wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We present polynomial-time kernelizations for Cluster Editing/Deletion , Path Contractions and Feedback Vertex Set that run with O(\mathrm{poly (k) \log n) bits and compute a kernel of size polynomial in k . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( \mathrm{poly(k) \log n) bits.
Revised:  We present polynomial-time kernelizations for Cluster Editing/Deletion as well as a kernel for Feedback Vertex Set that run with O(\mathrm{poly (k) \log n) bits and compute a kernel of size polynomial in k . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( \mathrm{poly(k) \log n) bits.
Edit intent: clarity
Original:  We present polynomial-time kernelizations for Cluster Editing/Deletion , Path Contractions and Feedback Vertex Set that run with O(\mathrm{poly (k) \log n) bits and compute a kernel of size polynomial in k . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( \mathrm{poly(k) \log n) bits.
Revised:  We present polynomial-time kernelizations for Cluster Editing/Deletion , Path Contractions and Feedback Vertex Set whose sizes are all polynomial in k, that are computable in polynomial time, and use O( (k) \log n) bits and compute a kernel of size polynomial in k . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( \mathrm{poly(k) \log n) bits.
Edit intent: meaning-changed
Original:  We present polynomial-time kernelizations for Cluster Editing/Deletion , Path Contractions and Feedback Vertex Set that run with O(\mathrm{poly (k) \log n) bits and compute a kernel of size polynomial in k . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( \mathrm{poly(k) \log n) bits.
Revised:  We present polynomial-time kernelizations for Cluster Editing/Deletion , Path Contractions and Feedback Vertex Set that run with O(\mathrm{poly (k) \log n) bits . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( \mathrm{poly(k) \log n) bits.
Edit intent: coherence
Original:  We present polynomial-time kernelizations for Cluster Editing/Deletion , Path Contractions and Feedback Vertex Set that run with O(\mathrm{poly (k) \log n) bits and compute a kernel of size polynomial in k . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( \mathrm{poly(k) \log n) bits.
Revised:  We present polynomial-time kernelizations for Cluster Editing/Deletion , Path Contractions and Feedback Vertex Set that run with O(\mathrm{poly (k) \log n) bits and compute a kernel of size polynomial in k . By first executing the new kernelizations and subsequently the best known polynomial-time kernelizations for the problem under consideration, we obtain the best known kernels in polynomial time with O( (k) \log n) bits.
Edit intent: fluency
Original: While reduced-order models (ROMs) have demonstrated success in many applications across computational science, challenges remain when applied both to extreme-scale models due to the prohibitive cost of generating requisite training data, and to decomposable systems due to many-query problems often requiring repeated reconfigurations of system components. Therefore, we propose the domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations .
Revised: A novel domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations .
Edit intent: clarity
Original:  Therefore, we propose the domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations .
Revised:  Therefore, we propose the domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations (e.g., arising from discretizing a parameterized partial-differential-equations problem) is proposed.
Edit intent: meaning-changed
Original:  Rather than constructing a low-dimensional subspace for the entire state space in a monolithic fashion---which would be infeasible for extreme-scale systems and decomposable models---the methodology constructs separate subspaces for the different subdomains/components characterizing the original model.
Revised:  Rather than constructing a low-dimensional subspace for the entire state space in a monolithic fashion, the methodology constructs separate subspaces for the different subdomains/components characterizing the original model.
Edit intent: coherence
Original:  In this paper, the trust-region-reflective algorithm is applied for parameter estimation. After that, based on these calculated parameters, the prediction is made under three scenarios of the control measures and one scenario without any control measures. Unlike the previous models in which the effects of control action is not quantified, the proposed model can do this in order to better predict the evolution of this pandemic. The case study considers Kazakhstan, a badly affected country by this pandemic but do not receive enough considerations .
Revised:  In this paper, the trust-region-reflective (TTR) algorithm is used to fit the model to estimate both time-invariant and time-varying (with bounded contsraints) parameters. The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but do not receive enough considerations .
Edit intent: clarity
Original:  The case study considers Kazakhstan, a badly affected country by this pandemic but do not receive enough considerations .
Revised:  The case study considers Kazakhstan, a badly affected country by this pandemic but has not been receiving much attention by the research community thus far. Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and better understood .
Edit intent: meaning-changed
Original: This paper presents the assessment and forecasts of the transmission dynamics of  COVID-19 with the effects of time-dependent control actions using the data in Kazakhstan as a case study.
