wanyu/IteraTeR_v2 huggingface dataset:
Original:  Numerical sensitivity analysis suggests that relatively low states of endemic addiction can also be obtained by focusing primarily on medical prevention , followed by aggressive treatment of remaining cases  even when the probability of relapse  remains high.
Revised:  Numerical sensitivity analysis suggests that relatively low states of endemic addiction can also be obtained by focusing primarily on medical prevention , followed by aggressive treatment of remaining cases even when the probability of relapse from treatment remains high.
Edit intent: clarity
Original:  Further empirical study focused on understanding the rate of illicit drug dependence verses overdose risk, along with the current and changing rates of opioid prescription and treatment, would shed significant light on optimal control efforts and feasible outcomes for this epidemic and drug epidemics in general.
Revised:  Further empirical study focused on understanding the rate of illicit drug dependence versus overdose risk, along with the current and changing rates of opioid prescription and treatment, would shed significant light on optimal control efforts and feasible outcomes for this epidemic and drug epidemics in general.
Edit intent: fluency
Original: We address single machine problems with optional job rejection, studied lately in Zhang et al. (2010) and Cao et al. (2006) .
Revised: We address single machine problems with optional jobs - rejection, studied lately in Zhang et al. (2010) and Cao et al. (2006) .
Edit intent: fluency
Original: We address single machine problems with optional job rejection, studied lately in Zhang et al. (2010) and Cao et al. (2006) .
Revised: We address single machine problems with optional job rejection, studied recently in Zhang et al. (2010) and Cao et al. (2006) .
Edit intent: fluency
Original:  The authors prove that the considered problems are ordinary NP hard and provide pseudo polynomial time Dynamic Programming (DP) solutions.
Revised:  They also prove that the considered problems are ordinary NP hard and provide pseudo polynomial time Dynamic Programming (DP) solutions.
Edit intent: clarity
Original:  The authors prove that the considered problems are ordinary NP hard and provide pseudo polynomial time Dynamic Programming (DP) solutions.
Revised:  The authors prove that the considered problems are ordinary NP-hard and provide pseudo-polynomial-time Dynamic Programming (DP) solutions.
Edit intent: clarity
Original:  In this paper, we focus on three of these problems: makespan with release dates ;
Revised:  In this paper, we focus on three of these problems: makespan with release-dates ;
Edit intent: fluency
Original:  Moreover, the extensive numerical study we executed proves that all updated DP algorithms are extremely efficient, even for large size problem instances .
Revised:  Moreover, we provide extensive numerical studies verifying their efficiency .
Edit intent: clarity
Original:  We write a cost function as a measurement error and a model error term .
Revised:  We write a cost function in terms of a measurement and a model error term .
Edit intent: clarity
Original:  We write a cost function as a measurement error and a model error term .
Revised:  We write a cost function as a measurement error and a model error .
Edit intent: clarity
Original:  The design for simulated experiments presented in this paper might apply in an experimental laboratory, to estimate properties of extremely small biological circuits.
Revised:  The design of simulated experiments presented in this paper might apply in an experimental laboratory, to estimate properties of extremely small biological circuits.
Edit intent: fluency
Original:  An iterative reweighting of these terms permits a systematic method to identify the lowest-minimum, within a specific region of state-and-parameter space, on the surface of a non-convex cost function.
Revised:  An iterative reweighting of these terms permits a systematic method to identify the lowest-minimum, within a local region of state-and-parameter space, on the surface of a non-convex cost function.
Edit intent: clarity
Original:  Finally, we show how this method can prune a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality required to explain the available experimental measurements.
Revised:  Finally, we show how this method can prune a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality required to explain the available measurements.
Edit intent: clarity
Original:  The input to the network and resulting network output are: time series of stimulating electrical current delivered to each constituent neuron, and time series of membrane voltage of each neuron, respectively.
Revised:  The input to the network and resulting network output are: stimulating electrical current delivered to each constituent neuron, and time series of membrane voltage of each neuron, respectively.
Edit intent: fluency
Original:  The input to the network and resulting network output are: time series of stimulating electrical current delivered to each constituent neuron, and time series of membrane voltage of each neuron, respectively.
Revised:  The input to the network and resulting network output are: time series of stimulating electrical current delivered to each constituent neuron, and membrane voltage of each neuron, respectively.
