wanyu/IteraTeR_v2 huggingface dataset:
Original:  An extensive evaluation on a variety of applications, including Facebook production workloads, the open-source compiler Clang , and SPEC CPU 2006 benchmarks, indicate that the new method outperforms existing block reordering techniques, improving the resulting performance of large-scale data-center applications .
Revised:  An extensive evaluation on a variety of applications, including Facebook production workloads, the open-source compiler Clang , and SPEC CPU benchmarks, indicate that the new method outperforms existing block reordering techniques, improving the resulting performance of large-scale data-center applications .
Edit intent: clarity
Original:  An extensive evaluation on a variety of applications, including Facebook production workloads, the open-source compiler Clang , and SPEC CPU 2006 benchmarks, indicate that the new method outperforms existing block reordering techniques, improving the resulting performance of large-scale data-center applications .
Revised:  An extensive evaluation on a variety of applications, including Facebook production workloads, the open-source compiler Clang , and SPEC CPU 2006 benchmarks, indicate that the new method outperforms existing block reordering techniques, improving the resulting performance of applications with large code size .
Edit intent: clarity
Original: Many social, economic, and information systems  can be represented as networks that grow with time, which makes it challenging to develop and validate methods to analyze their structure . Static methods are limiting as they miss essential information on the system's dynamics.
Revised: Many social, economic, and information systems that can be represented as networks that grow with time, which makes it challenging to develop and validate methods to analyze their structure . Static methods are limiting as they miss essential information on the system's dynamics.
Edit intent: fluency
Original: Many social, economic, and information systems  can be represented as networks that grow with time, which makes it challenging to develop and validate methods to analyze their structure . Static methods are limiting as they miss essential information on the system's dynamics.
Revised: Many social, economic, and information systems can be represented as networks that grow with time, which makes it challenging to develop and validate methods to analyze their structure . Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system's dynamics.
Edit intent: clarity
Original:  On the other hand, methods based on multi-layer temporal representations of the data lack clear guidelines on how to partition the input time-stamped data into layers .
Revised:  At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it .
Edit intent: clarity
Original:  We focus on the popular community detection problem , which aims to simplify the description of a given networkby partitioning its nodes into meaningful groups.
Revised:  We focus on the popular community detection problem which aims to simplify the description of a given networkby partitioning its nodes into meaningful groups.
Edit intent: fluency
Original:  We focus on the popular community detection problem , which aims to simplify the description of a given networkby partitioning its nodes into meaningful groups.
Revised:  We focus on the popular community detection problem , which aims to partition a network's nodes into meaningful groups.
Edit intent: clarity
Original:  We use a multi-layer quality function to show, on both synthetic and real datasets, that the temporal duration of the layers that leads to optimal communities is tightly related to the system's intrinsic aging timescale  .
Revised:  We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system's intrinsic aging timescale .
Edit intent: clarity
Original:  The use of temporal information leads to drastically different conclusions on the community structure of real  networks, which challenges our current understanding of the large-scale organization of growing networks.
Revised:  The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges our current understanding of the large-scale organization of growing networks.
Edit intent: clarity
Original:  The use of temporal information leads to drastically different conclusions on the community structure of real  networks, which challenges our current understanding of the large-scale organization of growing networks.
Revised:  The use of temporal information leads to drastically different conclusions on the community structure of real networks, which challenges the current understanding of the large-scale organization of growing networks.
Edit intent: fluency
Original:  Our findings point at the importance of understanding the timescales of the dynamical processes that generated the observed networks in order to properly assess their structural patterns .
Revised:  Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated the observed networks in order to properly assess their structural patterns .
Edit intent: clarity
Original:  Our findings point at the importance of understanding the timescales of the dynamical processes that generated the observed networks in order to properly assess their structural patterns .
Revised:  Our findings point at the importance of understanding the timescales of the dynamical processes that generated them .
Edit intent: clarity
Original:  For this case, the whole population is segregated into 3 clusters, and HR neurons in each cluster exhibit burst synchronization.
Revised:  For this case, HR neurons in each cluster exhibit burst synchronization.
Edit intent: clarity
Original:  However, as J_0 is increased and passes an intermediate threshold J^*_m  , HR neurons begin to make intermittent hoppings between the 3 clusters.
