wanyu/IteraTeR_v2 huggingface dataset:
Original:  We introduce a novel messaging protocol, which we call adaptive diffusion, and show that under the snapshot adversarial model, it spreads the messages fast and achieves a perfect obfuscation of the source when the underlying contact network is an infinite regular tree.
Revised:  We introduce a novel messaging protocol, which we call adaptive diffusion, and show that under the snapshot adversarial model, it spreads the messages fast and achieves perfect obfuscation of the source when the underlying contact network is an infinite regular tree.
Edit intent: fluency
Original:  Under the timestamp-based adversary, we show that it achieves optimal obfuscation asymptotically in the degree of the underlying regular tree . Experiments on a sampled Facebook network demonstrate that adaptive diffusion effectively hides the location of the source under both adversarial models, even when the graph is finite, irregular and has cycles.
Revised:  Under the timestamp-based adversary, we show that it achieves optimal obfuscation asymptotically in the degree of the underlying regular tree . Experiments on a sampled Facebook network demonstrate that adaptive diffusion effectively hides the location of the source even when the graph is finite, irregular and has cycles.
Edit intent: clarity
Original: The theoretical study of social learning by observation typically assumes that each agent's action only affects her own payoff.
Revised: The theoretical study of social learning typically assumes that each agent's action only affects her own payoff.
Edit intent: clarity
Original: The theoretical study of social learning by observation typically assumes that each agent's action only affects her own payoff.
Revised: The theoretical study of social learning by observation typically assumes that each agent's action affects only her own payoff.
Edit intent: fluency
Original:  In this paper, I present a model in which agents' actions directly affect one another's payoff .
Revised:  In this paper, I present a model in which agents' actions directly affect the payoffs of other agents .
Edit intent: clarity
Original:  On a discrete time line, there is a community of finitely many agents in each period.
Revised:  On a discrete time line, there is a community containing a random number of agents in each period.
Edit intent: clarity
Original:  Agents in the same community then simultaneously take an action, and each agent's payoff is higher if her action matches the state , and also higher if more agents take that same action .
Revised:  An agent's payoff is higher if her action matches the state , and also higher if more agents take that same action .
Edit intent: clarity
Original:  Agents in the same community then simultaneously take an action, and each agent's payoff is higher if her action matches the state , and also higher if more agents take that same action .
Revised:  Agents in the same community then simultaneously take an action, and each agent's payoff is higher if her action matches the state or if more agents take that same action .
Edit intent: coherence
Original:  Agents in the same community then simultaneously take an action, and each agent's payoff is higher if her action matches the state , and also higher if more agents take that same action .
Revised:  Agents in the same community then simultaneously take an action, and each agent's payoff is higher if her action matches the state , and also higher if more agents take the same action as hers .
Edit intent: clarity
Original:  I show that in both casesnetwork externalities in payoff enhance social learning , in the sense that the highest probability of agents taking the correct action in equilibrium is significantly higher with large communities than with small communities.
Revised:  I show that in both casesnetwork externalities in payoff enhance social learning in the sense that the highest probability of agents taking the correct action in equilibrium is significantly higher with large communities than with small communities.
Edit intent: fluency
Original:  I show that in both casesnetwork externalities in payoff enhance social learning , in the sense that the highest probability of agents taking the correct action in equilibrium is significantly higher with large communities than with small communities.
Revised:  I show that in both casesnetwork externalities in payoff enhance social learning , in the sense that agents take the correct action in equilibrium is significantly higher with large communities than with small communities.
Edit intent: clarity
Original:  In particular, when the community size is sufficiently large , this probability reaches one (asymptotic learning) when private beliefs are unbounded, and can get arbitrarily close to one when private beliefsare unbounded .
Revised:  In particular, when the community size is greater than a threshold. In particular , this probability reaches one (asymptotic learning) when private beliefs are unbounded, and can get arbitrarily close to one when private beliefsare unbounded .
Edit intent: clarity
Original:  In particular, when the community size is sufficiently large , this probability reaches one (asymptotic learning) when private beliefs are unbounded, and can get arbitrarily close to one when private beliefsare unbounded .
Revised:  In particular, when the community size is sufficiently large , this probability reaches one (asymptotic learning) with unbounded private beliefs and can be arbitrarily close to one when private beliefsare unbounded .
