wanyu/IteraTeR_v2 huggingface dataset:
Original:  Nevertheless, the latency-sensitive nature of in-memory services makes seamless integration of SCM in servers questionable.
Revised:  Nevertheless, the latency-sensitive nature of memory-resident services makes seamless integration of SCM in servers questionable.
Edit intent: clarity
Original:  In this paper, we ask the question of how best to introduce SCM for such servers to help improve overall performance per cost over existing DRAM-only architectures.
Revised:  In this paper, we ask the question of how best to introduce SCM for such servers to improve overall performance per cost over existing DRAM-only architectures.
Edit intent: clarity
Original:  In this paper, we ask the question of how best to introduce SCM for such servers to help improve overall performance per cost over existing DRAM-only architectures.
Revised:  In this paper, we ask the question of how best to introduce SCM for such servers to help improve overall performance / cost over existing DRAM-only architectures.
Edit intent: coherence
Original:  We first show that even with the best latency projections for SCM, the higher memory access latency results in prohibitive performance degradation.
Revised:  We first show that even with the most optimistic latency projections for SCM, the higher memory access latency results in prohibitive performance degradation.
Edit intent: clarity
Original:  However, we find that deploying a modestly sized high-bandwidth  stacked DRAM cache makes SCM-based memory competitive.
Revised:  However, we find that deployment of a modestly sized high-bandwidth stacked DRAM cache makes SCM-based memory competitive.
Edit intent: fluency
Original:  However, we find that deploying a modestly sized high-bandwidth  stacked DRAM cache makes SCM-based memory competitive.
Revised:  However, we find that deploying a modestly sized high-bandwidth stacked DRAM cache makes the performance of an SCM-mostly memory system competitive.
Edit intent: clarity
Original:  The high degree of spatial locality in-memory services exhibit not only simplifies the DRAM cache's design as page-based, but also enables the amortization of increased SCM access latencies and  mitigation of SCM's read/write latency disparity.
Revised:  The high degree of spatial locality that memory-resident services exhibit not only simplifies the DRAM cache's design as page-based, but also enables the amortization of increased SCM access latencies and mitigation of SCM's read/write latency disparity.
Edit intent: clarity
Original:  The high degree of spatial locality in-memory services exhibit not only simplifies the DRAM cache's design as page-based, but also enables the amortization of increased SCM access latencies and  mitigation of SCM's read/write latency disparity.
Revised:  The high degree of spatial locality in-memory services exhibit not only simplifies the DRAM cache's design as page-based, but also enables the amortization of increased SCM access latencies and the mitigation of SCM's read/write latency disparity.
Edit intent: fluency
Original:  We finally perform a case study with PCM, and show that a 2 bits/cell technology hits the performance/cost sweet spot, reducing the memory subsystem cost by 40\% while keeping performance within 5 \% of the best performing DRAM-only system, whereas single-level and triple-level cell organizations are impractical for use as memory replacements.
Revised:  We finally perform a case study with PCM, and show that a two bits/cell technology hits the performance/cost sweet spot, reducing the memory subsystem cost by 40\% while keeping performance within 5 \% of the best performing DRAM-only system, whereas single-level and triple-level cell organizations are impractical for use as memory replacements.
Edit intent: fluency
Original: Traditional modeling of inconsistency in database theory casts all possible "repairs" equally likely .
Revised: Most theoretical frameworks that focus on data errors and inconsistencies follow logic-based reasoning .
Edit intent: clarity
Original:  Yet, effective data cleaning needs to incorporate statistical reasoning .
Revised:  Yet, practical data cleaning tools need to incorporate statistical reasoning .
Edit intent: clarity
Original:  For example, yearly salary of%DIFDELCMD < \100k %%% and age of 22 are more likely than%DIFDELCMD < \100k %%% and 122 and two people with same address are likely to share their last name (i.e., a functional dependency tends to hold but may occasionally be violated).
Revised:  For example, yearly salary of%DIFDELCMD < \100k %%% %DIFDELCMD < \100k %%% and 122 and two people with same address are likely to share their last name (i.e., a functional dependency tends to hold but may occasionally be violated).
Edit intent: clarity
Original:  We propose a formal framework for unclean databases, where two types of statistical knowledge are incorporated .
Revised:  We propose a formal framework for unclean databases, where two types of statistical knowledge are incorporated :
Edit intent: fluency
Original:  Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the "intention" , a probabilistic data transformator that we call the "realization " , and a dirty database that we call the "observation" .
Revised:  Formally, a Probabilistic Unclean Database (PUD) , a triple that consists of a probabilistic database that we call the "intention" , a probabilistic data transformator that we call the "realization " , and a dirty database that we call the "observation" .
Edit intent: clarity
Original:  Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the "intention" , a probabilistic data transformator that we call the "realization " , and a dirty database that we call the "observation" .
