wanyu/IteraTeR_v2 huggingface dataset:
Original:  The proof requires a number of technical innovations, including proving soundness and completeness in a mutually-recursive fashion.
Revised:  The proof requires a number of technical innovations, including proving soundness and completeness in a mutually recursive fashion.
Edit intent: fluency
Original:  We use refinement types to avoid explicitly passing equality proofs in our term syntax, making our calculus close to languages such as Haskell and OCaml.
Revised:  We use refinement types to avoid explicitly passing equality proofs in our term syntax, making our calculus similar to languages such as Haskell and OCaml.
Edit intent: clarity
Original:  Pushing down the sampling operator through both sides of the join is inherently challenging due to data skew and correlation issues between output tuples.
Revised:  Pushing down the sampling operator through both sides of the join is inherently challenging due to the need to avoid correlation between output tuples.
Edit intent: clarity
Original:  This paper presents a unified strategy towards sampling over joins, comprising  two key contributions.
Revised:  This paper presents a unified two key contributions.
Edit intent: clarity
Original:  First, in the case that perfect sampling is a requirement, we introduce techniquesto generate aperfect random sample from both sides of a join .
Revised:  First, in the case perfect random sample from both sides of a join .
Edit intent: clarity
Original:  First, in the case that perfect sampling is a requirement, we introduce techniquesto generate aperfect random sample from both sides of a join .
Revised:  First, in the case that perfect sampling is a requirement, we introduce techniquesto generate a random sample from both sides of a join .
Edit intent: fluency
Original:  \emph{  Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Revised:  sample is\emph{ Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Edit intent: coherence
Original:  \emph{  Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Revised:  acceptable\emph{ Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Edit intent: fluency
Original:  \emph{  Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Revised:  \emph{ . Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Edit intent: fluency
Original:  \emph{  Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Revised:  \emph{ Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide maximize randomness of the join.
Edit intent: clarity
Original:  \emph{  Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic tomaximize randomness of the join.
Revised:  \emph{ Second, in the case that random sampling is not a requirement but is still preferred\emph{ , we provide a novel sampling heuristic to randomness of the join.
Edit intent: clarity
Original: For many use cases it is not feasible to access RDF data in a truly federated fashion.
Revised: For many use cases it is not feasible to access RDF data in a federated fashion.
Edit intent: clarity
Original:  For consistency, latency and performance reasons data needs to be replicated in order to be used locally.
Edit intent: coherence
Original:  To formalize hypotheses we utilize data that is publicly available onthe Web, namely Foursquare data and census data provided by anopen data platform.
Revised:  To formalize hypotheses we utilize data that is publicly available on the Web, namely Foursquare data and census data provided by anopen data platform.
Edit intent: fluency
Original:  To formalize hypotheses we utilize data that is publicly available onthe Web, namely Foursquare data and census data provided by anopen data platform.
Revised:  To formalize hypotheses we utilize data that is publicly available onthe Web, namely Foursquare data and census data provided by an open data platform.
Edit intent: fluency
Original:  By applying this combination of approaches to taxi data in Manhattan, we can discover and explain differentpatterns in human mobility that cannot be identified in a collective analysis.
Revised:  By applying this combination of approaches to taxi data in Manhattan, we can discover and explain different patterns in human mobility that cannot be identified in a collective analysis.
Edit intent: fluency
Original: Diffusion-based network models are widely used for protein function prediction using protein network data , and have been shown to outperform neighborhood-based and module-based methods.
Revised: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods.
Edit intent: fluency
Original: Diffusion-based network models are widely used for protein function prediction using protein network data , and have been shown to outperform neighborhood-based and module-based methods.
Revised: Diffusion-based network models are widely used for protein function prediction using protein network data , and have been shown to outperform neighborhood- and module-based methods.
Edit intent: fluency
Original:  However, previous methods usually  used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernels.
Revised:  However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernels.
Edit intent: coherence
Original:  We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information flows on this two-layer  model.
Revised:  We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer model.
Edit intent: clarity
Original:  BirgRank is a direct application of traditional PageRank with fixed decay parameters.
Revised:  BirgRank is an application of traditional PageRank with fixed decay parameters.
Edit intent: clarity
Original:  We  find that both BirgRank and AptRank outperform the previous methods, especially when only 10\% of the data are given for training.
Revised:  We find that both BirgRank and AptRank outperform the previous methods, especially when only 10\% of the data for training.
Edit intent: clarity
Original:  Super resolution optical fluctuation imaging (SOFI) is a fluorescence microscopy technique enabling sub-diffraction-limit imaging with high temporal resolution by calculating high order statistics of the fluctuating optical signal.
Revised:  Super-resolution optical fluctuation imaging (SOFI) is a fluorescence microscopy technique enabling sub-diffraction-limit imaging with high temporal resolution by calculating high order statistics of the fluctuating optical signal.
Edit intent: fluency
Original:  Super resolution optical fluctuation imaging (SOFI) is a fluorescence microscopy technique enabling sub-diffraction-limit imaging with high temporal resolution by calculating high order statistics of the fluctuating optical signal.
