wanyu/IteraTeR_v2 huggingface dataset:
Original:  Leisure travel has an important impact on the local and global economy and on the environment as well .
Revised:  Leisure travel has an important impact on the local and global economy and on the environment .
Edit intent: clarity
Original:  We first rank the touristic sites according to the spatial distribution of their visitors' place of residence.
Revised:  We first rank the touristic sites based on the spatial distribution of their visitors' place of residence.
Edit intent: fluency
Original:  We first rank the touristic sites according to the spatial distribution of their visitors' place of residence.
Revised:  We first rank the touristic sites according to the spatial distribution of the visitors' place of residence.
Edit intent: clarity
Original:  We then consider a coarser scale and classify the travelers by country of residence.
Revised:  We then pass to a coarser scale and classify the travelers by country of residence.
Edit intent: clarity
Original:  This allows us to unveil relations between touristic sites and find which ones are more tightly interconnected.
Revised:  This allow us to unveil relations between touristic sites and find which ones are more tightly interconnected.
Edit intent: fluency
Original: Let P \subset R^d be a set of n points in the d dimensions such that each point p \in P has an associated radius r_p > 0.
Revised: Let P \subset R^d be a set of n points in d dimensions such that each point p \in P has an associated radius r_p > 0.
Edit intent: clarity
Original:  In a geometric reachability query we ask whether a vertex p in G can "reach" a target q which is an arbitrary point  the plane (rather than restricted to be another vertex q of G in a standard reachability query).
Revised:  In a geometric reachability query we ask whether a vertex p in G can "reach" a target q which is an arbitrary point in the plane (rather than restricted to be another vertex q of G in a standard reachability query).
Edit intent: fluency
Original:  In addition  it maintains conservation laws on a discrete level.
Revised:  In addition , it maintains conservation laws on a discrete level.
Edit intent: fluency
Original:  Here, we present the theory for UDG-FEM forward modeling, its validation using quasianalytical solutions in multi-layer sphere models and an evaluation of the new method in a comparison with a discontinuous Galerkin (DG-FEM) method on hexahedral and on conforming tetrahedral meshes.
Revised:  Here, we present the theory for UDG-FEM forward modeling, its verification using quasi-analytical solutions in multi-layer sphere models and an evaluation of the new method in a comparison with a discontinuous Galerkin (DG-FEM) method on hexahedral and on conforming tetrahedral meshes.
Edit intent: clarity
Original:  Here, we present the theory for UDG-FEM forward modeling, its validation using quasianalytical solutions in multi-layer sphere models and an evaluation of the new method in a comparison with a discontinuous Galerkin (DG-FEM) method on hexahedral and on conforming tetrahedral meshes.
Revised:  Here, we present the theory for UDG-FEM forward modeling, its validation using quasianalytical solutions in multi-layer sphere models and an evaluation in a comparison with a discontinuous Galerkin (DG-FEM) method on hexahedral and on conforming tetrahedral meshes.
Edit intent: clarity
Original: In vivo experiments have highlighted the importance of understanding hemodynamic forces, i.e., shear stress and pressure, in vertebrate cardiogenesis.
Revised:  experiments have highlighted the importance of understanding hemodynamic forces, i.e., shear stress and pressure, in vertebrate cardiogenesis.
Edit intent: clarity
Original:  In particular, the formation of the cardiac trabeculae is governed by a delicate interaction between hemodynamic forces, myocardial activity,  and epigenetic signaling mechanisms, which are coupled with genetic regulatory networks.
Revised:  In particular, the formation of the cardiac trabeculae is governed by a delicate interaction between hemodynamic forces, myocardial activity, and epigenetic signaling , all of which are coupled with genetic regulatory networks.
Edit intent: clarity
Original:  The fluid dynamics at the stage of development in which the trabeculae form is particularly complex , given the balance between inertial and viscous forces.
Revised:  Recently both in vivo and in silico studies have tried to better understand hemodynamics at varying stages of vertebrate cardiogenesis. In particular, the intracardial hemodynamics during the onset of trabeculation is notably complex - the inertial and viscous forces.
Edit intent: clarity
Original:  Vortices, which cause changes in the magnitude and direction of flow, are sensitive to small perturbations in scale, morphology, and unsteadiness of the flow  .
Revised:  Vortices, which cause changes in the magnitude and direction of flow, are sensitive to small perturbations in morphology, scale, and steadiness of the flow .
Edit intent: clarity
Original:  The immersed boundary method was used to solve the fluid-structure interaction problem of fluid flow moving through an idealized chamber with trabeculae .
Revised:  The immersed boundary method was used to solve the computational fluid dynamics problem involving fluid flow moving through an idealized chamber with trabeculae .
Edit intent: clarity
Original: We give improved hitting-sets for two special cases of Read-once Oblivious Arithmetic Branching Programs (ROABP).
