wanyu/IteraTeR_v2 huggingface dataset:
Original:  We introduce the notion of "sequential " individual rationality (SIR) that requires that the "disutility " of existing passengers is non-increasing as additional passengers are picked up, and show that these constraints induce a natural limit on the incremental detours permissible as the ride progresses.
Revised:  We introduce the notion of sequential individual rationality (SIR) that requires that the "disutility " of existing passengers is non-increasing as additional passengers are picked up, and show that these constraints induce a natural limit on the incremental detours permissible as the ride progresses.
Edit intent: fluency
Original:  This observation inspires a "dual" notion of viewing cost sharingschemes as benefit sharing schemes, under which we introduce a natural definition of "sequential " fairness-the total incremental benefit due to the addition of a new passenger is (partly) shared among the existing passengers in proportion to the incremental inconvenience costs they suffer.
Revised:  This observation inspires a "dual" notion of viewing cost sharingschemes as benefit sharing schemes, under which we introduce a natural definition of sequential fairness (SF) - the total incremental benefit due to the addition of a new passenger is (partly) shared among the existing passengers in proportion to the incremental inconvenience costs they suffer.
Edit intent: clarity
Original:  This observation inspires a "dual" notion of viewing cost sharingschemes as benefit sharing schemes, under which we introduce a natural definition of "sequential " fairness-the total incremental benefit due to the addition of a new passenger is (partly) shared among the existing passengers in proportion to the incremental inconvenience costs they suffer.
Revised:  This observation inspires a "dual" notion of viewing cost sharingschemes as benefit sharing schemes, under which we introduce a natural definition of "sequential " fairness-the total incremental benefit due to a new customer is shared among existing customers in proportion to the incremental inconvenience costs they suffer.
Edit intent: clarity
Original:  We then provide an exact characterization of  sequentially fair cost sharing schemes, which brings out several useful structural properties, including a strong requirement that passengers must compensate each other for the detour inconveniences that they cause .
Revised:  We then provide an exact characterization of sequentially fair cost sharing schemes, which includes a strong requirement that passengers must compensate each other for the detour inconveniences that they cause .
Edit intent: clarity
Original:  Finally, we conclude with an extended discussion of new algorithmic problems related to and motivated by SIR , and important future work .
Revised:  Finally, we conclude with an extended discussion of new algorithmic problems related to and motivated by SIR .
Edit intent: clarity
Original:  We apply our approach using a dataset of over 81 ,000 mobile reviews over several years for the two of the most reviewed online travel agent apps from the iOS and Google Play marketplaces.
Revised:  We apply our approach using a dataset of over 81 ,000 mobile reviews over several years for the two of the most popular online travel agent apps from the iOS and Google Play marketplaces.
Edit intent: clarity
Original:  We apply our approach using a dataset of over 81 ,000 mobile reviews over several years for the two of the most reviewed online travel agent apps from the iOS and Google Play marketplaces.
Revised:  We apply our approach using a dataset of over 81 ,000 mobile reviews over several years for the two of the most reviewed online travel agent apps from the iTunes and Google Play marketplaces.
Edit intent: fluency
Original: We propose a new framework, called Hierarchical Multi-resolution Mesh Network (HMMN ), which establishes a dynamic brain network for each time resolution of the fMRI signal to represent the underlying cognitive process.
Revised: We propose a new framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs ), which establishes a dynamic brain network for each time resolution of the fMRI signal to represent the underlying cognitive process.
Edit intent: fluency
Original: We propose a new framework, called Hierarchical Multi-resolution Mesh Network (HMMN ), which establishes a dynamic brain network for each time resolution of the fMRI signal to represent the underlying cognitive process.
Revised: We propose a new framework, called Hierarchical Multi-resolution Mesh Network (HMMN ), which establishes a set of brain networks at multiple time resolutions of fMRI signal to represent the underlying cognitive process.
Edit intent: clarity
Original:  Then, for each time resolution, a local mesh is formed around each brain region .
Revised:  Then, a brain network, called mesh network, is formed at each subband by ensembling a set of local meshes .