Revised:  Super resolution optical fluctuation imaging (SOFI) is a fluorescence microscopy technique enabling sub-diffraction limit imaging with high temporal resolution by calculating high order statistics of the fluctuating optical signal.
Edit intent: fluency
Original:  The aim of this work is to achieve fast super resolution acoustic imaging by applying the tools used in SOFI to contrast-enhance ultrasound (CEUS) plane-wave scans.
Revised:  The aim of this work is to achieve fast super-resolution acoustic imaging by applying the tools used in SOFI to contrast-enhance ultrasound (CEUS) plane-wave scans.
Edit intent: fluency
Original:  The proposed methods were tested using numerical simulations and validated using two in-vivo rabbit models: scans of healthy kidneys and VX-2 tumor xenografts.
Revised:  The proposed method was tested using numerical simulations and validated using two in-vivo rabbit models: scans of healthy kidneys and VX-2 tumor xenografts.
Edit intent: fluency
Original:  The proposed methods were tested using numerical simulations and validated using two in-vivo rabbit models: scans of healthy kidneys and VX-2 tumor xenografts.
Revised:  The proposed methods were tested using numerical simulations and evaluated using two in-vivo rabbit models: scans of healthy kidneys and VX-2 tumor xenografts.
Edit intent: clarity
Original:  Improved spatial resolution was achieved with a reduction of up to 40\% in the full width half max of the point spread function.
Revised:  Improved spatial resolution was observed with a reduction of up to 40\% in the full width half max of the point spread function.
Edit intent: clarity
Original:  In addition, substantial reduction in the background level  compared to standard mean amplitude persistence images was achieved , revealing small vascular structures within tumors.
Revised:  In addition, substantial reduction in the background level was achieved compared to standard mean amplitude persistence images was achieved , revealing small vascular structures within tumors.
Edit intent: fluency
Original:  In addition, substantial reduction in the background level  compared to standard mean amplitude persistence images was achieved , revealing small vascular structures within tumors.
Revised:  In addition, substantial reduction in the background level compared to standard mean amplitude persistence images , revealing small vascular structures within tumors.
Edit intent: clarity
Original:  The scan duration required for the proposed method is less than a second while current super-localization techniques require acquisition duration of several minutes.
Revised:  The scan duration of the proposed method is less than a second while current super-localization techniques require acquisition duration of several minutes.
Edit intent: clarity
Original:  As a result, the proposed technique can be used for obtaining high spatio-temporal resolutionscans , facilitating flow-dynamics monitoring.
Revised:  As a result, the proposed technique may be used to obtain scans with sub-diffraction spatial resolution and high temporal resolution , facilitating flow-dynamics monitoring.
Edit intent: clarity
Original:  This technique can also be applied during a breath-hold, reducing the sensitivity to motion artifact .
Revised:  Our method can also be applied during a breath-hold, reducing the sensitivity to motion artifact .
Edit intent: clarity
Original:  This technique can also be applied during a breath-hold, reducing the sensitivity to motion artifact .
Revised:  This technique can also be applied during a breath-hold, reducing the sensitivity to motion artifacts .
Edit intent: fluency
Original:  The aim of this work is to achieve fast super-resolution acoustic imaging by applying the tools used in SOFI to contrast-enhance ultrasound (CEUS) plane-wave scans.
Revised:  The aim of this work is to achieve fast acoustic imaging with enhanced resolution by applying the tools used in SOFI to contrast-enhance ultrasound (CEUS) plane-wave scans.
Edit intent: clarity
Original:  The scan duration of the proposed method is less than a second while current super-localization techniques require acquisition duration of several minutes.
Revised:  The scan duration of the proposed method is less than a second while current superlocalization techniques require acquisition duration of several minutes.
Edit intent: fluency
Original:  As a result, the proposed technique may be used to obtain scans with sub-diffraction spatial resolution and high temporal resolution, facilitating flow-dynamics monitoring.
Revised:  As a result, the proposed technique may be used to obtain scans with enhanced spatial resolution and high temporal resolution, facilitating flow-dynamics monitoring.
Edit intent: clarity
Original: In local community detection by seed expansion a single cluster concentrated around few given query nodes in a graph is discovered in a localized way.
Revised: Local network community detection is the task of finding a single community of nodes concentrated around few given query nodes in a graph is discovered in a localized way.
Edit intent: clarity
Original: In local community detection by seed expansion a single cluster concentrated around few given query nodes in a graph is discovered in a localized way.
Revised: In local community detection by seed expansion a single cluster concentrated around few given seed nodes in a graph is discovered in a localized way.
Edit intent: clarity
Original: In local community detection by seed expansion a single cluster concentrated around few given query nodes in a graph is discovered in a localized way.
Revised: In local community detection by seed expansion a single cluster concentrated around few given query nodes in a localized way.
Edit intent: clarity
Original:  We show that in this setting continuous optimization leads to hard clusters .