Edit intent: clarity
Original:  In particular, when the community size is sufficiently large , this probability reaches one (asymptotic learning) when private beliefs are unbounded, and can get arbitrarily close to one when private beliefsare unbounded .
Revised:  In particular, when the community size is sufficiently large , this probability reaches one (asymptotic learning) when private beliefs are unbounded, and can get arbitrarily close to one with bounded private beliefs .
Edit intent: clarity
Original:  I find that in the selected equilibria, the community size has no effect on learning when observation is exogenous , facilitates learning when observation is endogenous and private beliefs are bounded, and may either help or hinder learning when observation is endogenous and private beliefs are bounded .
Revised:  I find that in the selected equilibria, the community size has no effect on learning under exogenous observation , facilitates learning when observation is endogenous and private beliefs are bounded, and may either help or hinder learning when observation is endogenous and private beliefs are bounded .
Edit intent: clarity
Original:  I find that in the selected equilibria, the community size has no effect on learning when observation is exogenous , facilitates learning when observation is endogenous and private beliefs are bounded, and may either help or hinder learning when observation is endogenous and private beliefs are bounded .
Revised:  I find that in the selected equilibria, the community size has no effect on learning when observation is exogenous , facilitates learning when observation is endogenous and either helps or hinders learning under endogenous observation and bounded private beliefs .
Edit intent: clarity
Original:  Results about probability of coverage, throughput or mean interference, have been provided for a wide variety of networks (cellular, ad hoc , cognitive, sensors, etc).
Revised:  Results about probability of coverage, throughput or mean interference, have been provided for a wide variety of networks (cellular, ad-hoc , cognitive, sensors, etc).
Edit intent: fluency
Original:  Nevertheless, all these works assume that the wireless network deployment is flat: nodes are placed on the Euclidean plane.
Revised:  Nevertheless, in their vast majority, these works assume that the wireless network deployment is flat: nodes are placed on the Euclidean plane.
Edit intent: clarity
Original:  We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties of the network and its kinetics.
Revised:  We give a description of the reduced network, its kinetics and conservations laws, and explore properties of the network and its kinetics.
Edit intent: clarity
Original:  We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties of the network and its kinetics.
Revised:  We give a description of the reduced reaction network, its kinetics and conservation laws, and explore properties of the network and its kinetics.
Edit intent: fluency
Original: The integration of incoming experimental data into coherent models of the brain is an increasing challenge of modern neuroscience.
Revised: The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience.
Edit intent: clarity
Original:  Nevertheless, structurally realistic network models of spiking neurons are necessarily under-constrained even if experimental data on brain connectivity are incorporated to the best of our knowledge.
Revised:  Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge.
Edit intent: fluency
Original:  We shape the phase space of a multi-scale network model of primate visual cortex by systematically refining its connectivity.
Revised:  We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity.
Edit intent: clarity
Original:  Only minimal adaptations of the structure are required, showing that the network operates close to an instability.
Revised:  Only small adaptations of the structure are required, showing that the network operates close to an instability.
Edit intent: clarity
Original:  In this paper, Bat Algorithm (BA) was applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN).
Revised:  In this paper, Bat Algorithm (BA) is applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN).
Edit intent: fluency
Original:  Initially the proposed method is tested against small artificial network without any noise and the efficiency was observed in term of number of iteration, number of population and BA optimization parameters.
Revised:  Initially the proposed method is tested against small artificial network without any noise and the efficiency is observed in term of number of iteration, number of population and BA optimization parameters.
Edit intent: fluency
Original:  The model was also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset.
Revised:  The model is also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset.
Edit intent: clarity
Original:  In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. Coli .
Revised:  In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. coli .
Edit intent: fluency
Original:  The results shown that it can able to identify the maximum  true positive regulation but also include some false positive regulations.
Revised:  The results prove that it can able to identify the maximum true positive regulation but also include some false positive regulations.
Edit intent: clarity
Original:  The results shown that it can able to identify the maximum  true positive regulation but also include some false positive regulations.
Revised:  The results shown that it can able to identify the maximum number of true positive regulation but also include some false positive regulations.
Edit intent: clarity
Original:  Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model 
Revised:  Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model .
Edit intent: fluency
Original: Multidimensional recurrent neural network (MDRNN) has shown a remarkable performance in  speech and handwriting recognition.
Revised: Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable performance in speech and handwriting recognition.