Edit intent: fluency
Original:  We use the estimates corresponding to the lowest minimum of the cost function to predict the particular mode of activity that the network is expressing , where each mode is known to be associated with a particular set of parameter values .
Revised:  We use the estimates corresponding to the lowest minimum of the cost function to predict the particular mode of activity that the network is expressing .
Edit intent: clarity
Original:  Finally, we show how this method can prune a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality required to explain the available measurements.
Revised:  Finally, we show how this method can reduce a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality required to explain the available measurements.
Edit intent: clarity
Original:  Finally, we show how this method can prune a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality required to explain the available measurements.
Revised:  Finally, we show how this method can prune a model of unnecessarily high complexity to a representation that contains the maximum dimensionality required to explain the available measurements.
Edit intent: coherence
Original:  Finally, we show how this method can prune a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality required to explain the available measurements.
Revised:  Finally, we show how this method can prune a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality contained in the available measurements.
Edit intent: clarity
Original: We employ an optimization procedure to estimate parameters of a small model biological neuronal network, which assumes multiple modes of activity, depending on parameter values .
Revised: A method of data assimilation (DA) is employed to estimate electrophysiological parameters of neurons simultaneously with their synaptic connectivity in a toy biological network .
Edit intent: clarity
Original:  The input to the network and resulting network output are: stimulating electrical current delivered to each constituent neuron, and membrane voltage of each neuron, respectively.
Revised:  The input to the network and resulting network output are: electrical current delivered to each constituent neuron, and membrane voltage of each neuron, respectively.
Edit intent: clarity
Original:  We write a cost function in terms of a measurement and a model error  .
Revised:  We write a cost function in terms of a measurement and a model error term .
Edit intent: clarity
Original:  An iterative reweighting of these terms permits a systematic method to identify the lowest-minimum , within a local region of state-and-parameter space, on the surface of a non-convex cost function.
Revised:  An iterative reweighting of these terms permits a systematic method to identify the lowest minimum , within a local region of state-and-parameter space, on the surface of a non-convex cost function.
Edit intent: fluency
Original:  We use the estimates corresponding to the lowest minimum of the cost function to predict the particular mode of activity that the network is expressing.
Revised:  The estimates corresponding to the lowest minimum of the cost function to predict the particular mode of activity that the network is expressing.
Edit intent: coherence
Original:  Finally, we show how this method can reduce a model of unnecessarily high complexity  to a representation that contains the maximum dimensionality contained in the available measurements.
Revised:  Finally, we show how this method can reduce a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality contained in the available measurements.
Edit intent: clarity
Original:  Finally, we show how this method can reduce a model of unnecessarily high complexity  to a representation that contains the maximum dimensionality contained in the available measurements.
Revised:  Finally, we show how this method can reduce a model of unnecessarily high complexity to a representation that contains the maximum dimensionality required to reproduce the available measurements.
Edit intent: clarity
Original:  The design of simulated experiments presented in this paper might apply in an experimental laboratory, to estimate properties of extremely small biological circuits .
Revised:  The design of simulated experiments presented in this paper might apply in an experimental laboratory, to estimate properties of extremely small functional circuits in a simulated scenario .
Edit intent: clarity
Original: A method of data assimilation (DA) is employed to estimate electrophysiological parameters of neurons simultaneously with their synaptic connectivity in a toy biological network.
Revised: A method of data assimilation (DA) is employed to estimate electrophysiological parameters of neurons simultaneously with their synaptic connectivity in a small model biological network.
Edit intent: clarity
Original:  An iterative reweighting of these terms permits a systematic method to identify the lowest minimum, within a local region of state-and-parameter space, on the surface of a non-convex cost function.
Revised:  An iterative reweighting of these terms permits a systematic method to identify the lowest minimum, within a local region of state space, on the surface of a non-convex cost function.
Edit intent: clarity
Original:  The pruning consists of identifying synaptic connections or membrane ion channels whose maximum conductances should be set to zero .
Revised:  The utility of DA for exploring the functionality of real biological networks is discussed .
Edit intent: clarity
Original:  This paper offers a proof-of-concept that DA can tackle the problem of estimating properties of extremely small functional circuits in a simulated scenario .
Revised:  This paper offers a proof-of-concept that DA has the potential to estimate properties in extremely small and isolatable functional circuits .
Edit intent: clarity
Original:  The DA procedure is cast as an optimization, where the cost function consists of both a measurement  and a model error term.