Revised:  Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the intention , a probabilistic data transformator that we call the "realization " , and a dirty database that we call the "observation" .
Edit intent: fluency
Original:  Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the "intention" , a probabilistic data transformator that we call the "realization " , and a dirty database that we call the "observation" .
Revised:  Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the "intention" , a probabilistic data transformator that we call the "realization " , and a dirty observed database instance that we call the "observation" .
Edit intent: clarity
Original:  Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the "intention" , a probabilistic data transformator that we call the "realization " , and a dirty database that we call the "observation" .
Revised:  Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the "intention" , a probabilistic data transformator that we call the "realization " , and a dirty database that we call the observation .
Edit intent: fluency
Original:  We define three computational problems in this framework: cleaning ( find the most likely intention ), probabilistic query answering (compute the probability of an answer tuple  ), and learning ( find the most likely parameters given examples of clean and dirty databases ).
Revised:  We define three computational problems in the PUD framework: cleaning ( find the most likely intention ), probabilistic query answering (compute the probability of an answer tuple ), and learning ( find the most likely parameters given examples of clean and dirty databases ).
Edit intent: clarity
Original:  We define three computational problems in this framework: cleaning ( find the most likely intention ), probabilistic query answering (compute the probability of an answer tuple  ), and learning ( find the most likely parameters given examples of clean and dirty databases ).
Revised:  We define three computational problems in this framework: cleaning ( find the most likely intention ), probabilistic query answering (compute the probability of an answer tuple ), and learning ( estimate the most likely parameters given examples of clean and dirty databases ).
Edit intent: clarity
Original:  We illustrate the  framework on concrete representations of PUDs , show that they generalize traditional concepts of repairs such as cardinality and value repairs, draw connection to consistent query answering, and prove tractability results.
Revised:  We illustrate the PUD framework on concrete representations of PUDs , show that they generalize traditional concepts of repairs such as cardinality and value repairs, draw connection to consistent query answering, and prove tractability results.
Edit intent: clarity
Original:  We further show that parameters can be learned in practical instantiations, and in fact, prove that under certain conditions we can learn  directly from a single dirty database  without any need for clean examples.
Revised:  We further show that parameters can be learned in practical instantiations, and in fact, prove that under certain conditions we can learn directly from a single dirty database instance without any need for clean examples.
Edit intent: clarity
Original: Learning and analyzing 3D point cloud with deep networks is challenging due to the sparseness and irregularity of the data.
Revised: Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.
Edit intent: fluency
Original:  The key idea is to learn multi-level features per point , and then expanding them via a multi-branch convolution unit , to implicitly expand the point set in feature space.
Revised:  The key idea is to learn multi-level features per point and expand the point set via a multi-branch convolution unit , to implicitly expand the point set in feature space.
Edit intent: clarity
Original:  The key idea is to learn multi-level features per point , and then expanding them via a multi-branch convolution unit , to implicitly expand the point set in feature space.
Revised:  The key idea is to learn multi-level features per point , and then expanding them via a multi-branch convolution unit implicitly in feature space.
Edit intent: clarity
Original:  The results show that our upsampled results have better uniformity , and sampled closer to the underlying surface .
Revised:  Results show that our upsampled results have better uniformity , and sampled closer to the underlying surface .
Edit intent: fluency
Original:  The results show that our upsampled results have better uniformity , and sampled closer to the underlying surface .
Revised:  The results show that our upsampled points have better uniformity , and sampled closer to the underlying surface .
Edit intent: clarity
Original:  The results show that our upsampled results have better uniformity , and sampled closer to the underlying surface .
Revised:  The results show that our upsampled results have better uniformity and are located closer to the underlying surface .
Edit intent: clarity
Original:  The results show that our upsampled results have better uniformity , and sampled closer to the underlying surface .
Revised:  The results show that our upsampled results have better uniformity , and sampled closer to the underlying surfaces .
Edit intent: fluency
Original:  In this paper, we discuss this important problem further and propose a new method to organize those eigenvectors by defining and measuring "natural" distances between eigenvectors using the Ramified Optimal Transport Theory followed by embedding the resulting distance matrix into a low-dimensional Euclidean domain for further grouping and organization of such eigenvectors .
Revised:  In this paper, we discuss this important problem further and propose a new method to organize those eigenvectors by defining and measuring "natural" distances between eigenvectors using the Ramified Optimal Transport Theory followed by embedding them into a low-dimensional Euclidean domain for further grouping and organization of such eigenvectors .
Edit intent: clarity
Original:  In this paper, we discuss this important problem further and propose a new method to organize those eigenvectors by defining and measuring "natural" distances between eigenvectors using the Ramified Optimal Transport Theory followed by embedding the resulting distance matrix into a low-dimensional Euclidean domain for further grouping and organization of such eigenvectors .