Revised: This paper presents the assessment and forecasts of the transmission dynamics of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 with the effects of time-dependent control actions using the data in Kazakhstan as a case study.
Edit intent: meaning-changed
Original: This paper presents the assessment and forecasts of the transmission dynamics of  COVID-19 with the effects of time-dependent control actions using the data in Kazakhstan as a case study. First, a modified SEIRD (susceptible, exposed, infectious, recovered, and death) model is introduced. The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic .
Revised: This paper presents the assessment and forecasts of the transmission dynamics of COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic .
Edit intent: coherence
Original:  The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic .
Revised:  The SEIRD model is developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic .
Edit intent: fluency
Original:  The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic .
Revised:  The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this epidemic .
Edit intent: fluency
Original:  The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic .
Revised:  The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this pandemic .
Edit intent: clarity
Original:  In this paper, the trust-region-reflective (TTR) algorithm is used to fit the model to estimate both time-invariant and time-varying (with bounded contsraints) parameters. The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far . Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and betterunderstood .
Revised:  In this paper, the model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far . Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and betterunderstood .
Edit intent: coherence
Original:  The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far . Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and betterunderstood .
Revised:  The model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations; time-invariant and time-varying (with bounded constraints) parameters . Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and betterunderstood .
Edit intent: meaning-changed
Original:  The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far . Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and betterunderstood .
Revised:  The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far . Finally, some forecasts are made using four scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and betterunderstood .
Edit intent: meaning-changed
Original:  The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far . Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and betterunderstood .
Revised:  The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far . Finally, some forecasts are made using five scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better .
Edit intent: clarity
Original:  The proposed method satisfies Pareto optimality, envy-freeness, strategy-proofness, and under an additional mild assumption, also proportionality. We show numerically that our method scales better to large problems than alternative approaches.
Revised:  The proposed method satisfies Pareto optimality, envy-freeness, strategy-proofness, and a notion of sharing incentive for our setting. To the best of our knowledge this is the first mechanism to achieve all four properties in our setting. Furthermore, we show numerically that our method scales better to large problems than alternative approaches.
Edit intent: meaning-changed
Original:  Here, we use a mathematical model describing the dynamics of resources, toxins, and microbial species in a chemostat where resource supplies switch. Over most of the explored parameter space, speciescompeted, but the strength of competition peaked at either low, high  or intermediate switching rates depending on the species' sensitivity to toxins.
Revised:  Here, we use a mathematical model describing two microbial species living in an environment where substrate supplies randomly switch between abundant and scarce. We then vary the rate of switching as well as different properties of the interacting species, and measure the probability of the weaker species driving the stronger one extinct. We find that this probability increases with the strength of demographic noise, and peaks at either low, high or intermediate switching rates depending on the species' sensitivity to toxins.
Edit intent: meaning-changed
Original:  Over most of the explored parameter space, speciescompeted, but the strength of competition peaked at either low, high  or intermediate switching rates depending on the species' sensitivity to toxins. Importantly , however, the strength of competition in species pairs was a good predictor for how community diversity changed over the switching rate .
Revised:  Over most of the explored parameter space, speciescompeted, but the strength of competition peaked at either low, high or intermediate switching rates depending on both species' ability to withstand the harsh environment. This complex relationship shows why finding patterns between environmental fluctuations and diversity has historically been difficult: response to fluctuations depends on species' properties. In parameter ranges where the fittest species was most likely to be excluded , however, the strength of competition in species pairs was a good predictor for how community diversity changed over the switching rate .
Edit intent: meaning-changed
Original:  In sum, while we find no simple rules on how the frequency of fluctuations shapes the diversity of a community, we show that their effect on interactions between two representative species predicts how diversity in the whole community will change .
Revised:  In sum, while we find no simple rules on how the frequency of fluctuations shapes the diversity of a community, we show that their effect on interactions between two representative species predicts how diversity in the whole community .
Edit intent: clarity
Original:  The baseline for ATSP here is a simple enumeration of cycle covers which can be done in time bounded by O^*(\mu(d)^n) for a function \mu(d)\le(d\rceil!)^{ n /{d\rceil}}.
Revised:  The baseline for ATSP here is a simple enumeration of cycle covers which can be done in time bounded by O^*(\mu(d)^n) for a function \mu(d)\le(d\rceil!)^{ 1 /{d\rceil}}.
