wanyu/IteraTeR_v2 huggingface dataset:
Original:  We argue that RNBRW provides an efficient and scalable alternative to LM . We give simulation results that suggest pre-weighting edges by the proposed methods can improve the performance of popular community detection algorithms substantially.
Revised:  We argue that RNBRW provides an efficient and scalable alternative to LM . We give simulation results that suggest pre-weighting edges through RNBRW may substantially improve the performance of popular community detection algorithms substantially.
Edit intent: clarity
Original:  We argue that RNBRW provides an efficient and scalable alternative to LM . We give simulation results that suggest pre-weighting edges by the proposed methods can improve the performance of popular community detection algorithms substantially.
Revised:  We argue that RNBRW provides an efficient and scalable alternative to LM . We give simulation results that suggest pre-weighting edges by the proposed methods can improve the performance of common community detection algorithms substantially.
Edit intent: clarity
Original:  We define  complexity and difficulty of a number sequence prediction task with the structure of the smallest automation that can generate the sequence.
Revised:  We define the complexity and difficulty of a number sequence prediction task with the structure of the smallest automation that can generate the sequence.
Edit intent: fluency
Original:  We define  complexity and difficulty of a number sequence prediction task with the structure of the smallest automation that can generate the sequence.
Revised:  We define complexity and difficulty of a number sequence prediction task with the structure of the smallest automaton that can generate the sequence.
Edit intent: fluency
Original:  The number-level problems format sequences as 2-dimensional grids of digits , and the digit-level problem provides a single digit input per a time step , hence solving this problem is equivalent to modeling a sequential state automation .
Revised:  The number-level problems format sequences as 2-dimensional grids of digits and the digit-level problem provides a single digit input per a time step , hence solving this problem is equivalent to modeling a sequential state automation .
Edit intent: fluency
Original:  The number-level problems format sequences as 2-dimensional grids of digits , and the digit-level problem provides a single digit input per a time step , hence solving this problem is equivalent to modeling a sequential state automation .
Revised:  The number-level problems format sequences as 2-dimensional grids of digits , and the digit-level problems provide a single digit input per a time step , hence solving this problem is equivalent to modeling a sequential state automation .
Edit intent: fluency
Original:  The number-level problems format sequences as 2-dimensional grids of digits , and the digit-level problem provides a single digit input per a time step , hence solving this problem is equivalent to modeling a sequential state automation .
Revised:  The number-level problems format sequences as 2-dimensional grids of digits , and the digit-level problem provides a single digit input per a time step .
Edit intent: coherence
Original:  The complexity of a number-level sequence problem can be defined with the depth of an equivalent combinatorial logic . Experimental results with CNN models suggest that they are capable of learning the compound operations of the number-level sequence generation rules  but the depths of the compound operations are limited.
Revised:  The complexity of a number-level sequence prediction can be defined with the depth of an equivalent combinatorial logic . Experimental results with CNN models suggest that they are capable of learning the compound operations of the number-level sequence generation rules but the depths of the compound operations are limited.
Edit intent: clarity
Original:  The complexity of a number-level sequence problem can be defined with the depth of an equivalent combinatorial logic . Experimental results with CNN models suggest that they are capable of learning the compound operations of the number-level sequence generation rules  but the depths of the compound operations are limited.
Revised:  The complexity of a number-level sequence problem can be defined with the depth of an equivalent combinatorial logic . Experimental results with CNN models suggest that they are capable of learning the compound operations of sequence generation rules but the depths of the compound operations are limited.
Edit intent: fluency
Original:  The complexity of a number-level sequence problem can be defined with the depth of an equivalent combinatorial logic . Experimental results with CNN models suggest that they are capable of learning the compound operations of the number-level sequence generation rules  but the depths of the compound operations are limited.
Revised:  The complexity of a number-level sequence problem can be defined with the depth of an equivalent combinatorial logic . Experimental results with CNN models suggest that they are capable of learning the compound operations of the number-level sequence generation rules , but the depths of the compound operations are limited.
