wanyu/IteraTeR_v2 huggingface dataset:
Original:  Preliminaries results indicate the feasibility and efficacy of our approach for the task of proximity sensing which could be relevant and accurate for its relevance to contact tracing and exposure notifications .
Revised:  Preliminaries results indicate the feasibility and efficacy of our approach for the task of proximity sensing which could be relevant and accurate for its relevance to contact tracing and exposure notification implementations .
Edit intent: clarity
Original:  The MscaleDNN solution to the Stokes problem is obtained by minimizing a loss function in terms of L^2 norm of the residual of the Stokes equation.
Revised:  The MscaleDNN solution to the Stokes problem is obtained by minimizing a loss function in terms of L2 normof the residual of the Stokes equation.
Edit intent: clarity
Original:  Three forms of loss functions are investigated based on vorticity-velocity-pressure, velocity-stress-pressure, and velocity gradient-velocity-pressure formulations of the Stokes equation.
Revised:  Three forms of loss functions are investigated based on vorticity-velocity-pressure, velocity-stress-pressure, and velocity-gradient of velocity-pressure formulations of the Stokes equation.
Edit intent: clarity
Original:  The results show that MscaleDNN has faster convergence and consistent error decays than normal fully connnected DNNs in the simulation of Kovasznay flow for all four tested loss functions.
Revised:  The results show that MscaleDNN has faster convergence and consistent error decays in the simulation of Kovasznay flow for all four tested loss functions.
Edit intent: clarity
Original:  More importantly, the MscaleDNN is capable of learning highly oscillatory solutions while the normal DNNs fail to converge.
Revised:  More importantly, the MscaleDNN is capable of learning highly oscillatory solutions when the normal DNNs fail to converge.
Edit intent: clarity
Original: In the study of infectious diseases on networks, researchers calculate epidemic thresholds as informative measures to help forecast whether a disease will eventually infect a large fraction of a population.
Revised: In the study of infectious diseases on networks, researchers calculate epidemic thresholds to help forecast whether a disease will eventually infect a large fraction of a population.
Edit intent: clarity
Original:  The structure of network typically changes in time, which fundamentally influence the dynamics of spreading processes on them and in turn affect epidemic thresholds for disease propagation  .
Revised:  Because network structure typically changes in time, which fundamentally influence the dynamics of spreading processes on them and in turn affect epidemic thresholds for disease propagation .
Edit intent: clarity
Original:  The structure of network typically changes in time, which fundamentally influence the dynamics of spreading processes on them and in turn affect epidemic thresholds for disease propagation  .
Revised:  The structure of network typically changes in time, which fundamentally influences the dynamics of spreading processes on them and in turn affect epidemic thresholds for disease propagation .
Edit intent: fluency
Original:  The structure of network typically changes in time, which fundamentally influence the dynamics of spreading processes on them and in turn affect epidemic thresholds for disease propagation  .
Revised:  The structure of network typically changes in time, which fundamentally influence the dynamics of spreading processes on them and in turn affects epidemic thresholds for disease propagation .
Edit intent: fluency
Original:  Most existing studies on epidemic thresholds in temporal networks have focused on models in discrete time, but most real-world networked systems evolve continuously in time.
Revised:  Most existing studies of epidemic thresholds in temporal networks have focused on models in discrete time, but most real-world networked systems evolve continuously in time.
Edit intent: fluency
Original:  In our work, we encode the continuous dependency of time into the evaluation of the epidemic threshold of an susceptible--infected--susceptible (SIS) process by studying an SIS model on tie-decay networks.
Revised:  In our work, we encode the continuous time-dependence of networks into the evaluation of the epidemic threshold of an susceptible--infected--susceptible (SIS) process by studying an SIS model on tie-decay networks.
Edit intent: fluency
Original:  In our work, we encode the continuous dependency of time into the evaluation of the epidemic threshold of an susceptible--infected--susceptible (SIS) process by studying an SIS model on tie-decay networks.
Revised:  In our work, we encode the continuous dependency of time into the evaluation of the epidemic threshold of a susceptible--infected--susceptible (SIS) process by studying an SIS model on tie-decay networks.
Edit intent: fluency
Original:  We formulate epidemic thresholds for an SIS compartmental model of disease spread on tie-decay networks , and we perform numerical experiments to verify the threshold condition that we derive .
Revised:  We derive the epidemic-threshold condition of this model , and we perform numerical experiments to verify the threshold condition that we derive .
Edit intent: clarity
Original:  We formulate epidemic thresholds for an SIS compartmental model of disease spread on tie-decay networks , and we perform numerical experiments to verify the threshold condition that we derive .
Revised:  We formulate epidemic thresholds for an SIS compartmental model of disease spread on tie-decay networks , and we perform numerical experiments to verify it .
