wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) obtained by an elegant application of simultaneous diophantine approximation due to [Gr\"otschel, Lov\'asz and Schrijver, Prog.
Revised:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) for polynomial time algorithms obtained by [Gr\"otschel, Lov\'asz and Schrijver, Prog.
Edit intent: meaning-changed
Original:  We conjecture that our oracle complexity is tight up to constant factors for polynomial time algorithms. Our result immediately implies a strongly polynomial algorithm for the Submodular Function Minimization problem that makes at most O(n^3) calls to an evaluation oracle .
Revised:  For the Submodular Function Minimization problem, our result immediately implies a strongly polynomial algorithm for the Submodular Function Minimization problem that makes at most O(n^3) calls to an evaluation oracle .
Edit intent: clarity
Original:  Our result immediately implies a strongly polynomial algorithm for the Submodular Function Minimization problem that makes at most O(n^3) calls to an evaluation oracle . This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V\'egh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work.
Revised:  Our result immediately implies a strongly polynomial algorithm for the Submodular Function Minimization problem that makes at most O(n^3) calls to an evaluation oracle , and an exponential time algorithm that makes at most O(n^2 \log(n)) calls to an evaluation oracle. These improve upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V\'egh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work.
Edit intent: fluency
Original:  We show how an approximately shortest vector of certain lattice can be used to  reduce the dimension of the problem , and how the oracle complexity of such a procedure is advantageous compared with the Gr\"otschel-Lov\'asz-Schrijver approach that uses simultaneous diophantine approximation .
Revised:  We show how an approximately shortest vector of certain lattice can be used to effectively reduce the dimension of the problem , and how the oracle complexity of such a procedure is advantageous compared with the Gr\"otschel-Lov\'asz-Schrijver approach that uses simultaneous diophantine approximation .
Edit intent: clarity
Original:  We show how an approximately shortest vector of certain lattice can be used to  reduce the dimension of the problem , and how the oracle complexity of such a procedure is advantageous compared with the Gr\"otschel-Lov\'asz-Schrijver approach that uses simultaneous diophantine approximation .
Revised:  We show how an approximately shortest vector of certain lattice can be used to reduce the dimension of the problem .
Edit intent: coherence
Original:  Our analysis of the oracle complexity is based on a potential function that captures simultaneously the size of the search set and the density of the lattice . To achieve the O(n^2) term in the oracle complexity, technical ingredients from convex geometry are applied .
Revised:  Our analysis of the oracle complexity is based on a potential function that captures simultaneously the size of the search set and the density of the lattice , which we analyze via technical tools from convex geometry are applied .
Edit intent: coherence
Original:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Revised:  Thus, in this study, based on big human mobility data and city POI data, we designed an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Edit intent: fluency
Original:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Revised:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system named EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Edit intent: fluency
Original:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Revised:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) . The system interactively simulates the changes in human mobility and the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Edit intent: clarity
Original:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Revised:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people in response to the implementation of a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Edit intent: clarity
Original:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Revised:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing;
Edit intent: fluency
Original:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies . EpiMob is made up of a set of coupled modules: a data processing module for data cleansing, interpolation, and indexing; a simulation module based on a modified trajectory-based SEIR model; an interaction visualization module to interactively visualize the analytical results in light of user's settings. Through multiple case studies for the biggest city of Japan (i.e., Tokyo) and domain expert interviews , we demonstrate that our system can be beneficial to give an illustrative insight in measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Revised:  Thus, in this study, based on big human mobility data and city POI data, we design an interactive visual analytics system called EpiMob (Epidemic Mobility) to intuitively demonstrate and simulate how the human mobility, as well as the number of infected people , will change according to a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies, and the result reflecting the infection situation under different policies is dynamically displayed and can be flexibly compared. We completed multiple case studies for the biggest city of Japan (i.e., Tokyo) and domain expert interviews , we demonstrate that our system can be beneficial to give an illustrative insight in measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Edit intent: meaning-changed
Original:  Through multiple case studies for the biggest city of Japan (i.e., Tokyo) and domain expert interviews , we demonstrate that our system can be beneficial to give an illustrative insight in measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Revised:  Through multiple case studies of the largest metropolitan area in Japan (i.e., Tokyo) and domain expert interviews , we demonstrate that our system can be beneficial to give an illustrative insight in measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Edit intent: clarity
Original:  Through multiple case studies for the biggest city of Japan (i.e., Tokyo) and domain expert interviews , we demonstrate that our system can be beneficial to give an illustrative insight in measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Revised:  Through multiple case studies for the biggest city of Japan (i.e., Greater Tokyo Area) and conducted interviews with domain experts to demonstrate that our system can be beneficial to give an illustrative insight in measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Edit intent: clarity
Original:  Through multiple case studies for the biggest city of Japan (i.e., Tokyo) and domain expert interviews , we demonstrate that our system can be beneficial to give an illustrative insight in measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Revised:  Through multiple case studies for the biggest city of Japan (i.e., Tokyo) and domain expert interviews , we demonstrate that our system can provide illustrative insight by measuring and comparing the effects of different human mobility restriction policies for epidemic control.
Edit intent: clarity
Original:  The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning , emergency management, and resource allocation. Urban traffic exhibits a high spatial correlation in which links adjacent to a congested link are likely to become congested due to spillback effects. The spillback behavior requires modeling the entire metropolitan area to recognize all of the upstream and downstream effects from intentional or unintentional perturbations to the network.