Edit intent: fluency
Original:  For the digit-level problems,  GRU and LSTM models can solve the problems with complexity of finite state automations, but they cannot solve the problems with complexity of pushdown automations or Turing machines.
Revised:  For the digit-level problems, GRU and LSTM models can solve some problems with the complexity of finite state automations, but they cannot solve the problems with complexity of pushdown automations or Turing machines.
Edit intent: clarity
Original: We consider artificial neurons which will update their weight coefficients with  internal rule based on backpropagation, rather than using it as an external training procedure.
Revised: We consider artificial neurons which will update their weight coefficients with an internal rule based on backpropagation, rather than using it as an external training procedure.
Edit intent: fluency
Original:  For recurrent neural networks we also demonstrate how to include backpropagation through time into their formalism with the help of some stack memory for reference inputs and external data inputs of neurons.
Revised:  For recurrent neural networks we also demonstrate how to integrate backpropagation through time into their formalism with the help of some stack memory for reference inputs and external data inputs of neurons.
Edit intent: fluency
Original:  As a useful consequence, our approach enables us to introduce neural networks with the adjustment of synaptic connections, tied to the incorporated backpropagation.
Edit intent: coherence
Original:  Also, for widely used neural networks, such as long short-term memory, radial basis function networks, multilayer perceptrons and convolutional neural networks  we demonstrate their alternative description within the framework of our new formalism.
Revised:  Also, for widely used neural networks, such as long short-term memory, radial basis function networks, multilayer perceptrons and convolutional neural networks , we demonstrate their alternative description within the framework of our new formalism.
Edit intent: fluency
Original:  Contrary to previous studies, we allow for general consumption processes, without any a priori boundedness constraint.
Revised:  Contrary to prior studies, we allow for general consumption processes, without any a priori boundedness constraint.
Edit intent: fluency
Original:  Today, advanced convolution artificial neural networks can successfully solve such tasks and are commonly used to build complex decision making systems.
Revised:  Today, advanced convolutional artificial neural networks can successfully solve such tasks and are commonly used to build complex decision making systems.
Edit intent: clarity
Original:  when modeled as leaky integrate and fire with gradient decent learning algorithm it was shown to posses a wide variety of complex computational capabilities.
Revised:  when modeled as leaky integrate and fire with gradient decent learning algorithm it was shown to posses a variety of complex computational capabilities.
Edit intent: clarity
Original:  Here we improve its learning algorithm .
Revised:  Here we improve its implementation .
Edit intent: clarity
Original:  In this counting MNIST task the neuron exploits the improved algorithm and succeeds while out performing the previously introduced single neuron learning algorithm as well as conventional ConvNet architecture under similar conditions .
Revised:  In this counting MNIST task the neuron exploits the improved implementation and outperforms conventional ConvNet architecture under similar conditions .
Edit intent: clarity
Original:  In this counting MNIST task the neuron exploits the improved algorithm and succeeds while out performing the previously introduced single neuron learning algorithm as well as conventional ConvNet architecture under similar conditions .
Revised:  In this counting MNIST task the neuron exploits the improved algorithm and succeeds while out performing the previously introduced single neuron learning algorithm as well as conventional ConvNet architecture under similar condtions .
Edit intent: fluency
Original:  On a uniform mesh the simplest Pad\'e scheme for generating fourth-order  compact finite differences gives exactly the derivatives at the interior nodes needed to guarantee twice-continuous differentiability ;
Revised:  On a uniform mesh the simplest Pad\'e scheme for generating fourth-order compact finite differences gives exactly the derivatives at the interior nodes needed to guarantee twice-continuous differentiability ;
Edit intent: fluency
Original:  I argue that for  nonuniform meshes the compact  approach offers some potential advantages, and even for uniform meshes it offers a new way to treat the edge effects .
Revised:  I argue that for nonuniform meshes the compact approach offers some potential advantages, and even for uniform meshes offers a simple way to treat the edge effects .
