wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The null hypothesis is that breaks in different parameters (either regression coefficients or elements of the covariance matrix of the errors) occur at a common locations or are separated by some positive fraction of the sample size  . Under the alternative hypothesis, the break dates  are not the same and also need not be separated by a positive fraction of the sample size across parameters .
Revised:  The null hypothesis is that breaks in different parameters (either regression coefficients or elements of the covariance matrix of the errors) occur at a common locations or are separated by some positive fraction of the sample size unless they occur across different equations . Under the alternative hypothesis, the break dates are not the same and also need not be separated by a positive fraction of the sample size across parameters .
Edit intent: meaning-changed
Original:  The null hypothesis is that breaks in different parameters (either regression coefficients or elements of the covariance matrix of the errors) occur at a common locations or are separated by some positive fraction of the sample size  . Under the alternative hypothesis, the break dates  are not the same and also need not be separated by a positive fraction of the sample size across parameters .
Revised:  The null hypothesis is that breaks in different parameters (either regression coefficients or elements of the covariance matrix of the errors) occur at a common locations or are separated by some positive fraction of the sample size . Under the alternative hypothesis, the break dates across parameters are not the same and also need not be separated by a positive fraction of the sample size across parameters .
Edit intent: fluency
Original:  The null hypothesis is that breaks in different parameters (either regression coefficients or elements of the covariance matrix of the errors) occur at a common locations or are separated by some positive fraction of the sample size  . Under the alternative hypothesis, the break dates  are not the same and also need not be separated by a positive fraction of the sample size across parameters .
Revised:  The null hypothesis is that breaks in different parameters (either regression coefficients or elements of the covariance matrix of the errors) occur at a common locations or are separated by some positive fraction of the sample size . Under the alternative hypothesis, the break dates are not the same and also need not be separated by a positive fraction of the sample size whether within or across equations .
Edit intent: clarity
Original:  Also of independent interest, we provide results about the rate of convergence  when searching over all possible partitions subject only to the requirement that each regime of different parameters contains at least as many observations as some positive fraction of the sample size . Simulation results show that the test has good finite sample properties.
Revised:  Of independent interest, we provide results about the rate of convergence when searching over all possible partitions subject only to the requirement that each regime of different parameters contains at least as many observations as some positive fraction of the sample size . Simulation results show that the test has good finite sample properties.
Edit intent: coherence
Original:  Also of independent interest, we provide results about the rate of convergence  when searching over all possible partitions subject only to the requirement that each regime of different parameters contains at least as many observations as some positive fraction of the sample size . Simulation results show that the test has good finite sample properties.
Revised:  Also of independent interest, we provide results about the rate of convergence of the estimates when searching over all possible partitions subject only to the requirement that each regime of different parameters contains at least as many observations as some positive fraction of the sample size . Simulation results show that the test has good finite sample properties.
Edit intent: clarity
Original:  Also of independent interest, we provide results about the rate of convergence  when searching over all possible partitions subject only to the requirement that each regime of different parameters contains at least as many observations as some positive fraction of the sample size . Simulation results show that the test has good finite sample properties.
Revised:  Also of independent interest, we provide results about the rate of convergence when searching over all possible partitions subject only to the requirement that each regime contains at least as many observations as some positive fraction of the sample size . Simulation results show that the test has good finite sample properties.
Edit intent: clarity
Original:  Also of independent interest, we provide results about the rate of convergence  when searching over all possible partitions subject only to the requirement that each regime of different parameters contains at least as many observations as some positive fraction of the sample size . Simulation results show that the test has good finite sample properties.
Revised:  Also of independent interest, we provide results about the rate of convergence when searching over all possible partitions subject only to the requirement that each regime of different parameters contains at least as many observations as some positive fraction of the sample size , allowing break dates not separated by a positive fraction of the sample size across equations. Simulations show that the test has good finite sample properties.
Edit intent: meaning-changed
Original:  Successful convergence of this process depends crucially on network topology and is most efficient for scale-free connectivity , typical of gene regulatory networks. For such networks, convergence to an adapted phenotype can be achieved without fine tuning of initial conditions or other model parameters, thus making it plausible for biological implementation  .
Revised:  Successful convergence of this process depends crucially on network topology and is most efficient for scale-free connectivity . Convergence to an adapted phenotype can be achieved without fine tuning of initial conditions or other model parameters, thus making it plausible for biological implementation .
Edit intent: clarity
Original:  For such networks, convergence to an adapted phenotype can be achieved without fine tuning of initial conditions or other model parameters, thus making it plausible for biological implementation  .
Revised:  For such networks, convergence to an adapted phenotype can be achieved without fine tuning of initial conditions or other model parameters, thus making it plausible for biological implementation . Experimental results have indeed shown that gene regulatory networks are characterized by this type of topology, suggesting a structural basis for exploratory adaptation .
Edit intent: meaning-changed
Original:  It can provide cross-protection between subtypes of influenza A which share epitopes; thus, the strength of the immune response to a given strain is dependent upon the individual's infectionhistory. We model cross-reactive cellular adaptive immune responses induced by multiple infections, and show how the formation and re-activation of memory T cells explains observed shortening of a second infection when cross-reactivity is present .
