wanyu/IteraTeR_v2 huggingface dataset:
Original:  Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth characterization of SBRS characteristics and challenges are available.
Revised:  Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available.
Edit intent: clarity
Original:  This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviors (e.g., users' clicks on items) and their properties (e.g., session length).
Revised:  This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users' clicks on items) and their properties (e.g., session length).
Edit intent: fluency
Original:  Here, we show that this " juxtracrine " property allows for an exact treatment of the pattern formation problem via a system of nine coupled ordinary differential equations.
Revised:  Here, exploiting this " juxtracrine " property allows for an exact treatment of the pattern formation problem via a system of nine coupled ordinary differential equations.
Edit intent: clarity
Original:  Here, we show that this " juxtracrine " property allows for an exact treatment of the pattern formation problem via a system of nine coupled ordinary differential equations.
Revised:  Here, we show that this " juxtacrine " property allows for an exact treatment of the pattern formation problem via a system of nine coupled ordinary differential equations.
Edit intent: fluency
Original:  Here, we show that this " juxtracrine " property allows for an exact treatment of the pattern formation problem via a system of nine coupled ordinary differential equations.
Revised:  Here, we show that this " juxtracrine " property , we present an exact treatment of the pattern formation problem via a system of nine coupled ordinary differential equations.
Edit intent: clarity
Original:  Furthermore, we show that the possible patterns that are realized  can be analyzed by considering a co-dimension 2 pitchfork bifurcation of this system.
Revised:  The possible patterns that are realized can be analyzed by considering a co-dimension 2 pitchfork bifurcation of this system.
Edit intent: coherence
Original:  Also, our theory suggests a simple strategy for producing  defect-free patterns.
Revised:  Also, our theory suggests a simple strategy for producing such defect-free patterns.
Edit intent: clarity
Original: With the fast development of peer to peer (P2P) lending , financial institutions have a substantial challenge from benefit loss due to the delinquent behaviors of the money borrowers .
Revised: In the peer to peer (P2P) lending , financial institutions have a substantial challenge from benefit loss due to the delinquent behaviors of the money borrowers .
Edit intent: clarity
Original:  Therefore, having a comprehensive understanding of the changing trend of the default rate in the P2P domain is crucial.
Revised:  Therefore, having a powerful model to describe the trend of the default rate in the P2P domain is crucial.
Edit intent: clarity
Original:  Therefore, having a comprehensive understanding of the changing trend of the default rate in the P2P domain is crucial.
Revised:  Therefore, having a comprehensive understanding of the changing trend of the default rate in the P2P market is crucial.
Edit intent: clarity
Original:  In this paper, we comprehensively study the changing trend of the default rate of P2P USA market at the aggregative level from August 2007 to January 2016.
Revised:  In this paper, we comprehensively study the changing trend of the default rate at the aggregative level from August 2007 to January 2016.
Edit intent: clarity
Original:  Furthermore, incorporating the macroeconomic feature unemp_rate  can improve the LSTM performance by decreasing RMSE on both training and testing datasets.
Revised:  Furthermore, incorporating the macroeconomic feature unemp\_rate can improve the LSTM performance by decreasing RMSE on both training and testing datasets.
Edit intent: fluency
Original:  Furthermore, incorporating the macroeconomic feature unemp_rate  can improve the LSTM performance by decreasing RMSE on both training and testing datasets.
Revised:  Furthermore, incorporating the macroeconomic feature unemp_rate can improve the LSTM performance by decreasing RMSE on both the training and the testing datasets.
Edit intent: fluency
Original:  Our study can broaden the applications of LSTM approach in the P2P market .
Revised:  Our study can broaden the applications of the LSTM algorithm by using it on the sequential P2P market .
Edit intent: clarity
Original:  Our study can broaden the applications of LSTM approach in the P2P market .
Revised:  Our study can broaden the applications of LSTM approach in the P2P data and guide the investors in making investment strategies .
Edit intent: clarity
Original:  These two geometric quantities are estimated by fitting a scaling-law model to the non-parametric multiscale bootstrap probabilities .
Revised:  These two geometric quantities are estimated by fitting a model of scaling-law to the non-parametric multiscale bootstrap probabilities .
Edit intent: clarity
Original: We describe a new method for reconstructing spatially explicit maps of seasonal palaeoclimate variables from site based reconstructions.