Revised:  In this paper, we discuss this important problem further and propose a new method to organize those eigenvectors by defining and measuring "natural" distances between eigenvectors using the Ramified Optimal Transport Theory followed by embedding the resulting distance matrix into a low-dimensional Euclidean domain .
Edit intent: clarity
Original: A message passing algorithm (MP) is derived for recovering a dense subgraph within a graph generated by a variation of the Barab\'{a}si-Albert preferential attachment model.
Revised: A message passing algorithm is derived for recovering a dense subgraph within a graph generated by a variation of the Barab\'{a}si-Albert preferential attachment model.
Edit intent: clarity
Original:   C significantly outperforms DT, showing it is beneficial to know the arrival times of the children, beyond simply knowing the number of them.
Revised:  Algorithm C significantly outperforms DT, showing it is beneficial to know the arrival times of the children, beyond simply knowing the number of them.
Edit intent: clarity
Original:  It is shown that for a fixed fraction of vertices in the community \rho , fixed number of new edges per arriving vertex m , and fixed affinity between vertices in the community \beta, the fraction of label errors  for either of the algorithms DT or C , or converges as T%DIFDELCMD < \to%%% \infty .
Revised:  For fixed fraction of vertices in the community \rho , fixed number of new edges per arriving vertex m , and fixed affinity between vertices in the community \beta, the fraction of label errors for either of the algorithms DT or C , or converges as T%DIFDELCMD < \to%%% \infty .
Edit intent: coherence
Original:  It is shown that for a fixed fraction of vertices in the community \rho , fixed number of new edges per arriving vertex m , and fixed affinity between vertices in the community \beta, the fraction of label errors  for either of the algorithms DT or C , or converges as T%DIFDELCMD < \to%%% \infty .
Revised:  It is shown that for a fixed fraction of vertices in the community , fixed number of new edges per arriving vertex m , and fixed affinity between vertices in the community \beta, the fraction of label errors for either of the algorithms DT or C , or converges as T%DIFDELCMD < \to%%% \infty .
Edit intent: clarity
Original:  It is shown that for a fixed fraction of vertices in the community \rho , fixed number of new edges per arriving vertex m , and fixed affinity between vertices in the community \beta, the fraction of label errors  for either of the algorithms DT or C , or converges as T%DIFDELCMD < \to%%% \infty .
Revised:  It is shown that for a fixed fraction of vertices in the community \rho , fixed number of new edges per arriving vertex , and fixed affinity between vertices in the community \beta, the fraction of label errors for either of the algorithms DT or C , or converges as T%DIFDELCMD < \to%%% \infty .
Edit intent: clarity
Original:  fixed number of new edges per arriving vertex, and fixed affinity between vertices in the community, the probability of error for recovering the label of a vertex is found as a function of the time of attachment, for either algorithm DT or C, in the large graph limit , for either algorithm DT or C . By averaging over the time of attachment, the limit in probability of the fraction of label errors made over all vertices is identified, for either of the algorithms DT or C.
Revised:  fixed number of new edges per arriving vertex, and fixed affinity between vertices in the community, the probability of error for recovering the label of a vertex is found as a function of the time of attachment, for either algorithm DT or C, in the large graph limit . By averaging over the time of attachment, the limit in probability of the fraction of label errors made over all vertices is identified, for either of the algorithms DT or C.
Edit intent: fluency
Original: A message passing algorithm is derived for recovering a dense subgraph within a graph generated by a variation of the Barab\'asi-Albert preferential attachment model.
Revised: A message passing algorithm is derived for recovering a dense subgraph within a graph generated by a variation of the Barab\'{a preferential attachment model.
Edit intent: fluency
Original:  Algorithm C significantly outperforms DT, showing it is beneficial to know the arrival times of the children, beyond simply knowing the numberof them.
Revised:  Comparison of the performance of the algorithms shows it is beneficial to know the arrival times of the children, beyond simply knowing the numberof them.
Edit intent: clarity
Original:  For fixed fraction of vertices in the community, fixed number of new edges per arriving vertex, and fixed affinity between vertices in the community, the probability of error for recovering the label of  a vertex is found as a function of the time of attachment, for either algorithm DT or C, in the large graph limit.
Revised:  For fixed fraction of vertices in the community, fixed number of new edges per arriving vertex, and fixed affinity between vertices in the community, the probability of error for recovering the label of the children. The probability of correct classification of a vertex is found as a function of the time of attachment, for either algorithm DT or C, in the large graph limit.
Edit intent: clarity
Original:  By averaging over the time of attachment, the limit in probability of the fraction of label errors made over all vertices is identified, for either of the algorithms DT or C. A message passing algorithm is also described and simulation results are given for the problem of recovering two symmetric communities in a graph .
