wanyu/IteraTeR_v2 huggingface dataset:
Original:  This paper defines the notion of safe interruptibility as a distributed computing problem,\emph{ \textit{  and studies this notion in the two main learning frameworks: joint action learners and independent learners  .
Revised:  This paper defines the notion of safe interruptibility as a distributed computing problem,\emph{ \textit{ and studies this notion in the two main learning frameworks: joint action learners and independent learners and .
Edit intent: coherence
Original:  We show however that if agents can detect interruptions, it is possible to prune the observations to ensure  safe interruptibility even for independent learners 
Revised:  We show however that if agents can detect interruptions, it is possible to prune the observations to ensure safe interruptibility even for independent learners .
Edit intent: fluency
Original:  A high support of the favored and therefore expressed opinion in the social environment, is treated as a positive social feedback which reinforces the value associated to this opinion.
Revised:  High support of the favored and therefore expressed opinion in the social environment, is treated as a positive social feedback which reinforces the value associated to this opinion.
Edit intent: coherence
Original:  A high support of the favored and therefore expressed opinion in the social environment, is treated as a positive social feedback which reinforces the value associated to this opinion.
Revised:  A high support of the favored opinion in the social environment, is treated as a positive social feedback which reinforces the value associated to this opinion.
Edit intent: clarity
Original:  A high support of the favored and therefore expressed opinion in the social environment, is treated as a positive social feedback which reinforces the value associated to this opinion.
Revised:  A high support of the favored and therefore expressed opinion in the social environment, is treated as a positive feedback which reinforces the value associated to this opinion.
Edit intent: fluency
Original: This paper describes an intuitive generalization to the Generative Adversarial Networks (GANs) to generate samples while capturing diverse modes of the true data distribution .
Revised: We propose an intuitive generalization to the Generative Adversarial Networks (GANs) to generate samples while capturing diverse modes of the true data distribution .
Edit intent: clarity
Original:  Firstly, we propose a very simple and intuitive multi-agent GAN architecture that incorporates multiple generators capable of generating samples from high probability modes . Secondly, in order to enforce different generators to generate samples from diverse modes, we propose two extensions to the standard GAN objective function.
Revised:  Firstly, we propose a multi-agent GAN architecture that incorporates multiple generators capable of generating samples from high probability modes . Secondly, in order to enforce different generators to generate samples from diverse modes, we propose two extensions to the standard GAN objective function.
Edit intent: coherence
Original:  Firstly, we propose a very simple and intuitive multi-agent GAN architecture that incorporates multiple generators capable of generating samples from high probability modes . Secondly, in order to enforce different generators to generate samples from diverse modes, we propose two extensions to the standard GAN objective function.
Revised:  Firstly, we propose a very simple and intuitive multi-agent GAN architecture that incorporates multiple generators capable of generating samples from high probability modes . Secondly, to enforce different generators to generate samples from diverse modes, we propose two extensions to the standard GAN objective function.
Edit intent: coherence
Original:  Firstly, we propose a very simple and intuitive multi-agent GAN architecture that incorporates multiple generators capable of generating samples from high probability modes . Secondly, in order to enforce different generators to generate samples from diverse modes, we propose two extensions to the standard GAN objective function.
Revised:  Firstly, we propose a very simple and intuitive multi-agent GAN architecture that incorporates multiple generators capable of generating samples from high probability modes . Secondly, in order to enforce different generators to capture diverse high probability modes, we propose two extensions to the standard GAN objective function.
Edit intent: clarity
Original:  (2) We modify the discriminator objective function where along with finding the real and fake samples, the discriminator has to predict the generator which generated the given fake sample.
Revised:  (2) We modify the discriminator objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the fake sample.
Edit intent: clarity
Original:  Intuitively, in order to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes.
Revised:  Intuitively, to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes.
Edit intent: coherence
Original:  Experimentally we show that our framework is able to produce high quality diverse samples for the challenging tasks such as image/face generation and image-to-image translation .
