wanyu/IteraTeR_v2 huggingface dataset:
Original:  We analytically demonstrate that, in the case when the recovery time \tau R is larger than the infection time \tau I , the infection flows on a one direction, i. e. from an infected node to its susceptible neighbor but, that neighbor can not reinfect that node during its infection time.
Revised:  We analytically demonstrate that, in the case when the recovery time \tau R is larger than the infection time I , the infection flows on a one direction, i. e. from an infected node to its susceptible neighbor but, that neighbor can not reinfect that node during its infection time.
Edit intent: fluency
Original:  This behavior leads us to deduce that,  when the infection rate \lambda is high enough  such that, each infected node on the network infects all  its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all nodes on the network become susceptible.
Revised:  This behavior leads us to deduce that, when the infection rate \lambda is high enough such that, each infected node on the network infects all its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all nodes on the network become susceptible.
Edit intent: fluency
Original:  This behavior leads us to deduce that,  when the infection rate \lambda is high enough  such that, each infected node on the network infects all  its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all nodes on the network become susceptible.
Revised:  This behavior leads us to deduce that, when the infection rate \lambda is high enough such that, any infected node on the network infects all its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all nodes on the network become susceptible.
Edit intent: clarity
Original:  This behavior leads us to deduce that,  when the infection rate \lambda is high enough  such that, each infected node on the network infects all  its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all nodes on the network become susceptible.
Revised:  This behavior leads us to deduce that, when the infection rate \lambda is high enough such that, each infected node on the network infects all of its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all nodes on the network become susceptible.
Edit intent: fluency
Original:  Moreover, we assert that, existing the loops on the network are necessary in this case in order to , the infection occurs repeatedly within the nodes on the network .
Revised:  Moreover, we assert that, in order to the infection occurs repeatedly within the nodes on the network .
Edit intent: coherence
Original:  In this case , unlike the other models such as SIS model and SIR model, SIRS model will have two critical thresholds separate the persistence phase from extinction phase.
Revised:  In this case , unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold separate the persistence phase from extinction phase.
Edit intent: clarity
Original:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Revised:  That means, for fixed values of I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Edit intent: fluency
Original:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Revised:  That means, for fixed values of \tau %DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Edit intent: clarity
Original:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Revised:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Edit intent: fluency
Original:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Revised:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Edit intent: fluency
Original:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Revised:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% 2, epidemic persists in between of those two points.
Edit intent: fluency
Original:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda 2, epidemic persists in between of those two points.
Revised:  That means, for fixed values of \tau I and%DIFDELCMD < {%%% \tau R there are two critical points of infection rate \lambda 1 and%DIFDELCMD < {%%% \lambda \lambda_1 and \lambda_2, where epidemic persists in between of those two points.
Edit intent: clarity
Original:  We also prove that, in between of these points, there is a value of \lambda%DIFDELCMD < } %%% in which the system settles down to stable state with highest infected nodes.
Revised:  We \lambda%DIFDELCMD < } %%% in which the system settles down to stable state with highest infected nodes.
Edit intent: coherence
Original:  We confirm these results numerically by a simulation of regular a one dimension system .
Revised:  We confirm these results numerically by the simulation of regular a one dimension system .
Edit intent: fluency
Original:  We confirm these results numerically by a simulation of regular a one dimension system .
Revised:  We confirm these results numerically by a simulation of regular a one dimensional system .
Edit intent: fluency
Original:  We analytically derive an expression for the probability of descendants nodes to reinfect their ancestors nodes.
Revised:  We derive an analytical expression of the probability for the descendants nodes to reinfect their ancestors nodes.
Edit intent: fluency
Original:  We analytically derive an expression for the probability of descendants nodes to reinfect their ancestors nodes.
Revised:  We analytically derive an expression for the probability of descendants nodes to re-infect their ancestors nodes.
Edit intent: fluency
Original:  We find that, in the case of the recovery time tau _R is larger than the infection time \tau_I, the infection will flow directionally from ancestors to descendants however,  descendants will not able to reinfect their ancestors during their infection time.
Revised:  We find that, in the case of the recovery time \tau _R is larger than the infection time \tau_I, the infection will flow directionally from ancestors to descendants however, descendants will not able to reinfect their ancestors during their infection time.