Edit intent: clarity
Original:  In the final step, the edge weights of the networks are used for brain decoding .
Revised:  In the final step, the adjacency matrices of mesh networks obtained at different subbands are ensembled for brain decoding .
Edit intent: clarity
Original:  This task is achieved by fusing the multi-resolution edge weights as the input to the Fuzzy Stacked Generalization (FSG) architecture .
Revised:  This task is achieved by fusing the multi-resolution edge weights as the input to the Fuzzy Stacked Generalization (FSG) .
Edit intent: clarity
Original:  Our results on Human Connectome Project task-fMRI dataset reflect that the suggested model, HMMN , can successfully discriminate tasks by extracting complementary information obtained from mesh edge weights of multiple subbands  .
Revised:  Our results on Human Connectome Project task-fMRI dataset reflect that the suggested HMMN model can successfully discriminate tasks by extracting complementary information obtained from mesh edge weights of multiple subbands .
Edit intent: fluency
Original:  Our results on Human Connectome Project task-fMRI dataset reflect that the suggested model, HMMN , can successfully discriminate tasks by extracting complementary information obtained from mesh edge weights of multiple subbands  .
Revised:  Our results on Human Connectome Project task-fMRI dataset reflect that the suggested model, HMMN , can successfully discriminate tasks by extracting complementary information obtained from mesh arc weights of multiple subbands .
Edit intent: clarity
Original: We propose a formal design framework for cooperative multi-robot systems through combining a top-down mission planning with a bottom-up motion planning.
Revised: We propose a hierarchical design framework to automatically synthesize coordination schemes and control policies for cooperative multi-agent systems to fulfill formal performance requirements, by associating a bottom-up motion planning.
Edit intent: clarity
Original:  A mission planning layer is sitting on the top of our framework, where an assume-guarantee reasoning and learning based approach is applied to decompose the global mission into local tasks according to the capabilitiesof each robot.
Revised:  A mission planning layer sits on the top of our framework, where an assume-guarantee reasoning and learning based approach is applied to decompose the global mission into local tasks according to the capabilitiesof each robot.
Edit intent: coherence
Original:  A mission planning layer is sitting on the top of our framework, where an assume-guarantee reasoning and learning based approach is applied to decompose the global mission into local tasks according to the capabilitiesof each robot.
Revised:  A mission planning layer is sitting on the top of the proposed framework, decomposing the global mission into local tasks according to the capabilitiesof each robot.
Edit intent: clarity
Original:  With respect to these local tasks , the motion planning for each robot is then solved by composing basic motion primitives that are verified safe by differential dynamic logic ( dL).
Revised:  With respect to these local tasks , the motion planning for each robot is then solved by composing basic motion primitives , which are verified safe by differential dynamic logic ( dL).
Edit intent: fluency
Original:  The motion primitive composition is through a Satisfiability Modulo Theories (SMT) solver that searches feasible solutions in face of constraints due to local task requirements and the environment description.
Revised:  The motion primitive composition is through a Satisfiability Modulo Theories (SMT) solver that searches feasible solutions in face of constraints imposed by local task requirements and the environment description.
Edit intent: clarity
Original:  Our method can handle changing environments as the motion primitives are reactive in nature which makes the motion planning adaptive to local environmental changes.
Revised:  It is shown that the proposed framework can handle dynamical environments as the motion primitives are reactive in nature which makes the motion planning adaptive to local environmental changes.
Edit intent: clarity
Original:  Our method can handle changing environments as the motion primitives are reactive in nature which makes the motion planning adaptive to local environmental changes.
Revised:  Our method can handle changing environments as the motion primitives possess reactive features, making the motion plans adaptive to local environmental changes.
Edit intent: clarity
Original:  Furthermore, on-line mission replanning can be triggered by lower motion planning layers once no feasible solutions can be found through the SMT solver.
Revised:  Furthermore, on-line mission reconfiguration can be triggered by lower motion planning layers once no feasible solutions can be found through the SMT solver.
