wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We propose a novel framework to generate synthetic three-dimensional histological models based on a generator-discriminator pattern optimizing constrained features that construct a 3D model via a blender interface exploiting smooth shape continuity typical for biological specimens.
Revised:  We propose a novel framework to generate synthetic three-dimensional histological models based on a generator-discriminator pattern optimizing constrained features that construct a 3D model via a Blender interface exploiting smooth shape continuity typical for biological specimens.
Edit intent: fluency
Original:  To capture the spatial context of entire cell clusters we deploy a similar architecture expanded by style transfer capability. Models based on clusters of adjoined embedding points are generated to analyze the characteristic cell mutation process of breast cancer .
Revised:  To capture the spatial context of entire cell clusters we deploy a novel deep topology transformer that implements and attention mechanism on cell group images to extract features for the frozen feature decoder. The proposed algorithms achieves high quantitative and qualitative synthesis evident in comparative evaluation metrics such as a low Frechet-Inception scores .
Edit intent: meaning-changed
Original:  In this work, we propose a new algorithm for graph matching and show that, for two Erdo s-R\'enyi graphs with edge correlation 1-\alpha, our algorithm recovers the underlying matching  with high probability when \alpha \le 1 / (\log \log n)^C, where n is the number of vertices in each graph and C denotes a positive universal constant.
Revised:  In this work, we propose a new algorithm for graph matching : Our algorithm associates each vertex with a signature vector using a multistage procedure and then matches a pair of vertices from the two graphs if their signature vectors are close to each other. We show that, for two Erdo s-R\'enyi graphs with edge correlation 1-\alpha, our algorithm recovers the underlying matching with high probability when \alpha \le 1 / (\log \log n)^C, where n is the number of vertices in each graph and C denotes a positive universal constant.
Edit intent: meaning-changed
Original:  In this work, we propose a new algorithm for graph matching and show that, for two Erdo s-R\'enyi graphs with edge correlation 1-\alpha, our algorithm recovers the underlying matching  with high probability when \alpha \le 1 / (\log \log n)^C, where n is the number of vertices in each graph and C denotes a positive universal constant.
Revised:  In this work, we propose a new algorithm for graph matching and show that, for two Erdo s--R\'enyi graphs with edge correlation 1-\alpha, our algorithm recovers the underlying matching with high probability when \alpha \le 1 / (\log \log n)^C, where n is the number of vertices in each graph and C denotes a positive universal constant.
Edit intent: fluency
Original:  In this work, we propose a new algorithm for graph matching and show that, for two Erdo s-R\'enyi graphs with edge correlation 1-\alpha, our algorithm recovers the underlying matching  with high probability when \alpha \le 1 / (\log \log n)^C, where n is the number of vertices in each graph and C denotes a positive universal constant.
Revised:  In this work, we propose a new algorithm for graph matching and show that, for two Erdo s-R\'enyi graphs with edge correlation 1-\alpha, our algorithm recovers the underlying matching exactly with high probability when \alpha \le 1 / (\log \log n)^C, where n is the number of vertices in each graph and C denotes a positive universal constant.
Edit intent: meaning-changed
Original: We study full acyclic join queries with general join predicates that involve conjunctions and disjunctions of inequality. Specifically, we provide the first non-trivial algorithms for the problem of ranked enumeration where the query results are returned incrementally in an order dictated by a given ranking function.
Revised: We study full acyclic join queries with general join predicates that involve conjunctions and disjunctions of inequalities, focusing on ranked enumeration where the query results are returned incrementally in an order dictated by a given ranking function.
Edit intent: clarity
Original:  Given an acyclic inequality join between tables of size n , it returns the join answers in order and guarantees that for every value of k, the k top-ranked answers are returned in O(n polylog n + k log k) time .
Revised:  With n denoting the number of tuples in the database, we guarantee that for every value of k, the k top-ranked answers are returned in O(n polylog n + k log k) time .
Edit intent: meaning-changed
Original:  Given an acyclic inequality join between tables of size n , it returns the join answers in order and guarantees that for every value of k, the k top-ranked answers are returned in O(n polylog n + k log k) time . This matches  the best-known guarantee for equi-joins up to a polylogarithmic factor .
Revised:  Given an acyclic inequality join between tables of size n , it returns the join answers in order and guarantees that for every value of k, the k top-ranked answers are returned in O(n polylog n + k log k) time and \mathcal{O the best-known guarantee for equi-joins up to a polylogarithmic factor .
Edit intent: meaning-changed
Original:  The key ingredient is an \mathcal{O}(n \operatorname{polylog} n)-size factorized representation of all query results that is constructed in a preprocessing step . As a side benefit, our techniques are also applicable to the easier task of unranked enumeration ( i.e., without an output ordering) where we are able to return k join results in \mathcal{O}(n \operatorname{polylog} n + k).
