wanyu/IteraTeR_v2 huggingface dataset:
Original:  Results Predictability is moderately high in most symptom networks, and differs considerable both within and between datasets.
Revised:  Results Predictability is moderately high in most symptom networks, and differed considerable both within and between datasets.
Edit intent: fluency
Original:  It provides a measure of practical relevance of connections among nodes that may be helpful to inform intervention strategies, and allows conclusions about how self-determined a symptom network is  . Limitations of predictability along with future directions are discussed.
Revised:  It provides a measure of practical relevance of connections among nodes that may be helpful to inform intervention strategies, and allows conclusions about how self-determined a symptom network is , and may help to inform intervention strategies . Limitations of predictability along with future directions are discussed.
Edit intent: coherence
Original:  However, existing work on virtual cluster resource allocation does not take into account the availability goal of tenants, which may cause degradation of tenant services and violation cost to the cloud when the cloud undergoes substrate failures.
Revised:  One issue arisen in virtual cluster allocation is the survivability of tenant services against physical failures. Existing works have studied virtual cluster backup provisioning with fixed primary embeddings, but have not considered the impact of primary embeddings on backup resource consumption.
Edit intent: clarity
Original:  We then propose a novel dynamic programming-based algorithm, which computes the most resource-efficient embedding given a tenant request.
Revised:  We then propose a novel algorithm, which computes the most resource-efficient embedding given a tenant request.
Edit intent: clarity
Original:  Since the optimal algorithm has high time complexity, we further propose an efficient heuristic algorithm, which is several orders faster than the optimal solution, yet able to achieve similar performance.
Revised:  Since the optimal algorithm has high time complexity, we further propose a faster heuristic algorithm, which is several orders faster than the optimal solution, yet able to achieve similar performance.
Edit intent: clarity
Original:  Besides theoretical analysis, we conduct extensive simulations to evaluate the performance of our proposed algorithms .
Revised:  Besides theoretical analysis, we evaluate our algorithms via extensive simulations .
Edit intent: clarity
Original: We show that any classical communication protocol that can approximately simulate the result of applying an arbitrary measurement (held by one party) to a quantum state of n qubits (held by another)  must transmit at least  2^n bits, up to constant factors.
Revised: We show that any classical communication protocol that can approximately simulate the result of applying an arbitrary measurement (held by one party) to a quantum state of n qubits (held by another) , up to constant accuracy, must transmit at least 2^n bits, up to constant factors.
Edit intent: clarity
Original:  First, a sampling problem which can be solved with one quantum query to the input, but which requires order-N classical queries for an input of size N. Second, a nonlocal task which can be solved using n Bell pairs, but for which any approximate classical solution must communicate  2^n bits, up to constant factors .
Revised:  First, a sampling problem which can be solved with one quantum query to the input, but which requires order-N classical queries for an input of size N. Second, a nonlocal task which can be solved using n Bell pairs, but for which any approximate classical solution must communicate 2^n ) bits .
Edit intent: clarity
Original: Type assignment systems are known to grant or characterize normalization.
Revised: Infinite types and formulas are known to grant or characterize normalization.
Edit intent: clarity
Original:  We show thatit is actually not the case, because they can also type every term . { \pounds The main theorem of this paper also yields a new non sensible relational model for pure \lambda} -calculus .
Revised:  We show thatit is actually not the case, because they can also type every term . { The main theorem of this paper also yields a new non sensible relational model for pure \lambda} -calculus .
Edit intent: fluency
Original:  We show thatit is actually not the case, because they can also type every term . { \pounds The main theorem of this paper also yields a new non sensible relational model for pure \lambda} -calculus .
Revised:  We show thatit is actually not the case, because they can also type every term . { \pounds \lambda} -calculus .
Edit intent: coherence
Original:  We investigate the problem of computing the probability that a realization of S contains inter-color dominances, which we call the colored stochastic dominance \textit{ (CSD) problem.
Revised:  We investigate the problem of computing the probability that a realization of S contains inter-color dominances, which we call the \textit{ (CSD) problem.
Edit intent: clarity
Original:  We investigate the problem of computing the probability that a realization of S contains inter-color dominances, which we call the colored stochastic dominance \textit{ (CSD) problem.
Revised:  We investigate the problem of computing the probability that a realization of S contains inter-color dominances, which we call the colored stochastic dominance \textit{colored stochastic dominance (CSD) problem.
Edit intent: fluency
Original:  In addition, even if the existence probabilities are restricted to be 1{2}, the problem remains  P-hard for d \geq 7. A simple FPRAS is then provided to approximate the desired probability in any dimension.