Edit intent: clarity
Original:  Furthermore, on-line mission replanning can be triggered by lower motion planning layers once no feasible solutions can be found through the SMT solver.
Revised:  Furthermore, on-line mission replanning can be triggered by the motion planning layer once no feasible solutions can be found through the SMT solver.
Edit intent: clarity
Original:  The  design framework is illustrated through an automated warehouse example .
Revised:  The design framework is validated by an automated warehouse example .
Edit intent: clarity
Original:  The  design framework is illustrated through an automated warehouse example .
Revised:  The design framework is illustrated through an automated warehouse case study .
Edit intent: clarity
Original: In the real world datasets (e.g., DBPedia query log),  well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Revised: In the real world datasets (e.g., DBpedia query log), well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Edit intent: fluency
Original: In the real world datasets (e.g., DBPedia query log),  well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Revised: In the real world datasets (e.g., DBPedia query log), queries built on well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Edit intent: clarity
Original: In the real world datasets (e.g., DBPedia query log),  well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Revised: In the real world datasets (e.g., DBPedia query log), well-designed patterns containing only AND and OPT operators (for short, WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Edit intent: clarity
Original: In the real world datasets (e.g., DBPedia query log),  well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Revised: In the real world datasets (e.g., DBPedia query log), well-designed and-opt patterns (or WDAO-patterns) account for a large proportion in all SPARQL queries.
Edit intent: fluency
Original: In the real world datasets (e.g., DBPedia query log),  well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion in all SPARQL queries.
Revised: In the real world datasets (e.g., DBPedia query log), well-designed and-opt patterns (or WDAO-patterns) accounts for a large proportion among all SPARQL queries.
Edit intent: clarity
Original:  In this paper, we present a plugin-based framework for all SELECT queries built on the notion WDAO-patterns, named PIWD.
Revised:  In this paper, we present a plugin-based framework for all SELECT queries built on WDAO-patterns, named PIWD.
Edit intent: clarity
Original:  Given a WDAO-query Q, PIWD firstly transform a WDAO-pattern into  WDAO-tree , whose leaves only contain the conjunctive queries (CQ ) and inner nodes only contain OPT operation.
Revised:  The framework is based on a parse tree called WDAO-tree , whose leaves only contain the conjunctive queries (CQ ) and inner nodes only contain OPT operation.
Edit intent: clarity
Original: The growth of the primary tumor of metastatic colorectal cancer (mCRC) and associated angiogenesis is modeled by a system of ordinary differential equationsfor basic quantities which summarize the number of proliferating and hypoxic cancer cells , the degree of angiogenesis and the density of VEGF.
Revised: The growth of the number of proliferating and hypoxic cancer cells , the degree of angiogenesis and the density of VEGF.
Edit intent: clarity
Original: The growth of the primary tumor of metastatic colorectal cancer (mCRC) and associated angiogenesis is modeled by a system of ordinary differential equationsfor basic quantities which summarize the number of proliferating and hypoxic cancer cells , the degree of angiogenesis and the density of VEGF.
Revised: The growth of the primary tumor of metastatic colorectal cancer (mCRC) and associated angiogenesis is modeled by a system of ordinary differential equationsfor basic quantities which summarize the number of proliferating and hypoxic cancer cells , the degree of angiogenesis and the average intensity of VEGF.
Edit intent: clarity
Original:  In this paper, we present a novel optical flow pipeline that uses patch-matching with CNN trained features at multiple scales.
Revised:  In this paper, we present an optical flow pipeline that uses patch-matching with CNN trained features at multiple scales.
Edit intent: clarity
Original:  In this paper, we present a novel optical flow pipeline that uses patch-matching with CNN trained features at multiple scales.
Revised:  In this paper, we present a novel optical flow pipeline that uses patch matching with CNN trained features at multiple scales.
Edit intent: fluency
Original:  We show a novel way for calculating CNN based features for different scales, which performs better than existing methods.
Revised:  We present a novel way for calculating CNN based features for different scales, which performs better than existing methods.