Edit intent: fluency
Original:  We find that, in the case of the recovery time tau _R is larger than the infection time \tau_I, the infection will flow directionally from ancestors to descendants however,  descendants will not able to reinfect their ancestors during their infection time.
Revised:  We find that, in the case of the recovery time tau _R is larger than the infection time \tau_I, the infection will flow directionally from the ancestors to the descendants however, descendants will not able to reinfect their ancestors during their infection time.
Edit intent: fluency
Original:  We find that, in the case of the recovery time tau _R is larger than the infection time \tau_I, the infection will flow directionally from ancestors to descendants however,  descendants will not able to reinfect their ancestors during their infection time.
Revised:  We find that, in the case of the recovery time tau _R is larger than the infection time \tau_I, the infection will flow directionally from ancestors to descendants however, the descendants will not able to reinfect their ancestors during their infection time.
Edit intent: coherence
Original:  This behavior leads us to deduce that, for this case and when the infection rate \lambda is high enough in such that, any infected node on the network infects all of its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all  nodes on the network become susceptible.
Revised:  This behavior leads us to deduce that, for this case and when the infection rate \lambda is high enough in such that, any infected node on the network infects all of its neighbors during its infection time, SIRS model on the network evolves to extinction state, where all the nodes on the network become susceptible.
Edit intent: fluency
Original:  Moreover, we assert that, in order to the infection occurs repeatedly within the nodes on the network, existing the loops on the network are necessary .
Revised:  Moreover, we assert that, in order to the infection occurs repeatedly inside the network, the loops on the network are necessary .
Edit intent: clarity
Original:  Therefore, for this case (when \tau_I<\tau_R) ,  unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold  separate the persistence phase from the extinction phase , which means, for fixed values of \tau_I and \tau_R there are a two critical points of infection rate \lambda_1 and \lambda_2, where epidemic persists in between of those two points.
Revised:  Therefore, for this case (when \tau_I<\tau_R) , unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold which separate the persistence phase from the extinction phase , which means, for fixed values of \tau_I and \tau_R there are a two critical points of infection rate \lambda_1 and \lambda_2, where epidemic persists in between of those two points.
Edit intent: clarity
Original:  Therefore, for this case (when \tau_I<\tau_R) ,  unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold  separate the persistence phase from the extinction phase , which means, for fixed values of \tau_I and \tau_R there are a two critical points of infection rate \lambda_1 and \lambda_2, where epidemic persists in between of those two points.
Revised:  Therefore, for this case (when \tau_I<\tau_R) , unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold separate the persistence phase from the extinction phase when \tau_I<\tau_R. That means, for fixed values of \tau_I and \tau_R there are a two critical points of infection rate \lambda_1 and \lambda_2, where epidemic persists in between of those two points.
Edit intent: coherence
Original:  Therefore, for this case (when \tau_I<\tau_R) ,  unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold  separate the persistence phase from the extinction phase , which means, for fixed values of \tau_I and \tau_R there are a two critical points of infection rate \lambda_1 and \lambda_2, where epidemic persists in between of those two points.
Revised:  Therefore, for this case (when \tau_I<\tau_R) , unlike the other models such as SIS model and SIR model, SIRS model has a two critical threshold separate the persistence phase from the extinction phase , which means, for fixed values of \tau_I and \tau_R there are a two critical points for infection rate \lambda_1 and \lambda_2, where epidemic persists in between of those two points.
Edit intent: fluency
Original: We propose a reduction for non-convex optimization that can (1) turn a stationary-point finding algorithm into a local-minimum finding one, and (2) replace the Hessian-vector product computations with only gradient computations.
Revised: We propose a reduction for non-convex optimization that can (1) turn an stationary-point finding algorithm into a local-minimum finding one, and (2) replace the Hessian-vector product computations with only gradient computations.
Edit intent: fluency
Original: We propose a reduction for non-convex optimization that can (1) turn a stationary-point finding algorithm into a local-minimum finding one, and (2) replace the Hessian-vector product computations with only gradient computations.
Revised: We propose a reduction for non-convex optimization that can (1) turn a stationary-point finding algorithm into an local-minimum finding one, and (2) replace the Hessian-vector product computations with only gradient computations.