Revised: We describe a method for reconstructing spatially explicit maps of seasonal palaeoclimate variables from site based reconstructions.
Edit intent: clarity
Original: We describe a new method for reconstructing spatially explicit maps of seasonal palaeoclimate variables from site based reconstructions.
Revised: We describe a new method for reconstructing spatially explicit maps of seasonal palaeoclimate variables from site-based reconstructions.
Edit intent: fluency
Original:  By assuming a set of Gaussian correlations in the error of the prior, the resulting climate is smoothed both from month to month and from grid cell to grid cell.
Revised:  By assuming a set of correlations in the error of the prior, the resulting climate is smoothed both from month to month and from grid cell to grid cell.
Edit intent: clarity
Original:  The amount of smoothing can be controlled through two length scales .
Revised:  The amount of smoothing can be controlled through the choice of two length-scale values .
Edit intent: clarity
Original: Cancer often spreads from a primary tumor and forms metastases, that are the main cause of cancer  mortality.
Revised: As cancer advances, cells often spread from the primary tumor to other parts of the body and form metastases. This is the main cause of cancer mortality.
Edit intent: clarity
Original: Cancer often spreads from a primary tumor and forms metastases, that are the main cause of cancer  mortality.
Revised: Cancer often spreads from a primary tumor and forms metastases, that are the main cause of cancer related mortality.
Edit intent: clarity
Original:  We find an alarmingly wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable.
Revised:  For some cancer types, we find a remarkably wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable.
Edit intent: clarity
Original:  Our model predicts that only very early resections can prevent recurrence, while small delays in the time of surgery  significantly increase the recurrence probability.
Revised:  Our model predicts that only very early resections can prevent recurrence, and that small delays in the time of surgery significantly increase the recurrence probability.
Edit intent: coherence
Original:  Our model predicts that only very early resections can prevent recurrence, while small delays in the time of surgery  significantly increase the recurrence probability.
Revised:  Our model predicts that only very early resections can prevent recurrence, while small delays in the time of surgery can significantly increase the recurrence probability.
Edit intent: fluency
Original:  The layered media Green's function for the Helmholtz equation, which satisfies the transmission conditions at material interfaces, is decomposed into a free space component and four types of reaction field components  from wave reflections and transmissions through the layered media.
Revised:  The layered media Green's function for the Helmholtz equation, which satisfies the transmission conditions at material interfaces, is decomposed into a free space component and four types of reaction field components arising from wave reflections and transmissions through the layered media.
Edit intent: clarity
Original:  The proposed algorithm is a combination of the classic FMM for the free space component and FMMs specifically designed for the four types reaction components, made possible by new multipole expansions (MEs)  as well as the multipole to local translation (M2L) operators for the reaction field components.
Revised:  The proposed algorithm is a combination of the classic FMM for the free space component and FMMs specifically designed for the four types reaction components, made possible by new multipole expansions (MEs) as well as the multipole-to-local translation (M2L) operators for the reaction field components.
Edit intent: fluency
Original:   The FMMs for the reaction components, which are implemented with the target particles and equivalent polarized sourcesassociated with the reaction field components, is found to be much more efficient than that for the free space component due to well-separateness between the equivalent polarized sources and the target particles .
Revised:  The FMMs for the reaction components, implemented with the target particles and equivalent polarized sourcesassociated with the reaction field components, is found to be much more efficient than that for the free space component due to well-separateness between the equivalent polarized sources and the target particles .
Edit intent: clarity
Original:   The FMMs for the reaction components, which are implemented with the target particles and equivalent polarized sourcesassociated with the reaction field components, is found to be much more efficient than that for the free space component due to well-separateness between the equivalent polarized sources and the target particles .
Revised:  The FMMs for the reaction components, which are implemented with the target particles and equivalent polarization sources, are found to be much more efficient than that for the free space component due to well-separateness between the equivalent polarized sources and the target particles .
Edit intent: clarity
Original:   The FMMs for the reaction components, which are implemented with the target particles and equivalent polarized sourcesassociated with the reaction field components, is found to be much more efficient than that for the free space component due to well-separateness between the equivalent polarized sources and the target particles .
Revised:  The FMMs for the reaction components, which are implemented with the target particles and equivalent polarized sourcesassociated with the reaction field components, is found to be much more efficient than the classic FMM for the free space component due to well-separateness between the equivalent polarized sources and the target particles .