Edit intent: clarity
Original:  Furthermore, we introduce a new thresholded loss for Siamese networks and demonstrate that our novel loss performs clearly better than existing losses.
Revised:  Furthermore, we introduce a new thresholded loss for Siamese networks and demonstrate that our loss performs clearly better than existing losses.
Edit intent: clarity
Original:  An interesting discovery in our paper is that low pass filtering feature maps can increase the robustness of features created by CNNs.
Revised:  An interesting discovery in our paper is that low-pass filtering of feature maps can increase the robustness of features created by CNNs.
Edit intent: clarity
Original:  We prove competitive performance of our approach by submitting it to the KITTI 2012 and KITTI 2015 evaluation portals and obtaining the best results on KITTI 2012 and the best for foreground objects in KITTI 2015.
Revised:  We proved the competitive performance of our approach by submitting it to the KITTI 2012 and KITTI 2015 evaluation portals and obtaining the best results on KITTI 2012 and the best for foreground objects in KITTI 2015.
Edit intent: fluency
Original:  We prove competitive performance of our approach by submitting it to the KITTI 2012 and KITTI 2015 evaluation portals and obtaining the best results on KITTI 2012 and the best for foreground objects in KITTI 2015.
Revised:  We prove competitive performance of our approach by submitting it to the KITTI 2012 , KITTI 2015 evaluation portals and obtaining the best results on KITTI 2012 and the best for foreground objects in KITTI 2015.
Edit intent: coherence
Original:  Furthermore, we introduce a new thresholded loss for Siamese networks and demonstrate that our loss performs clearly better than existing losses.
Revised:  Furthermore, we introduce a new thresholded loss for Siamese networks . We demonstrate that our loss performs clearly better than existing losses.
Edit intent: coherence
Original:  We show that the nondeterministic automatic complexity A_N(x) is close to maximal: n/2-A_N(x)=O(\log^2n), whereas Hyde has shown A_N(y)\le n/2+1 for all sequences y  .
Revised:  We show that the nondeterministic automatic complexity A_N(x) is close to maximal: n/2-A_N(x)=O(\log^2n), whereas Hyde has shown A_N(y)\le n/2+1 for all sequences y of length n .
Edit intent: clarity
Original: The growing populariy and adoption of differential privacy in academic and industrial settings has resulted in the development of increasingly sophisticated algorithms for releasing information while preserving privacy.
Revised: The growing popularity and adoption of differential privacy in academic and industrial settings has resulted in the development of increasingly sophisticated algorithms for releasing information while preserving privacy.
Edit intent: fluency
Original:  while existing type systems lacks expressive power for some sophisticated algorithms.
Revised:  while existing programming platforms lack expressive power for some sophisticated algorithms.
Edit intent: clarity
Original:  However, existing formal methods for differential privacy face a dilemma: methods based on customized logics can verify sophisticated algorithms but comes with a steep learning curve and significant annotation burden on the programmers ;
Revised:  However, existing formal methods for differential privacy face a dilemma: methods based on customized logics can verify sophisticated algorithms but come with a steep learning curve and significant annotation burden on the programmers ;
Edit intent: fluency
Original:  However, existing formal methods for differential privacy face a dilemma: methods based on customized logics can verify sophisticated algorithms but comes with a steep learning curve and significant annotation burden on the programmers ;
Revised:  However, existing formal methods for differential privacy face a dilemma: methods based on customized logics can verify sophisticated algorithms but comes with a steep learning curve and significant annotation burden on the programmers ,
Edit intent: fluency
Original:  In this paper, we present LightDP, a simple imperative language that strikes a better balance between expressive power and usefulness .
Revised:  In this paper, we present LightDP, a simple imperative language that strikes a better balance between expressive power and usability .
Edit intent: clarity
Original:  While our mathematical, algorithmic, and probabilistic understanding of the behavior of phylogenies in their metric space is relatively mature, rigorous inferential infrastructure is as yet undeveloped.
