wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Revised:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Edit intent: clarity
Original:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Revised:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data sets . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Edit intent: clarity
Original:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Revised:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Edit intent: clarity
Original:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Revised:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to determine nominal parameter values specific to each rat ;
Edit intent: clarity
Original:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ;
Revised:  In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values ;
Edit intent: clarity
Original:  and (4) both frequentist and Bayesian UQ methods to assess the predictive capability of the model  .
Revised:  and (4) both frequentist and Bayesian UQ methods to assess the predictive capability of the model . Our results show that it is possible to determine 5 identifiable model parameters that can be estimated to our experimental data from three rats, and that computed UQ intervals capture the measurement and model error .
Edit intent: meaning-changed
Original: Consider a countably infinite collection of coupled queuesrepresenting a large wireless network with a queue  at each point of the d-dimensional integer grid . These queues have independent Poisson arrivals, but are coupled through their service rateswhich is the signal to interference ratio of wireless network theory.
Revised: Consider a countably infinite collection of coupled queuesrepresenting a large wireless network with a queue at each point of the d-dimensional integer grid , having independent Poisson arrivals, but are coupled through their service rateswhich is the signal to interference ratio of wireless network theory.
Edit intent: clarity
Original:  These queues have independent Poisson arrivals, but are coupled through their service rateswhich is the signal to interference ratio of wireless network theory. More precisely, the service discipline is translation invariant and of the processor sharing type,with the service rate in each queue slowed down, when the neighboring queues have a larger workload.
Revised:  These queues have independent Poisson arrivals, but dependent service rates. The service discipline is translation invariant and of the processor sharing type,with the service rate in each queue slowed down, when the neighboring queues have a larger workload.
Edit intent: clarity
Original:  The dynamics is infinite dimensional Markov, with each queue having a non compact state space . It is neither reversible nor asymptotically product form, as in the mean-field setting . Coupling and percolation techniques are first used to show that this dynamics has well defined trajectories.
Revised:  The interactions are translation invariant in space and is neither of the Jackson Networks type, nor of the mean-field setting . Coupling and percolation techniques are first used to show that this dynamics has well defined trajectories.
Edit intent: meaning-changed
Original:  The rate conservation principle of Palm calculus is then used to identify the stability condition of this system, namely the condition on the interference sequence and arrival rates guaranteeing the finiteness of this minimal regime . Remarkably, the rate conservation principle also provides a closed form expression for its mean queue size.
Revised:  The rate conservation principle of Palm calculus is then used to identify the stability condition of this system, where the notion of stability is appropriately defined for an infinite dimensional process. We show that the identified condition is also necessary in certain special cases and conjecture it to be true in all cases . Remarkably, the rate conservation principle also provides a closed form expression for its mean queue size.
Edit intent: meaning-changed
Original:  The rate conservation principle of Palm calculus is then used to identify the stability condition of this system, namely the condition on the interference sequence and arrival rates guaranteeing the finiteness of this minimal regime . Remarkably, the rate conservation principle also provides a closed form expression for its mean queue size.
Revised:  The rate conservation principle of Palm calculus is then used to identify the stability condition of this system, namely the condition on the interference sequence and arrival rates guaranteeing the finiteness of this minimal regime . Remarkably, the rate conservation principle also provides a closed form expression for the mean queue size.
Edit intent: fluency
Original:  The main result obtained in the present paper is a generalization of the large deviation principle for the log-price process due to M. Forde and H. Zhang. In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity  , while the process \widehat{B} is a general Volterra type Gaussian process.
Revised:  The main result obtained in the present paper is a generalization of the large deviation principle for the log-price process due to is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity , while the process \widehat{B} is a general Volterra type Gaussian process.
Edit intent: coherence
Original:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity  , while the process \widehat{B} is a general Volterra type Gaussian process.
Revised:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o Forde and Zhang. It is assumed in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity , while the process \widehat{B} is a general Volterra type Gaussian process.
Edit intent: meaning-changed
Original:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity  , while the process \widehat{B} is a general Volterra type Gaussian process.
Revised:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper that the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity , while the process \widehat{B} is a general Volterra type Gaussian process.
