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Original:  The numerical results for homogeneous and non-homogeneous cases illustrate that the PCG method based on the proposed preconditioners have good stability in terms of the angular frequency, mesh parameters and the number of freedoms in each element.
Revised:  The numerical results for homogeneous and non-homogeneous cases illustrate that the PCG method based on the proposed preconditioners have good stability in terms of the angular frequency, mesh parameters and the number of degrees of freedom in each element.
Edit intent: clarity
Original:  Specifically, we first establish the spatial-temporal dual graph architecture to capture the complex correlations of both intersections and road segments. The adjacency relations of intersections and that of road segments are respectively characterized by node-wise graph and edge-wise graph .
Revised:  Specifically, we first establish the node-wise and edge-wise graphs to respectively characterize the adjacency relations of intersections and that of road segments are respectively characterized by node-wise graph and edge-wise graph .
Edit intent: clarity
Original:  In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale  spatial-temporal graph convolution networks and dual graphinteraction networks.
Revised:  In order to extract the joint spatio-temporal correlations of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale spatial-temporal graph convolution networks and dual graphinteraction networks.
Edit intent: clarity
Original:  In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale  spatial-temporal graph convolution networks and dual graphinteraction networks.
Revised:  In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with multi-scale spatial-temporal graph convolution networks and dual graphinteraction networks.
Edit intent: meaning-changed
Original:  In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale  spatial-temporal graph convolution networks and dual graphinteraction networks.
Revised:  In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale network architectures for capturing multi-level spatial-temporal graph convolution networks and dual graphinteraction networks.
Edit intent: clarity
Original:  In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale  spatial-temporal graph convolution networks and dual graphinteraction networks. Followed by the spatial-temporal learning layer, we also employ the multi-task learning layer to estimate the travel time of a given whole route and each road segment  simultaneously.
Revised:  In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale spatial-temporal information from the dual graph. Finally, we employ the multi-task learning layer to estimate the travel time of a given whole route and each road segment simultaneously.
Edit intent: clarity
Original:  In contrast to existing kNN augmentation techniques that blindly incorporate all samples, our method dynamically selects a subset of augmented samples with respect to the maximum KL-divergence of the training loss .
Revised:  In contrast to existing kNN augmentation techniques that blindly incorporate all samples, our method dynamically selects a subset of augmented samples with respect to the maximum KL-divergence between the teacher and student models .
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Original:  Using a portfolio of 50, liquid, continuous futures contracts over the period 1990-2020, the addition of the CPD module leads to an improvement in Sharpe ratio of 33%DIFDELCMD < \\%%% %DIF < . Even more notably, this module is especially beneficial in periods of significant nonstationarity, and in particular, over the most recent years tested (2015-2020) the performance boost is approximately 400\\%.
Revised:  Using a portfolio of 50, liquid, continuous futures contracts over the period 1990-2020, the addition of the CPD module leads to an improvement in Sharpe ratio of %DIFDELCMD < \\%%% %DIF < . Even more notably, this module is especially beneficial in periods of significant nonstationarity, and in particular, over the most recent years tested (2015-2020) the performance boost is approximately 400\\%.
Edit intent: coherence
Original:  This is especially interesting as traditional momentum strategies have been underperforming in this period.abstract 
Revised:  This is especially interesting as traditional momentum strategies have been underperforming in this period.abstract one-third. Even more notably, this module is especially beneficial in periods of significant nonstationarity, and in particular, over the most recent years tested (2015-2020) the performance boost is approximately two-thirds. This is especially interesting as traditional momentum strategies have been underperforming in this period.
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Original: The performances of GNNs for representation learning on the graph-structured data are generally limited to the issue that existing GNNs rely on one assumption, i. e., the original graph structure is reliable. However, since real-world graphs is inevitably noisy or incomplete, this assumption is often unrealistic. In this paper, we propose a structure learning graph convolutional networks (SLGCNs)to alleviate the issue from two aspects, and the proposed approach is applied to node classification.