Revised: We give improved hitting sets for two special cases of Read-once Oblivious Arithmetic Branching Programs (ROABP).
Edit intent: fluency
Original:  First is the case of an ROABP with known variable order .
Revised:  First is the case of an ROABP with known order of the variables .
Edit intent: clarity
Original:  The best hitting-set known for this case had cost (nw)^{O(\log n)} , where n is the number of variables and w is the width of the ROABP.
Revised:  The best previously known hitting set for this case had cost (nw)^{O(\log n)} , where n is the number of variables and w is the width of the ROABP.
Edit intent: clarity
Original:  The best hitting-set known for this case had cost (nw)^{O(\log n)} , where n is the number of variables and w is the width of the ROABP.
Revised:  The best hitting-set known for this case had cost (nw)^{O(\log n)} where n is the number of variables and w is the width of the ROABP.
Edit intent: fluency
Original:  In particular, this gives the first polynomial time hitting-set for constant-width ROABP (known-order).
Revised:  In particular, this gives the first polynomial-size hitting set for constant-width ROABP (known-order).
Edit intent: clarity
Original:  However, our hitting-set works only over those fields whose characteristic is zero or large enough.
Revised:  However, our hitting set only works when the characteristic of the field is zero or large enough.
Edit intent: clarity
Original:  To construct the hitting-set , we use the concept of the rank of  partial derivative matrix.
Revised:  To construct the hitting set , we use the concept of the rank of partial derivative matrix.
Edit intent: fluency
Original:  To construct the hitting-set , we use the concept of the rank of  partial derivative matrix.
Revised:  To construct the hitting-set , we use the concept of the rank of the partial derivative matrix.
Edit intent: fluency
Original:  Unlike previous approaches whose basic building block is a monomial map, we use a polynomial map .
Revised:  Unlike previous approaches which build up from mapping variables to monomials, we map variables to polynomials directly .
Edit intent: clarity
Original:  The best known hitting-set for this case had cost d^{O(\log w)}(nw)^{O(\log \log w)}, where d is the individual degree.
Revised:  The best previously known hitting set for this case had cost d^{O(\log w)}(nw)^{O(\log \log w)}, where d is the individual degree.
Edit intent: clarity
Original:  The best known hitting-set for this case had cost d^{O(\log w)}(nw)^{O(\log \log w)}, where d is the individual degree.
Revised:  The best known hitting-set for this case had size d^{O(\log w)}(nw)^{O(\log \log w)}, where d is the individual degree.
Edit intent: clarity
Original:  We improve this hitting-set complexity to (ndw)^{O(\log \log w)} . We get this by achieving rank concentration more efficiently .
Revised:  We improve the hitting-set complexity to (ndw)^{O(\log \log w)} . We get this by achieving rank concentration more efficiently .
Edit intent: fluency
Original:  We improve this hitting-set complexity to (ndw)^{O(\log \log w)} . We get this by achieving rank concentration more efficiently .
Revised:  We improve this hitting-set size to (ndw)^{O(\log \log w)} . We get this by achieving rank concentration more efficiently .
Edit intent: clarity
Original:  We improve this hitting-set complexity to (ndw)^{O(\log \log w)} . We get this by achieving rank concentration more efficiently .
Revised:  We improve this hitting-set complexity to (ndw)^{O(\log \log w)} .
Edit intent: coherence
Original: Evolving graphs arise in many different contexts where the interrelations between data elements change over time.
Revised: Evolving graphs arise in problems where interrelations between data elements change over time.
Edit intent: clarity
Original: Evolving graphs arise in many different contexts where the interrelations between data elements change over time.
Revised: Evolving graphs arise in many different contexts where the interrelations between data change over time.
Edit intent: clarity
Original:  Using simple examples, we show that naive matrix-matrix multiplication on time-dependent adjacency matrices miscounts the number of temporal paths.
Revised:  Using simple examples, we show that naive unfoldings of adjacency matrices miscount the number of temporal paths.
Edit intent: clarity
Original:  By mapping an evolving graph to an adjacency matrix of the equivalent static graph, we prove the properties of the BFS algorithm using the properties of the adjacency matrix .
Revised:  By mapping an evolving graph to an adjacency matrix of an equivalent static graph, we prove the properties of the BFS algorithm using the properties of the adjacency matrix .
Edit intent: fluency
Original:  By mapping an evolving graph to an adjacency matrix of the equivalent static graph, we prove the properties of the BFS algorithm using the properties of the adjacency matrix .
Revised:  By mapping an evolving graph to an adjacency matrix of the equivalent static graph, we prove that our generalization of the BFS algorithm using the properties of the adjacency matrix .
Edit intent: clarity
Original:  Finally,  demonstrate how the BFS over evolving graphs can be applied to mining citation networks.
Revised:  Finally, we demonstrate how the BFS over evolving graphs can be applied to mining citation networks.
