wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support.
Revised:  We further consider the problem under Poisson arrival and general workload distribution ,\ie, M/GI/N system), and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support.
Edit intent: meaning-changed
Original:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support.
Revised:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT achieves asymptotic optimal mean response time when the traffic intensity \rho approaches 1, if the job size distribution has bounded support.
Edit intent: meaning-changed
Original:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support.
Revised:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if job size distribution has bounded support.
Edit intent: fluency
Original:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support. In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has finite support. Beyond bounded job workload, the asymptotic optimality of M-SRPT also holds for unbounded job size distributions with certain probabilistic assumptions, for example, M/M/N system with upper bounded task workload. An byproduct of our analysis is a tight characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}} , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to all work-conserving algorithms.
Edit intent: meaning-changed
Original:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling . We prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}} , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to all work-conserving algorithms.
Edit intent: coherence
Original:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average response time in GI/GI/1 system scales with \frac{1{1-\rho}} , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to all work-conserving algorithms.
Edit intent: clarity
Original:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 {1-\rho}} , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to all work-conserving algorithms.
Edit intent: clarity
Original:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}} scales with 1/(1-\rho) , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to all work-conserving algorithms.
Edit intent: meaning-changed
Original:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}} , if the job size distribution has finite support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to all work-conserving algorithms.
Edit intent: clarity
Original:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}} , if the job size distribution has bounded support, which generalizes the growth rate in [Lin, Wierman and Zwart, 2011] to all work-conserving algorithms.
Edit intent: meaning-changed
Original:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}}  , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to  all work-conserving algorithms.
Revised:  In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling , and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}} , if the job size distribution has bounded support, which generalizes the result in [Lin, Wierman and Zwart, 2011] to general arrival processes and all work-conserving algorithms.
Edit intent: meaning-changed
Original:  Beyond bounded job workload, the asymptotic optimality of M-SRPT also holds for unbounded job size distributions with certain probabilistic assumptions, for example, M/M/N system with upper bounded task workload.
Revised:  Beyond finite job workload, the asymptotic optimality of M-SRPT also holds for unbounded job size distributions with certain probabilistic assumptions, for example, M/M/N system with upper bounded task workload.
Edit intent: clarity
Original:  Beyond bounded job workload, the asymptotic optimality of M-SRPT also holds for unbounded job size distributions with certain probabilistic assumptions, for example, M/M/N system with upper bounded task workload.
Revised:  Beyond bounded job workload, the asymptotic optimality of M-SRPT also holds for infinite job size distributions with certain probabilistic assumptions, for example, M/M/N system with upper bounded task workload.
Edit intent: clarity
Original:  Beyond bounded job workload, the asymptotic optimality of M-SRPT also holds for unbounded job size distributions with certain probabilistic assumptions, for example, M/M/N system with upper bounded task workload. An byproduct of our analysis is a tight characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling. We prove that the average response time in GI/GI/1 scales with 1/(1-\rho), if the job size distribution has finite support, which generalizes the growth rate in Lin, Wierman and Zwart, 2011%DIFDELCMD < ] %%% to general arrival processes and all work-conserving algorithms .
Revised:  Beyond bounded job workload, the asymptotic optimality of M-SRPT also holds for unbounded job size distributions with certain probabilistic assumptions, for example, M/M/N system with Lin, Wierman and Zwart, 2011%DIFDELCMD < ] %%% to general arrival processes and all work-conserving algorithms .
Edit intent: meaning-changed
Original:  We prove that the average response time in GI/GI/1 scales with 1/(1-\rho), if the job size distribution has finite support, which generalizes the growth rate in Lin, Wierman and Zwart, 2011%DIFDELCMD < ] %%% to general arrival processes and all work-conserving algorithms .
Revised:  We prove that the average response time in GI/GI/1 scales with 1/(1-\rho), if the job size distribution has finite support, which generalizes the growth rate in %DIFDELCMD < ] %%% to general arrival processes and all work-conserving algorithms .
Edit intent: meaning-changed
Original:  We prove that the average response time in GI/GI/1 scales with 1/(1-\rho), if the job size distribution has finite support, which generalizes the growth rate in Lin, Wierman and Zwart, 2011%DIFDELCMD < ] %%% to general arrival processes and all work-conserving algorithms .
Revised:  We prove that the average response time in GI/GI/1 scales with 1/(1-\rho), if the job size distribution has finite support, which generalizes the growth rate in Lin, Wierman and Zwart, 2011%DIFDELCMD < ] %%% finite task workload .