Revised:  Experiments where individuals are successively infected with different strains within a short timeframe provide insight into the underlying viral dynamics and the role of a cross-reactive immune response in resolving an acute infection. We construct a mathematical model of within-host influenza viral dynamics including three possible factors which determine the strength of the cross-reactive cellular adaptive immune responses induced by multiple infections, and show how the formation and re-activation of memory T cells explains observed shortening of a second infection when cross-reactivity is present .
Edit intent: meaning-changed
Original:  We model cross-reactive cellular adaptive immune responses induced by multiple infections, and show how the formation and re-activation of memory T cells explains observed shortening of a second infection when cross-reactivity is present .
Revised:  We model cross-reactive cellular adaptive immune response: the initial naive T cell number, the avidity of the interaction between T cells and the epitopes presented by infected cells, and the epitope abundance per infected cell. Our model explains the experimentally observed shortening of a second infection when cross-reactivity is present .
Edit intent: meaning-changed
Original:  We model cross-reactive cellular adaptive immune responses induced by multiple infections, and show how the formation and re-activation of memory T cells explains observed shortening of a second infection when cross-reactivity is present . We include three possible mechanisms which determine the strength of the cross-reactive immune response . Our model of cross-reactivity contributes to understanding how repeated exposures change an individual's immune profile over a lifetime .
Revised:  We model cross-reactive cellular adaptive immune responses induced by multiple infections, and show how the formation and re-activation of memory T cells explains observed shortening of a second infection when cross-reactivity is present , and shows that memory in the cellular adaptive immune response is necessary to protect against a second infection .
Edit intent: coherence
Original: In this paper, we extend the 3/2-model for VIX studied by Goard and Mazur (2013) and introduce  generalized 3/2 and 1/2 classes for volatility .
Revised: In this paper, we extend the 3/2-model for VIX studied by Goard and Mazur (2013) and introduce the generalized 3/2 and 1/2 classes for volatility .
Edit intent: fluency
Original: In this paper, we extend the 3/2-model for VIX studied by Goard and Mazur (2013) and introduce  generalized 3/2 and 1/2 classes for volatility .
Revised: In this paper, we extend the 3/2-model for VIX studied by Goard and Mazur (2013) and introduce generalized 3/2 and 1/2 classes of volatility processes .
Edit intent: clarity
Original:  Within this framework, we show that decidability imposes absolute limits to the  growth of complexity on computable dynamical systems up to a double logarithmic term . Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems.
Revised:  Within this framework, we show that decidability imposes absolute limits to the stable growth of complexity on computable dynamical systems up to a double logarithmic term . Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems.
Edit intent: clarity
Original:  Within this framework, we show that decidability imposes absolute limits to the  growth of complexity on computable dynamical systems up to a double logarithmic term . Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems.
Revised:  Within this framework, we show that decidability imposes absolute limits to the growth of complexity in computable dynamical systems up to a double logarithmic term . Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems.
Edit intent: fluency
Original:  Within this framework, we show that decidability imposes absolute limits to the  growth of complexity on computable dynamical systems up to a double logarithmic term . Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems.
Revised:  Within this framework, we show that decidability imposes absolute limits to the growth of complexity on computable dynamical systems . Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems.
Edit intent: clarity
Original:  We conjecture that, for similar complexity measures that assign low complexity values, decidability imposes comparable limits to the stable growth of complexity and such behaviour is necessary for non-trivial evolutionary systems.
Revised:  As time grows, the stated complexity measures allow for the existence of complex states during the evolution of a computable dynamical system. We show, however, that finding these states involves undecidable computations. We conjecture that for similar complexity measures that assign low complexity values, decidability imposes comparable limits to the stable growth of complexity and such behaviour is necessary for non-trivial evolutionary systems.
Edit intent: meaning-changed
Original: We consider the problem of embedding a discrete parameter queueing system into continuous space by using simulation-based interpolation. We first show that a discrete-time Geom/Geom/1 queue with service time T can be exactly simulated by making the service time a Bernoulli random variable which switches between T_1 and T_2 with T_1 < T < T_2 with expected value T.
Revised: In simulation-based optimization of queuing systems, the presence of discrete-valued parameters (such as buffer sizes and the number of servers) makes the optimization difficult. We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods. Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model. The interpolated value can be computed using a single simulation of this randomized model, irrespective of the number of parameters. We first study the theoretical properties of such a randomization scheme applied to M/M/1 and Geom/Geom/1 queue with service time T can be exactly simulated by making the service time a Bernoulli random variable which switches between T_1 and T_2 with T_1 < T < T_2 with expected value T.
Edit intent: meaning-changed
Original:  We first show that a discrete-time Geom/Geom/1 queue with service time T can be exactly simulated by making the service time a Bernoulli random variable which switches between T_1 and T_2 with T_1 < T < T_2 with expected value T. This is a form of simulation-based interpolation which can, in principle, be applied to more complex queueing networks. We show that such an interpolation is possible for  queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers, and empirically demonstrate that the interpolation is well behaved.