Edit intent: clarity
Original:  We also examine how different factors---the decay coefficients of the networks , the frequency of interactions  , and the sparsity of the underlying social network in which interactions occur---lead to decreases or increases of the critical values of the threshold condition and hence contribute to facilitating or impeding the spread of a disease.
Revised:  We also examine how different factors---the decay coefficients of the networks , the frequency of interactions , and the sparsity of the underlying social network in which interactions occur---lead to decreases or increases of the critical values of the threshold and hence contribute to facilitating or impeding the spread of a disease.
Edit intent: clarity
Original:  We thereby demonstrate how the tie-decay features of these networks alter the outcome of disease spread.
Revised:  We thereby demonstrate how the features of tie-decay networks alter the outcome of disease spread.
Edit intent: clarity
Original:  We combine an infrastructure network of flight connections with a population density dataset to derive the mobility network, and then  define an epidemic framework to model the spread of  disease.
Revised:  We combine an infrastructure network of flight connections with a population density dataset to derive the mobility network, and then we define an epidemic framework to model the spread of disease.
Edit intent: fluency
Original:  We combine an infrastructure network of flight connections with a population density dataset to derive the mobility network, and then  define an epidemic framework to model the spread of  disease.
Revised:  We combine an infrastructure network of flight connections with a population density dataset to derive the mobility network, and then define an epidemic framework to model the spread of the disease.
Edit intent: fluency
Original:  The resulting model is characterised by the dynamics of a metapopulation SEIRS, with amplification or reduction of the infection rate which is determined  by the mobility of individuals.
Revised:  The resulting model is characterised by the dynamics of a metapopulation SEIRS, with amplification or reduction of the infection rate which is determined also by the mobility of individuals.
Edit intent: clarity
Original:  Then, we use simulations to characterise and study a variety of realistic scenarios that resemble the recent spread of COVID-19.
Revised:  We use simulations to characterise and study a variety of realistic scenarios that resemble the recent spread of COVID-19.
Edit intent: coherence
Original: Partially Observable Stochastic Games (POSGs) , are the most general  model of games used in Multi-Agent Reinforcement Learning (MARL) , modeling actions and observations as happening sequentially for all agents.
Revised: Partially Observable Stochastic Games (POSGs) are the most general model of games used in Multi-Agent Reinforcement Learning (MARL) , modeling actions and observations as happening sequentially for all agents.
Edit intent: fluency
Original: Partially Observable Stochastic Games (POSGs) , are the most general  model of games used in Multi-Agent Reinforcement Learning (MARL) , modeling actions and observations as happening sequentially for all agents.
Revised: Partially Observable Stochastic Games (POSGs) , are the most general and common model of games used in Multi-Agent Reinforcement Learning (MARL) , modeling actions and observations as happening sequentially for all agents.
Edit intent: clarity
Original:  Most idealizations of closed reaction-diffusion systems employ no-flux boundary conditions, and often patterns will form up to or along these boundaries.
Revised:  Most idealizations of closed reaction-diffusion systems employ no-flux boundary conditions, and often patterns will form up to , or along, these boundaries.
Edit intent: fluency
Original: The analysis of daily life fashion trends can help us understand our societies and human culturesprofoundly .
Revised: The analysis of daily-life fashion trends can help us understand our societies and human culturesprofoundly .
Edit intent: fluency
Original: The analysis of daily life fashion trends can help us understand our societies and human culturesprofoundly .
Revised: The analysis of daily life fashion trends can provide us a profound understanding of our societies and human culturesprofoundly .
Edit intent: clarity
Original: The analysis of daily life fashion trends can help us understand our societies and human culturesprofoundly .
Revised: The analysis of daily life fashion trends can help us understand our societies and cultures .
Edit intent: clarity
Original:  However, no appropriate database exists that includes images illustrating what people wore in their daily lives over an extended period.
Revised:  However, no appropriate digital archive exists that includes images illustrating what people wore in their daily lives over an extended period.
Edit intent: clarity
Original:  In this study, we propose a new fashion image archive, Chronicle Archive of Tokyo Street-fashion (CAT STREET), to shed light on daily life fashion trends.
Revised:  In this study, we propose a new fashion image archive, Chronicle Archive of Tokyo Street-fashion (CAT STREET), to shed light on daily-life fashion trends.
Edit intent: fluency
Original:  CAT STREET includes images showing what people wore in their daily lives during the period 1970-2017 , and these images contain timestamps and street location annotations.
Revised:  CAT STREET includes images showing what people wore in their daily lives during 1970--2017 , and these images contain timestamps and street location annotations.
Edit intent: clarity
Original:  This novel database  enables us to observe long-term daily life fashion trends using quantitative methods .
Revised:  This novel database enables us to observe daily-life fashion trends over a long term and analyze them quantitatively .