Revised: The performances of GNNs for representation learning on the graph-structured data , under heterophily (or low homophily), many popular GNNs may fail to capture long-range dependencies, which leads to their performance degradation. To solve the above-mentioned issue, we propose a structure learning graph convolutional networks (SLGCNs)to alleviate the issue from two aspects, and the proposed approach is applied to node classification.
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Original:  Specifically, the first is node features, we design a efficient-spectral-clustering  with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Revised:  The proposed GCN-SL contains two improvements: corresponding to node features and edges, respectively. In the aspect of node features, we design a efficient-spectral-clustering with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Edit intent: meaning-changed
Original:  Specifically, the first is node features, we design a efficient-spectral-clustering  with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Revised:  Specifically, the first is node features, we propose an efficient-spectral-clustering with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Edit intent: clarity
Original:  Specifically, the first is node features, we design a efficient-spectral-clustering  with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Revised:  Specifically, the first is node features, we design a efficient-spectral-clustering (ESC) and an ESC with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Edit intent: meaning-changed
Original:  Specifically, the first is node features, we design a efficient-spectral-clustering  with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Revised:  Specifically, the first is node features, we design a efficient-spectral-clustering with anchors (ESC-ANCH) algorithms to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Edit intent: clarity
Original:  Specifically, the first is node features, we design a efficient-spectral-clustering  with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are.
Revised:  Specifically, the first is node features, we design a efficient-spectral-clustering with anchors (ESC-ANCH) approach to efficiently aggregate feature representations from all similar nodes , no matter how far away they are.
Edit intent: fluency
Original:  Specifically, the first is node features, we design a efficient-spectral-clustering  with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are. The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.
Revised:  Specifically, the first is node features, we design a efficient-spectral-clustering with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes . In the aspect of edges, we build a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.
Edit intent: clarity
Original:  The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.
Revised:  The second is edges, our approach generates a re-connected adjacency matrix by using a special data preprocessing technique and similarity learning, and the re-connected adjacency matrix can be optimized directly along with GCN-SL parameters. Considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.
Edit intent: meaning-changed
Original:  The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.
Revised:  The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix may provide misleading information for aggregation step of GCN in the graphs with low level of homophily.
Edit intent: clarity
Original:  The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.
Revised:  The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation in GCN, especially the graphs being with a low level of homophily.
Edit intent: clarity
Original:  Both the re-connected adjacency matrix and original adjacency matrix are applied to SLGCNs to aggregate feature representations from nearby nodes . Thus, SLGCNs can be applied to graphs with various levels of homophily.
Revised:  Both the re-connected adjacency matrix and original adjacency matrix are applied to SLGCNs to aggregate feature representations from nearby nodes via re-connected adjacency matrix and is applied to graphs with various levels of homophily.
Edit intent: coherence
Original: This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids. A recently developed deep learning model known as normalizing flows is used to generate quantile forecasts of renewable generation .
Revised: The core contribution is to propose a probabilistic forecast-driven strategy, modeled as a min-max-min robust optimization problem with recourse, and solved using a Benders-dual cutting plane algorithm in a tractable manner. The convergence is improved by building an initial set of cuts. In addition, a dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution is proposed. A secondary contribution is to use a recently developed deep learning model known as normalizing flows is used to generate quantile forecasts of renewable generation .
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Original:  A recently developed deep learning model known as normalizing flows is used to generate quantile forecasts of renewable generation . They provide a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations.
Revised:  A recently developed deep learning model known as normalizing flows is used to generate quantile forecasts of renewable generation for the robust optimization problem. This technique provides a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations.
Edit intent: meaning-changed
Original:  Then, a probabilistic forecast-driven strategy is designed, modeled as a min-max-min robust optimization problem with recourse, and solved using a Benders decomposition. The convergence is improved by building an initial set of cuts derived from domain knowledge. Robust optimization models the generation randomness using an uncertainty set that includes the worst-case generation scenario and protects this scenario under the minimal increment of costs. This approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies .
Revised:  Overall, the robust approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies .
