wanyu/IteraTeR_v2 huggingface dataset:
Original:  The hard part is human-realistic visualization of the periphery . Degradation of information with distance from fixation is far more complex than a mere reduction of acuity that might be mimicked using blur with a standard deviation that linearly increases with eccentricity .
Revised:  The hard part is human-realistic visualization of the periphery . Degradation of information with distance from fixation is far more complex than a mere reduction of acuity that might be mimicked with a radial blur .
Edit intent: clarity
Original:  Rather, behaviorally-validated models hypothesize that peripheral vision measures a large number of local texture statistics in pooling regions that overlap , grow with eccentricity , and tile the visual field.
Revised:  Rather, behaviorally-validated models hypothesize that peripheral vision measures a large number of local texture statistics in pooling regions that overlap and grow with eccentricity , and tile the visual field.
Edit intent: coherence
Original:  We focus on the case in which the agents are coupled not only via their cost functions, but also via affine constraints on the average of the agents' strategies.
Revised:  We focus on the case in which the agents are coupled not only via their cost functions, but also via constraints coupling their strategies.
Edit intent: clarity
Original: Monitoring key properties of dynamic graphs in real time can help detect spam accounts in social networks, identify anomalies and intrusions, and inform resource management.
Edit intent: coherence
Original:  Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes associated with the edges.
Revised:  Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes incident to the edges.
Edit intent: clarity
Original:  To demonstrate this higher accuracy using the framework, we present a new sampling algorithm, called Edge Sample and Discard , which generates an unbiased estimate of the total number of triangles in a fully dynamic graph.
Revised:  To demonstrate the advantages of this framework, we present a new sampling algorithm, called Edge Sample and Discard , which generates an unbiased estimate of the total number of triangles in a fully dynamic graph.
Edit intent: clarity
Original: Most existing algorithms for streaming SM only support the append-only model with cardinalityconstraints, which cannot meet the requirements of real-world problems considering either the data recency issues or more general d-knapsack constraints.
Revised: Most existing algorithms for streaming SM only support the append-only model with cardinalityconstraints, which cannot meet the requirements of real-world problems . To capture the data recency issues or more general d-knapsack constraints.
Edit intent: coherence
Original:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK)    . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Revised:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% for SM subject to a d-knapsack constraint (SMDK) . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Edit intent: coherence
Original:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK)    . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Revised:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraints (SMDK) . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Edit intent: fluency
Original:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK)    . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Revised:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK) over sliding windows. We propose a . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Edit intent: coherence
Original:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK)    . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Revised:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK) KnapStream . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Edit intent: fluency
Original:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK)    . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Revised:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK) . %DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Edit intent: coherence
Original:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK)    . Furthermore, we devise the%DIFDELCMD < {\sc %%% KnapWindow algorithm for SMDK over sliding windows to capture the recency constraints.
Revised:  Therefore, we first propose an append-only streaming algorithm%DIFDELCMD < {\sc %%% KnapStream for SM subject to a d-knapsack constraint (SMDK) . Furthermore, we devise the%DIFDELCMD < {\sc %%% algorithm for SMDK over sliding windows to capture the recency constraints.
Edit intent: fluency
Original:  The results show that the proposed algorithms achieve two orders of magnitude speedups over the greedy baseline in the batch setting while preserving high quality solutions .
Revised:  The experimental results demonstrate that KW achieves more than two orders of magnitude speedups over the greedy baseline in the batch setting while preserving high quality solutions .
Edit intent: clarity
Original: Comprehending complex systems by simplifying and highlighting important dynamical patterns often require modeling and mapping of higher-order network flows.
Revised: Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping of higher-order network flows.
Edit intent: clarity
Original: Comprehending complex systems by simplifying and highlighting important dynamical patterns often require modeling and mapping of higher-order network flows.
Revised: Comprehending complex systems by simplifying and highlighting important dynamical patterns often require modeling and mapping higher-order network flows.