Edit intent: clarity
Original:  To evaluate the potential of our  database, we corroborated the rules-of-thumb for two fashion trend phenomena , namely how economic conditions affect fashion style share in the long term and how fashion styles emerge in the street and diffuse from street to street.
Revised:  To evaluate the potential of our database, we corroborated the rules-of-thumb of two fashion trend phenomena , namely how economic conditions affect fashion style share in the long term and how fashion styles emerge in the street and diffuse from street to street.
Edit intent: fluency
Original:  Our study demonstrates CAT STREET's potential to promote understanding of societies and human cultures through quantitative analysis of daily life fashion trends .
Revised:  Our study demonstrates CAT STREET's potential to promote understanding of societies and cultures through fashion embedded in consumers' daily lives .
Edit intent: clarity
Original:  There 's a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers.
Revised:  There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers.
Edit intent: fluency
Original:  In this work , we focus on the literature works adopting neural networks fed by EEG signals.
Revised:  In this review , we focus on the literature works adopting neural networks fed by EEG signals.
Edit intent: clarity
Original:  Training machine learning models on biased data sets requires correction techniques to compensate for potential biases .
Revised:  Training machine learning models on biased data sets requires correction techniques to compensate for the bias .
Edit intent: clarity
Original:  Though statistically equivalent, it has been observed that reweighting outperforms resampling when combined with stochastic gradient algorithms.
Revised:  Though statistically equivalent, it has been observed that resampling outperforms reweighting when combined with stochastic gradient algorithms.
Edit intent: fluency
Original:  Their algorithm runs in time n^{ O (\log^3 n)}, where t is assumed to be a ~ constant.
Revised:  Their algorithm runs in time n^{ O (\log^3 n)}, where t is assumed to be a constant.
Edit intent: fluency
Original:  In recent years, several novel activation functions arising from these basic functions have been proposed, which have improved accuracy in some challenging datasets with complicated models .
Revised:  In recent years, several novel activation functions arising from these basic functions have been proposed, which have improved accuracy in some challenging datasets .
Edit intent: clarity
Original:  Also, x\ln(1+e^x){1+x^2} beats ReLU by 1.68\\% in DenseNet-169, 0.30\\% in Inception V3 in CIFAR100 dataset while 1.0\\% in Inception V3, 0.15\\% in DenseNet-169, 1.13\\% in SimpleNet model in CIFAR10 dataset 
Revised:  Also, x\ln(1+e^x){1+x^2} beats ReLU by 1.68\\% in DenseNet-169, 0.30\\% in Inception V3 in CIFAR100 dataset while 1.0\\% in Inception V3, 0.15\\% in DenseNet-169, 1.13\\% in SimpleNet model in CIFAR10 dataset .
Edit intent: fluency
Original:  An important question is how total protein production in the cell is regulated,despite this considerable variability.
Revised:  An important question is how protein production in the cell is regulated,despite this considerable variability.
Edit intent: clarity
Original:  An important question is how total protein production in the cell is regulated,despite this considerable variability.
Revised:  An important question is how total protein production in the cell is affected by this considerable variability.
Edit intent: clarity
Original:  This reveals a principle of universality: total protein production is insensitive to many of the details underlying the distribution of the random variables .
Revised:  This reveals a principle of universality: total protein production can be stabilized despite the overwhelming stochasticticity underlying cellular processes .
Edit intent: clarity
Original:  However, most existing GNN models have an implicit assumption of homophily among the nodes connected in the graph, and therefore have largely overlooked the important setting of heterophily  .
Revised:  However, many existing GNN models have an implicit assumption of homophily among the nodes connected in the graph, and therefore have largely overlooked the important setting of heterophily .
Edit intent: coherence
Original:  However, most existing GNN models have an implicit assumption of homophily among the nodes connected in the graph, and therefore have largely overlooked the important setting of heterophily  .
Revised:  However, most existing GNN models have implicitly assumed homophily among the nodes connected in the graph, and therefore have largely overlooked the important setting of heterophily .
Edit intent: coherence
Original:  To keep preference elicitation manageable, recent work has employed a machine learning (ML) algorithm that identifies a small set of bundles to query from each bidder.
Revised:  To keep preference elicitation manageable, recent work has employed machine learning (ML) algorithm that identifies a small set of bundles to query from each bidder.
Edit intent: fluency
Original:  To keep preference elicitation manageable, recent work has employed a machine learning (ML) algorithm that identifies a small set of bundles to query from each bidder.
Revised:  To keep preference elicitation manageable, recent work has employed a machine learning (ML) algorithms that identify a small set of bundles to query from each bidder.
Edit intent: fluency
Original:  To steer the auction towards an efficient allocation, we design a price-based refinement process asking bidders to tighten bounds on relevant bundles only , and we carefully integrate this refinement process into the ML-based query module .
Revised:  To steer the auction towards an efficient allocation, we introduce a new price-based refinement process asking bidders to tighten bounds on relevant bundles only , and we carefully integrate this refinement process into the ML-based query module .
