wanyu/IteraTeR_v2 huggingface dataset:
Original:  To do so, we first measured the prevalence  and identified the types of disclosures in over 500 thousand YouTube videos and 2.1 million Pinterest pins.
Revised:  To do so, we first measured the prevalence of and identified the types of disclosures in over 500 thousand YouTube videos and 2.1 million Pinterest pins.
Edit intent: fluency
Original:  To do so, we first measured the prevalence  and identified the types of disclosures in over 500 thousand YouTube videos and 2.1 million Pinterest pins.
Revised:  To do so, we first measured the prevalence and identified the types of disclosures in over 500 ,000 YouTube videos and 2.1 million Pinterest pins.
Edit intent: fluency
Original:  Based on our findings, we make various design and policy suggestions to help improve  disclosure practices on social media  .
Revised:  Based on our findings, we make various design and policy suggestions to help improve disclosure practices on social media platforms .
Edit intent: clarity
Original: We study the optimal convergence rates for distributed convex optimization problems over networks, where the objective is to minimize the sum i=1^{m}f_i(z) of local functions of the nodes in the network.
Revised: We study the optimal convergence rates for distributed convex optimization problems over networks, where the objective is to minimize a sum i=1^{m}f_i(z) of local functions of the nodes in the network.
Edit intent: fluency
Original: We study the optimal convergence rates for distributed convex optimization problems over networks, where the objective is to minimize the sum i=1^{m}f_i(z) of local functions of the nodes in the network.
Revised: We study the optimal convergence rates for distributed convex optimization problems over networks, where the objective is to minimize the sum i=1^{m}f_i(z) of functions over in a network.
Edit intent: clarity
Original:  We provide optimal complexity bounds for four different cases, namely: the case when each function f_i is strongly convex and smooth, the cases when it is either strongly convex or smooth and the case when it is convex but neither strongly convex nor smooth.
Revised:  We provide complexity bounds for four different cases, namely: the case when each function f_i is strongly convex and smooth, the cases when it is either strongly convex or smooth and the case when it is convex but neither strongly convex nor smooth.
Edit intent: clarity
Original:  We provide optimal complexity bounds for four different cases, namely: the case when each function f_i is strongly convex and smooth, the cases when it is either strongly convex or smooth and the case when it is convex but neither strongly convex nor smooth.
Revised:  We provide optimal complexity bounds for four different cases, namely: each function f_i is strongly convex and smooth, the cases when it is either strongly convex or smooth and the case when it is convex but neither strongly convex nor smooth.
Edit intent: fluency
Original:  We provide optimal complexity bounds for four different cases, namely: the case when each function f_i is strongly convex and smooth, the cases when it is either strongly convex or smooth and the case when it is convex but neither strongly convex nor smooth.
Revised:  We provide optimal complexity bounds for four different cases, namely: the case when each function f_i is strongly convex and smooth, each function is either strongly convex or smooth and the case when it is convex but neither strongly convex nor smooth.
Edit intent: clarity
Original:  We provide optimal complexity bounds for four different cases, namely: the case when each function f_i is strongly convex and smooth, the cases when it is either strongly convex or smooth and the case when it is convex but neither strongly convex nor smooth.
Revised:  We provide optimal complexity bounds for four different cases, namely: the case when each function f_i is strongly convex and smooth, the cases when it is either strongly convex or smooth , and when it is convex but neither strongly convex nor smooth.
Edit intent: coherence
Original:  Our approach is based on the dual of an appropriately formulated primal problem, which includes the underlying static graph that models the communication restrictions.
Revised:  Our approach is based on the dual of an appropriately formulated primal problem, which includes a graph that models the communication restrictions.
Edit intent: clarity
Original:  Our results show distributed algorithms that achieve the same optimal rates as their centralized counterparts (up to constant and logarithmic factors), with an additional  cost related to the spectral gap of the interaction matrix that captures the local communications of the nodes in the network .
Revised:  We propose distributed algorithms that achieve the same optimal rates as their centralized counterparts (up to constant and logarithmic factors), with an additional cost related to the spectral gap of the interaction matrix that captures the local communications of the nodes in the network .
Edit intent: clarity
Original:  Our results show distributed algorithms that achieve the same optimal rates as their centralized counterparts (up to constant and logarithmic factors), with an additional  cost related to the spectral gap of the interaction matrix that captures the local communications of the nodes in the network .
Revised:  Our results show distributed algorithms that achieve the same optimal rates as their centralized counterparts (up to constant and logarithmic factors), with an additional optimal cost related to the spectral gap of the interaction matrix that captures the local communications of the nodes in the network .