Edit intent: fluency
Original:  It also converts SGD, GD, SCSG, and SVRG into local-minimum finding algorithms outperforming some best known results.
Revised:  It also converts SGD, GD, SCSG, and SVRG into algorithms finding approximate local minima, outperforming some best known results.
Edit intent: clarity
Original:  In this paper, we extend it to solve f(x)+h%DIFDELCMD < \square %%% l(Ax) with differentiable l^* and prove its convergence under a weak condition (i. e., under a large dual stepsize).
Revised:  In this paper, we %DIFDELCMD < \square %%% l(Ax) with differentiable l^* and prove its convergence under a weak condition (i. e., under a large dual stepsize).
Edit intent: coherence
Original:  In this paper, we extend it to solve f(x)+h%DIFDELCMD < \square %%% l(Ax) with differentiable l^* and prove its convergence under a weak condition (i. e., under a large dual stepsize).
Revised:  In this paper, we extend it to solve f(x)+h%DIFDELCMD < \square %%% prove its convergence under a weak condition (i. e., under a large dual stepsize).
Edit intent: coherence
Original:  In addition, we show that this condition is optimal and can not be weaken .
Revised:  In addition, we show that this condition is optimal and can not be weakened .
Edit intent: fluency
Original:  This result recovers the recent proposed positive-indefinite linearized augmented Lagrangian method.
Revised:  This result also recovers a recently proposed positive-indefinite linearized augmented Lagrangian method.
Edit intent: clarity
Original:  We show that EXact firsT-ordeR Algorithm (EXTRA) and Proximal Gradient-EXTRA ( PG-EXTRA ) can be consider as the primal-dual algorithm applied on a problem in the form of h%DIFDELCMD < \square %%% l(Ax).
Revised:  We show that EXact firsT-ordeR Algorithm (EXTRA) and Proximal Gradient-EXTRA ( PG-EXTRA %DIFDELCMD < \square %%% l(Ax).
Edit intent: coherence
Original:  Our approach enables a robot to learn and  adapt its gaze control strategy for human-robot interaction without the use of external sensors or human supervision.
Revised:  Our approach enables a robot to learn and to adapt its gaze control strategy for human-robot interaction without the use of external sensors or human supervision.
Edit intent: fluency
Original:  Our approach enables a robot to learn and  adapt its gaze control strategy for human-robot interaction without the use of external sensors or human supervision.
Revised:  Our approach enables a robot to learn and adapt its gaze control strategy for human-robot interaction neither with the use of external sensors or human supervision.
Edit intent: clarity
Original:  Our approach enables a robot to learn and  adapt its gaze control strategy for human-robot interaction without the use of external sensors or human supervision.
Revised:  Our approach enables a robot to learn and adapt its gaze control strategy for human-robot interaction without the use of external sensors nor with human supervision.
Edit intent: clarity
Original:  The robot learns to focus its attention on groups of people from its own audio-visual experiences, and independently of the number of people in the environment, their position and physical appearance .
Revised:  The robot learns to focus its attention onto groups of people from its own audio-visual experiences, and independently of the number of people in the environment, their position and physical appearance .
Edit intent: fluency
Original:  The robot learns to focus its attention on groups of people from its own audio-visual experiences, and independently of the number of people in the environment, their position and physical appearance .
Revised:  The robot learns to focus its attention on groups of people from its own audio-visual experiences, independently of the number of people in the environment, their position and physical appearance .
Edit intent: coherence
Original:  The robot learns to focus its attention on groups of people from its own audio-visual experiences, and independently of the number of people in the environment, their position and physical appearance .
Revised:  The robot learns to focus its attention on groups of people from its own audio-visual experiences, and independently of the number of people , of their positions and of their physical appearances .
Edit intent: clarity
Original:  In particular, we use recurrent neural networks and Q-learning to find an optimal action-selection policy , and we pretrain on a synthetic environment that simulates sound sources and moving participantsto avoid the need of  interacting with people for hours .
Revised:  In particular, we use a recurrent neural network architecture in combination with Q-learning to find an optimal action-selection policy , and we pretrain on a synthetic environment that simulates sound sources and moving participantsto avoid the need of interacting with people for hours .