Edit intent: fluency
Original:  Finally,  demonstrate how the BFS over evolving graphs can be applied to mining citation networks.
Revised:  Finally, demonstrate how the BFS over evolving graphs can be applied to mine citation networks.
Edit intent: fluency
Original: Workload management for cloud databases deals with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources.
Revised: Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources.
Edit intent: clarity
Original:  Existing solutions have approached these  challenges in isolation  and with only a particular type of performance goal in mind.
Revised:  Existing solutions have approached these challenges in isolation , and with only a particular type of performance goal in mind.
Edit intent: fluency
Original:  In this paper, we introduce , WiSeDB, a learning-based framework for generating end-to-end workload management solutions customized to application-defined performance metrics and workload characteristics.
Revised:  In this paper, we introduce WiSeDB, a learning-based framework for generating end-to-end workload management solutions customized to application-defined performance metrics and workload characteristics.
Edit intent: fluency
Original:  In this paper, we introduce , WiSeDB, a learning-based framework for generating end-to-end workload management solutions customized to application-defined performance metrics and workload characteristics.
Revised:  In this paper, we introduce , WiSeDB, a learning-based framework for generating holistic workload management solutions customized to application-defined performance metrics and workload characteristics.
Edit intent: clarity
Original:  Our approach relies on decision tree learning to train offline cost-effective  models for guiding query placement, scheduling, and resource provisioning decisions.
Revised:  Our approach relies on decision tree learning to train cost-effective models for guiding query placement, scheduling, and resource provisioning decisions.
Edit intent: clarity
Original:  These models can be used for both batch and online scheduling of incoming workloads.
Revised:  Applications can use these models for both batch and online scheduling of incoming workloads.
Edit intent: clarity
Original:  This allows us to present to the user alternative workload management solutions that address the typical performance vs  cost trade-off of cloud services.
Revised:  This allows us to present alternative workload management solutions that address the typical performance vs cost trade-off of cloud services.
Edit intent: clarity
Original:  This allows us to present to the user alternative workload management solutions that address the typical performance vs  cost trade-off of cloud services.
Revised:  This allows us to present to the user alternative workload management strategies that address the typical performance vs cost trade-off of cloud services.
Edit intent: clarity
Original:  This allows us to present to the user alternative workload management solutions that address the typical performance vs  cost trade-off of cloud services.
Revised:  This allows us to present to the user alternative workload management solutions that address the typical performance vs . cost trade-off of cloud services.
Edit intent: fluency
Original:  Experimental results show that our approach has very low training overhead while it discovers near optimal solutions for a variety of performance goals and workload characteristics.
Revised:  Experimental results show that our approach has very low training overhead while offering low cost strategies for a variety of performance goals and workload characteristics.
Edit intent: clarity
Original: In network modeling of complex systems one is often required to sample random realizations of networks that obey a given set of constraints, often in form of graph measures.
Revised: In network modeling of complex systems one is often required to sample random realizations of networks that obey a given set of constraints, usually in form of graph measures.
Edit intent: fluency
Original:  While the conjecture is still open for the general case , it was proven for special degree sequences, in particular, for those of undirected and directed regular simple graphs (Cooper, Dyer, Greenhill, 2007;
Revised:  While the conjecture is still open , it was proven for special degree sequences, in particular, for those of undirected and directed regular simple graphs (Cooper, Dyer, Greenhill, 2007;
Edit intent: clarity
Original:  Greenhill, 2011), of half-regular bipartite graphs (Mikl\'os, Erd\H{o and of graphs with certain bounded maximum degrees (Greenhill, 2015) .
Revised:  Greenhill, 2011), of half-regular bipartite graphs , and of graphs with certain bounded maximum degrees (Greenhill, 2015) .
Edit intent: fluency
Original:  Greenhill, 2011), of half-regular bipartite graphs (Mikl\'os, Erd\H{o and of graphs with certain bounded maximum degrees (Greenhill, 2015) .
Revised:  Greenhill, 2011), of half-regular bipartite graphs (Mikl\'os, Erd\H{o and of graphs with certain bounded maximum degrees .
Edit intent: coherence
Original:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to Tyshkevich (1984, 2000), a structural theorem for the space of graph realizations by Barrus (2015) and on a factorization theorem for Markov chains (Erd\H{os, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize Tyshkevich's decomposition for bipartite and directed graphs.
Revised:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences , a structural theorem for the space of graph realizations by Barrus (2015) and on a factorization theorem for Markov chains (Erd\H{os, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize Tyshkevich's decomposition for bipartite and directed graphs.
Edit intent: clarity
Original:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to Tyshkevich (1984, 2000), a structural theorem for the space of graph realizations by Barrus (2015) and on a factorization theorem for Markov chains (Erd\H{os, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize Tyshkevich's decomposition for bipartite and directed graphs.