Edit intent: clarity
Original:  To steer the auction towards an efficient allocation, we design a price-based refinement process asking bidders to tighten bounds on relevant bundles only , and we carefully integrate this refinement process into the ML-based query module .
Revised:  To steer the auction towards an efficient allocation, we design a price-based activity rule, asking bidders to tighten bounds on relevant bundles only , and we carefully integrate this refinement process into the ML-based query module .
Edit intent: clarity
Original:  To keep preference elicitation manageable, recent work has employed machine learning (ML) algorithms that identify a small set of bundles to query from each bidder.
Revised:  Recent work has used machine learning (ML) algorithms that identify a small set of bundles to query from each bidder.
Edit intent: clarity
Original:  To keep preference elicitation manageable, recent work has employed machine learning (ML) algorithms that identify a small set of bundles to query from each bidder.
Revised:  To keep preference elicitation manageable, recent work has employed machine learning (ML) algorithms to identify a small set of bundles to query from each bidder.
Edit intent: clarity
Original:  However, a major limitation of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly for them .
Revised:  However, a shortcoming of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly for them .
Edit intent: clarity
Original:  However, a major limitation of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly for them .
Revised:  However, a major limitation of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly .
Edit intent: clarity
Original:  To steer the auction towards an efficient allocation, we introduce a new price-based activity rule, asking bidders to tighten bounds on relevant bundles only.
Revised:  To steer the auction towards an efficient allocation, we introduce a price-based activity rule, asking bidders to tighten bounds on relevant bundles only.
Edit intent: clarity
Original: The growth of science and technology is primarily a recombinative process, wherein new discoveries and inventions are generally built from prior knowledge.
Revised: The growth of science and technology is a recombinative process, wherein new discoveries and inventions are generally built from prior knowledge.
Edit intent: clarity
Original: The growth of science and technology is primarily a recombinative process, wherein new discoveries and inventions are generally built from prior knowledge.
Revised: The growth of science and technology is primarily a recombinative process, wherein new discoveries and inventions are built from prior knowledge.
Edit intent: coherence
Original:  While the recent past has seen rapid growth in scientific and technological knowledge, relatively little is known about the manner in which science and technology develop and coalesce knowledge into larger structures that enable or constrain future breakthroughs.
Revised:  Yet relatively little is known about the manner in which science and technology develop and coalesce knowledge into larger structures that enable or constrain future breakthroughs.
Edit intent: clarity
Original:  While helpful, these existing approaches struggle to capture the global structural properties of the underlying networks, leading to conflicting observations about the nature of scientific and technological progress.
Revised:  While helpful, existing approaches struggle to capture the global structural properties of the underlying networks, leading to conflicting observations about the nature of scientific and technological progress.
Edit intent: clarity
Original:  While helpful, these existing approaches struggle to capture the global structural properties of the underlying networks, leading to conflicting observations about the nature of scientific and technological progress.
Revised:  While helpful, these existing approaches struggle to capture the global properties of the underlying networks, leading to conflicting observations about the nature of scientific and technological progress.
Edit intent: clarity
Original:  We observe rapid and varied growth in the high-dimensional structure in many fields of science and technology, and find this high-dimensional growth coincides with decline in lower-dimensional structure.
Revised:  We observe rapid growth in the high-dimensional structure in many fields of science and technology, and find this high-dimensional growth coincides with decline in lower-dimensional structure.
Edit intent: clarity
Original:  This two-stage method consists of (1) mapping implicit human feedback to relevant task statistics such as rewards , optimality, and advantage;
Revised:  This two-stage method consists of (1) mapping implicit human feedback to relevant task statistics such as reward , optimality, and advantage;
Edit intent: fluency
Original: The nodes of a graph existing in a specific cluster are more likely to connect to each other than with other nodes in the graph.
Revised: The nodes of a graph existing in a cluster are more likely to connect to each other than with other nodes in the graph.
Edit intent: clarity
Original:  Then revealing some information about the nodes, the structure of the graph ( the graph edges) provides this opportunity to know more information about the other nodes.
Revised:  Then revealing some information about some nodes, the structure of the graph ( the graph edges) provides this opportunity to know more information about the other nodes.
Edit intent: fluency
Original:  Then revealing some information about the nodes, the structure of the graph ( the graph edges) provides this opportunity to know more information about the other nodes.
Revised:  Then revealing some information about the nodes, the structure of the graph ( graph edges) provides this opportunity to know more information about the other nodes.
Edit intent: fluency
Original:  Then revealing some information about the nodes, the structure of the graph ( the graph edges) provides this opportunity to know more information about the other nodes.
Revised:  Then revealing some information about the nodes, the structure of the graph ( the graph edges) provides this opportunity to know more information about other nodes.
Edit intent: fluency