Revised:  The key ingredient is an \mathcal{O}(n \operatorname{polylog} n)-size factorized representation of the query output, which is constructed on-the-fly for a given query and database . As a side benefit, our techniques are also applicable to the easier task of unranked enumeration ( i.e., without an output ordering) where we are able to return k join results in \mathcal{O}(n \operatorname{polylog} n + k).
Edit intent: clarity
Original:  The key ingredient is an \mathcal{O}(n \operatorname{polylog} n)-size factorized representation of all query results that is constructed in a preprocessing step . As a side benefit, our techniques are also applicable to the easier task of unranked enumeration ( i.e., without an output ordering) where we are able to return k join results in \mathcal{O}(n \operatorname{polylog} n + k).
Revised:  The key ingredient is an \mathcal{O}(n \operatorname{polylog} n)-size factorized representation of all query results that is constructed in a preprocessing step . As a side benefit, our techniques are also applicable to unranked enumeration ( i.e., without an output ordering) where we are able to return k join results in \mathcal{O}(n \operatorname{polylog} n + k).
Edit intent: clarity
Original:  The key ingredient is an \mathcal{O}(n \operatorname{polylog} n)-size factorized representation of all query results that is constructed in a preprocessing step . As a side benefit, our techniques are also applicable to the easier task of unranked enumeration ( i.e., without an output ordering) where we are able to return k join results in \mathcal{O}(n \operatorname{polylog} n + k).
Revised:  The key ingredient is an \mathcal{O}(n \operatorname{polylog} n)-size factorized representation of all query results that is constructed in a preprocessing step . As a side benefit, our techniques are also applicable to the easier task of unranked enumeration ( where answers can be returned in any order) for joins with inequalities, returning k answers in \mathcal{O}(n \operatorname{polylog} n + k).
Edit intent: meaning-changed
Original:  Our model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves . Individual waves are suppressed at the state of Transient Collective Immunity (TCI), which subsequently degrades due to the dynamic nature of social activity.
Revised:  Our model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves , which are transiently suppressed due to the dynamic nature of social activity.
Edit intent: clarity
Original:  This paper investigates the use of a Multilayer Perceptron (MLP), Classification and Regression Trees (CART),  and an Ordered Logit (OL) model for the prediction of sovereign credit ratings.
Revised:  This paper investigates the use of a Multilayer Perceptron (MLP), Classification and Regression Trees (CART), Support Vector Machines (SVM), Na\"ive Bayes (NB), and an Ordered Logit (OL) model for the prediction of sovereign credit ratings.
Edit intent: meaning-changed
Original:  We show that MLP is best suited for predicting sovereign credit ratings, with an accuracy of 68\%, followed by CART (59\%)  and OL (33\%).
Revised:  We show that MLP is best suited for predicting sovereign credit ratings, with an accuracy of 68\%, followed by CART (59\%) , SVM (41\%), NB (38\%), and OL (33\%).
Edit intent: meaning-changed
Original:  Investigation of the determining factors shows that roughly the same explanatory variables are important in all models, with regulatory quality,  GDP per capita and unemployment rate as common important variables.
Revised:  Investigation of the determining factors shows that there is some heterogeneity in the important variables across the models. However, the two models with the highest out-of-sample predictive accuracy, MLP and CART, show a lot of similarities in the influential variables, with regulatory quality, GDP per capita and unemployment rate as common important variables.
Edit intent: meaning-changed
Original: In this paper, we design and analyze third order positivity-preserving discontinuous Galerkin (DG) schemes for solving the time-dependent system of Poisson--Nernst--Planck (PNP) system , which has found much use in diverse applications.
Revised: In this paper, we design and analyze third order positivity-preserving discontinuous Galerkin (DG) schemes for solving the time-dependent system of Poisson--Nernst--Planck (PNP) equations , which has found much use in diverse applications.
Edit intent: clarity
Original:  Phys., 328, 2017%DIFDELCMD < ]%%% , solution positivity is only numerically enforced. In this paper we design a novel DG method, which with Euler forward time discretization is shown to preserve  positivity of cell averages at all time steps.
Revised:  Phys., 328, 2017%DIFDELCMD < ]%%% Our DG method with Euler forward time discretization is shown to preserve positivity of cell averages at all time steps.
Edit intent: coherence
Original:  Positivity of numerical solutions is then restored by a scaling limiter in reference to positive cell averages . Numerical examples are presented to demonstrate the third order accuracy and illustrate the positivity-preserving property in both one and two dimensions.
Revised:  The positivity of numerical solutions is then restored by a scaling limiter in reference to positive cell averages . Numerical examples are presented to demonstrate the third order accuracy and illustrate the positivity-preserving property in both one and two dimensions.