Revised:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to Tyshkevich (1984, 2000), a structural theorem for the space of graph realizations and on a factorization theorem for Markov chains (Erd\H{os, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize Tyshkevich's decomposition for bipartite and directed graphs.
Edit intent: coherence
Original:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to Tyshkevich (1984, 2000), a structural theorem for the space of graph realizations by Barrus (2015) and on a factorization theorem for Markov chains (Erd\H{os, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize Tyshkevich's decomposition for bipartite and directed graphs.
Revised:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to Tyshkevich (1984, 2000), a structural theorem for the space of graph realizations by Barrus (2015) and on a factorization theorem for Markov chains s, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize Tyshkevich's decomposition for bipartite and directed graphs.
Edit intent: coherence
Original:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to Tyshkevich (1984, 2000), a structural theorem for the space of graph realizations by Barrus (2015) and on a factorization theorem for Markov chains (Erd\H{os, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize Tyshkevich's decomposition for bipartite and directed graphs.
Revised:  Our method is based on a canonical decomposition of degree sequences into split graph degree sequences due to Tyshkevich (1984, 2000), a structural theorem for the space of graph realizations by Barrus (2015) and on a factorization theorem for Markov chains (Erd\H{os, Mikl\'os, Toroczkai, 2015)} . After introducing bipartite splitted degree sequences, we also generalize the canonical split graph decomposition for bipartite and directed graphs.
Edit intent: clarity
Original:  Both row stores and column stores miss key opportunities towards the efficient execution of relationship queries , making them unacceptably slow in real-world OLAP scenarios.
Revised:  Both row stores and column stores miss key opportunities towards the efficient execution of relationship queries , making them unacceptably slow in such OLAP scenarios.
Edit intent: clarity
Original:  We present the FastR in-memory analytics engine that utilizes a new form of bottom-up fully pipelined query processing execution strategy.
Revised:  We present the FastR in-memory analytics engine utilizes a bottom-up fully pipelined query processing execution strategy.
Edit intent: clarity
Original:  Furthermore, FastR compiles its query plans into query-aware executable C++ source codes.
Revised:  Furthermore, FastR compiles its query plans into executable C++ source codes.
Edit intent: clarity
Original:  FastR outperforms Postgres by 2-4 orders of magnitude and MonetDB by 1-3 orders of magnitude , when all of them are running on RAM.
Revised:  FastR outperforms Postgres by 2-4 orders of magnitude and MonetDB by 1-3 orders of magnitude when all of them are running on RAM.
Edit intent: fluency
Original:  With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-peripheriness , by defining the network-level measures for nestedness and core-peripheriness in case of weighted and bipartite networks.
Revised:  With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-peripheriness , by defining the network-level measures for nestedness and core-periphery-ness in the case of weighted and bipartite networks.
Edit intent: clarity
Original:  However, at the same time, via more sophisticated null-model analysis, we also discover that the degree (the number of connected neighbors of a node) distribution poses quite severe restrictions on the possible nestedness and core-peripheriness parameter space.
Revised:  However, at the same time, via more sophisticated null-model analysis, we also discover that the degree (the number of connected neighbors of a node) distribution poses quite severe restrictions on the possible nestedness and core-periphery parameter space.
Edit intent: fluency
Original: WeChat is a mobile messaging application that has 549 million active users as of Q1 2015, and "WeChat Moments" (WM) serves as its social-networking function that allows users to post/share links of web pages ) . WM differs from the other social networks as it imposes many restrictions on the information diffusion process to mitigate the information overload.
Revised: WeChat is a mobile messaging application that has 549 million active users as of Q1 2015, and "WeChat Moments" (WM) serves its social-networking function that allows users to post/share links of web pages ) . WM differs from the other social networks as it imposes many restrictions on the information diffusion process to mitigate the information overload.
Edit intent: clarity
Original: WeChat is a mobile messaging application that has 549 million active users as of Q1 2015, and "WeChat Moments" (WM) serves as its social-networking function that allows users to post/share links of web pages ) . WM differs from the other social networks as it imposes many restrictions on the information diffusion process to mitigate the information overload.
Revised: WeChat is a mobile messaging application that has 549 million active users as of Q1 2015, and "WeChat Moments" (WM) serves as its social-networking function that allows users to post/share links of web pages . WM differs from the other social networks as it imposes many restrictions on the information diffusion process to mitigate the information overload.
Edit intent: fluency
Original:  Specifically, we identify the relationship of the number of posted pages and the number of views, the diffusion path lengths , the similarity and distribution of users' locations as well as their connections to the GDP of the user' s province.
Revised:  Specifically, we identify the relationship of the number of posted pages and the number of views, the diffusion path length , the similarity and distribution of users' locations as well as their connections to the GDP of the user' s province.
Edit intent: fluency