Edit intent: fluency
Original:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity  , while the process \widehat{B} is a general Volterra type Gaussian process.
Revised:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a relatively mild condition expressed in terms of a local modulus of continuity , while the process \widehat{B} is a general Volterra type Gaussian process.
Edit intent: clarity
Original:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity  , while the process \widehat{B} is a general Volterra type Gaussian process.
Revised:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of its local modulus of continuity , while the process \widehat{B} is a general Volterra type Gaussian process.
Edit intent: clarity
Original:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity  , while the process \widehat{B} is a general Volterra type Gaussian process.
Revised:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity in L^2 , while the process \widehat{B} is a general Volterra type Gaussian process.
Edit intent: meaning-changed
Original:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity  , while the process \widehat{B} is a general Volterra type Gaussian process.
Revised:  In their work, Forde and Zhang assume that the function \sigma satisfies a global H\"{o is fractional Brownian motion, whereas in the present paper , the function \sigma satisfies a very mild condition expressed in terms of a local modulus of continuity , while the process \widehat{B} is a Volterra type Gaussian process.
Edit intent: clarity
Original:  Welder condition and the process }\widehat{B}  establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model, and under an additional condition of self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  The assumptions used by Forde and Zhang are more restrictive. It is supposed in their work that the function \sigma satisfies the global H\"{older condition and the process }\widehat{B} establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model, and under an additional condition of self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Edit intent: meaning-changed
Original:  Welder condition and the process }\widehat{B}  establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model, and under an additional condition of self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  Welder condition and the process }\widehat{B} is fractional Brownian motion. In the present paper, we establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model, and under an additional condition of self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Edit intent: meaning-changed
Original:  Welder condition and the process }\widehat{B}  establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model, and under an additional condition of self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Revised:  Welder condition and the process }\widehat{B} establish a small-noise large deviation principle for the log-price in a fractional stochastic volatility model, and under an additional assumption of the self-similarity of the process \widehat{B}, derive a similar large deviation principle in the small-time regime.
Edit intent: clarity
Original:  In this paper we consider the in- teraction due to payments and, by investigating a large proprietary dataset of Italian firms, we characterize the topological properties of the payment network. We then focus on the relation between the net- work of payments and the risk of firms.
Revised:  In this paper we consider the in- teraction due to payments and, by investigating a large proprietary dataset of Italian firms, we characterize the topological properties of the payment networks, and then we focus our attention on the relation between the net- work of payments and the risk of firms.
Edit intent: clarity
Original:  By applying machine learning techniques, we leverage this knowledge to show that network properties of a node can be used to predict the missing rating of a firm . Our results suggest that risk assessment should take quan- titatively into account also the network of interactions among firms .
Revised:  By applying machine learning techniques, we leverage this knowledge to show that network properties of a node can be used to predict the missing rating of a firm using only network properties of a node by means of machine learning methods .
Edit intent: clarity
Original:  A recent advancement in network science,multilayer network analysis , the study of network structures of multiple interconnected 'layers' and associated dynamical processes, offers a novel way to represent and analyze animal behavior.
Revised:  A recent advancement in network science,multilayer network analysis formalism, known as multilayer network analysis, has advanced the study of network structures of multiple interconnected 'layers' and associated dynamical processes, offers a novel way to represent and analyze animal behavior.
Edit intent: meaning-changed
Original:  A recent advancement in network science,multilayer network analysis , the study of network structures of multiple interconnected 'layers' and associated dynamical processes, offers a novel way to represent and analyze animal behavior. It also helps strengthen the links between animal social behavior and broader ecological and evolutionary contexts and processes.
Revised:  A recent advancement in network science,multilayer network analysis , the study of network structures of multiple interconnected 'layers' and associated dynamical processes, offers a novel way to represent and analyse animal behaviour as connected `layers' of interactions.
Edit intent: clarity
Original:  In this article, we describe how to encode animal behavior data as different types of multilayer networks, and we link recently-developed multilayer methods to individual-, group-, population-, and evolutionary-level questions in behavioral ecology.
