wanyu/IteraTeR_v2 huggingface dataset:
Original:  The corresponding question for the outside-in extension \sigma} _{ io } (L) instead of \sigma(L) . Our main result is the decidability of "\exists\sigma:io(L)\subseteq R?" if R is regular and L belongs to a class of tree languages closed under intersection with regular sets.
Revised:  The corresponding question for the outside-in extension \sigma} _{ oi } (L) instead of \sigma(L) . Our main result is the decidability of "\exists\sigma:io(L)\subseteq R?" if R is regular and L belongs to a class of tree languages closed under intersection with regular sets.
Edit intent: fluency
Original: We study the matching problem  "\exists \sigma:\sigma(L)\subseteq R?" where L,R are regular tree languages over  finite ranked alphabets X and \Sigma respectively, and \sigma is a substitution such that \sigma(x) is a set of trees in T(\Sigma\cup H)\setminus H for all x\in X . Here, H denotes a set of holes which are used to define a  concatenation of trees.
Revised: We study the matching problem of regular tree languages, that is, "\exists \sigma:\sigma(L)\subseteq R?" where L,R are regular tree languages over finite ranked alphabets X and \Sigma respectively, and \sigma is a substitution such that \sigma(x) is a set of trees in T(\Sigma\cup H)\setminus H for all x\in X . Here, H denotes a set of holes which are used to define a concatenation of trees.
Edit intent: coherence
Original: We study the matching problem  "\exists \sigma:\sigma(L)\subseteq R?" where L,R are regular tree languages over  finite ranked alphabets X and \Sigma respectively, and \sigma is a substitution such that \sigma(x) is a set of trees in T(\Sigma\cup H)\setminus H for all x\in X . Here, H denotes a set of holes which are used to define a  concatenation of trees.
Revised: We study the matching problem "\exists \sigma:\sigma(L)\subseteq R?" where L,R are regular tree languages over the union of finite ranked alphabets X and \Sigma respectively, and \sigma is a substitution such that \sigma(x) is a set of trees in T(\Sigma\cup H)\setminus H for all x\in X . Here, H denotes a set of holes which are used to define a concatenation of trees.
Edit intent: clarity
Original: We study the matching problem  "\exists \sigma:\sigma(L)\subseteq R?" where L,R are regular tree languages over  finite ranked alphabets X and \Sigma respectively, and \sigma is a substitution such that \sigma(x) is a set of trees in T(\Sigma\cup H)\setminus H for all x\in X . Here, H denotes a set of holes which are used to define a  concatenation of trees.
Revised: We study the matching problem "\exists \sigma:\sigma(L)\subseteq R?" where L,R are regular tree languages over finite ranked alphabets X and \Sigma respectively, and \sigma is a substitution such that \sigma(x) is a set of trees in T(\Sigma\cup H)\setminus H for all x\in X . Here, H denotes a set of "holes" which are used to define a concatenation of trees.
Edit intent: fluency
Original:  Conway studied this problem in the special case for languages of finite words in his classical textbook "Regular algebra and finite machines" and \emph{  showed that if L and R are regular, then the problem "\exists \sigma : \forall x\in X : \sigma(x)\neq \emptyset\wedge \sigma(L)\subseteq R?" is decidable.
Revised:  Conway studied this problem in the special case for languages of finite words in his classical textbook \emph{ showed that if L and R are regular, then the problem "\exists \sigma : \forall x\in X : \sigma(x)\neq \emptyset\wedge \sigma(L)\subseteq R?" is decidable.
Edit intent: clarity
Original:  Conway studied this problem in the special case for languages of finite words in his classical textbook "Regular algebra and finite machines" and \emph{  showed that if L and R are regular, then the problem "\exists \sigma : \forall x\in X : \sigma(x)\neq \emptyset\wedge \sigma(L)\subseteq R?" is decidable.
Revised:  Conway studied this problem in the special case for languages of finite words in his classical textbook "Regular algebra and finite machines" and \emph{Regular algebra and finite machines showed that if L and R are regular, then the problem "\exists \sigma : \forall x\in X : \sigma(x)\neq \emptyset\wedge \sigma(L)\subseteq R?" is decidable.
Edit intent: fluency
Original:  Conway studied this problem in the special case for languages of finite words in his classical textbook "Regular algebra and finite machines" and \emph{  showed that if L and R are regular, then the problem "\exists \sigma : \forall x\in X : \sigma(x)\neq \emptyset\wedge \sigma(L)\subseteq R?" is decidable.