Edit intent: clarity
Original: Multidimensional recurrent neural network (MDRNN) has shown a remarkable performance in  speech and handwriting recognition.
Revised: Multidimensional recurrent neural network (MDRNN) has shown a remarkable performance in the area of speech and handwriting recognition.
Edit intent: clarity
Original:  The performance of  MDRNN is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by  Hessian-free (HF) optimization.
Revised:  The performance of an MDRNN is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by Hessian-free (HF) optimization.
Edit intent: fluency
Original:  The performance of  MDRNN is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by  Hessian-free (HF) optimization.
Revised:  The performance of MDRNN is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by using Hessian-free (HF) optimization.
Edit intent: coherence
Original:  Considering that connectionist temporal classification (CTC) is utilized as an objective of learning  MDRNN for sequence labelling , the non-convexity of CTC poses a problem to apply HF to the network.
Revised:  Given that connectionist temporal classification (CTC) is utilized as an objective of learning MDRNN for sequence labelling , the non-convexity of CTC poses a problem to apply HF to the network.
Edit intent: fluency
Original:  Considering that connectionist temporal classification (CTC) is utilized as an objective of learning  MDRNN for sequence labelling , the non-convexity of CTC poses a problem to apply HF to the network.
Revised:  Considering that connectionist temporal classification (CTC) is utilized as an objective of learning an MDRNN for sequence labelling , the non-convexity of CTC poses a problem to apply HF to the network.
Edit intent: clarity
Original:  Considering that connectionist temporal classification (CTC) is utilized as an objective of learning  MDRNN for sequence labelling , the non-convexity of CTC poses a problem to apply HF to the network.
Revised:  Considering that connectionist temporal classification (CTC) is utilized as an objective of learning MDRNN for sequence labeling , the non-convexity of CTC poses a problem to apply HF to the network.
Edit intent: fluency
Original:  Considering that connectionist temporal classification (CTC) is utilized as an objective of learning  MDRNN for sequence labelling , the non-convexity of CTC poses a problem to apply HF to the network.
Revised:  Considering that connectionist temporal classification (CTC) is utilized as an objective of learning MDRNN for sequence labelling , the non-convexity of CTC poses a problem when applying HF to the network.
Edit intent: coherence
Original:  As a solution to this , a convex approximation of CTC is formulated and its relationship with the EM algorithm and the Fisher information matrix is discussed.
Revised:  As a solution , a convex approximation of CTC is formulated and its relationship with the EM algorithm and the Fisher information matrix is discussed.
Edit intent: coherence
Original:   MDRNN up to the depth of 15 layers is successfully trained using HF, resulting in  improved performance for sequence labelling .
Revised:  An MDRNN up to the depth of 15 layers is successfully trained using HF, resulting in improved performance for sequence labelling .
Edit intent: fluency
Original:   MDRNN up to the depth of 15 layers is successfully trained using HF, resulting in  improved performance for sequence labelling .
Revised:  MDRNN up to a depth of 15 layers is successfully trained using HF, resulting in improved performance for sequence labelling .
Edit intent: fluency
Original:   MDRNN up to the depth of 15 layers is successfully trained using HF, resulting in  improved performance for sequence labelling .
Revised:  MDRNN up to the depth of 15 layers is successfully trained using HF, resulting in an improved performance for sequence labelling .
Edit intent: fluency
Original:   MDRNN up to the depth of 15 layers is successfully trained using HF, resulting in  improved performance for sequence labelling .
Revised:  MDRNN up to the depth of 15 layers is successfully trained using HF, resulting in improved performance for sequence labeling .
Edit intent: fluency
Original: Measuring and maximising the influence of individuals is the key to understand and leverage the multiplicative potential of viral spreading over social networks.
Revised: Optimizing the spread of influence in online social networks (OSNs) is important for the design of efficient viral marketing strategies using online recommendations. It is commonly believed that, spreading is a global process, whose optimization would require the knowledge of the key to understand and leverage the multiplicative potential of viral spreading over social networks.
Edit intent: coherence
Original: Optimizing the spread of influence in  online social networks (OSNs) is important for the design of efficient viral marketing strategies using online recommendations.
Revised: Measuring and optimizing the spread of influence in online social networks (OSNs) is important for the design of efficient viral marketing strategies using online recommendations.
