wanyu/IteraTeR_v2 huggingface dataset:
Original:  The key is to take the influence of other agentsinto consideration when performing distributed decision making .
Revised:  The key is to understand the mutual interplay between agents .
Edit intent: clarity
Original:  However, multi-agent environment is highly dynamic, which makes it hard to learn abstract representations of influences between agents by only low-order features that existing methods exploit .
Revised:  However, multi-agent environments are highly dynamic, which makes it hard to learn abstract representations of influences between agents by only low-order features that existing methods exploit .
Edit intent: fluency
Original:  However, multi-agent environment is highly dynamic, which makes it hard to learn abstract representations of influences between agents by only low-order features that existing methods exploit .
Revised:  However, multi-agent environment is highly dynamic, which makes it hard to learn abstract representations of their mutual interplay .
Edit intent: clarity
Original:  In this paper, we propose a graph convolutional model for multi-agent cooperation .
Revised:  In this paper, we propose graph convolutional reinforcement learning for multi-agent cooperation .
Edit intent: clarity
Original:  The graph convolution architecture adapts to the dynamics of the underlying graph of the multi-agent environment, where the influence among agents is captured by their abstract relation representations.
Revised:  The graph convolution architecture adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations.
Edit intent: clarity
Original:  High-order features extracted by relation kernels of convolutional layers from gradually increased receptive fields are exploited to learn cooperative strategies.
Revised:  Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperative strategies.
Edit intent: clarity
Original:  Moreover, the relation representations are temporally regularized to make the cooperation more consistent . Empirically, we show that our model enables agents to develop more cooperative and sophisticated strategies than existing methods in jungle and battle games and routing in packet switching networks .
Revised:  Moreover, the relation representations are temporally regularized to make the cooperation more consistent . Empirically, we show that our method substantially outperforms existing methods in jungle and battle games and routing in packet switching networks .
Edit intent: clarity
Original:  Moreover, the relation representations are temporally regularized to make the cooperation more consistent . Empirically, we show that our model enables agents to develop more cooperative and sophisticated strategies than existing methods in jungle and battle games and routing in packet switching networks .
Revised:  Moreover, the relation representations are temporally regularized to make the cooperation more consistent . Empirically, we show that our model enables agents to develop more cooperative and sophisticated strategies than existing methods in a variety of cooperative scenarios .
Edit intent: clarity
Original:  In this paper , we propose graph convolutional reinforcement learning for multi-agent cooperation , where graph convolution adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations.
Revised:  In this paper , we propose graph convolutional reinforcement learning , where graph convolution adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations.
Edit intent: clarity
Original:  Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and the cooperation is further boosted by temporal relation regularization for consistency.
Revised:  Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and cooperation is further boosted by temporal relation regularization for consistency.
Edit intent: clarity
Original:  However, multi-agent environments are highly dynamic, which makes it hard to learn abstract representations of their mutual interplay .
Revised:  However, multi-agent environments are highly dynamic, which makes it hard to learn abstract representations of mutual interplay between agents .
Edit intent: clarity
Original:  Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and cooperation is further boosted by temporal relation regularization for consistency.
Revised:  Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and cooperation is further improved by temporal relation regularization for consistency.
Edit intent: clarity
Original:  It leverages hardware redundancies, both within a rack and inside the network fabric, to reduce both protocol complexity and communication over- head .
Revised:  It leverages hardware redundancies, both within a rack and inside the network fabric, to reduce both protocol complexity and communication overhead .
Edit intent: fluency
Original:  In this work we present a realization of a wireless sensor network for hazard monitoring which is based on an array of event-triggered seismic sensors with advanced signal processing and characterization capabilities for a novel co-detection technique.
Revised:  In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered seismic sensors with advanced signal processing and characterization capabilities for a novel co-detection technique.
Edit intent: coherence
Original:  In this work we present a realization of a wireless sensor network for hazard monitoring which is based on an array of event-triggered seismic sensors with advanced signal processing and characterization capabilities for a novel co-detection technique.
Revised:  In this work we present a realization of a wireless sensor network for hazard monitoring which is based on an array of event-triggered seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique.
Edit intent: coherence
Original:  On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly  when it matters most and  not wasting precious resources when nothing can be observed.
Revised:  On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and not wasting precious resources when nothing can be observed.
Edit intent: clarity
Original:  On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly  when it matters most and  not wasting precious resources when nothing can be observed.