Edit intent: fluency
Original:   Unfortunately, the constructions in CJLW18 do not work for other common errors such as block transpositions.
Revised:  \ Unfortunately, the constructions in CJLW18 do not work for other common errors such as block transpositions.
Edit intent: fluency
Original:  As far as we know, previously there are no known explicit deterministic document exchange protocols in this case, and the best known binary code needs \Omega(n) redundant bitseven to correct justone block transposition .
Revised:  As far as we know, previously there are no known explicit deterministic document exchange protocols in this case, and the best known binary code needs \Omega(n) redundant bitseven to correct just block transposition .
Edit intent: fluency
Original:  As far as we know, previously there are no known explicit deterministic document exchange protocols in this case, and the best known binary code needs \Omega(n) redundant bitseven to correct justone block transposition .
Revised:  As far as we know, previously there are no known explicit deterministic document exchange protocols in this case, and the best known binary code needs \Omega(n) redundant bitseven to correct justone redundant bits .
Edit intent: clarity
Original: Solute exchange between mother and fetus takes place in the placenta, where maternal and fetal blood are brought into close proximity .
Revised: Across mammalian species, solute exchange takes place in the placenta, where maternal and fetal blood are brought into close proximity .
Edit intent: clarity
Original: Solute exchange between mother and fetus takes place in the placenta, where maternal and fetal blood are brought into close proximity .
Revised: Solute exchange between mother and fetus takes place in complex microvascular networks .
Edit intent: clarity
Original:  In the human  , the primary exchange units from the fetal perspective are terminal villi , thin-walled protrusions at the periphery of villous trees that contain small but disordered networks of fetal capillaries , and which are surrounded externally by maternal blood.
Revised:  In the human placenta , the primary exchange units from the fetal perspective are terminal villi , thin-walled protrusions at the periphery of villous trees that contain small but disordered networks of fetal capillaries , and which are surrounded externally by maternal blood.
Edit intent: clarity
Original:  In the human  , the primary exchange units from the fetal perspective are terminal villi , thin-walled protrusions at the periphery of villous trees that contain small but disordered networks of fetal capillaries , and which are surrounded externally by maternal blood.
Revised:  In the human , the primary exchange units are terminal villi , thin-walled protrusions at the periphery of villous trees that contain small but disordered networks of fetal capillaries , and which are surrounded externally by maternal blood.
Edit intent: clarity
Original:  In the human  , the primary exchange units from the fetal perspective are terminal villi , thin-walled protrusions at the periphery of villous trees that contain small but disordered networks of fetal capillaries , and which are surrounded externally by maternal blood.
Revised:  In the human , the primary exchange units from the fetal perspective are terminal villi that contain disordered networks of fetal capillaries , and which are surrounded externally by maternal blood.
Edit intent: clarity
Original:  In the human  , the primary exchange units from the fetal perspective are terminal villi , thin-walled protrusions at the periphery of villous trees that contain small but disordered networks of fetal capillaries , and which are surrounded externally by maternal blood.
Revised:  In the human , the primary exchange units from the fetal perspective are terminal villi , thin-walled protrusions at the periphery of villous trees that contain small but disordered networks of fetal capillaries and are surrounded externally by maternal blood.
Edit intent: coherence
Original:  We use image-based simulation to examine how the irregular three-dimensional internal structure of a terminal villus determines its exchange capacity for a wide range of solutes.
Revised:  Here we show how the irregular three-dimensional internal structure of a terminal villus determines its exchange capacity for a wide range of solutes.
Edit intent: clarity
Original:  We use image-based simulation to examine how the irregular three-dimensional internal structure of a terminal villus determines its exchange capacity for a wide range of solutes.
Revised:  We use image-based simulation to examine how the irregular internal structure of a terminal villus determines its exchange capacity for a wide range of solutes.
Edit intent: clarity
Original:  Distilling key geometric features into three scalar parameters, obtained from image analysis and computational fluid dynamics, we demonstrate how the structure-function relationship of terminal villi is well described using a relatively simple algebraic approximation .
Revised:  Distilling geometric features into three scalar parameters, obtained from image analysis and computational fluid dynamics, we demonstrate how the structure-function relationship of terminal villi is well described using a relatively simple algebraic approximation .
Edit intent: clarity
Original:  Distilling key geometric features into three scalar parameters, obtained from image analysis and computational fluid dynamics, we demonstrate how the structure-function relationship of terminal villi is well described using a relatively simple algebraic approximation .
Revised:  Distilling key geometric features into three scalar parameters, obtained from image analysis and computational fluid dynamics, we demonstrate how the structure-function relationship of terminal villi using a simple algebraic approximation .