Edit intent: clarity
Original:  By mapping the problem onto bond percolation  , we give a theoretical explanation for the presence of this short influence radius, and a framework to quantify individual's influence in real OSNs.
Revised:  By mapping the problem onto bond percolation , and a framework to quantify individual's influence in real OSNs.
Edit intent: clarity
Original: Measuring and optimizing the spread of influence in big-data online social networks are important for designing efficient strategies for false information surveillance and viral marketing .
Revised: Measuring and optimizing the influence of nodes in big-data online social networks are important for designing efficient strategies for false information surveillance and viral marketing .
Edit intent: clarity
Original: Measuring and optimizing the spread of influence in big-data online social networks are important for designing efficient strategies for false information surveillance and viral marketing .
Revised: Measuring and optimizing the spread of influence in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products .
Edit intent: clarity
Original:  As the viral spread of information on social network is a global process, it is commonly believed that measuring the influence of nodes and optimizing viral spreading would require the whole networkinformation.
Revised:  As the viral spreading on social network is a global process, it is commonly believed that measuring the influence of nodes and optimizing viral spreading would require the whole networkinformation.
Edit intent: clarity
Original:   Thus, without the need of information about the global networkstructure , any nodes' global influence on the entire network can be accurately measured based on this local characteristic infection size.
Revised:  Thus, we find that, without the knowledge of entire network , any nodes' global influence on the entire network can be accurately measured based on this local characteristic infection size.
Edit intent: clarity
Original:   Thus, without the need of information about the global networkstructure , any nodes' global influence on the entire network can be accurately measured based on this local characteristic infection size.
Revised:  Thus, without the need of information about the global networkstructure , any nodes' global influence can be accurately measured based on this local characteristic infection size.
Edit intent: clarity
Original:   Thus, without the need of information about the global networkstructure , any nodes' global influence on the entire network can be accurately measured based on this local characteristic infection size.
Revised:  Thus, without the need of information about the global networkstructure , any nodes' global influence on the entire network can be accurately measured using this characteristic number, which is independent of the network size.
Edit intent: clarity
Original:  This motivates an efficient algorithm with constant time complexity on the long standing problem of best seed spreaders selection, whose performance remarkably close to the true optimum.
Revised:  This motivates an efficient algorithm with constant time complexity on the long standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.
Edit intent: clarity
Original:  Employing percolation theory, we show that the spreading process displays a nucleation behavior: once a piece of information spread from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure ;
Revised:  Employing percolation theory, we show that the spreading process displays a nucleation behavior: once a piece of information spread from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure ,
Edit intent: fluency
Original:  The %DIFDELCMD < {\it %%% coverage measure \mu_t(A) of a testing array A is defined by the ratio between the number of distinct t-tuples contained in the column vectors of A and the total number of t-tuples.
Revised:  The %DIFDELCMD < {\it %%% coverage measure \mu_t(A) of a testing array A is defined by the ratio between the number of distinct t-tuples contained in the column vectors of A and the total number of t-tuples.
Edit intent: fluency
Original:  The construction here is a generalisation of the construction methods used by Chateauneuf, Colbourn , Kreher, Meagher and Stevens.
Revised:  The construction here is a generalisation of the construction methods used by Chateauneuf, Colbourn and Kreher, and Meagher and Stevens.
Edit intent: coherence
Original:  Applying ESPIRiT to a combined set of physical and virtual conjugate coils yields sensitivities with unknown joint phase but known relative phase, which allows the determination of the absolute phase up to a sign ambiguity .
Revised:  Applying ESPIRiT to a combined set of physical and virtual conjugate coils yields sensitivities with unknown joint phase but known relative phase, which allows the determination of the absolute phase of the image .
Edit intent: clarity
Original:  The accuracy of the computed maps with phase is experimentally evaluated using a test based on projection onto fully sampled coil images .
Revised:  The accuracy of the computed maps is directly validated using a test based on projection onto fully sampled coil images .
Edit intent: clarity
Original:  The use of multiple ESPIRiT maps in a phase-constrained parallel imaging reconstruction is compared to the use of a single map . Results: The proposed method can estimate accurate sensitivities which include low-resolution image phase.
Revised:  The use of multiple ESPIRiT maps in a phase-constrained parallel-imaging reconstructions . Results: The proposed method can estimate accurate sensitivities which include low-resolution image phase.
Edit intent: clarity