Revised:  Experimentally we show that our framework is able to produce high quality diverse sample generations for the challenging tasks such as image/face generation and image-to-image translation .
Edit intent: clarity
Original:  Experimentally we show that our framework is able to produce high quality diverse samples for the challenging tasks such as image/face generation and image-to-image translation .
Revised:  Experimentally we show that our framework is able to produce high quality diverse samples for the challenging tasks such as image-to-image translation .
Edit intent: clarity
Original:  Secondly , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the  fake sample.
Revised:  Second , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the fake sample.
Edit intent: coherence
Original:  Secondly , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the  fake sample.
Revised:  Secondly , to enforce that different generators capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the fake sample.
Edit intent: fluency
Original:  Secondly , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the  fake sample.
Revised:  Secondly , to enforce different generators to capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, the discriminator has to identify the generator that generated the fake sample.
Edit intent: clarity
Original:  Secondly , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the  fake sample.
Revised:  Secondly , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, it is also required to identify the generator that generated the fake sample.
Edit intent: clarity
Original:  Secondly , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the  fake sample.
Revised:  Secondly , to enforce different generators to capture diverse high probability modes, we modify discriminator 's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the given fake sample.
Edit intent: clarity
Original:  Our framework (MAD-GAN) is generalizable in the sense that it can be easily combined with other existing variants of GANs to produce diverse samples.
Edit intent: coherence
Original:  We show high quality diverse sample generations for the challenging tasks such as image-to-image translation (known to learn delta distribution) and face generation.
Revised:  We show high quality diverse sample generations for challenging tasks such as image-to-image translation (known to learn delta distribution) and face generation.
Edit intent: clarity
Original:  In addition, we  show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging multi-view dataset (mixture of forests, icebergs, bedrooms etc ).
Revised:  In addition, we also show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging multi-view dataset (mixture of forests, icebergs, bedrooms etc ).
Edit intent: coherence
Original:  In addition, we  show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging multi-view dataset (mixture of forests, icebergs, bedrooms etc ).
Revised:  In addition, we show that MAD-GAN is able to disentangle different modalities when trained using highly challenging multi-view dataset (mixture of forests, icebergs, bedrooms etc ).
Edit intent: coherence
Original:  In addition, we  show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging multi-view dataset (mixture of forests, icebergs, bedrooms etc ).
Revised:  In addition, we show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, bedrooms etc ).
Edit intent: clarity
Original:  In addition, we  show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging multi-view dataset (mixture of forests, icebergs, bedrooms etc ).
Revised:  In addition, we show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging multi-view dataset (mixture of forests, icebergs, and bedrooms ).
Edit intent: clarity
Original:  In the end, we also show its efficacy for the unsupervised feature representation task.
Revised:  In the end, we show its efficacy for the unsupervised feature representation task.
Edit intent: clarity
Original:  In the end, we also show its efficacy for the unsupervised feature representation task.
Revised:  In the end, we also show its efficacy on the unsupervised feature representation task.
Edit intent: fluency
Original:  In the appendix we introduce a similarity based competing objective which encourages the different generators to generate varied samples judged by a user defined similarity metric.
Revised:  In Appendix, we introduce a similarity based competing objective which encourages the different generators to generate varied samples judged by a user defined similarity metric.
Edit intent: fluency
Original:  In the appendix we introduce a similarity based competing objective which encourages the different generators to generate varied samples judged by a user defined similarity metric.
Revised:  In the appendix we introduce a similarity based competing objective which encourages the different generators to generate diverse samples based on a user defined similarity metric.
Edit intent: clarity
Original: We consider the problem of duplicate detection  : given a large data set in which each entry has multiple attributes , detect which distinct entries refer to the same real world entity.
Revised: We consider the problem of duplicate detection : given a large data set in which each record has multiple entries (attributes) , detect which distinct entries refer to the same real world entity.
Edit intent: clarity
Original: We consider the problem of duplicate detection  : given a large data set in which each entry has multiple attributes , detect which distinct entries refer to the same real world entity.