Edit intent: clarity
Original:  I argue that for mildly nonuniform meshes the compact cubic approach offers some potential advantages, and even for uniform meshes offers a simple way to treat the edge conditions, relieving the user of the burden of deciding to use one of the three standard options: free (natural), complete (clamped), or `` not-a-knot '' conditions.
Revised:  I argue that for mildly nonuniform meshes the compact cubic approach offers some potential advantages, and even for uniform meshes offers a simple way to treat the edge conditions, relieving the user of the burden of deciding to use one of the three standard options: free (natural), complete (clamped), or " not-a-knot '' conditions.
Edit intent: fluency
Original:  I argue that for mildly nonuniform meshes the compact cubic approach offers some potential advantages, and even for uniform meshes offers a simple way to treat the edge conditions, relieving the user of the burden of deciding to use one of the three standard options: free (natural), complete (clamped), or `` not-a-knot '' conditions.
Revised:  I argue that for mildly nonuniform meshes the compact cubic approach offers some potential advantages, and even for uniform meshes offers a simple way to treat the edge conditions, relieving the user of the burden of deciding to use one of the three standard options: free (natural), complete (clamped), or `` not-a-knot " conditions.
Edit intent: fluency
Original:  Therefore, communication barely help , and could even impair the learning of multi-agent cooperation.
Revised:  Therefore, communication barely helps , and could even impair the learning of multi-agent cooperation.
Edit intent: fluency
Original:  When there is a large number of agents, agents hardly differentiate valuable information that helps cooperative decision making from globally shared information.
Revised:  When there is a large number of agents, agents cannot differentiate valuable information that helps cooperative decision making from globally shared information.
Edit intent: clarity
Original:  To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrates shared information for cooperative decision making.
Revised:  To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrate shared information for cooperative decision making.
Edit intent: fluency
Original:  Empirically, we show the strength of our model in various cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.
Revised:  Empirically, we show the strength of our model in a variety of cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.
Edit intent: fluency
Original: The ubiquitous complex networks are often composed of regular and irregular components, which makes uncovering the complexity of network structure into a fundamental challenge in network science . Exploring the regular information and identifying the roles of microscopic elements in network organization can help practitioners to recognize the universal principles of network formation and facilitate network data mining.
Revised: The ubiquitous complex networks are often composed of regular and irregular components, which makes uncovering the complexity of network structures to be a fundamental challenge in network science . Exploring the regular information and identifying the roles of microscopic elements in network organization can help practitioners to recognize the universal principles of network formation and facilitate network data mining.
Edit intent: clarity
Original: The ubiquitous complex networks are often composed of regular and irregular components, which makes uncovering the complexity of network structure into a fundamental challenge in network science . Exploring the regular information and identifying the roles of microscopic elements in network organization can help practitioners to recognize the universal principles of network formation and facilitate network data mining.
Revised: The ubiquitous complex networks are often composed of regular and irregular components, which makes uncovering the complexity of network structure into a fundamental challenge . Exploring the regular information and identifying the roles of microscopic elements in network organization can help practitioners to recognize the universal principles of network formation and facilitate network data mining.
Edit intent: clarity
Original:  With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model  in which the organization principles of networks are captured by representation matrix.
Revised:  With the realistic assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model in which the organization principles of networks are captured by representation matrix.
Edit intent: clarity
Original:  With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model  in which the organization principles of networks are captured by representation matrix.
Revised:  With the practical assumption that there are consistent features across the local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model in which the organization principles of networks are captured by representation matrix.
Edit intent: clarity
Original:  With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model  in which the organization principles of networks are captured by representation matrix.
Revised:  With the practical assumption that there has consistence between local structures of networks , we propose a low-rank pursuit based self-representation network model in which the organization principles of networks are captured by representation matrix.
Edit intent: clarity
Original:  With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model  in which the organization principles of networks are captured by representation matrix.
Revised:  With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model , in which the organization principles of networks are captured by representation matrix.
Edit intent: fluency
Original:  With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model  in which the organization principles of networks are captured by representation matrix.