Revised:  While our mathematical, algorithmic, and probabilistic understanding of phylogenies in their metric space is relatively mature, rigorous inferential infrastructure is as yet undeveloped.
Edit intent: clarity
Original:  While our mathematical, algorithmic, and probabilistic understanding of the behavior of phylogenies in their metric space is relatively mature, rigorous inferential infrastructure is as yet undeveloped.
Revised:  While our mathematical, algorithmic, and probabilistic understanding of the behavior of phylogenies in their metric space is mature, rigorous inferential infrastructure is as yet undeveloped.
Edit intent: clarity
Original:  In this manuscript we unify recent computational and probabilistic advancements to propose a method for constructing tree-valued confidence sets.
Revised:  In this manuscript we unify recent computational and probabilistic advances to construct tree--valued confidence sets.
Edit intent: clarity
Original:  We draw on statistical concepts of block replicates for improved testing, investigating the hypothesis of isotrophy in a turtle evolution dataset, identifying the best supported most recent ancestor of the Zika virus (Pacific strains, contrary to media releases in Latin America), and formally test the hypothesis that a Floridian dentist with AIDS infected two of his patients with HIV.
Revised:  We draw on statistical concepts of block replicates for improved testing, investigating the hypothesis of isotrophy in a turtle evolution dataset, identifying the best supported most recent ancestor of the Zika virus , and formally testing the hypothesis that a Floridian dentist with AIDS infected two of his patients with HIV.
Edit intent: coherence
Original:  We prove that if H_1 is a finitely generated subgroup of a free group F, then the Walter Neumann coefficient \sigma(H_1) of H_1 is rational and can be computed in deterministic exponential time of size of H_1.
Revised:  We prove that if H_1 is a finitely generated subgroup of a free group F, then the WN-coefficient \sigma(H_1) of H_1 is rational and can be computed in deterministic exponential time of size of H_1.
Edit intent: clarity
Original:  We prove that if H_1 is a finitely generated subgroup of a free group F, then the Walter Neumann coefficient \sigma(H_1) of H_1 is rational and can be computed in deterministic exponential time of size of H_1.
Revised:  We prove that if H_1 is a finitely generated subgroup of a free group F, then the Walter Neumann coefficient \sigma(H_1) of H_1 is rational and can be computed in deterministic exponential time in the size of H_1.
Edit intent: clarity
Original:  This coefficient \sigma(H_1) is a minimal nonnegative real number such that, for every finitely generated subgroup H_2 of F, it is true that %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_1, H_2) \le \sigma(H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_2), where {\rm r} (H) :
Revised:  This coefficient \sigma(H_1) is the minimal nonnegative real number such that, for every finitely generated subgroup H_2 of F, it is true that %DIFDELCMD < {\rm r}%%% \bar {\rm (H_1, H_2) \le \sigma(H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm (H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm (H_2), where {\rm r} (H) :
Edit intent: fluency
Original:  This coefficient \sigma(H_1) is a minimal nonnegative real number such that, for every finitely generated subgroup H_2 of F, it is true that %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_1, H_2) \le \sigma(H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_2), where {\rm r} (H) :
Revised:  This coefficient \sigma(H_1) is a minimal nonnegative real number such that, for every finitely generated subgroup H_2 of F, it is true that %DIFDELCMD < {\rm r}%%% \bar {\rm r (H_1, H_2) \le \sigma(H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm (H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm (H_2), where {\rm r} (H) :
Edit intent: fluency
Original:  This coefficient \sigma(H_1) is a minimal nonnegative real number such that, for every finitely generated subgroup H_2 of F, it is true that %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_1, H_2) \le \sigma(H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm  (H_2), where {\rm r} (H) :
Revised:  This coefficient \sigma(H_1) is a minimal nonnegative real number such that, for every finitely generated subgroup H_2 of F, it is true that %DIFDELCMD < {\rm r}%%% \bar {\rm (H_1, H_2) \le \sigma(H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm r (H_1) %DIFDELCMD < {\rm r}%%% \bar {\rm (H_2), where {\rm r} (H) :
Edit intent: fluency