Revised:  However, as J_0 passes an intermediate threshold J^*_m , HR neurons begin to make intermittent hoppings between the 3 clusters.
Edit intent: clarity
Original:  We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7\% on the test set, a 2.1\% absolute improvement over the model trained with traditional static oracles assuming a single correct target SQL query.
Revised:  We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7\% on the test set, a 2.1\% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query.
Edit intent: fluency
Original:  When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1\% . This is a work-in-progress technical report .
Revised:  When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1\% .
Edit intent: coherence
Original:  We are interested in understanding to what extent this relaxation can improve the achievable competitive ratio for the %DIFDELCMD < \nameOfProblem%%%  . In particular, using an O(k) shortlist, can an online algorithm achieve a competitive ratio close to the best achievable offline approximation factor for this problem?
Revised:  We are interested in understanding to what extent this relaxation can improve the achievable competitive ratio for the %DIFDELCMD < \nameOfProblem%%% . In particular, using an O(k) shortlist, can an online algorithm achieve a competitive ratio close to the best achievable online approximation factor for this problem?
Edit intent: clarity
Original:  This is especially surprising considering that the best known competitive ratio (in polynomial time) for the%DIFDELCMD < \nameOfProblem%%% ~is (1/e-O(k^{-1/2))(1-1/e) \mbox{ kesselheim}0pt .
Revised:  %DIFDELCMD < \nameOfProblem%%% ~is (1/e-O(k^{-1/2))(1-1/e) \mbox{ kesselheim}0pt .
Edit intent: coherence
Original:  This is especially surprising considering that the best known competitive ratio (in polynomial time) for the%DIFDELCMD < \nameOfProblem%%% ~is (1/e-O(k^{-1/2))(1-1/e) \mbox{ kesselheim}0pt .
Revised:  This is especially surprising considering that the best known competitive ratio (in polynomial time) for the%DIFDELCMD < \nameOfProblem%%% ))(1-1/e) \mbox{ kesselheim}0pt .
Edit intent: coherence
Original:  Further}  , for the special case of m-submodular functions, we demonstrate an algorithm that achieves 1-epsilon competitive ratio for any constant epsilon > 0, using an O(1) shortlist.
Revised:  Further} Also , for the special case of m-submodular functions, we demonstrate an algorithm that achieves 1-epsilon competitive ratio for any constant epsilon > 0, using an O(1) shortlist.
Edit intent: coherence
Original:  For submodular function maximization under random order streaming model and k-cardinality constraint , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1-1 / e- \epsilon-O(k^{-1}) approximation  .
Revised:  Finally , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1-1 / e- \epsilon-O(k^{-1}) approximation .
Edit intent: coherence
Original:  For submodular function maximization under random order streaming model and k-cardinality constraint , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1-1 / e- \epsilon-O(k^{-1}) approximation  .
Revised:  For submodular function maximization under random order streaming model and k-cardinality constraint , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1 - 1 / e- \epsilon-O(k^{-1}) approximation .
Edit intent: fluency
Original:  For submodular function maximization under random order streaming model and k-cardinality constraint , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1-1 / e- \epsilon-O(k^{-1}) approximation  .
Revised:  For submodular function maximization under random order streaming model and k-cardinality constraint , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1-1 / e - \epsilon-O(k^{-1}) approximation .
Edit intent: fluency
Original:  For submodular function maximization under random order streaming model and k-cardinality constraint , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1-1 / e- \epsilon-O(k^{-1}) approximation  .
Revised:  For submodular function maximization under random order streaming model and k-cardinality constraint , we show that our algorithm can be implemented in the streaming setting using a memory buffer of size \eta_\epsilon(k) = O(k) to achieve a 1-1 / e- \epsilon-O(k^{-1}) approximation for submodular function maximization in the random order streaming model .
Edit intent: clarity
Original:  This substantially improves upon \mbox{ \cite{norouzi}\hspace{0pt} , which achieved } the previously best known approximation factor of 1/2 + 8 \times 10^{-14} using  O(k \log k) memory .
Revised:  This substantially improves upon \mbox{ \cite{norouzi}\hspace{0pt} , which achieved } the previously best known approximation factor of 1/2 + 8 \times 10^{-14} using O(k \log k) .