Revised:  In addition, even if the existence probabilities are restricted to be 1{2}, the problem remains \# P-hard for d \geq 7. A simple FPRAS is then provided to approximate the desired probability in any dimension.
Edit intent: fluency
Original:  We investigate the problem of  computing the probability that a realization of \mathcal{S inter-color dominances, which we call the\textit{colored stochastic dominance (CSD)problem .
Revised:  We investigate the problem of computing the probability that a realization of \mathcal{S inter-color \textit{colored stochastic dominance (CSD)problem .
Edit intent: clarity
Original:  We investigate the problem of  computing the probability that a realization of \mathcal{S inter-color dominances, which we call the\textit{colored stochastic dominance (CSD)problem .
Revised:  We investigate the problem of computing the probability that a realization of \mathcal{S inter-color dominances, which we call the\textit{ (CSD)problem .
Edit intent: clarity
Original:  We propose the first algorithm to solve the CSD problem for d=2 in O(n^2 \log^2 n) time.
Revised:  We propose the first algorithm that solves the problem for d=2 in O(n^2 \log^2 n) time.
Edit intent: clarity
Original:  On the other hand, we prove that , for d \geq 3, even the CSD problem with a restricted color pattern is \#P-hard.
Revised:  On the other hand, we show that the problem is #P-hard for any d \geq 3, even the CSD problem with a restricted color pattern is \#P-hard.
Edit intent: clarity
Original:  On the other hand, we prove that , for d \geq 3, even the CSD problem with a restricted color pattern is \#P-hard.
Revised:  On the other hand, we prove that , for d \geq 3, even if the points have a restricted color pattern is \#P-hard.
Edit intent: clarity
Original:  In addition, even if the existence probabilities are restricted to be \frac{1 , the problem remains \# P-hard for d \geq 7. A simple FPRAS is then provided to approximate the desired probability in any dimension.
Revised:  In addition, we show that even when the existence probabilities are restricted to be \frac{1 , the problem remains \# P-hard for d \geq 7. A simple FPRAS is then provided to approximate the desired probability in any dimension.
Edit intent: coherence
Original:  In addition, even if the existence probabilities are restricted to be \frac{1 , the problem remains \# P-hard for d \geq 7. A simple FPRAS is then provided to approximate the desired probability in any dimension.
Revised:  In addition, even if the existence probabilities are restricted to be \frac{1 , the problem remains P-hard for d \geq 7. A simple FPRAS is then provided to approximate the desired probability in any dimension.
Edit intent: fluency
Original:  Specifically, this variant, which we call the }%DIFDELCMD < {\em %DIFDELCMD < %%% free-basis colored stochastic dominance (FBCSD) problem, considers the probability that a realization of \mathcal{S contains inter-color dominances with respect to any orthogonal basis of \mathbb{R}^d.
Revised:  Specifically, this variant, which we call the }%DIFDELCMD < {\em %DIFDELCMD < %%% (FBCSD) problem, considers the probability that a realization of \mathcal{S contains inter-color dominances with respect to any orthogonal basis of \mathbb{R}^d.
Edit intent: clarity
Original:  Specifically, this variant, which we call the }%DIFDELCMD < {\em %DIFDELCMD < %%% free-basis colored stochastic dominance (FBCSD) problem, considers the probability that a realization of \mathcal{S contains inter-color dominances with respect to any orthogonal basis of \mathbb{R}^d.
Revised:  Specifically, this variant, which we call the }%DIFDELCMD < {\em %DIFDELCMD < %%% free-basis colored stochastic dominance contains inter-color dominances with respect to any orthogonal basis of \mathbb{R}^d.
Edit intent: clarity
Original:  We show that the CSD problem is polynomial-time reducible to the FBCSD problem in the same dimension, which proves the \#}  P-hardness of the latter for d \geq 3. Conversely, we reduce the FBCSD problem in \mathbb{R^2 to the CSD problem in \mathbb{R}^2, by which an O(n^4 \log^2 n) time algorithm for the former is obtained}  .
Revised:  We show that the CSD problem is polynomial-time reducible to the FBCSD problem in the same dimension, which proves the \#} P-hardness of the latter for d \geq ^2 to the CSD problem in \mathbb{R}^2, by which an O(n^4 \log^2 n) time algorithm for the former is obtained} .
Edit intent: clarity
Original:  Linear models usually achieves state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations but with no much success . Recent analysis shows that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings.
Revised:  Linear models usually achieve state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations but with no much success . Recent analysis shows that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings.