Edit intent: clarity
Original:  Positivity of numerical solutions is then restored by a scaling limiter in reference to positive cell averages . Numerical examples are presented to demonstrate the third order accuracy and illustrate the positivity-preserving property in both one and two dimensions.
Revised:  Positivity of numerical solutions is then restored by a scaling limiter in reference to positive weighted cell averages. The method is also shown to preserve steady states . Numerical examples are presented to demonstrate the third order accuracy and illustrate the positivity-preserving property in both one and two dimensions.
Edit intent: meaning-changed
Original:  The results generate SteemOps, a new dataset URLanizes over 900 million operations from Steemit into three sub-datasets : 1 ) social-network operation dataset (SOD) ;
Revised:  The results generate SteemOps, a new dataset URLanizes more than 900 million operations from Steemit into three sub-datasets : 1 ) social-network operation dataset (SOD) ;
Edit intent: fluency
Original:  The results generate SteemOps, a new dataset URLanizes over 900 million operations from Steemit into three sub-datasets : 1 ) social-network operation dataset (SOD) ;
Revised:  The results generate SteemOps, a new dataset URLanizes over 900 million operations from Steemit into three sub-datasets namely (i ) social-network operation dataset (SOD) ;
Edit intent: meaning-changed
Original:  The results generate SteemOps, a new dataset URLanizes over 900 million operations from Steemit into three sub-datasets : 1 ) social-network operation dataset (SOD) ; 2 ) witness-election operation dataset (WOD) ;
Revised:  The results generate SteemOps, a new dataset URLanizes over 900 million operations from Steemit into three sub-datasets : 1 ) social-network operation dataset (SOD) , (ii ) witness-election operation dataset (WOD) ;
Edit intent: meaning-changed
Original: Although the sequence-to-sequence network with attention mechanism and neural vocoder has made great progress in the quality of speech synthesis, there are still some problems to be solved in large-scale real-time applications. For example, to avoid long sentence alignment failure while maintaining rich prosody, and to reduce the computational overhead while ensuring perceptual quality. In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet.
Revised: In this work, a robust and efficient text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet.
Edit intent: coherence
Original:  In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet.
Revised:  In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, is proposed for large-scale online application. The key components of Triple M are: 1) A seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet.
Edit intent: meaning-changed
Original:  In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet.
Revised:  In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms. Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss or online service modification. Compared with the our best result obtained by using single attention mechanism (GMM-based attention), the word error rate of long sentence synthesis decreases by 23.5\% when multi-guidance attention mechanism is applied. 2) A efficient multi-band multi-time LPCNet.
Edit intent: meaning-changed
Original:  In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet. The former uses alignment results of different attention mechanismsto guide the learning of the basic attention mechanism , and only retains the basic attention mechanism during inference. This approach can improve the performance of the text-to-feature module by absorbing the advantages of all guidance attention methods without modifying the basic inference architecture. The latter reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies .
Revised:  In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet, which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies .
Edit intent: coherence
Original:  The latter reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies . The multi-band strategy enables the LPCNet to generate sub-band signals in each inference. By predicting the sub-band signals of adjacent time in one forward operation, the multi-time strategy further decreases the number of inferences required. Due to the multi-band and multi-time strategy , the vocoder speed is increased by 2.75x on a single CPU and the MOS (mean opinion score)degradation is slight .
Revised:  The latter reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies (from 2.8 to 1.0 GFLOP). Due to these strategies , the vocoder speed is increased by 2.75x on a single CPU and the MOS (mean opinion score)degradation is slight .
Edit intent: meaning-changed
Original:  Due to the multi-band and multi-time strategy , the vocoder speed is increased by 2.75x on a single CPU and the MOS (mean opinion score)degradation is slight .
Revised:  Due to the multi-band and multi-time strategy , the vocoder speed is increased by 2.75x on a single CPU without much MOS degradatiaon (4.57 vs. 4.45) .
Edit intent: meaning-changed
Original:  In this paper we develop new algorithmic ideas that allow us to obtain a polynomial-time (1 - 1{e})-approximation algorithm (asymptotically and in expectation) for any matroid, and thus allow us to control the (1-1{e})-approximate regret.
Revised:  In this paper we develop new algorithmic ideas that allow us to obtain a polynomial-time (1 - 1{e})-approximation algorithm (asymptotically and in expectation) for any matroid, and thus to control the (1-1{e})-approximate regret.
Edit intent: fluency
Original:  A mean-field theory indicates that a new mechanism -- the dynamical reciprocity , as a counterpart to the well-known network reciprocity, is at work to foster cooperation, which is confirmed by the detailed analysis.