Edit intent: clarity
Original: Repeated exposure to low-level blast may initiate a range of adverse health problem such as traumatic brain injury , yet mechanisms underpinning its actions are not fully understood .
Revised: Repeated exposure to low-level blast may initiate a range of adverse health problem such as traumatic brain injury , yet mechanisms underpinning its actions are not fully elucidated .
Edit intent: clarity
Original:  Data for pre- and post-transcripts collected from 32 individuals in Army training sites combined with our system approach provided unique opportunity to compare the pre- and post-transcript networks . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Revised:  The data for pre- and post-transcripts collected from 32 individuals in Army training sites combined with our system approach provided unique opportunity to compare the pre- and post-transcript networks . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Edit intent: fluency
Original:  Data for pre- and post-transcripts collected from 32 individuals in Army training sites combined with our system approach provided unique opportunity to compare the pre- and post-transcript networks . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Revised:  Data for pre- and post-transcripts collected from 32 individuals in Army training program, combined with our system approach provided unique opportunity to compare the pre- and post-transcript networks . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Edit intent: clarity
Original:  Data for pre- and post-transcripts collected from 32 individuals in Army training sites combined with our system approach provided unique opportunity to compare the pre- and post-transcript networks . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Revised:  Data for pre- and post-transcripts collected from 32 individuals in Army training sites combined with our system approach provide unique opportunity to compare the pre- and post-transcript networks . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Edit intent: fluency
Original:  Data for pre- and post-transcripts collected from 32 individuals in Army training sites combined with our system approach provided unique opportunity to compare the pre- and post-transcript networks . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Revised:  Data for pre- and post-transcripts collected from 32 individuals in Army training sites combined with our system approach provided unique opportunity to investigate the effect of blast-wave exposure on gene-gene interactions . Digging into the networks, we identified four pathways that are dysregulated and disrupted due to the exposure waves produced by blast.
Edit intent: clarity
Original:  The pathways that are related to immune system and inflammatory process are complementary to experimental studies while they hypothesize new target for prevention and intervention .
Revised:  The pathways that are related to immune system and inflammatory process that are disrupted due to the exposure .
Edit intent: clarity
Original:  Among genes with relatively high Fold-Change in their transcript expression levels, genes PELI, PTGS2, TRIM58, B2M, BCL2AL, S100A8  and ZNF654 showed major impact on  dysregulation of the pathways.
Revised:  Among genes with relatively high fold change in their transcript expression levels, genes PELI, PTGS2, TRIM58, B2M, BCL2AL, S100A8 and ZNF654 showed major impact on dysregulation of the pathways.
Edit intent: fluency
Original:  Among genes with relatively high Fold-Change in their transcript expression levels, genes PELI, PTGS2, TRIM58, B2M, BCL2AL, S100A8  and ZNF654 showed major impact on  dysregulation of the pathways.
Revised:  Among genes with relatively high Fold-Change in their transcript expression levels, genes PELI, PTGS2, TRIM58, B2M, BCL2AL, S100A8 , TRIM58 and ZNF654 showed major impact on dysregulation of the pathways.
Edit intent: fluency
Original:  Among genes with relatively high Fold-Change in their transcript expression levels, genes PELI, PTGS2, TRIM58, B2M, BCL2AL, S100A8  and ZNF654 showed major impact on  dysregulation of the pathways.
Revised:  Among genes with relatively high Fold-Change in their transcript expression levels, genes PELI, PTGS2, TRIM58, B2M, BCL2AL, S100A8 and ZNF654 showed major impact on the dysregulation of the pathways.
Edit intent: fluency
Original:  In addition, this study reveals how repeated exposures to traumatic conditions increase the transcripts' Fold-Changes .
Revised:  In addition, this study reveals how repeated exposures to traumatic conditions increase the level of fold change of transcript expression and hypothesizes new targets for further experimental studies .
Edit intent: clarity
Original: We propose CM3, a new deep reinforcement learning method for cooperative multi-agent problems where agents must coordinate for joint success in achieving different individual goals .
Revised: A variety of cooperative multi-agent problems where agents must coordinate for joint success in achieving different individual goals .