Revised: We consider the problem of duplicate detection : given a large data set in which each entry has multiple attributes , detect which distinct records refer to the same real world entity.
Edit intent: clarity
Original:   Our method consists of three main steps: creating a similarity score between entries, grouping entries together into `unique entities' , and refining the groups.
Revised:  Our method consists of three main steps: creating a similarity score between records, grouping records together into "unique entities" , and refining the groups.
Edit intent: fluency
Original:  To develop a fully automated tracking algorithm , we developed a convolutional neural network  comprised of over 50 ,000 parameters, and employed deep learning to train the network on a diverse portfolio of video conditions.
Revised:  To develop a fully automated tracking method , we developed a convolutional neural network comprised of over 50 ,000 parameters, and employed deep learning to train the network on a diverse portfolio of video conditions.
Edit intent: clarity
Original:  To develop a fully automated tracking algorithm , we developed a convolutional neural network  comprised of over 50 ,000 parameters, and employed deep learning to train the network on a diverse portfolio of video conditions.
Revised:  To develop a fully automated tracking algorithm , we developed a convolutional neural network comprised of over 50 ,000 parameters, and employed machine learning techniques to train the network on a diverse portfolio of video conditions.
Edit intent: clarity
Original:  The neural network tracker , with no user-dependent input parameters, offered superior tracking performance, with exceptionally low lower false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.
Revised:  The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.
Edit intent: clarity
Original: Existing group mobility models were not designed to meet the requirements typical of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date information on the position of each node in the network, combined with a simple and flexible approach to  mobility modeling.
Revised: Existing group mobility models were not designed to meet the requirements for accurate simulation of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date information on the position of each node in the network, combined with a simple and flexible approach to mobility modeling.
Edit intent: clarity
Original: Existing group mobility models were not designed to meet the requirements typical of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date information on the position of each node in the network, combined with a simple and flexible approach to  mobility modeling.
Revised: Existing group mobility models were not designed to meet the requirements typical of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date informa- tion on the position of each node in the network, combined with a simple and flexible approach to mobility modeling.
Edit intent: fluency
Original: Existing group mobility models were not designed to meet the requirements typical of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date information on the position of each node in the network, combined with a simple and flexible approach to  mobility modeling.
Revised: Existing group mobility models were not designed to meet the requirements typical of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date information on the position of each node in the network, combined with a simple and flexible approach to group mobility modeling.
Edit intent: fluency
Original:  MoMo is capable of accurately describing all mobility scenarios, from individual mobility, in which nodes move independently one from the other, to tight group mobility, where mobility patterns of different nodes are strictly correlated.
Revised:  MoMo is capable of accurately describing all mobility scenarios, from individual mobility, in which nodes move inde- pendently one from the other, to tight group mobility, where mobility patterns of different nodes are strictly correlated.
Edit intent: fluency
Original:  A new set of intrinsic properties of a mobility model is proposed and adopted in the analysis and comparison of MoMo with existing models.
Revised:  A new set of intrinsic properties for a mobility model is proposed and adopted in the analysis and comparison of MoMo with existing models.
Edit intent: clarity
Original:   Results show that MoMo leads to accurate, robust and flexible modeling of mobility of groups of nodes  , making it suitable for the  wide range of network scenarios expected to characterize the deployment of 5G networks.
Revised:  Results show that MoMo leads to accurate, robust and flexible modeling of mobility of groups of nodes , making it suitable for the wide range of network scenarios expected to characterize 5G networks.
Edit intent: clarity
Original: A framework integrating information theory and network science is proposed, giving rise to a potentially new area of network information science .
Revised: A framework integrating information theory and network science is proposed, giving rise to a potentially new area .
Edit intent: clarity
Original:  Basically, an original network is supposed to be transmitted, with our without compaction, through a  time-series obtained by sampling its topology by some network dynamics, such as random walks.
Revised:  Basically, an original network is supposed to be transmitted, with or without compaction, through a time-series obtained by sampling its topology by some network dynamics, such as random walks.