Revised:  We first show that a discrete-time Geom/Geom/1 queues. We prove that the randomization produces valid interpolations of the steady-state performance functions with respect to an integer service-time parameter. We then show that such an interpolation is possible for queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers, and empirically demonstrate that the interpolation is well behaved.
Edit intent: clarity
Original:  We show that such an interpolation is possible for  queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers, and empirically demonstrate that the interpolation is well behaved.
Revised:  We show that such an embedding is possible for queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers, and empirically demonstrate that the interpolation is well behaved.
Edit intent: clarity
Original:  We show that such an interpolation is possible for  queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers, and empirically demonstrate that the interpolation is well behaved.
Revised:  We show that such an interpolation is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers, and empirically demonstrate that the interpolation is well behaved.
Edit intent: meaning-changed
Original:  To demonstrate the utility of the interpolation scheme , we solve a discrete parameter queuing network optimization problem by embedding the discrete parameters into  continuous space, and then using a continuous space optimization algorithm to find optimal configurations .
Revised:  To demonstrate the utility of the interpolation scheme , we solve a 6-parameter queuing network optimization problem by embedding the discrete parameters into continuous space, and then using a continuous space optimization algorithm to find optimal configurations .
Edit intent: meaning-changed
Original:  To demonstrate the utility of the interpolation scheme , we solve a discrete parameter queuing network optimization problem by embedding the discrete parameters into  continuous space, and then using a continuous space optimization algorithm to find optimal configurations .
Revised:  To demonstrate the utility of the interpolation scheme , we solve a discrete parameter queuing network optimization problem by embedding the discrete parameters into a continuous space. The technique produces smooth interpolations of the objective function, and a continuous space, and then using a continuous space optimization algorithm to find optimal configurations .
Edit intent: meaning-changed
Original: In simulation-based optimization of queuing systems, the presence of discrete-valued parameters (such as buffer sizes and the number of servers) makes the optimization difficult. We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model .
Revised: Motivated by the problem of discrete-parameter simulation optimization (DPSO) of queueing systems, we consider the problem of embedding the discrete parameter space into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model .
Edit intent: clarity
Original:  We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model .
Revised:  We propose a novel technique for embedding such discrete parameters into a continuous one so that descent-based continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model .
Edit intent: clarity
Original:  We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model .
Revised:  We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods could be directly applied for efficient optimization. We show that a randomization of the simulation model .
Edit intent: meaning-changed
Original:  We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model . The interpolated value can be computed using a single simulation of this randomized model, irrespective of the number of parameters .
Revised:  We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model itself can be used to achieve such an embedding when the objective function is a long-run average measure. Unlike spatial interpolation, the computational cost of this embedding is independent of the number of parameters .
Edit intent: meaning-changed
Original:  The interpolated value can be computed using a single simulation of this randomized model, irrespective of the number of parameters . We first study the theoretical properties of such a randomization scheme applied to M/M/1 and Geom/Geom/1 queues. We prove that the randomization produces valid interpolations of the steady-state performance functions with respect to an integer service-time parameter. We then show that such an embedding is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers , and empirically demonstrate that the interpolation is well behaved.
Revised:  The interpolated value can be computed using a single simulation of this randomized model, irrespective of the number of parameters in the system, making the approach ideally suited to high-dimensional problems. We describe in detail the application of this technique to discrete-time queues for embedding queue capacities, and the number of servers , and empirically demonstrate that the interpolation is well behaved.
Edit intent: clarity
Original:  We then show that such an embedding is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers , and empirically demonstrate that the interpolation is well behaved.
Revised:  We then show that such an embedding is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, number of servers , and empirically demonstrate that the interpolation is well behaved.
Edit intent: fluency
Original:  We then show that such an embedding is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers , and empirically demonstrate that the interpolation is well behaved. To demonstrate the utility of the embedding technique, we solve a 6-parameter queuing network optimization problem by embedding the discrete parameters into a continuous space . The technique produces smooth interpolations of the objective function , and a continuous optimizer applied directly over this embedding converges rapidly, producing good solutions.
Revised:  We then show that such an embedding is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers and server-delay parameters into continuous space and empirically show that the technique can produce smooth interpolations of the objective function , and a continuous optimizer applied directly over this embedding converges rapidly, producing good solutions.
Edit intent: coherence
Original:  Moreover, in our setting, the portfolio-generating function may depend on the entire history of the asset trajectories and on an additional continuous trajectory of bounded variation .
Revised:  Moreover, in our setting, the portfolio-generating function may depend on the current states of the market portfolio and an additional path of finite variation. The second version is formulated within the functional pathwise It\^o calculus of Dupire (2009) and Cont \& Fourni\'e (2010) and allows for portfolio-generating functionals that may depend additionally on the entire path of the market portfolio .
Edit intent: meaning-changed