Revised:  In this article, we outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at the individual-, group-, population-, and evolutionary-level questions in behavioral ecology.
Edit intent: clarity
Original:  In this article, we describe how to encode animal behavior data as different types of multilayer networks, and we link recently-developed multilayer methods to individual-, group-, population-, and evolutionary-level questions in behavioral ecology. We outline a variety of examples for how to apply multilayer methods in behavioral ecology research, including examples that demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure.
Revised:  In this article, we describe how to encode animal behavior data as different types of multilayer networks, and we link recently-developed multilayer methods to individual-, group-, population-, and evolutionary-levels. We give examples for how to apply multilayer methods in behavioral ecology research, including examples that demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure.
Edit intent: clarity
Original: \Re{R} \eps{\varepsilon} \Net{S} \tldO{{O}} \body{C} We revisit the problem of building weak%DIFDELCMD < \eps%%% -nets for convex ranges over a point set in \Re^d.
Revised: \Re{R} \eps{\varepsilon} \Net{S} \tldO{{O}} \body{C} We revisit %DIFDELCMD < \eps%%% -nets for convex ranges over a point set in \Re^d.
Edit intent: clarity
Original: \Re{R} \eps{\varepsilon} \Net{S} \tldO{{O}} \body{C} We revisit the problem of building weak%DIFDELCMD < \eps%%% -nets for convex ranges over a point set in \Re^d. Unfortunately, the known constructions of weak%DIFDELCMD < \eps%%% -nets yields sets that are of size \Omega(%DIFDELCMD < \eps%%% ^{-d e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% , where c is some constant.
Revised: \Re{R} \eps{\varepsilon} \Net{S} \tldO{{O}} \body{C} We revisit the problem of building weak%DIFDELCMD < \eps%%% %DIFDELCMD < \eps%%% -nets yields sets that are of size \Omega(%DIFDELCMD < \eps%%% ^{-d e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% , where c is some constant.
Edit intent: meaning-changed
Original:  Unfortunately, the known constructions of weak%DIFDELCMD < \eps%%% -nets yields sets that are of size \Omega(%DIFDELCMD < \eps%%% ^{-d e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% , where c is some constant.
Revised:  Unfortunately, the known constructions of weak%DIFDELCMD < \eps%%% %DIFDELCMD < \eps%%% ^{-d e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% , where c is some constant.
Edit intent: clarity
Original:  Unfortunately, the known constructions of weak%DIFDELCMD < \eps%%% -nets yields sets that are of size \Omega(%DIFDELCMD < \eps%%% ^{-d e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% , where c is some constant.
Revised:  Unfortunately, the known constructions of weak%DIFDELCMD < \eps%%% -nets yields sets that are of size \Omega(%DIFDELCMD < \eps%%% e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% , where c is some constant.
Edit intent: clarity
Original:  Unfortunately, the known constructions of weak%DIFDELCMD < \eps%%% -nets yields sets that are of size \Omega(%DIFDELCMD < \eps%%% ^{-d e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% , where c is some constant. We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Revised:  Unfortunately, the known constructions of weak%DIFDELCMD < \eps%%% -nets yields sets that are of size \Omega(%DIFDELCMD < \eps%%% ^{-d e^{c d^2} ) }%DIFDELCMD < \Bigr.%%% %DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Edit intent: meaning-changed
Original:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Revised:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% an algorithm of Clarkson etal ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Edit intent: meaning-changed
Original:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Revised:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size CEMST96] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Edit intent: meaning-changed
Original:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Revised:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ], that computes (roughly) a 1/(4d^2)-centerpoint in \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Edit intent: meaning-changed
Original:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Revised:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d %DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Edit intent: meaning-changed
Original:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^{-1 , where \tldO hides polylogarithmic terms.
Revised:  We offer two alternative schemes that yield significantly smaller sets, and two related results, as follows: (A)Let%DIFDELCMD < \Net %%% be a sample of size ] \tldO(d ^2%DIFDELCMD < \eps%%% ^9) time, for a point set in \Re^d , where \tldO hides polylogarithmic terms.
Edit intent: meaning-changed