Revised:  Conway studied this problem in the special case for languages of finite words in his classical textbook "Regular algebra and finite machines" and \emph{ showed that if L and R are regular, then the problem "\exists \sigma \forall x\in X : \sigma(x)\neq \emptyset\wedge \sigma(L)\subseteq R?" is decidable.
Edit intent: fluency
Original:  Unlike most existing strategies that rely on performance maximization in downstream tasks , here we propose a principled method for the identification of an optimal dimension such that all structural information of a network is parsimoniously encoded.
Revised:  Unlike most existing strategies that rely on performance maximization in downstream tasks , here we propose a principled method such that all structural information of a network is parsimoniously encoded.
Edit intent: coherence
Original:  Estimated values of the optimal dimension in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.
Revised:  The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.
Edit intent: clarity
Original:  An efficient allocation of R D budgets to accelerate technological advancement necessitates a better understanding of how LCETs rely on scientific knowledge.
Revised:  An efficient allocation of R \&D budgets requires a better understanding of how LCETs rely on scientific knowledge.
Edit intent: clarity
Original: We develop a new method to find the number of volatility regimes in a non-stationary financial time series  .
Revised: We develop a new method to find the number of volatility regimes in a nonstationary financial time series .
Edit intent: fluency
Original:  The distributions are clustered into a learned number of discrete volatility regimes via an optimisation routine.
Revised:  The distributions are clustered into a learned number of discrete volatility regimes via an optimization routine.
Edit intent: fluency
Original:  Finally, we create and validate a dynamic portfolio allocation strategy that learns the optimal match between the current distribution of a time series with its past regimes, thereby making online risk-avoidance decisions in the present.
Revised:  Finally, we create and validate a dynamic trading strategy that learns the optimal match between the current distribution of a time series with its past regimes, thereby making online risk-avoidance decisions in the present.
Edit intent: clarity
Original:  Finally, we create and validate a dynamic portfolio allocation strategy that learns the optimal match between the current distribution of a time series with its past regimes, thereby making online risk-avoidance decisions in the present.
Revised:  Finally, we create and validate a dynamic portfolio allocation strategy that learns the optimal match between the current distribution of a time series and its past regimes, thereby making online risk-avoidance decisions in the present.
Edit intent: fluency
Original:  This empirical analysis could be used with other regime-switching implementations, justifying the parametric structure encoded in any candidate model.
Edit intent: coherence
Original:  Therefore, data ethics should include a debate on what fundamental properties constitute data.
Revised:  Therefore, we need more debate on what fundamental properties constitute data.
Edit intent: clarity
Original:  In this paper, we provided one possible definition of statistical data.
Revised:  In this paper, we work from the fundamental properties necessary for statistical computation to a definition of statistical data.
Edit intent: clarity
Original:  We define statistical data as the coming-together of substantive and numerical properties and differentiate between categorical and quantitative data.
Revised:  We define a statistical datum as the coming together of substantive and numerical properties and differentiate between categorical and quantitative data.
Edit intent: clarity
Original:  We define statistical data as the coming-together of substantive and numerical properties and differentiate between categorical and quantitative data.
Revised:  We define statistical data as the coming-together of substantive and numerical properties and differentiate between qualitative and quantitative data.
Edit intent: clarity
Original: We introduce our Cyber-Physical Mobility Lab (CPM Lab) , a development environment for networked and autonomous vehicles .
Revised: This paper introduces our Cyber-Physical Mobility Lab (CPM Lab) , a development environment for networked and autonomous vehicles .
Edit intent: clarity
Original: We introduce our Cyber-Physical Mobility Lab (CPM Lab) , a development environment for networked and autonomous vehicles .
Revised: We introduce our Cyber-Physical Mobility Lab (CPM Lab) . It is an open-source development environment for networked and autonomous vehicles .
Edit intent: coherence
Original:  It consists of 20  model-scale vehicles for experiments and a simulation environment.
Revised:  It consists of 20 physical model-scale vehicles for experiments and a simulation environment.
Edit intent: clarity
Original:  We show our } four-layered architecture that enables the seamless use of the same software in simulations and in experiments without any  adaptions.
Revised:  We show our } four-layered architecture enables the seamless use of the same software in simulations and in experiments without any adaptions.