Revised:  The DA procedure is cast as an optimization, with a cost function consisting of both a measurement and a model error term.
Edit intent: coherence
Original:  The DA procedure is cast as an optimization, where the cost function consists of both a measurement  and a model error term.
Revised:  The DA procedure is cast as an optimization, where the cost function consists of both a measurement error and a model error term.
Edit intent: fluency
Original:  Two sets of parameter values are associated with two particular functional modes of network activity: simultaneous firing of all neurons, and a pattern-generating mode wherein the neurons burst in sequence.
Revised:  In the model, two sets of parameter values are associated with two particular functional modes of network activity: simultaneous firing of all neurons, and a pattern-generating mode wherein the neurons burst in sequence.
Edit intent: clarity
Original:  Further, this method is able to prune a model of unnecessarily high complexity down to a representation that contains the maximum dimensionality required to reproduce the available measurements.
Revised:  Further, this method is able to prune a model of unnecessarily high dimensionality to a representation that contains the maximum dimensionality required to reproduce the available measurements.
Edit intent: clarity
Original:  This paper offers a proof-of-concept that DA has the potential to estimate properties in extremely small and isolatable functional circuits.
Revised:  This paper offers a proof-of-concept that DA has the potential to inform laboratory designs for estimating properties in small and isolatable functional circuits.
Edit intent: clarity
Original:  Moreover, it also can be considered as an approximation of general nonconcave robust stochastic optimization problems.
Edit intent: coherence
Original: Antibodies, an essential part of our immune system, develop in an intricate process to guarantee a broad diversity of antibodies that are able to bind a continually diversifying array of pathogens.
Revised: Antibodies, an essential part of our immune system, develop through an intricate process to guarantee a broad diversity of antibodies that are able to bind a continually diversifying array of pathogens.
Edit intent: clarity
Original: Antibodies, an essential part of our immune system, develop in an intricate process to guarantee a broad diversity of antibodies that are able to bind a continually diversifying array of pathogens.
Revised: Antibodies, an essential part of our immune system, develop in an intricate process to bind a wide array of pathogens.
Edit intent: clarity
Original:  This process involves randomly mutating the DNA sequences that encode antibodies to find variants with improved binding .
Revised:  This process involves randomly mutating DNA sequences encoding these antibodies to find variants with improved binding .
Edit intent: clarity
Original:  The application of proportional hazards to analyses of mutation processes is, to our knowledge, novel and formalizes the current methods in a statistical framework that can be easily extended to analyze the effect of other biological features on mutation rates.
Revised:  The application of proportional hazards to mutation processes is, to our knowledge, novel and formalizes the current methods in a statistical framework that can be easily extended to analyze the effect of other biological features on mutation rates.
Edit intent: clarity
Original: Recent advances in language modeling such as word2vec motivate a number of graph embedding approaches by treating random walk sequences as sentences to encode structural proximity in a graph . However, most of the existing principles of neural graph embedding do not incorporate auxiliary information such as node content flexibly.
Revised: Recent advances in language modeling such as word2vec motivate a number of graph embedding approaches by treating random walk sequences as sentences to encode structural proximity in a graph . However, most of the existing principles of network embedding do not incorporate auxiliary information such as node content flexibly.
Edit intent: clarity
Original:  In this paper  we take a matrix factorization perspective of graph embeddingwhich generalizes to structural embedding as well as content embedding in a natural way .
Revised:  In this paper , we take a matrix factorization perspective of graph embeddingwhich generalizes to structural embedding as well as content embedding in a natural way .
Edit intent: fluency
Original:  In this paper  we take a matrix factorization perspective of graph embeddingwhich generalizes to structural embedding as well as content embedding in a natural way .
Revised:  In this paper we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously .
Edit intent: coherence
Original:  For structure embedding , we validate that the matrix we construct and factorize preserves the high-order proximities of the graph . Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding .
Revised:  For structure , we validate that the matrix we construct and factorize preserves the high-order proximities of the graph . Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding .
Edit intent: clarity
Original:  For structure embedding , we validate that the matrix we construct and factorize preserves the high-order proximities of the graph . Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding .
Revised:  For structure embedding , we validate that the matrix we construct preserves high-order proximities of the graph . Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding .
Edit intent: coherence
Original:  For structure embedding , we validate that the matrix we construct and factorize preserves the high-order proximities of the graph . Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding .
Revised:  For structure embedding , we validate that the matrix we construct and factorize preserves the high-order proximities of the network . Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding .
Edit intent: clarity
Original:  In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the graph in a matrix factorization framework.
Revised:  In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the network in a matrix factorization framework.
Edit intent: clarity
Original:  As a consequence, graph embedding can be learned in a unified framework integrating graph structure and node content as well as label information simultaneously.
Revised:  As a consequence, network embedding can be learned in a unified framework integrating graph structure and node content as well as label information simultaneously.
Edit intent: clarity
Original:  As a consequence, graph embedding can be learned in a unified framework integrating graph structure and node content as well as label information simultaneously.
Revised:  As a consequence, graph embedding can be learned in a unified framework integrating network structure and node content as well as label information simultaneously.
Edit intent: clarity
Original: A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs)  L^{\beta}u = W, where W is Gaussian white noise, L is a second-order differential operator, and \beta>0 is a parameter that determines the smoothness of u.
Revised: A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form L^{\beta}u = W, where W is Gaussian white noise, L is a second-order differential operator, and \beta>0 is a parameter that determines the smoothness of u.
Edit intent: clarity
Original:  We demonstrate how this restriction can be avoided by combining a finite element discretization in space with a rational approximation of the function x^{-\beta} to approximate the solution u.
Revised:  We demonstrate how this restriction can be avoided by combining a finite element discretization in space with a rational approximation of the function x^{-\beta} to approximate u.
Edit intent: clarity
Original:  However, this approach has been limited to the case 2\beta\inN, which excludes several important covariance models and makes it necessary to keep \beta fixed during inference.
Revised:  However, this approach has been limited to the case 2\beta\inN, which excludes several important models and makes it necessary to keep \beta fixed during inference.
Edit intent: clarity
Original:  We introduce a new method, the rational SPDE approach, which  is applicable for any \beta> 0 and therefore remedies the mentioned limitation.
Revised:  We propose a new method, the rational SPDE approach, which is applicable for any \beta> 0 and therefore remedies the mentioned limitation.
Edit intent: clarity
Original:  The presented scheme combines a finite element discretization in space with a rational approximation of the function x^{-\beta} to approximate u.
Revised:  The presented scheme combines a finite element discretization with a rational approximation of the function x^{-\beta} to approximate u.
Edit intent: clarity
Original:  For the resulting approximation, an explicit rate of strong convergence to u is derived and we show that the method has the same computational benefits as in the restricted case 2\beta\inN when used for statistical inference and prediction .
Revised:  For the resulting approximation, an explicit rate of convergence to u is derived and we show that the method has the same computational benefits as in the restricted case 2\beta\inN when used for statistical inference and prediction .
Edit intent: clarity
Original:  For the resulting approximation, an explicit rate of strong convergence to u is derived and we show that the method has the same computational benefits as in the restricted case 2\beta\inN when used for statistical inference and prediction .
Revised:  For the resulting approximation, an explicit rate of strong convergence to u is derived and we show that our method has the same computational benefits as in the restricted case 2\beta\inN when used for statistical inference and prediction .
Edit intent: clarity
Original:  For the resulting approximation, an explicit rate of strong convergence to u is derived and we show that the method has the same computational benefits as in the restricted case 2\beta\inN when used for statistical inference and prediction .
Revised:  For the resulting approximation, an explicit rate of strong convergence to u is derived and we show that the method has the same computational benefits as in the restricted case 2\beta\inN .
Edit intent: clarity
Original:  Several numerical experiments are performed to illustrate the accuracy of the method, and to show how it can be used for likelihood-based inference for all model parameters including \beta.
Revised:  Several numerical experiments are performed to illustrate the accuracy of the method, and to show that it facilitates likelihood-based inference for all model parameters including \beta.
Edit intent: clarity
Original:  Data Augmentation \mbox{ krizhevsky2012imagenet alleviates this by using existing data more effectively.
Revised:  Data Augmentation alleviates this by using existing data more effectively.
Edit intent: coherence
Original: The amplification cycle of many replicators (natural or artificial) involves the usage of a host compartment, inside  which the replicator express phenotypic compounds necessary to carry out its genetic replication.
Revised: The amplification cycle of many replicators (natural or artificial) involves the usage of a host compartment, inside of which the replicator express phenotypic compounds necessary to carry out its genetic replication.
Edit intent: clarity