Edit intent: fluency
Original:  The potential of the proposed approach is illustrated with respect to the efficiency of transmitting topologies of several network models by using a variety of random walks.
Revised:  The potential of the proposed approach is illustrated with respect to the efficiency of transmitting several types of topologies by using a variety of random walks.
Edit intent: clarity
Original:  Several interesting results are obtained, including the behavior of the BA model oscillating between high and low performance depending on the considered dynamics, and the distinct performances obtained for two analogous geographical models.
Revised:  Several interesting results are obtained, including the behavior of the BA model oscillating between high and low performance depending on the considered dynamics, and the distinct performances obtained for two geographical models.
Edit intent: clarity
Original: It is a long standing open problem whether Yao-Yao graphs YY_{k} are all spanners  . Bauer and Damian  bauer2013infinite showed that all YY_{6k} for k \geq 6 are spanners.
Revised: It is a long standing open problem whether Yao-Yao graphs YY_{k} are all spanners . Bauer and Damian ~ bauer2013infinite showed that all YY_{6k} for k \geq 6 are spanners.
Edit intent: fluency
Original:  Li and Zhan  li2016almost generalized their result and proved that all even Yao-Yao graphs YY_{2k} are spanners (for k\geq 42).
Revised:  Li and Zhan ~ li2016almost generalized their result and proved that all even Yao-Yao graphs YY_{2k} are spanners (for k\geq 42).
Edit intent: fluency
Original: It is a long standing open problem whether Yao-Yao graphs YY_{k} are all spanners ~\mbox{ li2002sparse . Bauer and Damian ~\mbox{ bauer2013infinite showed that all YY_{6k} for k \geq 6 are spanners.
Revised: It is a long standing open problem whether Yao-Yao graphs YY_{k} are all spanners li2002sparse . Bauer and Damian ~\mbox{ bauer2013infinite showed that all YY_{6k} for k \geq 6 are spanners.
Edit intent: fluency
Original: It is a long standing open problem whether Yao-Yao graphs YY_{k} are all spanners ~\mbox{ li2002sparse . Bauer and Damian ~\mbox{ bauer2013infinite showed that all YY_{6k} for k \geq 6 are spanners.
Revised: It is a long standing open problem whether Yao-Yao graphs YY_{k} are all spanners ~\mbox{ li2002sparse . Bauer and Damian bauer2013infinite showed that all YY_{6k} for k \geq 6 are spanners.
Edit intent: fluency
Original:  Structural identity has been studied in theory and practice over the past decades, but has only recently been addressed with techniques from representational learning .
Revised:  Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with techniques from representational learning .
Edit intent: clarity
Original:  Structural identity has been studied in theory and practice over the past decades, but has only recently been addressed with techniques from representational learning .
Revised:  Structural identity has been studied in theory and practice over the past decades, but has only recently been addressed with representational learning techniques .
Edit intent: clarity
Original:  This work presents struc2vec, a novel and flexible framework for learning latent representations of node's structural identity . struc2vec assesses structural similarity without using node or edge attributes, uses a hierarchy to measure  similarity at different scales, and constructs a multilayer graph to encode the structural similarities and generate structural context for nodes.
Revised:  This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes . struc2vec assesses structural similarity without using node or edge attributes, uses a hierarchy to measure similarity at different scales, and constructs a multilayer graph to encode the structural similarities and generate structural context for nodes.
Edit intent: clarity
Original:  This work presents struc2vec, a novel and flexible framework for learning latent representations of node's structural identity . struc2vec assesses structural similarity without using node or edge attributes, uses a hierarchy to measure  similarity at different scales, and constructs a multilayer graph to encode the structural similarities and generate structural context for nodes.
Revised:  This work presents struc2vec, a novel and flexible framework for learning latent representations of node's structural identity . struc2vec uses a hierarchy to measure similarity at different scales, and constructs a multilayer graph to encode the structural similarities and generate structural context for nodes.
Edit intent: clarity