Edit intent: coherence
Original:  We show our } four-layered architecture that enables the seamless use of the same software in simulations and in experiments without any  adaptions.
Revised:  We show our } four-layered architecture that enables the seamless use of the same software in simulations and in experiments without any further adaptions.
Edit intent: clarity
Original:  A Data Distribution Service (DDS) based middleware allows to adapt the number of vehicles during experiments in a seamless manner.
Revised:  A Data Distribution Service (DDS) based middleware allows adapting the number of vehicles during experiments in a seamless manner.
Edit intent: fluency
Original: Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system.
Revised: In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) system.
Edit intent: coherence
Original:  With graph interaction mechanism, our framework has the advantage to automatically extract the relevant intents information to guide each token slot prediction , making a fine-grained intent information integration for the token-level slot prediction.
Revised:  With graph interaction mechanism, our framework has the advantage to automatically extract the relevant intents information , making a fine-grained intent information integration for the token-level slot prediction.
Edit intent: clarity
Original:  However, the existence of outliers could make the problems to be much more challenging than their vanilla versions.
Revised:  However, the presence of outliers could make the problems to be much more challenging than their vanilla versions.
Edit intent: clarity
Original: The unprecedented requirements of IoT networks have made fine-grained optimization of spectrum resources an urgent necessity.
Revised: The unprecedented requirements of the Internet of Things (IoT) have made fine-grained optimization of spectrum resources an urgent necessity.
Edit intent: clarity
Original:  In this paper, we first discuss the need for real-time deep learning at the physical layer, and then summarize the current state of the art and existing limitations.
Revised:  In this paper, we first discuss the need for real-time DL at the physical layer, and then summarize the current state of the art and existing limitations.
Edit intent: clarity
Original:  We conclude the paper by discussing an agenda of research challenges and how deep learning can be applied to address crucial problems in 5G-and-beyond networks.
Revised:  We conclude the paper by discussing an agenda of research challenges and how DL can be applied to address crucial problems in 5G-and-beyond networks.
Edit intent: clarity
Original:  We conclude the paper by discussing an agenda of research challenges and how deep learning can be applied to address crucial problems in 5G-and-beyond networks.
Revised:  We conclude the paper by discussing an agenda of research challenges and how deep learning can be applied to address crucial problems in 5GB networks.
Edit intent: clarity
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death  despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death despite maximal supportive care.
Edit intent: fluency
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death  despite maximal supportive care.
Revised: In Coronavirus Disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death despite maximal supportive care.
Edit intent: fluency
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death  despite maximal supportive care.
Revised: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm syndrome ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death despite maximal supportive care.
Edit intent: fluency
Original: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm  ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death  despite maximal supportive care.
Revised: In Coronavirus Disease 2019 (COVID-19), the initial viral-replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm ') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death - despite maximal supportive care.
Edit intent: fluency
Original:  Preventing hyperinflammation is key to avoiding this progression to severe stages of COVID-19.
Revised:  Preventing hyperinflammation is key to avoiding progression to severe stages of COVID-19.
Edit intent: clarity
Original:  Here  we present a retrospective study of outcomes in patients with ARDS (n = 13,125) or pneumonia (n = 108,956).
Revised:  Here , we present a retrospective study of outcomes in patients with ARDS (n = 13,125) or pneumonia (n = 108,956).
Edit intent: fluency
Original: In  Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death- despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death- despite maximal supportive care.
Edit intent: clarity
Original: In  Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death- despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death- despite maximal supportive care.
Edit intent: clarity
Original: In  Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), multi-organ failure, and death- despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and other organ systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Edit intent: clarity
Original:  We have previously demonstrated that alpha-1 adrenergic receptor (\alpha_1-AR) antagonists can prevent cytokine storm syndrome and resulting death in mice.
Revised:  We have previously demonstrated that alpha-1 adrenergic receptor (\alpha_1-AR) antagonists can prevent cytokine storm syndrome and death in mice.
Edit intent: clarity
Original:  Here, we present a retrospective study of outcomes in patients with acute respiratory distress (n = 13,125) or pneumonia (n = 108,956) .
Revised:  Here, we conduct a retrospective analysis of patients with acute respiratory distress (n = 13,125) or pneumonia (n = 108,956) .