Edit intent: fluency
Original:  Linear models usually achieves state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations but with no much success . Recent analysis shows that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings.
Revised:  Linear models usually achieves state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations . Recent analysis shows that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings.
Edit intent: coherence
Original:  Linear models usually achieves state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations but with no much success . Recent analysis shows that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings.
Revised:  Linear models usually achieves state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations but with no much success . Recent analysis show that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings.
Edit intent: fluency
Original:  In this paper we propose a boolean kernel, called Disjunctive Kernel , which is less expressive than the linear one but it is able to alleviate the sparsity issue in CF contexts.
Revised:  In this paper we propose a boolean kernel, called Disjunctive kernel , which is less expressive than the linear one but it is able to alleviate the sparsity issue in CF contexts.
Edit intent: fluency
Original:  The embedding of this kernel is composed by all the combinations of a certain degree d of the input variables, and these combined features are semantically interpreted as disjunctions of the input variables.
Revised:  The embedding of this kernel is composed by all the combinations of a certain arity d of the input variables, and these combined features are semantically interpreted as disjunctions of the input variables.
Edit intent: fluency
Original: Nowadays, denialofservice (DoS) attacksrepresentasignificantfrac- tion of all attacks that take place in the Internet and their intensity is always growing.
Revised: Nowadays, denial of service (DoS) attacksrepresentasignificantfrac- tion of all attacks that take place in the Internet and their intensity is always growing.
Edit intent: fluency
Original: Nowadays, denialofservice (DoS) attacksrepresentasignificantfrac- tion of all attacks that take place in the Internet and their intensity is always growing.
Revised: Nowadays, denialofservice (DoS) attacks represent a significant fraction of all attacks that take place in the Internet and their intensity is always growing.
Edit intent: fluency
Original:  The main DoS attack methods consist of flooding their victims with bo- gus packets, queries or replies, so as to prevent them from fulfilling their roles.
Revised:  The main DoS attack methods consist of flooding their victims with bogus packets, queries or replies, so as to prevent them from fulfilling their roles.
Edit intent: fluency
Original:  In- stead of being known by public stable IP addresses, hosts use ephemeral IP Iden- tifiers cryptographically generated and bound to its usage context.
Revised:  Instead of being known by public stable IP addresses, hosts use ephemeral IP Iden- tifiers cryptographically generated and bound to its usage context.
Edit intent: fluency
Original:  In- stead of being known by public stable IP addresses, hosts use ephemeral IP Iden- tifiers cryptographically generated and bound to its usage context.
Revised:  In- stead of being known by public stable IP addresses, hosts use ephemeral IP Identifiers cryptographically generated and bound to its usage context.
Edit intent: fluency
Original:  Communication in the open Internet while using these special IP addresses is supported by the so- called Map/Encap approaches, which in our point of view will be sooner or later required for the future Internet.
Revised:  Communication in the open Internet while using these special IP addresses is supported by the so-called Map/Encap approaches, which in our point of view will be sooner or later required for the future Internet.
Edit intent: fluency
Original:  We use the susceptible-infected-recovered model -- the generic model for diseases making patients immune upon recovery -- as our disease-spreading scenario, and analyze outbreaks on both empirical and model networks to find the most cost-efficient vaccination protocols .
Revised:  We use the susceptible-infected-recovered model -- the generic model for diseases making patients immune upon recovery -- as our disease-spreading scenario, and analyze outbreaks on both empirical and model networks .
Edit intent: coherence
Original: We obtain a closed form of generating functions of RNA substructure using hermitian matrix model with the Chebyshev polynomial of the second kind, which turns out to be the hypergeometric function.
Revised: We obtain a closed form of generating functions of RNA substructure using hermitian matrix model with the Chebyshev polynomial of the second kind, which has the form of the hypergeometric function.
Edit intent: clarity
Original: We obtain a closed form of generating functions of RNA substructure using hermitian matrix model with the Chebyshev polynomial of the second kind , which has the form of the hypergeometric function .
Revised: We obtain a closed form of generating functions of RNA substructure using hermitian matrix model with Chebyshev polynomial of the second kind , which has the form of the hypergeometric function .
Edit intent: fluency
Original:  To match the experimental findings of the statistical behavior, we regard the substructure as a grand canonical ensemble and find its fugacity value corresponding to the number of stems .
Revised:  To match the experimental findings of the statistical behavior, we consider the structures in a grand canonical ensemble and find its fugacity value corresponding to the number of stems .