Edit intent: clarity
Original: We consider the possibility of social and economical collapse of population caused by epidemics. We exploit a simplest toy model with negative feedback for the dynamics of epidemic spreading in population with its mutual influence on some formal resource (economical, financial, etc).
Revised: We consider a possibility of socioeconomic collapse caused by the spread of epidemic in a basic dynamical model with negative feedback for the dynamics of epidemic spreading in population with its mutual influence on some formal resource (economical, financial, etc).
Edit intent: clarity
Original:  We exploit a simplest toy model with negative feedback for the dynamics of epidemic spreading in population with its mutual influence on some formal resource (economical, financial, etc). For epidemics spreading we use the simple SIS model, supplemented with the simplest equation for the dynamics of the resource whose generation is determined by the active labor resource . We note that the patient's characteristic rehabilitation rate or recovery time depends on the cost of health services, cost of the bare subsistence level of consumption, E, and the availability of some formal resource \rho, e. g. money. Since the cost of services is fixed, the service is terminated if there is not enough personal, private, or collective financial resource\rho\ll E, the service is absent. Therefore, we suppose that recovery rate should have an activation character \sim\exp(-E/\rho). We show that depending on the rate of epidemics spreading and on the availability of formal resource, the system can come back to normal life, can overcome the stress or move to another stable but more "poor" state. Otherwise, the system can collapse . While collapsing, the system can pass through a number of quasi-stable states, its dynamics being resembling the so-called devil's staircase .
Revised:  We exploit a simplest toy model with negative feedback between the infected population size and a formal collective economic resource. The epidemic-resource coupling is supposed to be of activation type, with the recovery rate governed by the Arrhenius-like law and resource playing the role of temperature. Such a coupling can result in the collapsing effect opposite to thermal explosion because of the limited resource. In this case, the system can no longer stabilize and return to the stable pre- or post-epidemic states. We demonstrate that such a collapse can partially be mitigated by means of a negative resource or debt .
Edit intent: meaning-changed
Original: We analyze preconditioned Nesterov's accelerated gradient descent methods (PAGD) for approximating the minimizer of locally Lipschitz smooth, strongly convex objective functionals.
Revised: We analyze preconditioned Nesterov's accelerated gradient descent method (AGD) to the approximation of solutions of a wide class of partial differential equations (PDEs). This is achieved by proving the existence of an invariant set and exponential convergence rates when its preconditioned version (PAGD) is applied to minimize locally Lipschitz smooth, strongly convex objective functionals.
Edit intent: meaning-changed
Original:  To facilitate our analysis, we introduce a second-order ordinary differential equation (ODE) and demonstrate that this ODE is the limiting case of PAGD as the step size tends to zero. Using a simple energy argument , we show an exponential convergence of the ODE solution to its steady state .
Revised:  To facilitate our analysis, we introduce a second-order ordinary differential equation (ODE) with a preconditioner built-in and show that PAGD is an explicit time-discretization of this ODE, which requires a natural time step restriction for energy stability. At the continuous time level , we show an exponential convergence of the ODE solution to its steady state .
Edit intent: meaning-changed
Original:  Using a simple energy argument , we show an exponential convergence of the ODE solution to its steady state . The PAGD method may be viewed as an explicit-type time-discretization scheme of the ODE system, which requires a natural time step restriction for energy stability. Assuming this restriction, an exponential rate of convergence of the PAGD sequence is demonstrated, by mimicking the convergence of the solution to the ODE via energy methods.
Revised:  Using a simple energy argument , we show an exponential convergence of the ODE solution to its steady state using a simple energy argument. At the discrete level, assuming the aforementioned step size restriction, the existence of an invariant set is proved and a matching exponential rate of convergence of the PAGD sequence is demonstrated, by mimicking the convergence of the solution to the ODE via energy methods.
Edit intent: meaning-changed
Original:  Assuming this restriction, an exponential rate of convergence of the PAGD sequence is demonstrated, by mimicking the convergence of the solution to the ODE via energy methods. Application of the PAGD method is made in the context of solving certain nonlinear elliptic PDE using pseudo-spectral methods  , and several numerical experiments are conducted.
Revised:  Assuming this restriction, an exponential rate of convergence of the PAGD sequence is demonstrated, by mimicking the energy argument and the convergence at the continuous level. Applications of the PAGD method is made in the context of solving certain nonlinear elliptic PDE using pseudo-spectral methods , and several numerical experiments are conducted.
Edit intent: meaning-changed
Original:  Our evaluation shows that Ansor improves the execution performance of deep neural networks  on the Intel CPU, ARM CPU, and NVIDIA GPU by up to 3.8\times, 2.6\times, and 1.7\times, respectively.