Edit intent: fluency
Original:  We show that various  higher-order network flow representations can be represented with sparse memory networks , in which the multilevel map equation can identify overlapping and nested modules that capture network flows .
Revised:  Here we show that various higher-order network flow representations can be represented with sparse memory networks , in which the multilevel map equation can identify overlapping and nested modules that capture network flows .
Edit intent: coherence
Original:  We show that various  higher-order network flow representations can be represented with sparse memory networks , in which the multilevel map equation can identify overlapping and nested modules that capture network flows .
Revised:  We show that various forms of higher-order network flow representations can be represented with sparse memory networks , in which the multilevel map equation can identify overlapping and nested modules that capture network flows .
Edit intent: clarity
Original:  We show that various  higher-order network flow representations can be represented with sparse memory networks , in which the multilevel map equation can identify overlapping and nested modules that capture network flows .
Revised:  We show that various higher-order network flows can be represented with sparse memory networks , in which the multilevel map equation can identify overlapping and nested modules that capture network flows .
Edit intent: fluency
Original:  However, the  nucleocapsid core is not required for assembly of infectious particles.
Revised:  However, the presence of a nucleocapsid core is not required for assembly of infectious particles.
Edit intent: clarity
Original:  To determine the role of the nucleocapsid , we perform coarse-grained computational modeling of budding in the presence and absence of a nucleocapsid.
Revised:  To determine the role of the nucleocapsid , we perform coarse-grained computational modeling of budding in the absence of a nucleocapsid.
Edit intent: fluency
Original:  The simulations demonstrate that glycoprotein-directed budding leads to larger and more polydisperse particles than nucleocapsid-directed budding .
Revised:  The simulations demonstrate that glycoprotein-directed budding leads to significantly increased size polydispersity and particle polymorphism .
Edit intent: clarity
Original:  This polydispersity is explained by a theoretical model  for the competition between bending energy of the membrane and the glycoprotein shell.
Revised:  This polydispersity is explained by a theoretical model accounting for the competition between bending energy of the membrane and the glycoprotein shell.
Edit intent: clarity
Original:  The simulations also show that the geometry of a budding particle impedes glycoprotein diffusion, which can result in a stalled, partially budded state.
Revised:  The simulations also show that the geometry of a budding particle leads to a barrier to subunit diffusion, which can result in a stalled, partially budded state.
Edit intent: clarity
Original:  We present a phase diagram for  morphologies of budded particles , which can be compared against experiments to establish bounds on whether budding is directed by glycoprotein or nucleocapsid interactions  .
Revised:  We present a phase diagram for morphologies of budded particles . Comparison of these structures against experiments could establish bounds on whether budding is directed by glycoprotein or nucleocapsid interactions .
Edit intent: clarity
Original:  It is \NP-complete  even for very restricted graph classes such as planar bipartite graphs with maximum degree 3 and is solvable in  linear time for P_7-free graphs, and in polynomial time for S_{1,2,3 -free graphs as well as for S _{ 2 , 2 , 2 }-free graphs  .
Revised:  It is \NP-complete even for very restricted graph classes such as planar bipartite graphs with maximum degree 3 and is solvable in linear time for P_7-free graphs, and in polynomial time for S_{1,2,3 -free graphs as well as for S _{ 2 , j , 2 }-free graphs .
Edit intent: fluency
Original:  In this paper we solve it in polynomial time for S_{2,2,3 -free graphs (generalizing two known cases ).
Revised:  In this paper we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing two known cases ).
Edit intent: fluency
Original: Let G=(V,E) be a finite undirected graph without loops and multiple edges .
Revised: Let G=(V,E) be a finite undirected graph .
Edit intent: clarity
Original:  it is the %DIFDELCMD < {\em %%% Efficient Domination problem for line graphs.
Revised:  it is the %DIFDELCMD < {\em %%% Efficient Domination problem for line graphs.
Edit intent: fluency
Original:  The DIM problem is  \NP-complete in general, and even for very restricted graph classes such as planar bipartite graphs with maximum degree 3.