Edit intent: clarity
Original:  To match the experimental findings of the statistical behavior, we regard the substructure as a grand canonical ensemble and find its fugacity value corresponding to the number of stems .
Revised:  To match the experimental findings of the statistical behavior, we regard the substructure as a grand canonical ensemble and find a fugacity value corresponding to the number of stems .
Edit intent: clarity
Original:  To match the experimental findings of the statistical behavior, we regard the substructure as a grand canonical ensemble and find its fugacity value corresponding to the number of stems .
Revised:  To match the experimental findings of the statistical behavior, we regard the substructure as a grand canonical ensemble and find its fugacity value corresponding to an appropriate number of stems .
Edit intent: clarity
Original: In order to better understand the advantages and disadvantages of a constrained multi-objective evolutionary algorithm (CMOEA) , it is important to understand the nature of difficulty of a constrained multi-objective optimization problem (CMOP) that the CMOEA is going to deal with.
Revised: Multi-objective evolutionary algorithms (MOEAs) have achieved great progress in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems. In fact, many real-world multi-objective problems usually contain a number of constraints. To promote the research of constrained multi-objective optimization problem (CMOP) that the CMOEA is going to deal with.
Edit intent: coherence
Original:  In this paper , we first propose three primary types of difficulty  to characterize the constraints in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness.
Revised:  In this paper , we first propose three primary types of difficulty to characterize the constraint functions in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness.
Edit intent: clarity
Original:  In fact, combination of the three primary constraint functions with different parameters can lead to construct a large variety of CMOPs and CMaOPs , whose difficulty can be uniquely defined by a triplet with each of its parameter specifying the level of each primary difficulty type respectively.
Revised:  In fact, combination of the three primary constraint functions with different parameters can lead to construct a large variety of CMOPs , whose difficulty can be uniquely defined by a triplet with each of its parameter specifying the level of each primary difficulty type respectively.
Edit intent: coherence
Original: Multi-objective evolutionary algorithms (MOEAs) have achieved great progress in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems.
Revised: Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems.
Edit intent: clarity
Original:  In fact, many real-world multi-objective problems usually contain a number of constraints.
Revised:  In fact, many real-world multi-objective problems contain a number of constraints.
Edit intent: clarity
Original:  To promote the research of constrained multi-objective optimization, we first propose  three primary types of difficulty, which reflect the challenges in the real-world optimization problems,  to characterize the constraint functions in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness.
Revised:  To promote research on constrained multi-objective optimization, we first propose three primary types of difficulty, which reflect the challenges in the real-world optimization problems, to characterize the constraint functions in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness.
Edit intent: clarity
Original:  To promote the research of constrained multi-objective optimization, we first propose  three primary types of difficulty, which reflect the challenges in the real-world optimization problems,  to characterize the constraint functions in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness.
Revised:  To promote the research of constrained multi-objective optimization, we first propose three primary types of difficulty, which reflect the challenges in the real-world optimization problems, in order to characterize the constraint functions in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness.
Edit intent: coherence
Original:  To promote the research of constrained multi-objective optimization, we first propose  three primary types of difficulty, which reflect the challenges in the real-world optimization problems,  to characterize the constraint functions in CMOPs, including feasibility-hardness, convergence-hardness and diversity-hardness.
Revised:  To promote the research of constrained multi-objective optimization, we first propose three primary types of difficulty, which reflect the challenges in the real-world optimization problems, to characterize the constraint functions in constrained multi-objective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness and diversity-hardness.
Edit intent: coherence
Original:  We then develop a general toolkit to construct difficulty adjustable and scalable constrained multi-objective optimization problems (CMOPs ) with three types of parameterized constraint functions according to the proposed three primary types of difficulty.
Revised:  We then develop a general toolkit to construct difficulty adjustable and scalable constrained multi-objective optimization problems (CMOPs ) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty.
Edit intent: clarity
Original:  Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs named DAS-CMOP1-9.
Revised:  Based on this toolkit, we suggest nine difficulty-adjustable and scalable CMOPs named DAS-CMOP1-9.
Edit intent: fluency
Original:  For example, navigation behavior might change in different phases of a website visit , or movement behavior may vary between groups of user .
Revised:  For example, navigation behavior might change in different phases of browsing a website , or movement behavior may vary between groups of user .
Edit intent: clarity
Original:  For example, navigation behavior might change in different phases of a website visit , or movement behavior may vary between groups of user .
Revised:  For example, navigation behavior might change in different phases of a website visit , or movement behavior may vary between groups of users .
Edit intent: fluency