Edit intent: clarity
Original:   The FMMs for the reaction components, which are implemented with the target particles and equivalent polarized sourcesassociated with the reaction field components, is found to be much more efficient than that for the free space component due to well-separateness between the equivalent polarized sources and the target particles .
Revised:  The FMMs for the reaction components, which are implemented with the target particles and equivalent polarized sourcesassociated with the reaction field components, is found to be much more efficient than that for the free space component due to the fact that the equivalent polarization sources and the target particles .
Edit intent: clarity
Original:  Numerical results validate the fast convergence of the ME and the O(N logN) complexity and efficiency of the FMM for  wave sources in 3-D layered media.
Revised:  Numerical results validate the fast convergence of the MEs and the O(N logN) complexity and efficiency of the FMM for wave sources in 3-D layered media.
Edit intent: clarity
Original:  Numerical results validate the fast convergence of the ME and the O(N logN) complexity and efficiency of the FMM for  wave sources in 3-D layered media.
Revised:  Numerical results validate the fast convergence of the ME and the O(N ) complexity of the FMM for wave sources in 3-D layered media.
Edit intent: clarity
Original:  In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities , and computing citation-based metrics  .
Revised:  In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities and computing citation-based metrics .
Edit intent: fluency
Original:  We analyze the resilience of the resulting self-assembly behaviors by causing damages to the assembled pattern and observe the self-organized self-repair process .
Revised:  We analyze the resilience of the resulting self-assembly behaviors by causing damages to the assembled pattern and observe the self-organized reassembly of the structure .
Edit intent: clarity
Original:  When a limb initiates a link-up action  and there is another limb nearby, the latter is magnetically connected to the 'parent' limb's motor.
Revised:  When a limb initiates a link-up action , and there is another limb nearby, the latter is magnetically connected to the 'parent' limb's motor.
Edit intent: fluency
Original:  We evaluate the performance of these 'dynamic ' and 'modular ' agents in simulated environments.
Revised:  We evaluate the performance of these dynamic and modular agents in simulated environments.
Edit intent: fluency
Original:  We demonstrate better generalization to test-time changes both in the environment, as well as in the agent morphology , compared to static and monolithic baselines.
Revised:  We demonstrate better generalization to test-time changes both in the environment, as well as in the structure of the agent , compared to static and monolithic baselines.
Edit intent: clarity
Original:  Project videos and code are available at URL
Revised:  Project video and code are available at URL
Edit intent: fluency
Original:  In our work, an archetype comprises of progressive stages of distinct behavior.
Revised:  In our work, an archetype comprises of progressive stages of distinct behavior.
Edit intent: fluency
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: ,Diverse ,Evolving andDiffuse .
Edit intent: clarity
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: Steady Diverse ,Evolving andDiffuse .
Edit intent: fluency
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: Steady , ,Evolving andDiffuse .
Edit intent: fluency
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: Steady ,Diverse Evolving andDiffuse .
Edit intent: fluency
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: Steady ,Diverse , andDiffuse .
Edit intent: clarity
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving Diffuse .
Edit intent: fluency
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andSteady .
Edit intent: clarity
Original:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse .
Revised:  We identify four archetypes for researchers: Steady ,Diverse ,Evolving andDiffuse, .
Edit intent: fluency
Original:  Specifically, women and men differ within an archetype (e.g. Diverse ) in how they start, how they transition and the time spent in mid-career.
Revised:  Specifically, women and men differ within an archetype (e.g. Diverse ) in how they start, how they transition and the time spent in mid-career.
Edit intent: fluency
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Edit intent: fluency
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as : ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Edit intent: fluency
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as :Experts \emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Edit intent: fluency
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{ ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Edit intent: clarity
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers \emph{Enthusiasts and\emph{Facilitators \textit{ .
Edit intent: fluency
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{ and\emph{Facilitators \textit{ .
Edit intent: clarity
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts \emph{Facilitators \textit{ .
Edit intent: fluency
Original:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{Facilitators \textit{ .
Revised:  For StackOverflow, discovered archetypes could be labeled as :Experts ,\emph{Seekers ,\emph{Enthusiasts and\emph{ \textit{ .
Edit intent: clarity
Original:  The algorithm assumes that M is given implicitly: It takes as input a short chain complex of free bipersistence modules \[ F^2 \partial^2 F^1 \partial^1 F^0 \] such that M\cong \partial^1/im{\partial^2}.