Edit intent: clarity
Original:  This approximation provides a convenient tool for understanding transport at both the vessel and network level , revealing transitions between flow-limited and diffusion-limited transport for different solutes .
Revised:  This approximation provides a convenient tool for understanding transport at both the vessel and network level , revealing transitions between flow- and diffusion-limited transport for different solutes .
Edit intent: fluency
Original:  This approximation provides a convenient tool for understanding transport at both the vessel and network level , revealing transitions between flow-limited and diffusion-limited transport for different solutes .
Revised:  This approximation provides a convenient tool for understanding transport at both the vessel and network level , revealing transitions between flow-limited and diffusion-limited transport at vessel and network levels .
Edit intent: clarity
Original:  Our theory accommodates countercurrent effects, incorporates nonlinear blood rheology and offers a low-cost method for testing network robustness under perturbations .
Revised:  Our theory accommodates countercurrent effects, incorporates nonlinear blood rheology and offers an efficient method for testing network robustness under perturbations .
Edit intent: clarity
Original:  Our theory accommodates countercurrent effects, incorporates nonlinear blood rheology and offers a low-cost method for testing network robustness under perturbations .
Revised:  Our theory accommodates countercurrent effects, incorporates nonlinear blood rheology and offers a low-cost method for testing network robustness .
Edit intent: clarity
Original:  Our results suggest that physical estimates of placental solute transport, based on carefully defined geometrical statistics, provide a viable method for linking placental structure and function in health and disease .
Revised:  Our results show how physical estimates of placental solute transport, based on carefully defined geometrical statistics, provide a viable method for linking placental structure and function in health and disease .
Edit intent: clarity
Original:  Our results suggest that physical estimates of placental solute transport, based on carefully defined geometrical statistics, provide a viable method for linking placental structure and function in health and disease .
Revised:  Our results suggest that physical estimates of solute transport, based on carefully defined geometrical statistics, provide a viable method for linking placental structure and function in health and disease .
Edit intent: fluency
Original: In this short paper, we analyze the convergence of the Conjugate Gradient (CG) method in exact arithmetic, when the coefficient matrix A is symmetric positive semidefinite and the system is consistent.
Revised: We analyze the convergence of the Conjugate Gradient (CG) method in exact arithmetic, when the coefficient matrix A is symmetric positive semidefinite and the system is consistent.
Edit intent: clarity
Original:  In this contribution we present an Additive-Schwarz type preconditioner that enables efficient and parallel scalable iterative solutions of large-scale multi-level hp-refined finite cell analy- ses .
Revised:  In this contribution we present an Additive-Schwarz type preconditioner that enables efficient and parallel scalable iterative solutions of large-scale multi-level hp-refined finite cell analyses .
Edit intent: fluency
Original: Co-evolutionary processes are according to the evolutive urban theory at the center of urban systems dynamics.
Revised: Co-evolutionary processes are according to the evolutionary urban theory at the center of urban systems dynamics.
Edit intent: fluency
Original:  We develop an effective framework for the three balanced clustering problems to address this issue, and our idea is based on a novel spatial partition  in geometry.
Revised:  We develop an effective framework for the three balanced clustering problems to address this issue, and our method is based on a novel spatial partition in geometry.
Edit intent: clarity
Original:  We develop an effective framework for the three balanced clustering problems to address this issue, and our idea is based on a novel spatial partition  in geometry.
Revised:  We develop an effective framework for the three balanced clustering problems to address this issue, and our idea is based on a novel spatial partition idea in geometry.
Edit intent: clarity
Original:  For the balanced k-center clustering, we provide a 4-approximation algorithm that improves the existing approximation factor 7 ;
Revised:  For the balanced k-center clustering, we provide a 4-approximation algorithm that improves the existing approximation factors ;
Edit intent: clarity
Original: We reprove three known (algorithmic ) bounds for terminal-clustering problems, using a single framework that leads to simpler proofs.
Revised: We reprove three known algorithmic bounds for terminal-clustering problems, using a single framework that leads to simpler proofs.