Revised:  A mean-field theory indicates that a new mechanism -- the dynamical reciprocity , as a counterpart to the well-known network reciprocity, is at work to foster cooperation, which is confirmed by the detailed analysis.
Edit intent: fluency
Original:  Our findings point out the great potential towards high cooperation for many issues occurred in the real world, and may also provide clues to resolve those imminent crises like the climate change and trade wars .
Revised:  Our findings point out the great potential towards high cooperation for many issues are interwoven with each other in the real world, and may also provide clues to resolve those imminent crises like the climate change and trade wars .
Edit intent: meaning-changed
Original:  On 2d square lattices, we reveal that the game-game interaction can promote the cooperation prevalence in all cases, and the cooperation-defection phase transitions even become absent and fairly high cooperation is expected when the interaction goes to be extremely strong.
Revised:  On 2d square lattices, we reveal that the game-game interaction can promote the cooperation prevalence in all cases, and the cooperation-defection phase transitions even become absent and fairly high cooperation is expected when the interaction goes to be very strong.
Edit intent: clarity
Original:  The structural complexities of the underlying population, such as Newman-Watts small world networks, Erdo s-R\'enyi random networks, and Barab\'asi-Albert networks, also do not alter the working of the dynamical reciprocity.
Revised:  The structural complexities of the underlying population, such as Newman--Watts small world networks, Erdo s-R\'enyi random networks, and Barab\'asi-Albert networks, also do not alter the working of the dynamical reciprocity.
Edit intent: fluency
Original:  The structural complexities of the underlying population, such as Newman-Watts small world networks, Erdo s-R\'enyi random networks, and Barab\'asi-Albert networks, also do not alter the working of the dynamical reciprocity.
Revised:  The structural complexities of the underlying population, such as Newman-Watts small world networks, Erdo s--R\'enyi random networks, and Barab\'asi-Albert networks, also do not alter the working of the dynamical reciprocity.
Edit intent: fluency
Original:  The structural complexities of the underlying population, such as Newman-Watts small world networks, Erdo s-R\'enyi random networks, and Barab\'asi-Albert networks, also do not alter the working of the dynamical reciprocity.
Revised:  The structural complexities of the underlying population, such as Newman-Watts small world networks, Erdo s-R\'enyi random networks, and Barab\'asi--Albert networks, also do not alter the working of the dynamical reciprocity.
Edit intent: fluency
Original:  We implement our technique into a Python library called Surfer and find that it makes accurate iteration execution time predictions  on ResNet-50, Inception v3, the Transformer, GNMT, and DCGAN across six different GPU architectures.
Revised:  We implement our technique into a Python library called Habitat and find that it makes accurate iteration execution time predictions on ResNet-50, Inception v3, the Transformer, GNMT, and DCGAN across six different GPU architectures.
Edit intent: meaning-changed
Original:  We implement our technique into a Python library called Surfer and find that it makes accurate iteration execution time predictions  on ResNet-50, Inception v3, the Transformer, GNMT, and DCGAN across six different GPU architectures.
Revised:  We implement our technique into a Python library called Surfer and find that it makes accurate iteration execution time predictions (with an average error of 11.8\%) on ResNet-50, Inception v3, the Transformer, GNMT, and DCGAN across six different GPU architectures.
Edit intent: meaning-changed
Original: Objective: This paper proposes an neural network approach for predicting heart surface potentials (HSPs) from body surface potentials (BSPs), which reframes the traditional inverse problem of electrocardiography into a regression problem through the use of Gaussian 3D (G3D) basis function decomposition.
Revised: The proposed method re-frames traditional inverse problems of electrocardiography into a regression problem through the use of Gaussian 3D (G3D) basis function decomposition.
Edit intent: clarity
Original: Objective: This paper proposes an neural network approach for predicting heart surface potentials (HSPs) from body surface potentials (BSPs), which reframes the traditional inverse problem of electrocardiography into a regression problem through the use of Gaussian 3D (G3D) basis function decomposition. Methods: HSPs were generated using G3D basis functions and passed through a boundary element forward model to obtain corresponding BSPs . The generated BSPs (input) and HSPs (output) were used to train a neural network , which was then used to predict a variety of synthesized and decomposed real-world HSPs.
Revised: Objective: This paper proposes an neural network approach for predicting heart surface potentials (HSPs) from body surface potentials (BSPs), which reframes the traditional inverse problem of electrocardiography into regression problems, constraining the solution space by decomposing signals with multidimensional Gaussian impulse basis functions. Impulse HSPs were generated using G3D basis functions and passed through a boundary element forward model to obtain corresponding BSPs . The generated BSPs (input) and HSPs (output) were used to train a neural network , which was then used to predict a variety of synthesized and decomposed real-world HSPs.
Edit intent: meaning-changed