Revised:  The DIM problem is \NP-complete even for very restricted graph classes such as planar bipartite graphs with maximum degree 3.
Edit intent: coherence
Original:  However, DIM is solvable in polynomial time for claw-free (i.e., S_{1,1,1 linear time for P_7-free graphs, and in polynomial time for P_8 -free graphs .
Revised:  However, DIM is solvable in linear time for P_7-free graphs, and in polynomial time for P_8 -free graphs .
Edit intent: coherence
Original:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ i , j , k }-free graphs for every fixed i,j,k . In this paper  we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Revised:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ 2 , j , k }-free graphs for every fixed i,j,k . In this paper we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Edit intent: fluency
Original:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ i , j , k }-free graphs for every fixed i,j,k . In this paper  we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Revised:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ i , 2 , k }-free graphs for every fixed i,j,k . In this paper we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Edit intent: fluency
Original:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ i , j , k }-free graphs for every fixed i,j,k . In this paper  we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Revised:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ i , j , k }-free graphs . In this paper we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Edit intent: coherence
Original:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ i , j , k }-free graphs for every fixed i,j,k . In this paper  we solve it in polynomial time for (S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Revised:  In a paper by Hertz, Lozin, Ries, Zamaraev and de Werra, it was conjectured that DIM is solvable in polynomial time for S _{ i , j , k }-free graphs for every fixed i,j,k . In this paper we solve it in polynomial time for S_{2,2,3 -free graphs (generalizing S_{1,2,3 .
Edit intent: fluency
Original: Cellular electron cryo-tomography enables the 3D visualization of cellular organization in a near-native state  at submolecular resolution.
Revised: Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state at submolecular resolution.
Edit intent: fluency
Original: Cellular electron cryo-tomography enables the 3D visualization of cellular organization in a near-native state  at submolecular resolution.
Revised: Cellular electron cryo-tomography enables the 3D visualization of cellular organization in a near-native state and at submolecular resolution.
Edit intent: coherence
Original:  However, the content of a cellular tomogram is often complex, making it difficult to automatically isolate different in situ cellular components.
Revised:  However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components.
Edit intent: fluency
Original:  In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse characterization of 3D patches extracted from tomograms.
Revised:  In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D patches extracted from tomograms.
Edit intent: clarity
Original:  We demonstrate that the autoencoder can be used for the efficient and coarse characterizing of features that correspond to macromolecular complexes and surfaces, like membranes.
Revised:  We demonstrate that the autoencoder can be used for efficient and coarse characterizing of features that correspond to macromolecular complexes and surfaces, like membranes.
Edit intent: fluency
Original:  We demonstrate that the autoencoder can be used for the efficient and coarse characterizing of features that correspond to macromolecular complexes and surfaces, like membranes.
Revised:  We demonstrate that the autoencoder can be used for the efficient and coarse characterization of features of macromolecular complexes and surfaces, like membranes.
Edit intent: clarity
Original:  We demonstrate that the autoencoder can be used for the efficient and coarse characterizing of features that correspond to macromolecular complexes and surfaces, like membranes.
Revised:  We demonstrate that the autoencoder can be used for the efficient and coarse characterizing of features that correspond to macromolecular complexes and surfaces, such as membranes.
Edit intent: fluency
Original:  In addition, it can be used to detect non-cellular features related to sample preparation and data collection like carbon edges from the grid , and tomogram boundaries.
Revised:  In addition, it can be used to detect non-cellular features related to sample preparation and data collection , such as carbon edges from the grid , and tomogram boundaries.
Edit intent: fluency
Original:  In addition, it can be used to detect non-cellular features related to sample preparation and data collection like carbon edges from the grid , and tomogram boundaries.
Revised:  In addition, it can be used to detect non-cellular features related to sample preparation and data collection like carbon edges from the grid and tomogram boundaries.
Edit intent: fluency
Original:  The autoencoder is also able to detect patterns that may indicate spatial interactions between cell components.
Revised:  The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components.
Edit intent: fluency
Original:  Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components requiring very small amount of manual annotation.