Revised:  The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning . This requires modeling entire metropolitan areas to recognize the upstream and downstream effects from intentional or unintentional perturbations to the network.
Edit intent: coherence
Original:   This paper addresses these performance shortcomings by introducing a new platform MANTA for traffic microsimulation at the metropolitan-scale.
Revised:  To achieve the level of detail required for traffic microsimulation, current implementations often compromise by simulating small spatial scales, and those that operate at larger scales often require access to expensive high-performance computing systems or have computation times on the order of days or weeks that discourage productive research and real-time planning. This paper addresses these performance shortcomings by introducing a new platform MANTA for traffic microsimulation at the metropolitan-scale.
Edit intent: meaning-changed
Original:  We test our platform to simulate the entire Bay Area metropolitan region over the course of the morning using half-second time steps. The runtime for the nine-county Bay Area simulation is just over four minutes, not including routing and initialization.
Revised:  We test our platform to simulate the entire Bay Area metropolitan region over the course of the morning using half-second timesteps, for the nine-county Bay Area simulation is just over four minutes, not including routing and initialization.
Edit intent: clarity
Original:  In this work, we show that standard beam search is a computationally inefficient choice for many decoding tasks; specifically, when the scoring function is a monotonic function in sequence length, other search algorithms can be used to reduce the number of calls to the scoring function (e.g., a neural network), which is often the bottleneck computation.
Revised:  In this work, we show that the standard implementation of beam search can be made up to 10x faster in practice. Our method assumes that the scoring function is a monotonic function in sequence length, other search algorithms can be used to reduce the number of calls to the scoring function (e.g., a neural network), which is often the bottleneck computation.
Edit intent: meaning-changed
Original:  specifically, when the scoring function is a monotonic function in sequence length, other search algorithms can be used to reduce the number of calls to the scoring function (e.g., a neural network), which is often the bottleneck computation.
Revised:  specifically, when the scoring function is monotonic in the sequence length, other search algorithms can be used to reduce the number of calls to the scoring function (e.g., a neural network), which is often the bottleneck computation.
Edit intent: fluency
Original:  specifically, when the scoring function is a monotonic function in sequence length, other search algorithms can be used to reduce the number of calls to the scoring function (e.g., a neural network), which is often the bottleneck computation. We propose best-first beam search, an algorithm that provably returns the same set of results as standard beam search, albeit in the minimum number of scoring function calls to guarantee optimality (modulo beam size). We show that best-first beam search can be used with length normalization and mutual information decoding , among other rescoring functions . Lastly, we propose a memory-reduced variant of best-first beam search , which has a similar  search bias in terms of downstream performance, but runs in a fraction of the time.
Revised:  specifically, when the scoring function is a monotonic function in sequence length, which allows us to safely prune hypotheses that cannot be in the final set of hypotheses early on. We devise effective monotonic approximations to popular nonmonontic scoring functions, including length normalization and mutual information decoding , among other rescoring functions . Lastly, we propose a memory-reduced variant of best-first beam search , which has a similar search bias in terms of downstream performance, but runs in a fraction of the time.
Edit intent: coherence
Original:  We show that best-first beam search can be used with length normalization and mutual information decoding , among other rescoring functions . Lastly, we propose a memory-reduced variant of best-first beam search , which has a similar  search bias in terms of downstream performance, but runs in a fraction of the time.
Revised:  We show that best-first beam search can be used with length normalization and mutual information decoding . Lastly, we propose a memory-reduced variant of best-first beam search , which has a similar search bias in terms of downstream performance, but runs in a fraction of the time.
Edit intent: clarity
Original:  We show that best-first beam search can be used with length normalization and mutual information decoding , among other rescoring functions . Lastly, we propose a memory-reduced variant of best-first beam search , which has a similar  search bias in terms of downstream performance, but runs in a fraction of the time.
Revised:  We show that best-first beam search can be used with length normalization and mutual information decoding , among other rescoring functions . Lastly, we propose a memory-reduced variant of Best-First Beam Search , which has a similar search bias in terms of downstream performance, but runs in a fraction of the time.
Edit intent: fluency
Original:  We show that best-first beam search can be used with length normalization and mutual information decoding , among other rescoring functions . Lastly, we propose a memory-reduced variant of best-first beam search , which has a similar  search bias in terms of downstream performance, but runs in a fraction of the time.
Revised:  We show that best-first beam search can be used with length normalization and mutual information decoding , among other rescoring functions . Lastly, we propose a memory-reduced variant of best-first beam search , which has a similar beneficial search bias in terms of downstream performance, but runs in a fraction of the time.
Edit intent: clarity
Original: Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to detect and discriminate different levels of n-back tasks that involve working memory across different experiment sessions and subjects .
Revised: Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory across different experiment sessions and subjects .
Edit intent: clarity
Original: Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to detect and discriminate different levels of n-back tasks that involve working memory across different experiment sessions and subjects .
Revised: Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to detect and discriminate different levels of n-back tasks that involve working memory .
Edit intent: clarity
Original:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W)  . Approach:  We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Revised:  Aim: Domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) . Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Edit intent: clarity
Original:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W)  . Approach:  We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Revised:  Aim: To address the domain shift in fNIRS data is a challenge in the workload level alignment across different experiment sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) . Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Edit intent: meaning-changed
Original:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W)  . Approach:  We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Revised:  Aim: To address the domain shift in fNIRS data across sessions and subjects . In order to address this problem, two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) . Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Edit intent: clarity
Original:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W)  . Approach:  We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Revised:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches -- Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) . Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Edit intent: fluency