Edit intent: meaning-changed
Original:  We present two new deterministic algorithms  : the first running in time O(2^{0.441(d-1)n}) and polynomial space, and the second in exponential space with running time of O^*(\tau(d)^{n/2}) for a function \tau(d)\le d.
Revised:  We present two new deterministic algorithms for ATSP : the first running in time O(2^{0.441(d-1)n}) and polynomial space, and the second in exponential space with running time of O^*(\tau(d)^{n/2}) for a function \tau(d)\le d.
Edit intent: meaning-changed
Original: Research on infodemics, i.e., the rapid spread of (mis)information related to a hazardous event  such as the COVID-19 pandemic, requires the integration of a multiplicity of scientific disciplines.
Revised: Research on infodemics, i.e., the rapid spread of (mis)information related to a hazardous event , such as the COVID-19 pandemic, requires the integration of a multiplicity of scientific disciplines.
Edit intent: fluency
Original:  For the field of Business and Economics, understanding these dynamics is of ultimate importance : it supports, for example, anticipating individual behavior, which might help reduce the uncertainty entailed by the COVID-19 pandemic and allows for assessing the efficiency of policy decisions to contain its effects.
Revised:  In order to react appropriately, it is of ultimate importance : it supports, for example, anticipating individual behavior, which might help reduce the uncertainty entailed by the COVID-19 pandemic and allows for assessing the efficiency of policy decisions to contain its effects.
Edit intent: coherence
Original:  For the field of Business and Economics, understanding these dynamics is of ultimate importance : it supports, for example, anticipating individual behavior, which might help reduce the uncertainty entailed by the COVID-19 pandemic and allows for assessing the efficiency of policy decisions to contain its effects. In addition to the field of Business and Economics , we take into account the following disciplines: Through the lens of Computer Science and Information Systems, the information accessible to individuals is central, whereby the way information spreads in a society is strongly affected by the employed algorithms for information provision and by personalization.
Revised:  For the field of Business and Economics, understanding these dynamics is of ultimate importance for the fields of Business and Economics , we take into account the following disciplines: Through the lens of Computer Science and Information Systems, the information accessible to individuals is central, whereby the way information spreads in a society is strongly affected by the employed algorithms for information provision and by personalization.
Edit intent: coherence
Original:  In addition to the field of Business and Economics , we take into account the following disciplines: Through the lens of Computer Science and Information Systems, the information accessible to individuals is central, whereby the way information spreads in a society is strongly affected by the employed algorithms for information provision and by personalization. From the perspective of Linguistics, specific language signals in communication which emerge during pandemics have to be taken into account (e. g., emotion-related words, avoiding causal terms). Considering linguistic patterns in the context of infodemics appears to be highly relevant as they strongly affect how information is interpreted, fact-checked, made sense of by non-expert persons, and the way misinformationis automatically detected. From a Cognitive Psychology point of view, the focus is on how motives, intuition and affect influence the search and evaluation of information , and on how cognitive processes, the digital information environment and linguistic patterns together shape individuals' understanding of critical events, risk perception and behavior. The perspective of Business and Economics allows for integrating these perspectives into the wider context of economic systems URLanizations or the society) .
Revised:  In addition to the field of Business and Economics to understand the dynamics emerging from it. In the short run, dynamics might lead to an adaptation in household spending or to a shift in buying behavior towards online providers. In the long run, changes in investments, consumer behavior, and markets are to be expected. We argue that the dynamics emerge from complex interactions among multiple factors, such as information and misinformation accessible for individuals and the formation and revision of beliefs. (Mis)information accessible to individuals is, amongst others, affected by algorithms specifically designed to provide personalized information, while automated fact-checking algorithms can help reduce the amount of circulating misinformation. The formation and revision of individual (and probably false) beliefs and individual fact-checking and interpretation of information are heavily affected by linguistic patterns inherent to information during pandemics and infodemics and further factors, such as affect, intuition and motives. We argue that, in order to get a deep(er) understanding of the dynamics emerging from infodemics, the fields of Business and Economics allows for integrating these perspectives into the wider context of economic systems URLanizations or the society) .
Edit intent: meaning-changed
Original:  As a general conclusion from our results, more " homogeneous" marginals lead to a larger aggregation set, and thus more severe model uncertainty, although the situation for quantile mixtures is much more complicated than that for distribution mixtures.
Revised:  As a general conclusion from our results, more `` homogeneous" marginals lead to a larger aggregation set, and thus more severe model uncertainty, although the situation for quantile mixtures is much more complicated than that for distribution mixtures.
Edit intent: fluency