Edit intent: coherence
Original: We study objective robustness failures, a type of out-of-distribution robustness failure in reinforcement learning (RL). Objective robustness failures occur when an RL agent retains its capabilities off-distribution yet pursues the wrong objective.
Revised: We study objective robustness failures, a type of out-of-distribution robustness failure in reinforcement learning (RL). Objective robustness failures occur when an RL agent retains its capabilities out-of-distribution yet pursues the wrong objective.
Edit intent: fluency
Original:   We provide the first explicit empirical demonstrations of objective robustness failures and argue that this type of failure is critical to address .
Revised:  This kind of failure presents different risks than the robustness problems usually considered in the literature, since it involves agents that leverage their capabilities to pursue the wrong objective rather than simply failing to do anything useful. We provide the first explicit empirical demonstrations of objective robustness failures and argue that this type of failure is critical to address .
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Original:  The influence of this new similarity measure on the optimization of neural network have been thoroughly analyzed.
Revised:  We first develop a theoretical foundation for AdderNets, by showing that both the single hidden layer AdderNet and the width-bounded deep AdderNet with ReLU activation functions are universal function approximators. An approximation bound for AdderNets with a single hidden layer is also presented. We further analyze the influence of this new similarity measure on the optimization of neural network have been thoroughly analyzed.
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Original:  To achieve a better performance, we develop a special training approach for AdderNetsby investigating the \ell_p-norm. We then propose an adaptive learning rate strategy  to enhance the training procedure of AdderNets according to the magnitude of each neuron's gradient.
Revised:  To achieve a better performance, we develop a special training scheme for AdderNets. Based on the gradient magnitude, an adaptive learning rate strategy to enhance the training procedure of AdderNets according to the magnitude of each neuron's gradient.
Edit intent: clarity
Original:  We then propose an adaptive learning rate strategy  to enhance the training procedure of AdderNets according to the magnitude of each neuron's gradient. As a result, the proposed AdderNets can achieve  75.7\% Top-1 accuracy  92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer.
Revised:  We then propose an adaptive learning rate strategy to enhance the training procedure of AdderNets . AdderNets can achieve 75.7\% Top-1 accuracy 92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer.
Edit intent: clarity
Original:  As a result, the proposed AdderNets can achieve  75.7\% Top-1 accuracy  92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer.
Revised:  As a result, the proposed AdderNets can achieve a 75.7\% Top-1 accuracy 92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer.
Edit intent: fluency
Original:  As a result, the proposed AdderNets can achieve  75.7\% Top-1 accuracy  92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer.
Revised:  As a result, the proposed AdderNets can achieve 75.7\% Top-1 accuracy and a 92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer.
Edit intent: meaning-changed
Original:  As a result, the proposed AdderNets can achieve  75.7\% Top-1 accuracy  92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer. Moreover, we develop a theoretical foundation for AdderNets, by showing that both the single hidden layer AdderNet and the width-bounded deep AdderNet with ReLU activation functions are universal function approximators. These results match those of the traditional neural networks using the more complex multiplication units. An approximation bound for AdderNets with a single hidden layer is also presented .
Revised:  As a result, the proposed AdderNets can achieve 75.7\% Top-1 accuracy 92.3\% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in the convolutional layer .
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Original: Recent researches show that deep learning model is susceptible to backdoor attacks where the backdoor embedded in the model will be triggered when a backdoor instance arrives .
Revised: Recent researches show that deep learning model is susceptible to backdoor attacks . Many defenses against backdoor attacks have been proposed. However, existing defense works require high computational overhead or backdoor attack information such as the trigger size, which is difficult to satisfy in realistic scenarios .
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Original:  The proposed method leverages intentional adversarial perturbations to detect whether the image contains a trigger, which can be applied in two scenarios (sanitize the training set in training stage and detect the backdoor instances in inference stage).
Revised:  The proposed method leverages intentional adversarial perturbations to detect whether an image contains a trigger, which can be applied in two scenarios (sanitize the training set in training stage and detect the backdoor instances in inference stage).
Edit intent: fluency