Revised:  The algorithm takes as input a short chain complex of free bipersistence modules \[ F^2 \partial^2 F^1 \partial^1 F^0 \] such that M\cong \partial^1/im{\partial^2}.
Edit intent: coherence
Original:  The algorithm assumes that M is given implicitly: It takes as input a short chain complex of free bipersistence modules \[ F^2 \partial^2 F^1 \partial^1 F^0 \] such that M\cong \partial^1/im{\partial^2}.
Revised:  The algorithm assumes that M is given implicitly: It takes as input a short chain complex of free modules \[ F^2 \partial^2 F^1 \partial^1 F^0 \] such that M\cong \partial^1/im{\partial^2}.
Edit intent: fluency
Original:  The algorithm runs in time O(\sum_i |F^i|^3) and requires O(\sum_i |F^i|^2) storage , where |F^i| denotes the size of a basis of F^i.
Revised:  It runs in time O(\sum_i |F^i|^3) and requires O(\sum_i |F^i|^2) storage , where |F^i| denotes the size of a basis of F^i.
Edit intent: clarity
Original:  The algorithm runs in time O(\sum_i |F^i|^3) and requires O(\sum_i |F^i|^2) storage , where |F^i| denotes the size of a basis of F^i.
Revised:  The algorithm runs in time O(\sum_i |F^i|^3) and requires O(\sum_i |F^i|^2) memory , where |F^i| denotes the size of a basis of F^i.
Edit intent: clarity
Original:  Given the presentation , the bigraded Betti numbers of the module are readily computed.
Revised:  Given the presentation , the bigraded Betti numbers of M are readily computed.
Edit intent: clarity
Original:  We also present a different but related algorithm, based on Koszul homology, which computes the bigraded Betti numbers without computing a presentation, with these same complexity bounds.
Revised:  We also introduce a different but related algorithm, based on Koszul homology, which computes the bigraded Betti numbers without computing a presentation, with these same complexity bounds.
Edit intent: clarity
Original:  In preliminary experiments on topological data analysis problems, our approach outperforms the standard computational commutative algebra packages Singular and Macaulay2 by a wide margin.
Revised:  In experiments on topological data analysis problems, our approach outperforms the standard computational commutative algebra packages Singular and Macaulay2 by a wide margin.
Edit intent: clarity
Original:  In experiments on topological data analysis problems, our approach outperforms the standard computational commutative algebra packages Singular and Macaulay2 by a wide margin.
Revised:  In experiments on topological data analysis problems, our implementation outperforms the standard computational commutative algebra packages Singular and Macaulay2 by a wide margin.
Edit intent: clarity
Original:  The algorithm takes as input a short chain complex of free modules %DIFDELCMD < \[ F^2 \partial^2 F^1 \partial^1 %DIFDELCMD < F^0 \] %%%  such that M\cong \ker{\partial^1 / \mathrm{im{\partial^2}}  . It runs in time O( \sum_i | F^i |^3  ) and requires O( \sum_i | F^i |^2  ) memory, where | F^i | denotes the size of a basis of F^i.
Revised:  The algorithm takes as input a short chain complex of free modules %DIFDELCMD < \[ F^2 \partial^2 F^1 \partial^1 %DIFDELCMD < F^0 \] %%% such that M\cong \ker{\partial^1 / {\partial^2}} . It runs in time O( \sum_i | F^i |^3 ) and requires O( \sum_i | F^i |^2 ) memory, where | F^i | denotes the size of a basis of F^i.
Edit intent: clarity
Original:  The algorithm takes as input a short chain complex of free modules %DIFDELCMD < \[ F^2 \partial^2 F^1 \partial^1 %DIFDELCMD < F^0 \] %%%  such that M\cong \ker{\partial^1 / \mathrm{im{\partial^2}}  . It runs in time O( \sum_i | F^i |^3  ) and requires O( \sum_i | F^i |^2  ) memory, where | F^i | denotes the size of a basis of F^i.
Revised:  The algorithm takes as input a short chain complex of free modules %DIFDELCMD < \[ F^2 \partial^2 F^1 \partial^1 %DIFDELCMD < F^0 \] %%% such that M\cong \ker{\partial^1 / \mathrm{im{\partial^2}} . It runs in time O( | F^i |^3 ) and requires O( \sum_i | F^i |^2 ) memory, where | F^i | denotes the size of a basis of F^i.
Edit intent: fluency