Revised:  On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly when it matters most and consequentially not wasting precious resources when nothing can be observed.
Edit intent: coherence
Original:  On the other hand we use machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones.
Revised:  On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones.
Edit intent: fluency
Original:  In this paper we will construct three graphs from D whose chromatic numbers will bound \chi_A(D).
Revised:  In this paper we will construct three different graphs whose chromatic numbers will bound \chi_A(D).
Edit intent: clarity
Original:  In this paper we will construct three graphs from D whose chromatic numbers will bound \chi_A(D).
Revised:  In this paper we will construct three graphs from D whose chromatic numbers bound \chi_A(D).
Edit intent: clarity
Original:  ii) if D has girth g , the length of a shortest cycle, and circumference c, then \chi_A(D)\leq%DIFDELCMD < \lceil %%% \frac{c-1{g-1} }%DIFDELCMD < \rceil %DIFDELCMD < %%% +1, which improves, substantially, the bound proposed by Bondy;
Revised:  ii) if D has girth g %DIFDELCMD < \lceil %%% \frac{c-1{g-1} }%DIFDELCMD < \rceil %DIFDELCMD < %%% +1, which improves, substantially, the bound proposed by Bondy;
Edit intent: coherence
Original:  ii) if D has girth g , the length of a shortest cycle, and circumference c, then \chi_A(D)\leq%DIFDELCMD < \lceil %%% \frac{c-1{g-1} }%DIFDELCMD < \rceil %DIFDELCMD < %%% +1, which improves, substantially, the bound proposed by Bondy;
Revised:  ii) if D has girth g , the length of a shortest cycle, and circumference c, then \chi_A(D)\leq%DIFDELCMD < \lceil %%% {g-1} }%DIFDELCMD < \rceil %DIFDELCMD < %%% +1, which improves, substantially, the bound proposed by Bondy;
Edit intent: coherence
Original: The impact of information dissemination on epidemic control essentially affects individual behaviors.
Revised: The impact of information dissemination on epidemic control is essentially subject to individual behaviors.
Edit intent: clarity
Original:  Among the information-driven behaviors, vaccination is determined by the cost-related factors, and the correlation with information dissemination is not clear yet .
Revised:  Unlike information-driven behaviors, vaccination is determined by the cost-related factors, and the correlation with information dissemination is not clear yet .
Edit intent: fluency
Original:  Among the information-driven behaviors, vaccination is determined by the cost-related factors, and the correlation with information dissemination is not clear yet .
Revised:  Among the information-driven behaviors, vaccination is determined by many cost-related factors, and the correlation with information dissemination is not clear yet .
Edit intent: clarity
Original:  Among the information-driven behaviors, vaccination is determined by the cost-related factors, and the correlation with information dissemination is not clear yet .
Revised:  Among the information-driven behaviors, vaccination is determined by the cost-related factors, whose correlation with the information dissemination should be better understood .
Edit intent: clarity
Original:  To this end, we present a model to integrate the information-epidemic spread process into an evolutionary vaccination game  in multiplex networks  , and explore how the spread of information on epidemic influences the vaccinationbehavior.
Revised:  To this end, we propose an evolutionary vaccination game in multiplex networks , and explore how the spread of information on epidemic influences the vaccinationbehavior.
Edit intent: clarity
Original:  To this end, we present a model to integrate the information-epidemic spread process into an evolutionary vaccination game  in multiplex networks  , and explore how the spread of information on epidemic influences the vaccinationbehavior.
Revised:  To this end, we present a model to integrate the information-epidemic spread process into an evolutionary vaccination game model in multiplex networks , and explore how the spread of information on epidemic influences the vaccinationbehavior.
Edit intent: clarity
Original:  We propose a two-layer coupled susceptible-alert-infected-susceptible (SAIS) model on a multiplex network , where the strength coefficient  is defined to characterize the tendency and intensity of information dissemination.
Revised:  We propose a two-layer coupled susceptible-alert-infected-susceptible (SAIS) model , where the strength coefficient is defined to characterize the tendency and intensity of information dissemination.
Edit intent: clarity
Original:  Specifically, when the vaccination cost is within a certain range, increasing information dissemination even leads to a decline in the equilibrium vaccination level .
Revised:  Specifically, increasing information dissemination even leads to a decline in the equilibrium vaccination level .