Revised:  Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components , requiring a very small amount of manual annotation.
Edit intent: fluency
Original: We investigate the integration of a planning mechanism into an encoder-decoder architecture with attention for character-level machine translation.
Revised: We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation.
Edit intent: clarity
Original: For  past 40 years, cellular industry has been relying on static radio access deployments with gross over-provisioning.
Revised: For the past 40 years, cellular industry has been relying on static radio access deployments with gross over-provisioning.
Edit intent: fluency
Original:  This work puts forward a novel vision of moving networks to match dynamic user demand with network access supply in the beyond-5G heterogeneous cellular systems.
Revised:  This work puts forward a vision of moving networks to match dynamic user demand with network access supply in the beyond-5G heterogeneous cellular systems.
Edit intent: clarity
Original:  This work puts forward a novel vision of moving networks to match dynamic user demand with network access supply in the beyond-5G heterogeneous cellular systems.
Revised:  This work puts forward a novel vision of moving networks to match dynamic user demand with network access supply in the beyond-5G cellular systems.
Edit intent: clarity
Original:  The resulting adaptive and flexible network infrastructures will leverage intelligent capable devices (e.g., cars , drones) by employing appropriate user involvement schemes.
Revised:  The resulting adaptive and flexible network infrastructures will leverage intelligent capable devices (e.g., cars and drones) by employing appropriate user involvement schemes.
Edit intent: fluency
Original:  This work is a recollection of our systematic scientific efforts in this space with the goal to contribute a holistic research agenda.
Revised:  This work is a recollection of our efforts in this space with the goal to contribute a holistic research agenda.
Edit intent: clarity
Original:  This work is a recollection of our systematic scientific efforts in this space with the goal to contribute a holistic research agenda.
Revised:  This work is a recollection of our systematic scientific efforts in this space with the goal to contribute a comprehensive research agenda.
Edit intent: clarity
Original:  Particular attention is paid to quantifying the network capacity scaling and session continuity performance with ultra-dense moving cells.
Revised:  Particular attention is paid to quantifying the network performance scaling and session continuity performance with ultra-dense moving cells.
Edit intent: clarity
Original:  Particular attention is paid to quantifying the network capacity scaling and session continuity performance with ultra-dense moving cells.
Revised:  Particular attention is paid to quantifying the network capacity scaling and session continuity gains with ultra-dense moving cells.
Edit intent: clarity
Original:  Our findings argue for non-incremental benefits of integrating moving access points (MAPs) on a par with conventional (static) cellular access infrastructure.
Revised:  Our findings argue for non-incremental benefits of integrating moving access points on a par with conventional (static) cellular access infrastructure.
Edit intent: clarity
Original: In serial cognitive tasks, information from preceding trials  can bias performance in the current trial, a phenomenon referred to as interference.
Revised: Information from preceding trials can bias performance in the current trial, a phenomenon referred to as interference.
Edit intent: clarity
Original: In serial cognitive tasks, information from preceding trials  can bias performance in the current trial, a phenomenon referred to as interference.
Revised: In serial cognitive tasks, information from preceding trials of cognitive tasks can bias performance in the current trial, a phenomenon referred to as interference.
Edit intent: clarity
Original:  Recent experiments by Papadimitriou et al. (2015) demonstrated such biases in spatial working memory tasks , wherein subjects recalled the location of a target presented in continuous space.
Revised:  Subjects performing visual working memory tasks , wherein subjects recalled the location of a target presented in continuous space.
Edit intent: clarity
Original:  We build on their heuristic computational model to: (a) provide a Bayesian interpretation of the history-dependent bias ;
Revised:  We present modeling work that (a) provide a Bayesian interpretation of the history-dependent bias ;
Edit intent: clarity
Original:  We build on their heuristic computational model to: (a) provide a Bayesian interpretation of the history-dependent bias ;
Revised:  We build on their heuristic computational model to: (a) provide a Bayesian interpretation of the history-dependent bias ,
Edit intent: fluency