Revised:  By averaging over the time of attachment, the limit in probability of the fraction of label errors made over all vertices is identified, for either of the algorithms DT or C. A message passing algorithm is also described and simulation results are given .
Edit intent: coherence
Original:  We propose a scalable LPP method based on the paradigm of counterfeiting locations.
Revised:  We propose a scalable LPP method based on the paradigm of counterfeiting locations.
Edit intent: fluency
Original:  To make fake locations extremely plausible, we forge them through synthesizing artificial impostors (AIs).
Revised:  To make fake locations extremely plausible, we forge them through synthesizing artificial impostors (AIs).
Edit intent: fluency
Original:  The AIs refer to the synthesized traces which have similar semantic features to the actual traces, and do not contain any target location.
Revised:  The AIs refer to the synthesized traces which have similar semantic features to the actual traces, and do not contain any target location.
Edit intent: fluency
Original:  Two dedicated techniques are devised: the sampling-based synthesis method andpopulation-level semantic model .
Revised:  Two dedicated techniques are devised: the andpopulation-level semantic model .
Edit intent: clarity
Original:  Two dedicated techniques are devised: the sampling-based synthesis method andpopulation-level semantic model .
Revised:  Two dedicated techniques are devised: the sampling-based synthesis method population-level semantic model .
Edit intent: fluency
Original:  Two dedicated techniques are devised: the sampling-based synthesis method andpopulation-level semantic model .
Revised:  Two dedicated techniques are devised: the sampling-based synthesis method andsampling-based synthesis method and population-level semantic model .
Edit intent: fluency
Original:  In these two datasets, the experimental results show that our method achieves the preservation efficacy of 97.65\\%DIF < and 96.12%DIFDELCMD < \\%%% %DIF < , and its run time of building the generators is only 230.47 and %DIF > and 96.12\\%, and its run time of building  215.92 seconds, respectively.
Revised:  In these two datasets, the experimental results show that our method achieves the preservation efficacy of 97.65\\%DIF < %DIFDELCMD < \\%%% %DIF < , and its run time of building the generators is only 230.47 and %DIF > and 96.12\\%, and its run time of building 215.92 seconds, respectively.
Edit intent: fluency
Original:  In these two datasets, the experimental results show that our method achieves the preservation efficacy of 97.65\\%DIF < and 96.12%DIFDELCMD < \\%%% %DIF < , and its run time of building the generators is only 230.47 and %DIF > and 96.12\\%, and its run time of building  215.92 seconds, respectively.
Revised:  In these two datasets, the experimental results show that our method achieves the preservation efficacy of 97.65\\%DIF < and 96.12%DIFDELCMD < \\%%% %DIF < , and its run time of building the generators is only 230.47 and %DIF > and 96.12\\%, and its run time of building the generators is only 230.47 and 215.92 seconds, respectively.
Edit intent: fluency
Original: Intracellular bidirectional transport of cargo on Microtubule filaments is achieved by the collective action of oppositely directed dynein and kinesin motors.
Revised: Intracellular bidirectional transport of cargo on microtubule filaments is achieved by the collective action of oppositely directed dynein and kinesin motors.
Edit intent: fluency
Original:  Experimental investigations probing the nature of bidirectional transport have found that in certain cases, inhibiting the activity of one type of motor results in an overall decline in the motility of the cellular cargo in both directions.
Revised:  Experiments have found that in certain cases, inhibiting the activity of one type of motor results in an overall decline in the motility of the cellular cargo in both directions.
Edit intent: clarity
Original:  Existing theoretical models do not take into account a key difference in the functionality of kinesin and dynein.
Edit intent: coherence
Original:  Unlike kinesin, dynein motors exhibit catchbonding, wherein the unbinding rates of these motors from the filaments are seen to decrease with increasing force on them.
Revised:  Unlike kinesin, dynein motors exhibit catchbonding, wherein the unbinding rates of these motors decrease with increasing force on them.
Edit intent: clarity
Original: We study the problem of maximising terminal utility for an agent facing model uncertainty, in a frictionless discrete-time market consisting of one safe asset and finitely many risky assets.
Revised: We study the problem of maximising terminal utility for an agent facing model uncertainty, in a frictionless discrete-time market with one safe asset and finitely many risky assets.
Edit intent: clarity
Original:  We also find that , when wealth is required to satisfy the no-bankruptcy constraint, the boundedness assumption can be dropped provided that we impose a certain integrability condition , related to some strengthened form of no-arbitrage.
Revised:  We further find that the boundedness assumption can be dropped provided that we impose a certain integrability condition , related to some strengthened form of no-arbitrage.
Edit intent: clarity
Original: This paper introduces the sequence covering similarity, that we have formally define for evaluating the similarity between a symbolic sequence  and a set of symbolic sequences  .
Revised: This paper introduces the sequence covering similarity, that we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences .
Edit intent: fluency