Revised:  We show that continuous optimization of this objective still leads to discrete communities .
Edit intent: clarity
Original:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Revised:  We investigate the relation of conductance with weighted kernel k-means for a single community , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Edit intent: clarity
Original:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Revised:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Edit intent: coherence
Original:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Revised:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Edit intent: coherence
Original:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Revised:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where %DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Edit intent: coherence
Original:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Revised:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% . Conductance is obtained by setting \sigma to 0.
Edit intent: clarity
Original:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Revised:  We investigate the relation of conductance with weighted kernel k-means for a single cluster , which leads to the introduction of a weighted kernel 1-mean objective function, called \sigma-conductance , where \sigma%DIFDELCMD < } %%% is a parameter which influences the size of the community . Conductance is obtained by setting \sigma to 0.
Edit intent: fluency
Original:  Two algorithms for local optimization of \sigma-conductance based on the expectation maximization and the projected gradient descend method are developed, called EMc and PGDc, respectively.
Revised:  Two algorithms for local optimization of \sigma-conductance based on the expectation maximization and projected gradient descent, respectively.
Edit intent: clarity
Original:  We design a procedure to automatically select a value for \sigma%DIFDELCMD < }%%% . Performance guarantee for these algorithms is proven for a class of dense  communities centered around the seeds and well separated from the rest of the network.
Revised:  We design a procedure to automatically select a value for %DIFDELCMD < }%%% . Performance guarantee for these algorithms is proven for a class of dense communities centered around the seeds and well separated from the rest of the network.
Edit intent: coherence
Original:  A comparative experimental analysis on networks with ground-truth communities is performed using state-of-the-art algorithms based on the graph diffusion method.
Revised:  A comparative experimental analysis on networks with ground-truth communities , comparing to state-of-the-art algorithms based on the graph diffusion method.
Edit intent: clarity
Original: We give the first multivariate version of Descartes' rule of signs to bound the number of positive real roots of a system of polynomial equations in n variables with n+2 monomials, in terms of the sign variation of a sequence associated both to the exponent vectors and the given coefficients.
Revised: We give a multivariate version of Descartes' rule of signs to bound the number of positive real roots of a system of polynomial equations in n variables with n+2 monomials, in terms of the sign variation of a sequence associated both to the exponent vectors and the given coefficients.
Edit intent: clarity
Original:  The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents ;
Revised:  The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents ,
Edit intent: fluency
Original:  We show that the social network sensing problem can be viewed as a blind compressed sensing problem with a sparse measurement matrix.
Revised:  The social network sensing problem can be viewed as a blind compressed sensing problem with a sparse measurement matrix.
Edit intent: coherence
Original:  We show that the social network sensing problem can be viewed as a blind compressed sensing problem with a sparse measurement matrix.
Revised:  We show that the social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix.
Edit intent: clarity
Original:  We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator for the steady states  . Simulation results on synthetic and real world networks support our findings.
Revised:  We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states . Simulation results on synthetic and real world networks support our findings.
Edit intent: fluency
Original:  In addition  a measure that bases on mutual information is introduced in order to best quantify the performance of the network with the time series.
Revised:  In addition , a measure that bases on mutual information is introduced in order to best quantify the performance of the network with the time series.
Edit intent: fluency
Original:  In addition , a measure that bases on mutual information is introduced in order to best quantify the performance of the network with the time series.
Revised:  In addition a measure that bases on mutual information is introduced in order to best quantify the performance of the network with the time series.
Edit intent: fluency
Original: In this paper we report the likelihood estimates that are performed on time series using a echo state network with orthogonal recurrent connectivity.
Revised: We report about probabilistic likelihood estimates that are performed on time series using a echo state network with orthogonal recurrent connectivity.
Edit intent: clarity
Original: In this paper we report the likelihood estimates that are performed on time series using a echo state network with orthogonal recurrent connectivity.
Revised: In this paper we report the likelihood estimates that are performed on time series using an echo state network with orthogonal recurrent connectivity.
Edit intent: fluency
Original:  The results  indicate that the optimal performance depends on the way of balancing the input strength with the recurrent activity, which also has an influence on the network with regard to the quality of  the short term prediction versus prediction that accounts for influences that date back a long time in the input history.
Revised:  The results indicate that the capability of the network to infer depends on the way of balancing the input strength with the recurrent activity, which also has an influence on the network with regard to the quality of the short term prediction versus prediction that accounts for influences that date back a long time in the input history.
Edit intent: clarity
Original:  The results  indicate that the optimal performance depends on the way of balancing the input strength with the recurrent activity, which also has an influence on the network with regard to the quality of  the short term prediction versus prediction that accounts for influences that date back a long time in the input history.
Revised:  The results indicate that the optimal performance depends on the balance between input strength and recurrent activity. This balance has an influence on the network with regard to the quality of the short term prediction versus prediction that accounts for influences that date back a long time in the input history.
Edit intent: clarity