Revised:  We show that Ansor improves the execution performance of deep neural networks on the Intel CPU, ARM CPU, and NVIDIA GPU by up to 3.8\times, 2.6\times, and 1.7\times, respectively.
Edit intent: clarity
Original:  Our evaluation shows that Ansor improves the execution performance of deep neural networks  on the Intel CPU, ARM CPU, and NVIDIA GPU by up to 3.8\times, 2.6\times, and 1.7\times, respectively.
Revised:  Our evaluation shows that Ansor improves the execution performance of deep neural networks relative to the state-of-the-art on the Intel CPU, ARM CPU, and NVIDIA GPU by up to 3.8\times, 2.6\times, and 1.7\times, respectively.
Edit intent: meaning-changed
Original:  In our work, we (1) propose modelling the hierarchical structure between dialogue policy and natural language generator (NLG) with the option framework, called HDNO  ;
Revised:  In our work, we (1) propose modelling the hierarchical structure between dialogue policy and natural language generator (NLG) with the option framework, called HDNO , where the latent dialogue act is applied to avoid designing specific dialogue act representations ;
Edit intent: meaning-changed
Original:  (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during HRL inspired by fictitious play, to preserve the comprehensibility of generated system utterances while improving fulfilling user requests ;
Revised:  (2) train HDNO via hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during HRL inspired by fictitious play, to preserve the comprehensibility of generated system utterances while improving fulfilling user requests ;
Edit intent: clarity
Original:  (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during HRL inspired by fictitious play, to preserve the comprehensibility of generated system utterances while improving fulfilling user requests ;
Revised:  (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest the asynchronous updates between dialogue policy and NLG during HRL inspired by fictitious play, to preserve the comprehensibility of generated system utterances while improving fulfilling user requests ;
Edit intent: fluency
Original:  (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during HRL inspired by fictitious play, to preserve the comprehensibility of generated system utterances while improving fulfilling user requests ;
Revised:  (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during training to theoretically guarantee their convergence to a local maximizer ;
Edit intent: clarity
Original:  We test HDNO on MultiWoz 2.0 and MultiWoz 2.1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA, showing a significant improvement on the total performance evaluated with automatic metrics .
Revised:  We test HDNO on MultiWoz 2.0 and MultiWoz 2.1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA, showing improvements on the performance evaluated by automatic evaluation metrics and human evaluation. Finally, we demonstrate the semantic meanings of latent dialogue acts to show the ability of explanation .
Edit intent: meaning-changed
Original:  In o gur work, we (1) propose modelling the hierarchical structure between dialogue policy and natural language generator (NLG) with the option framework, called HDNO, where the latent dialogue act is applied to avoid designing specific dialogue act representations;
Revised:  In our work, we (1) propose modelling the hierarchical structure between dialogue policy and natural language generator (NLG) with the option framework, called HDNO, where the latent dialogue act is applied to avoid designing specific dialogue act representations;
Edit intent: fluency
Original:  We primarily focus on the computation of the finite and infinite spectral structures (e.g., eigenvalues, zeros, poles) as well as the left and right singular structures (e.g., Kronecker indices), which play a fundamental role in the structure of the solution of many problems involving polynomial  matrices.
Revised:  We primarily focus on the computation of the finite and infinite spectral structures (e.g., eigenvalues, zeros, poles) as well as the left and right singular structures (e.g., Kronecker indices), which play a fundamental role in the structure of the solution of many problems involving polynomial and rational matrices.
Edit intent: meaning-changed
Original:  The basic analysis tool is the determination of the Kronecker structure of linear matrix pencils using numerically reliable algorithms, which is used in conjunction with several linearization techniques of polynomial matrices. An example of a polynomial matrix which exhibits all relevant structural features is considered to illustrate the main mathematical concepts and the capabilities of implemented tools.
Revised:  The basic analysis tool is the determination of the Kronecker structure of linear matrix pencils using numerically reliable algorithms, which is used in conjunction with several linearization techniques of polynomial and rational matrices. Examples of polynomial and rational matrices, which exhibit all relevant structural features is considered to illustrate the main mathematical concepts and the capabilities of implemented tools.
Edit intent: coherence
Original:  The ensemble of specialized classifiers (ESC) can better generalize, leading to an average improvement of 56 \% in F1 score for unseen accounts across datasets. Furthermore, novel bot behaviors are learned with fewer labeled examples during retraining. We are deploying ESC in the newest version of Botometer, a popular tool to detect social bots in the wild.
Revised:  The ensemble of specialized classifiers (ESC) can better generalize, leading to an average improvement of 56 
Edit intent: coherence