Edit intent: clarity
Original: The present paper provides a mathematical description of high-order moments of spiking activity in a recurrently-connected network of  Hawkes processes.
Revised: The present paper provides exact mathematical expressions for the high-order moments of spiking activity in a recurrently-connected network of Hawkes processes.
Edit intent: clarity
Original:  The proposed architecture relies exclusively on neural components, and is built upon a tree2tree autoencoder trained on abstract syntax trees, combined with a pretrained word embedding and a bi-directional multi-layer LSTM for NL processing .
Revised:  The proposed architecture relies exclusively on neural components, and is trained on abstract syntax trees, combined with a pretrained word embedding and a bi-directional multi-layer LSTM for NL processing .
Edit intent: clarity
Original:  The proposed architecture relies exclusively on neural components, and is built upon a tree2tree autoencoder trained on abstract syntax trees, combined with a pretrained word embedding and a bi-directional multi-layer LSTM for NL processing .
Revised:  The proposed architecture relies exclusively on neural components, and is built upon a tree2tree autoencoder trained on abstract syntax trees, combined with a pretrained word embedding and a bi-directional multi-layer LSTM for processing of word sequences .
Edit intent: clarity
Original:  The decoder features a doubly-recurrent LSTM with a novel signal propagation scheme and soft attention mechanism.
Revised:  The decoder features a doubly-recurrent LSTM , for which we propose novel signal propagation scheme and soft attention mechanism.
Edit intent: coherence
Original:  The decoder features a doubly-recurrent LSTM with a novel signal propagation scheme and soft attention mechanism.
Revised:  The decoder features a doubly-recurrent LSTM with a novel signal propagation schemes and soft attention mechanism.
Edit intent: fluency
Original:  In contrast to other methods, it does not involve any non-neural components to post-process the resulting programs, and uses a fixed-dimensional latent representation as the only link between the NL analyzer and  source code generator.
Revised:  In contrast to other methods, it does not require post-processing of the resulting programs, and uses a fixed-dimensional latent representation as the only link between the NL analyzer and source code generator.
Edit intent: clarity
Original:  In contrast to other methods, it does not involve any non-neural components to post-process the resulting programs, and uses a fixed-dimensional latent representation as the only link between the NL analyzer and  source code generator.
Revised:  In contrast to other methods, it does not involve any non-neural components to post-process the resulting programs, and uses a fixed-dimensional latent representation as the only interface between the NL analyzer and source code generator.
Edit intent: clarity
Original:  In contrast to other methods, it does not involve any non-neural components to post-process the resulting programs, and uses a fixed-dimensional latent representation as the only link between the NL analyzer and  source code generator.
Revised:  In contrast to other methods, it does not involve any non-neural components to post-process the resulting programs, and uses a fixed-dimensional latent representation as the only link between the NL analyzer and the source code generator.
Edit intent: fluency
Original: Principles of network theoretical topology have been widely studied in the human connectome.
Revised: Principles of network topology have been widely studied in the human connectome.
Edit intent: clarity
Original:  Of particular interest is the modular nature of the human brain, where the connectome is divided into subnetworks and subsequently studied to investigate changes with development, aging or disease .
Revised:  Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks and subsequently studied to investigate changes with development, aging or disease .
Edit intent: clarity
Original:  Of particular interest is the modular nature of the human brain, where the connectome is divided into subnetworks and subsequently studied to investigate changes with development, aging or disease .
Revised:  Of particular interest is the modular nature of the human brain, where the connectome is divided into subnetworks and subsequently changes with development, aging or disease .
Edit intent: clarity
Original:  Here, we present a new, weighted network measure called the Network Dependency Index (NDI), which can be used to identify an individual region's importance to the global functioning of the network.
Revised:  Here, we present a new, weighted network measure , the Network Dependency Index (NDI), which can be used to identify an individual region's importance to the global functioning of the network.
Edit intent: coherence
Original:  Here, we present a new, weighted network measure called the Network Dependency Index (NDI), which can be used to identify an individual region's importance to the global functioning of the network.
Revised:  Here, we present a new, weighted network measure called the Network Dependency Index (NDI), to identify an individual region's importance to the global functioning of the network.
Edit intent: clarity
Original:  Importantly, we utilize NDI to differentiate four different subnetworks (Tiers) in the human connectome following Gaussian Mixture Model fitting.
Revised:  Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian Mixture Model fitting.