Edit intent: clarity
Original:  Here, we conduct a retrospective analysis of patients with acute respiratory distress  (n = 13,125 ) or pneumonia ( n = 108,956  ) from all causes;
Revised:  Here, we conduct a retrospective analysis of patients with acute respiratory distress or pneumonia (n = 13,125 ) or pneumonia ( n = 108,956 ) from all causes;
Edit intent: fluency
Original: In severe  pneumonias, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Revised: In severe viral pneumonias, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Edit intent: fluency
Original:  We previously demonstrated that alpha-1 adrenergic receptor antagonists (\alpha-blockers) can prevent cytokine storm syndrome and death in mice.
Revised:  We previously demonstrated that alpha-1 adrenergic receptor (\alpha_1-AR) antagonists can prevent cytokine storm syndrome and death in mice.
Edit intent: clarity
Original:  We previously demonstrated that alpha-1 adrenergic receptor antagonists (\alpha-blockers) can prevent cytokine storm syndrome and death in mice.
Revised:  We previously demonstrated that alpha-1 adrenergic receptor antagonists (\alpha-blockers) can prevent cytokine storm syndrome in mice.
Edit intent: clarity
Original:  Here, we conduct a retrospective analysis of patients with acute respiratory distress or pneumonia ( n= 13,125 and n= 108,956, respectively)from all causes;
Revised:  Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress or pneumonia ( n= 13,125 and n= 108,956, respectively)from all causes;
Edit intent: clarity
Original: In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation ('cytokine storm syndrome') , which can lead to acute respiratory distress syndrome, multi-organ failure, and death.
Revised: In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation , which can lead to acute respiratory distress syndrome, multi-organ failure, and death.
Edit intent: coherence
Original:  We previously demonstrated that alpha-1 adrenergic receptor (\alpha_1-AR) antagonists can prevent  cytokine storm syndrome  in mice.
Revised:  We previously demonstrated that alpha-1 adrenergic receptor (\alpha_1-AR) antagonists can prevent cytokine storm syndrome ) in mice.
Edit intent: fluency
Original:  All results were robust to various sensitivity analyses.
Revised:  All results were robust to sensitivity analyses.
Edit intent: clarity
Original: The ternary betweenness relation of a tree, B(x,y,z) , indicates that y is on the unique path between x and z.
Revised: The ternary betweenness relation of a tree, B(x,y,z) expresses that y is on the unique path between x and z.
Edit intent: clarity
Original:  This notion can be extended to order-theoretic trees defined as partial orders such that the set of nodes greater than any node is linearly ordered.
Revised:  This notion can be extended to order-theoretic trees defined as partial orders such that the set of nodes larger than any node is linearly ordered.
Edit intent: fluency
Original:  The drugs studied include hydroxychloroquine, ivermectin, remdesivir and \alpha -difluoromethylornithine (DMFO).
Revised:  The drugs studied include hydroxychloroquine, ivermectin, remdesivir and -difluoromethylornithine (DMFO).
Edit intent: clarity
Original:  The drugs studied include hydroxychloroquine, ivermectin, remdesivir and \alpha -difluoromethylornithine (DMFO).
Revised:  The drugs studied include hydroxychloroquine, ivermectin, remdesivir and \alpha alpha-difluoromethylornithine (DMFO).
Edit intent: fluency
Original: We report results of our study of approved drugs as potential treatments for COVID-19 based on the application of various bioinformatics predictive methods.
Revised: We report results of our study of approved drugs as potential treatments for COVID 19, based on the application of various bioinformatics predictive methods.
Edit intent: clarity
Original:  The drugs studied include hydroxychloroquine, ivermectin, remdesivir and alpha-difluoromethylornithine (DMFO).
Revised:  The drugs studied include hydroxychloroquine, Ivermectin, Remdesivir and alpha-difluoromethylornithine (DMFO).
Edit intent: fluency
Original:  The drugs studied include hydroxychloroquine , Ivermectin, Remdesivir and alpha-difluoromethylornithine (DMFO).
Revised:  The drugs studied include Chloroquine , Ivermectin, Remdesivir and alpha-difluoromethylornithine (DMFO).
Edit intent: clarity
Original:  Our results indicate that these small drug molecules selectively bind to kinetically active and residues adjoining them on the surface  and inside protein  , and that some prefer hydrophobic over other active sites.
Revised:  Our results indicate that these small drug molecules selectively bind to kinetically active and residues adjoining them on the surface of proteins and inside protein , and that some prefer hydrophobic over other active sites.
Edit intent: fluency