Edit intent: clarity
Original:  In particular, we use recurrent neural networks and Q-learning to find an optimal action-selection policy , and we pretrain on a synthetic environment that simulates sound sources and moving participantsto avoid the need of  interacting with people for hours .
Revised:  In particular, we use recurrent neural networks and Q-learning to find an optimal action-selection policy ; we pre-train the network using a simulated environment that mimics realistic scenarios that involve speaking/silent participants, thus avoiding the need of interacting with people for hours .
Edit intent: clarity
Original:  In particular, we use recurrent neural networks and Q-learning to find an optimal action-selection policy , and we pretrain on a synthetic environment that simulates sound sources and moving participantsto avoid the need of  interacting with people for hours .
Revised:  In particular, we use recurrent neural networks and Q-learning to find an optimal action-selection policy , and we pretrain on a synthetic environment that simulates sound sources and moving participantsto avoid the need of interacting with people .
Edit intent: coherence
Original:  Our experimental evaluation suggests that the proposed method is robust in terms of parameters configuration ( i.e. the selection of the parameter values has not a decisive impact on the performance ) .
Revised:  Our experimental evaluation suggests that the proposed method is robust in terms of parameters configuration ( i.e. the parameter values yielded by the method do not have a decisive impact on the performance ) .
Edit intent: clarity
Original:  Our experimental evaluation suggests that the proposed method is robust in terms of parameters configuration ( i.e. the selection of the parameter values has not a decisive impact on the performance ) .
Revised:  Our experimental evaluation suggests that the proposed method is robust in terms of parameters configuration ( i.e. the selection of the parameter values has not a decisive impact on the performance .
Edit intent: fluency
Original:  Successful experiments on a real environment with the Nao robot indicate that our framework is a step forward towards the autonomous learning of a perceivable and socially acceptable gaze behavior.
Revised:  Successful experiments on a real environment with the Nao robot indicate that our framework is a step forward towards the autonomous learning of socially acceptable gaze behavior.
Edit intent: clarity
Original:  We consider cooperation of self-interested agents  and show that agents who have social inhibition can converge to a Nash stable partition (i.e., social agreement) using our proposed decentralised algorithm within polynomial time.
Revised:  We consider cooperation of self-interested agents , and show that agents who have social inhibition can converge to a Nash stable partition (i.e., social agreement) using our proposed decentralised algorithm within polynomial time.
Edit intent: fluency
Original:  We consider cooperation of self-interested agents  and show that agents who have social inhibition can converge to a Nash stable partition (i.e., social agreement) using our proposed decentralised algorithm within polynomial time.
Revised:  We consider cooperation of self-interested agents and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) using our proposed decentralised algorithm within polynomial time.
Edit intent: clarity
Original:  We consider cooperation of self-interested agents  and show that agents who have social inhibition can converge to a Nash stable partition (i.e., social agreement) using our proposed decentralised algorithm within polynomial time.
Revised:  We consider cooperation of self-interested agents and show that agents who have social inhibition can converge to a Nash stable partition (i.e., social agreement) within polynomial time.
Edit intent: clarity
Original:  The algorithm is simple and executable based on local interactions with neighbour agents under a strongly-connected communication network and even in asynchronous environments.
Revised:  The algorithm is simple and executable based on local interactions with neighbor agents under a strongly-connected communication network and even in asynchronous environments.
Edit intent: fluency
Original:  We analytically present a mathematical formulation for computing the lower bound of a converged solution's suboptimality and additionally show that 50\% of suboptimality can be minimally guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents.
Revised:  We analytically present a mathematical formulation for computing the lower bound of suboptimality of the solution, and additionally show that 50\% of suboptimality can be minimally guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents.
Edit intent: clarity
Original:  We analytically present a mathematical formulation for computing the lower bound of a converged solution's suboptimality and additionally show that 50\% of suboptimality can be minimally guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents.
Revised:  We analytically present a mathematical formulation for computing the lower bound of a converged solution's suboptimality and additionally show that 50\% of suboptimality can be at least guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents.
Edit intent: clarity
Original:  Through numerical experiments , it is confirmed that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation where some of the agents temporarily somehow do not operate during a mission .
Revised:  The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation where some of the agents temporarily somehow do not operate during a mission .
Edit intent: clarity