Revised:  With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model in which the principle of network organization can be uncovered by a representation matrix.
Edit intent: clarity
Original:  According to the model, original  networks can be reconstructed based on observed structure.
Revised:  According to this model, original networks can be reconstructed based on observed structure.
Edit intent: coherence
Original:  What's more, the model enables us to estimate to what extent networks are regulable, in other words, measure the reconstructability of complex networks.
Revised:  What's more, the model enables us to estimate the extent to which the networks are regulable, in other words, measure the reconstructability of complex networks.
Edit intent: clarity
Original:  What's more, the model enables us to estimate to what extent networks are regulable, in other words, measure the reconstructability of complex networks.
Revised:  What's more, the model enables us to estimate to what extent networks are regulable, i.e., measuring the reconstructability of complex networks.
Edit intent: coherence
Original:  What's more, the model enables us to estimate to what extent networks are regulable, in other words, measure the reconstructability of complex networks.
Revised:  What's more, the model enables us to estimate to what extent networks are regulable, in other words, measure the reconstructability of networks.
Edit intent: clarity
Original:  In addition, the model enables us to measure the importance of  network links for network regularity thereby allowing us to regulate the reconstructability of networks .
Revised:  In addition, the model is capable of measuring the importance of network links for network regularity thereby allowing us to regulate the reconstructability of networks .
Edit intent: clarity
Original:  In addition, the model enables us to measure the importance of  network links for network regularity thereby allowing us to regulate the reconstructability of networks .
Revised:  In addition, the model enables us to measure the importance of network regularity thereby allowing us to regulate the reconstructability of networks .
Edit intent: clarity
Original:  The extensive experiments on disparate  networks demonstrate the effectiveness of the proposed algorithm and measure . Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms .
Revised:  The extensive experiments on disparate real-world networks demonstrate the effectiveness of the proposed algorithm and measure . Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms .
Edit intent: clarity
Original:  The extensive experiments on disparate  networks demonstrate the effectiveness of the proposed algorithm and measure . Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms .
Revised:  The extensive experiments on disparate networks demonstrate the effectiveness of the proposed network reconstruction and regulation algorithm . Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms .
Edit intent: clarity
Original:  The extensive experiments on disparate  networks demonstrate the effectiveness of the proposed algorithm and measure . Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms .
Revised:  The extensive experiments on disparate networks demonstrate the effectiveness of the proposed algorithm and measure . Specifically, the network regularity metric can reflect the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms .
Edit intent: clarity
Original:  The extensive experiments on disparate  networks demonstrate the effectiveness of the proposed algorithm and measure . Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms .
Revised:  The extensive experiments on disparate networks demonstrate the effectiveness of the proposed algorithm and measure . Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be improved by removing irregular network links independent of specific algorithms .
Edit intent: clarity
Original:  It is generally assumed that regulatory mechanisms must be in place to enforce some asymmetry at cell division and, therefore, at the single cell level.
Edit intent: coherence
Original:  Within a mathematical model, we show the possibility of unimodal or bimodal distributions depending on the variance of partition errors and the growth rate tolerance around the optimal copy number.
Revised:  We show that the existence of unimodal or bimodal distributions depending on the variance of partition errors and the growth rate tolerance around the optimal copy number.
Edit intent: clarity
Original:  Within a mathematical model, we show the possibility of unimodal or bimodal distributions depending on the variance of partition errors and the growth rate tolerance around the optimal copy number.
Revised:  Within a mathematical model, we show the possibility of unimodal or bimodal distributions depends on the variance of partition errors and the growth rate tolerance around the optimal copy number.
Edit intent: fluency
Original:  Our theory provides a simple explanation for the emergence of aneuploid states in a population  .
Revised:  In particular, our theory provides a simple explanation for the emergence of aneuploid states in a population .
Edit intent: coherence
Original:  Our theory provides a simple explanation for the emergence of aneuploid states in a population  .
Revised:  Our theory provides a consistent explanation for the emergence of aneuploid states in a population .
Edit intent: clarity