Edit intent: fluency
Original:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset  K\subset X of terminals , and the goal is to partition the points X , such that each part, called  cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Revised:  In this genre of problems, the input is a metric space (X,d) (possibly arising from a graph) and a subset K\subset X of terminals , and the goal is to partition the points X , such that each part, called cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Edit intent: clarity
Original:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset  K\subset X of terminals , and the goal is to partition the points X , such that each part, called  cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Revised:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset of terminals K\subset X of terminals , and the goal is to partition the points X , such that each part, called cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Edit intent: clarity
Original:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset  K\subset X of terminals , and the goal is to partition the points X , such that each part, called  cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Revised:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset K\subset X , and the goal is to partition the points X , such that each part, called cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Edit intent: clarity
Original:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset  K\subset X of terminals , and the goal is to partition the points X , such that each part, called  cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Revised:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset K\subset X of terminals , and the goal is to partition the points X such that each part, called cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Edit intent: fluency
Original:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset  K\subset X of terminals , and the goal is to partition the points X , such that each part, called  cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Revised:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset K\subset X of terminals , and the goal is to partition the points X , such that each part, called a cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Edit intent: fluency
Original:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset  K\subset X of terminals , and the goal is to partition the points X , such that each part, called  cluster, contains exactly one terminal (possibly with connectivity requirements) , so as to minimize some objective.
Revised:  The input in terminal-clustering problems is a metric space (X,d) (possibly arising from a graph) and a subset K\subset X of terminals , and the goal is to partition the points X , such that each part, called cluster, contains exactly one terminal (possibly with connectivity requirements) so as to minimize some objective.
Edit intent: fluency
Original:  The three bounds we reprove are for Steiner Point Removal on trees [Gupta, SODA 2001],  Metric 0-Extension for bounded doubling dimension [Lee and Naor, unpublished 2003], and  Connected Metric 0-Extension [Englert et al., SICOMP 2014].
Revised:  The three bounds we reprove are for Steiner Point Removal on trees [Gupta, SODA 2001], for Metric 0-Extension for bounded doubling dimension [Lee and Naor, unpublished 2003], and Connected Metric 0-Extension [Englert et al., SICOMP 2014].
Edit intent: fluency
Original:  The three bounds we reprove are for Steiner Point Removal on trees [Gupta, SODA 2001],  Metric 0-Extension for bounded doubling dimension [Lee and Naor, unpublished 2003], and  Connected Metric 0-Extension [Englert et al., SICOMP 2014].
Revised:  The three bounds we reprove are for Steiner Point Removal on trees [Gupta, SODA 2001], Metric 0-Extension in bounded doubling dimension [Lee and Naor, unpublished 2003], and Connected Metric 0-Extension [Englert et al., SICOMP 2014].
Edit intent: clarity
Original:  The three bounds we reprove are for Steiner Point Removal on trees [Gupta, SODA 2001],  Metric 0-Extension for bounded doubling dimension [Lee and Naor, unpublished 2003], and  Connected Metric 0-Extension [Englert et al., SICOMP 2014].
Revised:  The three bounds we reprove are for Steiner Point Removal on trees [Gupta, SODA 2001], Metric 0-Extension for bounded doubling dimension [Lee and Naor, unpublished 2003], and for Connected Metric 0-Extension [Englert et al., SICOMP 2014].
Edit intent: fluency
Original:  A now-standard fix is to enlarge each Voronoi cell computed in some order to obtain disjoint clusters, which defines theNoisy-Voronoi algorithm .
Revised:  A now-standard fix is to obtain disjoint clusters, which defines theNoisy-Voronoi algorithm .
Edit intent: clarity
Original:  A now-standard fix is to enlarge each Voronoi cell computed in some order to obtain disjoint clusters, which defines theNoisy-Voronoi algorithm .
Revised:  A now-standard fix is to enlarge each Voronoi cell computed in some order to obtain disjoint clusters, Noisy-Voronoi algorithm .
Edit intent: clarity
Original:  A now-standard fix is to enlarge each Voronoi cell computed in some order to obtain disjoint clusters, which defines theNoisy-Voronoi algorithm .
Revised:  A now-standard fix is to enlarge each Voronoi cell computed in some order to obtain disjoint clusters, which defines the algorithm .
Edit intent: coherence
Original:  A now-standard fix is to enlarge each Voronoi cell computed in some order to obtain disjoint clusters, which defines theNoisy-Voronoi algorithm .
Revised:  A now-standard fix is to enlarge each Voronoi cell computed in some order to obtain disjoint clusters, which defines theNoisy-Voronoi the cells are computed greedily according to some order .
Edit intent: clarity
Original:  Our main contribution is to demonstrate that the \texttt{Noisy-Voronoi algorithm is not only applicable to restricted metrics, but actually leads to relatively simple algorithms and analyses.
Revised:  Our main contribution is to demonstrate that the \texttt{Noisy-Voronoi algorithm is not only applicable to restricted metrics, and actually leads to relatively simple algorithms and analyses.
Edit intent: fluency
Original:  The  algorithm is not our own, it is a variant of radix sort with counting sort as a subroutine.
Revised:  The sorting algorithm is not our own, it is a variant of radix sort with counting sort as a subroutine.
Edit intent: clarity