Edit intent: clarity
Original:  The key novelty is that we propose to employ adversarial training to improve the generalization of a recurrent neural network model.
Revised:  The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model.
Edit intent: clarity
Original:  As such, normal training with stationary price-based features (e.g. the closing price) can easily overfit the data, being insufficient to obtain reliable models.
Revised:  As such, normal training with static price-based features (e.g. the closing price) can easily overfit the data, being insufficient to obtain reliable models.
Edit intent: clarity
Original:  As such, normal training with stationary price-based features (e.g. the closing price) can easily overfit the data, being insufficient to obtain reliable models.
Revised:  As such, normal training with stationary price-based features (e.g. the close price) can easily overfit the data, being insufficient to obtain reliable models.
Edit intent: fluency
Original:  To address this problem, we propose to add perturbations to simulate the stochasticity of continuous price variable, and train the model to work well under small yet intentional perturbations.
Revised:  To address this problem, we propose to add perturbations to simulate the stochasticity of price variable, and train the model to work well under small yet intentional perturbations.
Edit intent: clarity
Original: The analysis of neuroimaging data poses several strong challenges, in particular, due to its high dimensionality, its strong spatio-temporal correlation and the comparably small sample sizes of the respective datasets.
Revised: The analysis of neuroimaging data poses several challenges, due to the high dimensionality, its strong spatio-temporal correlation and the comparably small sample sizes of the respective datasets.
Edit intent: coherence
Original:  In order to maintain interpretability of the neuroimaging data, we adapt the layer-wise relevance propagation (LRP) method .
Revised:  In order to maintain interpretability of the layer-wise relevance propagation (LRP) method .
Edit intent: clarity
Original:  In order to maintain interpretability of the neuroimaging data, we adapt the layer-wise relevance propagation (LRP) method .
Revised:  In order to maintain interpretability of the neuroimaging data, we adapt the layer-wise relevance propagation (LRP) technique .
Edit intent: clarity
Original:  We demonstrate the versatility of DLight by applying it to a large fMRI dataset of the Human Connectome Project.
Revised:  We demonstrate the versatility of DeepLight, we apply it to a large fMRI dataset of the Human Connectome Project.
Edit intent: coherence
Original:  [1] recently presented an algorithm for constructing general confluent drawings, by leveraging power graph decomposition to generate an auxiliary routing graph.
Revised:  [1] recently presented an algorithm for constructing confluent drawings, by leveraging power graph decomposition to generate an auxiliary routing graph.
Edit intent: clarity
Original:  We identify two problems with their method and offer a single solution to solve both .
Revised:  We identify two issues with their method and offer a single solution to solve both .
Edit intent: fluency
Original:  Such  behavior is commonplace in social networks.
Revised:  Such a behavior is commonplace in social networks.
Edit intent: fluency
Original:  Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available  relate to the same graph.
Revised:  Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph.
Edit intent: fluency
Original:  We propose a novel generative model that explains a collection of distinct data naturally living on different graphs.
Revised:  We propose a novel generative model that represents a collection of distinct data naturally living on different graphs.
Edit intent: clarity
Original:  We propose a novel generative model that explains a collection of distinct data naturally living on different graphs.
Revised:  We propose a novel generative model that explains a collection of distinct data which naturally live on different graphs.
Edit intent: clarity
Original:  Experiments in both synthetic and real-world datasets demonstrate promising performance both in terms of data clustering , as well as multiple graph inference from mixture data .
Revised:  Experiments demonstrate promising performance both in terms of data clustering , as well as multiple graph inference from mixture data .
Edit intent: clarity
Original:  Our main results are that by training deep residual neural models  specifically for retrieval purposes  can yield significant gains when it is used to augment existing embeddings.
Revised:  Our main results are that by training deep residual neural models , specifically for retrieval purposes can yield significant gains when it is used to augment existing embeddings.
Edit intent: fluency
Original:  Our main results are that by training deep residual neural models  specifically for retrieval purposes  can yield significant gains when it is used to augment existing embeddings.
Revised:  Our main results are that by training deep residual neural models specifically for retrieval purposes , can yield significant gains when it is used to augment existing embeddings.
Edit intent: fluency
Original:  In this work  we investigate object compositionality as an inductive bias for Generative Adversarial Networks (GANs).
Revised:  In this work , we investigate object compositionality as an inductive bias for Generative Adversarial Networks (GANs).
Edit intent: fluency