Edit intent: clarity
Original:  We restructure multi-agent learning into a two-stage curriculum, consisting of a single-agent stage for learning to accomplish individual tasks, followed by a multi-agent stage for learning to cooperate in the presence of other agents.
Revised:  We restructure multi-agent learning into a two-stage curriculum, in which single-agent stage for learning to accomplish individual tasks, followed by a multi-agent stage for learning to cooperate in the presence of other agents.
Edit intent: clarity
Original:  We restructure multi-agent learning into a two-stage curriculum, consisting of a single-agent stage for learning to accomplish individual tasks, followed by a multi-agent stage for learning to cooperate in the presence of other agents.
Revised:  We restructure multi-agent learning into a two-stage curriculum, consisting of a single-agent goal attainment is learned prior to learning multi-agent stage for learning to cooperate in the presence of other agents.
Edit intent: clarity
Original:  To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for fine-grained credit assignment.
Revised:  To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.
Edit intent: clarity
Original:  We further demonstrate a function augmentation scheme to reduce computational cost of learning value and policy functions in synergy with the curriculum.
Revised:  We use a function augmentation scheme to reduce computational cost of learning value and policy functions in synergy with the curriculum.
Edit intent: clarity
Original:  We further demonstrate a function augmentation scheme to reduce computational cost of learning value and policy functions in synergy with the curriculum.
Revised:  We further demonstrate a function augmentation scheme to bridge value and policy functions in synergy with the curriculum.
Edit intent: clarity
Original:  We further demonstrate a function augmentation scheme to reduce computational cost of learning value and policy functions in synergy with the curriculum.
Revised:  We further demonstrate a function augmentation scheme to reduce computational cost of learning value and policy functions across the curriculum.
Edit intent: clarity
Original:  Particularly, the loss function of the adapted WAE is re-designed by introducing two additional loss terms: (1) the mutual information loss between the distribution of latent variables and the assumed ground truth distribution, and (2) the L_1 regularization loss introduced to restrict the encoded latent variables to be sparse.
Revised:  Particularly, the loss function of the adapted WAE is re-designed by introducing two additional loss terms: (1) the mutual information loss between the distribution of latent variables and the assumed ground truth distribution, and (2) the L1 regularization loss introduced to restrict the encoded latent variables to be sparse.
Edit intent: fluency
Original:  For person(pedestrian) category, we observed significant improvements:+46.45 \% for CM and 45.22 \% for ACM, compared to Faster R-CNN. Finally, we demonstrate the performance of accident forecasting in our dataset using Faster R-CNN and an Accident LSTM architecture.
Revised:  Finally, we demonstrate the performance of accident forecasting in our dataset using Faster R-CNN and an Accident LSTM architecture.
Edit intent: coherence
Original: Spiking neural networks (SNNs) are gaining more attention as a promising way that enables energy efficient implementation on emerging neuromorphic hardware  .
Revised: Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware .
Edit intent: clarity
Original:  We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for large-scale SNNs. (2) We present a Pytorch-based implementation method towards the training of deep SNNs by narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version .
Revised:  We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for deep SNNs. (2) We present a Pytorch-based implementation method towards the training of deep SNNs by narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version .
Edit intent: clarity
Original:  We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for large-scale SNNs. (2) We present a Pytorch-based implementation method towards the training of deep SNNs by narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version .
Revised:  We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for large-scale SNNs. (2) Via narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version .
Edit intent: coherence
Original: In this paper, we present a method to determine if a lift-and-project cut for a mixed-integer linear program is regular , in which case the cut is equivalent to an intersection cut from some basis of the linear relaxation.
Revised: In this paper, we present a method to determine if a lift-and-project cut for a mixed-integer linear program is irregular , in which case the cut is equivalent to an intersection cut from some basis of the linear relaxation.
Edit intent: fluency
Original: In this paper, we present a method to determine if a lift-and-project cut for a mixed-integer linear program is regular , in which case the cut is equivalent to an intersection cut from some basis of the linear relaxation.
Revised: In this paper, we present a method to determine if a lift-and-project cut for a mixed-integer linear program is regular , in which case the cut is not equivalent to any intersection cut from some basis of the linear relaxation.
Edit intent: clarity